From 9b97430a74fd5b43b5e0c0b11fddfeb38e60bd92 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Eren=20G=C3=B6lge?= <egolge@mozilla.com>
Date: Wed, 26 Feb 2020 16:14:10 +0100
Subject: [PATCH 001/185] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 19d7fa24..918328b2 100644
--- a/README.md
+++ b/README.md
@@ -54,7 +54,7 @@ Below you see Tacotron model state after 16K iterations with batch-size 32 with
 
 > "Recent research at Harvard has shown meditating for as little as 8 weeks can actually increase the grey matter in the parts of the brain responsible for emotional regulation and learning."
 
-Audio examples: [https://soundcloud.com/user-565970875](https://soundcloud.com/user-565970875)
+Audio examples: [soundcloud](https://soundcloud.com/user-565970875/pocket-article-wavernn-and-tacotron2)
 
 <img src="images/example_model_output.png?raw=true" alt="example_output" width="400"/>
 

From fab74dd5be681d5fb51080515f60e1b20c6b8d40 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Eren=20G=C3=B6lge?= <egolge@mozilla.com>
Date: Thu, 12 Mar 2020 13:23:13 +0100
Subject: [PATCH 002/185] Update ISSUE_TEMPLATE.md

---
 .github/ISSUE_TEMPLATE.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/ISSUE_TEMPLATE.md b/.github/ISSUE_TEMPLATE.md
index ff377f69..694bb36f 100644
--- a/.github/ISSUE_TEMPLATE.md
+++ b/.github/ISSUE_TEMPLATE.md
@@ -6,8 +6,9 @@ labels: ''
 assignees: ''
 
 ---
+<b>Questions</b> will not be answered here!! 
 
-Please consider posting on [TTS Discourse](https://discourse.mozilla.org/c/tts) page, if your issue is not directly related to TTS development process.
+Please consider posting on [TTS Discourse](https://discourse.mozilla.org/c/tts) page if your issue is not directly related to TTS development (Bugs, code updates etc.). 
 
 You can also check https://github.com/mozilla/TTS/wiki/FAQ for common questions and answers.
 

From 70a8210283bbbd613fcd55f17ef8ebb7bdaac32e Mon Sep 17 00:00:00 2001
From: fatihkiralioglu <38240476+fatihkiralioglu@users.noreply.github.com>
Date: Sat, 25 Apr 2020 15:18:46 +0300
Subject: [PATCH 003/185] Tacotron1 + wavernn configuration fix

Tacotron1 + wavernn configuration: corrected the input format for wavernn vocoder, converted spectrograms to mels
---
 server/synthesizer.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/server/synthesizer.py b/server/synthesizer.py
index e9205bf1..3268be46 100644
--- a/server/synthesizer.py
+++ b/server/synthesizer.py
@@ -184,7 +184,12 @@ class Synthesizer(object):
                     vocoder_input.cuda()
                 wav = self.pwgan.inference(vocoder_input, hop_size=self.ap.hop_length)
             elif self.wavernn:
-                vocoder_input = torch.FloatTensor(postnet_output.T).unsqueeze(0)
+                vocoder_input = None
+                if self.tts_config.model == "Tacotron" :
+                    vocoder_input = torch.FloatTensor(self.ap.out_linear_to_mel(linear_spec = postnet_output.T).T).T.unsqueeze(0)
+                else:
+                    vocoder_input = torch.FloatTensor(postnet_output.T).unsqueeze(0)
+
                 if self.use_cuda:
                     vocoder_input.cuda()
                 wav = self.wavernn.generate(vocoder_input, batched=self.config.is_wavernn_batched, target=11000, overlap=550)

From cc11be06d7efd1e9a2a1cd5a35e7f790592e5067 Mon Sep 17 00:00:00 2001
From: fatihkiralioglu <38240476+fatihkiralioglu@users.noreply.github.com>
Date: Mon, 27 Apr 2020 09:53:52 +0300
Subject: [PATCH 004/185] fixing "No space allowed before..." compile errors

fixing "No space allowed before..." compile errors
---
 server/synthesizer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/server/synthesizer.py b/server/synthesizer.py
index 3268be46..10f4fb0a 100644
--- a/server/synthesizer.py
+++ b/server/synthesizer.py
@@ -185,8 +185,8 @@ class Synthesizer(object):
                 wav = self.pwgan.inference(vocoder_input, hop_size=self.ap.hop_length)
             elif self.wavernn:
                 vocoder_input = None
-                if self.tts_config.model == "Tacotron" :
-                    vocoder_input = torch.FloatTensor(self.ap.out_linear_to_mel(linear_spec = postnet_output.T).T).T.unsqueeze(0)
+                if self.tts_config.model == "Tacotron":
+                    vocoder_input = torch.FloatTensor(self.ap.out_linear_to_mel(linear_spec=postnet_output.T).T).T.unsqueeze(0)
                 else:
                     vocoder_input = torch.FloatTensor(postnet_output.T).unsqueeze(0)
 

From 5a99986e86a7b7dddeef7692cb2ca83a237cc2e3 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 20 May 2020 17:30:25 +0200
Subject: [PATCH 005/185] update numpy setup.py

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index f92dac8a..a1987a60 100644
--- a/setup.py
+++ b/setup.py
@@ -93,7 +93,7 @@ setup(
     install_requires=[
         "scipy>=0.19.0",
         "torch>=0.4.1",
-        "numpy==1.15.4",
+        "numpy>=1.16.0",
         "librosa==0.6.2",
         "unidecode==0.4.20",
         "attrdict",

From 7931a106e234430c449a42c4e2032b9f0e5098ea Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 20 May 2020 17:41:55 +0200
Subject: [PATCH 006/185] update version

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index a1987a60..5e89723b 100644
--- a/setup.py
+++ b/setup.py
@@ -19,7 +19,7 @@ args, unknown_args = parser.parse_known_args()
 # Remove our arguments from argv so that setuptools doesn't see them
 sys.argv = [sys.argv[0]] + unknown_args
 
-version = '0.0.1'
+version = '0.0.2'
 
 # Adapted from https://github.com/pytorch/pytorch
 cwd = os.path.dirname(os.path.abspath(__file__))

From 2a071a75055b5b266fe982602585be35d2667ad0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Eren=20G=C3=B6lge?= <egolge@mozilla.com>
Date: Wed, 20 May 2020 17:51:42 +0200
Subject: [PATCH 007/185] Update README.md

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index 4adae507..6a90ffbf 100644
--- a/README.md
+++ b/README.md
@@ -9,6 +9,8 @@ TTS includes two different model implementations which are based on [Tacotron](h
 
 If you are new, you can also find [here](http://www.erogol.com/text-speech-deep-learning-architectures/) a brief post about TTS architectures and their comparisons.
 
+[![](https://sourcerer.io/fame/erogol/erogol/TTS/images/0)](https://sourcerer.io/fame/erogol/erogol/TTS/links/0)[![](https://sourcerer.io/fame/erogol/erogol/TTS/images/1)](https://sourcerer.io/fame/erogol/erogol/TTS/links/1)[![](https://sourcerer.io/fame/erogol/erogol/TTS/images/2)](https://sourcerer.io/fame/erogol/erogol/TTS/links/2)[![](https://sourcerer.io/fame/erogol/erogol/TTS/images/3)](https://sourcerer.io/fame/erogol/erogol/TTS/links/3)[![](https://sourcerer.io/fame/erogol/erogol/TTS/images/4)](https://sourcerer.io/fame/erogol/erogol/TTS/links/4)[![](https://sourcerer.io/fame/erogol/erogol/TTS/images/5)](https://sourcerer.io/fame/erogol/erogol/TTS/links/5)[![](https://sourcerer.io/fame/erogol/erogol/TTS/images/6)](https://sourcerer.io/fame/erogol/erogol/TTS/links/6)[![](https://sourcerer.io/fame/erogol/erogol/TTS/images/7)](https://sourcerer.io/fame/erogol/erogol/TTS/links/7)
+
 ## TTS Performance 
 <p align="center"><img src="https://camo.githubusercontent.com/9fa79f977015e55eb9ec7aa32045555f60d093d3/68747470733a2f2f646973636f757273652d706161732d70726f64756374696f6e2d636f6e74656e742e73332e6475616c737461636b2e75732d656173742d312e616d617a6f6e6177732e636f6d2f6f7074696d697a65642f33582f362f342f363432386639383065396563373531633234386535393134363038393566373838316165633063365f325f363930783339342e706e67"/></p>
 

From cedc22847beff4a3f8f0b088463b37355717b36d Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 20 May 2020 18:16:24 +0200
Subject: [PATCH 008/185] fix synthesize.py

---
 config_template.json | 134 -------------------------------------------
 synthesize.py        |   3 +-
 2 files changed, 2 insertions(+), 135 deletions(-)
 delete mode 100644 config_template.json

diff --git a/config_template.json b/config_template.json
deleted file mode 100644
index e525ec31..00000000
--- a/config_template.json
+++ /dev/null
@@ -1,134 +0,0 @@
-{
-    "model": "Tacotron2",          // one of the model in models/  
-    "run_name": "ljspeech-stft_params",
-    "run_description": "tacotron2 cosntant stf parameters",
-
-    // AUDIO PARAMETERS
-    "audio":{
-        // Audio processing parameters
-        "num_mels": 80,         // size of the mel spec frame. 
-        "num_freq": 513,       // number of stft frequency levels. Size of the linear spectogram frame.
-        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
-        "win_length": 1024,     // stft window length in ms.
-        "hop_length": 256,      // stft window hop-lengh in ms.
-        "preemphasis": 0.0,    // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
-        "frame_length_ms": null,  // stft window length in ms.If null, 'win_length' is used.
-        "frame_shift_ms": null,   // stft window hop-lengh in ms. If null, 'hop_length' is used.
-        "min_level_db": -100,   // normalization range
-        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
-        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
-        "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
-        // Normalization parameters
-        "signal_norm": true,    // normalize the spec values in range [0, 1]
-        "symmetric_norm": true, // move normalization to range [-1, 1]
-        "max_norm": 1.0,          // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
-        "clip_norm": true,      // clip normalized values into the range.
-        "mel_fmin": 0.0,         // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
-        "mel_fmax": 8000.0,        // maximum freq level for mel-spec. Tune for dataset!!
-        "do_trim_silence": true,  // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
-        "trim_db": 60          // threshold for timming silence. Set this according to your dataset.
-    },
-
-    // VOCABULARY PARAMETERS
-    // if custom character set is not defined,
-    // default set in symbols.py is used
-    "characters":{
-        "pad": "_",
-        "eos": "~",
-        "bos": "^",
-        "characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!'(),-.:;? ",
-        "punctuations":"!'(),-.:;? ",
-        "phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫ"
-    },
-    
-    // DISTRIBUTED TRAINING
-    "distributed":{
-        "backend": "nccl",
-        "url": "tcp:\/\/localhost:54321"
-    },
-
-    "reinit_layers": [],    // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
-
-    // TRAINING
-    "batch_size": 32,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
-    "eval_batch_size":16,   
-    "r": 7,                 // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.  
-    "gradual_training": [[0, 7, 64], [1, 5, 64], [50000, 3, 32], [130000, 2, 32], [290000, 1, 32]], //set gradual training steps [first_step, r, batch_size]. If it is null, gradual training is disabled. For Tacotron, you might need to reduce the 'batch_size' as you proceeed.
-    "loss_masking": true,         // enable / disable loss masking against the sequence padding.
-    "ga_alpha": 10.0,        // weight for guided attention loss. If > 0, guided attention is enabled.
-
-    // VALIDATION
-    "run_eval": true,
-    "test_delay_epochs": 10,  //Until attention is aligned, testing only wastes computation time.
-    "test_sentences_file": null,  // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
-
-    // OPTIMIZER
-    "noam_schedule": false,        // use noam warmup and lr schedule.
-    "grad_clip": 1.0,                // upper limit for gradients for clipping.
-    "epochs": 1000,                // total number of epochs to train.
-    "lr": 0.0001,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
-    "wd": 0.000001,         // Weight decay weight.
-    "warmup_steps": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
-    "seq_len_norm": false,	   // Normalize eash sample loss with its length to alleviate imbalanced datasets. Use it if your dataset is small or has skewed distribution of sequence lengths.
-    
-    // TACOTRON PRENET
-    "memory_size": -1,              // ONLY TACOTRON - size of the memory queue used fro storing last decoder predictions for auto-regression. If < 0, memory queue is disabled and decoder only uses the last prediction frame. 
-    "prenet_type": "original",     // "original" or "bn".
-    "prenet_dropout": true,        // enable/disable dropout at prenet. 
-
-    // ATTENTION
-    "attention_type": "original",  // 'original' or 'graves'
-    "attention_heads": 4,          // number of attention heads (only for 'graves')
-    "attention_norm": "sigmoid",   // softmax or sigmoid. Suggested to use softmax for Tacotron2 and sigmoid for Tacotron.
-    "windowing": false,            // Enables attention windowing. Used only in eval mode.
-    "use_forward_attn": false,      // if it uses forward attention. In general, it aligns faster.
-    "forward_attn_mask": false,    // Additional masking forcing monotonicity only in eval mode.
-    "transition_agent": false,     // enable/disable transition agent of forward attention.
-    "location_attn": true,        // enable_disable location sensitive attention. It is enabled for TACOTRON by default.
-    "bidirectional_decoder": false,  // use https://arxiv.org/abs/1907.09006. Use it, if attention does not work well with your dataset.
-
-    // STOPNET
-    "stopnet": true,               // Train stopnet predicting the end of synthesis. 
-    "separate_stopnet": true,     // Train stopnet seperately if 'stopnet==true'. It prevents stopnet loss to influence the rest of the model. It causes a better model, but it trains SLOWER.
-
-    // TENSORBOARD and LOGGING
-    "print_step": 25,       // Number of steps to log traning on console.
-    "save_step": 10000,      // Number of training steps expected to save traninpg stats and checkpoints.
-    "checkpoint": true,     // If true, it saves checkpoints per "save_step"
-    "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging. 
-    
-    // DATA LOADING
-    "text_cleaner": "phoneme_cleaners",
-    "enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.
-    "num_loader_workers": 4,        // number of training data loader processes. Don't set it too big. 4-8 are good values.
-    "num_val_loader_workers": 4,    // number of evaluation data loader processes.
-    "batch_group_size": 0,  //Number of batches to shuffle after bucketing.
-    "min_seq_len": 6,       // DATASET-RELATED: minimum text length to use in training
-    "max_seq_len": 153,     // DATASET-RELATED: maximum text length
-
-    // PATHS
-    "output_path": "/data4/rw/home/Trainings/",
- 
-    // PHONEMES
-    "phoneme_cache_path": "mozilla_us_phonemes_3",  // phoneme computation is slow, therefore, it caches results in the given folder.
-    "use_phonemes": true,           // use phonemes instead of raw characters. It is suggested for better pronounciation.
-    "phoneme_language": "en-us",     // depending on your target language, pick one from  https://github.com/bootphon/phonemizer#languages
-
-    // MULTI-SPEAKER and GST
-    "use_speaker_embedding": false,     // use speaker embedding to enable multi-speaker learning.
-    "style_wav_for_test": null,          // path to style wav file to be used in TacotronGST inference.
-    "use_gst": false,       // TACOTRON ONLY: use global style tokens
-
-    // DATASETS
-    "datasets":   // List of datasets. They all merged and they get different speaker_ids.
-        [
-            {
-                "name": "ljspeech",
-                "path": "/root/LJSpeech-1.1/",
-                "meta_file_train": "metadata.csv",
-                "meta_file_val": null
-            }
-        ]
-
-}
-
diff --git a/synthesize.py b/synthesize.py
index 1a760268..18048c2f 100644
--- a/synthesize.py
+++ b/synthesize.py
@@ -7,7 +7,8 @@ import json
 import string
 
 from TTS.utils.synthesis import synthesis
-from TTS.utils.generic_utils import load_config, setup_model
+from TTS.utils.generic_utils import setup_model
+from TTS.utils.io import load_config
 from TTS.utils.text.symbols import make_symbols, symbols, phonemes
 from TTS.utils.audio import AudioProcessor
 

From c27fd4238afb7a7c7e059f9b4cedcf37146b2aa2 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 22 May 2020 13:09:07 +0200
Subject: [PATCH 009/185] update torch version and solve compat issue with
 isinf

---
 requirements.txt  |  2 +-
 setup.py          |  2 +-
 utils/training.py | 12 +++++++++---
 3 files changed, 11 insertions(+), 5 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index 5f31db70..862cb229 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy>=1.16.0
-torch>=0.4.1
+torch>=1.5
 librosa>=0.5.1
 Unidecode>=0.4.20
 tensorboard
diff --git a/setup.py b/setup.py
index 5e89723b..84a31488 100644
--- a/setup.py
+++ b/setup.py
@@ -92,7 +92,7 @@ setup(
     },
     install_requires=[
         "scipy>=0.19.0",
-        "torch>=0.4.1",
+        "torch>=1.5",
         "numpy>=1.16.0",
         "librosa==0.6.2",
         "unidecode==0.4.20",
diff --git a/utils/training.py b/utils/training.py
index ebf8fd13..5ce7948b 100644
--- a/utils/training.py
+++ b/utils/training.py
@@ -9,9 +9,15 @@ def check_update(model, grad_clip, ignore_stopnet=False):
         grad_norm = torch.nn.utils.clip_grad_norm_([param for name, param in model.named_parameters() if 'stopnet' not in name], grad_clip)
     else:
         grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
-    if torch.isinf(grad_norm):
-        print(" | > Gradient is INF !!")
-        skip_flag = True
+    # compatibility with different torch versions
+    if isinstance(grad_norm, float):
+        if np.isinf(grad_norm):
+            print(" | > Gradient is INF !!")
+            skip_flag = True
+    else:
+        if torch.isinf(grad_norm):
+            print(" | > Gradient is INF !!")
+            skip_flag = True
     return grad_norm, skip_flag
 
 

From b5f61d81c0e2e08b5be2b5e222377cc590731281 Mon Sep 17 00:00:00 2001
From: Ubuntu <ubuntu@ip-172-31-12-20.ec2.internal>
Date: Sat, 23 May 2020 12:39:47 +0000
Subject: [PATCH 010/185] fix error mshg grammar

---
 datasets/preprocess.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/datasets/preprocess.py b/datasets/preprocess.py
index ce876edc..e8700c6b 100644
--- a/datasets/preprocess.py
+++ b/datasets/preprocess.py
@@ -120,7 +120,7 @@ def mailabs(root_path, meta_files=None):
                     text = cols[1].strip()
                     items.append([text, wav_file, speaker_name])
                 else:
-                    raise RuntimeError("> File %s is not exist!"%(wav_file))
+                    raise RuntimeError("> File %s does not exist!"%(wav_file))
     return items
 
 
@@ -185,7 +185,7 @@ def libri_tts(root_path, meta_files=None):
                 text = cols[1]
                 items.append([text, wav_file, speaker_name])
     for item in items:
-        assert os.path.exists(item[1]), f" [!] wav file is not exist - {item[1]}"
+        assert os.path.exists(item[1]), f" [!] wav files don't exist - {item[1]}"
     return items
 
 
@@ -203,5 +203,5 @@ def custom_turkish(root_path, meta_file):
                 continue
             text = cols[1].strip()
             items.append([text, wav_file, speaker_name])
-    print(f" [!] {len(skipped_files)} files skipped. They are not exist...")
+    print(f" [!] {len(skipped_files)} files skipped. They don't exist...")
     return items

From 0333483617953bd105d68032c5d03b5819270e7f Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sat, 23 May 2020 14:47:32 +0200
Subject: [PATCH 011/185] set the initial color right

---
 utils/console_logger.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/utils/console_logger.py b/utils/console_logger.py
index 5c6ec75f..0b361bb8 100644
--- a/utils/console_logger.py
+++ b/utils/console_logger.py
@@ -87,9 +87,9 @@ class ConsoleLogger():
             diff = 0
             if self.old_eval_loss_dict is not None:
                 diff = value - self.old_eval_loss_dict[key]
-                if diff < 0:
+                if diff <= 0:
                     color = tcolors.OKGREEN
                     sign = ''
             log_text += "{}{}:{} {:.5f} {}({}{:.5f})\n".format(indent, key, color, value, tcolors.ENDC, sign, diff)
         self.old_eval_loss_dict = avg_loss_dict
-        print(log_text, flush=True)
\ No newline at end of file
+        print(log_text, flush=True)

From ac55f198f17ec4ef550b82ab28f22b5eea9ebbad Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sat, 23 May 2020 14:48:34 +0200
Subject: [PATCH 012/185] commenting config.json

---
 config.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/config.json b/config.json
index 03907fb0..32debf86 100644
--- a/config.json
+++ b/config.json
@@ -103,7 +103,7 @@
 
     // TENSORBOARD and LOGGING
     "print_step": 25,       // Number of steps to log traning on console.
-    "print_eval": false,     // If True, it prints loss values in evalulation.
+    "print_eval": false,     // If True, it prints intermediate loss values in evalulation.
     "save_step": 10000,      // Number of training steps expected to save traninpg stats and checkpoints.
     "checkpoint": true,     // If true, it saves checkpoints per "save_step"
     "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.

From 1d8ac4c571f543acf666cccdc0e4d4d214c8b96e Mon Sep 17 00:00:00 2001
From: George Roussos <Georgerussos@icloud.com>
Date: Sat, 23 May 2020 23:22:04 +0200
Subject: [PATCH 013/185] Update compute_statistics.py

change import of load_config (is in io)
---
 compute_statistics.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/compute_statistics.py b/compute_statistics.py
index bbedf7af..9cd073cd 100755
--- a/compute_statistics.py
+++ b/compute_statistics.py
@@ -8,7 +8,7 @@ import numpy as np
 from tqdm import tqdm
 
 from TTS.datasets.preprocess import load_meta_data
-from TTS.utils.generic_utils import load_config
+from TTS.utils.io import load_config
 from TTS.utils.audio import AudioProcessor
 
 def main():

From ef7e31c498f4fdd12acc1aad5ce12fb36d80a970 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Mon, 25 May 2020 10:32:14 +0200
Subject: [PATCH 014/185] fix #412

---
 speaker_encoder/train.py | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/speaker_encoder/train.py b/speaker_encoder/train.py
index 0a137360..f74d0880 100644
--- a/speaker_encoder/train.py
+++ b/speaker_encoder/train.py
@@ -13,12 +13,11 @@ from TTS.speaker_encoder.model import SpeakerEncoder
 from TTS.speaker_encoder.visual import plot_embeddings
 from TTS.speaker_encoder.generic_utils import save_best_model
 from TTS.utils.audio import AudioProcessor
-from TTS.utils.generic_utils import (NoamLR, check_update, copy_config_file,
-                                     count_parameters,
-                                     create_experiment_folder, get_git_branch,
-                                     load_config,
+from TTS.utils.generic_utils import (create_experiment_folder, get_git_branch,
                                      remove_experiment_folder, set_init_dict)
-from TTS.utils.logger import Logger
+from TTS.utils.io import load_config, copy_config_file
+from TTS.utils.training import check_update, NoamLR
+from TTS.utils.tensorboard_logger import TensorboardLogger
 from TTS.utils.radam import RAdam
 
 torch.backends.cudnn.enabled = True
@@ -237,7 +236,7 @@ if __name__ == '__main__':
                      new_fields)
 
     LOG_DIR = OUT_PATH
-    tb_logger = Logger(LOG_DIR)
+    tb_logger = TensorboardLogger(LOG_DIR)
 
     try:
         main(args)

From 3eacab9f6ac58be816ff7b4c5201705e3b5d73c5 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Mon, 25 May 2020 10:32:42 +0200
Subject: [PATCH 015/185] add requirements for speaker encoder

---
 speaker_encoder/requirements.txt | 2 ++
 1 file changed, 2 insertions(+)
 create mode 100644 speaker_encoder/requirements.txt

diff --git a/speaker_encoder/requirements.txt b/speaker_encoder/requirements.txt
new file mode 100644
index 00000000..a486cc45
--- /dev/null
+++ b/speaker_encoder/requirements.txt
@@ -0,0 +1,2 @@
+umap-learn
+numpy>=1.17.0

From e71a3772021d47bbf6a9c4f1e38420ba316f7e8c Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sat, 30 May 2020 14:05:19 +0200
Subject: [PATCH 016/185] add a non-existing value to avg values

---
 utils/generic_utils.py | 19 ++++++++++++-------
 1 file changed, 12 insertions(+), 7 deletions(-)

diff --git a/utils/generic_utils.py b/utils/generic_utils.py
index 1c7dd5e4..5a811907 100644
--- a/utils/generic_utils.py
+++ b/utils/generic_utils.py
@@ -197,14 +197,19 @@ class KeepAverage():
         self.iters[name] = init_iter
 
     def update_value(self, name, value, weighted_avg=False):
-        if weighted_avg:
-            self.avg_values[name] = 0.99 * self.avg_values[name] + 0.01 * value
-            self.iters[name] += 1
+        if name not in self.avg_values:
+            # add value if not exist before
+            self.add_value(name, init_val=value)
         else:
-            self.avg_values[name] = self.avg_values[name] * \
-                self.iters[name] + value
-            self.iters[name] += 1
-            self.avg_values[name] /= self.iters[name]
+            # else update existing value
+            if weighted_avg:
+                self.avg_values[name] = 0.99 * self.avg_values[name] + 0.01 * value
+                self.iters[name] += 1
+            else:
+                self.avg_values[name] = self.avg_values[name] * \
+                    self.iters[name] + value
+                self.iters[name] += 1
+                self.avg_values[name] /= self.iters[name]
 
     def add_values(self, name_dict):
         for key, value in name_dict.items():

From 5116a20c86608a2790b1b7cecf0765a003134218 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sat, 30 May 2020 18:07:01 +0200
Subject: [PATCH 017/185] differentiate modules on TB with a prefix

---
 train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/train.py b/train.py
index e4963ee7..82f91cd5 100644
--- a/train.py
+++ b/train.py
@@ -667,7 +667,7 @@ if __name__ == '__main__':
         os.chmod(OUT_PATH, 0o775)
 
         LOG_DIR = OUT_PATH
-        tb_logger = TensorboardLogger(LOG_DIR)
+        tb_logger = TensorboardLogger(LOG_DIR, model_name='TTS')
 
         # write model desc to tensorboard
         tb_logger.tb_add_text('model-description', c['run_description'], 0)

From 0372c5ce5d066667102193133aa46133037c1d09 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sat, 30 May 2020 18:08:26 +0200
Subject: [PATCH 018/185] set state dict using direct state_dict dict

---
 utils/generic_utils.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/utils/generic_utils.py b/utils/generic_utils.py
index 5a811907..5b135061 100644
--- a/utils/generic_utils.py
+++ b/utils/generic_utils.py
@@ -106,15 +106,15 @@ def sequence_mask(sequence_length, max_len=None):
     return seq_range_expand < seq_length_expand
 
 
-def set_init_dict(model_dict, checkpoint, c):
+def set_init_dict(model_dict, checkpoint_state, c):
     # Partial initialization: if there is a mismatch with new and old layer, it is skipped.
-    for k, v in checkpoint['model'].items():
+    for k, v in checkpoint_state.items():
         if k not in model_dict:
             print(" | > Layer missing in the model definition: {}".format(k))
     # 1. filter out unnecessary keys
     pretrained_dict = {
         k: v
-        for k, v in checkpoint['model'].items() if k in model_dict
+        for k, v in checkpoint_state.items() if k in model_dict
     }
     # 2. filter out different size layers
     pretrained_dict = {

From 4ecd47e072ab594dc0c7af952b341a8f6f8090bb Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sat, 30 May 2020 18:08:56 +0200
Subject: [PATCH 019/185] use model_name prefix in tb_logger

---
 utils/tensorboard_logger.py | 21 +++++++++++----------
 1 file changed, 11 insertions(+), 10 deletions(-)

diff --git a/utils/tensorboard_logger.py b/utils/tensorboard_logger.py
index 15fe04e4..cbf68ad6 100644
--- a/utils/tensorboard_logger.py
+++ b/utils/tensorboard_logger.py
@@ -3,7 +3,8 @@ from tensorboardX import SummaryWriter
 
 
 class TensorboardLogger(object):
-    def __init__(self, log_dir):
+    def __init__(self, log_dir, model_name):
+        self.model_name = model_name
         self.writer = SummaryWriter(log_dir)
         self.train_stats = {}
         self.eval_stats = {}
@@ -50,31 +51,31 @@ class TensorboardLogger(object):
                 traceback.print_exc()
 
     def tb_train_iter_stats(self, step, stats):
-        self.dict_to_tb_scalar("TrainIterStats", stats, step)
+        self.dict_to_tb_scalar(f"{self.model_name}_TrainIterStats", stats, step)
 
     def tb_train_epoch_stats(self, step, stats):
-        self.dict_to_tb_scalar("TrainEpochStats", stats, step)
+        self.dict_to_tb_scalar(f"{self.model_name}_TrainEpochStats", stats, step)
 
     def tb_train_figures(self, step, figures):
-        self.dict_to_tb_figure("TrainFigures", figures, step)
+        self.dict_to_tb_figure(f"{self.model_name}_TrainFigures", figures, step)
 
     def tb_train_audios(self, step, audios, sample_rate):
-        self.dict_to_tb_audios("TrainAudios", audios, step, sample_rate)
+        self.dict_to_tb_audios(f"{self.model_name}_TrainAudios", audios, step, sample_rate)
 
     def tb_eval_stats(self, step, stats):
-        self.dict_to_tb_scalar("EvalStats", stats, step)
+        self.dict_to_tb_scalar(f"{self.model_name}_EvalStats", stats, step)
 
     def tb_eval_figures(self, step, figures):
-        self.dict_to_tb_figure("EvalFigures", figures, step)
+        self.dict_to_tb_figure(f"{self.model_name}_EvalFigures", figures, step)
 
     def tb_eval_audios(self, step, audios, sample_rate):
-        self.dict_to_tb_audios("EvalAudios", audios, step, sample_rate)
+        self.dict_to_tb_audios(f"{self.model_name}_EvalAudios", audios, step, sample_rate)
 
     def tb_test_audios(self, step, audios, sample_rate):
-        self.dict_to_tb_audios("TestAudios", audios, step, sample_rate)
+        self.dict_to_tb_audios(f"{self.model_name}_TestAudios", audios, step, sample_rate)
 
     def tb_test_figures(self, step, figures):
-        self.dict_to_tb_figure("TestFigures", figures, step)
+        self.dict_to_tb_figure(f"{self.model_name}_TestFigures", figures, step)
 
     def tb_add_text(self, title, text, step):
         self.writer.add_text(title, text, step)

From d47c9987dbaa2dbd129b3f897c6fb45b392ae455 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sat, 30 May 2020 18:09:25 +0200
Subject: [PATCH 020/185] initial commit intro. to vocoder submodule

---
 vocoder/README.md                             |  35 +
 vocoder/__init__.py                           |   0
 vocoder/compute_tts_features.py               |   0
 vocoder/configs/melgan_config.json            | 138 ++++
 vocoder/datasets/__init__.py                  |   0
 vocoder/datasets/gan_dataset.py               | 133 ++++
 vocoder/datasets/preprocess.py                |  16 +
 vocoder/layers/__init__.py                    |   0
 vocoder/layers/losses.py                      | 273 +++++++
 vocoder/layers/melgan.py                      |  48 ++
 vocoder/layers/pqmf.py                        |  55 ++
 vocoder/layers/pqmf2.py                       | 127 ++++
 vocoder/layers/qmf.dat                        | 640 +++++++++++++++++
 vocoder/models/__init__.py                    |   0
 vocoder/models/melgan_discriminator.py        |  80 +++
 vocoder/models/melgan_generator.py            |  91 +++
 .../models/melgan_multiscale_discriminator.py |  41 ++
 vocoder/models/multiband_melgan_generator.py  |  38 +
 vocoder/models/random_window_discriminator.py | 225 ++++++
 vocoder/notebooks/Untitled.ipynb              | 678 ++++++++++++++++++
 vocoder/notebooks/Untitled1.ipynb             |   6 +
 vocoder/pqmf_output.wav                       | Bin 0 -> 83812 bytes
 vocoder/tests/__init__.py                     |   1 +
 vocoder/tests/test_config.json                |  24 +
 vocoder/tests/test_datasets.py                |  95 +++
 vocoder/tests/test_losses.py                  |  62 ++
 vocoder/tests/test_melgan_discriminator.py    |  26 +
 vocoder/tests/test_melgan_generator.py        |  15 +
 vocoder/tests/test_pqmf.py                    |  33 +
 vocoder/tests/test_rwd.py                     |  21 +
 vocoder/train.py                              | 585 +++++++++++++++
 vocoder/utils/__init__.py                     |   0
 vocoder/utils/console_logger.py               |  97 +++
 vocoder/utils/generic_utils.py                | 102 +++
 vocoder/utils/io.py                           |  52 ++
 35 files changed, 3737 insertions(+)
 create mode 100644 vocoder/README.md
 create mode 100644 vocoder/__init__.py
 create mode 100644 vocoder/compute_tts_features.py
 create mode 100644 vocoder/configs/melgan_config.json
 create mode 100644 vocoder/datasets/__init__.py
 create mode 100644 vocoder/datasets/gan_dataset.py
 create mode 100644 vocoder/datasets/preprocess.py
 create mode 100644 vocoder/layers/__init__.py
 create mode 100644 vocoder/layers/losses.py
 create mode 100644 vocoder/layers/melgan.py
 create mode 100644 vocoder/layers/pqmf.py
 create mode 100644 vocoder/layers/pqmf2.py
 create mode 100644 vocoder/layers/qmf.dat
 create mode 100644 vocoder/models/__init__.py
 create mode 100644 vocoder/models/melgan_discriminator.py
 create mode 100644 vocoder/models/melgan_generator.py
 create mode 100644 vocoder/models/melgan_multiscale_discriminator.py
 create mode 100644 vocoder/models/multiband_melgan_generator.py
 create mode 100644 vocoder/models/random_window_discriminator.py
 create mode 100644 vocoder/notebooks/Untitled.ipynb
 create mode 100644 vocoder/notebooks/Untitled1.ipynb
 create mode 100644 vocoder/pqmf_output.wav
 create mode 100644 vocoder/tests/__init__.py
 create mode 100644 vocoder/tests/test_config.json
 create mode 100644 vocoder/tests/test_datasets.py
 create mode 100644 vocoder/tests/test_losses.py
 create mode 100644 vocoder/tests/test_melgan_discriminator.py
 create mode 100644 vocoder/tests/test_melgan_generator.py
 create mode 100644 vocoder/tests/test_pqmf.py
 create mode 100644 vocoder/tests/test_rwd.py
 create mode 100644 vocoder/train.py
 create mode 100644 vocoder/utils/__init__.py
 create mode 100644 vocoder/utils/console_logger.py
 create mode 100644 vocoder/utils/generic_utils.py
 create mode 100644 vocoder/utils/io.py

diff --git a/vocoder/README.md b/vocoder/README.md
new file mode 100644
index 00000000..48fc24ee
--- /dev/null
+++ b/vocoder/README.md
@@ -0,0 +1,35 @@
+# Mozilla TTS Vocoders (Experimental)
+
+We provide here different vocoder implementations which can be combined with our TTS models to enable "FASTER THAN REAL-TIME" end-to-end TTS stack.
+
+Currently, there are implementations of the following models.
+
+- Melgan
+- MultiBand-Melgan
+- GAN-TTS (Discriminator Only)
+
+It is also very easy to adapt different vocoder models as we provide here a flexible and modular (but not too modular) framework.
+
+## Training a model
+
+You can see here an example (Soon)[Colab Notebook]() training MelGAN with LJSpeech dataset.
+
+In order to train a new model, you need to collecto all your wav files under a common parent folder and give this path to `data_path` field in '''config.json'''
+
+You need to define other relevant parameters in your ```config.json``` and then start traning with the following command from Mozilla TTS root path.
+
+```CUDA_VISIBLE_DEVICES='1' python vocoder/train.py --config_path path/to/config.json```
+
+Exampled config files can be found under `vocoder/configs/` folder.
+
+You can continue a previous training by the following command.
+
+```CUDA_VISIBLE_DEVICES='1' python vocoder/train.py --continue_path path/to/your/model/folder```
+
+You can fine-tune a pre-trained model by the following command.
+
+```CUDA_VISIBLE_DEVICES='1' python vocoder/train.py --restore_path path/to/your/model.pth.tar```
+
+Restoring a model starts a new training in a different output folder. It only restores model weights with the given checkpoint file. However, continuing a training starts from the same conditions the previous training run left off.
+
+You can also follow your training runs on Tensorboard as you do with our TTS models.
\ No newline at end of file
diff --git a/vocoder/__init__.py b/vocoder/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/vocoder/compute_tts_features.py b/vocoder/compute_tts_features.py
new file mode 100644
index 00000000..e69de29b
diff --git a/vocoder/configs/melgan_config.json b/vocoder/configs/melgan_config.json
new file mode 100644
index 00000000..9a3ded37
--- /dev/null
+++ b/vocoder/configs/melgan_config.json
@@ -0,0 +1,138 @@
+{
+    "run_name": "melgan",
+    "run_description": "melgan initial run",
+
+    // AUDIO PARAMETERS
+    "audio":{
+        // stft parameters
+        "num_freq": 513,         // number of stft frequency levels. Size of the linear spectogram frame.
+        "win_length": 1024,      // stft window length in ms.
+        "hop_length": 256,       // stft window hop-lengh in ms.
+        "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
+        "frame_shift_ms": null,  // stft window hop-lengh in ms. If null, 'hop_length' is used.
+
+        // Audio processing parameters
+        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
+        "preemphasis": 0.0,     // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
+        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
+
+        // Silence trimming
+        "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
+        "trim_db": 60,          // threshold for timming silence. Set this according to your dataset.
+
+        // Griffin-Lim
+        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
+        "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
+
+        // MelSpectrogram parameters
+        "num_mels": 80,         // size of the mel spec frame.
+        "mel_fmin": 0.0,        // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
+        "mel_fmax": 8000.0,     // maximum freq level for mel-spec. Tune for dataset!!
+
+        // Normalization parameters
+        "signal_norm": true,    // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
+        "min_level_db": -100,   // lower bound for normalization
+        "symmetric_norm": true, // move normalization to range [-1, 1]
+        "max_norm": 4.0,        // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
+        "clip_norm": true,      // clip normalized values into the range.
+        "stats_path": null    // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
+    },
+
+    // DISTRIBUTED TRAINING
+    // "distributed":{
+    //     "backend": "nccl",
+    //     "url": "tcp:\/\/localhost:54321"
+    // },
+
+    // MODEL PARAMETERS
+    "use_pqmf": true,
+
+    // LOSS PARAMETERS
+    "use_stft_loss": true,
+    "use_mse_gan_loss": true,
+    "use_hinge_gan_loss": false,
+    "use_feat_match_loss": false,  // use only with melgan discriminators
+
+    "stft_loss_alpha": 1,
+    "mse_gan_loss_alpha": 1,
+    "hinge_gan_loss_alpha": 1,
+    "feat_match_loss_alpha": 10.0,
+
+    "stft_loss_params": {
+        "n_ffts": [1024, 2048, 512],
+        "hop_lengths": [120, 240, 50],
+        "win_lengths": [600, 1200, 240]
+    },
+    "target_loss": "avg_G_loss",  // loss value to pick the best model
+
+    // DISCRIMINATOR
+    "discriminator_model": "melgan_multiscale_discriminator",
+    "discriminator_model_params":{
+        "base_channels": 16,
+        "max_channels":1024,
+        "downsample_factors":[4, 4, 4, 4]
+    },
+    "steps_to_start_discriminator": 100000,      // steps required to start GAN trainining.1
+
+    // "discriminator_model": "random_window_discriminator",
+    // "discriminator_model_params":{
+    //     "uncond_disc_donwsample_factors": [8, 4],
+    //     "cond_disc_downsample_factors": [[8, 4, 2, 2, 2], [8, 4, 2, 2], [8, 4, 2], [8, 4], [4, 2, 2]],
+    //     "cond_disc_out_channels": [[128, 128, 256, 256], [128, 256, 256], [128, 256], [256], [128, 256]],
+    //     "window_sizes": [512, 1024, 2048, 4096, 8192]
+    // },
+
+
+    // GENERATOR
+    "generator_model": "multiband_melgan_generator",
+    "generator_model_params": {
+        "upsample_factors":[2 ,2, 4, 4],
+        "num_res_blocks": 4
+    },
+
+    // DATASET
+    "data_path": "/home/erogol/Data/LJSpeech-1.1/wavs/",
+    "seq_len": 16384,
+    "pad_short": 2000,
+    "conv_pad": 0,
+    "use_noise_augment": true,
+    "use_cache": true,
+
+    "reinit_layers": [],    // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
+
+    // TRAINING
+    "batch_size": 64,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
+
+    // VALIDATION
+    "run_eval": true,
+    "test_delay_epochs": 10,  //Until attention is aligned, testing only wastes computation time.
+    "test_sentences_file": null,  // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
+
+    // OPTIMIZER
+    "noam_schedule": true,        // use noam warmup and lr schedule.
+    "grad_clip": 1.0,              // upper limit for gradients for clipping.
+    "epochs": 1000,                // total number of epochs to train.
+    "wd": 0.000001,                // Weight decay weight.
+    "lr_gen": 0.0001,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
+    "lr_disc": 0.0001,
+    "warmup_steps_gen": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
+    "warmup_steps_disc": 4000,
+    "gen_clip_grad": 10.0,
+    "disc_clip_grad": 10.0,
+
+    // TENSORBOARD and LOGGING
+    "print_step": 25,       // Number of steps to log traning on console.
+    "print_eval": false,     // If True, it prints intermediate loss values in evalulation.
+    "save_step": 10000,      // Number of training steps expected to save traninpg stats and checkpoints.
+    "checkpoint": true,     // If true, it saves checkpoints per "save_step"
+    "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
+
+    // DATA LOADING
+    "num_loader_workers": 4,        // number of training data loader processes. Don't set it too big. 4-8 are good values.
+    "num_val_loader_workers": 4,    // number of evaluation data loader processes.
+    "eval_split_size": 10,
+
+    // PATHS
+    "output_path": "/home/erogol/Models/LJSpeech/"
+}
+
diff --git a/vocoder/datasets/__init__.py b/vocoder/datasets/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/vocoder/datasets/gan_dataset.py b/vocoder/datasets/gan_dataset.py
new file mode 100644
index 00000000..10d36cab
--- /dev/null
+++ b/vocoder/datasets/gan_dataset.py
@@ -0,0 +1,133 @@
+import os
+import glob
+import torch
+import random
+import numpy as np
+from torch.utils.data import Dataset, DataLoader
+from multiprocessing import Manager
+
+
+def create_dataloader(hp, args, train):
+    dataset = MelFromDisk(hp, args, train)
+
+    if train:
+        return DataLoader(dataset=dataset,
+                          batch_size=hp.train.batch_size,
+                          shuffle=True,
+                          num_workers=hp.train.num_workers,
+                          pin_memory=True,
+                          drop_last=True)
+    else:
+        return DataLoader(dataset=dataset,
+                          batch_size=1,
+                          shuffle=False,
+                          num_workers=hp.train.num_workers,
+                          pin_memory=True,
+                          drop_last=False)
+
+
+class GANDataset(Dataset):
+    """
+    GAN Dataset searchs for all the wav files under root path
+    and converts them to acoustic features on the fly and returns
+    random segments of (audio, feature) couples.
+    """
+    def __init__(self,
+                 ap,
+                 items,
+                 seq_len,
+                 hop_len,
+                 pad_short,
+                 conv_pad=2,
+                 is_training=True,
+                 return_segments=True,
+                 use_noise_augment=False,
+                 use_cache=False,
+                 verbose=False):
+
+        self.ap = ap
+        self.item_list = items
+        self.seq_len = seq_len
+        self.hop_len = hop_len
+        self.pad_short = pad_short
+        self.conv_pad = conv_pad
+        self.is_training = is_training
+        self.return_segments = return_segments
+        self.use_cache = use_cache
+        self.use_noise_augment = use_noise_augment
+
+        assert seq_len % hop_len == 0, " [!] seq_len has to be a multiple of hop_len."
+        self.feat_frame_len = seq_len // hop_len + (2 * conv_pad)
+
+        # map G and D instances
+        self.G_to_D_mappings = [i for i in range(len(self.item_list))]
+        self.shuffle_mapping()
+
+        # cache acoustic features
+        if use_cache:
+            self.create_feature_cache()
+
+
+
+    def create_feature_cache(self):
+        self.manager = Manager()
+        self.cache = self.manager.list()
+        self.cache += [None for _ in range(len(self.item_list))]
+
+    def find_wav_files(self, path):
+        return glob.glob(os.path.join(path, '**', '*.wav'), recursive=True)
+
+    def __len__(self):
+        return len(self.item_list)
+
+    def __getitem__(self, idx):
+        """ Return different items for Generator and Discriminator and
+        cache acoustic features """
+        if self.return_segments:
+            idx2 = self.G_to_D_mappings[idx]
+            item1 = self.load_item(idx)
+            item2 = self.load_item(idx2)
+            return item1, item2
+        else:
+            item1 = self.load_item(idx)
+            return item1
+
+    def shuffle_mapping(self):
+        random.shuffle(self.G_to_D_mappings)
+
+    def load_item(self, idx):
+        """ load (audio, feat) couple """
+        wavpath = self.item_list[idx]
+        # print(wavpath)
+
+        if self.use_cache and self.cache[idx] is not None:
+            audio, mel = self.cache[idx]
+        else:
+            audio = self.ap.load_wav(wavpath)
+            mel = self.ap.melspectrogram(audio)
+
+        if len(audio) < self.seq_len + self.pad_short:
+            audio = np.pad(audio, (0, self.seq_len + self.pad_short - len(audio)), \
+                    mode='constant', constant_values=0.0)
+
+        # correct the audio length wrt padding applied in stft
+        audio = np.pad(audio, (0, self.hop_len), mode="edge")
+        audio = audio[:mel.shape[-1] * self.hop_len]
+        assert mel.shape[-1] * self.hop_len == audio.shape[-1], f' [!] {mel.shape[-1] * self.hop_len} vs {audio.shape[-1]}'
+
+        audio = torch.from_numpy(audio).float().unsqueeze(0)
+        mel = torch.from_numpy(mel).float().squeeze(0)
+
+        if self.return_segments:
+            max_mel_start = mel.shape[1] - self.feat_frame_len
+            mel_start = random.randint(0, max_mel_start)
+            mel_end = mel_start + self.feat_frame_len
+            mel = mel[:, mel_start:mel_end]
+
+            audio_start = mel_start * self.hop_len
+            audio = audio[:, audio_start:audio_start +
+                          self.seq_len]
+
+        if self.use_noise_augment and self.is_training and self.return_segments:
+            audio = audio + (1 / 32768) * torch.randn_like(audio)
+        return (mel, audio)
diff --git a/vocoder/datasets/preprocess.py b/vocoder/datasets/preprocess.py
new file mode 100644
index 00000000..01e01e3e
--- /dev/null
+++ b/vocoder/datasets/preprocess.py
@@ -0,0 +1,16 @@
+import glob
+import os
+
+import numpy as np
+
+
+def find_wav_files(data_path):
+    wav_paths = glob.glob(os.path.join(data_path, '**', '*.wav'), recursive=True)
+    return wav_paths
+
+
+def load_wav_data(data_path, eval_split_size):
+    wav_paths = find_wav_files(data_path)
+    np.random.seed(0)
+    np.random.shuffle(wav_paths)
+    return wav_paths[:eval_split_size], wav_paths[eval_split_size:]
diff --git a/vocoder/layers/__init__.py b/vocoder/layers/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/vocoder/layers/losses.py b/vocoder/layers/losses.py
new file mode 100644
index 00000000..11985629
--- /dev/null
+++ b/vocoder/layers/losses.py
@@ -0,0 +1,273 @@
+import torch
+
+from torch import nn
+from torch.nn import functional as F
+
+
+class TorchSTFT():
+    def __init__(self, n_fft, hop_length, win_length, window='hann_window'):
+        self.n_fft = n_fft
+        self.hop_length = hop_length
+        self.win_length = win_length
+        self.window = getattr(torch, window)(win_length)
+
+    def __call__(self, x):
+        # B x D x T x 2
+        o = torch.stft(x,
+                       self.n_fft,
+                       self.hop_length,
+                       self.win_length,
+                       self.window,
+                       center=True,
+                       pad_mode="constant",  # compatible with audio.py
+                       normalized=False,
+                       onesided=True)
+        M = o[:, :, :, 0]
+        P = o[:, :, :, 1]
+        return torch.sqrt(torch.clamp(M ** 2 + P ** 2, min=1e-8))
+
+
+#################################
+# GENERATOR LOSSES
+#################################
+
+
+class STFTLoss(nn.Module):
+    def __init__(self, n_fft, hop_length, win_length):
+        super(STFTLoss, self).__init__()
+        self.n_fft = n_fft
+        self.hop_length = hop_length
+        self.win_length = win_length
+        self.stft = TorchSTFT(n_fft, hop_length, win_length)
+
+    def forward(self, y_hat, y):
+        y_hat_M = self.stft(y_hat)
+        y_M = self.stft(y)
+        # magnitude loss
+        loss_mag = F.l1_loss(torch.log(y_M), torch.log(y_hat_M))
+        # spectral convergence loss
+        loss_sc = torch.norm(y_M - y_hat_M, p="fro") / torch.norm(y_M, p="fro")
+        return loss_mag, loss_sc
+
+
+class MultiScaleSTFTLoss(torch.nn.Module):
+    def __init__(self,
+                 n_ffts=[1024, 2048, 512],
+                 hop_lengths=[120, 240, 50],
+                 win_lengths=[600, 1200, 240]):
+        super(MultiScaleSTFTLoss, self).__init__()
+        self.loss_funcs = torch.nn.ModuleList()
+        for idx in range(len(n_ffts)):
+            self.loss_funcs.append(STFTLoss(n_ffts[idx], hop_lengths[idx], win_lengths[idx]))
+
+    def forward(self, y_hat, y):
+        N = len(self.loss_funcs)
+        loss_sc = 0
+        loss_mag = 0
+        for f in self.loss_funcs:
+            lm, lsc = f(y_hat, y)
+            loss_mag += lm
+            loss_sc += lsc
+        loss_sc /= N
+        loss_mag /= N
+        return loss_mag, loss_sc
+
+
+class MSEGLoss(nn.Module):
+    """ Mean Squared Generator Loss """
+    def __init__(self,):
+        super(MSEGLoss, self).__init__()
+
+    def forward(self, score_fake, ):
+        loss_fake = torch.mean(torch.sum(torch.pow(score_fake, 2), dim=[1, 2]))
+        return loss_fake
+
+
+class HingeGLoss(nn.Module):
+    """ Hinge Discriminator Loss """
+    def __init__(self,):
+        super(HingeGLoss, self).__init__()
+
+    def forward(self, score_fake, score_real):
+        loss_fake = torch.mean(F.relu(1. + score_fake))
+        return loss_fake
+
+
+##################################
+# DISCRIMINATOR LOSSES
+##################################
+
+
+class MSEDLoss(nn.Module):
+    """ Mean Squared Discriminator Loss """
+    def __init__(self,):
+        super(MSEDLoss, self).__init__()
+
+    def forward(self, score_fake, score_real):
+        loss_real = torch.mean(torch.sum(torch.pow(score_real - 1.0, 2), dim=[1, 2]))
+        loss_fake = torch.mean(torch.sum(torch.pow(score_fake, 2), dim=[1, 2]))
+        loss_d = loss_real + loss_fake
+        return loss_d, loss_real, loss_fake
+
+
+class HingeDLoss(nn.Module):
+    """ Hinge Discriminator Loss """
+    def __init__(self,):
+        super(HingeDLoss, self).__init__()
+
+    def forward(self, score_fake, score_real):
+        loss_real = torch.mean(F.relu(1. - score_real))
+        loss_fake = torch.mean(F.relu(1. + score_fake))
+        loss_d = loss_real + loss_fake
+        return loss_d, loss_real, loss_fake
+
+
+class MelganFeatureLoss(nn.Module):
+    def __init__(self, ):
+        super(MelganFeatureLoss, self).__init__()
+
+    def forward(self, fake_feats, real_feats):
+        loss_feats = 0
+        for fake_feat, real_feat in zip(fake_feats, real_feats):
+            loss_feats += hp.model.feat_match * torch.mean(torch.abs(fake_feat - real_feat))
+        return loss_feats
+
+
+##################################
+# LOSS WRAPPERS
+##################################
+
+
+class GeneratorLoss(nn.Module):
+    def __init__(self, C):
+        super(GeneratorLoss, self).__init__()
+        assert C.use_mse_gan_loss and C.use_hinge_gan_loss == False,\
+            " [!] Cannot use HingeGANLoss and MSEGANLoss together."
+
+        self.use_stft_loss = C.use_stft_loss
+        self.use_mse_gan_loss = C.use_mse_gan_loss
+        self.use_hinge_gan_loss = C.use_hinge_gan_loss
+        self.use_feat_match_loss = C.use_feat_match_loss
+
+        self.stft_loss_alpha = C.stft_loss_alpha
+        self.mse_gan_loss_alpha = C.mse_gan_loss_alpha
+        self.hinge_gan_loss_alpha = C.hinge_gan_loss_alpha
+        self.feat_match_loss_alpha = C.feat_match_loss_alpha
+
+        if C.use_stft_loss:
+            self.stft_loss = MultiScaleSTFTLoss(**C.stft_loss_params)
+        if C.use_mse_gan_loss:
+            self.mse_loss = MSEGLoss()
+        if C.use_hinge_gan_loss:
+            self.hinge_loss = HingeGLoss()
+        if C.use_feat_match_loss:
+            self.feat_match_loss = MelganFeatureLoss()
+
+    def forward(self, y_hat=None, y=None, scores_fake=None, feats_fake=None, feats_real=None):
+        loss = 0
+        return_dict = {}
+
+        # STFT Loss
+        if self.use_stft_loss:
+            stft_loss_mg, stft_loss_sc = self.stft_loss(y_hat.squeeze(1), y.squeeze(1))
+            return_dict['G_stft_loss_mg'] = stft_loss_mg
+            return_dict['G_stft_loss_sc'] = stft_loss_sc
+            loss += self.stft_loss_alpha * (stft_loss_mg + stft_loss_sc)
+
+        # Fake Losses
+        if self.use_mse_gan_loss and scores_fake is not None:
+            mse_fake_loss = 0
+            if isinstance(scores_fake, list):
+                for score_fake in scores_fake:
+                    fake_loss = self.mse_loss(score_fake)
+                    mse_fake_loss += fake_loss
+            else:
+                fake_loss = self.mse_loss(scores_fake)
+                mse_fake_loss = fake_loss
+            return_dict['G_mse_fake_loss'] = mse_fake_loss
+            loss += self.mse_gan_loss_alpha * mse_fake_loss
+
+        if self.use_hinge_gan_loss and not scores_fake is not None:
+            hinge_fake_loss = 0
+            if isinstance(scores_fake, list):
+                for score_fake in scores_fake:
+                    fake_loss = self.hinge_loss(score_fake)
+                    hinge_fake_loss += fake_loss
+            else:
+                fake_loss = self.hinge_loss(scores_fake)
+                hinge_fake_loss = fake_loss
+            return_dict['G_hinge_fake_loss'] = hinge_fake_loss
+            loss += self.hinge_gan_loss_alpha * hinge_fake_loss
+
+        # Feature Matching Loss
+        if self.use_feat_match_loss and not feats_fake:
+            feat_match_loss = self.feat_match_loss(feats_fake, feats_real)
+            return_dict['G_feat_match_loss'] = feat_match_loss
+            loss += self.feat_match_loss_alpha * feat_match_loss
+        return_dict['G_loss'] = loss
+        return return_dict
+
+
+class DiscriminatorLoss(nn.Module):
+    def __init__(self, C):
+        super(DiscriminatorLoss, self).__init__()
+        assert C.use_mse_gan_loss and C.use_hinge_gan_loss == False,\
+            " [!] Cannot use HingeGANLoss and MSEGANLoss together."
+
+        self.use_mse_gan_loss = C.use_mse_gan_loss
+        self.use_hinge_gan_loss = C.use_hinge_gan_loss
+
+        self.mse_gan_loss_alpha = C.mse_gan_loss_alpha
+        self.hinge_gan_loss_alpha = C.hinge_gan_loss_alpha
+
+        if C.use_mse_gan_loss:
+            self.mse_loss = MSEDLoss()
+        if C.use_hinge_gan_loss:
+            self.hinge_loss = HingeDLoss()
+
+    def forward(self, scores_fake, scores_real):
+        loss = 0
+        return_dict = {}
+
+        if self.use_mse_gan_loss:
+            mse_gan_loss = 0
+            mse_gan_real_loss = 0
+            mse_gan_fake_loss = 0
+            if isinstance(scores_fake, list):
+                for score_fake, score_real in zip(scores_fake, scores_real):
+                    total_loss, real_loss, fake_loss = self.mse_loss(score_fake, score_real)
+                    mse_gan_loss += total_loss
+                    mse_gan_real_loss += real_loss
+                    mse_gan_fake_loss += fake_loss
+            else:
+                total_loss, real_loss, fake_loss = self.mse_loss(scores_fake, scores_real)
+                mse_gan_loss = total_loss
+                mse_gan_real_loss = real_loss
+                mse_gan_fake_loss = fake_loss
+            return_dict['D_mse_gan_loss'] = mse_gan_loss
+            return_dict['D_mse_gan_real_loss'] = mse_gan_real_loss
+            return_dict['D_mse_gan_fake_loss'] = mse_gan_fake_loss
+            loss += self.mse_gan_loss_alpha * mse_gan_loss
+
+        if self.use_hinge_gan_loss:
+            hinge_gan_loss = 0
+            hinge_gan_real_loss = 0
+            hinge_gan_fake_loss = 0
+            if isinstance(scores_fake, list):
+                for score_fake, score_real in zip(scores_fake, scores_real):
+                    total_loss, real_loss, fake_loss = self.hinge_loss(score_fake, score_real)
+                    hinge_gan_loss += total_loss
+                    hinge_gan_real_loss += real_loss
+                    hinge_gan_fake_loss += fake_loss
+            else:
+                total_loss, real_loss, fake_loss = self.hinge_loss(scores_fake, scores_real)
+                hinge_gan_loss = total_loss
+                hinge_gan_real_loss = real_loss
+                hinge_gan_fake_loss = fake_loss
+            return_dict['D_hinge_gan_loss'] = hinge_gan_loss
+            return_dict['D_hinge_gan_real_loss'] = hinge_gan_real_loss
+            return_dict['D_hinge_gan_fake_loss'] = hinge_gan_fake_loss
+            loss += self.hinge_gan_loss_alpha * hinge_gan_loss
+
+        return_dict['D_loss'] = loss
+        return return_dict
\ No newline at end of file
diff --git a/vocoder/layers/melgan.py b/vocoder/layers/melgan.py
new file mode 100644
index 00000000..cda0413c
--- /dev/null
+++ b/vocoder/layers/melgan.py
@@ -0,0 +1,48 @@
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.nn.utils import weight_norm
+
+
+class ResidualStack(nn.Module):
+    def __init__(self, channels, num_res_blocks, kernel_size):
+        super(ResidualStack, self).__init__()
+
+        assert (kernel_size - 1) % 2 == 0, " [!] kernel_size has to be odd."
+        base_padding = (kernel_size - 1) // 2
+
+        self.blocks = nn.ModuleList()
+        for idx in range(num_res_blocks):
+            layer_kernel_size = kernel_size
+            layer_dilation = layer_kernel_size**idx
+            layer_padding = base_padding * layer_dilation
+            self.blocks += [nn.Sequential(
+                nn.LeakyReLU(0.2),
+                nn.ReflectionPad1d(layer_padding),
+                weight_norm(
+                    nn.Conv1d(channels,
+                              channels,
+                              kernel_size=kernel_size,
+                              dilation=layer_padding,
+                              bias=True)),
+                nn.LeakyReLU(0.2),
+                weight_norm(
+                    nn.Conv1d(channels, channels, kernel_size=1, bias=True)),
+            )]
+
+        self.shortcuts = nn.ModuleList([
+            weight_norm(nn.Conv1d(channels, channels, kernel_size=1,
+                                  bias=True)) for i in range(num_res_blocks)
+        ])
+
+    def forward(self, x):
+        for block, shortcut in zip(self.blocks, self.shortcuts):
+            x = shortcut(x) + block(x)
+        return x
+
+    def remove_weight_norm(self):
+        for block, shortcut in zip(self.blocks, self.shortcuts):
+            nn.utils.remove_weight_norm(block[2])
+            nn.utils.remove_weight_norm(block[4])
+            nn.utils.remove_weight_norm(shortcut)
diff --git a/vocoder/layers/pqmf.py b/vocoder/layers/pqmf.py
new file mode 100644
index 00000000..f438ea00
--- /dev/null
+++ b/vocoder/layers/pqmf.py
@@ -0,0 +1,55 @@
+"""Pseudo QMF modules."""
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from scipy import signal as sig
+
+
+# adapted from
+# https://github.com/kan-bayashi/ParallelWaveGAN/tree/master/parallel_wavegan
+class PQMF(torch.nn.Module):
+    def __init__(self, N=4, taps=62, cutoff=0.15, beta=9.0):
+        super(PQMF, self).__init__()
+
+        self.N = N
+        self.taps = taps
+        self.cutoff = cutoff
+        self.beta = beta
+
+        QMF = sig.firwin(taps + 1, cutoff, window=('kaiser', beta))
+        H = np.zeros((N, len(QMF)))
+        G = np.zeros((N, len(QMF)))
+        for k in range(N):
+            constant_factor = (2 * k + 1) * (np.pi /
+                                             (2 * N)) * (np.arange(taps + 1) -
+                                                         ((taps - 1) / 2))
+            phase = (-1)**k * np.pi / 4
+            H[k] = 2 * QMF * np.cos(constant_factor + phase)
+
+            G[k] = 2 * QMF * np.cos(constant_factor - phase)
+
+        H = torch.from_numpy(H[:, None, :]).float()
+        G = torch.from_numpy(G[None, :, :]).float()
+
+        self.register_buffer("H", H)
+        self.register_buffer("G", G)
+
+        updown_filter = torch.zeros((N, N, N)).float()
+        for k in range(N):
+            updown_filter[k, k, 0] = 1.0
+        self.register_buffer("updown_filter", updown_filter)
+        self.N = N
+
+        self.pad_fn = torch.nn.ConstantPad1d(taps // 2, 0.0)
+
+    def analysis(self, x):
+        return F.conv1d(x, self.H, padding=self.taps // 2, stride=self.N)
+
+    def synthesis(self, x):
+        x = F.conv_transpose1d(x,
+                               self.updown_filter * self.N,
+                               stride=self.N)
+        x = F.conv1d(x, self.G, padding=self.taps // 2)
+        return x
diff --git a/vocoder/layers/pqmf2.py b/vocoder/layers/pqmf2.py
new file mode 100644
index 00000000..4cffb819
--- /dev/null
+++ b/vocoder/layers/pqmf2.py
@@ -0,0 +1,127 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2020 Tomoki Hayashi
+#  MIT License (https://opensource.org/licenses/MIT)
+
+"""Pseudo QMF modules."""
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from scipy.signal import kaiser
+
+
+def design_prototype_filter(taps=62, cutoff_ratio=0.15, beta=9.0):
+    """Design prototype filter for PQMF.
+
+    This method is based on `A Kaiser window approach for the design of prototype
+    filters of cosine modulated filterbanks`_.
+
+    Args:
+        taps (int): The number of filter taps.
+        cutoff_ratio (float): Cut-off frequency ratio.
+        beta (float): Beta coefficient for kaiser window.
+
+    Returns:
+        ndarray: Impluse response of prototype filter (taps + 1,).
+
+    .. _`A Kaiser window approach for the design of prototype filters of cosine modulated filterbanks`:
+        https://ieeexplore.ieee.org/abstract/document/681427
+
+    """
+    # check the arguments are valid
+    assert taps % 2 == 0, "The number of taps mush be even number."
+    assert 0.0 < cutoff_ratio < 1.0, "Cutoff ratio must be > 0.0 and < 1.0."
+
+    # make initial filter
+    omega_c = np.pi * cutoff_ratio
+    with np.errstate(invalid='ignore'):
+        h_i = np.sin(omega_c * (np.arange(taps + 1) - 0.5 * taps)) \
+            / (np.pi * (np.arange(taps + 1) - 0.5 * taps))
+    h_i[taps // 2] = np.cos(0) * cutoff_ratio  # fix nan due to indeterminate form
+
+    # apply kaiser window
+    w = kaiser(taps + 1, beta)
+    h = h_i * w
+
+    return h
+
+
+class PQMF(torch.nn.Module):
+    """PQMF module.
+
+    This module is based on `Near-perfect-reconstruction pseudo-QMF banks`_.
+
+    .. _`Near-perfect-reconstruction pseudo-QMF banks`:
+        https://ieeexplore.ieee.org/document/258122
+
+    """
+
+    def __init__(self, subbands=4, taps=62, cutoff_ratio=0.15, beta=9.0):
+        """Initilize PQMF module.
+
+        Args:
+            subbands (int): The number of subbands.
+            taps (int): The number of filter taps.
+            cutoff_ratio (float): Cut-off frequency ratio.
+            beta (float): Beta coefficient for kaiser window.
+
+        """
+        super(PQMF, self).__init__()
+
+        # define filter coefficient
+        h_proto = design_prototype_filter(taps, cutoff_ratio, beta)
+        h_analysis = np.zeros((subbands, len(h_proto)))
+        h_synthesis = np.zeros((subbands, len(h_proto)))
+        for k in range(subbands):
+            h_analysis[k] = 2 * h_proto * np.cos((2 * k + 1) * (np.pi / (2 * subbands)) * (np.arange(taps + 1) - ((taps - 1) / 2)) + (-1) ** k * np.pi / 4)
+            h_synthesis[k] = 2 * h_proto * np.cos((2 * k + 1) * (np.pi / (2 * subbands)) * (np.arange(taps + 1) - ((taps - 1) / 2)) - (-1) ** k * np.pi / 4)
+
+        # convert to tensor
+        analysis_filter = torch.from_numpy(h_analysis).float().unsqueeze(1)
+        synthesis_filter = torch.from_numpy(h_synthesis).float().unsqueeze(0)
+
+        # register coefficients as beffer
+        self.register_buffer("analysis_filter", analysis_filter)
+        self.register_buffer("synthesis_filter", synthesis_filter)
+
+        # filter for downsampling & upsampling
+        updown_filter = torch.zeros((subbands, subbands, subbands)).float()
+        for k in range(subbands):
+            updown_filter[k, k, 0] = 1.0
+        self.register_buffer("updown_filter", updown_filter)
+        self.subbands = subbands
+
+        # keep padding info
+        self.pad_fn = torch.nn.ConstantPad1d(taps // 2, 0.0)
+
+    def analysis(self, x):
+        """Analysis with PQMF.
+
+        Args:
+            x (Tensor): Input tensor (B, 1, T).
+
+        Returns:
+            Tensor: Output tensor (B, subbands, T // subbands).
+
+        """
+        x = F.conv1d(self.pad_fn(x), self.analysis_filter)
+        return F.conv1d(x, self.updown_filter, stride=self.subbands)
+
+    def synthesis(self, x):
+        """Synthesis with PQMF.
+
+        Args:
+            x (Tensor): Input tensor (B, subbands, T // subbands).
+
+        Returns:
+            Tensor: Output tensor (B, 1, T).
+
+        """
+        # NOTE(kan-bayashi): Power will be dreased so here multipy by # subbands.
+        #   Not sure this is the correct way, it is better to check again.
+        # TODO(kan-bayashi): Understand the reconstruction procedure
+        x = F.conv_transpose1d(x, self.updown_filter * self.subbands, stride=self.subbands)
+        x = F.conv1d(self.pad_fn(x), self.synthesis_filter)
+        return x
diff --git a/vocoder/layers/qmf.dat b/vocoder/layers/qmf.dat
new file mode 100644
index 00000000..17eab137
--- /dev/null
+++ b/vocoder/layers/qmf.dat
@@ -0,0 +1,640 @@
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+  6.2393761e-004
+  5.1073885e-004
+  4.0265402e-004
+  2.9495311e-004
+  2.0430171e-004
+  1.0943831e-004
+  1.3494974e-005
+ -6.1733441e-005
+ -1.4463809e-004
+ -2.0983373e-004
+ -2.8969812e-004
+ -3.5011759e-004
+ -4.0951215e-004
+ -4.6063255e-004
+ -5.1455722e-004
+ -5.5645764e-004
+ -5.9461189e-004
+ -6.3415949e-004
+ -6.6504151e-004
+ -6.9179375e-004
+ -7.2153920e-004
+ -7.3193572e-004
+ -7.5300014e-004
+ -7.6307936e-004
+ -7.7579773e-004
+ -7.8014496e-004
+ -7.8036647e-004
+ -7.7798695e-004
+ -7.8343323e-004
+ -7.7248486e-004
+ -7.6813719e-004
+ -7.4905981e-004
+ -7.4409419e-004
+ -7.2550431e-004
+ -7.1577365e-004
+ -6.9416146e-004
+ -6.7776908e-004
+ -6.5403334e-004
+ -6.3124935e-004
+ -6.1327474e-004
+ -5.8709305e-004
+ -5.6778026e-004
+ -5.4665656e-004
+ -5.2265643e-004
+ -5.0407143e-004
+ -4.8937912e-004
+ -4.8752280e-004
+ -4.9475181e-004
+ -5.6176926e-004
+ -5.5252865e-004
diff --git a/vocoder/models/__init__.py b/vocoder/models/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/vocoder/models/melgan_discriminator.py b/vocoder/models/melgan_discriminator.py
new file mode 100644
index 00000000..55d3f585
--- /dev/null
+++ b/vocoder/models/melgan_discriminator.py
@@ -0,0 +1,80 @@
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.nn.utils import weight_norm
+
+
+class MelganDiscriminator(nn.Module):
+    def __init__(self,
+                 in_channels=1,
+                 out_channels=1,
+                 kernel_sizes=(5, 3),
+                 base_channels=16,
+                 max_channels=1024,
+                 downsample_factors=(4, 4, 4, 4)):
+        super(MelganDiscriminator, self).__init__()
+        self.layers = nn.ModuleList()
+
+        layer_kernel_size = np.prod(kernel_sizes)
+        layer_padding = (layer_kernel_size - 1) // 2
+
+        # initial layer
+        self.layers += [
+            nn.Sequential(
+                nn.ReflectionPad1d(layer_padding),
+                weight_norm(
+                    nn.Conv1d(in_channels,
+                              base_channels,
+                              layer_kernel_size,
+                              stride=1)), nn.LeakyReLU(0.2, inplace=True))
+        ]
+
+        # downsampling layers
+        layer_in_channels = base_channels
+        for idx, downsample_factor in enumerate(downsample_factors):
+            layer_out_channels = min(layer_in_channels * downsample_factor,
+                                     max_channels)
+            layer_kernel_size = downsample_factor * 10 + 1
+            layer_padding = (layer_kernel_size - 1) // 2
+            layer_groups = layer_in_channels // 4
+            self.layers += [
+                nn.Sequential(
+                    weight_norm(
+                        nn.Conv1d(layer_in_channels,
+                                  layer_out_channels,
+                                  kernel_size=layer_kernel_size,
+                                  stride=downsample_factor,
+                                  padding=layer_padding,
+                                  groups=layer_groups)),
+                    nn.LeakyReLU(0.2, inplace=True))
+            ]
+            layer_in_channels = layer_out_channels
+
+        # last 2 layers
+        layer_padding1 = (kernel_sizes[0] - 1) // 2
+        layer_padding2 = (kernel_sizes[1] - 1) // 2
+        self.layers += [
+            nn.Sequential(
+                weight_norm(
+                    nn.Conv1d(layer_out_channels,
+                              layer_out_channels,
+                              kernel_size=kernel_sizes[0],
+                              stride=1,
+                              padding=layer_padding1)),
+                nn.LeakyReLU(0.2, inplace=True),
+            ),
+            weight_norm(
+                nn.Conv1d(layer_out_channels,
+                          out_channels,
+                          kernel_size=kernel_sizes[1],
+                          stride=1,
+                          padding=layer_padding2)),
+        ]
+
+    def forward(self, x):
+        feats = []
+        for layer in self.layers:
+            x = layer(x)
+            feats.append(x)
+        return x, feats
diff --git a/vocoder/models/melgan_generator.py b/vocoder/models/melgan_generator.py
new file mode 100644
index 00000000..2d266f29
--- /dev/null
+++ b/vocoder/models/melgan_generator.py
@@ -0,0 +1,91 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.nn.utils import weight_norm
+
+from TTS.vocoder.layers.melgan import ResidualStack
+
+
+class MelganGenerator(nn.Module):
+    def __init__(self,
+                 in_channels=80,
+                 out_channels=1,
+                 proj_kernel=7,
+                 base_channels=512,
+                 upsample_factors=(8, 8, 2, 2),
+                 res_kernel=3,
+                 num_res_blocks=3):
+        super(MelganGenerator, self).__init__()
+
+        # assert model parameters
+        assert (proj_kernel -
+                1) % 2 == 0, " [!] proj_kernel should be an odd number."
+
+        # setup additional model parameters
+        base_padding = (proj_kernel - 1) // 2
+        act_slope = 0.2
+        self.inference_padding = 2
+
+        # initial layer
+        layers = []
+        layers += [
+            nn.ReflectionPad1d(base_padding),
+            weight_norm(
+                nn.Conv1d(in_channels,
+                          base_channels,
+                          kernel_size=proj_kernel,
+                          stride=1,
+                          bias=True))
+        ]
+
+        # upsampling layers and residual stacks
+        for idx, upsample_factor in enumerate(upsample_factors):
+            layer_in_channels = base_channels // (2**idx)
+            layer_out_channels = base_channels // (2**(idx + 1))
+            layer_filter_size = upsample_factor * 2
+            layer_stride = upsample_factor
+            layer_output_padding = upsample_factor % 2
+            layer_padding = upsample_factor // 2 + layer_output_padding
+            layers += [
+                nn.LeakyReLU(act_slope),
+                weight_norm(
+                    nn.ConvTranspose1d(layer_in_channels,
+                                       layer_out_channels,
+                                       layer_filter_size,
+                                       stride=layer_stride,
+                                       padding=layer_padding,
+                                       output_padding=layer_output_padding,
+                                       bias=True)),
+                ResidualStack(
+                    channels=layer_out_channels,
+                    num_res_blocks=num_res_blocks,
+                    kernel_size=res_kernel
+                )
+            ]
+
+        layers += [nn.LeakyReLU(act_slope)]
+
+        # final layer
+        layers += [
+            nn.ReflectionPad1d(base_padding),
+            weight_norm(
+                nn.Conv1d(layer_out_channels,
+                          out_channels,
+                          proj_kernel,
+                          stride=1,
+                          bias=True)),
+            nn.Tanh()
+        ]
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, cond_features):
+        return self.layers(cond_features)
+
+    def inference(self, cond_features):
+        cond_features = cond_features.to(self.layers[1].weight.device)
+        cond_features = torch.nn.functional.pad(
+            cond_features,
+            (self.inference_padding, self.inference_padding),
+            'replicate')
+        return self.layers(cond_features)
diff --git a/vocoder/models/melgan_multiscale_discriminator.py b/vocoder/models/melgan_multiscale_discriminator.py
new file mode 100644
index 00000000..d77b9ceb
--- /dev/null
+++ b/vocoder/models/melgan_multiscale_discriminator.py
@@ -0,0 +1,41 @@
+from torch import nn
+
+from TTS.vocoder.models.melgan_discriminator import MelganDiscriminator
+
+
+class MelganMultiscaleDiscriminator(nn.Module):
+    def __init__(self,
+                 in_channels=1,
+                 out_channels=1,
+                 num_scales=3,
+                 kernel_sizes=(5, 3),
+                 base_channels=16,
+                 max_channels=1024,
+                 downsample_factors=(4, 4, 4, 4),
+                 pooling_kernel_size=4,
+                 pooling_stride=2,
+                 pooling_padding=1):
+        super(MelganMultiscaleDiscriminator, self).__init__()
+
+        self.discriminators = nn.ModuleList([
+            MelganDiscriminator(in_channels=in_channels,
+                                out_channels=out_channels,
+                                kernel_sizes=kernel_sizes,
+                                base_channels=base_channels,
+                                max_channels=max_channels,
+                                downsample_factors=downsample_factors)
+            for _ in range(num_scales)
+        ])
+
+        self.pooling = nn.AvgPool1d(kernel_size=pooling_kernel_size, stride=pooling_stride, padding=pooling_padding, count_include_pad=False)
+
+
+    def forward(self, x):
+        scores = list()
+        feats = list()
+        for disc in self.discriminators:
+            score, feat = disc(x)
+            scores.append(score)
+            feats.append(feat)
+            x = self.pooling(x)
+        return scores, feats
\ No newline at end of file
diff --git a/vocoder/models/multiband_melgan_generator.py b/vocoder/models/multiband_melgan_generator.py
new file mode 100644
index 00000000..8feacd25
--- /dev/null
+++ b/vocoder/models/multiband_melgan_generator.py
@@ -0,0 +1,38 @@
+import torch
+
+from TTS.vocoder.models.melgan_generator import MelganGenerator
+from TTS.vocoder.layers.pqmf import PQMF
+
+
+class MultibandMelganGenerator(MelganGenerator):
+    def __init__(self,
+                 in_channels=80,
+                 out_channels=4,
+                 proj_kernel=7,
+                 base_channels=384,
+                 upsample_factors=(2, 8, 2, 2),
+                 res_kernel=3,
+                 num_res_blocks=3):
+        super(MultibandMelganGenerator,
+              self).__init__(in_channels=in_channels,
+                             out_channels=out_channels,
+                             proj_kernel=proj_kernel,
+                             base_channels=base_channels,
+                             upsample_factors=upsample_factors,
+                             res_kernel=res_kernel,
+                             num_res_blocks=num_res_blocks)
+        self.pqmf_layer = PQMF(N=4, taps=62, cutoff=0.15, beta=9.0)
+
+    def pqmf_analysis(self, x):
+        return self.pqmf_layer.analysis(x)
+
+    def pqmf_synthesis(self, x):
+        return self.pqmf_layer.synthesis(x)
+
+    def inference(self, cond_features):
+        cond_features = cond_features.to(self.layers[1].weight.device)
+        cond_features = torch.nn.functional.pad(
+            cond_features,
+            (self.inference_padding, self.inference_padding),
+            'replicate')
+        return self.pqmf.synthesis(self.layers(cond_features))
diff --git a/vocoder/models/random_window_discriminator.py b/vocoder/models/random_window_discriminator.py
new file mode 100644
index 00000000..3efd395e
--- /dev/null
+++ b/vocoder/models/random_window_discriminator.py
@@ -0,0 +1,225 @@
+import numpy as np
+from torch import nn
+
+
+class GBlock(nn.Module):
+    def __init__(self, in_channels, cond_channels, downsample_factor):
+        super(GBlock, self).__init__()
+
+        self.in_channels = in_channels
+        self.cond_channels = cond_channels
+        self.downsample_factor = downsample_factor
+
+        self.start = nn.Sequential(
+            nn.AvgPool1d(downsample_factor, stride=downsample_factor),
+            nn.ReLU(),
+            nn.Conv1d(in_channels, in_channels * 2, kernel_size=3, padding=1))
+        self.lc_conv1d = nn.Conv1d(cond_channels,
+                                   in_channels * 2,
+                                   kernel_size=1)
+        self.end = nn.Sequential(
+            nn.ReLU(),
+            nn.Conv1d(in_channels * 2,
+                      in_channels * 2,
+                      kernel_size=3,
+                      dilation=2,
+                      padding=2))
+        self.residual = nn.Sequential(
+            nn.Conv1d(in_channels, in_channels * 2, kernel_size=1),
+            nn.AvgPool1d(downsample_factor, stride=downsample_factor))
+
+    def forward(self, inputs, conditions):
+        outputs = self.start(inputs) + self.lc_conv1d(conditions)
+        outputs = self.end(outputs)
+        residual_outputs = self.residual(inputs)
+        outputs = outputs + residual_outputs
+
+        return outputs
+
+
+class DBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, downsample_factor):
+        super(DBlock, self).__init__()
+
+        self.in_channels = in_channels
+        self.downsample_factor = downsample_factor
+        self.out_channels = out_channels
+
+        self.donwsample_layer = nn.AvgPool1d(downsample_factor,
+                                             stride=downsample_factor)
+        self.layers = nn.Sequential(
+            nn.ReLU(),
+            nn.Conv1d(in_channels, out_channels, kernel_size=3, padding=1),
+            nn.ReLU(),
+            nn.Conv1d(out_channels,
+                      out_channels,
+                      kernel_size=3,
+                      dilation=2,
+                      padding=2))
+        self.residual = nn.Sequential(
+            nn.Conv1d(in_channels, out_channels, kernel_size=1), )
+
+    def forward(self, inputs):
+        if self.downsample_factor > 1:
+            outputs = self.layers(self.donwsample_layer(inputs))\
+                + self.donwsample_layer(self.residual(inputs))
+        else:
+            outputs = self.layers(inputs) + self.residual(inputs)
+        return outputs
+
+
+class ConditionalDiscriminator(nn.Module):
+    def __init__(self,
+                 in_channels,
+                 cond_channels,
+                 downsample_factors=(2, 2, 2),
+                 out_channels=(128, 256)):
+        super(ConditionalDiscriminator, self).__init__()
+
+        assert len(downsample_factors) == len(out_channels) + 1
+
+        self.in_channels = in_channels
+        self.cond_channels = cond_channels
+        self.downsample_factors = downsample_factors
+        self.out_channels = out_channels
+
+        self.pre_cond_layers = nn.ModuleList()
+        self.post_cond_layers = nn.ModuleList()
+
+        # layers before condition features
+        self.pre_cond_layers += [DBlock(in_channels, 64, 1)]
+        in_channels = 64
+        for (i, channel) in enumerate(out_channels):
+            self.pre_cond_layers.append(
+                DBlock(in_channels, channel, downsample_factors[i]))
+            in_channels = channel
+
+        # condition block
+        self.cond_block = GBlock(in_channels, cond_channels,
+                                 downsample_factors[-1])
+
+        # layers after condition block
+        self.post_cond_layers += [
+            DBlock(in_channels * 2, in_channels * 2, 1),
+            DBlock(in_channels * 2, in_channels * 2, 1),
+            nn.AdaptiveAvgPool1d(1),
+            nn.Conv1d(in_channels * 2, 1, kernel_size=1),
+        ]
+
+    def forward(self, inputs, conditions):
+        batch_size = inputs.size()[0]
+        outputs = inputs.view(batch_size, self.in_channels, -1)
+        for layer in self.pre_cond_layers:
+            outputs = layer(outputs)
+        outputs = self.cond_block(outputs, conditions)
+        for layer in self.post_cond_layers:
+            outputs = layer(outputs)
+
+        return outputs
+
+
+class UnconditionalDiscriminator(nn.Module):
+    def __init__(self,
+                 in_channels,
+                 base_channels=64,
+                 downsample_factors=(8, 4),
+                 out_channels=(128, 256)):
+        super(UnconditionalDiscriminator, self).__init__()
+
+        self.downsample_factors = downsample_factors
+        self.in_channels = in_channels
+        self.downsample_factors = downsample_factors
+        self.out_channels = out_channels
+
+        self.layers = nn.ModuleList()
+        self.layers += [DBlock(self.in_channels, base_channels, 1)]
+        in_channels = base_channels
+        for (i, factor) in enumerate(downsample_factors):
+            self.layers.append(DBlock(in_channels, out_channels[i], factor))
+            in_channels *= 2
+        self.layers += [
+            DBlock(in_channels, in_channels, 1),
+            DBlock(in_channels, in_channels, 1),
+            nn.AdaptiveAvgPool1d(1),
+            nn.Conv1d(in_channels, 1, kernel_size=1),
+        ]
+
+    def forward(self, inputs):
+        batch_size = inputs.size()[0]
+        outputs = inputs.view(batch_size, self.in_channels, -1)
+        for layer in self.layers:
+            outputs = layer(outputs)
+        return outputs
+
+
+class RandomWindowDiscriminator(nn.Module):
+    """Random Window Discriminator as described in
+    http://arxiv.org/abs/1909.11646"""
+    def __init__(self,
+                 cond_channels,
+                 hop_length,
+                 uncond_disc_donwsample_factors=(8, 4),
+                 cond_disc_downsample_factors=((8, 4, 2, 2, 2), (8, 4, 2, 2),
+                                               (8, 4, 2), (8, 4), (4, 2, 2)),
+                 cond_disc_out_channels=((128, 128, 256, 256), (128, 256, 256),
+                                         (128, 256), (256, ), (128, 256)),
+                 window_sizes=(512, 1024, 2048, 4096, 8192)):
+
+        super(RandomWindowDiscriminator, self).__init__()
+        self.cond_channels = cond_channels
+        self.window_sizes = window_sizes
+        self.hop_length = hop_length
+        self.base_window_size = self.hop_length * 2
+        self.ks = [ws // self.base_window_size for ws in window_sizes]
+
+        # check arguments
+        assert len(cond_disc_downsample_factors) == len(
+            cond_disc_out_channels) == len(window_sizes)
+        for ws in window_sizes:
+            assert ws % hop_length == 0
+
+        for idx, cf in enumerate(cond_disc_downsample_factors):
+            assert np.prod(cf) == hop_length // self.ks[idx]
+
+        # define layers
+        self.unconditional_discriminators = nn.ModuleList([])
+        for k in self.ks:
+            layer = UnconditionalDiscriminator(
+                in_channels=k,
+                base_channels=64,
+                downsample_factors=uncond_disc_donwsample_factors)
+            self.unconditional_discriminators.append(layer)
+
+        self.conditional_discriminators = nn.ModuleList([])
+        for idx, k in enumerate(self.ks):
+            layer = ConditionalDiscriminator(
+                in_channels=k,
+                cond_channels=cond_channels,
+                downsample_factors=cond_disc_downsample_factors[idx],
+                out_channels=cond_disc_out_channels[idx])
+            self.conditional_discriminators.append(layer)
+
+    def forward(self, x, c):
+        scores = []
+        feats = []
+        # unconditional pass
+        for (window_size, layer) in zip(self.window_sizes,
+                                        self.unconditional_discriminators):
+            index = np.random.randint(x.shape[-1] - window_size)
+
+            score = layer(x[:, :, index:index + window_size])
+            scores.append(score)
+
+        # conditional pass
+        for (window_size, layer) in zip(self.window_sizes,
+                                        self.conditional_discriminators):
+            frame_size = window_size // self.hop_length
+            lc_index = np.random.randint(c.shape[-1] - frame_size)
+            sample_index = lc_index * self.hop_length
+            x_sub = x[:, :,
+                      sample_index:(lc_index + frame_size) * self.hop_length]
+            c_sub = c[:, :, lc_index:lc_index + frame_size]
+
+            score = layer(x_sub, c_sub)
+            scores.append(score)
+        return scores, feats
diff --git a/vocoder/notebooks/Untitled.ipynb b/vocoder/notebooks/Untitled.ipynb
new file mode 100644
index 00000000..ce49d6fa
--- /dev/null
+++ b/vocoder/notebooks/Untitled.ipynb
@@ -0,0 +1,678 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "#function example with several unknowns (variables) for optimization\n",
+    "#Gerald Schuller, Nov. 2016\n",
+    "import numpy as np\n",
+    "\n",
+    "def functionexamp(x):\n",
+    "    #x: array with 2 variables\n",
+    "    \n",
+    "    y=np.sin(x[0])+np.cos(x[1])\n",
+    "    return y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     fun: -1.9999999999888387\n",
+      "     jac: array([4.7236681e-06, 0.0000000e+00])\n",
+      " message: 'Optimization terminated successfully.'\n",
+      "    nfev: 12\n",
+      "     nit: 2\n",
+      "    njev: 3\n",
+      "  status: 0\n",
+      " success: True\n",
+      "       x: array([-1.5707916 , -3.14159265])\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Optimization example, see also:\n",
+    "#https://docs.scipy.org/doc/scipy-0.18.1/reference/optimize.html\n",
+    "#Gerald Schuller, Nov. 2016\n",
+    "#run it with \"python optimizationExample.py\" in a termina shell\n",
+    "#or type \"ipython\" in a termina shell and copy lines below:\n",
+    "\n",
+    "import numpy as np\n",
+    "import scipy.optimize as optimize\n",
+    "\n",
+    "#Example for 2 unknowns, args: function-name, starting point, method:\n",
+    "xmin = optimize.minimize(functionexamp, [-1.0, -3.0], method='CG')\n",
+    "print(xmin)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "function [p,passedge] = opt_filter(filtorder,N)\n",
+    "\n",
+    "% opt_filter      Create Lowpass Prototype Filter for the Pseudo-QMF \n",
+    "%                 Filter Bank with N Subbands\n",
+    "%\n",
+    "% Adapted from the paper by C. D. Creusere and S. K. Mitra, titled \n",
+    "%   \"A simple method for designing high-quality prototype filters for \n",
+    "%   M-band pseudo-QMF banks,\" IEEE Trans. Signal Processing,vol. 43, \n",
+    "%   pp. 1005-1007, Apr. 1995 and the book by S. K. Mitra titled \"\n",
+    "%   Digital Signal Processing: A Computer-Based Approach, McGraw-Hill, 2001\n",
+    "%\n",
+    "% Arguments:\n",
+    "% filtorder     Filter order (i.e., filter length - 1)\n",
+    "% N             Number of subbands\n",
+    "\n",
+    "stopedge = 1/N;       % Stopband edge fixed at (1/N)pi\n",
+    "passedge = 1/(4*N);   % Start value for passband edge\n",
+    "tol = 0.000001;       % Tolerance\n",
+    "step = 0.1*passedge;  % Step size for searching the passband edge\n",
+    "way = -1;             % Search direction, increase or reduce the passband edge\n",
+    "tcost = 0;            % Current error calculated with the cost function\n",
+    "pcost = 10;           % Previous error calculated with the cost function\n",
+    "flag = 0;             % Set to 1 to stop the search\n",
+    "\n",
+    "while flag == 0\n",
+    "    \n",
+    "% Design the lowpass filter using Parks-McClellan algorithm\n",
+    " \n",
+    "    p = remez(filtorder,[0,passedge,stopedge,1],[1,1,0,0],[5,1]);\n",
+    "    \n",
+    "% Calculates the cost function according to Eq. (2.36)\n",
+    "\n",
+    "    P = fft(p,4096);\n",
+    "    OptRange = floor(2048/N);           % 0 to pi/N\n",
+    "    phi = zeros(OptRange,1);            % Initialize to zeros\n",
+    "\n",
+    "% Compute the flatness in the range from 0 to pi/N\n",
+    "\n",
+    "\tfor k = 1:OptRange\n",
+    "          phi(k) = abs(P(OptRange-k+2))^2 + abs(P(k))^2;\n",
+    "\tend\n",
+    "\ttcost = max(abs(phi - ones(max(size(phi)),1)));\n",
+    "   \t\n",
+    "\tif tcost > pcost                % If search in wrong direction\n",
+    "\t\tstep = step/2;          % Reduce step size by half \n",
+    "\t\tway = -way;             % Change the search direction \n",
+    "\tend\n",
+    "\t\n",
+    "\tif abs(pcost - tcost) < tol     % If improvement is below tol         \n",
+    "\t\tflag = 1;               % Stop the search                    \n",
+    "\tend\n",
+    "\t\n",
+    "\tpcost = tcost;\n",
+    "\tpassedge = passedge + way*step; % Adjust the passband edge\n",
+    "   \n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "sig.remez"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0125"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 / 4. / 20.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Band edges should be less than 1/2 the sampling frequency",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-90-fb4445f5709b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremez\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m16.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m4.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/scipy/signal/fir_filter_design.py\u001b[0m in \u001b[0;36mremez\u001b[0;34m(numtaps, bands, desired, weight, Hz, type, maxiter, grid_density, fs)\u001b[0m\n\u001b[1;32m    854\u001b[0m     \u001b[0mbands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    855\u001b[0m     return sigtools._remez(numtaps, bands, desired, weight, tnum, fs,\n\u001b[0;32m--> 856\u001b[0;31m                            maxiter, grid_density)\n\u001b[0m\u001b[1;32m    857\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: Band edges should be less than 1/2 the sampling frequency"
+     ]
+    }
+   ],
+   "source": [
+    "p = sig.remez(65, [0, 1/16.0, 1/4.0, 1], [1, 0], [5, 1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "def create_pqmf_filter(filter_len=64, N=4):\n",
+    "    stop_edge = 1 / N\n",
+    "    pass_edge = 1 / (4 * N)\n",
+    "    tol = 1e-8\n",
+    "    cutoff = 0.1 * pass_edge\n",
+    "    cost = 0\n",
+    "    cost_prev = float('inf')\n",
+    "    \n",
+    "    p = sig.remez(filter_len, [0, pass_edge, stop_edge, 1], [1, 1, 0, 0], [5, 1])\n",
+    "    \n",
+    "    P = sig.freqz(p, workN=2048)\n",
+    "    opt_range = 2048 // N\n",
+    "    phi = np.zeros(opt_range)\n",
+    "    \n",
+    "    H = np.abs(P)\n",
+    "    phi = H[opt_range  + 2] \n",
+    "    for i in range(opt_range):\n",
+    "        phi[i] = abs(P(opt_range - i + 2)) ** 2 + abs(P[i]) ** 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import scipy as sp\n",
+    "import scipy.signal as sig\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "\n",
+    "def optimfuncQMF(x):\n",
+    "    \"\"\"Optimization function for a PQMF Filterbank\n",
+    "    x: coefficients to optimize (first half of prototype h because of symmetry)\n",
+    "    err: resulting total error\n",
+    "    \"\"\"\n",
+    "    K = ntaps * N \n",
+    "    h = np.append(x, np.flipud(x))\n",
+    "    cutoff = 0.15\n",
+    "    \n",
+    "#     breakpoint()\n",
+    "    f, H_im = sig.freqz(h, worN=K)\n",
+    "    H = np.abs(H_im) #only keeping the real part\n",
+    "    \n",
+    "    posfreq = np.square(H[0:K//N])\n",
+    "    \n",
+    "    #Negative frequencies are symmetric around 0:\n",
+    "    negfreq = np.flipud(np.square(H[0:K//N]))\n",
+    "    \n",
+    "    #Sum of magnitude squared frequency responses should be closed to unity (or N)\n",
+    "    unitycond = np.sum(np.abs(posfreq + negfreq - 2*(N*N)*np.ones(K//N)))/K\n",
+    "    \n",
+    "    #plt.plot(posfreq+negfreq)\n",
+    "    \n",
+    "    #High attenuation after the next subband:\n",
+    "    att = np.sum(np.abs(H[int(cutoff*K//N):]))/K\n",
+    "    \n",
+    "    #Total (weighted) error:\n",
+    "    err = unitycond + 100*att\n",
+    "    return err"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(32,)"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xmin.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8.684549400499243\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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MsRsYY9sYY9u6uroaeq5VET+Oz6Qaeg6FQqFYSixV4TAEoF/6v0/b1hR6Wn0YnVPCQaFQLB+WqnB4EsBGIlpHRB4AbwXwu2YNpiekhINCoVheLEnhwBjLAfgwgDsB7AFwM2Nsd7PGs6LVi3gmj2gq26whKBQKxaLiavYA7GCM3QHgjmaPAwB6wj4AwOhcCiGfu8mjUSgUisazJC2HpYYQDiOzqkpaoVAsD5RwqIKukBcAMBlXwkGhUCwPlHCogs4gFw4TMbVcqEKhWB4o4VAFYb8LLgdhUjXfUygUywQlHKqAiNDR4sGkshwUCsUyQQmHKukIelXMQaFQLBuUcKiSjhaPijkoFIplgxIOVdLZ4sWEijkoFIplghIOVRIJuDGbUBXSCoVieaCEQ5W0+t2IpnPI5QvNHopCoVA0HCUcqiTi520z5lK5Jo9EoVAoGo8SDlXSGuDCYSahgtIKheLkRwmHKon4PQCA2aSKOygUipMfJRyqJKy5lWaUcFAoFMsAJRyqJKK5lVTGkkKhWA4o4VAlrZrloNxKCoViOaCEQ5Uo4aBQKJYTSjhUidvpgMflQDytUlkVCsXJjxIONdDidSGmhINCoVgGKOFQA0GvU1kOCoViWaCEQw0EPS7E0vlmD0OhUCgajhIONdDidSGRUZaDQqE4+VHCoQaCXpdyKykUimWBEg41EPQ6VUBaoVAsC5RwqIGgx4W4ijkoFIplgBIONaDcSgqFYrmghEMNtHhdiGdyYIw1eygKhULRUJRwqIGg14UCA1JZtRqcQqE4uWmKcCCiLxPRXiJ6loh+TUQR6bVPEtEBItpHRK9sxvjsCHqdAIC4SmdVKBQnOc2yHO4GcDpj7EwALwD4JAAQ0WkA3gpgK4CrAHybiJxNGmMJHie/XJmcshwUCsXJTVOEA2PsLsaYUL8fA9Cn/X0NgJ8zxtKMscMADgA4vxljtMLjUsJBoVAsD5ZCzOEvAfxe+3sVgAHptUFtWwlE9D4i2k5E28fHxxs8RI4uHPJKOCgUipObhgkHIrqHiJ6z+LlG2udTAHIAflbr8RljNzDGtjHGtnV1ddVz6LYIt1JaBaQVipOWe54fxY2PHkEqu7xrmlyNOjBj7PJyrxPRXwB4DYDLWDE3dAhAv7Rbn7ZtSVC0HJb3TaNQnKzc9MQxfPLWXQCAB18Yx/fftQ1E1ORRNYdmZStdBeATAF7HGEtIL/0OwFuJyEtE6wBsBPBEM8ZohRAOaRVzUChOOpKZPL70h7148bp2fOzKTbhnzxgeOjDR7GE1jWbFHK4HEAJwNxE9TUTfBQDG2G4ANwN4HsAfAHyIMbZk1HSvCkgrFCct9+wZxXQii49cthHve9kGdAQ9+PmTA5XfeJLSMLdSORhjp5R57fMAPr+Iw6kaj5Nn1S4n4ZAvMDDG4HIuhdwFhaJx/OG5EXSHvLhgfQecDsLVZ/biF08OIJnJw+9ZMhn1i4Z64mtguWUrpbJ5vP37j+GCL9yLZwdnmj0chaJhMMbw6KFJXLyxE04HjzFcurkb6VwBO45ON3l0zUEJhxpYbnUOv316CI8fnsJELIOv3vVCs4dTNQ8fmMBf/ehJ7B6ebfZQFCcIB8ZimIpncMH6Dn3beWvb4XIQHjm4POMOSjjUwHKLOfxm5zDWdwbxkcs24oEXxjEZSzd7SBVJZvJ4/0934N69Y/jbm3aiUFBNEhWV2TXEFYmz+/VOPgh6Xdi6MoynB5an1ayEQw0sJ7dSRjOnLz+tBy/b2AkA2H4CmNcPvDCGaCqHa7f14dB4HI8emmz2kBQnAHuOz8HjcmBdZ9CwfeuqVjw3NFv3TsyMsSXf3VkJhxpYTm6lvSNzyOQLeFF/BGf0tcLjcmD7kamGnzeTK+Cvb9yOq77x4LzWznjghXGEfS58+jWnwe0kPPjC4lTPK05s9hyP4tSeUEnixekrWzGXymFgKlm3c82lsnjDtx/B6Z+5E/ftHa3bceuNEg41oFdILwPh8MwgN7PP7GuF1+XEpp4W7BuNNfy89+wZxd3Pj2LvSBQ3Pnq05vc/fzyKrStbEfK5cfbqNjy8TP3Fito4OB7Dxu6Wku2be0MAgBdGo3U713fuP4hnBmfgdBA+/stnl2wlthIONSB3ZU1l80veLFwIh8ZjCHicWBXxAwDWdbbg8ETjhcNtzw6jJ+zFGata8ce9YzW9t1Bg2D8axakr+AO9bU0b9h6PIp2r38M3NJPENd96GB/82Q7kloF7cTmQyuYxMpfC6o5AyWsbOrnAOFSnez+XL+AXTw7gytN68N13novJeAa3PXu8LseuN0o41IDDQXA7Cb96ahCbP/0H3LJjsNlDahjHJhNY3R7QWwes6wxiaDpZ14nWil1Ds9i2th3nrW3Hs0MzyNYwAQ9OJ5HI5LFZEw5besPIFRj219Hi+fztz+OZgRncsWsEv9i+fAukTiaGZpJgDFjdXiocWgNudAQ9ODQer8u5nh2axVQ8g9edtQoXru/AylYf7to9Updj1xslHGrE43RgcJr7H3cPzzV5NI3j6FQCayRNan1nEAUGDEwlyrxrYcwmsxiYSmLryjDOWRNBKlvAvpHqzfnBaT42oQFu6Q0D4MHGehBNZXHP82P4y4vW4dSeEG5fohqfojaOafe0lXAAgPVdwboJh0cP8gSJCzd0gIjw8s3deOjAxJKMYyrhUCMiKA0Ax2frF6RaShQKDMemEljTUczcWNXG3UvHZ1MNO+9ebRLf0hvGes2cP1aDMBqZ42NbEfYB4NaOx+nA/rH6WA4P7Z9AJl/AVaevwCs2d+OJw1OIzSNorlhaDFYQDqvbg7risVB2HpvGKd0taA96AAAv2dCBRCaPvSNLT9FUwqFGjMKBT0bxdO6kqqKcSmSQyRX0eAMAdLV4AQDj0WKtw23PDuOlX7oPX71rX13iL8IiW9sR1IVRLZbK6BwfW48mHJwOQn+7H0cn66P17RyYgcflwIv6Izh/XRtyBaYLtOXC0EwSv9w+cFLVj4zOpeF0EDq0e9zMqogPI3OpmlycduwdieoWLQCcvboNAJZkLYUSDjUiC4fhGS4c/u7nT+NN33mkbpNQs5nQit06pYelM2QUDtFUFp+8dRcGppL47/sO4I5dC/ebCkust9WHVr8bYZ9LFxhW/PbpIVz61fsxPMP3GZ1LIeR1Iegttgxb2xHE0cn6aH3PDMxgS28YHpcDm3p4XGNfHbNYljrpXB6v+++H8PFbnsUPHjrU7OHUjfFoGh1Bj942w8zKiB8Fxu+vhRBNZTE4ndRjYgCwstWHrpAXTx9TwuGExyPlQU/E0oilc7hnD89VvvWpJbP0xIKYjGUAAJ0tHn1b0OOE3+3UhcOjBycRTeXws/e+GFt6w/jiH/YuOHtneDaF9qAHPjdvctbfHsCAjTmfLzD83c+fxqHxOH76GE95HZ1LoTts1P7WdnLhsFDLhjGG3cNzOGMV1/pWRfwIepw1xUROdB4/NIXJOL83bt5+8iRjjMfS6ApZWw1A0aUqlMH5ItybQrEAACLClt7wklQylHCokVa/2/C/nP/83NDJ0ctHWA6ymU1E6Ap5Ma699sjBSfjcDmxb24a/v3wjjk0lcPuuhQVoj88k0dvq0//vbfXjuM0DeXC8GEf4035eyzAWTesuJcHq9gCS2bw+7vkyHuWKwMZu/mATEU7pCRnGcbJz394x+NwOfPSKTTgwFtMtthOd8Wh54bBSc68OzSzMAhUu0rWmlNlN3S04MBZDfom56pRwqBExYQqt+sgEdyX53U5bLfdEQ1gHXSYfbFfIq7+2e3gWZ66KwOty4ootPdjY3YJv//HggnzRx2dT6G0txjnag27MJDOW+z6rFem9ZEMHjkzGwRjDXDJbIryFsBmdXZhwOKx9z2ul9gqrIr6GBuiXGs8Pz+H0la245FS+LO/J0ql3PJouuddlRIKDiGnNF+EiFZaIYNOKENK5Qk3JF4uBEg41IiYf4Y8/ovmzz13ThsHp5AlZGHfbs8P4yaNH9Il9Mp6B20kI+43LfbQHPbrL6dhUQk8ZdTgIH3zFBuwbjeLeGgvXZCbjGXSFiq6sSMCD6UTW8pruPT4Hn9uBV5zajWgqh+lEFtFUDiGfccwrNOGw0MyyI1o8aZ2UwbUi7MfIbKou33kuX8CPHzmypBMb9o9FsbGnBeu7RGHYiR9jKxQYJiq4lYJeF/xuJyai1sIhly/gy3fuxX/ds7/svTA4nURH0IOAx3iPbtCu52IUmdaCEg414tf84SIVTVgO56xpQyKTx3Qi27SxzYedx6bx4f/diU//djdueYr7kSeiaXQEvSVr54a8LsTSOaSyeYzOpQ2pf689cyX62vy4/o8H5j1ZziWzCEuafyTg1qrRS2MZI3PcyljfxSfrI5NxxNI5tHiNlkNR61uYhn9kMgG3k7AyIru9fEhk8ojWIZ31+j8ewGd+txtv+s4jGFqC7prJWBrTiSxO6Q6hxetCd8iLw1Lufyqbx83bB3QL60QhlskhV2D682xHZ8iju1vN3Lx9EN/640F8/Z4XyrpWB6cT6DNZDQDQ3y4y85bW966EQ4343PySubXA9JHJODwuB7au5IFKkQ9dKLCGtVcoFBj+uG+sLrnXP3v8GEJeFzpbvPi1FlCfS5W6ZwCuQcUzOd08loWDy+nA+y/ZgGcGZvRCn1pIZfNI5woI+4rnbQvwB3Y6UepaGtP8xP3aGAamElw4mCyHjhYvXA7SayBkhmaSiKaqE+bDM0msaPUZGrMJq2Rkga6lrGY1bF4RgstBeoB9KSGshA2aMF7XGTQIgi/+YS8+ccuz+PD/PnVCWc9zSf79my1OM10tXkzErF2ct+wYwMbuFvSEvfi/Z4ZtjzE8k9TjF+Zj+9yOhhaYzgclHGpEWA5CqT46mUB3yKtrHjOJLEbnUrjwunvx7v95oiFj+Ma9+/Ge/3kSf/6Dx+fVuVSQyRVw1+4RXLG1B687ayV2DkyjUGCIp/MIekuXRWzxuRBL5fTYilkLevO5fegOefGt+w9Udf4DY1F84fd7kMsX9IfUYDlof08nMmCM4Z7nR5HI8M87Hk2jO+TV9xG+/7DpIXc6CN0hb0ls4N49o3jpF+/D277/WFUtQUZmU+gJGYPdvbrLih97NpHFJ255Bk8crq177WOHJjGdyOIfr9iEs1dH8FgVbcYZYzg0Hlu0egNheQmB2N8e0C2cbL6A3+zkisXu4Tk8eaT5rrHr79uPP//B4xUTBqIpfj+FfKXKkExni9dQ41N8fxY7B2Zw9Zm9uPK0FXjwhQnbwPJkPGNIDxcQEfra7DPzkpk87nl+FDMWSlIjUcKhRkTVsNCaZ5NZtHhduuYRTeVw754xjM6l8fCBybovkJPJFXDjo0cAcFfHbc/aayqVePjABOZSOVx9Ri/WdwWRyhZwfC6FWDpnqBUQtHhdyBUYRrXJ0Fw05HM78dcvXY+HD0xaBivNguyTt+7C9x44hJ8/OYA5TYNvNbiVuMCdTWTxy+2DeO+N2/H9Bw8DAMbmUugO+XRhIjJnWizG3dPqK3Erffv+gygw4LmhOdy7p3KcZCyaRk+rUTgIhWBaS+/8wu/34Obtg/jAT3fU1IPqycNTcBBw0Smd2La2Hc8NzSKZKf/+nzx2FJd+9QH8+23PV32ehSAmxm5NQHa08PgTF1JxTCey+PdrtoIITV857f59Y/jKXS/goQMT+PRvnjO8NhXP4M3feQT/8IunARSFQ7iScAh5Ld1Ku4ZmwRjwov4IzuxrRTKbt3StZfMFzCSy6Gixdl/1t/kt3UqFAsN7b3wS771xO9747UcWtSJfCYcaeeM5q/C9d56Lv7p4nb7N63Lomkc0lTWktz50oL4Pyt6ROcwksrj+7WdjfVcQv95ZubYiX2D4+C+fwVXfeBBPSmsy3L7rOEI+Fy7e2Kn77g+Px5HI5BD0lE6yQW2R9WFNOFi5nt56fj/cTirxvX7h93uw9TN34iEt7TSVzeOZAZ5xdPfzo5gVloOk+bcFheWQxa+0eMizgzOIpXOIZ/LoDnvhdTngdlJROFi4B3pbfQbXz8hsCjuOTuNjV25C2OfCfRWC6IwxjMym9PiFQHz+uRQPmv9p/wQcxDXEWoqadg7MYPOKMIJeF/JbofoAACAASURBVM7qiyCbZ9g/Zp/3zhjD9fdx6+wnjx3Vr91COKplfNkxFk3D7STdUusMepHJFxBN5/SxnrumDVtXhquyfBrJzx4/hq6QF3932UY8cnASY9Hid3/bs8PYfnQav945hGOTiardSp0tXkwlMiVWwS69tX2kbC8voUDYVWH3tQUs3cQPH5zAwwcm8YpTu3BoIo6bn1y8Zo9KONQIEeGVW1cYNGuPy6FPatFUDntH5nD6qjCIUHXDLsYYbnjwIL557/6y+4kUzrP6Injl1hXYfmS6omvplh0D+OWOQewdieIDP92B2US26FI6rQdel1PPmDg0EdPcShbCQdt2XJuIrR6okM+N89a24/69xUV2svkC/ufhIwCAX+7gN/fgdEJfUW9gKoG5JP8MBsvBr7nqkhndhbH96LSeNdLZwoPmYZ9bj4NYuQd6wkbhsP0oF5Av29SFi07prDiZzaVySGbzJcJBWC2zCV75OjSTxD9cvglEqHoFOsYYnhmYwVna8pRrO7lFWi6t8dBEHGPRNN5ybh/yBYb7980/QwzgLrZLvnw/vnLXPtt9xubS6GzxwqFVEQsNeDKWwYGxGIh41s1ZfRE8PzzXtLhDMpPH/fvG8LqzVuKyLd0AYIiB3bl7RF/u9969o4imqxMOEb8bjKEkRnVgLIYuza28sacFLgdZFkaKeEWnTeC7v92PuVSuRND/ascgIgE3vvvOc3H26ghuXsROwEo4zJOAp+iT97qcCHpcIOI3z+GJOLasCGNF2Gfb/uGh/RN6phMAPHlkGv95x1587e4XMFYms+a5oVm0Bdzoa/PjJRs6kCswPFFhhbZf7RjiXUQ/cjGm4hl88c69uHfPKOZSObz2zJUAeFDMQcVirxaLmIN4gIZnkwh4nHpQ3swF6zuwbzSqm8C7hmaRyRXgczt0y0FMfuevbcfgdFIPOssxBxH8T2byGJlNwekgzCazunkvxhP2u8u6lVaEfYhn8vqDvePoNPxuJ7b0hnFabxiD08my5vq4pnmaq6/dTgcCHifmUlndt33hhg6c2hPCM1X2ypmIZTCXymFTDxfO/W1cOJRr+SFW5PubSzYg6HFiZw1WynNDsyUB7+8+cBAA8P0/Hbb1l4/HeIxHIDTgyVgaB8fj6Gvzw+d2YlNPCHOpHMZs0j4bzfajU8jmGV66sRNbV7aixevCU1J68N7jUbzxnFVoC7hxYCxWdCtZWMEyQmkxT97HphJYo7mYvS4nesI+y2yzybim0NikzIrvXQ5KC2v00lO74XU58arTV2DvSHTRig8rCgciWkNEndrfFxDRx4joDY0f2tLG55KFgwMOB6HF68JcKoeZRBbtQQ/62vyWpuJELI0//+HjePlX7tfNTaHNAjDUCjw7OGMQIkcnE1jf1QIiwrY17XA6qOzyndPxDLYfncIrt/Zg68pWvOeidfjfx4/hwzftxKqIHy/bxAuaHA5C2O/GTCKLuE3MQWwbnkmV9dEK83qf1mlSmN5vPW81JuMZxNI5HNMmv4s3diKTL+jtA1oNwoFf44GpBHIFhjNWtfL/tWsqXF8hH7/u4m8zIogq4g57j0exuTcEt9OhLwy0v0z7gqk4nxCs0h3DPjdmk1k9KN0b8WNDd0vVNQBCqAjLLahljpXLXDk8kYDHydc73tgTqrqj52wyi9f890P4l988h0c0d2ehwNuCtHhdyOQKej2HGXMVcYd2LSZiGYxHiy43vedUk9qKPH5oCk4H4by1/NlY1xnEYe1em45nMBnPYH1nC1a1+TE4nZQC0uUth3LCQV4kaGXEZzl5i/qgDlvLgR9Dni9eGI1hMp7BBRs6AACvOJVbQkLBajRlhQMRfRrAfQAeI6L/APANAJ0APkJE31iE8S1ZHA7SM5dEM76wz42JWJqnZPrd6G8LWFoOv5f88TsHuFbz1NEZrOsMIuRz6Z0+ZxIZvO76h3HZ1x7QzfSRuZQ+2fk9TmzpDZXVHJ8ZnEGBARdu6AQA/MMVm3BKdwsKjOFfrt5iaDbW6ndjPJpGrsAqCIekZbxBsEVbWvH543yCGJxOwOty4Nw1vAPlwFQCx6aS8LudeJHmThGTiaz5iz5WYqI9s08TDlrgzq9Zb7KgEt+JjJi4xAR+cDymT8ZCOJRbBnJKE+AitVam1a8Jh5kkHAR0h7xY3xnEwFSiqh79wu0oYj4AdzHIbqW7do/gDumeGZhKYFWbH04HYfOKEPaNRKty48jus19pactHpxJIZPJ487l9AHgVNMCvx1/fuF1fjS+ayhqus8i6mYilMR3P6tfmFG2pzWa1Fdk1NIuN3S36vcpTbvlYxGpuG7qD6IvwbKu5ZBYelwNeV+l9I9MaKBUO+gpyUkp3b6vfsmpeWMZW9xBQtBzk+WLnMT43nLe2HQC/tq1+tz5nNJpKlsPbAGwBsA3ARwBcyhj7JwBXALi8wWNb8ojJSfgwQz6XblKG/W6sjPhxfDZZkm749MAsWrzcDbVrkD+MhydiOLUnhFURv36D/PZpnomULzA8dWwajDGeKy1lzZyzug3PDMzYugOe1wTNaVodRovXhT/83Uvx6D9dhled0WvYN+J3Y1irJA56LFJZtQeOCz97TWtVxI+Ax4mDWqOxoZkkVkX8+uJBx6YSGItyIRfRHjqhWcmuKoeD4HEW8783r+CfQfwv0m3lsbicpZ01e6T2B9FUFmPRtC4cVkX8cBAwVKb7q0ghbLOyHPwuzCVzOD6bQlfIC7em0RdYdWtRHNXqZFa2GtujC4H03NAs3veTHfjgz57S3WIDUjHVxp4QphNZvSFeOR47xPthvXhdO/aN8vtCWHevPasXTslf/uudQ7j7+VF87e4XAKDEmhTKQTSVw2Q8o8cgOls88LudlkqRKJQ70qBCOd4ccRanaxYmwNudDE0nkckVdEG8TrccEphL5UrSn62wshyOz6bAGA8mC3ojPL5lfuZjmoVilTAB8Pso4HEamvsJi064rYgIZ/VHanIjLoRKwiHFGMswxmYAHGSMJQCAMZYDsLhJt0sQoaUKrSPkc+mTTNjnQlvQgwIrpssJjk3FcdrKMNZ1BvHcMHe5jEfT6Al70ddWzB8/IC1Ss28khplEFulcASukieTs1RHEM3nb7Jbnh+fQ1+Y3aPoup0O3PmTCfrc+frtUVn3fMm4lIkKvlD46NJ3Eqja/wa86l8oh7Hfr55mKZ+B0UEnbZK/LgRntgdQrSc1uJakq2knlhEOqRFN3OR3oDvkwVKbjpqh6by9nOUh9oUS687GpypPg8GwKva0+PdALcPeVEA6PSzUTj2iB1WNTCd0NIYSEXYNCmd1DvDfSGatasX+UN3oTWu66zhZ0tnj070y08RAN4cxJCkIhSmRymE5kdJcbz9m3dqde93teKPeh/32qIfUZ47E0JmIZvSAVANa0B1BgXEERQeGesBerIn6euj2b1JW8clgJBxH7kmMxK1v9yOQLJcI6ls5pmXXWU654ZuQ2L7uHZ3Fab9hwb5zV14oXRqNIZRu7XC9QWThEiOiNRPQmAK3a3/r/Cz05EX2UiJgU0yAi+iYRHSCiZ4nonIWeo5EIzdWjWw5uPRAX9rv1tD9z87gjkzyItbo9gJHZFFLZPOZSOXSHfdqDxXs0HZ6I44xVrfC4HDg6GS/6taWJ/ex+7qp56qi1NnFwPG5oEVyOVr9bv6mtArvyjV3OrcTHWDSvh2aS6GvzIxJww+UgTMYzvFWGz6WfZyaRMbRDF3jdTsxok3OfLlyMbiX54XZY9OT3e5wI+1wYm0uV+PgB7icu13tpOpGB1+WwnETCmnCQ+/OIyWIiWll/GplNlmRB8Z5SvIbgqaPT6A554XE68NTRaaSyecwksrr1KCyOalpuiNX95EZv41G+0E3E70ZnC8/lZ4zh2cEZhLwuJLNc8cjkCwa/vHCrjs6lkC8wg7tE3MMyyUwev9LWXN89PIddDehgLNp5yN+tCABPxdOYjKXhczsQ8Lj0+3cylrGdsGWEMmQQDlHRvbj42YW7bcpCOFSKa6yM+PU0ccYY9o1EdRetYFNPCAVWfRbkQqh0VR4A8BoAV0t/i58HFnJiIuoHcCWAY9LmVwHYqP28D8B3FnKORiM0KaFFeV3GyVO4TGakfkuJTA7j0TTWdga1RnbpYhfUkBd9bX7E0jyl7fBEHOu7gljdHsDhibjedlrWVNZ0BNAe9Oj+STOD0wn0W/RzsUKe8AMWwkGed30VtK0VWm1BLl/ARCyDnrAPRISQz4VoKos5zYctrmE8k4fbwiUkX9P2oAdel0OfCIXlIFsbVpYDoKWzapaD00FGP3HE2k8smI5nbHvvBDxOpLJ53vRP+yxCSMj59XYcn02VtFRoD7qRzTPE0jkcHI/hzL5W9EZ8GJ5N6ZOTKBDsjVTXWDCVzWM0yv3jQqCMzaUwEeML3TgcxZbss8ksUtkCLtVSQUXmldnV6Pc49e9CniD72gIlAfWdx6YRTedw3RvPAMBTkmX2jUT1Qsj5clQ7p7z2uQgAT8YymIpn0BHk341ssVopJWZ8bgc8TodROGgCQO7oahe4tisslVnZ6pcWrkojnsnrMRyBUPTK1cHUi0pX5TkAu7WfXaa/nyvzvmr4OoBPAJDty2sA3Mg4j4FbLr2W714CCK1XWA6y1hr2ScJBulGET7GvzY/OFi8m4xl9EukOeXUNbCKWxvBsEms6gljbEcDRyYTut5Rz+YkIZ/dHsNMidXI2yTuVyj7RcojxArCcqKuZhAW9rT6MRVP6ZxcPTcjnRjSVQzSV435Wt1NvReKxCAp63cVbNOBx6tecqJjqKo/LynIAuLAanUvj4HgMa9oDhhX9Vrby9EO7oO50IqNPxmY8TicyuQIvHNTG5nNzS8Wq3YJMocAwKiUYCPSeUvEsbxMS9mFF2IfjM0ld0RDfVUfQA4/LUbF1OP98wOoOvy7opuIZTMSKLR06W7yYiGZ0d4mYiOxcjX530UcuWw69ER/mUjm91QkA/f686vQVWBXxG9JLdw/P4pXfeBCXffUBw3tq5egkF/yysG2TPqscGxH30awWkK4EEc/mE/U4QNFykBWHokJoshxSOUtrXKY34sNELK3FR0otXIAH2O1qKepNpavSov2cC+ADAHoBrATwfgDzdvkQ0TUAhhhjz5heWgVArvIY1LYtScwxB5csHPwutGpFXLIWIWctdAQ9SOcKODzBNZ7ukE83PblriT/8XSEuRERA0hzUOnt1BAfGYiXayqBNDyQ7ZMvBavKXu7TazME6PWGfwfwVAi2k9WfibiU3HA7SLQCPhUDySdfW7Sz6bHldCd/fILTshIOWf35gLGbIDAL4dc/kCrbdVWeTWd1FaMbtImTyBcTTeQSk2hB5YSSACwKzJTEZzyCbZyVuJTHZjEVTmEpk0NXi1ZIbipaD+K6ICCtbK68rITT5/raAvhbJRDxjSFHlykpad42KiWnQpujR73HqNTmywiJabIxJ6x88PTCD9Z1BRAIebOk1LpL0w4d4S5TxaBoP7CsWT9bK0ckEVkX8BjeRbjnEM5iMp/X/hUs4ls5V5VYS75GF10QsjbaA29CMcaGWA9OWI9XdnybLweNyoL89YJtyXE/KXhXG2L8xxv4NQB+AcxhjH2OMfRRcWKwu914iuoeInrP4uQbAPwP414UMnIjeR0TbiWj7+Pj8b6iFIG4qod3KE6psOcxKWoTQ/NoCHn0SEIHn9qBHf8iERhb2uxD2uTGXyuqFWuaHVCxSbi68sltcxA6PKVPIjLzJ3M7bjNAkhYASGSEtXhcm4hk93Rcojd3IiGsrBLHbxc8r+//l625n0WzobsF4NI39YzE9fVWga3txa7dGzKZiHAC8TgfSuQIy+QJapJYj8sJIAPCp3+zC+Z+/Fzc8eFDfNqW3VDBaJULb3T8WA2O8+E4E+Ke0YipRPQ6IAHZ5K0UEY7tC3qI2HeNWQqe0gFU2z6SsniCcDrK1HAIeJ+JaDyjZ/SfcnrJwPDAWw2bNf74q4jfESHYem8Flm7vRFnDjbm3J3fnAYypGK9nndiLgcWIqnsFULIN2za0ka/FWVrIVfrcTCann1WSstJFeuIxwCFVhOQA8VfzgeBxBj9PgQhas0TwJjabaCukeGLOTMto2WxhjlzPGTjf/ADgEYB2AZ4joCLjgeYqIVgAYAtAvHaZP22Z1/BsYY9sYY9u6urqq/Bj1RdxUYlKVJ1Svy6FrEXLMQVgOkYBbv7FE9o3f49QnfrEkYavfjbCfr2sgHnBz36Mz+1pBBDxlijuIycm8dKYd8vitFPBqNHSBsG70bqmSW0n4VYXAEJOOlQYnJh0R4yhaDtZBaIfNHX2KZJ6LIj2B7max6XoZt6kYN49ZjtPIXTyPTSZw0xPcIP7mvQf0icMu913cN0Jp6GrhwiFXYDioTdyyC7A96MG0jWATyOm4bie/N6fiaS3TyG0Yh37ekBdhn0tXMszCwec2FoIK9JiLZjlk8wUMTCWwTltFr68tgKjWKmI6nsHhiTi2rW3HOavbFrTU7tHJUuEgPtdUPIOpRNGtFDQIh+qmwYDHaWiIOJ3IlKQ3h7QU9TkL4WCXxioQ2W7HZ7nlsKG7xVIJW9MewLE6rIteiWqFw40AniCizxLRZwE8DuBH8zkhY2wXY6ybMbaWMbYW3HV0DmNsBMDvALxLy1q6AMAsY2xhCxM3EHFTCY1XuJUcxDVrt9OBFq/LsADQrOQzFpOSePj8bkk46Cmxbn0SHZ5JosXrKpmYQz43Tu0pLYYrV7xlhcPgNrKyHKp3K4nJVK/70Cwi2RcvrKSWssKBH0e0KxGCWF5Ny1lh3AAMgb3TTMIhUmbdCKA0x19GtnZkAdLqd+uW3p8OcMv2S28+E7F0Dn/az/+fsREOcmU4AHSHi91nxb3SKgkHkd1Ujql4Bi4H6dprR9CDiVgGqWwBfu1aCmtscDqhZzBFAh699sWs+cotZDwWloNwow1OJ5ErMKzr1GpLNEt2aDqJA5r7ZEtvCJtWhHBoPF5V8aCZ2UQWs8ks1rQHS17raOEL9aSyBV2xkhUsbxUxB4Dfc7JbKZYurZFwOAitfrchzgiUv4cEYiGp4dkkDo3HS+INgjUdQUTTuZKMqHpT1VVhjH0ewHsATGs/72GMfaEB47kD3LI4AOD7AD7YgHPUDZdpMhMarDx5m/2U0wn+kLZ4XbomMRFNw+UgeKTurmISCGuWA1AUDlacvTqCpwdmDPnjU/EMQj5XVQE3oEbhUMly8BpbaYtCNdklJv7WYw4W4/SZ3Uq6cJDcStLb7NxK/e0BvPqMFXjHi1djbYdxAjG33jbDe01ZX3eD5SBNOC0+l17f8vihKawI+/CGs1ch4HHi8UO8dkEoDaL7rMCnXQdhdbUHPPo1GtYqsWUXVnuwsnAQQXWhiXa0eHTBLa6t+D08k0LE79YnOqGgWrmVBPJ31xbwwOUgPXYhKpTXaU0FV0WK6bfiM66M+LGppwW5ApuXP/2oVlOy2sJyEG4loHg/yeuVVGs5+D1Gt5LdfSFqX2TkbDY7Ah4XIlrPp6GZJNZ3lgo6QG610dgeS5VLAzUYY08BeKreA9CsB/E3A/Chep+jUYgAalbrLuq0CJB6XU6kJU1oJplFJOAGEekay2wyqz+YIclKAITlwCePoZmkba702f1tuOmJARyeLGock/GMbS8XKwyTbIWYg52GLtDdSjPGgKUcuBRWgZh0LOsctH18unDg55WzmIxuJetxOR2Eb7/jXMvX2gLF1uBmcvkC0rlClZaDJPi8Lh6L0PoVbexpgdvJW4iIAjM7t5L4rKI+xu9x6sHu4ZkkwtrELYgE3EhlC0hm8rYFXVPxovsI4NdcxBb82rXUz5vI6H/L7iuzhm10KxndfF0hr+5WEpOYKIIUrp3peEb/jCtafUhry8EekmpzRmZT+NubnkImz3D9287WJ0YzVqsTyuPUrXPt+ricDnhdPF5UtXBwO5GUis+iKWtXkRAO2XwBB8dj2NjN60qqKbbb0NWid0Ywx8YEvdK66KKbbyNQXVkXgLAcdOGgPbAuyfHtdTkM1YwziYzuUxYTSyyd028ct9MBn9uhF8OE/S5d6z4+m7L1W569mt8kchO+qXi64tq4MrJ/02rud1QR+BWIiXJ4NgmiokvCWEgl9i0TkHZZWw7yZFRpLJUI+9xwkLXlEE/z785WOEgTi7yP+PzxdA4DUwk9nXhjdwiHJ/jaCTOJLHxuh2GSBYqfWcSqfG6HbjlMxjMImPZvr+AW469lDULI63Lobi1zMeFMMqtr2IYMNpPgtbMcAO5aEm6l47MpuBykx9jkjJ7jsykEPU6EvC49pVfO6vra3fvw5JFpPDMwg4/+0pzcWERUdlvF1/hn1a6ldN+Uc2daETBbDqnSNcsBTYhk8vjBnw7jqm/8SV8ZsJrzbF0Z1lvhvMhm4i9aXgtbnrYSSjgsAJduOfAv0ynFHAQ+t9FymE0W12eWJzhZqxA3nNvJq1DlVhV2yxme0t2Cla0+3COtajYpZWdUgyHrx8pyqBCwlhEPXjSVQ4vHpb83YJFlVAxIW6Syuo0Tl5iEZC22UnC8Eg4HIRLwWAakY5pL0DYg7SqeW/5sLb7i8qXTiayu0a7tDCCZzWMsmsZ0PGMZD3I5HXA5SHdL+dxO3Q0yk8iUTMQiZlLOB20+l9fl1DvZimssBEIik9fvTbmRoTk4Kr9mtvq6Qj49tnR8JomecLFFSNDjgoP4Ikkjs7zOg4jQEfTA6SB97Y1kJo/fPTOMt53fj8++9jQ8cXgKO45adyAWixG1BUqfD5/bqcd/ZItTt1irdLv6pYB0OpcvqRrXr4XLgUy+oHfLFWswVJMVJeJhbieh2yaRJBJww+d26OuqNAolHBaA8AmKPHXdcnAaLQd5ychkJq/7puUJTn7QRJAr7HPrxTcCu0mKiHD5aT340/5x3VKZqtGtJGf62LmNxOZKqaxOqWutzxAfKJ5ECFdzMaGMveVQP+EAcIvGatEksc3ecijVROW/ReND0RdKxDsOT8QxIykKZsSELeo7xD1TYKUaaJtFx1Azwp2pj9vi3pPvQSEoyikEfinuYZ74usPFbC1eBV6c6ER7+NkkX29daPsObb3vUc0d9dzwLFLZAi7b3INrz+tH0OPELVoLDjNjc2l0aYs/mZGtBdlKK7ozq7t/Ah7uVmKMFRvpWdwXXpcT6WxBd6uJleFcdql0Elec1oM3n9uH/36bfRkZEWmtNpRwWLJcu60fP/7L8/HGc3idnphQ5YnV53YilS1aDulcwbLdhmw5FDU5o08esPbLC166sQupbAFPa/UOc6msIaulErVkI1UzIQsXmFwc6DJMNkbhYJmtpE1SYvLRYw6yW6kOwiHgcekuJBmhvdsJB3lSlIvghEYpKl2FAiHSOY9O8uVY7QLdegqvxT1gvk5ByYVlh6yUyMcHJLeSRQyhXJypnFXRrRVuZvMFHJ9NGZpFAkW/fDSVMwjInrBPdyuJup2z+iMIeFy4dEsP7tw9atmBeCyaQpeNpi1bC/KYhaJVvVvJhXyBIZMv6JaItXDglsN+LSVY9CurxnLoaPHiK285C1edvqLsfr1VFD4uFCUcFgAR4ZJNXfqD4XIYfwOllkM6V9BvViLSJ3v5ptXdU9q34zTEAuxvsPPXtcNBvDVzvsAMqXvVUClbybhv5eOJOINdewuhSZUz7y/Z1I1IwI0z+rj/1Zw+XM1YqyHocVq2boiXmQTM47CyHMx1HqIGYCKWQTKTL4k3CMyuHtll5TZdJ/Ga7A83k8zm9WMBRuEq3u+VhYOwHMq4Gu2sWKBYJT0eTWNkNmVoMw9w4TCXLF1Yqifs1d1Ku4fn0Nvq06/Z5Vu6MRXPYPdwaS3EeDRtWTAGGK0F+W8hLM3X0w7xjCYzeV1psIoBel0OJDN5vQ3JpPbbnN24ELolt12jUMKhjlilsnrdDpPlkDc8mEKDkx9+8XbxYMpzX7l5sNXvxrrOIPYcn9MnumCZB9iM00Krt6NSKis/dwXLQQ9I22crnb+uHU//65X4q4vXGfapt1sp4HXp1b4y+nW0EbJiPEQmrdSUdSYsCVGxOx3nNQZ2wsFryiByOx26IDK7QYrNC43C7ZO37sJnfvsc0rk88gVmtAwkQeGzcivploN9kkK5vH0xoe8b4R1dzf2jxAp65nTQDmkti8HphCH76EJtRbRHDpauzz1WRjjI94osIMvdd1bIQljvVmBxDTwuhyE5QBg6rjrcpwJRgd/IQjglHOqI02Iy97mcRsshWzDcrOZJAJCEjIWbqtKkvb6rBYfG47oWGajJcpA+i82NTPq+lW908TntLAexvZbAoLllCbDwbCVAsxws3DJCsNulIYoxy72egOKkISwHOZGgTQt+p0zavIzPlMIrxgiUukHEpCVX7yYzedz0xDH8+NGjeFhbElT+DFbxLrezuJ6GleVg/s7LCQcxUQsXZ6+NWymeyRsUmJBUHzI8Y+xY2x3yYX1XEE8eNgalM7kCpuIZ3VoxY2c5lGvbYoVfFg4VLAfx/MkCq1r3VTV0tXiRzhX0pIJGoIRDHbFahazUcjAJB1MFMFBqMRj9vuXHsL4riKOTCb1JXy2WQzktUcD0fSsfT1wPORBnZUUEa/D9ikOZ8+oXCq9+LbUcRLWunb9Yb+dhus6ilcbIbAoOMrb7aAvytbpT2bzlsqaArDSUFtmVCgcRcyiOf4+0rvT+0Zh2TOu6BDHpEUlJBFXEHMp1Ge0O80nx2UEhHEyWg5+vfZI3LUkb9rmRyfOajZE5YyAbAF7UF8Ezg7MGjVlfdCdc2XLwWwSka+mtBHDBK+odAhZKgyxseiXhZjU/zBfxWRvpWlLCoY5YadM8cyGPqXgGc6ksMrmC4SH1mLJxgNJiOkP9AcrfYBs6W5DJF7BvhE8ItVkO5VNZ7fa1QwgFO3eVOSBdjQYn5gSP5CFgIAAAIABJREFUwa1U8W0VCXqdJW4ZAMhoNSx2rgfZcpAREw5Pd3QbvkPR6yeVs3cr+SxSSe2uk9NB8LkdhpjJbqlHkejwaQw4W0+YQhjpzSTLZCtZTYyCzhYviIBnB/k4ek2TvN/tLAb7pWsn3G8Hx/kKdGaL48y+VkzE0oZgrKjEtnUr2bjTaq1zEPvlCgVJabAv3ARgiLVUk61ULWINiUYKh+pnDkVFxH0iz5tetwNzqRzO+dzd8LkdXDhID6aYdOR0z6LFINxKxeNVur96tJtRtCCwWgvafvy1BKSrEA6mDCPAaDmY3UrVPKQFTToYYw4Lf+gCHhcSFtlKosDRTnAVLQfjoyS7usy58G0BD45NJbSAtI1bycLdqNd6WFyngMdlEG5DMym4nYSwz6036zMEpC1iDvLf4vqSZMWakyHKWQ5upwPtAQ8m4xm4nYROU72NfE9YFQ+KxWzMloNYH3rvyJzuchJtw23dSi7rz1prnYO4X/MFpt8XVveswXKQhFu1Fko1dFl0vq03ynKoI3aWgyCVLaDAjBObmOzkFszmlFhj5XL5G0zkvItAqNWKbnZUG/gGqgsCW1kOVn+LCaGaBmgiuFfPCmmAC9FMvqA/9IJyGiIfR2lCAWD8nObCxbaAW7McyriVXE7Db8AYGzAT8DgNwk0Uva1q8+vptFapqvJnAErTqM3JETKVGsmJCWxDV0uJ6092sbQYYg5abzFtKdhWv7FOR7SGkZfJ1C0HG7eSzyLQLo+/WstBKDY5STiUq80BjO60emYr6c0iG9h8TwmHOmKVjWClGcoPpvBzy5Wd5lRWQH5Iy49BCBnRVK2c6W+mlpXeqoo5WLQTsRIOQrOuRjgIt5I8udTHrcTHYI47lNMQ5e1mLZqISj6foDXgQTSVA2NGl4eM3nDQU2o5WI0laIqZTGpLm3aHfHrPKDu3kqxwiO9MvG5lvernrNhllGvNGy3WMJfvCfk44lod1xcRMlldQb4OirxY0Fg0DSLYFnzKn9VgsZSxxKwQ32ehwPSuB5Ush85QcUzuOmYrRfR+YEo4nBBYadOyIBDIN4/wE8vLUJrdSvLfldw5kaDJcqhBONSSFVXJggGKE3glyyES8OBLbzoTrztrZcVjikBkLWOtBhFQNtc6ZLTWKPYBab7dykITn8/c1tln49Ix7iMCwxZZRRZCNGCKmYi+WoYMJRvhICNateiWg8P+vqvksrxkE19nxWrylZ8BS+Fgs/ocwDsTHJQsh/EoX+HNTjOXr7F839ZsOThly4HfF+WaRQLG57qeloPb6UDI5zKsFVNvlHCoI+JBkoPG1pZDcVu8nOVgkT1UaRoMaes96Kt3zbcIroKWU51bSbMcnDbCQTrftef12/aSkRE5KvLZ61UhDaCkSjqTK8DjdNgKQzHJWRWEic9vTgqQJxQ7t5KY0ORiYLHNOuZgbAo3nciiPeixDTwLQWH+WF2aa0ZfF13cdxYfv9Jk95Ztfbj6zF68/5L1pe+VvjNjN1vjSohWcY0NXS26qwyAYTU7K+wEYS2JEEAxtpWvwa3UbhAO9bMcAB67UpbDCYKVW8nKcpCDgcKnLXdPtTLlySL+YAURX6RFCJ1ATams1n9X2tcOMXnIk7dVQLoWRIzG4HKri3Cwthyy+ULZQGJxfQl7y8GsmXoMPn7rR/DVZ/QCMNXMmFqJGMdv7A01GePatF0BmNhuLuLq0YK6omZC3IvzqbUKeFz41tvPsXYrGdbBKG07IvoGWSk3G7qDmIhl9IWz5HWwrRDX27xPf1sALgeVBL3tkGMOmVwBDrK+h81rWwjcdcxW4sd2W7aZrxcqW6mOWAeky5udAtn8FMexSiOsxoXSGnBjMp6B00FV+1PN56tPKisZfgPWRXC1ICapWtqHV4OYrHKmvj2ZXKGsZulxOmzX+rX6/IBRWNhZDueva8fNf3Mh1ncFS/a1Go+81kA2z4uj2oIezCWLAsOqCC5savwngroiyFvpO3I7CZdu7i67j937BFYupmiKV05bCf712opyBydiOGd1GyZiab1nlRXimG8/37js/eqOAHZ99pVVrbMAyNlKBU1pKJ+kABhX7Ku35VDNCoALQQmHOlKsSyhus6ugNCN3zCya8lYxh8rj4NpKHF6XvTvECqvz2VFLKmsjLAerIOpC0LXCvFE4lJsEAC7sbvvIS/XGejLCDWGeFOTj2cUcAC4gZITFaZUv73KS3pBOrF8c8bsN7eKt+neZM6ku2dSF636/Fxeu560qil14rce4//Ovth1/OQxrnkidbWWhYbew1QZtydeDYzGc3R+paDls6gnhzr9/GTb1lC67Wa1g4GOWLIe8vdJg1fEWqG8qK8Ath0MTsco7zhMlHOqI1WTXbtGvX7Yc1nYEcGQyYZgwxHGs1kauZq4XZnqtGnVtvZUqH09MAPJEUEuhnRXCcpDfWQ+3klN/8E2prGUmAYGd1iomk3JuJa+NW8n6eHxfKw+Pk4rCIaUJBJ/baZmmChTbgrSblijd0hvGvv+4SurKOn+3UjnsLAeR5ZUvMNs6ir42P5wOwtHJBGLpHNK5gl4UZofdqmq1INc5iFiUFfLzbazxqa9bKRLwNDQgrYRDHbEUDhbpdfKEcOsHLzKsfAVIVoJ0L5HptXKISclZo6ZiFEbl961pHLLlIAen5+EO+shlG7FvNIqXbewqHqcOwkE8xOZ20OUmgUo4LT4/YGycZ+dWssIluTVKXpMsh7TmXuLCwTpD6ZzVEbzzgjX44Cs2lBzL0JqkvsqujiwwzRq1LhxsLAe304GVER+OTSX0CmE5ZbRRiMk9l+cB6UqWQ8DjNFq4dbYcWrw8zsQYq8lDUC1KONQRvfGetM1SOMjZDFretoxV+mDRvK98E+jujBqfbPnQ9XErlY6j0mpzlTh1RQj3/OMlNY+lEuKamWMOldxK5dB7S5kmBY+NNl8Jh+TWMCMmVKBoFXhdDvt6BqcDn3v96ZXP2YBJh5/fOBbDaw5CBvarHgLAmvYgjk4lMBHjPveuluqCygvBKSkQmTJrT4skk3PXtBm219tyCHpdKDDer62W+6haVLZSHbFyb1jVGVgFpA3HsQg+FwVG5XGIe7bWB9uue2qlfe2olMpaL22nHpaDfcyBVZ3qaDcuc5ZKtTEHuzHm8xbCgUgXGqILsM/tlNYOqWHgErW4M2uh3EQprpt5rWyZ/vYABhbdcigK52ye2cYQzlkTwbsuXIOvXnuW8f11thxEbU6szCJPC0EJhzpipalbTYDlMisAuVV3cVstD6lrnpZDLcKkulTWUguoHhO5mXpkKzlNLptMroBP3roLB8dj8w4kWmWdAdXVOZQdo0UAwOlwlFoOboeuiMw3jVIMvZExBzN6rKaMUF7TEcBUPKP3EKsUc6gH8j3CY1H2rU/+/ZrTS3o91TuVNajX5jRGOCi3Uh3Ri+BsJqu3nNuHF6/vqKiJksWkUksqa7H9Rq2WQ/X7VlUhbXH+RgiHejxzbqn6FQAePjiBm544BgCWmUjzObb+fxV1DlaI61mwcCsZYg6a5eB1FQPS89VaG/F9AeUL6Kpxi67RFgHacXQaTgcZ6gkahbnOodq1p/X3N8hysFreth4o4VBH7DRYt5OQzTN8+S1nWb5uxiqVVUQyapmUa485VL9/Ndq61QTQEMuhLtlKxWAjYGxoNl+3kmj1Yb4Onnm6lZw2tRiAdczB5y7GHOZ7jRoR6ASqsxzK3b/9knDoCHrqkrFWCXNX1lpjUfUXDtYrANYLJRzqiN3N/NSnr4DF82yLVSprsd9S5fdbLVda1XlrcStVlcrKjye7JBohHOpa56B9UaI3FVB9Y7ZKx9aPZ1jsqfpjr9fckZssKo55zIELBYPlIC0zOh8aFZAuNx5xj5SzLlZ3cOEwm8zi9FXh+g7OBuGuTecKZbOV7Ki7W8mr3EonDHbaS7msCyuEtmZ1L1Va7AewTiGthtpiDtWPg0mZ+fXO2Kh2LJUQ1+rBF8bxxOFJg0Cb78QqDlEiHKTj1aKZX3RKJ3734YtwhramgYzTQSgw4LX//ZCeGu1zO/TGffMVynXsFWegnEAvriBov0/Y50Z7kC+atLW39Ho0AnENv3znPgDFxoLVUm/rJmjTD6xeKOFQR/QK6QUfh/+2mvSqy1YSwqG2J7ua3a3aV9hh6VZqgCZal2wlbUL63TPDAIzVyfN3K4ljm7KV5nk8ADizL2K5XUyku6QV4AyWw5JzK1VjOZQ/twhKn7ZysSwHU+yoUZKzSvSYQ4PcSipbqY7Uy2VSrj13NdrHfGMOtWjgtaSyGtxKdfa7AvWxHMwWzYS0/OJCJ4FylkO9sLov5JjDfNtF6xXS8x+aJeUmfrvKcjMfu/JUALVr8PPFfI1rcQk2gkZnKzXt0xHR3xLRXiLaTURfkrZ/kogOENE+Inpls8Y3H+qlFZfro1TNKfSaiJqzlSrvX6nXjmEcFjstVcvBfAx5+UWPa37HryYgXS+sF5pyWtaa1MJiVEibERZvpe/1olM6ceS6q7G2Qmp4o6h3r6RaOSljDkT0CgDXADiLMZYmom5t+2kA3gpgK4CVAO4hok2MscY41eqMfjMv8J4pts+wcis1Mlup+n2r0ta1XWStc6lmK5mvVTRVfODmO5mLz22eRObrpiqH+Ro4iH8msSjNfIOh9XKVmik3HnG567lyWiOYSTaur1E1eFwOeJwOvT1/vWlWzOEDAK5jjKUBgDE2pm2/BsDPte2HiegAgPMBPNqcYdaGXqi2wOOUizlUc+xqNa+S99XZrVTP95WjLm4lCy1wVcSPF69vx5vO7VvQsc2fuREap1m4+dy8r8+6ziB6wl586uot8zpuo2IO5SyZYvHg0vZ6H5tKVLXff77hDDx+eLIhY/juO8/Bmo7GWE7NEg6bALyUiD4PIAXgY4yxJwGsAvCYtN+gtq0EInofgPcBwOrVq612WXTqlcesPxxyKqvptbLjsEiFreW85SgGpGs6tHSO+b2vHPVJZS2diLatbcPXrn3RvI+pB6RNx67ncpECswAS/nC/x4nH//nyeR9Xr5Ce9xGsKfesWK0TvtT4s239eOeFa6ra9+0vXo23v7gxc9Slm3saclyggcKBiO4BsMLipU9p520HcAGA8wDcTETrazk+Y+wGADcAwLZt2+p9786Lek18xZhBcZu+PGZN2Uo1Coca9q9FW5cD0o3QROuRImh1iIUGokUK72L4ps1adr0asTUiRgSUdyst5nWbL+96yRpsXbk4KbTNomHCgTFmq64Q0QcA3Mp4xO4JIioA6AQwBKBf2rVP23ZCUK/+M+VaZdTSPqPW57qaOZaIf85qxrFYj3Y9XFVWQqtesYFGtaCQKVdotxCoTq5SM9VYBUvZrdTsTKXFoFmf8DcAXgEARLQJgAfABIDfAXgrEXmJaB2AjQCeaNIY581CteNi4z2LmEMNlkPN563ifbXUOejvqbtTwkijtNuFZhWJa7UY+fBm66kerjagcW6lctekeN2WruVQqbPyyUCzYg7/D8D/I6LnAGQAvFuzInYT0c0AngeQA/ChEyVTqZ7oFdLSszGfmEOt1FQhXcV8pwvJBjv9GqVgLlT7LsYcSq/rJZu6cPEpnQs6voz5HJ116lLaKKunGuHQiGr6etGIjLOlRlOEA2MsA+DPbV77PIDPL+6I6kO95sByk3QtFdK1urnq3T5j0dxKTWgOVwtWLpQf/+X5dTm2wDyJ10s4NKq3Ui1FlEsR5VZSzIt6pbLKFAPS1cccaqWWt9VyjkZnC9RbuxWX2OOsj+tgMTTgUsuhPi2s9aLHuhytlL+7bGPJNr0n1RJ2KynLQVETp3S14N0XrsG7XrJ2QccpJwCqW+yncTGHYkC68vEapHRanKe+Jwp6XIilc3VwK4kK6cZfCHPM4TypN9SCjtug9hkAcOS6qy2321WWLyUaUeW+1FDCoY44HIR/u6byuryVsJqka4k5zDfLo5pJVriqqtn3Vaf34p49o/jEVafOazy1snlFaSvr+eBzOxFL5+rnVlpky+H2j1xctzTLxci0smMpu5WWsuCqF0o4LEHKPRPVPC+L8VBV4+f3e5z49jvObfhYAOC2v70Y/W2Buhwr6HViIrZwv/JiukfkSbye+feLZf1ZsZSFw3JACYclSDnroBqNfTFWxWpUoHK+nG6xxsF8CWjdLhdcBCdSMhfBcmiUht/M77nZLbGXO+rqL0GsHkg9IF3F+xdD41rCWYYLJuDhgegFxxy0b60RbcrNzLfwsdrjLiZCqDbTpaVQwmFJUu4Br6VCupEsNcuhnvjc/LGol+XQqFRbGRHXqPf30oz5WQjVpZyttBxQbqUTBD0gXcvazQ1MIl3IJPQvV29BV6g+efiNQAT0F245cBZDjjaqtXajurJWg3IrNRclHE4wlozlsIDn9r0vranH4qIjhOuJZB05GzTm/7+9uw+2o67vOP7+3OSGYEIIENQUCEFAESiGJCIPDkalTshQUjU62PIUH1JbIWqHUVs7FrTVGUY6FTtDyiDGUAFB1AkRBh8gBYGgEUkQkZoiHVFGHtqGplY6Id/+sb+TrHfPPffc3Hv24ZzPa+bM3ad79vs7e85+d3+/3d9W8Rm4WqkenBwaooyb4MajSTvO8Wp9fi/umtiZ15pzF3L13Y9P2t3KnUztVZtDFckh/S2jIX+81py78HceBNXPnBxqqNPPsZufahnJoYqdRlmmTlJyWHT4gfzTeZNzM9pYenXmUOmlrDVsc1h6/NyqQyhN/VKztW0pGN9NcD5zmIjW57dz166KI+ler65WqnI7u1qpWk4ODdNVtxUldHmnPv7mLDhsNpA9JrQpenXmUM2lrNnhkVNDtVyt1DBVXj2S14/VSn955jE8u+MF3vP6IzjlyIMa9aSvXrU5VHnw3odfsUZxcmiYuvxg+rFa6U/fcOTu4SYlBuhlm0N6vxIfxFuLZ/6aq5Wapi475RpeSDLQ9iSH3rxvqXZnh3p81weVf+INM57f6mQ907p9HP7h1kmnR8tOxO7vW4mb+yNLX8U+U4ca1ebTj1ytVEOdfod12Sn3Y5tDo7UeytMHVystPX4uj/3t4FwyWlc+c2iaEn6rnfYHl519HNOmDNWm7cMy43nOxnjsfjs3BAwcnzk0TK+P5L730Tey7/Doj8e84NT5XDDBJ93Z5Nt/32EAVrd57OZE+F6DweXk0DDd/FYPmJHtKI566cxxv/+hk/TAHCvX9OEpoz52cyJ2H4w4RwwcJ4eG6ebM4bjf25/r3/s6Fs0/oISIrJ+5WmlwOTk0TLcHcKceNaencdhg8IUHg8sN0g1TlzukbTDU5eo4K5+TQw11OoN3+6CVyclhcDk5NMyQs4OVqJ87WLTOvOlrqPNNcKWFYeY2hwHm5NA4/rFaeVytNLgqSQ6SFkjaJOkhSZslnZSmS9KVkrZJ2ippYRXxVe30Vx4MwPITDynM85mDlcm5YXBVdSnr5cBlEXG7pGVpfAlwJnB0er0OuCr9HSivOHjmqDc0+UjOyuSv2+CqqlopgFlpeH/gV2l4ObAuMpuA2ZLcA1eOk4OVaXhoiCHBX5/16qpDsZJVdebwIeAOSZ8lS1CnpumHAL/ILfdkmvbUyDeQtApYBTBv3ryeBlsnzg1WpqEh8fhnJr9bDqu/niUHSd8BXt5m1seBNwMfjohbJL0T+AJwxnjePyKuBq4GWLx4cd/f3L+7ixsnBzMrQc+SQ0SMurOXtA74YBq9GbgmDf8SOCy36KFp2sBrdcnsaiUzK0NVbQ6/At6Qht8E/CwNrwfOT1ctnQxsj4hCldIgc3IwszJU1ebwPuBzkqYCvyW1HQC3AcuAbcBvgJXVhFdfzg1mVoZKkkNEfA9Y1GZ6AB8oP6L6ayUF3+dgZmXwHdIN415ZzawMTg4N4QZpMyuTk0PDODWYWRmcHBpiT5uD04OZ9Z6TQ8M4N5hZGZwcGsYP+zGzMjg5NMSeBulq4zCzweDk0DByk7SZlcDJoSHc1mBmZXJyMDOzAieHhoi+75TczOrEycHMzAqcHBrCbQ5mViYnBzMzK3ByaIiXTMt6V/cZhJmVoaqH/dg4rV35WjZsfYqX7rdP1aGY2QBwcmiIww+awQfeeFTVYZjZgHC1kpmZFTg5mJlZgZODmZkVODmYmVmBk4OZmRU4OZiZWYGTg5mZFTg5mJlZgaIP+oKW9Azw73v573OAZycxnCo0vQxNjx9chjpoevxQfhkOj4iD283oi+QwEZI2R8TiquOYiKaXoenxg8tQB02PH+pVBlcrmZlZgZODmZkVODnA1VUHMAmaXoamxw8uQx00PX6oURkGvs3BzMyKfOZgZmYFTg5mZlYwMMlB0lJJj0naJuljbebvI+kraf4DkuaXH2VnXZThQknPSHoovd5bRZyjkXStpKcl/XiU+ZJ0ZSrfVkkLy46xky7iXyJpe+7z/0TZMXYi6TBJd0n6iaRHJH2wzTJ13wbdlKG220HSdEnfl7QlxX9Zm2XqsS+KiL5/AVOAfwNeAUwDtgDHjljmz4E1afgc4CtVx70XZbgQ+MeqY+1QhtOBhcCPR5m/DLgdEHAy8EDVMY8z/iXAhqrj7BD/XGBhGt4P+Nc236G6b4NuylDb7ZA+15lpeBh4ADh5xDK12BcNypnDScC2iHg8Iv4PuBFYPmKZ5cCX0vBXgTdLUokxjqWbMtRaRNwN/EeHRZYD6yKzCZgtaW450Y2ti/hrLSKeiogH0/B/A48Ch4xYrO7boJsy1Fb6XHek0eH0GnlVUC32RYOSHA4BfpEbf5LiF2r3MhGxE9gOHFRKdN3ppgwAb0/VAV+VdFg5oU2abstYZ6ekKoPbJR1XdTCjSVUVJ5IdueY1Zht0KAPUeDtImiLpIeBp4NsRMeo2qHJfNCjJYVDcCsyPiBOAb7Pn6MPK8SBZXzWvAT4PfKPieNqSNBO4BfhQRDxfdTx7Y4wy1Ho7RMSLEbEAOBQ4SdLxVcfUzqAkh18C+aPoQ9O0tstImgrsDzxXSnTdGbMMEfFcRLyQRq8BFpUU22TpZjvVVkQ836oyiIjbgGFJcyoO63dIGibbqX45Ir7WZpHab4OxytCE7QAQEf8F3AUsHTGrFvuiQUkOPwCOlnSEpGlkjTzrRyyzHrggDa8A7ozUIlQTY5ZhRN3w2WT1sU2yHjg/XTFzMrA9Ip6qOqhuSXp5q25Y0klkv6/aHGCk2L4APBoRfz/KYrXeBt2Uoc7bQdLBkman4X2BPwB+OmKxWuyLppa9wipExE5JFwF3kF31c21EPCLpk8DmiFhP9oW7TtI2skbHc6qLuKjLMqyWdDawk6wMF1YWcBuSbiC7kmSOpCeBvyFrkCMi1gC3kV0tsw34DbCymkjb6yL+FcCfSdoJ/C9wTs0OME4DzgMeTnXeAH8FzINmbAO6K0Odt8Nc4EuSppAlrZsiYkMd90XuPsPMzAoGpVrJzMzGwcnBzMwKnBzMzKzAycHMzAqcHMzMrMDJwWpBUki6Ijd+iaRLS45ho6TFafi21vXoE3i/JZI2jDI932vodyayHrNecHKwungBeNve3sma7iSdNBGxLN3B2iv3RMSC9DojP2Oyy2K2N5wcrC52kj0/98MjZ0iaL+nO1KHgdyXNS9PXSloj6QHg8jR+laRNkh5PR+jXSnpU0trc+10lafNo/emnZZ6QNEfS+3NH+D+XdFea/xZJ90t6UNLNqa+f1jM3firpQeBt3RZe2bM41ku6E/iupBkp9u9L+pGk5Wm5fSXdmMr0dWX9/bfOdnbk3m9Fq8zprtxbJP0gvU5L0y9N69iYPq/Vuf8/P33eWyRdJ2m/VP7hNH9Wftz6UBX9hPvl18gXsAOYBTxB1pfMJcClad6twAVp+N3AN9LwWmADMCU3fiNZn/nLgeeB3yc7CPohsCAtd2D6OwXYCJyQxjcCi9PwE8CcXHzDwD3AHwJzgLuBGWneR4FPANPJetM8OsVwE22eK0B2l/V24KH0+jjZ3exP5mL7NHBuGp5N9tyCGcBfkN0dD3ACWVJtxbwjt44VwNo0fD3w+jQ8j6zrCYBLgfuAfVKZnkvlPC6tb86Iz+uLwB+l4VXAFVV/b/zq3cunr1YbEfG8pHXAarJuD1pOYc9R+HXA5bl5N0fEi7nxWyMiJD0M/DoiHgaQ9Agwn2xn/E5Jq8i6j5kLHAtsHSO8z5H1cXOrpLPS/9ybuvCZBtwPHAP8PCJ+ltb5z2Q70XbuiYizWiOSLiTrvrn1vIi3AGdLuiSNTyfbsZ8OXAkQEVsljRU3wBnAsdrzSIBZrTMd4JuRddb4gqSngZcBbyL7XJ9N62nFdA3wEbJeTlcC7+ti3dZQTg5WN/9A1uXyF7tc/n9GjLd6pd2VG26NT5V0BNlZyWsj4j9T1cv0TitIO+7DgYtak8h25O8asdyCLmMeTb4sAt4eEY+NWEen/8/3hZMv0xDZ08Z+2+a98p/Ri3TYJ0TEvamKbwnZ2Vrbx6Vaf3Cbg9VKOkq9CXhPbvJ97Ol87E/Iqnf21iyynfB2SS8Dzuy0sKRFZMnk3IjYlSZvAk6TdFRaZoakV5L1rjlf0pFpuXcV3rB7dwAXS7t7Fz0xTb8b+OM07XiyqqWWX0t6taQh4K256d8CLs6VaawkdifwDkkHpeUPzM1bR1ZN1W3ytoZycrA6uoKsDrzlYmBlqkI5Dyg8VL5bEbEF+BHZjvx64N4x/uUi4EDgrtQofU1EPEPWRnBDiul+4Jh0ZL4K+GZqkH56b+MEPkVW/781VYl9Kk2/Cpgp6VHgk2RtKS0fI2uDuQ/Id7O9GlicGph/Ary/04oj4hHg74B/kbQFyHeN/WXgAOCGvS2YNYN7ZTVrMEkbgUsiYnNJ61sBLI+I88pYn1XHbQ5m1hVJnyerhltWdSzWez5zMDOzArc5mJlZgZODmZkVODmYmVmBk4OZmRU4OZiZWcH/A09HDwZiAAAAA0lEQVQgq1zuqXC/AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import scipy.optimize as opt\n",
+    "import scipy.signal as sig\n",
+    "\n",
+    "ntaps = 64\n",
+    "N = 4\n",
+    "\n",
+    "#optimize for 16 filter coefficients:\n",
+    "xmin = opt.minimize(optimfuncQMF, ntaps*np.ones(ntaps), method='SLSQP', tol=1e-8)\n",
+    "xmin = xmin[\"x\"]\n",
+    "\n",
+    "err = optimfuncQMF(xmin)\n",
+    "print(err)\n",
+    "\n",
+    "#Restore symmetric upper half of window:\n",
+    "h = np.concatenate((xmin, np.flipud(xmin)))\n",
+    "plt.plot(h)\n",
+    "plt.title('Resulting PQMF Window Function')\n",
+    "plt.xlabel('Sample')\n",
+    "plt.ylabel('Value')\n",
+    "plt.show()\n",
+    "\n",
+    "f, H = sig.freqz(h)\n",
+    "plt.plot(f, 20*np.log10(np.abs(H)))\n",
+    "plt.title('Resulting PQMF Magnitude Response')\n",
+    "plt.xlabel('Normalized Frequency')\n",
+    "plt.ylabel('dB')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "N = 4\n",
+    "f, H_im = sig.freqz(h)\n",
+    "posfreq = np.square(H[0:512//N])\n",
+    "negfreq = np.flipud(np.square(H[0:512//N]))\n",
+    "plt.plot((np.abs(posfreq) + np.abs(negfreq)))\n",
+    "plt.xlabel('Frequency (512 is Nyquist)')\n",
+    "plt.ylabel('Magnitude')\n",
+    "plt.title('Unity Condition, Sum of Squared Magnitude of 2 Neighboring Subbands')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "b = sig.firwin(80, 0.5, window=('kaiser', 8))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, H_im = sig.freqz(h)\n",
+    "posfreq = np.square(H[0:512//N])\n",
+    "negfreq = np.flipud(np.square(H[0:512//N]))\n",
+    "plt.plot((np.abs(posfreq) + np.abs(negfreq)))\n",
+    "plt.xlabel('Frequency (512 is Nyquist)')\n",
+    "plt.ylabel('Magnitude')\n",
+    "plt.title('Unity Condition, Sum of Squared Magnitude of 2 Neighboring Subbands')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(63,)"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cutoff = 0.15\n",
+    "beta = 9\n",
+    "ntaps = 63\n",
+    "N = 4\n",
+    "\n",
+    "b = sig.firwin(ntaps, cutoff, window=('kaiser', beta))\n",
+    "w, h = sig.freqz(b)\n",
+    "\n",
+    "plt.plot(b)\n",
+    "plt.title('Resulting PQMF Window Function')\n",
+    "plt.xlabel('Sample')\n",
+    "plt.ylabel('Value')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "fig, ax1 = plt.subplots()\n",
+    "ax1.set_title('Digital filter frequency response')\n",
+    "\n",
+    "ax1.plot(w / (2 * np.pi), 20 * np.log10(abs(h)), 'b')\n",
+    "ax1.set_ylabel('Amplitude [dB]', color='b')\n",
+    "ax1.set_xlabel('Frequency [rad/sample]')\n",
+    "\n",
+    "ax2 = ax1.twinx()\n",
+    "angles = np.unwrap(np.angle(h))\n",
+    "ax2.plot(w / (2 * np.pi), angles, 'g')\n",
+    "ax2.set_ylabel('Angle (radians)', color='g')\n",
+    "ax2.grid()\n",
+    "ax2.axis('tight')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(63,)"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "def optimfuncQMF(x):\n",
+    "    \"\"\"Optimization function for a PQMF Filterbank\n",
+    "    x: coefficients to optimize (first half of prototype h because of symmetry)\n",
+    "    err: resulting total error\n",
+    "    \"\"\"\n",
+    "    N = 2 #4 subbands\n",
+    "    cutoff = 1.5 #1.5\n",
+    "    h = np.append(x, np.flipud(x))\n",
+    "    f, H_im = sig.freqz(h)\n",
+    "    H = np.abs(H_im) #only keeping the real part\n",
+    "    \n",
+    "    posfreq = np.square(H[0:512//N])\n",
+    "    \n",
+    "    #Negative frequencies are symmetric around 0:\n",
+    "    negfreq = np.flipud(np.square(H[0:512//N]))\n",
+    "    \n",
+    "    #Sum of magnitude squared frequency responses should be closed to unity (or N)\n",
+    "    unitycond = np.sum(np.abs(posfreq + negfreq - 2*(N*N)*np.ones(512//N)))//512\n",
+    "    \n",
+    "    #plt.plot(posfreq+negfreq)\n",
+    "    \n",
+    "    #High attenuation after the next subband:\n",
+    "    att = np.sum(np.abs(H[int(cutoff*512//N):]))//512\n",
+    "    \n",
+    "    #Total (weighted) error:\n",
+    "    err = unitycond + 100*att\n",
+    "    return err"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "err = optimfuncQMF(b)\n",
+    "print(err)\n",
+    "\n",
+    "#Restore symmetric upper half of window:\n",
+    "h = np.concatenate((xmin, np.flipud(xmin)))\n",
+    "plt.plot(h)\n",
+    "plt.title('Resulting PQMF Window Function')\n",
+    "plt.xlabel('Sample')\n",
+    "plt.ylabel('Value')\n",
+    "plt.show()\n",
+    "\n",
+    "f, H = sig.freqz(h)\n",
+    "plt.plot(f, 20*np.log10(np.abs(H)))\n",
+    "plt.title('Resulting PQMF Magnitude Response')\n",
+    "plt.xlabel('Normalized Frequency')\n",
+    "plt.ylabel('dB')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.7.4 64-bit ('base': conda)",
+   "language": "python",
+   "name": "python37464bitbaseconda58faf23c4b5f4fef93406f29a1005f35"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/vocoder/notebooks/Untitled1.ipynb b/vocoder/notebooks/Untitled1.ipynb
new file mode 100644
index 00000000..7fec5150
--- /dev/null
+++ b/vocoder/notebooks/Untitled1.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/vocoder/pqmf_output.wav b/vocoder/pqmf_output.wav
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diff --git a/vocoder/tests/__init__.py b/vocoder/tests/__init__.py
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/vocoder/tests/__init__.py
@@ -0,0 +1 @@
+
diff --git a/vocoder/tests/test_config.json b/vocoder/tests/test_config.json
new file mode 100644
index 00000000..08acc48c
--- /dev/null
+++ b/vocoder/tests/test_config.json
@@ -0,0 +1,24 @@
+{
+    "audio":{
+        "num_mels": 80,         // size of the mel spec frame.
+        "num_freq": 513,       // number of stft frequency levels. Size of the linear spectogram frame.
+        "sample_rate": 22050,   // wav sample-rate. If different than the original data, it is resampled.
+        "frame_length_ms": null,  // stft window length in ms.
+        "frame_shift_ms": null, // stft window hop-lengh in ms.
+        "hop_length": 256,
+        "win_length": 1024,
+        "preemphasis": 0.97,    // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
+        "min_level_db": -100,   // normalization range
+        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
+        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
+        "griffin_lim_iters": 30,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
+        "signal_norm": true,    // normalize the spec values in range [0, 1]
+        "symmetric_norm": true, // move normalization to range [-1, 1]
+        "clip_norm": true,       // clip normalized values into the range.
+        "max_norm": 4,          // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
+        "mel_fmin": 0,         // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
+        "mel_fmax": 8000,        // maximum freq level for mel-spec. Tune for dataset!!
+        "do_trim_silence": false
+    }
+}
+
diff --git a/vocoder/tests/test_datasets.py b/vocoder/tests/test_datasets.py
new file mode 100644
index 00000000..3d6280f0
--- /dev/null
+++ b/vocoder/tests/test_datasets.py
@@ -0,0 +1,95 @@
+import os
+import numpy as np
+from torch.utils.data import DataLoader
+
+from TTS.vocoder.datasets.gan_dataset import GANDataset
+from TTS.vocoder.datasets.preprocess import load_wav_data
+from TTS.utils.audio import AudioProcessor
+from TTS.utils.io import load_config
+
+
+file_path = os.path.dirname(os.path.realpath(__file__))
+OUTPATH = os.path.join(file_path, "../../tests/outputs/loader_tests/")
+os.makedirs(OUTPATH, exist_ok=True)
+
+C = load_config(os.path.join(file_path, 'test_config.json'))
+
+test_data_path = os.path.join(file_path, "../../tests/data/ljspeech/")
+ok_ljspeech = os.path.exists(test_data_path)
+
+
+
+def gan_dataset_case(batch_size, seq_len, hop_len, conv_pad, return_segments, use_noise_augment, use_cache, num_workers):
+    ''' run dataloader with given parameters and check conditions '''
+    ap = AudioProcessor(**C.audio)
+    eval_items, train_items = load_wav_data(test_data_path, 10)
+    dataset = GANDataset(ap,
+                         train_items,
+                         seq_len=seq_len,
+                         hop_len=hop_len,
+                         pad_short=2000,
+                         conv_pad=conv_pad,
+                         return_segments=return_segments,
+                         use_noise_augment=use_noise_augment,
+                         use_cache=use_cache)
+    loader = DataLoader(dataset=dataset,
+                        batch_size=batch_size,
+                        shuffle=True,
+                        num_workers=num_workers,
+                        pin_memory=True,
+                        drop_last=True)
+
+    max_iter = 10
+    count_iter = 0
+
+    # return random segments or return the whole audio
+    if return_segments:
+        for item1, item2 in loader:
+            feat1, wav1 = item1
+            feat2, wav2 = item2
+            expected_feat_shape = (batch_size, ap.num_mels, seq_len // hop_len + conv_pad * 2)
+
+            # check shapes
+            assert np.all(feat1.shape == expected_feat_shape), f" [!] {feat1.shape} vs {expected_feat_shape}"
+            assert (feat1.shape[2] - conv_pad * 2) * hop_len == wav1.shape[2]
+
+            # check feature vs audio match
+            if not use_noise_augment:
+                for idx in range(batch_size):
+                    audio = wav1[idx].squeeze()
+                    feat = feat1[idx]
+                    mel = ap.melspectrogram(audio)
+                    # the first 2 and the last frame is skipped due to the padding
+                    # applied in spec. computation.
+                    assert (feat - mel[:, :feat1.shape[-1]])[:, 2:-1].sum() == 0, f' [!] {(feat - mel[:, :feat1.shape[-1]])[:, 2:-1].sum()}'
+
+            count_iter += 1
+            # if count_iter == max_iter:
+            #     break
+    else:
+        for item in loader:
+            feat, wav = item
+            expected_feat_shape = (batch_size, ap.num_mels, (wav.shape[-1] // hop_len) + (conv_pad * 2))
+            assert np.all(feat.shape == expected_feat_shape), f" [!] {feat.shape} vs {expected_feat_shape}"
+            assert (feat.shape[2] - conv_pad * 2) * hop_len == wav.shape[2]
+            count_iter += 1
+            if count_iter == max_iter:
+                break
+
+
+def test_parametrized_gan_dataset():
+    ''' test dataloader with different parameters '''
+    params = [
+        [32, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, True, False, True, 0],
+        [32, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, True, False, True, 4],
+        [1, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, True, True, True, 0],
+        [1, C.audio['hop_length'], C.audio['hop_length'], 0, True, True, True, 0],
+        [1, C.audio['hop_length'] * 10,  C.audio['hop_length'], 2, True, True, True, 0],
+        [1, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, False, True, True, 0],
+        [1, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, True, False, True, 0],
+        [1, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, True, True, False, 0],
+        [1, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, False, False, False, 0],
+    ]
+    for param in params:
+        print(param)
+        gan_dataset_case(*param)
diff --git a/vocoder/tests/test_losses.py b/vocoder/tests/test_losses.py
new file mode 100644
index 00000000..83314832
--- /dev/null
+++ b/vocoder/tests/test_losses.py
@@ -0,0 +1,62 @@
+import os
+import unittest
+import torch
+
+from TTS.vocoder.layers.losses import TorchSTFT, STFTLoss, MultiScaleSTFTLoss
+
+from TTS.tests import get_tests_path, get_tests_input_path, get_tests_output_path
+from TTS.utils.audio import AudioProcessor
+from TTS.utils.io import load_config
+
+TESTS_PATH = get_tests_path()
+
+OUT_PATH = os.path.join(get_tests_output_path(), "audio_tests")
+os.makedirs(OUT_PATH, exist_ok=True)
+
+WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
+
+file_path = os.path.dirname(os.path.realpath(__file__))
+C = load_config(os.path.join(file_path, 'test_config.json'))
+ap = AudioProcessor(**C.audio)
+
+
+def test_torch_stft():
+    torch_stft = TorchSTFT(ap.n_fft, ap.hop_length, ap.win_length)
+    # librosa stft
+    wav = ap.load_wav(WAV_FILE)
+    M_librosa = abs(ap._stft(wav))
+    # torch stft
+    wav = torch.from_numpy(wav[None, :]).float()
+    M_torch = torch_stft(wav)
+    # check the difference b/w librosa and torch outputs
+    assert (M_librosa - M_torch[0].data.numpy()).max() < 1e-5
+
+
+def test_stft_loss():
+    stft_loss = STFTLoss(ap.n_fft, ap.hop_length, ap.win_length)
+    wav = ap.load_wav(WAV_FILE)
+    wav = torch.from_numpy(wav[None, :]).float()
+    loss_m, loss_sc = stft_loss(wav, wav)
+    assert loss_m + loss_sc == 0
+    loss_m, loss_sc = stft_loss(wav, torch.rand_like(wav))
+    assert loss_sc < 1.0
+    assert loss_m + loss_sc > 0
+
+
+def test_multiscale_stft_loss():
+    stft_loss = MultiScaleSTFTLoss([ap.n_fft//2, ap.n_fft, ap.n_fft*2],
+                                   [ap.hop_length // 2, ap.hop_length, ap.hop_length * 2],
+                                   [ap.win_length // 2, ap.win_length, ap.win_length * 2])
+    wav = ap.load_wav(WAV_FILE)
+    wav = torch.from_numpy(wav[None, :]).float()
+    loss_m, loss_sc = stft_loss(wav, wav)
+    assert loss_m + loss_sc == 0
+    loss_m, loss_sc = stft_loss(wav, torch.rand_like(wav))
+    assert loss_sc < 1.0
+    assert loss_m + loss_sc > 0
+
+
+
+
+
+
diff --git a/vocoder/tests/test_melgan_discriminator.py b/vocoder/tests/test_melgan_discriminator.py
new file mode 100644
index 00000000..83acec8f
--- /dev/null
+++ b/vocoder/tests/test_melgan_discriminator.py
@@ -0,0 +1,26 @@
+import numpy as np
+import torch
+
+from TTS.vocoder.models.melgan_discriminator import MelganDiscriminator
+from TTS.vocoder.models.melgan_multiscale_discriminator import MelganMultiscaleDiscriminator
+
+
+def test_melgan_discriminator():
+    model = MelganDiscriminator()
+    print(model)
+    dummy_input = torch.rand((4, 1, 256 * 10))
+    output, _ = model(dummy_input)
+    assert np.all(output.shape == (4, 1, 10))
+
+
+def test_melgan_multi_scale_discriminator():
+    model = MelganMultiscaleDiscriminator()
+    print(model)
+    dummy_input = torch.rand((4, 1, 256 * 16))
+    scores, feats = model(dummy_input)
+    assert len(scores) == 3
+    assert len(scores) == len(feats)
+    assert np.all(scores[0].shape == (4, 1, 16))
+    assert np.all(feats[0][0].shape == (4, 16, 4096))
+    assert np.all(feats[0][1].shape == (4, 64, 1024))
+    assert np.all(feats[0][2].shape == (4, 256, 256))
diff --git a/vocoder/tests/test_melgan_generator.py b/vocoder/tests/test_melgan_generator.py
new file mode 100644
index 00000000..e9c4ad60
--- /dev/null
+++ b/vocoder/tests/test_melgan_generator.py
@@ -0,0 +1,15 @@
+import numpy as np
+import unittest
+import torch
+
+from TTS.vocoder.models.melgan_generator import MelganGenerator
+
+def test_melgan_generator():
+    model = MelganGenerator()
+    print(model)
+    dummy_input = torch.rand((4, 80, 64))
+    output = model(dummy_input)
+    assert np.all(output.shape == (4, 1, 64 * 256))
+    output = model.inference(dummy_input)
+    assert np.all(output.shape == (4, 1, (64 + 4) * 256))
+
diff --git a/vocoder/tests/test_pqmf.py b/vocoder/tests/test_pqmf.py
new file mode 100644
index 00000000..8444fc5a
--- /dev/null
+++ b/vocoder/tests/test_pqmf.py
@@ -0,0 +1,33 @@
+import os
+import torch
+import unittest
+
+import numpy as np
+import soundfile as sf
+from librosa.core import load
+
+from TTS.tests import get_tests_path, get_tests_input_path, get_tests_output_path
+from TTS.utils.audio import AudioProcessor
+from TTS.utils.io import load_config
+from TTS.vocoder.layers.pqmf import PQMF
+from TTS.vocoder.layers.pqmf2 import PQMF as PQMF2
+
+
+TESTS_PATH = get_tests_path()
+WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
+
+
+def test_pqmf():
+    w, sr = load(WAV_FILE)
+
+    layer = PQMF(N=4, taps=62, cutoff=0.15, beta=9.0)
+    w, sr = load(WAV_FILE)
+    w2 = torch.from_numpy(w[None, None, :])
+    b2 = layer.analysis(w2)
+    w2_ = layer.synthesis(b2)
+
+    print(w2_.max())
+    print(w2_.min())
+    print(w2_.mean())
+    sf.write('pqmf_output.wav', w2_.flatten().detach(), sr)
+
diff --git a/vocoder/tests/test_rwd.py b/vocoder/tests/test_rwd.py
new file mode 100644
index 00000000..424d3b49
--- /dev/null
+++ b/vocoder/tests/test_rwd.py
@@ -0,0 +1,21 @@
+import torch
+import numpy as np
+
+from TTS.vocoder.models.random_window_discriminator import RandomWindowDiscriminator
+
+
+def test_rwd():
+    layer = RandomWindowDiscriminator(cond_channels=80,
+                                      window_sizes=(512, 1024, 2048, 4096,
+                                                    8192),
+                                      cond_disc_downsample_factors=[
+                                          (8, 4, 2, 2, 2), (8, 4, 2, 2),
+                                          (8, 4, 2), (8, 4), (4, 2, 2)
+                                      ],
+                                      hop_length=256)
+    x = torch.rand([4, 1, 22050])
+    c = torch.rand([4, 80, 22050 // 256])
+
+    scores, _ = layer(x, c)
+    assert len(scores) == 10
+    assert np.all(scores[0].shape == (4, 1, 1))
diff --git a/vocoder/train.py b/vocoder/train.py
new file mode 100644
index 00000000..b0b4833a
--- /dev/null
+++ b/vocoder/train.py
@@ -0,0 +1,585 @@
+import argparse
+import glob
+import os
+import sys
+import time
+import traceback
+
+import torch
+from torch.utils.data import DataLoader
+
+from inspect import signature
+
+from TTS.utils.audio import AudioProcessor
+from TTS.utils.generic_utils import (KeepAverage, count_parameters,
+                                     create_experiment_folder, get_git_branch,
+                                     remove_experiment_folder, set_init_dict)
+from TTS.utils.io import copy_config_file, load_config
+from TTS.utils.radam import RAdam
+from TTS.utils.tensorboard_logger import TensorboardLogger
+from TTS.utils.training import NoamLR
+from TTS.vocoder.datasets.gan_dataset import GANDataset
+from TTS.vocoder.datasets.preprocess import load_wav_data
+# from distribute import (DistributedSampler, apply_gradient_allreduce,
+#                         init_distributed, reduce_tensor)
+from TTS.vocoder.layers.losses import DiscriminatorLoss, GeneratorLoss
+from TTS.vocoder.utils.io import save_checkpoint, save_best_model
+from TTS.vocoder.utils.console_logger import ConsoleLogger
+from TTS.vocoder.utils.generic_utils import (check_config, plot_results,
+                                             setup_discriminator,
+                                             setup_generator)
+
+torch.backends.cudnn.enabled = True
+torch.backends.cudnn.benchmark = True
+torch.manual_seed(54321)
+use_cuda = torch.cuda.is_available()
+num_gpus = torch.cuda.device_count()
+print(" > Using CUDA: ", use_cuda)
+print(" > Number of GPUs: ", num_gpus)
+
+
+def setup_loader(ap, is_val=False, verbose=False):
+    if is_val and not c.run_eval:
+        loader = None
+    else:
+        dataset = GANDataset(ap=ap,
+                             items=eval_data if is_val else train_data,
+                             seq_len=c.seq_len,
+                             hop_len=ap.hop_length,
+                             pad_short=c.pad_short,
+                             conv_pad=c.conv_pad,
+                             is_training=not is_val,
+                             return_segments=False if is_val else True,
+                             use_noise_augment=c.use_noise_augment,
+                             use_cache=c.use_cache,
+                             verbose=verbose)
+        # sampler = DistributedSampler(dataset) if num_gpus > 1 else None
+        loader = DataLoader(dataset,
+                            batch_size=1 if is_val else c.batch_size,
+                            shuffle=False,
+                            drop_last=False,
+                            sampler=None,
+                            num_workers=c.num_val_loader_workers
+                            if is_val else c.num_loader_workers,
+                            pin_memory=False)
+    return loader
+
+
+def format_data(data):
+    if isinstance(data[0], list):
+        # setup input data
+        c_G, x_G = data[0]
+        c_D, x_D = data[1]
+
+        # dispatch data to GPU
+        if use_cuda:
+            c_G = c_G.cuda(non_blocking=True)
+            x_G = x_G.cuda(non_blocking=True)
+            c_D = c_D.cuda(non_blocking=True)
+            x_D = x_D.cuda(non_blocking=True)
+
+        return c_G, x_G, c_D, x_D
+
+    # return a whole audio segment
+    c, x = data
+    if use_cuda:
+        c = c.cuda(non_blocking=True)
+        x = x.cuda(non_blocking=True)
+    return c, x
+
+
+def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
+          scheduler_G, scheduler_D, ap, global_step, epoch):
+    data_loader = setup_loader(ap, is_val=False, verbose=(epoch == 0))
+    model_G.train()
+    model_D.train()
+    epoch_time = 0
+    keep_avg = KeepAverage()
+    if use_cuda:
+        batch_n_iter = int(
+            len(data_loader.dataset) / (c.batch_size * num_gpus))
+    else:
+        batch_n_iter = int(len(data_loader.dataset) / c.batch_size)
+    end_time = time.time()
+    c_logger.print_train_start()
+    for num_iter, data in enumerate(data_loader):
+        start_time = time.time()
+
+        # format data
+        c_G, y_G, c_D, y_D = format_data(data)
+        loader_time = time.time() - end_time
+
+        global_step += 1
+
+        # get current learning rates
+        current_lr_G = list(optimizer_G.param_groups)[0]['lr']
+        current_lr_D = list(optimizer_D.param_groups)[0]['lr']
+
+        ##############################
+        # GENERATOR
+        ##############################
+
+        # generator pass
+        optimizer_G.zero_grad()
+        y_hat = model_G(c_G)
+
+        in_real_D = y_hat
+        in_fake_D = y_G
+
+        # PQMF formatting
+        if y_hat.shape[1] > 1:
+            in_real_D = y_G
+            in_fake_D = model_G.pqmf_synthesis(y_hat)
+            y_G = model_G.pqmf_analysis(y_G)
+            y_hat = y_hat.view(-1, 1, y_hat.shape[2])
+            y_G = y_G.view(-1, 1, y_G.shape[2])
+
+        if global_step > c.steps_to_start_discriminator:
+
+            # run D with or without cond. features
+            if len(signature(model_D).parameters) == 2:
+                D_out_fake = model_D(in_fake_D, c_G)
+            else:
+                D_out_fake = model_D(in_fake_D)
+            D_out_real = None
+
+            if c.use_feat_match_loss:
+                with torch.no_grad():
+                    D_out_real = model_D(in_real_D)
+
+            # format D outputs
+            if isinstance(D_out_fake, tuple):
+                scores_fake, feats_fake = D_out_fake
+                if D_out_real is None:
+                    scores_real, feats_real = None, None
+                else:
+                    scores_real, feats_real = D_out_real
+            else:
+                scores_fake = D_out_fake
+                scores_real = D_out_real
+        else:
+            scores_fake, feats_fake, feats_real = None, None, None
+
+        # compute losses
+        loss_G_dict = criterion_G(y_hat, y_G, scores_fake, feats_fake,
+                                  feats_real)
+        loss_G = loss_G_dict['G_loss']
+
+        # optimizer generator
+        loss_G.backward()
+        if c.gen_clip_grad > 0:
+            torch.nn.utils.clip_grad_norm_(model_G.parameters(),
+                                           c.gen_clip_grad)
+        optimizer_G.step()
+
+        # setup lr
+        if c.noam_schedule:
+            scheduler_G.step()
+
+        loss_dict = dict()
+        for key, value in loss_G_dict.items():
+            loss_dict[key] = value.item()
+
+        ##############################
+        # DISCRIMINATOR
+        ##############################
+        if global_step > c.steps_to_start_discriminator:
+            # discriminator pass
+            with torch.no_grad():
+                y_hat = model_G(c_D)
+
+            # PQMF formatting
+            if y_hat.shape[1] > 1:
+                y_hat = model_G.pqmf_synthesis(y_hat)
+
+            optimizer_D.zero_grad()
+
+            # run D with or without cond. features
+            if len(signature(model_D).parameters) == 2:
+                D_out_fake = model_D(y_hat.detach(), c_D)
+                D_out_real = model_D(y_D, c_D)
+            else:
+                D_out_fake = model_D(y_hat.detach())
+                D_out_real = model_D(y_D)
+
+            # format D outputs
+            if isinstance(D_out_fake, tuple):
+                scores_fake, feats_fake = D_out_fake
+                if D_out_real is None:
+                    scores_real, feats_real = None, None
+                else:
+                    scores_real, feats_real = D_out_real
+            else:
+                scores_fake = D_out_fake
+                scores_real = D_out_real
+
+            # compute losses
+            loss_D_dict = criterion_D(scores_fake, scores_real)
+            loss_D = loss_D_dict['D_loss']
+
+            # optimizer discriminator
+            loss_D.backward()
+            if c.disc_clip_grad > 0:
+                torch.nn.utils.clip_grad_norm_(model_D.parameters(),
+                                               c.disc_clip_grad)
+            optimizer_D.step()
+
+            # setup lr
+            if c.noam_schedule:
+                scheduler_D.step()
+
+            for key, value in loss_D_dict.items():
+                loss_dict[key] = value.item()
+
+        step_time = time.time() - start_time
+        epoch_time += step_time
+
+        # update avg stats
+        update_train_values = dict()
+        for key, value in loss_dict.items():
+            update_train_values['avg_' + key] = value
+        update_train_values['avg_loader_time'] = loader_time
+        update_train_values['avg_step_time'] = step_time
+        keep_avg.update_values(update_train_values)
+
+        # print training stats
+        if global_step % c.print_step == 0:
+            c_logger.print_train_step(batch_n_iter, num_iter, global_step,
+                                      step_time, loader_time, current_lr_G,
+                                      loss_dict, keep_avg.avg_values)
+
+        # plot step stats
+        if global_step % 10 == 0:
+            iter_stats = {
+                "lr_G": current_lr_G,
+                "lr_D": current_lr_D,
+                "step_time": step_time
+            }
+            iter_stats.update(loss_dict)
+            tb_logger.tb_train_iter_stats(global_step, iter_stats)
+
+        # save checkpoint
+        if global_step % c.save_step == 0:
+            if c.checkpoint:
+                # save model
+                save_checkpoint(model_G,
+                                optimizer_G,
+                                model_D,
+                                optimizer_D,
+                                global_step,
+                                epoch,
+                                OUT_PATH,
+                                model_losses=loss_dict)
+
+            # compute spectrograms
+            figures = plot_results(in_fake_D, in_real_D, ap, global_step,
+                                   'train')
+            tb_logger.tb_train_figures(global_step, figures)
+
+            # Sample audio
+            sample_voice = in_fake_D[0].squeeze(0).detach().cpu().numpy()
+            tb_logger.tb_train_audios(global_step,
+                                      {'train/audio': sample_voice},
+                                      c.audio["sample_rate"])
+        end_time = time.time()
+
+    # print epoch stats
+    c_logger.print_train_epoch_end(global_step, epoch, epoch_time, keep_avg)
+
+    # Plot Training Epoch Stats
+    epoch_stats = {"epoch_time": epoch_time}
+    epoch_stats.update(keep_avg.avg_values)
+    tb_logger.tb_train_epoch_stats(global_step, epoch_stats)
+    # TODO: plot model stats
+    # if c.tb_model_param_stats:
+    # tb_logger.tb_model_weights(model, global_step)
+    return keep_avg.avg_values, global_step
+
+
+@torch.no_grad()
+def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
+    data_loader = setup_loader(ap, is_val=True, verbose=(epoch == 0))
+    model_G.eval()
+    model_D.eval()
+    epoch_time = 0
+    keep_avg = KeepAverage()
+    end_time = time.time()
+    c_logger.print_eval_start()
+    for num_iter, data in enumerate(data_loader):
+        start_time = time.time()
+
+        # format data
+        c_G, y_G = format_data(data)
+        loader_time = time.time() - end_time
+
+        global_step += 1
+
+        ##############################
+        # GENERATOR
+        ##############################
+
+        # generator pass
+        y_hat = model_G(c_G)
+
+        in_real_D = y_hat
+        in_fake_D = y_G
+
+        # PQMF formatting
+        if y_hat.shape[1] > 1:
+            in_real_D = y_G
+            in_fake_D = model_G.pqmf_synthesis(y_hat)
+            y_G = model_G.pqmf_analysis(y_G)
+            y_hat = y_hat.view(-1, 1, y_hat.shape[2])
+            y_G = y_G.view(-1, 1, y_G.shape[2])
+
+        D_out_fake = model_D(in_fake_D)
+        D_out_real = None
+        if c.use_feat_match_loss:
+            with torch.no_grad():
+                D_out_real = model_D(in_real_D)
+
+        # format D outputs
+        if isinstance(D_out_fake, tuple):
+            scores_fake, feats_fake = D_out_fake
+            if D_out_real is None:
+                feats_real = None
+            else:
+                _, feats_real = D_out_real
+        else:
+            scores_fake = D_out_fake
+
+        # compute losses
+        loss_G_dict = criterion_G(y_hat, y_G, scores_fake, feats_fake,
+                                  feats_real)
+
+        loss_dict = dict()
+        for key, value in loss_G_dict.items():
+            loss_dict[key] = value.item()
+
+        step_time = time.time() - start_time
+        epoch_time += step_time
+
+        # update avg stats
+        update_eval_values = dict()
+        for key, value in loss_G_dict.items():
+            update_eval_values['avg_' + key] = value.item()
+        update_eval_values['avg_loader_time'] = loader_time
+        update_eval_values['avg_step_time'] = step_time
+        keep_avg.update_values(update_eval_values)
+
+        # print eval stats
+        if c.print_eval:
+            c_logger.print_eval_step(num_iter, loss_dict, keep_avg.avg_values)
+
+    # compute spectrograms
+    figures = plot_results(y_hat, y_G, ap, global_step, 'eval')
+    tb_logger.tb_eval_figures(global_step, figures)
+
+    # Sample audio
+    sample_voice = y_hat[0].squeeze(0).detach().cpu().numpy()
+    tb_logger.tb_eval_audios(global_step, {'eval/audio': sample_voice},
+                             c.audio["sample_rate"])
+
+    # synthesize a full voice
+    data_loader.return_segments = False
+
+    return keep_avg.avg_values
+
+
+# FIXME: move args definition/parsing inside of main?
+def main(args):  # pylint: disable=redefined-outer-name
+    # pylint: disable=global-variable-undefined
+    global train_data, eval_data
+    eval_data, train_data = load_wav_data(c.data_path, c.eval_split_size)
+
+    # setup audio processor
+    ap = AudioProcessor(**c.audio)
+    # DISTRUBUTED
+    # if num_gpus > 1:
+    # init_distributed(args.rank, num_gpus, args.group_id,
+    #  c.distributed["backend"], c.distributed["url"])
+
+    # setup models
+    model_gen = setup_generator(c)
+    model_disc = setup_discriminator(c)
+
+    # setup optimizers
+    optimizer_gen = RAdam(model_gen.parameters(), lr=c.lr_gen, weight_decay=0)
+    optimizer_disc = RAdam(model_disc.parameters(),
+                           lr=c.lr_disc,
+                           weight_decay=0)
+
+    # setup criterion
+    criterion_gen = GeneratorLoss(c)
+    criterion_disc = DiscriminatorLoss(c)
+
+    if args.restore_path:
+        checkpoint = torch.load(args.restore_path, map_location='cpu')
+        try:
+            model_gen.load_state_dict(checkpoint['model'])
+            optimizer_gen.load_state_dict(checkpoint['optimizer'])
+            model_disc.load_state_dict(checkpoint['model_disc'])
+            optimizer_disc.load_state_dict(checkpoint['optimizer_disc'])
+        except:
+            print(" > Partial model initialization.")
+            model_dict = model_gen.state_dict()
+            model_dict = set_init_dict(model_dict, checkpoint['model'], c)
+            model_gen.load_state_dict(model_dict)
+
+            model_dict = model_disc.state_dict()
+            model_dict = set_init_dict(model_dict, checkpoint['model_disc'], c)
+            model_disc.load_state_dict(model_dict)
+            del model_dict
+
+        # reset lr if not countinuining training.
+        for group in optimizer_gen.param_groups:
+            group['lr'] = c.lr_gen
+
+        for group in optimizer_disc.param_groups:
+            group['lr'] = c.lr_disc
+
+        print(" > Model restored from step %d" % checkpoint['step'],
+              flush=True)
+        args.restore_step = checkpoint['step']
+    else:
+        args.restore_step = 0
+
+    if use_cuda:
+        model_gen.cuda()
+        criterion_gen.cuda()
+        model_disc.cuda()
+        criterion_disc.cuda()
+
+    # DISTRUBUTED
+    # if num_gpus > 1:
+    #     model = apply_gradient_allreduce(model)
+
+    if c.noam_schedule:
+        scheduler_gen = NoamLR(optimizer_gen,
+                               warmup_steps=c.warmup_steps_gen,
+                               last_epoch=args.restore_step - 1)
+        scheduler_disc = NoamLR(optimizer_disc,
+                                warmup_steps=c.warmup_steps_gen,
+                                last_epoch=args.restore_step - 1)
+    else:
+        scheduler_gen, scheduler_disc = None, None
+
+    num_params = count_parameters(model_gen)
+    print(" > Generator has {} parameters".format(num_params), flush=True)
+    num_params = count_parameters(model_disc)
+    print(" > Discriminator has {} parameters".format(num_params), flush=True)
+
+    if 'best_loss' not in locals():
+        best_loss = float('inf')
+
+    global_step = args.restore_step
+    for epoch in range(0, c.epochs):
+        c_logger.print_epoch_start(epoch, c.epochs)
+        _, global_step = train(model_gen, criterion_gen, optimizer_gen,
+                               model_disc, criterion_disc, optimizer_disc,
+                               scheduler_gen, scheduler_disc, ap, global_step,
+                               epoch)
+        eval_avg_loss_dict = evaluate(model_gen, criterion_gen, model_disc, ap,
+                                      global_step, epoch)
+        c_logger.print_epoch_end(epoch, eval_avg_loss_dict)
+        target_loss = eval_avg_loss_dict[c.target_loss]
+        best_loss = save_best_model(target_loss,
+                                    best_loss,
+                                    model_gen,
+                                    optimizer_gen,
+                                    model_disc,
+                                    optimizer_disc,
+                                    global_step,
+                                    epoch,
+                                    OUT_PATH,
+                                    model_losses=eval_avg_loss_dict)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--continue_path',
+        type=str,
+        help=
+        'Training output folder to continue training. Use to continue a training. If it is used, "config_path" is ignored.',
+        default='',
+        required='--config_path' not in sys.argv)
+    parser.add_argument(
+        '--restore_path',
+        type=str,
+        help='Model file to be restored. Use to finetune a model.',
+        default='')
+    parser.add_argument('--config_path',
+                        type=str,
+                        help='Path to config file for training.',
+                        required='--continue_path' not in sys.argv)
+    parser.add_argument('--debug',
+                        type=bool,
+                        default=False,
+                        help='Do not verify commit integrity to run training.')
+
+    # DISTRUBUTED
+    parser.add_argument(
+        '--rank',
+        type=int,
+        default=0,
+        help='DISTRIBUTED: process rank for distributed training.')
+    parser.add_argument('--group_id',
+                        type=str,
+                        default="",
+                        help='DISTRIBUTED: process group id.')
+    args = parser.parse_args()
+
+    if args.continue_path != '':
+        args.output_path = args.continue_path
+        args.config_path = os.path.join(args.continue_path, 'config.json')
+        list_of_files = glob.glob(
+            args.continue_path +
+            "/*.pth.tar")  # * means all if need specific format then *.csv
+        latest_model_file = max(list_of_files, key=os.path.getctime)
+        args.restore_path = latest_model_file
+        print(f" > Training continues for {args.restore_path}")
+
+    # setup output paths and read configs
+    c = load_config(args.config_path)
+    check_config(c)
+    _ = os.path.dirname(os.path.realpath(__file__))
+
+    OUT_PATH = args.continue_path
+    if args.continue_path == '':
+        OUT_PATH = create_experiment_folder(c.output_path, c.run_name,
+                                            args.debug)
+
+    AUDIO_PATH = os.path.join(OUT_PATH, 'test_audios')
+
+    c_logger = ConsoleLogger()
+
+    if args.rank == 0:
+        os.makedirs(AUDIO_PATH, exist_ok=True)
+        new_fields = {}
+        if args.restore_path:
+            new_fields["restore_path"] = args.restore_path
+        new_fields["github_branch"] = get_git_branch()
+        copy_config_file(args.config_path,
+                         os.path.join(OUT_PATH, 'config.json'), new_fields)
+        os.chmod(AUDIO_PATH, 0o775)
+        os.chmod(OUT_PATH, 0o775)
+
+        LOG_DIR = OUT_PATH
+        tb_logger = TensorboardLogger(LOG_DIR, model_name='VOCODER')
+
+        # write model desc to tensorboard
+        tb_logger.tb_add_text('model-description', c['run_description'], 0)
+
+    try:
+        main(args)
+    except KeyboardInterrupt:
+        remove_experiment_folder(OUT_PATH)
+        try:
+            sys.exit(0)
+        except SystemExit:
+            os._exit(0)  # pylint: disable=protected-access
+    except Exception:  # pylint: disable=broad-except
+        remove_experiment_folder(OUT_PATH)
+        traceback.print_exc()
+        sys.exit(1)
diff --git a/vocoder/utils/__init__.py b/vocoder/utils/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/vocoder/utils/console_logger.py b/vocoder/utils/console_logger.py
new file mode 100644
index 00000000..4fe132bb
--- /dev/null
+++ b/vocoder/utils/console_logger.py
@@ -0,0 +1,97 @@
+import datetime
+from TTS.utils.io import AttrDict
+
+
+tcolors = AttrDict({
+    'OKBLUE': '\033[94m',
+    'HEADER': '\033[95m',
+    'OKGREEN': '\033[92m',
+    'WARNING': '\033[93m',
+    'FAIL': '\033[91m',
+    'ENDC': '\033[0m',
+    'BOLD': '\033[1m',
+    'UNDERLINE': '\033[4m'
+})
+
+
+class ConsoleLogger():
+    def __init__(self):
+        # TODO: color code for value changes
+        # use these to compare values between iterations
+        self.old_train_loss_dict = None
+        self.old_epoch_loss_dict = None
+        self.old_eval_loss_dict = None
+
+    # pylint: disable=no-self-use
+    def get_time(self):
+        now = datetime.datetime.now()
+        return now.strftime("%Y-%m-%d %H:%M:%S")
+
+    def print_epoch_start(self, epoch, max_epoch):
+        print("\n{}{} > EPOCH: {}/{}{}".format(tcolors.UNDERLINE, tcolors.BOLD,
+                                               epoch, max_epoch, tcolors.ENDC),
+              flush=True)
+
+    def print_train_start(self):
+        print(f"\n{tcolors.BOLD} > TRAINING ({self.get_time()}) {tcolors.ENDC}")
+
+    def print_train_step(self, batch_steps, step, global_step,
+                         step_time, loader_time, lr,
+                         loss_dict, avg_loss_dict):
+        indent = "     | > "
+        print()
+        log_text = "{}   --> STEP: {}/{} -- GLOBAL_STEP: {}{}\n".format(
+            tcolors.BOLD, step, batch_steps, global_step, tcolors.ENDC)
+        for key, value in loss_dict.items():
+            # print the avg value if given
+            if f'avg_{key}' in avg_loss_dict.keys():
+                log_text += "{}{}: {:.5f}  ({:.5f})\n".format(indent, key, value, avg_loss_dict[f'avg_{key}'])
+            else:
+                log_text += "{}{}: {:.5f} \n".format(indent, key, value)
+        log_text += f"{indent}step_time: {step_time:.2f}\n{indent}loader_time: {loader_time:.2f}\n{indent}lr: {lr:.5f}"
+        print(log_text, flush=True)
+
+    # pylint: disable=unused-argument
+    def print_train_epoch_end(self, global_step, epoch, epoch_time,
+                              print_dict):
+        indent = "     | > "
+        log_text = f"\n{tcolors.BOLD}   --> TRAIN PERFORMACE -- EPOCH TIME: {epoch} sec -- GLOBAL_STEP: {global_step}{tcolors.ENDC}\n"
+        for key, value in print_dict.items():
+            log_text += "{}{}: {:.5f}\n".format(indent, key, value)
+        print(log_text, flush=True)
+
+    def print_eval_start(self):
+        print(f"{tcolors.BOLD} > EVALUATION {tcolors.ENDC}\n")
+
+    def print_eval_step(self, step, loss_dict, avg_loss_dict):
+        indent = "     | > "
+        print()
+        log_text = f"{tcolors.BOLD}   --> STEP: {step}{tcolors.ENDC}\n"
+        for key, value in loss_dict.items():
+            # print the avg value if given
+            if f'avg_{key}' in avg_loss_dict.keys():
+                log_text += "{}{}: {:.5f}  ({:.5f})\n".format(indent, key, value, avg_loss_dict[f'avg_{key}'])
+            else:
+                log_text += "{}{}: {:.5f} \n".format(indent, key, value)
+        print(log_text, flush=True)
+
+    def print_epoch_end(self, epoch, avg_loss_dict):
+        indent = "     | > "
+        log_text = "  {}--> EVAL PERFORMANCE{}\n".format(
+            tcolors.BOLD, tcolors.ENDC)
+        for key, value in avg_loss_dict.items():
+            # print the avg value if given
+            color = ''
+            sign = '+'
+            diff = 0
+            if self.old_eval_loss_dict is not None:
+                diff = value - self.old_eval_loss_dict[key]
+                if diff < 0:
+                    color = tcolors.OKGREEN
+                    sign = ''
+                elif diff > 0:
+                    color = tcolors.FAIL
+                    sing = '+'
+            log_text += "{}{}:{} {:.5f} {}({}{:.5f})\n".format(indent, key, color, value, tcolors.ENDC, sign, diff)
+        self.old_eval_loss_dict = avg_loss_dict
+        print(log_text, flush=True)
diff --git a/vocoder/utils/generic_utils.py b/vocoder/utils/generic_utils.py
new file mode 100644
index 00000000..062de160
--- /dev/null
+++ b/vocoder/utils/generic_utils.py
@@ -0,0 +1,102 @@
+import re
+import importlib
+import numpy as np
+from matplotlib import pyplot as plt
+
+from TTS.utils.visual import plot_spectrogram
+
+
+def plot_results(y_hat, y, ap, global_step, name_prefix):
+    """ Plot vocoder model results """
+
+    # select an instance from batch
+    y_hat = y_hat[0].squeeze(0).detach().cpu().numpy()
+    y = y[0].squeeze(0).detach().cpu().numpy()
+
+    spec_fake = ap.spectrogram(y_hat).T
+    spec_real = ap.spectrogram(y).T
+    spec_diff = np.abs(spec_fake - spec_real)
+
+    # plot figure and save it
+    fig_wave = plt.figure()
+    plt.subplot(2, 1, 1)
+    plt.plot(y)
+    plt.title("groundtruth speech")
+    plt.subplot(2, 1, 2)
+    plt.plot(y_hat)
+    plt.title(f"generated speech @ {global_step} steps")
+    plt.tight_layout()
+    plt.close()
+
+    figures = {
+        name_prefix + "/spectrogram/fake": plot_spectrogram(spec_fake, ap),
+        name_prefix + "spectrogram/real": plot_spectrogram(spec_real, ap),
+        name_prefix + "spectrogram/diff": plot_spectrogram(spec_diff, ap),
+        name_prefix + "speech_comparison": fig_wave,
+    }
+    return figures
+
+
+def to_camel(text):
+    text = text.capitalize()
+    return re.sub(r'(?!^)_([a-zA-Z])', lambda m: m.group(1).upper(), text)
+
+
+def setup_generator(c):
+    print(" > Generator Model: {}".format(c.generator_model))
+    MyModel = importlib.import_module('TTS.vocoder.models.' +
+                                      c.generator_model.lower())
+    MyModel = getattr(MyModel, to_camel(c.generator_model))
+    if c.generator_model in 'melgan_generator':
+        model = MyModel(
+            in_channels=c.audio['num_mels'],
+            out_channels=1,
+            proj_kernel=7,
+            base_channels=512,
+            upsample_factors=c.generator_model_params['upsample_factors'],
+            res_kernel=3,
+            num_res_blocks=c.generator_model_params['num_res_blocks'])
+    if c.generator_model in 'melgan_fb_generator':
+        pass
+    if c.generator_model in 'multiband_melgan_generator':
+        model = MyModel(
+            in_channels=c.audio['num_mels'],
+            out_channels=4,
+            proj_kernel=7,
+            base_channels=384,
+            upsample_factors=c.generator_model_params['upsample_factors'],
+            res_kernel=3,
+            num_res_blocks=c.generator_model_params['num_res_blocks'])
+    return model
+
+
+def setup_discriminator(c):
+    print(" > Discriminator Model: {}".format(c.discriminator_model))
+    MyModel = importlib.import_module('TTS.vocoder.models.' +
+                                      c.discriminator_model.lower())
+    MyModel = getattr(MyModel, to_camel(c.discriminator_model))
+    if c.discriminator_model in 'random_window_discriminator':
+        model = MyModel(
+            cond_channels=c.audio['num_mels'],
+            hop_length=c.audio['hop_length'],
+            uncond_disc_donwsample_factors=c.
+            discriminator_model_params['uncond_disc_donwsample_factors'],
+            cond_disc_downsample_factors=c.
+            discriminator_model_params['cond_disc_downsample_factors'],
+            cond_disc_out_channels=c.
+            discriminator_model_params['cond_disc_out_channels'],
+            window_sizes=c.discriminator_model_params['window_sizes'])
+    if c.discriminator_model in 'melgan_multiscale_discriminator':
+        model = MyModel(
+            in_channels=1,
+            out_channels=1,
+            kernel_sizes=(5, 3),
+            base_channels=c.discriminator_model_params['base_channels'],
+            max_channels=c.discriminator_model_params['max_channels'],
+            downsample_factors=c.
+            discriminator_model_params['downsample_factors'])
+    return model
+
+
+# def check_config(c):
+#     pass
\ No newline at end of file
diff --git a/vocoder/utils/io.py b/vocoder/utils/io.py
new file mode 100644
index 00000000..9526b9db
--- /dev/null
+++ b/vocoder/utils/io.py
@@ -0,0 +1,52 @@
+import os
+import torch
+import datetime
+
+
+def save_model(model, optimizer, model_disc, optimizer_disc, current_step,
+               epoch, output_path, **kwargs):
+    model_state = model.state_dict()
+    model_disc_state = model_disc.state_dict()
+    optimizer_state = optimizer.state_dict() if optimizer is not None else None
+    optimizer_disc_state = optimizer_disc.state_dict(
+    ) if optimizer_disc is not None else None
+    state = {
+        'model': model_state,
+        'optimizer': optimizer_state,
+        'model_disc': model_disc_state,
+        'optimizer_disc': optimizer_disc_state,
+        'step': current_step,
+        'epoch': epoch,
+        'date': datetime.date.today().strftime("%B %d, %Y"),
+    }
+    state.update(kwargs)
+    torch.save(state, output_path)
+
+
+def save_checkpoint(model, optimizer, model_disc, optimizer_disc, current_step,
+                    epoch, output_folder, **kwargs):
+    file_name = 'checkpoint_{}.pth.tar'.format(current_step)
+    checkpoint_path = os.path.join(output_folder, file_name)
+    print(" > CHECKPOINT : {}".format(checkpoint_path))
+    save_model(model, optimizer, model_disc, optimizer_disc, current_step,
+               epoch, checkpoint_path, **kwargs)
+
+
+def save_best_model(target_loss, best_loss, model, optimizer, model_disc,
+                    optimizer_disc, current_step, epoch, output_folder,
+                    **kwargs):
+    if target_loss < best_loss:
+        file_name = 'best_model.pth.tar'
+        checkpoint_path = os.path.join(output_folder, file_name)
+        print(" > BEST MODEL : {}".format(checkpoint_path))
+        save_model(model,
+                   optimizer,
+                   model_disc,
+                   optimizer_disc,
+                   current_step,
+                   epoch,
+                   checkpoint_path,
+                   model_loss=target_loss,
+                   **kwargs)
+        best_loss = target_loss
+    return best_loss
\ No newline at end of file

From 8e7a15f07b20d0b3134d8c33cd4e582de30db10e Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sun, 31 May 2020 03:31:13 +0200
Subject: [PATCH 021/185] version update

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 84a31488..e8a626c6 100644
--- a/setup.py
+++ b/setup.py
@@ -19,7 +19,7 @@ args, unknown_args = parser.parse_known_args()
 # Remove our arguments from argv so that setuptools doesn't see them
 sys.argv = [sys.argv[0]] + unknown_args
 
-version = '0.0.2'
+version = '0.1.0'
 
 # Adapted from https://github.com/pytorch/pytorch
 cwd = os.path.dirname(os.path.abspath(__file__))

From d5bbcd0487be1b87d5d75b78608a44e19f868b68 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sun, 31 May 2020 03:31:42 +0200
Subject: [PATCH 022/185] plot fix

---
 vocoder/configs/melgan_config.json | 7 +++----
 vocoder/train.py                   | 4 ++--
 vocoder/utils/generic_utils.py     | 8 ++++----
 3 files changed, 9 insertions(+), 10 deletions(-)

diff --git a/vocoder/configs/melgan_config.json b/vocoder/configs/melgan_config.json
index 9a3ded37..7da42f75 100644
--- a/vocoder/configs/melgan_config.json
+++ b/vocoder/configs/melgan_config.json
@@ -109,14 +109,13 @@
     "test_sentences_file": null,  // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
 
     // OPTIMIZER
-    "noam_schedule": true,        // use noam warmup and lr schedule.
-    "grad_clip": 1.0,              // upper limit for gradients for clipping.
+    "noam_schedule": false,        // use noam warmup and lr schedule.
+    "warmup_steps_gen": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
+    "warmup_steps_disc": 4000,
     "epochs": 1000,                // total number of epochs to train.
     "wd": 0.000001,                // Weight decay weight.
     "lr_gen": 0.0001,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
     "lr_disc": 0.0001,
-    "warmup_steps_gen": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
-    "warmup_steps_disc": 4000,
     "gen_clip_grad": 10.0,
     "disc_clip_grad": 10.0,
 
diff --git a/vocoder/train.py b/vocoder/train.py
index b0b4833a..0d039e73 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -372,11 +372,11 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
             c_logger.print_eval_step(num_iter, loss_dict, keep_avg.avg_values)
 
     # compute spectrograms
-    figures = plot_results(y_hat, y_G, ap, global_step, 'eval')
+    figures = plot_results(in_fake_D, in_real_D, ap, global_step, 'eval')
     tb_logger.tb_eval_figures(global_step, figures)
 
     # Sample audio
-    sample_voice = y_hat[0].squeeze(0).detach().cpu().numpy()
+    sample_voice = in_fake_D[0].squeeze(0).detach().cpu().numpy()
     tb_logger.tb_eval_audios(global_step, {'eval/audio': sample_voice},
                              c.audio["sample_rate"])
 
diff --git a/vocoder/utils/generic_utils.py b/vocoder/utils/generic_utils.py
index 062de160..179c4064 100644
--- a/vocoder/utils/generic_utils.py
+++ b/vocoder/utils/generic_utils.py
@@ -13,8 +13,8 @@ def plot_results(y_hat, y, ap, global_step, name_prefix):
     y_hat = y_hat[0].squeeze(0).detach().cpu().numpy()
     y = y[0].squeeze(0).detach().cpu().numpy()
 
-    spec_fake = ap.spectrogram(y_hat).T
-    spec_real = ap.spectrogram(y).T
+    spec_fake = ap.melspectrogram(y_hat).T
+    spec_real = ap.melspectrogram(y).T
     spec_diff = np.abs(spec_fake - spec_real)
 
     # plot figure and save it
@@ -98,5 +98,5 @@ def setup_discriminator(c):
     return model
 
 
-# def check_config(c):
-#     pass
\ No newline at end of file
+def check_config(c):
+    pass
\ No newline at end of file

From 3f789187159e5bd385f49f26b8643e801d5838a5 Mon Sep 17 00:00:00 2001
From: ForceCore <ssanzing@gmail.com>
Date: Sun, 31 May 2020 19:51:16 +0900
Subject: [PATCH 023/185] Fix phoneme cache file name aliasing problem

When the wav file has multiple dots in the file name,
_load_or_generate_phoneme_sequence would only use only the first segment
of the file name and cause overwrite of *_phoneme.npy
---
 datasets/TTSDataset.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/datasets/TTSDataset.py b/datasets/TTSDataset.py
index 0d884c00..7fe966d7 100644
--- a/datasets/TTSDataset.py
+++ b/datasets/TTSDataset.py
@@ -92,7 +92,7 @@ class MyDataset(Dataset):
         return phonemes
 
     def _load_or_generate_phoneme_sequence(self, wav_file, text):
-        file_name = os.path.basename(wav_file).split('.')[0]
+        file_name = os.path.splitext(os.path.basename(wav_file))[0]
         cache_path = os.path.join(self.phoneme_cache_path,
                                   file_name + '_phoneme.npy')
         try:

From 29915ba85c77db944071ea1e0be2b153c2e74102 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sun, 31 May 2020 14:18:29 +0200
Subject: [PATCH 024/185] bug fix

---
 vocoder/train.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/vocoder/train.py b/vocoder/train.py
index 0d039e73..a30e3bff 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -137,7 +137,7 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
         if global_step > c.steps_to_start_discriminator:
 
             # run D with or without cond. features
-            if len(signature(model_D).parameters) == 2:
+            if len(signature(model_D.forward).parameters) == 2:
                 D_out_fake = model_D(in_fake_D, c_G)
             else:
                 D_out_fake = model_D(in_fake_D)
@@ -195,7 +195,7 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
             optimizer_D.zero_grad()
 
             # run D with or without cond. features
-            if len(signature(model_D).parameters) == 2:
+            if len(signature(model_D.forward).parameters) == 2:
                 D_out_fake = model_D(y_hat.detach(), c_D)
                 D_out_real = model_D(y_D, c_D)
             else:

From bb6729b42ad3b46eeaf95ca5243159ef62e1f899 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sun, 31 May 2020 18:56:57 +0200
Subject: [PATCH 025/185] add multiband melgan config and rename loss weights
 for vocoder

---
 vocoder/configs/melgan_config.json |  8 ++++----
 vocoder/layers/losses.py           | 24 ++++++++++++------------
 2 files changed, 16 insertions(+), 16 deletions(-)

diff --git a/vocoder/configs/melgan_config.json b/vocoder/configs/melgan_config.json
index 7da42f75..b97206fa 100644
--- a/vocoder/configs/melgan_config.json
+++ b/vocoder/configs/melgan_config.json
@@ -53,10 +53,10 @@
     "use_hinge_gan_loss": false,
     "use_feat_match_loss": false,  // use only with melgan discriminators
 
-    "stft_loss_alpha": 1,
-    "mse_gan_loss_alpha": 1,
-    "hinge_gan_loss_alpha": 1,
-    "feat_match_loss_alpha": 10.0,
+    "stft_loss_weight": 1,
+    "mse_gan_loss_weight": 2.5,
+    "hinge_gan_loss_weight": 2.5,
+    "feat_match_loss_weight": 10.0,
 
     "stft_loss_params": {
         "n_ffts": [1024, 2048, 512],
diff --git a/vocoder/layers/losses.py b/vocoder/layers/losses.py
index 11985629..27091c0a 100644
--- a/vocoder/layers/losses.py
+++ b/vocoder/layers/losses.py
@@ -149,10 +149,10 @@ class GeneratorLoss(nn.Module):
         self.use_hinge_gan_loss = C.use_hinge_gan_loss
         self.use_feat_match_loss = C.use_feat_match_loss
 
-        self.stft_loss_alpha = C.stft_loss_alpha
-        self.mse_gan_loss_alpha = C.mse_gan_loss_alpha
-        self.hinge_gan_loss_alpha = C.hinge_gan_loss_alpha
-        self.feat_match_loss_alpha = C.feat_match_loss_alpha
+        self.stft_loss_weight = C.stft_loss_weight
+        self.mse_gan_loss_weight = C.mse_gan_loss_weight
+        self.hinge_gan_loss_weight = C.hinge_gan_loss_weight
+        self.feat_match_loss_weight = C.feat_match_loss_weight
 
         if C.use_stft_loss:
             self.stft_loss = MultiScaleSTFTLoss(**C.stft_loss_params)
@@ -172,7 +172,7 @@ class GeneratorLoss(nn.Module):
             stft_loss_mg, stft_loss_sc = self.stft_loss(y_hat.squeeze(1), y.squeeze(1))
             return_dict['G_stft_loss_mg'] = stft_loss_mg
             return_dict['G_stft_loss_sc'] = stft_loss_sc
-            loss += self.stft_loss_alpha * (stft_loss_mg + stft_loss_sc)
+            loss += self.stft_loss_weight * (stft_loss_mg + stft_loss_sc)
 
         # Fake Losses
         if self.use_mse_gan_loss and scores_fake is not None:
@@ -185,7 +185,7 @@ class GeneratorLoss(nn.Module):
                 fake_loss = self.mse_loss(scores_fake)
                 mse_fake_loss = fake_loss
             return_dict['G_mse_fake_loss'] = mse_fake_loss
-            loss += self.mse_gan_loss_alpha * mse_fake_loss
+            loss += self.mse_gan_loss_weight * mse_fake_loss
 
         if self.use_hinge_gan_loss and not scores_fake is not None:
             hinge_fake_loss = 0
@@ -197,13 +197,13 @@ class GeneratorLoss(nn.Module):
                 fake_loss = self.hinge_loss(scores_fake)
                 hinge_fake_loss = fake_loss
             return_dict['G_hinge_fake_loss'] = hinge_fake_loss
-            loss += self.hinge_gan_loss_alpha * hinge_fake_loss
+            loss += self.hinge_gan_loss_weight * hinge_fake_loss
 
         # Feature Matching Loss
         if self.use_feat_match_loss and not feats_fake:
             feat_match_loss = self.feat_match_loss(feats_fake, feats_real)
             return_dict['G_feat_match_loss'] = feat_match_loss
-            loss += self.feat_match_loss_alpha * feat_match_loss
+            loss += self.feat_match_loss_weight * feat_match_loss
         return_dict['G_loss'] = loss
         return return_dict
 
@@ -217,8 +217,8 @@ class DiscriminatorLoss(nn.Module):
         self.use_mse_gan_loss = C.use_mse_gan_loss
         self.use_hinge_gan_loss = C.use_hinge_gan_loss
 
-        self.mse_gan_loss_alpha = C.mse_gan_loss_alpha
-        self.hinge_gan_loss_alpha = C.hinge_gan_loss_alpha
+        self.mse_gan_loss_weight = C.mse_gan_loss_weight
+        self.hinge_gan_loss_weight = C.hinge_gan_loss_weight
 
         if C.use_mse_gan_loss:
             self.mse_loss = MSEDLoss()
@@ -247,7 +247,7 @@ class DiscriminatorLoss(nn.Module):
             return_dict['D_mse_gan_loss'] = mse_gan_loss
             return_dict['D_mse_gan_real_loss'] = mse_gan_real_loss
             return_dict['D_mse_gan_fake_loss'] = mse_gan_fake_loss
-            loss += self.mse_gan_loss_alpha * mse_gan_loss
+            loss += self.mse_gan_loss_weight * mse_gan_loss
 
         if self.use_hinge_gan_loss:
             hinge_gan_loss = 0
@@ -267,7 +267,7 @@ class DiscriminatorLoss(nn.Module):
             return_dict['D_hinge_gan_loss'] = hinge_gan_loss
             return_dict['D_hinge_gan_real_loss'] = hinge_gan_real_loss
             return_dict['D_hinge_gan_fake_loss'] = hinge_gan_fake_loss
-            loss += self.hinge_gan_loss_alpha * hinge_gan_loss
+            loss += self.hinge_gan_loss_weight * hinge_gan_loss
 
         return_dict['D_loss'] = loss
         return return_dict
\ No newline at end of file

From 1a470ba6b94914a4170e4687639d58c4ba9efb81 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sun, 31 May 2020 19:03:07 +0200
Subject: [PATCH 026/185] multiband rwd D config

---
 .../multiband-melgan_and_rwd_config.json      | 136 ++++++++++++++++++
 1 file changed, 136 insertions(+)
 create mode 100644 vocoder/configs/multiband-melgan_and_rwd_config.json

diff --git a/vocoder/configs/multiband-melgan_and_rwd_config.json b/vocoder/configs/multiband-melgan_and_rwd_config.json
new file mode 100644
index 00000000..9aeb7933
--- /dev/null
+++ b/vocoder/configs/multiband-melgan_and_rwd_config.json
@@ -0,0 +1,136 @@
+{
+    "run_name": "multiband-melgan-rwd",
+    "run_description": "multibadn melgan with random window discriminator",
+
+    // AUDIO PARAMETERS
+    "audio":{
+        // stft parameters
+        "num_freq": 513,         // number of stft frequency levels. Size of the linear spectogram frame.
+        "win_length": 1024,      // stft window length in ms.
+        "hop_length": 256,       // stft window hop-lengh in ms.
+        "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
+        "frame_shift_ms": null,  // stft window hop-lengh in ms. If null, 'hop_length' is used.
+
+        // Audio processing parameters
+        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
+        "preemphasis": 0.0,     // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
+        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
+
+        // Silence trimming
+        "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
+        "trim_db": 60,          // threshold for timming silence. Set this according to your dataset.
+
+        // Griffin-Lim
+        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
+        "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
+
+        // MelSpectrogram parameters
+        "num_mels": 80,         // size of the mel spec frame.
+        "mel_fmin": 0.0,        // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
+        "mel_fmax": 8000.0,     // maximum freq level for mel-spec. Tune for dataset!!
+
+        // Normalization parameters
+        "signal_norm": true,    // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
+        "min_level_db": -100,   // lower bound for normalization
+        "symmetric_norm": true, // move normalization to range [-1, 1]
+        "max_norm": 4.0,        // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
+        "clip_norm": true,      // clip normalized values into the range.
+        "stats_path": null    // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
+    },
+
+    // DISTRIBUTED TRAINING
+    // "distributed":{
+    //     "backend": "nccl",
+    //     "url": "tcp:\/\/localhost:54321"
+    // },
+
+    // MODEL PARAMETERS
+    "use_pqmf": true,
+
+    // LOSS PARAMETERS
+    "use_stft_loss": true,
+    "use_mse_gan_loss": true,
+    "use_hinge_gan_loss": false,
+    "use_feat_match_loss": false,  // use only with melgan discriminators
+
+    "stft_loss_weight": 1,
+    "mse_gan_loss_weight": 2.5,
+    "hinge_gan_loss_weight": 1,
+    "feat_match_loss_weight": 10.0,
+
+    "stft_loss_params": {
+        "n_ffts": [1024, 2048, 512],
+        "hop_lengths": [120, 240, 50],
+        "win_lengths": [600, 1200, 240]
+    },
+    "target_loss": "avg_G_loss",  // loss value to pick the best model
+
+    // DISCRIMINATOR
+    // "discriminator_model": "melgan_multiscale_discriminator",
+    // "discriminator_model_params":{
+    //     "base_channels": 16,
+    //     "max_channels":1024,
+    //     "downsample_factors":[4, 4, 4, 4]
+    // },
+    "steps_to_start_discriminator": 100000,      // steps required to start GAN trainining.1
+
+    "discriminator_model": "random_window_discriminator",
+    "discriminator_model_params":{
+        "uncond_disc_donwsample_factors": [8, 4],
+        "cond_disc_downsample_factors": [[8, 4, 2, 2, 2], [8, 4, 2, 2], [8, 4, 2], [8, 4], [4, 2, 2]],
+        "cond_disc_out_channels": [[128, 128, 256, 256], [128, 256, 256], [128, 256], [256], [128, 256]],
+        "window_sizes": [512, 1024, 2048, 4096, 8192]
+    },
+
+    // GENERATOR
+    "generator_model": "multiband_melgan_generator",
+    "generator_model_params": {
+        "upsample_factors":[2 ,2, 4, 4],
+        "num_res_blocks": 4
+    },
+
+    // DATASET
+    "data_path": "/root/LJSpeech-1.1/wavs/",
+    "seq_len": 16384,
+    "pad_short": 2000,
+    "conv_pad": 0,
+    "use_noise_augment": true,
+    "use_cache": true,
+
+    "reinit_layers": [],    // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
+
+    // TRAINING
+    "batch_size": 64,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
+
+    // VALIDATION
+    "run_eval": true,
+    "test_delay_epochs": 10,  //Until attention is aligned, testing only wastes computation time.
+    "test_sentences_file": null,  // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
+
+    // OPTIMIZER
+    "noam_schedule": false,        // use noam warmup and lr schedule.
+    "warmup_steps_gen": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
+    "warmup_steps_disc": 4000,
+    "epochs": 1000,                // total number of epochs to train.
+    "wd": 0.000001,                // Weight decay weight.
+    "lr_gen": 0.0001,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
+    "lr_disc": 0.0001,
+    "gen_clip_grad": 10.0,
+    "disc_clip_grad": 10.0,
+
+    // TENSORBOARD and LOGGING
+    "print_step": 25,       // Number of steps to log traning on console.
+    "print_eval": false,     // If True, it prints intermediate loss values in evalulation.
+    "save_step": 10000,      // Number of training steps expected to save traninpg stats and checkpoints.
+    "checkpoint": true,     // If true, it saves checkpoints per "save_step"
+    "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
+
+    // DATA LOADING
+    "num_loader_workers": 4,        // number of training data loader processes. Don't set it too big. 4-8 are good values.
+    "num_val_loader_workers": 4,    // number of evaluation data loader processes.
+    "eval_split_size": 10,
+
+    // PATHS
+    "output_path": "/home/erogol/Models/LJSpeech/"
+}
+

From 5662aa3bbe4d73120a89b4eb59a3ce8a03698abd Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Mon, 1 Jun 2020 03:02:51 +0200
Subject: [PATCH 027/185] tb eval print

---
 vocoder/train.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/vocoder/train.py b/vocoder/train.py
index a30e3bff..27e14e8a 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -383,6 +383,7 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
     # synthesize a full voice
     data_loader.return_segments = False
 
+    tb_logger.tb_eval_stats(global_step, keep_avg.avg_values)
     return keep_avg.avg_values
 
 

From 4619b84ddbda458b202e74f28db9a02191c0a499 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 2 Jun 2020 17:07:15 +0200
Subject: [PATCH 028/185] multiscale subband stft loss and missing tb_logger
 for eval stats

---
 vocoder/configs/melgan_config.json |  8 +++++
 vocoder/layers/losses.py           | 21 ++++++++++--
 vocoder/train.py                   | 53 ++++++++++++++----------------
 3 files changed, 51 insertions(+), 31 deletions(-)

diff --git a/vocoder/configs/melgan_config.json b/vocoder/configs/melgan_config.json
index b97206fa..4aeba82e 100644
--- a/vocoder/configs/melgan_config.json
+++ b/vocoder/configs/melgan_config.json
@@ -49,11 +49,13 @@
 
     // LOSS PARAMETERS
     "use_stft_loss": true,
+    "use_subband_stft_loss": true,
     "use_mse_gan_loss": true,
     "use_hinge_gan_loss": false,
     "use_feat_match_loss": false,  // use only with melgan discriminators
 
     "stft_loss_weight": 1,
+    "subband_stft_loss_weight": 1,
     "mse_gan_loss_weight": 2.5,
     "hinge_gan_loss_weight": 2.5,
     "feat_match_loss_weight": 10.0,
@@ -63,6 +65,12 @@
         "hop_lengths": [120, 240, 50],
         "win_lengths": [600, 1200, 240]
     },
+    "subband_stft_loss_params":{
+        "n_ffts": [384, 683, 171],
+        "hop_lengths": [30, 60, 10],
+        "win_lengths": [150, 300, 60]
+    },
+
     "target_loss": "avg_G_loss",  // loss value to pick the best model
 
     // DISCRIMINATOR
diff --git a/vocoder/layers/losses.py b/vocoder/layers/losses.py
index 27091c0a..e242d209 100644
--- a/vocoder/layers/losses.py
+++ b/vocoder/layers/losses.py
@@ -49,7 +49,6 @@ class STFTLoss(nn.Module):
         loss_sc = torch.norm(y_M - y_hat_M, p="fro") / torch.norm(y_M, p="fro")
         return loss_mag, loss_sc
 
-
 class MultiScaleSTFTLoss(torch.nn.Module):
     def __init__(self,
                  n_ffts=[1024, 2048, 512],
@@ -73,6 +72,13 @@ class MultiScaleSTFTLoss(torch.nn.Module):
         return loss_mag, loss_sc
 
 
+class MultiScaleSubbandSTFTLoss(MultiScaleSTFTLoss):
+    def forward(self, y_hat, y):
+        y_hat = y_hat.view(-1, 1, y_hat.shape[2])
+        y = y.view(-1, 1, y.shape[2])
+        return super().forward(y_hat.squeeze(1), y.squeeze(1))
+
+
 class MSEGLoss(nn.Module):
     """ Mean Squared Generator Loss """
     def __init__(self,):
@@ -145,17 +151,21 @@ class GeneratorLoss(nn.Module):
             " [!] Cannot use HingeGANLoss and MSEGANLoss together."
 
         self.use_stft_loss = C.use_stft_loss
+        self.use_subband_stft_loss = C.use_subband_stft_loss
         self.use_mse_gan_loss = C.use_mse_gan_loss
         self.use_hinge_gan_loss = C.use_hinge_gan_loss
         self.use_feat_match_loss = C.use_feat_match_loss
 
         self.stft_loss_weight = C.stft_loss_weight
+        self.subband_stft_loss_weight = C.subband_stft_loss_weight
         self.mse_gan_loss_weight = C.mse_gan_loss_weight
         self.hinge_gan_loss_weight = C.hinge_gan_loss_weight
         self.feat_match_loss_weight = C.feat_match_loss_weight
 
         if C.use_stft_loss:
             self.stft_loss = MultiScaleSTFTLoss(**C.stft_loss_params)
+        if C.use_subband_stft_loss:
+            self.subband_stft_loss = MultiScaleSubbandSTFTLoss(**C.subband_stft_loss_params)
         if C.use_mse_gan_loss:
             self.mse_loss = MSEGLoss()
         if C.use_hinge_gan_loss:
@@ -163,7 +173,7 @@ class GeneratorLoss(nn.Module):
         if C.use_feat_match_loss:
             self.feat_match_loss = MelganFeatureLoss()
 
-    def forward(self, y_hat=None, y=None, scores_fake=None, feats_fake=None, feats_real=None):
+    def forward(self, y_hat=None, y=None, scores_fake=None, feats_fake=None, feats_real=None, y_hat_sub=None, y_sub=None):
         loss = 0
         return_dict = {}
 
@@ -174,6 +184,13 @@ class GeneratorLoss(nn.Module):
             return_dict['G_stft_loss_sc'] = stft_loss_sc
             loss += self.stft_loss_weight * (stft_loss_mg + stft_loss_sc)
 
+        # subband STFT Loss
+        if self.use_subband_stft_loss:
+            subband_stft_loss_mg, subband_stft_loss_sc = self.subband_stft_loss(y_hat_sub, y_sub)
+            return_dict['G_subband_stft_loss_mg'] = subband_stft_loss_mg
+            return_dict['G_subband_stft_loss_sc'] = subband_stft_loss_sc
+            loss += self.subband_stft_loss_weight * (subband_stft_loss_mg + subband_stft_loss_sc)
+
         # Fake Losses
         if self.use_mse_gan_loss and scores_fake is not None:
             mse_fake_loss = 0
diff --git a/vocoder/train.py b/vocoder/train.py
index 27e14e8a..6341bb6c 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -122,30 +122,27 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
         # generator pass
         optimizer_G.zero_grad()
         y_hat = model_G(c_G)
-
-        in_real_D = y_hat
-        in_fake_D = y_G
+        y_hat_sub = None
+        y_G_sub = None
 
         # PQMF formatting
         if y_hat.shape[1] > 1:
-            in_real_D = y_G
-            in_fake_D = model_G.pqmf_synthesis(y_hat)
-            y_G = model_G.pqmf_analysis(y_G)
-            y_hat = y_hat.view(-1, 1, y_hat.shape[2])
-            y_G = y_G.view(-1, 1, y_G.shape[2])
+            y_hat_sub = y_hat
+            y_hat = model_G.pqmf_synthesis(y_hat)
+            y_G_sub = model_G.pqmf_analysis(y_G)
 
         if global_step > c.steps_to_start_discriminator:
 
             # run D with or without cond. features
             if len(signature(model_D.forward).parameters) == 2:
-                D_out_fake = model_D(in_fake_D, c_G)
+                D_out_fake = model_D(y_hat, c_G)
             else:
-                D_out_fake = model_D(in_fake_D)
+                D_out_fake = model_D(y_hat)
             D_out_real = None
 
             if c.use_feat_match_loss:
                 with torch.no_grad():
-                    D_out_real = model_D(in_real_D)
+                    D_out_real = model_D(y_G)
 
             # format D outputs
             if isinstance(D_out_fake, tuple):
@@ -162,7 +159,7 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
 
         # compute losses
         loss_G_dict = criterion_G(y_hat, y_G, scores_fake, feats_fake,
-                                  feats_real)
+                                  feats_real, y_hat_sub, y_G_sub)
         loss_G = loss_G_dict['G_loss']
 
         # optimizer generator
@@ -272,12 +269,12 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
                                 model_losses=loss_dict)
 
             # compute spectrograms
-            figures = plot_results(in_fake_D, in_real_D, ap, global_step,
+            figures = plot_results(y_hat, y_G, ap, global_step,
                                    'train')
             tb_logger.tb_train_figures(global_step, figures)
 
             # Sample audio
-            sample_voice = in_fake_D[0].squeeze(0).detach().cpu().numpy()
+            sample_voice = y_hat[0].squeeze(0).detach().cpu().numpy()
             tb_logger.tb_train_audios(global_step,
                                       {'train/audio': sample_voice},
                                       c.audio["sample_rate"])
@@ -320,23 +317,20 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
 
         # generator pass
         y_hat = model_G(c_G)
-
-        in_real_D = y_hat
-        in_fake_D = y_G
+        y_hat_sub = None
+        y_G_sub = None
 
         # PQMF formatting
         if y_hat.shape[1] > 1:
-            in_real_D = y_G
-            in_fake_D = model_G.pqmf_synthesis(y_hat)
-            y_G = model_G.pqmf_analysis(y_G)
-            y_hat = y_hat.view(-1, 1, y_hat.shape[2])
-            y_G = y_G.view(-1, 1, y_G.shape[2])
+            y_hat_sub = y_hat
+            y_hat = model_G.pqmf_synthesis(y_hat)
+            y_G_sub = model_G.pqmf_analysis(y_G)
 
-        D_out_fake = model_D(in_fake_D)
+        D_out_fake = model_D(y_hat)
         D_out_real = None
         if c.use_feat_match_loss:
             with torch.no_grad():
-                D_out_real = model_D(in_real_D)
+                D_out_real = model_D(y_G)
 
         # format D outputs
         if isinstance(D_out_fake, tuple):
@@ -350,7 +344,7 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
 
         # compute losses
         loss_G_dict = criterion_G(y_hat, y_G, scores_fake, feats_fake,
-                                  feats_real)
+                                  feats_real, y_hat_sub, y_G_sub)
 
         loss_dict = dict()
         for key, value in loss_G_dict.items():
@@ -364,7 +358,7 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
         for key, value in loss_G_dict.items():
             update_eval_values['avg_' + key] = value.item()
         update_eval_values['avg_loader_time'] = loader_time
-        update_eval_values['avg_step_time'] = step_time
+        update_eval_values['avgP_step_time'] = step_time
         keep_avg.update_values(update_eval_values)
 
         # print eval stats
@@ -372,18 +366,19 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
             c_logger.print_eval_step(num_iter, loss_dict, keep_avg.avg_values)
 
     # compute spectrograms
-    figures = plot_results(in_fake_D, in_real_D, ap, global_step, 'eval')
+    figures = plot_results(y_hat, y_G, ap, global_step, 'eval')
     tb_logger.tb_eval_figures(global_step, figures)
 
     # Sample audio
-    sample_voice = in_fake_D[0].squeeze(0).detach().cpu().numpy()
+    sample_voice = y_hat[0].squeeze(0).detach().cpu().numpy()
     tb_logger.tb_eval_audios(global_step, {'eval/audio': sample_voice},
                              c.audio["sample_rate"])
 
     # synthesize a full voice
     data_loader.return_segments = False
 
-    tb_logger.tb_eval_stats(global_step, keep_avg.avg_values)
+    tb_logger.tb_evals_stats(global_step, keep_avg.avg_values)
+
     return keep_avg.avg_values
 
 

From fd2d0dab41764890dda0aadbad782eda29005d93 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 2 Jun 2020 17:09:05 +0200
Subject: [PATCH 029/185] bug fix

---
 vocoder/train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vocoder/train.py b/vocoder/train.py
index 6341bb6c..398051a9 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -377,7 +377,7 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
     # synthesize a full voice
     data_loader.return_segments = False
 
-    tb_logger.tb_evals_stats(global_step, keep_avg.avg_values)
+    tb_logger.tb_eval_stats(global_step, keep_avg.avg_values)
 
     return keep_avg.avg_values
 

From 808da15e7d50fd014687664c7664b989405a9d85 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 2 Jun 2020 17:28:52 +0200
Subject: [PATCH 030/185] update travis script

---
 .travis/script         | 4 +++-
 requirements_tests.txt | 1 +
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/.travis/script b/.travis/script
index ca6f4cd3..278ba4f2 100755
--- a/.travis/script
+++ b/.travis/script
@@ -12,7 +12,9 @@ fi
 if [[ "$TEST_SUITE" == "unittest" ]]; then
   # Run tests on all pushes
   pushd tts_namespace
-  python -m unittest
+  nosetests speaker_encoder.tests --nocapture
+  nosetests vocoder.tests --nocapture
+  nosetests tests --nocapture
   popd
   # Test server package
   ./tests/test_server_package.sh
diff --git a/requirements_tests.txt b/requirements_tests.txt
index 1e0615b2..243aa3a6 100644
--- a/requirements_tests.txt
+++ b/requirements_tests.txt
@@ -13,3 +13,4 @@ tqdm
 soundfile
 phonemizer
 bokeh==1.4.0
+nose

From af516aff7fd6e8ee5a182af92507f6863b6b4783 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 2 Jun 2020 18:54:03 +0200
Subject: [PATCH 031/185] cardboard config update

---
 .cardboardlint.yml | 3 +++
 .pylintrc          | 3 ++-
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/.cardboardlint.yml b/.cardboardlint.yml
index 464ea733..4a115a37 100644
--- a/.cardboardlint.yml
+++ b/.cardboardlint.yml
@@ -1,2 +1,5 @@
 linters:
 - pylint:
+    # pylintrc: pylintrc
+    filefilter: ['- test_*.py', '+ *.py', '- *.npy']
+    # exclude:
\ No newline at end of file
diff --git a/.pylintrc b/.pylintrc
index b6e04944..a78b521e 100644
--- a/.pylintrc
+++ b/.pylintrc
@@ -157,7 +157,8 @@ disable=missing-docstring,
         xreadlines-attribute,
         deprecated-sys-function,
         exception-escape,
-        comprehension-escape
+        comprehension-escape,
+        duplicate-code
 
 # Enable the message, report, category or checker with the given id(s). You can
 # either give multiple identifier separated by comma (,) or put this option

From 054c1fce39bd89b8ce2194de641cb4e2c63f919b Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 2 Jun 2020 18:54:29 +0200
Subject: [PATCH 032/185] travis script for nose

---
 .travis/script | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.travis/script b/.travis/script
index 278ba4f2..ce886fc2 100755
--- a/.travis/script
+++ b/.travis/script
@@ -12,9 +12,9 @@ fi
 if [[ "$TEST_SUITE" == "unittest" ]]; then
   # Run tests on all pushes
   pushd tts_namespace
-  nosetests speaker_encoder.tests --nocapture
-  nosetests vocoder.tests --nocapture
-  nosetests tests --nocapture
+  nosetests TTS.speaker_encoder.tests --nocapture
+  nosetests TTS.vocoder.tests --nocapture
+  nosetests TTS.tests --nocapture
   popd
   # Test server package
   ./tests/test_server_package.sh

From e051dd41b18fed43a71c9d6b7280548184fd3341 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 2 Jun 2020 18:58:10 +0200
Subject: [PATCH 033/185] Ton of linter fixes

---
 train.py                               |  12 +--
 utils/training.py                      |  11 +++
 vocoder/datasets/gan_dataset.py        |  34 ++-----
 vocoder/layers/losses.py               |  35 ++-----
 vocoder/layers/melgan.py               |   3 -
 vocoder/layers/pqmf.py                 |   3 +
 vocoder/layers/pqmf2.py                | 127 -------------------------
 vocoder/models/melgan_discriminator.py |   4 +-
 vocoder/models/melgan_generator.py     |   2 -
 vocoder/tests/test_datasets.py         |  22 ++---
 vocoder/tests/test_losses.py           |   3 +-
 vocoder/tests/test_melgan_generator.py |   1 -
 vocoder/tests/test_pqmf.py             |   7 +-
 vocoder/train.py                       |  24 ++---
 vocoder/utils/console_logger.py        |   2 +-
 vocoder/utils/io.py                    |   1 -
 16 files changed, 60 insertions(+), 231 deletions(-)
 delete mode 100644 vocoder/layers/pqmf2.py

diff --git a/train.py b/train.py
index 82f91cd5..5a345b59 100644
--- a/train.py
+++ b/train.py
@@ -20,7 +20,8 @@ from TTS.utils.generic_utils import (count_parameters, create_experiment_folder,
 from TTS.utils.io import (save_best_model, save_checkpoint,
                           load_config, copy_config_file)
 from TTS.utils.training import (NoamLR, check_update, adam_weight_decay,
-                                gradual_training_scheduler, set_weight_decay)
+                                gradual_training_scheduler, set_weight_decay,
+                                setup_torch_training_env)
 from TTS.utils.tensorboard_logger import TensorboardLogger
 from TTS.utils.console_logger import ConsoleLogger
 from TTS.utils.speakers import load_speaker_mapping, save_speaker_mapping, \
@@ -32,13 +33,8 @@ from TTS.datasets.preprocess import load_meta_data
 from TTS.utils.radam import RAdam
 from TTS.utils.measures import alignment_diagonal_score
 
-torch.backends.cudnn.enabled = True
-torch.backends.cudnn.benchmark = False
-torch.manual_seed(54321)
-use_cuda = torch.cuda.is_available()
-num_gpus = torch.cuda.device_count()
-print(" > Using CUDA: ", use_cuda)
-print(" > Number of GPUs: ", num_gpus)
+
+use_cuda, num_gpus = setup_torch_training_env(True, False)
 
 
 def setup_loader(ap, r, is_val=False, verbose=False):
diff --git a/utils/training.py b/utils/training.py
index 5ce7948b..9046f9e0 100644
--- a/utils/training.py
+++ b/utils/training.py
@@ -2,6 +2,17 @@ import torch
 import numpy as np
 
 
+def setup_torch_training_env(cudnn_enable, cudnn_benchmark):
+    torch.backends.cudnn.enabled = cudnn_enable
+    torch.backends.cudnn.benchmark = cudnn_benchmark
+    torch.manual_seed(54321)
+    use_cuda = torch.cuda.is_available()
+    num_gpus = torch.cuda.device_count()
+    print(" > Using CUDA: ", use_cuda)
+    print(" > Number of GPUs: ", num_gpus)
+    return use_cuda, num_gpus
+
+
 def check_update(model, grad_clip, ignore_stopnet=False):
     r'''Check model gradient against unexpected jumps and failures'''
     skip_flag = False
diff --git a/vocoder/datasets/gan_dataset.py b/vocoder/datasets/gan_dataset.py
index 10d36cab..5e410151 100644
--- a/vocoder/datasets/gan_dataset.py
+++ b/vocoder/datasets/gan_dataset.py
@@ -3,29 +3,10 @@ import glob
 import torch
 import random
 import numpy as np
-from torch.utils.data import Dataset, DataLoader
+from torch.utils.data import Dataset
 from multiprocessing import Manager
 
 
-def create_dataloader(hp, args, train):
-    dataset = MelFromDisk(hp, args, train)
-
-    if train:
-        return DataLoader(dataset=dataset,
-                          batch_size=hp.train.batch_size,
-                          shuffle=True,
-                          num_workers=hp.train.num_workers,
-                          pin_memory=True,
-                          drop_last=True)
-    else:
-        return DataLoader(dataset=dataset,
-                          batch_size=1,
-                          shuffle=False,
-                          num_workers=hp.train.num_workers,
-                          pin_memory=True,
-                          drop_last=False)
-
-
 class GANDataset(Dataset):
     """
     GAN Dataset searchs for all the wav files under root path
@@ -55,26 +36,26 @@ class GANDataset(Dataset):
         self.return_segments = return_segments
         self.use_cache = use_cache
         self.use_noise_augment = use_noise_augment
+        self.verbose = verbose
 
         assert seq_len % hop_len == 0, " [!] seq_len has to be a multiple of hop_len."
         self.feat_frame_len = seq_len // hop_len + (2 * conv_pad)
 
         # map G and D instances
-        self.G_to_D_mappings = [i for i in range(len(self.item_list))]
+        self.G_to_D_mappings = list(range(len(self.item_list)))
         self.shuffle_mapping()
 
         # cache acoustic features
         if use_cache:
             self.create_feature_cache()
 
-
-
     def create_feature_cache(self):
         self.manager = Manager()
         self.cache = self.manager.list()
         self.cache += [None for _ in range(len(self.item_list))]
 
-    def find_wav_files(self, path):
+    @staticmethod
+    def find_wav_files(path):
         return glob.glob(os.path.join(path, '**', '*.wav'), recursive=True)
 
     def __len__(self):
@@ -88,9 +69,8 @@ class GANDataset(Dataset):
             item1 = self.load_item(idx)
             item2 = self.load_item(idx2)
             return item1, item2
-        else:
-            item1 = self.load_item(idx)
-            return item1
+        item1 = self.load_item(idx)
+        return item1
 
     def shuffle_mapping(self):
         random.shuffle(self.G_to_D_mappings)
diff --git a/vocoder/layers/losses.py b/vocoder/layers/losses.py
index e242d209..1c3d4442 100644
--- a/vocoder/layers/losses.py
+++ b/vocoder/layers/losses.py
@@ -51,13 +51,13 @@ class STFTLoss(nn.Module):
 
 class MultiScaleSTFTLoss(torch.nn.Module):
     def __init__(self,
-                 n_ffts=[1024, 2048, 512],
-                 hop_lengths=[120, 240, 50],
-                 win_lengths=[600, 1200, 240]):
+                 n_ffts=(1024, 2048, 512),
+                 hop_lengths=(120, 240, 50),
+                 win_lengths=(600, 1200, 240)):
         super(MultiScaleSTFTLoss, self).__init__()
         self.loss_funcs = torch.nn.ModuleList()
-        for idx in range(len(n_ffts)):
-            self.loss_funcs.append(STFTLoss(n_ffts[idx], hop_lengths[idx], win_lengths[idx]))
+        for n_fft, hop_length, win_length in zip(n_ffts, hop_lengths, win_lengths):
+            self.loss_funcs.append(STFTLoss(n_fft, hop_length, win_length))
 
     def forward(self, y_hat, y):
         N = len(self.loss_funcs)
@@ -81,20 +81,14 @@ class MultiScaleSubbandSTFTLoss(MultiScaleSTFTLoss):
 
 class MSEGLoss(nn.Module):
     """ Mean Squared Generator Loss """
-    def __init__(self,):
-        super(MSEGLoss, self).__init__()
-
-    def forward(self, score_fake, ):
+    def forward(self, score_fake):
         loss_fake = torch.mean(torch.sum(torch.pow(score_fake, 2), dim=[1, 2]))
         return loss_fake
 
 
 class HingeGLoss(nn.Module):
     """ Hinge Discriminator Loss """
-    def __init__(self,):
-        super(HingeGLoss, self).__init__()
-
-    def forward(self, score_fake, score_real):
+    def forward(self, score_fake):
         loss_fake = torch.mean(F.relu(1. + score_fake))
         return loss_fake
 
@@ -106,9 +100,6 @@ class HingeGLoss(nn.Module):
 
 class MSEDLoss(nn.Module):
     """ Mean Squared Discriminator Loss """
-    def __init__(self,):
-        super(MSEDLoss, self).__init__()
-
     def forward(self, score_fake, score_real):
         loss_real = torch.mean(torch.sum(torch.pow(score_real - 1.0, 2), dim=[1, 2]))
         loss_fake = torch.mean(torch.sum(torch.pow(score_fake, 2), dim=[1, 2]))
@@ -118,9 +109,6 @@ class MSEDLoss(nn.Module):
 
 class HingeDLoss(nn.Module):
     """ Hinge Discriminator Loss """
-    def __init__(self,):
-        super(HingeDLoss, self).__init__()
-
     def forward(self, score_fake, score_real):
         loss_real = torch.mean(F.relu(1. - score_real))
         loss_fake = torch.mean(F.relu(1. + score_fake))
@@ -129,13 +117,10 @@ class HingeDLoss(nn.Module):
 
 
 class MelganFeatureLoss(nn.Module):
-    def __init__(self, ):
-        super(MelganFeatureLoss, self).__init__()
-
     def forward(self, fake_feats, real_feats):
         loss_feats = 0
         for fake_feat, real_feat in zip(fake_feats, real_feats):
-            loss_feats += hp.model.feat_match * torch.mean(torch.abs(fake_feat - real_feat))
+            loss_feats += torch.mean(torch.abs(fake_feat - real_feat))
         return loss_feats
 
 
@@ -147,7 +132,7 @@ class MelganFeatureLoss(nn.Module):
 class GeneratorLoss(nn.Module):
     def __init__(self, C):
         super(GeneratorLoss, self).__init__()
-        assert C.use_mse_gan_loss and C.use_hinge_gan_loss == False,\
+        assert not(C.use_mse_gan_loss and C.use_hinge_gan_loss),\
             " [!] Cannot use HingeGANLoss and MSEGANLoss together."
 
         self.use_stft_loss = C.use_stft_loss
@@ -228,7 +213,7 @@ class GeneratorLoss(nn.Module):
 class DiscriminatorLoss(nn.Module):
     def __init__(self, C):
         super(DiscriminatorLoss, self).__init__()
-        assert C.use_mse_gan_loss and C.use_hinge_gan_loss == False,\
+        assert not(C.use_mse_gan_loss and C.use_hinge_gan_loss),\
             " [!] Cannot use HingeGANLoss and MSEGANLoss together."
 
         self.use_mse_gan_loss = C.use_mse_gan_loss
diff --git a/vocoder/layers/melgan.py b/vocoder/layers/melgan.py
index cda0413c..61f7a96b 100644
--- a/vocoder/layers/melgan.py
+++ b/vocoder/layers/melgan.py
@@ -1,7 +1,4 @@
-import numpy as np
-import torch
 from torch import nn
-from torch.nn import functional as F
 from torch.nn.utils import weight_norm
 
 
diff --git a/vocoder/layers/pqmf.py b/vocoder/layers/pqmf.py
index f438ea00..3985aad4 100644
--- a/vocoder/layers/pqmf.py
+++ b/vocoder/layers/pqmf.py
@@ -44,6 +44,9 @@ class PQMF(torch.nn.Module):
 
         self.pad_fn = torch.nn.ConstantPad1d(taps // 2, 0.0)
 
+    def forward(self, x):
+        return self.analysis(x)
+
     def analysis(self, x):
         return F.conv1d(x, self.H, padding=self.taps // 2, stride=self.N)
 
diff --git a/vocoder/layers/pqmf2.py b/vocoder/layers/pqmf2.py
deleted file mode 100644
index 4cffb819..00000000
--- a/vocoder/layers/pqmf2.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Copyright 2020 Tomoki Hayashi
-#  MIT License (https://opensource.org/licenses/MIT)
-
-"""Pseudo QMF modules."""
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-from scipy.signal import kaiser
-
-
-def design_prototype_filter(taps=62, cutoff_ratio=0.15, beta=9.0):
-    """Design prototype filter for PQMF.
-
-    This method is based on `A Kaiser window approach for the design of prototype
-    filters of cosine modulated filterbanks`_.
-
-    Args:
-        taps (int): The number of filter taps.
-        cutoff_ratio (float): Cut-off frequency ratio.
-        beta (float): Beta coefficient for kaiser window.
-
-    Returns:
-        ndarray: Impluse response of prototype filter (taps + 1,).
-
-    .. _`A Kaiser window approach for the design of prototype filters of cosine modulated filterbanks`:
-        https://ieeexplore.ieee.org/abstract/document/681427
-
-    """
-    # check the arguments are valid
-    assert taps % 2 == 0, "The number of taps mush be even number."
-    assert 0.0 < cutoff_ratio < 1.0, "Cutoff ratio must be > 0.0 and < 1.0."
-
-    # make initial filter
-    omega_c = np.pi * cutoff_ratio
-    with np.errstate(invalid='ignore'):
-        h_i = np.sin(omega_c * (np.arange(taps + 1) - 0.5 * taps)) \
-            / (np.pi * (np.arange(taps + 1) - 0.5 * taps))
-    h_i[taps // 2] = np.cos(0) * cutoff_ratio  # fix nan due to indeterminate form
-
-    # apply kaiser window
-    w = kaiser(taps + 1, beta)
-    h = h_i * w
-
-    return h
-
-
-class PQMF(torch.nn.Module):
-    """PQMF module.
-
-    This module is based on `Near-perfect-reconstruction pseudo-QMF banks`_.
-
-    .. _`Near-perfect-reconstruction pseudo-QMF banks`:
-        https://ieeexplore.ieee.org/document/258122
-
-    """
-
-    def __init__(self, subbands=4, taps=62, cutoff_ratio=0.15, beta=9.0):
-        """Initilize PQMF module.
-
-        Args:
-            subbands (int): The number of subbands.
-            taps (int): The number of filter taps.
-            cutoff_ratio (float): Cut-off frequency ratio.
-            beta (float): Beta coefficient for kaiser window.
-
-        """
-        super(PQMF, self).__init__()
-
-        # define filter coefficient
-        h_proto = design_prototype_filter(taps, cutoff_ratio, beta)
-        h_analysis = np.zeros((subbands, len(h_proto)))
-        h_synthesis = np.zeros((subbands, len(h_proto)))
-        for k in range(subbands):
-            h_analysis[k] = 2 * h_proto * np.cos((2 * k + 1) * (np.pi / (2 * subbands)) * (np.arange(taps + 1) - ((taps - 1) / 2)) + (-1) ** k * np.pi / 4)
-            h_synthesis[k] = 2 * h_proto * np.cos((2 * k + 1) * (np.pi / (2 * subbands)) * (np.arange(taps + 1) - ((taps - 1) / 2)) - (-1) ** k * np.pi / 4)
-
-        # convert to tensor
-        analysis_filter = torch.from_numpy(h_analysis).float().unsqueeze(1)
-        synthesis_filter = torch.from_numpy(h_synthesis).float().unsqueeze(0)
-
-        # register coefficients as beffer
-        self.register_buffer("analysis_filter", analysis_filter)
-        self.register_buffer("synthesis_filter", synthesis_filter)
-
-        # filter for downsampling & upsampling
-        updown_filter = torch.zeros((subbands, subbands, subbands)).float()
-        for k in range(subbands):
-            updown_filter[k, k, 0] = 1.0
-        self.register_buffer("updown_filter", updown_filter)
-        self.subbands = subbands
-
-        # keep padding info
-        self.pad_fn = torch.nn.ConstantPad1d(taps // 2, 0.0)
-
-    def analysis(self, x):
-        """Analysis with PQMF.
-
-        Args:
-            x (Tensor): Input tensor (B, 1, T).
-
-        Returns:
-            Tensor: Output tensor (B, subbands, T // subbands).
-
-        """
-        x = F.conv1d(self.pad_fn(x), self.analysis_filter)
-        return F.conv1d(x, self.updown_filter, stride=self.subbands)
-
-    def synthesis(self, x):
-        """Synthesis with PQMF.
-
-        Args:
-            x (Tensor): Input tensor (B, subbands, T // subbands).
-
-        Returns:
-            Tensor: Output tensor (B, 1, T).
-
-        """
-        # NOTE(kan-bayashi): Power will be dreased so here multipy by # subbands.
-        #   Not sure this is the correct way, it is better to check again.
-        # TODO(kan-bayashi): Understand the reconstruction procedure
-        x = F.conv_transpose1d(x, self.updown_filter * self.subbands, stride=self.subbands)
-        x = F.conv1d(self.pad_fn(x), self.synthesis_filter)
-        return x
diff --git a/vocoder/models/melgan_discriminator.py b/vocoder/models/melgan_discriminator.py
index 55d3f585..3847babb 100644
--- a/vocoder/models/melgan_discriminator.py
+++ b/vocoder/models/melgan_discriminator.py
@@ -1,7 +1,5 @@
 import numpy as np
-import torch
 from torch import nn
-from torch.nn import functional as F
 from torch.nn.utils import weight_norm
 
 
@@ -32,7 +30,7 @@ class MelganDiscriminator(nn.Module):
 
         # downsampling layers
         layer_in_channels = base_channels
-        for idx, downsample_factor in enumerate(downsample_factors):
+        for downsample_factor in downsample_factors:
             layer_out_channels = min(layer_in_channels * downsample_factor,
                                      max_channels)
             layer_kernel_size = downsample_factor * 10 + 1
diff --git a/vocoder/models/melgan_generator.py b/vocoder/models/melgan_generator.py
index 2d266f29..1e47816d 100644
--- a/vocoder/models/melgan_generator.py
+++ b/vocoder/models/melgan_generator.py
@@ -1,7 +1,5 @@
-import math
 import torch
 from torch import nn
-from torch.nn import functional as F
 from torch.nn.utils import weight_norm
 
 from TTS.vocoder.layers.melgan import ResidualStack
diff --git a/vocoder/tests/test_datasets.py b/vocoder/tests/test_datasets.py
index 3d6280f0..5d409b3f 100644
--- a/vocoder/tests/test_datasets.py
+++ b/vocoder/tests/test_datasets.py
@@ -22,7 +22,7 @@ ok_ljspeech = os.path.exists(test_data_path)
 def gan_dataset_case(batch_size, seq_len, hop_len, conv_pad, return_segments, use_noise_augment, use_cache, num_workers):
     ''' run dataloader with given parameters and check conditions '''
     ap = AudioProcessor(**C.audio)
-    eval_items, train_items = load_wav_data(test_data_path, 10)
+    _, train_items = load_wav_data(test_data_path, 10)
     dataset = GANDataset(ap,
                          train_items,
                          seq_len=seq_len,
@@ -44,9 +44,9 @@ def gan_dataset_case(batch_size, seq_len, hop_len, conv_pad, return_segments, us
 
     # return random segments or return the whole audio
     if return_segments:
-        for item1, item2 in loader:
+        for item1, _ in loader:
             feat1, wav1 = item1
-            feat2, wav2 = item2
+            # feat2, wav2 = item2
             expected_feat_shape = (batch_size, ap.num_mels, seq_len // hop_len + conv_pad * 2)
 
             # check shapes
@@ -80,15 +80,15 @@ def gan_dataset_case(batch_size, seq_len, hop_len, conv_pad, return_segments, us
 def test_parametrized_gan_dataset():
     ''' test dataloader with different parameters '''
     params = [
-        [32, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, True, False, True, 0],
-        [32, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, True, False, True, 4],
-        [1, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, True, True, True, 0],
+        [32, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, True, False, True, 0],
+        [32, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, True, False, True, 4],
+        [1, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, True, True, True, 0],
         [1, C.audio['hop_length'], C.audio['hop_length'], 0, True, True, True, 0],
-        [1, C.audio['hop_length'] * 10,  C.audio['hop_length'], 2, True, True, True, 0],
-        [1, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, False, True, True, 0],
-        [1, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, True, False, True, 0],
-        [1, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, True, True, False, 0],
-        [1, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, False, False, False, 0],
+        [1, C.audio['hop_length'] * 10, C.audio['hop_length'], 2, True, True, True, 0],
+        [1, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, False, True, True, 0],
+        [1, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, True, False, True, 0],
+        [1, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, True, True, False, 0],
+        [1, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, False, False, False, 0],
     ]
     for param in params:
         print(param)
diff --git a/vocoder/tests/test_losses.py b/vocoder/tests/test_losses.py
index 83314832..44a1321d 100644
--- a/vocoder/tests/test_losses.py
+++ b/vocoder/tests/test_losses.py
@@ -1,5 +1,4 @@
 import os
-import unittest
 import torch
 
 from TTS.vocoder.layers.losses import TorchSTFT, STFTLoss, MultiScaleSTFTLoss
@@ -24,7 +23,7 @@ def test_torch_stft():
     torch_stft = TorchSTFT(ap.n_fft, ap.hop_length, ap.win_length)
     # librosa stft
     wav = ap.load_wav(WAV_FILE)
-    M_librosa = abs(ap._stft(wav))
+    M_librosa = abs(ap._stft(wav)) # pylint: disable=protected-access
     # torch stft
     wav = torch.from_numpy(wav[None, :]).float()
     M_torch = torch_stft(wav)
diff --git a/vocoder/tests/test_melgan_generator.py b/vocoder/tests/test_melgan_generator.py
index e9c4ad60..e03da31b 100644
--- a/vocoder/tests/test_melgan_generator.py
+++ b/vocoder/tests/test_melgan_generator.py
@@ -1,5 +1,4 @@
 import numpy as np
-import unittest
 import torch
 
 from TTS.vocoder.models.melgan_generator import MelganGenerator
diff --git a/vocoder/tests/test_pqmf.py b/vocoder/tests/test_pqmf.py
index 8444fc5a..a26bdd59 100644
--- a/vocoder/tests/test_pqmf.py
+++ b/vocoder/tests/test_pqmf.py
@@ -1,16 +1,11 @@
 import os
 import torch
-import unittest
 
-import numpy as np
 import soundfile as sf
 from librosa.core import load
 
-from TTS.tests import get_tests_path, get_tests_input_path, get_tests_output_path
-from TTS.utils.audio import AudioProcessor
-from TTS.utils.io import load_config
+from TTS.tests import get_tests_path, get_tests_input_path
 from TTS.vocoder.layers.pqmf import PQMF
-from TTS.vocoder.layers.pqmf2 import PQMF as PQMF2
 
 
 TESTS_PATH = get_tests_path()
diff --git a/vocoder/train.py b/vocoder/train.py
index 398051a9..d401e72e 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -17,7 +17,7 @@ from TTS.utils.generic_utils import (KeepAverage, count_parameters,
 from TTS.utils.io import copy_config_file, load_config
 from TTS.utils.radam import RAdam
 from TTS.utils.tensorboard_logger import TensorboardLogger
-from TTS.utils.training import NoamLR
+from TTS.utils.training import setup_torch_training_env, NoamLR
 from TTS.vocoder.datasets.gan_dataset import GANDataset
 from TTS.vocoder.datasets.preprocess import load_wav_data
 # from distribute import (DistributedSampler, apply_gradient_allreduce,
@@ -29,13 +29,8 @@ from TTS.vocoder.utils.generic_utils import (check_config, plot_results,
                                              setup_discriminator,
                                              setup_generator)
 
-torch.backends.cudnn.enabled = True
-torch.backends.cudnn.benchmark = True
-torch.manual_seed(54321)
-use_cuda = torch.cuda.is_available()
-num_gpus = torch.cuda.device_count()
-print(" > Using CUDA: ", use_cuda)
-print(" > Number of GPUs: ", num_gpus)
+
+use_cuda, num_gpus = setup_torch_training_env(True, True)
 
 
 def setup_loader(ap, is_val=False, verbose=False):
@@ -49,10 +44,11 @@ def setup_loader(ap, is_val=False, verbose=False):
                              pad_short=c.pad_short,
                              conv_pad=c.conv_pad,
                              is_training=not is_val,
-                             return_segments=False if is_val else True,
+                             return_segments=not is_val,
                              use_noise_augment=c.use_noise_augment,
                              use_cache=c.use_cache,
                              verbose=verbose)
+        dataset.shuffle_mapping()
         # sampler = DistributedSampler(dataset) if num_gpus > 1 else None
         loader = DataLoader(dataset,
                             batch_size=1 if is_val else c.batch_size,
@@ -81,11 +77,11 @@ def format_data(data):
         return c_G, x_G, c_D, x_D
 
     # return a whole audio segment
-    c, x = data
+    co, x = data
     if use_cuda:
-        c = c.cuda(non_blocking=True)
+        co = co.cuda(non_blocking=True)
         x = x.cuda(non_blocking=True)
-    return c, x
+    return co, x, None, None
 
 
 def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
@@ -306,7 +302,7 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
         start_time = time.time()
 
         # format data
-        c_G, y_G = format_data(data)
+        c_G, y_G, _, _ = format_data(data)
         loader_time = time.time() - end_time
 
         global_step += 1
@@ -416,7 +412,7 @@ def main(args):  # pylint: disable=redefined-outer-name
             optimizer_gen.load_state_dict(checkpoint['optimizer'])
             model_disc.load_state_dict(checkpoint['model_disc'])
             optimizer_disc.load_state_dict(checkpoint['optimizer_disc'])
-        except:
+        except KeyError:
             print(" > Partial model initialization.")
             model_dict = model_gen.state_dict()
             model_dict = set_init_dict(model_dict, checkpoint['model'], c)
diff --git a/vocoder/utils/console_logger.py b/vocoder/utils/console_logger.py
index 4fe132bb..ff7edfd0 100644
--- a/vocoder/utils/console_logger.py
+++ b/vocoder/utils/console_logger.py
@@ -91,7 +91,7 @@ class ConsoleLogger():
                     sign = ''
                 elif diff > 0:
                     color = tcolors.FAIL
-                    sing = '+'
+                    sign = '+'
             log_text += "{}{}:{} {:.5f} {}({}{:.5f})\n".format(indent, key, color, value, tcolors.ENDC, sign, diff)
         self.old_eval_loss_dict = avg_loss_dict
         print(log_text, flush=True)
diff --git a/vocoder/utils/io.py b/vocoder/utils/io.py
index 9526b9db..b6ea7484 100644
--- a/vocoder/utils/io.py
+++ b/vocoder/utils/io.py
@@ -2,7 +2,6 @@ import os
 import torch
 import datetime
 
-
 def save_model(model, optimizer, model_disc, optimizer_disc, current_step,
                epoch, output_path, **kwargs):
     model_state = model.state_dict()

From b25af49c34b161cf0d2d7e6887af35a811bde33d Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 2 Jun 2020 23:58:18 +0200
Subject: [PATCH 034/185] espeak install travis

---
 .travis.yml | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/.travis.yml b/.travis.yml
index 645f9861..83ba25a3 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -3,6 +3,10 @@ language: python
 git:
   quiet: true
 
+before_install:
+  - sudo apt-get update
+  - sudo apt-get -y install espeak
+
 matrix:
   include:
   - name: "Lint check"

From 1e017f866abf5d1249d19ec77a3a91a7b7ff227d Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 3 Jun 2020 12:15:48 +0200
Subject: [PATCH 035/185] reset loss weights

---
 vocoder/configs/melgan_config.json | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/vocoder/configs/melgan_config.json b/vocoder/configs/melgan_config.json
index 4aeba82e..bdfdf88a 100644
--- a/vocoder/configs/melgan_config.json
+++ b/vocoder/configs/melgan_config.json
@@ -54,11 +54,11 @@
     "use_hinge_gan_loss": false,
     "use_feat_match_loss": false,  // use only with melgan discriminators
 
-    "stft_loss_weight": 1,
-    "subband_stft_loss_weight": 1,
+    "stft_loss_weight": 0.5,
+    "subband_stft_loss_weight": 0.5,
     "mse_gan_loss_weight": 2.5,
     "hinge_gan_loss_weight": 2.5,
-    "feat_match_loss_weight": 10.0,
+    "feat_match_loss_weight": 25,
 
     "stft_loss_params": {
         "n_ffts": [1024, 2048, 512],

From 20a9d51cccbe4e995aa34d4965bde8dcd25b8f6b Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 3 Jun 2020 12:16:08 +0200
Subject: [PATCH 036/185] correct loss normalization and function refactoring

---
 vocoder/layers/losses.py | 141 +++++++++++++++++++++------------------
 1 file changed, 77 insertions(+), 64 deletions(-)

diff --git a/vocoder/layers/losses.py b/vocoder/layers/losses.py
index 1c3d4442..fb4e85d4 100644
--- a/vocoder/layers/losses.py
+++ b/vocoder/layers/losses.py
@@ -6,6 +6,7 @@ from torch.nn import functional as F
 
 class TorchSTFT():
     def __init__(self, n_fft, hop_length, win_length, window='hann_window'):
+        """ Torch based STFT operation """
         self.n_fft = n_fft
         self.hop_length = hop_length
         self.win_length = win_length
@@ -33,6 +34,7 @@ class TorchSTFT():
 
 
 class STFTLoss(nn.Module):
+    """ Single scale  STFT Loss """
     def __init__(self, n_fft, hop_length, win_length):
         super(STFTLoss, self).__init__()
         self.n_fft = n_fft
@@ -50,6 +52,7 @@ class STFTLoss(nn.Module):
         return loss_mag, loss_sc
 
 class MultiScaleSTFTLoss(torch.nn.Module):
+    """ Multi scale STFT loss """
     def __init__(self,
                  n_ffts=(1024, 2048, 512),
                  hop_lengths=(120, 240, 50),
@@ -73,6 +76,7 @@ class MultiScaleSTFTLoss(torch.nn.Module):
 
 
 class MultiScaleSubbandSTFTLoss(MultiScaleSTFTLoss):
+    """ Multiscale STFT loss for multi band model outputs """
     def forward(self, y_hat, y):
         y_hat = y_hat.view(-1, 1, y_hat.shape[2])
         y = y.view(-1, 1, y.shape[2])
@@ -121,16 +125,62 @@ class MelganFeatureLoss(nn.Module):
         loss_feats = 0
         for fake_feat, real_feat in zip(fake_feats, real_feats):
             loss_feats += torch.mean(torch.abs(fake_feat - real_feat))
+        loss_feats /= len(fake_feats) + len(real_feats)
         return loss_feats
 
 
-##################################
+#####################################
 # LOSS WRAPPERS
+#####################################
+
+
+def _apply_G_adv_loss(scores_fake, loss_func):
+    """ Compute G adversarial loss function
+    and normalize values """
+    adv_loss = 0
+    if isinstance(scores_fake, list):
+        for score_fake in scores_fake:
+            fake_loss = loss_func(score_fake)
+            adv_loss += fake_loss
+        adv_loss /= len(scores_fake)
+    else:
+        fake_loss = loss_func(scores_fake)
+        adv_loss = fake_loss
+    return adv_loss
+
+
+def _apply_D_loss(scores_fake, scores_real, loss_func):
+    """ Compute D loss func and normalize loss values """
+    loss = 0
+    real_loss = 0
+    fake_loss = 0
+    if isinstance(scores_fake, list):
+        # multi-scale loss
+        for score_fake, score_real in zip(scores_fake, scores_real):
+            total_loss, real_loss, fake_loss = loss_func(score_fake, score_real)
+            loss += total_loss
+            real_loss += real_loss
+            fake_loss += fake_loss
+        # normalize loss values with number of scales
+        loss /= len(scores_fake)
+        real_loss /= len(scores_real)
+        fake_loss /= len(scores_fake)
+    else:
+        # single scale loss
+        total_loss, real_loss, fake_loss = loss_func(scores_fake, scores_real)
+        loss = total_loss
+    return loss, real_loss, fake_loss
+
+
+##################################
+# MODEL LOSSES
 ##################################
 
 
 class GeneratorLoss(nn.Module):
     def __init__(self, C):
+        """ Compute Generator Loss values depending on training
+        configuration """
         super(GeneratorLoss, self).__init__()
         assert not(C.use_mse_gan_loss and C.use_hinge_gan_loss),\
             " [!] Cannot use HingeGANLoss and MSEGANLoss together."
@@ -159,7 +209,8 @@ class GeneratorLoss(nn.Module):
             self.feat_match_loss = MelganFeatureLoss()
 
     def forward(self, y_hat=None, y=None, scores_fake=None, feats_fake=None, feats_real=None, y_hat_sub=None, y_sub=None):
-        loss = 0
+        gen_loss = 0
+        adv_loss = 0
         return_dict = {}
 
         # STFT Loss
@@ -167,50 +218,41 @@ class GeneratorLoss(nn.Module):
             stft_loss_mg, stft_loss_sc = self.stft_loss(y_hat.squeeze(1), y.squeeze(1))
             return_dict['G_stft_loss_mg'] = stft_loss_mg
             return_dict['G_stft_loss_sc'] = stft_loss_sc
-            loss += self.stft_loss_weight * (stft_loss_mg + stft_loss_sc)
+            gen_loss += self.stft_loss_weight * (stft_loss_mg + stft_loss_sc)
 
         # subband STFT Loss
         if self.use_subband_stft_loss:
             subband_stft_loss_mg, subband_stft_loss_sc = self.subband_stft_loss(y_hat_sub, y_sub)
             return_dict['G_subband_stft_loss_mg'] = subband_stft_loss_mg
             return_dict['G_subband_stft_loss_sc'] = subband_stft_loss_sc
-            loss += self.subband_stft_loss_weight * (subband_stft_loss_mg + subband_stft_loss_sc)
+            gen_loss += self.subband_stft_loss_weight * (subband_stft_loss_mg + subband_stft_loss_sc)
 
-        # Fake Losses
+        # multiscale MSE adversarial loss
         if self.use_mse_gan_loss and scores_fake is not None:
-            mse_fake_loss = 0
-            if isinstance(scores_fake, list):
-                for score_fake in scores_fake:
-                    fake_loss = self.mse_loss(score_fake)
-                    mse_fake_loss += fake_loss
-            else:
-                fake_loss = self.mse_loss(scores_fake)
-                mse_fake_loss = fake_loss
+            mse_fake_loss = _apply_G_adv_loss(scores_fake, self.mse_loss)
             return_dict['G_mse_fake_loss'] = mse_fake_loss
-            loss += self.mse_gan_loss_weight * mse_fake_loss
+            adv_loss += self.mse_gan_loss_weight * mse_fake_loss
 
+        # multiscale Hinge adversarial loss
         if self.use_hinge_gan_loss and not scores_fake is not None:
-            hinge_fake_loss = 0
-            if isinstance(scores_fake, list):
-                for score_fake in scores_fake:
-                    fake_loss = self.hinge_loss(score_fake)
-                    hinge_fake_loss += fake_loss
-            else:
-                fake_loss = self.hinge_loss(scores_fake)
-                hinge_fake_loss = fake_loss
+            hinge_fake_loss = _apply_G_adv_loss(scores_fake, self.hinge_loss)
             return_dict['G_hinge_fake_loss'] = hinge_fake_loss
-            loss += self.hinge_gan_loss_weight * hinge_fake_loss
+            adv_loss += self.hinge_gan_loss_weight * hinge_fake_loss
 
         # Feature Matching Loss
         if self.use_feat_match_loss and not feats_fake:
             feat_match_loss = self.feat_match_loss(feats_fake, feats_real)
             return_dict['G_feat_match_loss'] = feat_match_loss
-            loss += self.feat_match_loss_weight * feat_match_loss
-        return_dict['G_loss'] = loss
+            adv_loss += self.feat_match_loss_weight * feat_match_loss
+        return_dict['G_loss'] = gen_loss + adv_loss
+        return_dict['G_gen_loss'] = gen_loss
+        return_dict['G_adv_loss'] = adv_loss
         return return_dict
 
 
 class DiscriminatorLoss(nn.Module):
+    """ Compute Discriminator Loss values depending on training
+    configuration """
     def __init__(self, C):
         super(DiscriminatorLoss, self).__init__()
         assert not(C.use_mse_gan_loss and C.use_hinge_gan_loss),\
@@ -219,9 +261,6 @@ class DiscriminatorLoss(nn.Module):
         self.use_mse_gan_loss = C.use_mse_gan_loss
         self.use_hinge_gan_loss = C.use_hinge_gan_loss
 
-        self.mse_gan_loss_weight = C.mse_gan_loss_weight
-        self.hinge_gan_loss_weight = C.hinge_gan_loss_weight
-
         if C.use_mse_gan_loss:
             self.mse_loss = MSEDLoss()
         if C.use_hinge_gan_loss:
@@ -232,44 +271,18 @@ class DiscriminatorLoss(nn.Module):
         return_dict = {}
 
         if self.use_mse_gan_loss:
-            mse_gan_loss = 0
-            mse_gan_real_loss = 0
-            mse_gan_fake_loss = 0
-            if isinstance(scores_fake, list):
-                for score_fake, score_real in zip(scores_fake, scores_real):
-                    total_loss, real_loss, fake_loss = self.mse_loss(score_fake, score_real)
-                    mse_gan_loss += total_loss
-                    mse_gan_real_loss += real_loss
-                    mse_gan_fake_loss += fake_loss
-            else:
-                total_loss, real_loss, fake_loss = self.mse_loss(scores_fake, scores_real)
-                mse_gan_loss = total_loss
-                mse_gan_real_loss = real_loss
-                mse_gan_fake_loss = fake_loss
-            return_dict['D_mse_gan_loss'] = mse_gan_loss
-            return_dict['D_mse_gan_real_loss'] = mse_gan_real_loss
-            return_dict['D_mse_gan_fake_loss'] = mse_gan_fake_loss
-            loss += self.mse_gan_loss_weight * mse_gan_loss
+            mse_D_loss, mse_D_real_loss, mse_D_fake_loss = _apply_D_loss(scores_fake, scores_real, self.mse_loss)
+            return_dict['D_mse_gan_loss'] = mse_D_loss
+            return_dict['D_mse_gan_real_loss'] = mse_D_real_loss
+            return_dict['D_mse_gan_fake_loss'] = mse_D_fake_loss
+            loss += mse_D_loss
 
         if self.use_hinge_gan_loss:
-            hinge_gan_loss = 0
-            hinge_gan_real_loss = 0
-            hinge_gan_fake_loss = 0
-            if isinstance(scores_fake, list):
-                for score_fake, score_real in zip(scores_fake, scores_real):
-                    total_loss, real_loss, fake_loss = self.hinge_loss(score_fake, score_real)
-                    hinge_gan_loss += total_loss
-                    hinge_gan_real_loss += real_loss
-                    hinge_gan_fake_loss += fake_loss
-            else:
-                total_loss, real_loss, fake_loss = self.hinge_loss(scores_fake, scores_real)
-                hinge_gan_loss = total_loss
-                hinge_gan_real_loss = real_loss
-                hinge_gan_fake_loss = fake_loss
-            return_dict['D_hinge_gan_loss'] = hinge_gan_loss
-            return_dict['D_hinge_gan_real_loss'] = hinge_gan_real_loss
-            return_dict['D_hinge_gan_fake_loss'] = hinge_gan_fake_loss
-            loss += self.hinge_gan_loss_weight * hinge_gan_loss
+            hinge_D_loss, hinge_D_real_loss, hinge_D_fake_loss = _apply_D_loss(scores_fake, scores_real, self.hinge_loss)
+            return_dict['D_hinge_gan_loss'] = hinge_D_loss
+            return_dict['D_hinge_gan_real_loss'] = hinge_D_real_loss
+            return_dict['D_hinge_gan_fake_loss'] = hinge_D_fake_loss
+            loss += hinge_D_loss
 
         return_dict['D_loss'] = loss
         return return_dict
\ No newline at end of file

From be861d7aa0ebfa0d926f579c4d9793314aac2808 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 3 Jun 2020 12:33:53 +0200
Subject: [PATCH 037/185] suppress pylint no-self-use for loss layers

---
 vocoder/layers/losses.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/vocoder/layers/losses.py b/vocoder/layers/losses.py
index fb4e85d4..22e2fb54 100644
--- a/vocoder/layers/losses.py
+++ b/vocoder/layers/losses.py
@@ -77,6 +77,7 @@ class MultiScaleSTFTLoss(torch.nn.Module):
 
 class MultiScaleSubbandSTFTLoss(MultiScaleSTFTLoss):
     """ Multiscale STFT loss for multi band model outputs """
+    # pylint: disable=no-self-use
     def forward(self, y_hat, y):
         y_hat = y_hat.view(-1, 1, y_hat.shape[2])
         y = y.view(-1, 1, y.shape[2])
@@ -85,6 +86,7 @@ class MultiScaleSubbandSTFTLoss(MultiScaleSTFTLoss):
 
 class MSEGLoss(nn.Module):
     """ Mean Squared Generator Loss """
+    # pylint: disable=no-self-use
     def forward(self, score_fake):
         loss_fake = torch.mean(torch.sum(torch.pow(score_fake, 2), dim=[1, 2]))
         return loss_fake
@@ -92,6 +94,7 @@ class MSEGLoss(nn.Module):
 
 class HingeGLoss(nn.Module):
     """ Hinge Discriminator Loss """
+    # pylint: disable=no-self-use
     def forward(self, score_fake):
         loss_fake = torch.mean(F.relu(1. + score_fake))
         return loss_fake
@@ -104,6 +107,7 @@ class HingeGLoss(nn.Module):
 
 class MSEDLoss(nn.Module):
     """ Mean Squared Discriminator Loss """
+    # pylint: disable=no-self-use
     def forward(self, score_fake, score_real):
         loss_real = torch.mean(torch.sum(torch.pow(score_real - 1.0, 2), dim=[1, 2]))
         loss_fake = torch.mean(torch.sum(torch.pow(score_fake, 2), dim=[1, 2]))
@@ -113,6 +117,7 @@ class MSEDLoss(nn.Module):
 
 class HingeDLoss(nn.Module):
     """ Hinge Discriminator Loss """
+    # pylint: disable=no-self-use
     def forward(self, score_fake, score_real):
         loss_real = torch.mean(F.relu(1. - score_real))
         loss_fake = torch.mean(F.relu(1. + score_fake))
@@ -121,6 +126,7 @@ class HingeDLoss(nn.Module):
 
 
 class MelganFeatureLoss(nn.Module):
+    # pylint: disable=no-self-use
     def forward(self, fake_feats, real_feats):
         loss_feats = 0
         for fake_feat, real_feat in zip(fake_feats, real_feats):

From ed466c8581058d5d9eeaffd237344270ba1328c7 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 02:58:25 +0200
Subject: [PATCH 038/185] update README.md

---
 README.md        | 8 +++++---
 vocoder/train.py | 2 +-
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 7d9884b0..20799943 100644
--- a/README.md
+++ b/README.md
@@ -17,9 +17,11 @@ If you are new, you can also find [here](http://www.erogol.com/text-speech-deep-
 [Details...](https://github.com/mozilla/TTS/wiki/Mean-Opinion-Score-Results)
 
 ## Features
-- High performance Text2Speech models on Torch and Tensorflow 2.0.
-- High performance Speaker Encoder to compute speaker embeddings efficiently. 
-- Integration with various Neural Vocoders (PWGAN, MelGAN, WaveRNN)
+- High performance Deep Learning models for Text2Speech related tasks.
+    - Text2Speech models (Tacotron, Tacotron2).
+    - Speaker Encoder to compute speaker embeddings efficiently. 
+    - Vocoder models (MelGAN, Multiband-MelGAN, GAN-TTS)
+- Ability to convert Torch models to Tensorflow 2.0 for inference.
 - Released trained models.
 - Efficient training codes for PyTorch. (soon for Tensorflow 2.0)
 - Codes to convert Torch models to Tensorflow 2.0.
diff --git a/vocoder/train.py b/vocoder/train.py
index d401e72e..03e14f4a 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -52,7 +52,7 @@ def setup_loader(ap, is_val=False, verbose=False):
         # sampler = DistributedSampler(dataset) if num_gpus > 1 else None
         loader = DataLoader(dataset,
                             batch_size=1 if is_val else c.batch_size,
-                            shuffle=False,
+                            shuffle=True,
                             drop_last=False,
                             sampler=None,
                             num_workers=c.num_val_loader_workers

From 2b361f0b50daed9e4555d7c09f371ba201564ed7 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 09:51:20 +0200
Subject: [PATCH 039/185] visualization fix

---
 vocoder/train.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/vocoder/train.py b/vocoder/train.py
index 03e14f4a..a75c6a12 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -120,11 +120,13 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
         y_hat = model_G(c_G)
         y_hat_sub = None
         y_G_sub = None
+        y_hat_vis = y_hat  # for visualization
 
         # PQMF formatting
         if y_hat.shape[1] > 1:
             y_hat_sub = y_hat
             y_hat = model_G.pqmf_synthesis(y_hat)
+            y_hat_vis = y_hat
             y_G_sub = model_G.pqmf_analysis(y_G)
 
         if global_step > c.steps_to_start_discriminator:
@@ -265,12 +267,12 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
                                 model_losses=loss_dict)
 
             # compute spectrograms
-            figures = plot_results(y_hat, y_G, ap, global_step,
+            figures = plot_results(y_hat_vis, y_G, ap, global_step,
                                    'train')
             tb_logger.tb_train_figures(global_step, figures)
 
             # Sample audio
-            sample_voice = y_hat[0].squeeze(0).detach().cpu().numpy()
+            sample_voice = y_hat_vis[0].squeeze(0).detach().cpu().numpy()
             tb_logger.tb_train_audios(global_step,
                                       {'train/audio': sample_voice},
                                       c.audio["sample_rate"])

From 1de68250407cb1b711d22fcd00018d912eb09849 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 14:26:30 +0200
Subject: [PATCH 040/185] tacotron abstract class

---
 models/tacotron_abstract.py | 160 ++++++++++++++++++++++++++++++++++++
 1 file changed, 160 insertions(+)
 create mode 100644 models/tacotron_abstract.py

diff --git a/models/tacotron_abstract.py b/models/tacotron_abstract.py
new file mode 100644
index 00000000..db6cbdee
--- /dev/null
+++ b/models/tacotron_abstract.py
@@ -0,0 +1,160 @@
+import copy
+from abc import ABC, abstractmethod
+
+import torch
+from torch import nn
+
+from TTS.layers.gst_layers import GST
+from TTS.utils.generic_utils import sequence_mask
+
+
+class TacotronAbstract(ABC, nn.Module):
+    def __init__(self, num_chars,
+                 num_speakers,
+                 r,
+                 postnet_output_dim=80,
+                 decoder_output_dim=80,
+                 attn_type='original',
+                 attn_win=False,
+                 attn_norm="softmax",
+                 prenet_type="original",
+                 prenet_dropout=True,
+                 forward_attn=False,
+                 trans_agent=False,
+                 forward_attn_mask=False,
+                 location_attn=True,
+                 attn_K=5,
+                 separate_stopnet=True,
+                 bidirectional_decoder=False,
+                 double_decoder_consistency=False,
+                 ddc_r=None,
+                 gst=False):
+        """ Abstract Tacotron class """
+        super().__init__()
+        self.r = r
+        self.decoder_output_dim = decoder_output_dim
+        self.postnet_output_dim = postnet_output_dim
+        self.gst = gst
+        self.num_speakers = num_speakers
+        self.bidirectional_decoder = bidirectional_decoder
+        self.double_decoder_consistency = double_decoder_consistency
+        self.ddc_r = ddc_r
+
+        # layers
+        self.embedding = None
+        self.encoder = None
+        self.decoder = None
+        self.postnet = None
+
+        # global style token
+        if self.gst:
+            gst_embedding_dim = None
+            self.gst_layer = None
+
+    #############################
+    # INIT FUNCTIONS
+    #############################
+
+    def _init_states(self):
+        self.speaker_embeddings = None
+        self.speaker_embeddings_projected = None
+
+    def _init_backward_decoder(self):
+        self.decoder_backward = copy.deepcopy(self.decoder)
+
+    def _init_coarse_decoder(self):
+        self.coarse_decoder = copy.deepcopy(self.decoder)
+        self.coarse_decoder.r_init = self.ddc_r
+        self.coarse_decoder.set_r(self.ddc_r)
+
+    #############################
+    # CORE FUNCTIONS
+    #############################
+
+    @abstractmethod
+    def forward(self):
+        pass
+
+    @abstractmethod
+    def inference(self):
+        pass
+
+    #############################
+    # COMMON COMPUTE FUNCTIONS
+    #############################
+
+    def compute_masks(self, text_lengths, mel_lengths):
+        """Compute masks  against sequence paddings."""
+        # B x T_in_max (boolean)
+        device = text_lengths.device
+        input_mask = sequence_mask(text_lengths).to(device)
+        output_mask = None
+        if mel_lengths is not None:
+            max_len = mel_lengths.max()
+            r = self.decoder.r
+            max_len = max_len + (r - (max_len % r)) if max_len % r > 0 else max_len
+            output_mask = sequence_mask(mel_lengths, max_len=max_len).to(device)
+        return input_mask, output_mask
+
+    def _backward_pass(self, mel_specs, encoder_outputs, mask):
+        """ Run backwards decoder """
+        decoder_outputs_b, alignments_b, _ = self.decoder_backward(
+            encoder_outputs, torch.flip(mel_specs, dims=(1,)), mask,
+            self.speaker_embeddings_projected)
+        decoder_outputs_b = decoder_outputs_b.transpose(1, 2).contiguous()
+        return decoder_outputs_b, alignments_b
+
+    def _coarse_decoder_pass(self, mel_specs, encoder_outputs, alignments,
+                             input_mask):
+        """ Double Decoder Consistency """
+        T = mel_specs.shape[1]
+        if T % self.coarse_decoder.r > 0:
+            padding_size = self.coarse_decoder.r - (T % self.coarse_decoder.r)
+            mel_specs = torch.nn.functional.pad(mel_specs,
+                                                (0, 0, 0, padding_size, 0, 0))
+        decoder_outputs_backward, alignments_backward, _ = self.coarse_decoder(
+            encoder_outputs.detach(), mel_specs, input_mask)
+        scale_factor = self.decoder.r_init / self.decoder.r
+        alignments_backward = torch.nn.functional.interpolate(
+            alignments_backward.transpose(1, 2),
+            size=alignments.shape[1],
+            mode='nearest').transpose(1, 2)
+        decoder_outputs_backward = decoder_outputs_backward.transpose(1, 2)
+        decoder_outputs_backward = decoder_outputs_backward[:, :T, :]
+        return decoder_outputs_backward, alignments_backward
+
+    #############################
+    # EMBEDDING FUNCTIONS
+    #############################
+
+    def compute_speaker_embedding(self, speaker_ids):
+        """ Compute speaker embedding vectors """
+        if hasattr(self, "speaker_embedding") and speaker_ids is None:
+            raise RuntimeError(
+                " [!] Model has speaker embedding layer but speaker_id is not provided"
+            )
+        if hasattr(self, "speaker_embedding") and speaker_ids is not None:
+            self.speaker_embeddings = self.speaker_embedding(speaker_ids).unsqueeze(1)
+        if hasattr(self, "speaker_project_mel") and speaker_ids is not None:
+            self.speaker_embeddings_projected = self.speaker_project_mel(
+                self.speaker_embeddings).squeeze(1)
+
+    def compute_gst(self, inputs, mel_specs):
+        """ Compute global style token """
+        gst_outputs = self.gst_layer(mel_specs)
+        inputs = self._add_speaker_embedding(inputs, gst_outputs)
+        return inputs
+
+    @staticmethod
+    def _add_speaker_embedding(outputs, speaker_embeddings):
+        speaker_embeddings_ = speaker_embeddings.expand(
+            outputs.size(0), outputs.size(1), -1)
+        outputs = outputs + speaker_embeddings_
+        return outputs
+
+    @staticmethod
+    def _concat_speaker_embedding(outputs, speaker_embeddings):
+        speaker_embeddings_ = speaker_embeddings.expand(
+            outputs.size(0), outputs.size(1), -1)
+        outputs = torch.cat([outputs, speaker_embeddings_], dim=-1)
+        return outputs

From ef2369e0dac6eaa88489611c925bc929bc31571f Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 14:26:51 +0200
Subject: [PATCH 041/185] update README.md

---
 README.md | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/README.md b/README.md
index 20799943..f6ee1ade 100644
--- a/README.md
+++ b/README.md
@@ -11,7 +11,7 @@ If you are new, you can also find [here](http://www.erogol.com/text-speech-deep-
 
 [![](https://sourcerer.io/fame/erogol/erogol/TTS/images/0)](https://sourcerer.io/fame/erogol/erogol/TTS/links/0)[![](https://sourcerer.io/fame/erogol/erogol/TTS/images/1)](https://sourcerer.io/fame/erogol/erogol/TTS/links/1)[![](https://sourcerer.io/fame/erogol/erogol/TTS/images/2)](https://sourcerer.io/fame/erogol/erogol/TTS/links/2)[![](https://sourcerer.io/fame/erogol/erogol/TTS/images/3)](https://sourcerer.io/fame/erogol/erogol/TTS/links/3)[![](https://sourcerer.io/fame/erogol/erogol/TTS/images/4)](https://sourcerer.io/fame/erogol/erogol/TTS/links/4)[![](https://sourcerer.io/fame/erogol/erogol/TTS/images/5)](https://sourcerer.io/fame/erogol/erogol/TTS/links/5)[![](https://sourcerer.io/fame/erogol/erogol/TTS/images/6)](https://sourcerer.io/fame/erogol/erogol/TTS/links/6)[![](https://sourcerer.io/fame/erogol/erogol/TTS/images/7)](https://sourcerer.io/fame/erogol/erogol/TTS/links/7)
 
-## TTS Performance 
+## TTS Performance
 <p align="center"><img src="https://camo.githubusercontent.com/9fa79f977015e55eb9ec7aa32045555f60d093d3/68747470733a2f2f646973636f757273652d706161732d70726f64756374696f6e2d636f6e74656e742e73332e6475616c737461636b2e75732d656173742d312e616d617a6f6e6177732e636f6d2f6f7074696d697a65642f33582f362f342f363432386639383065396563373531633234386535393134363038393566373838316165633063365f325f363930783339342e706e67"/></p>
 
 [Details...](https://github.com/mozilla/TTS/wiki/Mean-Opinion-Score-Results)
@@ -19,8 +19,9 @@ If you are new, you can also find [here](http://www.erogol.com/text-speech-deep-
 ## Features
 - High performance Deep Learning models for Text2Speech related tasks.
     - Text2Speech models (Tacotron, Tacotron2).
-    - Speaker Encoder to compute speaker embeddings efficiently. 
+    - Speaker Encoder to compute speaker embeddings efficiently.
     - Vocoder models (MelGAN, Multiband-MelGAN, GAN-TTS)
+- Support for multi-speaker TTS training.
 - Ability to convert Torch models to Tensorflow 2.0 for inference.
 - Released trained models.
 - Efficient training codes for PyTorch. (soon for Tensorflow 2.0)
@@ -86,7 +87,7 @@ Audio length is approximately 6 secs.
 
 
 ## Datasets and Data-Loading
-TTS provides a generic dataloder easy to use for new datasets. You need to write an preprocessor function to integrate your own dataset.Check ```datasets/preprocess.py``` to see some examples. After the function, you need to set ```dataset``` field in ```config.json```. Do not forget other data related fields too.  
+TTS provides a generic dataloder easy to use for new datasets. You need to write an preprocessor function to integrate your own dataset.Check ```datasets/preprocess.py``` to see some examples. After the function, you need to set ```dataset``` field in ```config.json```. Do not forget other data related fields too.
 
 Some of the open-sourced datasets that we successfully applied TTS, are linked below.
 
@@ -98,9 +99,9 @@ Some of the open-sourced datasets that we successfully applied TTS, are linked b
 - [Spanish](https://drive.google.com/file/d/1Sm_zyBo67XHkiFhcRSQ4YaHPYM0slO_e/view?usp=sharing) - thx! @carlfm01
 
 ## Training and Fine-tuning LJ-Speech
-Here you can find a [CoLab](https://gist.github.com/erogol/97516ad65b44dbddb8cd694953187c5b) notebook for a hands-on example, training LJSpeech. Or you can manually follow the guideline below. 
+Here you can find a [CoLab](https://gist.github.com/erogol/97516ad65b44dbddb8cd694953187c5b) notebook for a hands-on example, training LJSpeech. Or you can manually follow the guideline below.
 
-To start with, split ```metadata.csv``` into train and validation subsets respectively ```metadata_train.csv``` and ```metadata_val.csv```. Note that for text-to-speech, validation performance might be misleading since the loss value does not directly measure the voice quality to the human ear and it also does not measure the attention module performance. Therefore, running the model with new sentences and listening to the results is the best way to go. 
+To start with, split ```metadata.csv``` into train and validation subsets respectively ```metadata_train.csv``` and ```metadata_val.csv```. Note that for text-to-speech, validation performance might be misleading since the loss value does not directly measure the voice quality to the human ear and it also does not measure the attention module performance. Therefore, running the model with new sentences and listening to the results is the best way to go.
 
 ```
 shuf metadata.csv > metadata_shuf.csv
@@ -139,10 +140,10 @@ cardboardlinter --refspec master
 ```
 
 ## Collaborative Experimentation Guide
-If you like to use TTS to try a new idea and like to share your experiments with the community, we urge you to use the following guideline for a better collaboration. 
+If you like to use TTS to try a new idea and like to share your experiments with the community, we urge you to use the following guideline for a better collaboration.
 (If you have an idea for better collaboration, let us know)
 - Create a new branch.
-- Open an issue pointing your branch. 
+- Open an issue pointing your branch.
 - Explain your experiment.
 - Share your results as you proceed. (Tensorboard log files, audio results, visuals etc.)
 - Use LJSpeech dataset (for English) if you like to compare results with the released models. (It is the most open scalable dataset for quick experimentation)

From 1ca078ae7e06ab9ccb79cf6022ad56052db84964 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 14:27:06 +0200
Subject: [PATCH 042/185] update model config.json with double decoder
 consistency

---
 config.json | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/config.json b/config.json
index 32debf86..74021970 100644
--- a/config.json
+++ b/config.json
@@ -96,6 +96,8 @@
     "transition_agent": false,     // enable/disable transition agent of forward attention.
     "location_attn": true,         // enable_disable location sensitive attention. It is enabled for TACOTRON by default.
     "bidirectional_decoder": false,  // use https://arxiv.org/abs/1907.09006. Use it, if attention does not work well with your dataset.
+    "double_decoder_consistency": true,  // use DDC explained here https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency-draft/
+    "ddc_r": 7,                           // reduction rate for coarse decoder.
 
     // STOPNET
     "stopnet": true,               // Train stopnet predicting the end of synthesis.

From b4ac68df7ba1f50c3f8f40cd61962a773367a661 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 14:27:40 +0200
Subject: [PATCH 043/185] add DDC loss

---
 layers/losses.py | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/layers/losses.py b/layers/losses.py
index 608e247d..f7745b6e 100644
--- a/layers/losses.py
+++ b/layers/losses.py
@@ -184,7 +184,7 @@ class TacotronLoss(torch.nn.Module):
 
     def forward(self, postnet_output, decoder_output, mel_input, linear_input,
                 stopnet_output, stopnet_target, output_lens, decoder_b_output,
-                alignments, alignment_lens, input_lens):
+                alignments, alignment_lens, alignments_backwards, input_lens):
 
         return_dict = {}
         # decoder and postnet losses
@@ -226,6 +226,15 @@ class TacotronLoss(torch.nn.Module):
             return_dict['decoder_b_loss'] = decoder_b_loss
             return_dict['decoder_c_loss'] = decoder_c_loss
 
+        # double decoder consistency loss (if enabled)
+        if self.config.double_decoder_consistency:
+            decoder_b_loss = self.criterion(decoder_b_output, mel_input, output_lens)
+            # decoder_c_loss = torch.nn.functional.l1_loss(decoder_b_output, decoder_output)
+            attention_c_loss = torch.nn.functional.l1_loss(alignments, alignments_backwards)
+            loss += decoder_b_loss + attention_c_loss
+            return_dict['decoder_coarse_loss'] = decoder_b_loss
+            return_dict['decoder_ddc_loss'] = attention_c_loss
+
         # guided attention loss (if enabled)
         if self.config.ga_alpha > 0:
             ga_loss = self.criterion_ga(alignments, input_lens, alignment_lens)

From bd7237d916823ffd9d3832360aa057b5203947b2 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 14:28:16 +0200
Subject: [PATCH 044/185] inherit TacotronAbstact with both tacotron and
 tacotron2

---
 models/tacotron.py  | 135 ++++++++++++++++++-------------------------
 models/tacotron2.py | 136 ++++++++++++++++++++++++++++----------------
 2 files changed, 142 insertions(+), 129 deletions(-)

diff --git a/models/tacotron.py b/models/tacotron.py
index fba82b1b..c526374a 100644
--- a/models/tacotron.py
+++ b/models/tacotron.py
@@ -1,23 +1,21 @@
 # coding: utf-8
 import torch
-import copy
 from torch import nn
-from TTS.layers.tacotron import Encoder, Decoder, PostCBHG
-from TTS.utils.generic_utils import sequence_mask
+
 from TTS.layers.gst_layers import GST
+from TTS.layers.tacotron import Decoder, Encoder, PostCBHG
+from TTS.models.tacotron_abstract import TacotronAbstract
 
 
-class Tacotron(nn.Module):
+class Tacotron(TacotronAbstract):
     def __init__(self,
                  num_chars,
                  num_speakers,
                  r=5,
                  postnet_output_dim=1025,
                  decoder_output_dim=80,
-                 memory_size=5,
                  attn_type='original',
                  attn_win=False,
-                 gst=False,
                  attn_norm="sigmoid",
                  prenet_type="original",
                  prenet_dropout=True,
@@ -27,38 +25,41 @@ class Tacotron(nn.Module):
                  location_attn=True,
                  attn_K=5,
                  separate_stopnet=True,
-                 bidirectional_decoder=False):
-        super(Tacotron, self).__init__()
-        self.r = r
-        self.decoder_output_dim = decoder_output_dim
-        self.postnet_output_dim = postnet_output_dim
-        self.gst = gst
-        self.num_speakers = num_speakers
-        self.bidirectional_decoder = bidirectional_decoder
-        decoder_dim = 512 if num_speakers > 1 else 256
-        encoder_dim = 512 if num_speakers > 1 else 256
+                 bidirectional_decoder=False,
+                 double_decoder_consistency=False,
+                 ddc_r=None,
+                 gst=False,
+                 memory_size=5):
+        super(Tacotron,
+              self).__init__(num_chars, num_speakers, r, postnet_output_dim,
+                             decoder_output_dim, attn_type, attn_win,
+                             attn_norm, prenet_type, prenet_dropout,
+                             forward_attn, trans_agent, forward_attn_mask,
+                             location_attn, attn_K, separate_stopnet,
+                             bidirectional_decoder, double_decoder_consistency,
+                             ddc_r, gst)
+        decoder_in_features = 512 if num_speakers > 1 else 256
+        encoder_in_features = 512 if num_speakers > 1 else 256
+        speaker_embedding_dim = 256
         proj_speaker_dim = 80 if num_speakers > 1 else 0
-        # embedding layer
+        # base model layers
         self.embedding = nn.Embedding(num_chars, 256, padding_idx=0)
         self.embedding.weight.data.normal_(0, 0.3)
-        # boilerplate model
-        self.encoder = Encoder(encoder_dim)
-        self.decoder = Decoder(decoder_dim, decoder_output_dim, r, memory_size, attn_type, attn_win,
+        self.encoder = Encoder(encoder_in_features)
+        self.decoder = Decoder(decoder_in_features, decoder_output_dim, r, memory_size, attn_type, attn_win,
                                attn_norm, prenet_type, prenet_dropout,
                                forward_attn, trans_agent, forward_attn_mask,
                                location_attn, attn_K, separate_stopnet,
                                proj_speaker_dim)
-        if self.bidirectional_decoder:
-            self.decoder_backward = copy.deepcopy(self.decoder)
         self.postnet = PostCBHG(decoder_output_dim)
         self.last_linear = nn.Linear(self.postnet.cbhg.gru_features * 2,
                                      postnet_output_dim)
         # speaker embedding layers
         if num_speakers > 1:
-            self.speaker_embedding = nn.Embedding(num_speakers, 256)
+            self.speaker_embedding = nn.Embedding(num_speakers, speaker_embedding_dim)
             self.speaker_embedding.weight.data.normal_(0, 0.3)
             self.speaker_project_mel = nn.Sequential(
-                nn.Linear(256, proj_speaker_dim), nn.Tanh())
+                nn.Linear(speaker_embedding_dim, proj_speaker_dim), nn.Tanh())
             self.speaker_embeddings = None
             self.speaker_embeddings_projected = None
         # global style token layers
@@ -68,28 +69,15 @@ class Tacotron(nn.Module):
                                  num_heads=4,
                                  num_style_tokens=10,
                                  embedding_dim=gst_embedding_dim)
+        # backward pass decoder
+        if self.bidirectional_decoder:
+            self._init_backward_decoder()
+        # setup DDC
+        if self.double_decoder_consistency:
+            self._init_coarse_decoder()
 
-    def _init_states(self):
-        self.speaker_embeddings = None
-        self.speaker_embeddings_projected = None
 
-    def compute_speaker_embedding(self, speaker_ids):
-        if hasattr(self, "speaker_embedding") and speaker_ids is None:
-            raise RuntimeError(
-                " [!] Model has speaker embedding layer but speaker_id is not provided"
-            )
-        if hasattr(self, "speaker_embedding") and speaker_ids is not None:
-            self.speaker_embeddings = self._compute_speaker_embedding(
-                speaker_ids)
-            self.speaker_embeddings_projected = self.speaker_project_mel(
-                self.speaker_embeddings).squeeze(1)
-
-    def compute_gst(self, inputs, mel_specs):
-        gst_outputs = self.gst_layer(mel_specs)
-        inputs = self._add_speaker_embedding(inputs, gst_outputs)
-        return inputs
-
-    def forward(self, characters, text_lengths, mel_specs, speaker_ids=None):
+    def forward(self, characters, text_lengths, mel_specs, mel_lengths=None, speaker_ids=None):
         """
         Shapes:
             - characters: B x T_in
@@ -98,45 +86,59 @@ class Tacotron(nn.Module):
             - speaker_ids: B x 1
         """
         self._init_states()
-        mask = sequence_mask(text_lengths).to(characters.device)
+        input_mask, output_mask = self.compute_masks(text_lengths, mel_lengths)
         # B x T_in x embed_dim
         inputs = self.embedding(characters)
         # B x speaker_embed_dim
-        self.compute_speaker_embedding(speaker_ids)
+        if speaker_ids is not None:
+            self.compute_speaker_embedding(speaker_ids)
         if self.num_speakers > 1:
             # B x T_in x embed_dim + speaker_embed_dim
             inputs = self._concat_speaker_embedding(inputs,
                                                     self.speaker_embeddings)
-        # B x T_in x encoder_dim
+        # B x T_in x encoder_in_features
         encoder_outputs = self.encoder(inputs)
+        # sequence masking
+        encoder_outputs = encoder_outputs * input_mask.unsqueeze(2).expand_as(encoder_outputs)
+        # global style token
         if self.gst:
             # B x gst_dim
             encoder_outputs = self.compute_gst(encoder_outputs, mel_specs)
         if self.num_speakers > 1:
             encoder_outputs = self._concat_speaker_embedding(
                 encoder_outputs, self.speaker_embeddings)
-        # decoder_outputs: B x decoder_dim x T_out
-        # alignments: B x T_in x encoder_dim
+        # decoder_outputs: B x decoder_in_features x T_out
+        # alignments: B x T_in x encoder_in_features
         # stop_tokens: B x T_in
         decoder_outputs, alignments, stop_tokens = self.decoder(
-            encoder_outputs, mel_specs, mask,
+            encoder_outputs, mel_specs, input_mask,
             self.speaker_embeddings_projected)
-        # B x T_out x decoder_dim
+        # sequence masking
+        if output_mask is not None:
+            decoder_outputs = decoder_outputs * output_mask.unsqueeze(1).expand_as(decoder_outputs)
+        # B x T_out x decoder_in_features
         postnet_outputs = self.postnet(decoder_outputs)
+        # sequence masking
+        if output_mask is not None:
+            postnet_outputs = postnet_outputs * output_mask.unsqueeze(2).expand_as(postnet_outputs)
         # B x T_out x posnet_dim
         postnet_outputs = self.last_linear(postnet_outputs)
-        # B x T_out x decoder_dim
+        # B x T_out x decoder_in_features
         decoder_outputs = decoder_outputs.transpose(1, 2).contiguous()
         if self.bidirectional_decoder:
-            decoder_outputs_backward, alignments_backward = self._backward_inference(mel_specs, encoder_outputs, mask)
+            decoder_outputs_backward, alignments_backward = self._backward_pass(mel_specs, encoder_outputs, input_mask)
             return decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward
+        if self.double_decoder_consistency:
+            decoder_outputs_backward, alignments_backward = self._coarse_decoder_pass(mel_specs, encoder_outputs, alignments, input_mask)
+            return  decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward
         return decoder_outputs, postnet_outputs, alignments, stop_tokens
 
     @torch.no_grad()
     def inference(self, characters, speaker_ids=None, style_mel=None):
         inputs = self.embedding(characters)
         self._init_states()
-        self.compute_speaker_embedding(speaker_ids)
+        if speaker_ids is not None:
+            self.compute_speaker_embedding(speaker_ids)
         if self.num_speakers > 1:
             inputs = self._concat_speaker_embedding(inputs,
                                                     self.speaker_embeddings)
@@ -152,28 +154,3 @@ class Tacotron(nn.Module):
         postnet_outputs = self.last_linear(postnet_outputs)
         decoder_outputs = decoder_outputs.transpose(1, 2)
         return decoder_outputs, postnet_outputs, alignments, stop_tokens
-
-    def _backward_inference(self, mel_specs, encoder_outputs, mask):
-        decoder_outputs_b, alignments_b, _ = self.decoder_backward(
-            encoder_outputs, torch.flip(mel_specs, dims=(1,)), mask,
-            self.speaker_embeddings_projected)
-        decoder_outputs_b = decoder_outputs_b.transpose(1, 2).contiguous()
-        return decoder_outputs_b, alignments_b
-
-    def _compute_speaker_embedding(self, speaker_ids):
-        speaker_embeddings = self.speaker_embedding(speaker_ids)
-        return speaker_embeddings.unsqueeze_(1)
-
-    @staticmethod
-    def _add_speaker_embedding(outputs, speaker_embeddings):
-        speaker_embeddings_ = speaker_embeddings.expand(
-            outputs.size(0), outputs.size(1), -1)
-        outputs = outputs + speaker_embeddings_
-        return outputs
-
-    @staticmethod
-    def _concat_speaker_embedding(outputs, speaker_embeddings):
-        speaker_embeddings_ = speaker_embeddings.expand(
-            outputs.size(0), outputs.size(1), -1)
-        outputs = torch.cat([outputs, speaker_embeddings_], dim=-1)
-        return outputs
diff --git a/models/tacotron2.py b/models/tacotron2.py
index 3e7adfca..bbce4be9 100644
--- a/models/tacotron2.py
+++ b/models/tacotron2.py
@@ -1,13 +1,15 @@
-import copy
-import torch
 from math import sqrt
+
+import torch
 from torch import nn
-from TTS.layers.tacotron2 import Encoder, Decoder, Postnet
-from TTS.utils.generic_utils import sequence_mask
+
+from TTS.layers.gst_layers import GST
+from TTS.layers.tacotron2 import Decoder, Encoder, Postnet
+from TTS.models.tacotron_abstract import TacotronAbstract
 
 
 # TODO: match function arguments with tacotron
-class Tacotron2(nn.Module):
+class Tacotron2(TacotronAbstract):
     def __init__(self,
                  num_chars,
                  num_speakers,
@@ -25,16 +27,22 @@ class Tacotron2(nn.Module):
                  location_attn=True,
                  attn_K=5,
                  separate_stopnet=True,
-                 bidirectional_decoder=False):
-        super(Tacotron2, self).__init__()
-        self.postnet_output_dim = postnet_output_dim
-        self.decoder_output_dim = decoder_output_dim
-        self.r = r
-        self.bidirectional_decoder = bidirectional_decoder
-        decoder_dim = 512 if num_speakers > 1 else 512
-        encoder_dim = 512 if num_speakers > 1 else 512
+                 bidirectional_decoder=False,
+                 double_decoder_consistency=False,
+                 ddc_r=None,
+                 gst=False):
+        super(Tacotron2,
+              self).__init__(num_chars, num_speakers, r, postnet_output_dim,
+                             decoder_output_dim, attn_type, attn_win,
+                             attn_norm, prenet_type, prenet_dropout,
+                             forward_attn, trans_agent, forward_attn_mask,
+                             location_attn, attn_K, separate_stopnet,
+                             bidirectional_decoder, double_decoder_consistency,
+                             ddc_r, gst)
+        decoder_in_features = 512 if num_speakers > 1 else 512
+        encoder_in_features = 512 if num_speakers > 1 else 512
         proj_speaker_dim = 80 if num_speakers > 1 else 0
-        # embedding layer
+        # base layers
         self.embedding = nn.Embedding(num_chars, 512, padding_idx=0)
         std = sqrt(2.0 / (num_chars + 512))
         val = sqrt(3.0) * std  # uniform bounds for std
@@ -42,20 +50,25 @@ class Tacotron2(nn.Module):
         if num_speakers > 1:
             self.speaker_embedding = nn.Embedding(num_speakers, 512)
             self.speaker_embedding.weight.data.normal_(0, 0.3)
-            self.speaker_embeddings = None
-            self.speaker_embeddings_projected = None
-        self.encoder = Encoder(encoder_dim)
-        self.decoder = Decoder(decoder_dim, self.decoder_output_dim, r, attn_type, attn_win,
+        self.encoder = Encoder(encoder_in_features)
+        self.decoder = Decoder(decoder_in_features, self.decoder_output_dim, r, attn_type, attn_win,
                                attn_norm, prenet_type, prenet_dropout,
                                forward_attn, trans_agent, forward_attn_mask,
                                location_attn, attn_K, separate_stopnet, proj_speaker_dim)
-        if self.bidirectional_decoder:
-            self.decoder_backward = copy.deepcopy(self.decoder)
         self.postnet = Postnet(self.postnet_output_dim)
-
-    def _init_states(self):
-        self.speaker_embeddings = None
-        self.speaker_embeddings_projected = None
+        # global style token layers
+        if self.gst:
+            gst_embedding_dim = encoder_in_features
+            self.gst_layer = GST(num_mel=80,
+                                 num_heads=4,
+                                 num_style_tokens=10,
+                                 embedding_dim=gst_embedding_dim)
+        # backward pass decoder
+        if self.bidirectional_decoder:
+            self._init_backward_decoder()
+        # setup DDC
+        if self.double_decoder_consistency:
+            self._init_coarse_decoder()
 
     @staticmethod
     def shape_outputs(mel_outputs, mel_outputs_postnet, alignments):
@@ -63,31 +76,60 @@ class Tacotron2(nn.Module):
         mel_outputs_postnet = mel_outputs_postnet.transpose(1, 2)
         return mel_outputs, mel_outputs_postnet, alignments
 
-    def forward(self, text, text_lengths, mel_specs=None, speaker_ids=None):
+    def forward(self, text, text_lengths, mel_specs=None, mel_lengths=None, speaker_ids=None):
         self._init_states()
         # compute mask for padding
-        mask = sequence_mask(text_lengths).to(text.device)
+        # B x T_in_max (boolean)
+        input_mask, output_mask = self.compute_masks(text_lengths, mel_lengths)
+        # B x D_embed x T_in_max
         embedded_inputs = self.embedding(text).transpose(1, 2)
+        # B x T_in_max x D_en
         encoder_outputs = self.encoder(embedded_inputs, text_lengths)
-        encoder_outputs = self._add_speaker_embedding(encoder_outputs,
-                                                      speaker_ids)
+        # adding speaker embeddding to encoder output
+        # TODO: multi-speaker
+        # B x speaker_embed_dim
+        if speaker_ids is not None:
+            self.compute_speaker_embedding(speaker_ids)
+        if self.num_speakers > 1:
+            # B x T_in x embed_dim + speaker_embed_dim
+            encoder_outputs = self._add_speaker_embedding(encoder_outputs,
+                                                          self.speaker_embeddings)
+        encoder_outputs = encoder_outputs * input_mask.unsqueeze(2).expand_as(encoder_outputs)
+        # global style token
+        if self.gst:
+            # B x gst_dim
+            encoder_outputs = self.compute_gst(encoder_outputs, mel_specs)
+        # B x mel_dim x T_out -- B x T_out//r x T_in -- B x T_out//r
         decoder_outputs, alignments, stop_tokens = self.decoder(
-            encoder_outputs, mel_specs, mask)
+            encoder_outputs, mel_specs, input_mask)
+        # sequence masking
+        if mel_lengths is not None:
+            decoder_outputs = decoder_outputs * output_mask.unsqueeze(1).expand_as(decoder_outputs)
+        # B x mel_dim x T_out
         postnet_outputs = self.postnet(decoder_outputs)
-        postnet_outputs = decoder_outputs + postnet_outputs
+        # sequence masking
+        if output_mask is not None:
+            postnet_outputs = postnet_outputs * output_mask.unsqueeze(1).expand_as(postnet_outputs)
+        # B x T_out x mel_dim -- B x T_out x mel_dim -- B x T_out//r x T_in
         decoder_outputs, postnet_outputs, alignments = self.shape_outputs(
             decoder_outputs, postnet_outputs, alignments)
         if self.bidirectional_decoder:
-            decoder_outputs_backward, alignments_backward = self._backward_inference(mel_specs, encoder_outputs, mask)
+            decoder_outputs_backward, alignments_backward = self._backward_pass(mel_specs, encoder_outputs, input_mask)
             return decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward
+        if self.double_decoder_consistency:
+            decoder_outputs_backward, alignments_backward = self._coarse_decoder_pass(mel_specs, encoder_outputs, alignments, input_mask)
+            return  decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward
         return decoder_outputs, postnet_outputs, alignments, stop_tokens
 
     @torch.no_grad()
     def inference(self, text, speaker_ids=None):
         embedded_inputs = self.embedding(text).transpose(1, 2)
         encoder_outputs = self.encoder.inference(embedded_inputs)
-        encoder_outputs = self._add_speaker_embedding(encoder_outputs,
-                                                      speaker_ids)
+        if speaker_ids is not None:
+            self.compute_speaker_embedding(speaker_ids)
+        if self.num_speakers > 1:
+            encoder_outputs = self._add_speaker_embedding(encoder_outputs,
+                                                    self.speaker_embeddings)
         mel_outputs, alignments, stop_tokens = self.decoder.inference(
             encoder_outputs)
         mel_outputs_postnet = self.postnet(mel_outputs)
@@ -112,22 +154,16 @@ class Tacotron2(nn.Module):
             mel_outputs, mel_outputs_postnet, alignments)
         return mel_outputs, mel_outputs_postnet, alignments, stop_tokens
 
-    def _backward_inference(self, mel_specs, encoder_outputs, mask):
-        decoder_outputs_b, alignments_b, _ = self.decoder_backward(
-            encoder_outputs, torch.flip(mel_specs, dims=(1,)), mask,
-            self.speaker_embeddings_projected)
-        decoder_outputs_b = decoder_outputs_b.transpose(1, 2)
-        return decoder_outputs_b, alignments_b
 
-    def _add_speaker_embedding(self, encoder_outputs, speaker_ids):
-        if hasattr(self, "speaker_embedding") and speaker_ids is None:
-            raise RuntimeError(" [!] Model has speaker embedding layer but speaker_id is not provided")
-        if hasattr(self, "speaker_embedding") and speaker_ids is not None:
-            speaker_embeddings = self.speaker_embedding(speaker_ids)
+    def _speaker_embedding_pass(self, encoder_outputs, speaker_ids):
+        # TODO: multi-speaker
+        # if hasattr(self, "speaker_embedding") and speaker_ids is None:
+        #     raise RuntimeError(" [!] Model has speaker embedding layer but speaker_id is not provided")
+        # if hasattr(self, "speaker_embedding") and speaker_ids is not None:
 
-            speaker_embeddings.unsqueeze_(1)
-            speaker_embeddings = speaker_embeddings.expand(encoder_outputs.size(0),
-                                                           encoder_outputs.size(1),
-                                                           -1)
-            encoder_outputs = encoder_outputs + speaker_embeddings
-        return encoder_outputs
+        #     speaker_embeddings = speaker_embeddings.expand(encoder_outputs.size(0),
+        #                                                    encoder_outputs.size(1),
+        #                                                    -1)
+        #     encoder_outputs = encoder_outputs + speaker_embeddings
+        # return encoder_outputs
+        pass

From e272e07ce99f6789dbf5461e3daec1e3b4bc3fc3 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 14:28:55 +0200
Subject: [PATCH 045/185] update dummy model config

---
 tests/outputs/dummy_model_config.json | 20 +++++++++++---------
 1 file changed, 11 insertions(+), 9 deletions(-)

diff --git a/tests/outputs/dummy_model_config.json b/tests/outputs/dummy_model_config.json
index 2f56c6ce..d301b61a 100644
--- a/tests/outputs/dummy_model_config.json
+++ b/tests/outputs/dummy_model_config.json
@@ -4,7 +4,7 @@
 
     "audio":{
         // Audio processing parameters
-        "num_mels": 80,         // size of the mel spec frame. 
+        "num_mels": 80,         // size of the mel spec frame.
         "num_freq": 1025,       // number of stft frequency levels. Size of the linear spectogram frame.
         "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
         "frame_length_ms": 50,  // stft window length in ms.
@@ -31,19 +31,19 @@
 
     "reinit_layers": [],
 
-    "model": "Tacotron2",          // one of the model in models/    
+    "model": "Tacotron2",          // one of the model in models/
     "grad_clip": 1,                // upper limit for gradients for clipping.
     "epochs": 1000,                // total number of epochs to train.
     "lr": 0.0001,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
     "lr_decay": false,             // if true, Noam learning rate decaying is applied through training.
     "warmup_steps": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
     "windowing": false,            // Enables attention windowing. Used only in eval mode.
-    "memory_size": 5,              // ONLY TACOTRON - memory queue size used to queue network predictions to feed autoregressive connection. Useful if r < 5. 
+    "memory_size": 5,              // ONLY TACOTRON - memory queue size used to queue network predictions to feed autoregressive connection. Useful if r < 5.
     "attention_norm": "sigmoid",   // softmax or sigmoid. Suggested to use softmax for Tacotron2 and sigmoid for Tacotron.
     "prenet_type": "original",     // ONLY TACOTRON2 - "original" or "bn".
-    "prenet_dropout": true,        // ONLY TACOTRON2 - enable/disable dropout at prenet. 
+    "prenet_dropout": true,        // ONLY TACOTRON2 - enable/disable dropout at prenet.
     "use_forward_attn": true,      // ONLY TACOTRON2 - if it uses forward attention. In general, it aligns faster.
-    "forward_attn_mask": false, 
+    "forward_attn_mask": false,
     "attention_type": "original",
     "attention_heads": 5,
     "bidirectional_decoder": false,
@@ -51,13 +51,15 @@
     "location_attn": false,        // ONLY TACOTRON2 - enable_disable location sensitive attention. It is enabled for TACOTRON by default.
     "loss_masking": true,         // enable / disable loss masking against the sequence padding.
     "enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.
-    "stopnet": true,               // Train stopnet predicting the end of synthesis. 
+    "stopnet": true,               // Train stopnet predicting the end of synthesis.
     "separate_stopnet": true,     // Train stopnet seperately if 'stopnet==true'. It prevents stopnet loss to influence the rest of the model. It causes a better model, but it trains SLOWER.
     "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
-    "use_gst": false, 
-    
+    "use_gst": false,
+    "double_decoder_consistency": true,  // use DDC explained here https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency-draft/
+    "ddc_r": 7,                           // reduction rate for coarse decoder.
+
     "batch_size": 32,       // Batch size for training. Lower values than 32 might cause hard to learn attention.
-    "eval_batch_size":16,   
+    "eval_batch_size":16,
     "r": 1,                 // Number of frames to predict for step.
     "wd": 0.000001,         // Weight decay weight.
     "checkpoint": true,     // If true, it saves checkpoints per "save_step"

From 43d278be2f744cef27bfb25e100f74f0e6952a40 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 14:29:37 +0200
Subject: [PATCH 046/185] update model tests for ddc

---
 tests/test_tacotron2_model.py | 2 +-
 tests/test_tacotron_model.py  | 5 +++--
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/tests/test_tacotron2_model.py b/tests/test_tacotron2_model.py
index eb91b3cc..cf1d0778 100644
--- a/tests/test_tacotron2_model.py
+++ b/tests/test_tacotron2_model.py
@@ -51,7 +51,7 @@ class TacotronTrainTest(unittest.TestCase):
         optimizer = optim.Adam(model.parameters(), lr=c.lr)
         for i in range(5):
             mel_out, mel_postnet_out, align, stop_tokens = model.forward(
-                input, input_lengths, mel_spec, speaker_ids)
+                input, input_lengths, mel_spec, mel_lengths, speaker_ids)
             assert torch.sigmoid(stop_tokens).data.max() <= 1.0
             assert torch.sigmoid(stop_tokens).data.min() >= 0.0
             optimizer.zero_grad()
diff --git a/tests/test_tacotron_model.py b/tests/test_tacotron_model.py
index 7053a580..2bbb3c8d 100644
--- a/tests/test_tacotron_model.py
+++ b/tests/test_tacotron_model.py
@@ -66,7 +66,7 @@ class TacotronTrainTest(unittest.TestCase):
         optimizer = optim.Adam(model.parameters(), lr=c.lr)
         for _ in range(5):
             mel_out, linear_out, align, stop_tokens = model.forward(
-                input_dummy, input_lengths, mel_spec, speaker_ids)
+                input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids)
             optimizer.zero_grad()
             loss = criterion(mel_out, mel_spec, mel_lengths)
             stop_loss = criterion_st(stop_tokens, stop_targets)
@@ -95,6 +95,7 @@ class TacotronGSTTrainTest(unittest.TestCase):
         mel_spec = torch.rand(8, 120, c.audio['num_mels']).to(device)
         linear_spec = torch.rand(8, 120, c.audio['num_freq']).to(device)
         mel_lengths = torch.randint(20, 120, (8, )).long().to(device)
+        mel_lengths[-1] = 120
         stop_targets = torch.zeros(8, 120, 1).float().to(device)
         speaker_ids = torch.randint(0, 5, (8, )).long().to(device)
 
@@ -130,7 +131,7 @@ class TacotronGSTTrainTest(unittest.TestCase):
         optimizer = optim.Adam(model.parameters(), lr=c.lr)
         for _ in range(10):
             mel_out, linear_out, align, stop_tokens = model.forward(
-                input_dummy, input_lengths, mel_spec, speaker_ids)
+                input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids)
             optimizer.zero_grad()
             loss = criterion(mel_out, mel_spec, mel_lengths)
             stop_loss = criterion_st(stop_tokens, stop_targets)

From 0f88f8ec5a90ec0cf6cc355e1f45a2a77930deb6 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 14:30:12 +0200
Subject: [PATCH 047/185] train.py update for DDC

---
 train.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/train.py b/train.py
index 5a345b59..612ee8a6 100644
--- a/train.py
+++ b/train.py
@@ -158,13 +158,14 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
             optimizer_st.zero_grad()
 
         # forward pass model
-        if c.bidirectional_decoder:
+        if c.bidirectional_decoder or c.double_decoder_consistency:
             decoder_output, postnet_output, alignments, stop_tokens, decoder_backward_output, alignments_backward = model(
-                text_input, text_lengths, mel_input, speaker_ids=speaker_ids)
+                text_input, text_lengths, mel_input, mel_lengths, speaker_ids=speaker_ids)
         else:
             decoder_output, postnet_output, alignments, stop_tokens = model(
-                text_input, text_lengths, mel_input, speaker_ids=speaker_ids)
+                text_input, text_lengths, mel_input, mel_lengths, speaker_ids=speaker_ids)
             decoder_backward_output = None
+            alignments_backward = None
 
         # set the alignment lengths wrt reduction factor for guided attention
         if mel_lengths.max() % model.decoder.r != 0:
@@ -176,7 +177,8 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
         loss_dict = criterion(postnet_output, decoder_output, mel_input,
                               linear_input, stop_tokens, stop_targets,
                               mel_lengths, decoder_backward_output,
-                              alignments, alignment_lengths, text_lengths)
+                              alignments, alignment_lengths, alignments_backward,
+                              text_lengths)
         if c.bidirectional_decoder:
             keep_avg.update_values({'avg_decoder_b_loss': loss_dict['decoder_backward_loss'].item(),
                                     'avg_decoder_c_loss': loss_dict['decoder_c_loss'].item()})

From 66ec1b2e2a89800a2b7ca93ce0dd50373c2bd99c Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 14:30:43 +0200
Subject: [PATCH 048/185] setup_model update following TacotronAbstract

---
 utils/generic_utils.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/utils/generic_utils.py b/utils/generic_utils.py
index 5b135061..3fbe84b1 100644
--- a/utils/generic_utils.py
+++ b/utils/generic_utils.py
@@ -160,13 +160,16 @@ def setup_model(num_chars, num_speakers, c):
                         location_attn=c.location_attn,
                         attn_K=c.attention_heads,
                         separate_stopnet=c.separate_stopnet,
-                        bidirectional_decoder=c.bidirectional_decoder)
+                        bidirectional_decoder=c.bidirectional_decoder,
+                        double_decoder_consistency=c.double_decoder_consistency,
+                        ddc_r=c.ddc_r)
     elif c.model.lower() == "tacotron2":
         model = MyModel(num_chars=num_chars,
                         num_speakers=num_speakers,
                         r=c.r,
                         postnet_output_dim=c.audio['num_mels'],
                         decoder_output_dim=c.audio['num_mels'],
+                        gst=c.use_gst,
                         attn_type=c.attention_type,
                         attn_win=c.windowing,
                         attn_norm=c.attention_norm,
@@ -178,7 +181,9 @@ def setup_model(num_chars, num_speakers, c):
                         location_attn=c.location_attn,
                         attn_K=c.attention_heads,
                         separate_stopnet=c.separate_stopnet,
-                        bidirectional_decoder=c.bidirectional_decoder)
+                        bidirectional_decoder=c.bidirectional_decoder,
+                        double_decoder_consistency=c.double_decoder_consistency,
+                        ddc_r=c.ddc_r)
     return model
 
 class KeepAverage():

From a82f7d129d7b5c3068d4c19dc86bf93ce09f330a Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 14:31:17 +0200
Subject: [PATCH 049/185] check DDC parameters in config.json

---
 utils/generic_utils.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/utils/generic_utils.py b/utils/generic_utils.py
index 3fbe84b1..eb6d21a9 100644
--- a/utils/generic_utils.py
+++ b/utils/generic_utils.py
@@ -318,6 +318,8 @@ def check_config(c):
     _check_argument('transition_agent', c, restricted=True, val_type=bool)
     _check_argument('location_attn', c, restricted=True, val_type=bool)
     _check_argument('bidirectional_decoder', c, restricted=True, val_type=bool)
+    _check_argument('double_decoder_consistency', c, restricted=True, val_type=bool)
+    _check_argument('ddc_r', c, restricted='double_decoder_consistency' in c.keys(), min_val=1, max_val=7, val_type=int)
 
     # stopnet
     _check_argument('stopnet', c, restricted=True, val_type=bool)

From fd8f1ecb7d776918980379b59d79cc94e0970644 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 14:58:18 +0200
Subject: [PATCH 050/185] lint updates

---
 models/tacotron_abstract.py | 28 ++++++++++++++++++++++++----
 1 file changed, 24 insertions(+), 4 deletions(-)

diff --git a/models/tacotron_abstract.py b/models/tacotron_abstract.py
index db6cbdee..75a1a5cd 100644
--- a/models/tacotron_abstract.py
+++ b/models/tacotron_abstract.py
@@ -4,12 +4,12 @@ from abc import ABC, abstractmethod
 import torch
 from torch import nn
 
-from TTS.layers.gst_layers import GST
 from TTS.utils.generic_utils import sequence_mask
 
 
 class TacotronAbstract(ABC, nn.Module):
-    def __init__(self, num_chars,
+    def __init__(self,
+                 num_chars,
                  num_speakers,
                  r,
                  postnet_output_dim=80,
@@ -31,6 +31,7 @@ class TacotronAbstract(ABC, nn.Module):
                  gst=False):
         """ Abstract Tacotron class """
         super().__init__()
+        self.num_chars = num_chars
         self.r = r
         self.decoder_output_dim = decoder_output_dim
         self.postnet_output_dim = postnet_output_dim
@@ -39,6 +40,17 @@ class TacotronAbstract(ABC, nn.Module):
         self.bidirectional_decoder = bidirectional_decoder
         self.double_decoder_consistency = double_decoder_consistency
         self.ddc_r = ddc_r
+        self.attn_type = attn_type
+        self.attn_win = attn_win
+        self.attn_norm = attn_norm
+        self.prenet_type = prenet_type
+        self.prenet_dropout = prenet_dropout
+        self.forward_attn = forward_attn
+        self.trans_agent = trans_agent
+        self.forward_attn_mask = forward_attn_mask
+        self.location_attn = location_attn
+        self.attn_K = attn_K
+        self.separate_stopnet = separate_stopnet
 
         # layers
         self.embedding = None
@@ -48,9 +60,16 @@ class TacotronAbstract(ABC, nn.Module):
 
         # global style token
         if self.gst:
-            gst_embedding_dim = None
             self.gst_layer = None
 
+        # model states
+        self.speaker_embeddings = None
+        self.speaker_embeddings_projected = None
+
+        # additional layers
+        self.decoder_backward = None
+        self.coarse_decoder = None
+
     #############################
     # INIT FUNCTIONS
     #############################
@@ -114,7 +133,7 @@ class TacotronAbstract(ABC, nn.Module):
                                                 (0, 0, 0, padding_size, 0, 0))
         decoder_outputs_backward, alignments_backward, _ = self.coarse_decoder(
             encoder_outputs.detach(), mel_specs, input_mask)
-        scale_factor = self.decoder.r_init / self.decoder.r
+        # scale_factor = self.decoder.r_init / self.decoder.r
         alignments_backward = torch.nn.functional.interpolate(
             alignments_backward.transpose(1, 2),
             size=alignments.shape[1],
@@ -141,6 +160,7 @@ class TacotronAbstract(ABC, nn.Module):
 
     def compute_gst(self, inputs, mel_specs):
         """ Compute global style token """
+        # pylint: disable=not-callable
         gst_outputs = self.gst_layer(mel_specs)
         inputs = self._add_speaker_embedding(inputs, gst_outputs)
         return inputs

From 189227c7412da63f3f4253e3296c4b95f005ff93 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 5 Jun 2020 13:23:11 +0200
Subject: [PATCH 051/185] bug fix vocoder training

---
 vocoder/layers/losses.py | 4 ++--
 vocoder/train.py         | 5 ++++-
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/vocoder/layers/losses.py b/vocoder/layers/losses.py
index 22e2fb54..a24b129c 100644
--- a/vocoder/layers/losses.py
+++ b/vocoder/layers/losses.py
@@ -199,8 +199,8 @@ class GeneratorLoss(nn.Module):
 
         self.stft_loss_weight = C.stft_loss_weight
         self.subband_stft_loss_weight = C.subband_stft_loss_weight
-        self.mse_gan_loss_weight = C.mse_gan_loss_weight
-        self.hinge_gan_loss_weight = C.hinge_gan_loss_weight
+        self.mse_gan_loss_weight = C.mse_G_loss_weight
+        self.hinge_gan_loss_weight = C.hinge_G_loss_weight
         self.feat_match_loss_weight = C.feat_match_loss_weight
 
         if C.use_stft_loss:
diff --git a/vocoder/train.py b/vocoder/train.py
index a75c6a12..d1e6befe 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -173,7 +173,10 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
 
         loss_dict = dict()
         for key, value in loss_G_dict.items():
-            loss_dict[key] = value.item()
+            if isinstance(value, int):
+                loss_dict[key] = value
+            else:
+                loss_dict[key] = value.item()
 
         ##############################
         # DISCRIMINATOR

From c5c3f6094cfe2eadc16d2c970fbe95cb2b3bb917 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 5 Jun 2020 13:25:53 +0200
Subject: [PATCH 052/185] bug fix for rwd discriminator

---
 vocoder/train.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/vocoder/train.py b/vocoder/train.py
index d1e6befe..82f3bee6 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -327,8 +327,12 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
             y_hat = model_G.pqmf_synthesis(y_hat)
             y_G_sub = model_G.pqmf_analysis(y_G)
 
-        D_out_fake = model_D(y_hat)
+        if len(signature(model_D.forward).parameters) == 2:
+            D_out_fake = model_D(y_hat, c_G)
+        else:
+            D_out_fake = model_D(y_hat)
         D_out_real = None
+
         if c.use_feat_match_loss:
             with torch.no_grad():
                 D_out_real = model_D(y_G)

From 72fe5a8217f1e28439aec902f72bf8eaff7a7521 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 5 Jun 2020 13:27:09 +0200
Subject: [PATCH 053/185] correct name

---
 vocoder/train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vocoder/train.py b/vocoder/train.py
index 82f3bee6..2c3a2e0a 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -363,7 +363,7 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
         for key, value in loss_G_dict.items():
             update_eval_values['avg_' + key] = value.item()
         update_eval_values['avg_loader_time'] = loader_time
-        update_eval_values['avgP_step_time'] = step_time
+        update_eval_values['avg_step_time'] = step_time
         keep_avg.update_values(update_eval_values)
 
         # print eval stats

From 81af469be053e6109808f2bbd73105ebd410efc8 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 5 Jun 2020 13:28:16 +0200
Subject: [PATCH 054/185] model config update

---
 .../multiband-melgan_and_rwd_config.json      | 21 ++++++++++++-------
 1 file changed, 14 insertions(+), 7 deletions(-)

diff --git a/vocoder/configs/multiband-melgan_and_rwd_config.json b/vocoder/configs/multiband-melgan_and_rwd_config.json
index 9aeb7933..c783877d 100644
--- a/vocoder/configs/multiband-melgan_and_rwd_config.json
+++ b/vocoder/configs/multiband-melgan_and_rwd_config.json
@@ -49,21 +49,28 @@
 
     // LOSS PARAMETERS
     "use_stft_loss": true,
+    "use_subband_stft_loss": true,
     "use_mse_gan_loss": true,
     "use_hinge_gan_loss": false,
     "use_feat_match_loss": false,  // use only with melgan discriminators
 
-    "stft_loss_weight": 1,
+    "stft_loss_weight": 0.5,
+    "subband_stft_loss_weight": 0.5,
     "mse_gan_loss_weight": 2.5,
-    "hinge_gan_loss_weight": 1,
-    "feat_match_loss_weight": 10.0,
+    "hinge_gan_loss_weight": 2.5,
+    "feat_match_loss_weight": 25.0,
 
     "stft_loss_params": {
         "n_ffts": [1024, 2048, 512],
         "hop_lengths": [120, 240, 50],
         "win_lengths": [600, 1200, 240]
     },
-    "target_loss": "avg_G_loss",  // loss value to pick the best model
+    "subband_stft_loss_params":{
+        "n_ffts": [384, 683, 171],
+        "hop_lengths": [30, 60, 10],
+        "win_lengths": [150, 300, 60]
+    },
+    "target_loss": "avg_G_loss",
 
     // DISCRIMINATOR
     // "discriminator_model": "melgan_multiscale_discriminator",
@@ -85,7 +92,7 @@
     // GENERATOR
     "generator_model": "multiband_melgan_generator",
     "generator_model_params": {
-        "upsample_factors":[2 ,2, 4, 4],
+        "upsample_factors":[8, 4, 2],
         "num_res_blocks": 4
     },
 
@@ -111,7 +118,7 @@
     "noam_schedule": false,        // use noam warmup and lr schedule.
     "warmup_steps_gen": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
     "warmup_steps_disc": 4000,
-    "epochs": 1000,                // total number of epochs to train.
+    "epochs": 100000,                // total number of epochs to train.
     "wd": 0.000001,                // Weight decay weight.
     "lr_gen": 0.0001,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
     "lr_disc": 0.0001,
@@ -131,6 +138,6 @@
     "eval_split_size": 10,
 
     // PATHS
-    "output_path": "/home/erogol/Models/LJSpeech/"
+    "output_path": "/data/rw/home/Trainings/"
 }
 

From 79db7d931b213ed9a75d64fe8c5c5e1b27bcd863 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 5 Jun 2020 14:47:25 +0200
Subject: [PATCH 055/185] bug fix for DDC model eval run

---
 train.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/train.py b/train.py
index 612ee8a6..13bda5ef 100644
--- a/train.py
+++ b/train.py
@@ -331,13 +331,14 @@ def evaluate(model, criterion, ap, global_step, epoch):
             assert mel_input.shape[1] % model.decoder.r == 0
 
             # forward pass model
-            if c.bidirectional_decoder:
+            if c.bidirectional_decoder or c.double_decoder_consistency:
                 decoder_output, postnet_output, alignments, stop_tokens, decoder_backward_output, alignments_backward = model(
                     text_input, text_lengths, mel_input, speaker_ids=speaker_ids)
             else:
                 decoder_output, postnet_output, alignments, stop_tokens = model(
                     text_input, text_lengths, mel_input, speaker_ids=speaker_ids)
                 decoder_backward_output = None
+                alignments_backward = None
 
             # set the alignment lengths wrt reduction factor for guided attention
             if mel_lengths.max() % model.decoder.r != 0:
@@ -349,7 +350,8 @@ def evaluate(model, criterion, ap, global_step, epoch):
             loss_dict = criterion(postnet_output, decoder_output, mel_input,
                                   linear_input, stop_tokens, stop_targets,
                                   mel_lengths, decoder_backward_output,
-                                  alignments, alignment_lengths, text_lengths)
+                                  alignments, alignment_lengths, alignments_backward, 
+                                  text_lengths)
             if c.bidirectional_decoder:
                 keep_avg.update_values({'avg_decoder_b_loss': loss_dict['decoder_b_loss'].item(),
                                         'avg_decoder_c_loss': loss_dict['decoder_c_loss'].item()})

From b86af6538f7044a0785cde6196c4b42748a6c04e Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sat, 6 Jun 2020 12:59:59 +0200
Subject: [PATCH 056/185] bug fix for TTS model restore

---
 train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/train.py b/train.py
index 13bda5ef..a47828e0 100644
--- a/train.py
+++ b/train.py
@@ -542,7 +542,7 @@ def main(args):  # pylint: disable=redefined-outer-name
         except:
             print(" > Partial model initialization.")
             model_dict = model.state_dict()
-            model_dict = set_init_dict(model_dict, checkpoint, c)
+            model_dict = set_init_dict(model_dict, checkpoint['model'], c)
             model.load_state_dict(model_dict)
             del model_dict
         for group in optimizer.param_groups:

From fedb2542be72c6f96d10c0b379968337412b24ee Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sat, 6 Jun 2020 13:00:46 +0200
Subject: [PATCH 057/185] bug fix vocoder model restore

---
 vocoder/configs/multiband-melgan_and_rwd_config.json | 4 ++--
 vocoder/train.py                                     | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/vocoder/configs/multiband-melgan_and_rwd_config.json b/vocoder/configs/multiband-melgan_and_rwd_config.json
index c783877d..736f3459 100644
--- a/vocoder/configs/multiband-melgan_and_rwd_config.json
+++ b/vocoder/configs/multiband-melgan_and_rwd_config.json
@@ -56,8 +56,8 @@
 
     "stft_loss_weight": 0.5,
     "subband_stft_loss_weight": 0.5,
-    "mse_gan_loss_weight": 2.5,
-    "hinge_gan_loss_weight": 2.5,
+    "mse_G_loss_weight": 2.5,
+    "hinge_G_loss_weight": 2.5,
     "feat_match_loss_weight": 25.0,
 
     "stft_loss_params": {
diff --git a/vocoder/train.py b/vocoder/train.py
index 2c3a2e0a..05805c68 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -421,7 +421,7 @@ def main(args):  # pylint: disable=redefined-outer-name
             optimizer_gen.load_state_dict(checkpoint['optimizer'])
             model_disc.load_state_dict(checkpoint['model_disc'])
             optimizer_disc.load_state_dict(checkpoint['optimizer_disc'])
-        except KeyError:
+        except RuntimeError:
             print(" > Partial model initialization.")
             model_dict = model_gen.state_dict()
             model_dict = set_init_dict(model_dict, checkpoint['model'], c)

From 1d3c0c88467d01e114063b54279693b464ac656a Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sat, 6 Jun 2020 13:44:03 +0200
Subject: [PATCH 058/185] raise error if training set is < 100

---
 setup.py               | 2 +-
 utils/generic_utils.py | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 84a31488..a7840568 100644
--- a/setup.py
+++ b/setup.py
@@ -19,7 +19,7 @@ args, unknown_args = parser.parse_known_args()
 # Remove our arguments from argv so that setuptools doesn't see them
 sys.argv = [sys.argv[0]] + unknown_args
 
-version = '0.0.2'
+version = '0.0.3'
 
 # Adapted from https://github.com/pytorch/pytorch
 cwd = os.path.dirname(os.path.abspath(__file__))
diff --git a/utils/generic_utils.py b/utils/generic_utils.py
index 1c7dd5e4..298f5970 100644
--- a/utils/generic_utils.py
+++ b/utils/generic_utils.py
@@ -75,6 +75,7 @@ def split_dataset(items):
     is_multi_speaker = len(set(speakers)) > 1
     eval_split_size = 500 if len(items) * 0.01 > 500 else int(
         len(items) * 0.01)
+    assert len(eval_split_size) > 0, " [!] You do not have enough samples to train. You need at least 100 samples."
     np.random.seed(0)
     np.random.shuffle(items)
     if is_multi_speaker:

From 2404f96cba51bdb642a65296e49bda905ae70830 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Mon, 8 Jun 2020 03:22:17 +0200
Subject: [PATCH 059/185] bug fix and var renaming

---
 models/tacotron2.py | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/models/tacotron2.py b/models/tacotron2.py
index bbce4be9..bce21e9e 100644
--- a/models/tacotron2.py
+++ b/models/tacotron2.py
@@ -107,6 +107,7 @@ class Tacotron2(TacotronAbstract):
             decoder_outputs = decoder_outputs * output_mask.unsqueeze(1).expand_as(decoder_outputs)
         # B x mel_dim x T_out
         postnet_outputs = self.postnet(decoder_outputs)
+        postnet_outputs = decoder_outputs + postnet_outputs
         # sequence masking
         if output_mask is not None:
             postnet_outputs = postnet_outputs * output_mask.unsqueeze(1).expand_as(postnet_outputs)
@@ -130,13 +131,13 @@ class Tacotron2(TacotronAbstract):
         if self.num_speakers > 1:
             encoder_outputs = self._add_speaker_embedding(encoder_outputs,
                                                     self.speaker_embeddings)
-        mel_outputs, alignments, stop_tokens = self.decoder.inference(
+        decoder_outputs, alignments, stop_tokens = self.decoder.inference(
             encoder_outputs)
-        mel_outputs_postnet = self.postnet(mel_outputs)
-        mel_outputs_postnet = mel_outputs + mel_outputs_postnet
-        mel_outputs, mel_outputs_postnet, alignments = self.shape_outputs(
-            mel_outputs, mel_outputs_postnet, alignments)
-        return mel_outputs, mel_outputs_postnet, alignments, stop_tokens
+        postnet_outputs = self.postnet(decoder_outputs)
+        postnet_outputs = decoder_outputs + postnet_outputs
+        decoder_outputs, postnet_outputs, alignments = self.shape_outputs(
+            decoder_outputs, postnet_outputs, alignments)
+        return decoder_outputs, postnet_outputs, alignments, stop_tokens
 
     def inference_truncated(self, text, speaker_ids=None):
         """

From 8d307f21336352949ff56caa29f3e582e4e7a115 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Mon, 8 Jun 2020 10:43:09 +0200
Subject: [PATCH 060/185] run D in eval

---
 vocoder/configs/melgan_config.json |  4 +--
 vocoder/train.py                   | 47 +++++++++++++++++++++++++++---
 2 files changed, 45 insertions(+), 6 deletions(-)

diff --git a/vocoder/configs/melgan_config.json b/vocoder/configs/melgan_config.json
index bdfdf88a..0b6b16db 100644
--- a/vocoder/configs/melgan_config.json
+++ b/vocoder/configs/melgan_config.json
@@ -56,8 +56,8 @@
 
     "stft_loss_weight": 0.5,
     "subband_stft_loss_weight": 0.5,
-    "mse_gan_loss_weight": 2.5,
-    "hinge_gan_loss_weight": 2.5,
+    "mse_G_loss_weight": 2.5,
+    "hinge_G_loss_weight": 2.5,
     "feat_match_loss_weight": 25,
 
     "stft_loss_params": {
diff --git a/vocoder/train.py b/vocoder/train.py
index 05805c68..54b36e6b 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -181,7 +181,7 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
         ##############################
         # DISCRIMINATOR
         ##############################
-        if global_step > c.steps_to_start_discriminator:
+        if global_step >= c.steps_to_start_discriminator:
             # discriminator pass
             with torch.no_grad():
                 y_hat = model_G(c_D)
@@ -295,7 +295,7 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
 
 
 @torch.no_grad()
-def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
+def evaluate(model_G, criterion_G, model_D, criterion_D, ap, global_step, epoch):
     data_loader = setup_loader(ap, is_val=True, verbose=(epoch == 0))
     model_G.eval()
     model_D.eval()
@@ -355,13 +355,52 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
         for key, value in loss_G_dict.items():
             loss_dict[key] = value.item()
 
+        ##############################
+        # DISCRIMINATOR
+        ##############################
+
+        if global_step >= c.steps_to_start_discriminator:
+            # discriminator pass
+            with torch.no_grad():
+                y_hat = model_G(c_G)
+
+            # PQMF formatting
+            if y_hat.shape[1] > 1:
+                y_hat = model_G.pqmf_synthesis(y_hat)
+
+            # run D with or without cond. features
+            if len(signature(model_D.forward).parameters) == 2:
+                D_out_fake = model_D(y_hat.detach(), c_G)
+                D_out_real = model_D(y_G, c_G)
+            else:
+                D_out_fake = model_D(y_hat.detach())
+                D_out_real = model_D(y_G)
+
+            # format D outputs
+            if isinstance(D_out_fake, tuple):
+                scores_fake, feats_fake = D_out_fake
+                if D_out_real is None:
+                    scores_real, feats_real = None, None
+                else:
+                    scores_real, feats_real = D_out_real
+            else:
+                scores_fake = D_out_fake
+                scores_real = D_out_real
+
+            # compute losses
+            loss_D_dict = criterion_D(scores_fake, scores_real)
+
+            for key, value in loss_D_dict.items():
+                loss_dict[key] = value.item()
+
+
         step_time = time.time() - start_time
         epoch_time += step_time
 
         # update avg stats
         update_eval_values = dict()
-        for key, value in loss_G_dict.items():
-            update_eval_values['avg_' + key] = value.item()
+        for key, value in loss_dict.items():
+            update_eval_values['avg_' + key] = value
         update_eval_values['avg_loader_time'] = loader_time
         update_eval_values['avg_step_time'] = step_time
         keep_avg.update_values(update_eval_values)

From dbe381a18647d60f9ee548f11162ec57fc50e5f0 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Mon, 8 Jun 2020 10:45:38 +0200
Subject: [PATCH 061/185] linter fix

---
 models/tacotron2.py | 2 +-
 train.py            | 2 +-
 vocoder/train.py    | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/models/tacotron2.py b/models/tacotron2.py
index bce21e9e..46b915b5 100644
--- a/models/tacotron2.py
+++ b/models/tacotron2.py
@@ -130,7 +130,7 @@ class Tacotron2(TacotronAbstract):
             self.compute_speaker_embedding(speaker_ids)
         if self.num_speakers > 1:
             encoder_outputs = self._add_speaker_embedding(encoder_outputs,
-                                                    self.speaker_embeddings)
+                                                          self.speaker_embeddings)
         decoder_outputs, alignments, stop_tokens = self.decoder.inference(
             encoder_outputs)
         postnet_outputs = self.postnet(decoder_outputs)
diff --git a/train.py b/train.py
index a47828e0..a5f3d2e7 100644
--- a/train.py
+++ b/train.py
@@ -350,7 +350,7 @@ def evaluate(model, criterion, ap, global_step, epoch):
             loss_dict = criterion(postnet_output, decoder_output, mel_input,
                                   linear_input, stop_tokens, stop_targets,
                                   mel_lengths, decoder_backward_output,
-                                  alignments, alignment_lengths, alignments_backward, 
+                                  alignments, alignment_lengths, alignments_backward,
                                   text_lengths)
             if c.bidirectional_decoder:
                 keep_avg.update_values({'avg_decoder_b_loss': loss_dict['decoder_b_loss'].item(),
diff --git a/vocoder/train.py b/vocoder/train.py
index 54b36e6b..259c5eb7 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -519,7 +519,7 @@ def main(args):  # pylint: disable=redefined-outer-name
                                model_disc, criterion_disc, optimizer_disc,
                                scheduler_gen, scheduler_disc, ap, global_step,
                                epoch)
-        eval_avg_loss_dict = evaluate(model_gen, criterion_gen, model_disc, ap,
+        eval_avg_loss_dict = evaluate(model_gen, criterion_gen, model_disc, criterion_disc, ap,
                                       global_step, epoch)
         c_logger.print_epoch_end(epoch, eval_avg_loss_dict)
         target_loss = eval_avg_loss_dict[c.target_loss]

From 625ea70c98c67cdfa87dcbc71478a002370ac41a Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 9 Jun 2020 23:01:13 +0200
Subject: [PATCH 062/185] TTS config update for using DDC

---
 config.json | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/config.json b/config.json
index 74021970..5e10c535 100644
--- a/config.json
+++ b/config.json
@@ -1,7 +1,7 @@
 {
     "model": "Tacotron2",
-    "run_name": "ljspeech",
-    "run_description": "tacotron2",
+    "run_name": "ljspeech-ddc-bn",
+    "run_description": "tacotron2 with ddc and batch-normalization",
 
     // AUDIO PARAMETERS
     "audio":{
@@ -83,8 +83,8 @@
 
     // TACOTRON PRENET
     "memory_size": -1,             // ONLY TACOTRON - size of the memory queue used fro storing last decoder predictions for auto-regression. If < 0, memory queue is disabled and decoder only uses the last prediction frame.
-    "prenet_type": "original",     // "original" or "bn".
-    "prenet_dropout": true,        // enable/disable dropout at prenet.
+    "prenet_type": "bn",     // "original" or "bn".
+    "prenet_dropout": false,        // enable/disable dropout at prenet.
 
     // ATTENTION
     "attention_type": "original",  // 'original' or 'graves'
@@ -124,7 +124,7 @@
 
     // PHONEMES
     "phoneme_cache_path": "/media/erogol/data_ssd2/mozilla_us_phonemes_3",  // phoneme computation is slow, therefore, it caches results in the given folder.
-    "use_phonemes": false,           // use phonemes instead of raw characters. It is suggested for better pronounciation.
+    "use_phonemes": true,           // use phonemes instead of raw characters. It is suggested for better pronounciation.
     "phoneme_language": "en-us",     // depending on your target language, pick one from  https://github.com/bootphon/phonemizer#languages
 
     // MULTI-SPEAKER and GST

From 4ea649f59b87dd1bebbf31fa9a7015d765c21e35 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 9 Jun 2020 23:01:34 +0200
Subject: [PATCH 063/185] change alignment_score to alignment_error

---
 train.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/train.py b/train.py
index a5f3d2e7..869557e6 100644
--- a/train.py
+++ b/train.py
@@ -291,7 +291,7 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
             "loss_postnet": keep_avg['avg_postnet_loss'],
             "loss_decoder": keep_avg['avg_decoder_loss'],
             "stopnet_loss": keep_avg['avg_stopnet_loss'],
-            "alignment_score": keep_avg['avg_align_error'],
+            "alignment_error": keep_avg['avg_align_error'],
             "epoch_time": epoch_time
         }
         if c.ga_alpha > 0:
@@ -413,7 +413,7 @@ def evaluate(model, criterion, ap, global_step, epoch):
                 "loss_postnet": keep_avg['avg_postnet_loss'],
                 "loss_decoder": keep_avg['avg_decoder_loss'],
                 "stopnet_loss": keep_avg['avg_stopnet_loss'],
-                "alignment_score": keep_avg['avg_align_error'],
+                "alignment_error": keep_avg['avg_align_error'],
             }
 
             if c.bidirectional_decoder:

From f3aa295834007a956f115f53deed3e6b5907c9af Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 9 Jun 2020 23:02:05 +0200
Subject: [PATCH 064/185] correction for vocoder configs

---
 vocoder/configs/melgan_config.json            | 145 ------------------
 .../multiband-melgan_and_rwd_config.json      |   2 +-
 2 files changed, 1 insertion(+), 146 deletions(-)
 delete mode 100644 vocoder/configs/melgan_config.json

diff --git a/vocoder/configs/melgan_config.json b/vocoder/configs/melgan_config.json
deleted file mode 100644
index 0b6b16db..00000000
--- a/vocoder/configs/melgan_config.json
+++ /dev/null
@@ -1,145 +0,0 @@
-{
-    "run_name": "melgan",
-    "run_description": "melgan initial run",
-
-    // AUDIO PARAMETERS
-    "audio":{
-        // stft parameters
-        "num_freq": 513,         // number of stft frequency levels. Size of the linear spectogram frame.
-        "win_length": 1024,      // stft window length in ms.
-        "hop_length": 256,       // stft window hop-lengh in ms.
-        "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
-        "frame_shift_ms": null,  // stft window hop-lengh in ms. If null, 'hop_length' is used.
-
-        // Audio processing parameters
-        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
-        "preemphasis": 0.0,     // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
-        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
-
-        // Silence trimming
-        "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
-        "trim_db": 60,          // threshold for timming silence. Set this according to your dataset.
-
-        // Griffin-Lim
-        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
-        "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
-
-        // MelSpectrogram parameters
-        "num_mels": 80,         // size of the mel spec frame.
-        "mel_fmin": 0.0,        // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
-        "mel_fmax": 8000.0,     // maximum freq level for mel-spec. Tune for dataset!!
-
-        // Normalization parameters
-        "signal_norm": true,    // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
-        "min_level_db": -100,   // lower bound for normalization
-        "symmetric_norm": true, // move normalization to range [-1, 1]
-        "max_norm": 4.0,        // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
-        "clip_norm": true,      // clip normalized values into the range.
-        "stats_path": null    // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
-    },
-
-    // DISTRIBUTED TRAINING
-    // "distributed":{
-    //     "backend": "nccl",
-    //     "url": "tcp:\/\/localhost:54321"
-    // },
-
-    // MODEL PARAMETERS
-    "use_pqmf": true,
-
-    // LOSS PARAMETERS
-    "use_stft_loss": true,
-    "use_subband_stft_loss": true,
-    "use_mse_gan_loss": true,
-    "use_hinge_gan_loss": false,
-    "use_feat_match_loss": false,  // use only with melgan discriminators
-
-    "stft_loss_weight": 0.5,
-    "subband_stft_loss_weight": 0.5,
-    "mse_G_loss_weight": 2.5,
-    "hinge_G_loss_weight": 2.5,
-    "feat_match_loss_weight": 25,
-
-    "stft_loss_params": {
-        "n_ffts": [1024, 2048, 512],
-        "hop_lengths": [120, 240, 50],
-        "win_lengths": [600, 1200, 240]
-    },
-    "subband_stft_loss_params":{
-        "n_ffts": [384, 683, 171],
-        "hop_lengths": [30, 60, 10],
-        "win_lengths": [150, 300, 60]
-    },
-
-    "target_loss": "avg_G_loss",  // loss value to pick the best model
-
-    // DISCRIMINATOR
-    "discriminator_model": "melgan_multiscale_discriminator",
-    "discriminator_model_params":{
-        "base_channels": 16,
-        "max_channels":1024,
-        "downsample_factors":[4, 4, 4, 4]
-    },
-    "steps_to_start_discriminator": 100000,      // steps required to start GAN trainining.1
-
-    // "discriminator_model": "random_window_discriminator",
-    // "discriminator_model_params":{
-    //     "uncond_disc_donwsample_factors": [8, 4],
-    //     "cond_disc_downsample_factors": [[8, 4, 2, 2, 2], [8, 4, 2, 2], [8, 4, 2], [8, 4], [4, 2, 2]],
-    //     "cond_disc_out_channels": [[128, 128, 256, 256], [128, 256, 256], [128, 256], [256], [128, 256]],
-    //     "window_sizes": [512, 1024, 2048, 4096, 8192]
-    // },
-
-
-    // GENERATOR
-    "generator_model": "multiband_melgan_generator",
-    "generator_model_params": {
-        "upsample_factors":[2 ,2, 4, 4],
-        "num_res_blocks": 4
-    },
-
-    // DATASET
-    "data_path": "/home/erogol/Data/LJSpeech-1.1/wavs/",
-    "seq_len": 16384,
-    "pad_short": 2000,
-    "conv_pad": 0,
-    "use_noise_augment": true,
-    "use_cache": true,
-
-    "reinit_layers": [],    // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
-
-    // TRAINING
-    "batch_size": 64,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
-
-    // VALIDATION
-    "run_eval": true,
-    "test_delay_epochs": 10,  //Until attention is aligned, testing only wastes computation time.
-    "test_sentences_file": null,  // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
-
-    // OPTIMIZER
-    "noam_schedule": false,        // use noam warmup and lr schedule.
-    "warmup_steps_gen": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
-    "warmup_steps_disc": 4000,
-    "epochs": 1000,                // total number of epochs to train.
-    "wd": 0.000001,                // Weight decay weight.
-    "lr_gen": 0.0001,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
-    "lr_disc": 0.0001,
-    "gen_clip_grad": 10.0,
-    "disc_clip_grad": 10.0,
-
-    // TENSORBOARD and LOGGING
-    "print_step": 25,       // Number of steps to log traning on console.
-    "print_eval": false,     // If True, it prints intermediate loss values in evalulation.
-    "save_step": 10000,      // Number of training steps expected to save traninpg stats and checkpoints.
-    "checkpoint": true,     // If true, it saves checkpoints per "save_step"
-    "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
-
-    // DATA LOADING
-    "num_loader_workers": 4,        // number of training data loader processes. Don't set it too big. 4-8 are good values.
-    "num_val_loader_workers": 4,    // number of evaluation data loader processes.
-    "eval_split_size": 10,
-
-    // PATHS
-    "output_path": "/home/erogol/Models/LJSpeech/"
-}
-
diff --git a/vocoder/configs/multiband-melgan_and_rwd_config.json b/vocoder/configs/multiband-melgan_and_rwd_config.json
index 736f3459..f4b91aae 100644
--- a/vocoder/configs/multiband-melgan_and_rwd_config.json
+++ b/vocoder/configs/multiband-melgan_and_rwd_config.json
@@ -79,7 +79,7 @@
     //     "max_channels":1024,
     //     "downsample_factors":[4, 4, 4, 4]
     // },
-    "steps_to_start_discriminator": 100000,      // steps required to start GAN trainining.1
+    "steps_to_start_discriminator": 200000,      // steps required to start GAN trainining.1
 
     "discriminator_model": "random_window_discriminator",
     "discriminator_model_params":{

From aedbd318d55681d23562d4a84e843aea9b0bd396 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 9 Jun 2020 23:02:28 +0200
Subject: [PATCH 065/185] remove weight norm for melgan generator

---
 vocoder/models/melgan_generator.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/vocoder/models/melgan_generator.py b/vocoder/models/melgan_generator.py
index 1e47816d..e69e6ef3 100644
--- a/vocoder/models/melgan_generator.py
+++ b/vocoder/models/melgan_generator.py
@@ -87,3 +87,12 @@ class MelganGenerator(nn.Module):
             (self.inference_padding, self.inference_padding),
             'replicate')
         return self.layers(cond_features)
+
+    def remove_weight_norm(self):
+        for _, layer in enumerate(self.layers):
+            if len(layer.state_dict()) != 0:
+                try:
+                    nn.utils.remove_weight_norm(layer)
+                except ValueError:
+                    layer.remove_weight_norm()
+

From 8552c1d991955c8545e59b7d1af492449ee82c9a Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 9 Jun 2020 23:02:55 +0200
Subject: [PATCH 066/185] fix multiband melgan inference

---
 vocoder/models/multiband_melgan_generator.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/vocoder/models/multiband_melgan_generator.py b/vocoder/models/multiband_melgan_generator.py
index 8feacd25..15e7426e 100644
--- a/vocoder/models/multiband_melgan_generator.py
+++ b/vocoder/models/multiband_melgan_generator.py
@@ -29,10 +29,11 @@ class MultibandMelganGenerator(MelganGenerator):
     def pqmf_synthesis(self, x):
         return self.pqmf_layer.synthesis(x)
 
+    @torch.no_grad()
     def inference(self, cond_features):
         cond_features = cond_features.to(self.layers[1].weight.device)
         cond_features = torch.nn.functional.pad(
             cond_features,
             (self.inference_padding, self.inference_padding),
             'replicate')
-        return self.pqmf.synthesis(self.layers(cond_features))
+        return self.pqmf_synthesis(self.layers(cond_features))

From f0144bfcba8411fd68a934158307ec49b00a0ea2 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 9 Jun 2020 23:03:37 +0200
Subject: [PATCH 067/185] add lr schedulers for generator and discriminator

---
 vocoder/train.py | 56 +++++++++++++++++++++++++++++-------------------
 1 file changed, 34 insertions(+), 22 deletions(-)

diff --git a/vocoder/train.py b/vocoder/train.py
index 259c5eb7..c563dff0 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -107,10 +107,6 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
 
         global_step += 1
 
-        # get current learning rates
-        current_lr_G = list(optimizer_G.param_groups)[0]['lr']
-        current_lr_D = list(optimizer_D.param_groups)[0]['lr']
-
         ##############################
         # GENERATOR
         ##############################
@@ -166,9 +162,7 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
             torch.nn.utils.clip_grad_norm_(model_G.parameters(),
                                            c.gen_clip_grad)
         optimizer_G.step()
-
-        # setup lr
-        if c.noam_schedule:
+        if scheduler_G is not None:
             scheduler_G.step()
 
         loss_dict = dict()
@@ -221,9 +215,7 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
                 torch.nn.utils.clip_grad_norm_(model_D.parameters(),
                                                c.disc_clip_grad)
             optimizer_D.step()
-
-            # setup lr
-            if c.noam_schedule:
+            if c.scheduler_D is not None:
                 scheduler_D.step()
 
             for key, value in loss_D_dict.items():
@@ -232,6 +224,10 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
         step_time = time.time() - start_time
         epoch_time += step_time
 
+        # get current learning rates
+        current_lr_G = list(optimizer_G.param_groups)[0]['lr']
+        current_lr_D = list(optimizer_D.param_groups)[0]['lr']
+
         # update avg stats
         update_train_values = dict()
         for key, value in loss_dict.items():
@@ -244,7 +240,8 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
         if global_step % c.print_step == 0:
             c_logger.print_train_step(batch_n_iter, num_iter, global_step,
                                       step_time, loader_time, current_lr_G,
-                                      loss_dict, keep_avg.avg_values)
+                                      current_lr_D, loss_dict,
+                                      keep_avg.avg_values)
 
         # plot step stats
         if global_step % 10 == 0:
@@ -262,8 +259,10 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
                 # save model
                 save_checkpoint(model_G,
                                 optimizer_G,
+                                scheduler_G,
                                 model_D,
                                 optimizer_D,
+                                scheduler_D,
                                 global_step,
                                 epoch,
                                 OUT_PATH,
@@ -434,6 +433,7 @@ def main(args):  # pylint: disable=redefined-outer-name
 
     # setup audio processor
     ap = AudioProcessor(**c.audio)
+
     # DISTRUBUTED
     # if num_gpus > 1:
     # init_distributed(args.rank, num_gpus, args.group_id,
@@ -449,6 +449,12 @@ def main(args):  # pylint: disable=redefined-outer-name
                            lr=c.lr_disc,
                            weight_decay=0)
 
+    # schedulers
+    scheduler_gen = getattr(torch.optim.lr_scheduler, c.lr_scheduler_gen)
+    scheduler_disc = getattr(torch.optim.lr_scheduler, c.lr_scheduler_disc)
+    scheduler_gen = scheduler_gen(optimizer_gen, **c.lr_scheduler_gen_params)
+    scheduler_disc = scheduler_disc(optimizer_disc, **c.lr_scheduler_disc_params)
+
     # setup criterion
     criterion_gen = GeneratorLoss(c)
     criterion_disc = DiscriminatorLoss(c)
@@ -456,12 +462,26 @@ def main(args):  # pylint: disable=redefined-outer-name
     if args.restore_path:
         checkpoint = torch.load(args.restore_path, map_location='cpu')
         try:
+            print(" > Restoring Generator Model...")
             model_gen.load_state_dict(checkpoint['model'])
+            print(" > Restoring Generator Optimizer...")
             optimizer_gen.load_state_dict(checkpoint['optimizer'])
+            print(" > Restoring Discriminator Model...")
             model_disc.load_state_dict(checkpoint['model_disc'])
+            print(" > Restoring Discriminator Optimizer...")
             optimizer_disc.load_state_dict(checkpoint['optimizer_disc'])
+            if 'scheduler' in checkpoint:
+                print(" > Restoring Generator LR Scheduler...")
+                scheduler_gen.load_state_dict(checkpoint['scheduler'])
+                # NOTE: Not sure if necessary
+                scheduler_gen.optimizer = optimizer_gen
+            if 'scheduler_disc' in checkpoint:
+                print(" > Restoring Discriminator LR Scheduler...")
+                scheduler_disc.load_state_dict(checkpoint['scheduler_disc'])
+                scheduler_disc.optimizer = optimizer_disc
         except RuntimeError:
-            print(" > Partial model initialization.")
+            # retore only matching layers.
+            print(" > Partial model initialization...")
             model_dict = model_gen.state_dict()
             model_dict = set_init_dict(model_dict, checkpoint['model'], c)
             model_gen.load_state_dict(model_dict)
@@ -494,16 +514,6 @@ def main(args):  # pylint: disable=redefined-outer-name
     # if num_gpus > 1:
     #     model = apply_gradient_allreduce(model)
 
-    if c.noam_schedule:
-        scheduler_gen = NoamLR(optimizer_gen,
-                               warmup_steps=c.warmup_steps_gen,
-                               last_epoch=args.restore_step - 1)
-        scheduler_disc = NoamLR(optimizer_disc,
-                                warmup_steps=c.warmup_steps_gen,
-                                last_epoch=args.restore_step - 1)
-    else:
-        scheduler_gen, scheduler_disc = None, None
-
     num_params = count_parameters(model_gen)
     print(" > Generator has {} parameters".format(num_params), flush=True)
     num_params = count_parameters(model_disc)
@@ -526,9 +536,11 @@ def main(args):  # pylint: disable=redefined-outer-name
         best_loss = save_best_model(target_loss,
                                     best_loss,
                                     model_gen,
+                                    scheduler_gen,
                                     optimizer_gen,
                                     model_disc,
                                     optimizer_disc,
+                                    scheduler_disc,
                                     global_step,
                                     epoch,
                                     OUT_PATH,

From dcfd62678480a1b3d02e9b91dbb22fa6d2c0ea8c Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 9 Jun 2020 23:04:02 +0200
Subject: [PATCH 068/185] print lrg and lrD

---
 vocoder/utils/console_logger.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/vocoder/utils/console_logger.py b/vocoder/utils/console_logger.py
index ff7edfd0..50882160 100644
--- a/vocoder/utils/console_logger.py
+++ b/vocoder/utils/console_logger.py
@@ -15,8 +15,8 @@ tcolors = AttrDict({
 
 
 class ConsoleLogger():
+    # TODO: merge this with TTS ConsoleLogger
     def __init__(self):
-        # TODO: color code for value changes
         # use these to compare values between iterations
         self.old_train_loss_dict = None
         self.old_epoch_loss_dict = None
@@ -36,7 +36,7 @@ class ConsoleLogger():
         print(f"\n{tcolors.BOLD} > TRAINING ({self.get_time()}) {tcolors.ENDC}")
 
     def print_train_step(self, batch_steps, step, global_step,
-                         step_time, loader_time, lr,
+                         step_time, loader_time, lrG, lrD,
                          loss_dict, avg_loss_dict):
         indent = "     | > "
         print()
@@ -48,7 +48,7 @@ class ConsoleLogger():
                 log_text += "{}{}: {:.5f}  ({:.5f})\n".format(indent, key, value, avg_loss_dict[f'avg_{key}'])
             else:
                 log_text += "{}{}: {:.5f} \n".format(indent, key, value)
-        log_text += f"{indent}step_time: {step_time:.2f}\n{indent}loader_time: {loader_time:.2f}\n{indent}lr: {lr:.5f}"
+        log_text += f"{indent}step_time: {step_time:.2f}\n{indent}loader_time: {loader_time:.2f}\n{indent}lrG: {lrG}\n{indent}lrD: {lrD}"
         print(log_text, flush=True)
 
     # pylint: disable=unused-argument

From b445bcb9624905b58cbcac1196f708bb55e5a154 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 9 Jun 2020 23:04:16 +0200
Subject: [PATCH 069/185] save schedulers with checkpoints

---
 vocoder/utils/io.py | 38 +++++++++++++++++++++++++-------------
 1 file changed, 25 insertions(+), 13 deletions(-)

diff --git a/vocoder/utils/io.py b/vocoder/utils/io.py
index b6ea7484..9d350238 100644
--- a/vocoder/utils/io.py
+++ b/vocoder/utils/io.py
@@ -2,18 +2,27 @@ import os
 import torch
 import datetime
 
-def save_model(model, optimizer, model_disc, optimizer_disc, current_step,
-               epoch, output_path, **kwargs):
+
+def save_model(model, optimizer, scheduler, model_disc, optimizer_disc,
+               scheduler_disc, current_step, epoch, output_path, **kwargs):
     model_state = model.state_dict()
-    model_disc_state = model_disc.state_dict()
-    optimizer_state = optimizer.state_dict() if optimizer is not None else None
-    optimizer_disc_state = optimizer_disc.state_dict(
-    ) if optimizer_disc is not None else None
+    model_disc_state = model_disc.state_dict()\
+         if model_disc is not None else None
+    optimizer_state = optimizer.state_dict()\
+         if optimizer is not None else None
+    optimizer_disc_state = optimizer_disc.state_dict()\
+        if optimizer_disc is not None else None
+    scheduler_state = scheduler.state_dict()\
+        if scheduler is not None else None
+    scheduler_disc_state = scheduler_disc.state_dict()\
+        if scheduler_disc is not None else None
     state = {
         'model': model_state,
         'optimizer': optimizer_state,
+        'scheduler': scheduler_state,
         'model_disc': model_disc_state,
         'optimizer_disc': optimizer_disc_state,
+        'scheduler_disc': scheduler_disc_state,
         'step': current_step,
         'epoch': epoch,
         'date': datetime.date.today().strftime("%B %d, %Y"),
@@ -22,26 +31,29 @@ def save_model(model, optimizer, model_disc, optimizer_disc, current_step,
     torch.save(state, output_path)
 
 
-def save_checkpoint(model, optimizer, model_disc, optimizer_disc, current_step,
-                    epoch, output_folder, **kwargs):
+def save_checkpoint(model, optimizer, scheduler, model_disc, optimizer_disc,
+                    scheduler_disc, current_step, epoch, output_folder,
+                    **kwargs):
     file_name = 'checkpoint_{}.pth.tar'.format(current_step)
     checkpoint_path = os.path.join(output_folder, file_name)
     print(" > CHECKPOINT : {}".format(checkpoint_path))
-    save_model(model, optimizer, model_disc, optimizer_disc, current_step,
-               epoch, checkpoint_path, **kwargs)
+    save_model(model, optimizer, scheduler, model_disc, optimizer_disc,
+               scheduler_disc, current_step, epoch, checkpoint_path, **kwargs)
 
 
-def save_best_model(target_loss, best_loss, model, optimizer, model_disc,
-                    optimizer_disc, current_step, epoch, output_folder,
-                    **kwargs):
+def save_best_model(target_loss, best_loss, model, optimizer, scheduler,
+                    model_disc, optimizer_disc, scheduler_disc, current_step,
+                    epoch, output_folder, **kwargs):
     if target_loss < best_loss:
         file_name = 'best_model.pth.tar'
         checkpoint_path = os.path.join(output_folder, file_name)
         print(" > BEST MODEL : {}".format(checkpoint_path))
         save_model(model,
                    optimizer,
+                   scheduler,
                    model_disc,
                    optimizer_disc,
+                   scheduler_disc,
                    current_step,
                    epoch,
                    checkpoint_path,

From a1368e3eb741f9e4c9448fbab0b8bd936786ac02 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 9 Jun 2020 23:05:02 +0200
Subject: [PATCH 070/185] multiband melga nconfig

---
 vocoder/configs/multiband_melgan_config.json | 150 +++++++++++++++++++
 1 file changed, 150 insertions(+)
 create mode 100644 vocoder/configs/multiband_melgan_config.json

diff --git a/vocoder/configs/multiband_melgan_config.json b/vocoder/configs/multiband_melgan_config.json
new file mode 100644
index 00000000..3a5f0673
--- /dev/null
+++ b/vocoder/configs/multiband_melgan_config.json
@@ -0,0 +1,150 @@
+{
+    "run_name": "multiband-melgan",
+    "run_description": "multiband melgan",
+
+    // AUDIO PARAMETERS
+    "audio":{
+        // stft parameters
+        "num_freq": 513,         // number of stft frequency levels. Size of the linear spectogram frame.
+        "win_length": 1024,      // stft window length in ms.
+        "hop_length": 256,       // stft window hop-lengh in ms.
+        "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
+        "frame_shift_ms": null,  // stft window hop-lengh in ms. If null, 'hop_length' is used.
+
+        // Audio processing parameters
+        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
+        "preemphasis": 0.0,     // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
+        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
+
+        // Silence trimming
+        "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
+        "trim_db": 60,          // threshold for timming silence. Set this according to your dataset.
+
+        // Griffin-Lim
+        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
+        "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
+
+        // MelSpectrogram parameters
+        "num_mels": 80,         // size of the mel spec frame.
+        "mel_fmin": 0.0,        // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
+        "mel_fmax": 8000.0,     // maximum freq level for mel-spec. Tune for dataset!!
+
+        // Normalization parameters
+        "signal_norm": true,    // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
+        "min_level_db": -100,   // lower bound for normalization
+        "symmetric_norm": true, // move normalization to range [-1, 1]
+        "max_norm": 4.0,        // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
+        "clip_norm": true,      // clip normalized values into the range.
+        "stats_path": null    // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
+    },
+
+    // DISTRIBUTED TRAINING
+    // "distributed":{
+    //     "backend": "nccl",
+    //     "url": "tcp:\/\/localhost:54321"
+    // },
+
+    // MODEL PARAMETERS
+    "use_pqmf": true,
+
+    // LOSS PARAMETERS
+    "use_stft_loss": true,
+    "use_subband_stft_loss": true,
+    "use_mse_gan_loss": true,
+    "use_hinge_gan_loss": false,
+    "use_feat_match_loss": false,  // use only with melgan discriminators
+
+    // loss weights
+    "stft_loss_weight": 0.5,
+    "subband_stft_loss_weight": 0.5,
+    "mse_G_loss_weight": 2.5,
+    "hinge_G_loss_weight": 2.5,
+    "feat_match_loss_weight": 25,
+
+    // multiscale stft loss parameters
+    "stft_loss_params": {
+        "n_ffts": [1024, 2048, 512],
+        "hop_lengths": [120, 240, 50],
+        "win_lengths": [600, 1200, 240]
+    },
+
+    // subband multiscale stft loss parameters
+    "subband_stft_loss_params":{
+        "n_ffts": [384, 683, 171],
+        "hop_lengths": [30, 60, 10],
+        "win_lengths": [150, 300, 60]
+    },
+
+    "target_loss": "avg_G_loss",  // loss value to pick the best model to save after each epoch
+
+    // DISCRIMINATOR
+    "discriminator_model": "melgan_multiscale_discriminator",
+    "discriminator_model_params":{
+        "base_channels": 16,
+        "max_channels":1024,
+        "downsample_factors":[4, 4, 4, 4]
+    },
+    "steps_to_start_discriminator": 200000,      // steps required to start GAN trainining.1
+
+    // GENERATOR
+    "generator_model": "multiband_melgan_generator",
+    "generator_model_params": {
+        "upsample_factors":[8, 4, 2],
+        "num_res_blocks": 4
+    },
+
+    // DATASET
+    "data_path": "/home/erogol/Data/LJSpeech-1.1/wavs/",
+    "seq_len": 16384,
+    "pad_short": 2000,
+    "conv_pad": 0,
+    "use_noise_augment": false,
+    "use_cache": true,
+
+    "reinit_layers": [],    // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
+
+    // TRAINING
+    "batch_size": 64,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
+
+    // VALIDATION
+    "run_eval": true,
+    "test_delay_epochs": 10,  //Until attention is aligned, testing only wastes computation time.
+    "test_sentences_file": null,  // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
+
+    // OPTIMIZER
+    "noam_schedule": false,        // use noam warmup and lr schedule.
+    "warmup_steps_gen": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
+    "warmup_steps_disc": 4000,
+    "epochs": 10000,                // total number of epochs to train.
+    "wd": 0.0,                // Weight decay weight.
+    "gen_clip_grad": -1,      // Generator gradient clipping threshold. Apply gradient clipping if > 0
+    "disc_clip_grad": -1,     // Discriminator gradient clipping threshold.
+    "lr_scheduler_gen": "MultiStepLR",   // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
+    "lr_scheduler_gen_params": {
+        "gamma": 0.5,
+        "milestones": [100000, 200000, 300000, 400000, 500000, 600000]
+    },
+    "lr_scheduler_disc": "MultiStepLR",   // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
+    "lr_scheduler_disc_params": {
+        "gamma": 0.5,
+        "milestones": [100000, 200000, 300000, 400000, 500000, 600000]
+    },
+    "lr_gen": 1e-4,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
+    "lr_disc": 1e-4,
+
+    // TENSORBOARD and LOGGING
+    "print_step": 25,       // Number of steps to log traning on console.
+    "print_eval": false,     // If True, it prints loss values for each step in eval run.
+    "save_step": 25000,      // Number of training steps expected to plot training stats on TB and save model checkpoints.
+    "checkpoint": true,     // If true, it saves checkpoints per "save_step"
+    "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
+
+    // DATA LOADING
+    "num_loader_workers": 4,        // number of training data loader processes. Don't set it too big. 4-8 are good values.
+    "num_val_loader_workers": 4,    // number of evaluation data loader processes.
+    "eval_split_size": 10,
+
+    // PATHS
+    "output_path": "/home/erogol/Models/LJSpeech/"
+}
+

From 6d2cda97c8709f4697b03fb040f0b8afcb91c6ad Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 9 Jun 2020 23:14:10 +0200
Subject: [PATCH 071/185] q

---
 .../multiband-melgan_and_rwd_config.json      | 52 +++++++++++--------
 vocoder/train.py                              |  2 +-
 2 files changed, 31 insertions(+), 23 deletions(-)

diff --git a/vocoder/configs/multiband-melgan_and_rwd_config.json b/vocoder/configs/multiband-melgan_and_rwd_config.json
index f4b91aae..0b751854 100644
--- a/vocoder/configs/multiband-melgan_and_rwd_config.json
+++ b/vocoder/configs/multiband-melgan_and_rwd_config.json
@@ -1,6 +1,6 @@
 {
     "run_name": "multiband-melgan-rwd",
-    "run_description": "multibadn melgan with random window discriminator",
+    "run_description": "multiband melgan with random window discriminator from https://arxiv.org/pdf/1909.11646.pdf",
 
     // AUDIO PARAMETERS
     "audio":{
@@ -54,33 +54,30 @@
     "use_hinge_gan_loss": false,
     "use_feat_match_loss": false,  // use only with melgan discriminators
 
+    // loss weights
     "stft_loss_weight": 0.5,
     "subband_stft_loss_weight": 0.5,
     "mse_G_loss_weight": 2.5,
     "hinge_G_loss_weight": 2.5,
-    "feat_match_loss_weight": 25.0,
+    "feat_match_loss_weight": 25,
 
+    // multiscale stft loss parameters
     "stft_loss_params": {
         "n_ffts": [1024, 2048, 512],
         "hop_lengths": [120, 240, 50],
         "win_lengths": [600, 1200, 240]
     },
+
+    // subband multiscale stft loss parameters
     "subband_stft_loss_params":{
         "n_ffts": [384, 683, 171],
         "hop_lengths": [30, 60, 10],
         "win_lengths": [150, 300, 60]
     },
-    "target_loss": "avg_G_loss",
+
+    "target_loss": "avg_G_loss",  // loss value to pick the best model to save after each epoch
 
     // DISCRIMINATOR
-    // "discriminator_model": "melgan_multiscale_discriminator",
-    // "discriminator_model_params":{
-    //     "base_channels": 16,
-    //     "max_channels":1024,
-    //     "downsample_factors":[4, 4, 4, 4]
-    // },
-    "steps_to_start_discriminator": 200000,      // steps required to start GAN trainining.1
-
     "discriminator_model": "random_window_discriminator",
     "discriminator_model_params":{
         "uncond_disc_donwsample_factors": [8, 4],
@@ -88,6 +85,7 @@
         "cond_disc_out_channels": [[128, 128, 256, 256], [128, 256, 256], [128, 256], [256], [128, 256]],
         "window_sizes": [512, 1024, 2048, 4096, 8192]
     },
+    "steps_to_start_discriminator": 200000,      // steps required to start GAN trainining.1
 
     // GENERATOR
     "generator_model": "multiband_melgan_generator",
@@ -97,11 +95,11 @@
     },
 
     // DATASET
-    "data_path": "/root/LJSpeech-1.1/wavs/",
+    "data_path": "/home/erogol/Data/LJSpeech-1.1/wavs/",
     "seq_len": 16384,
     "pad_short": 2000,
     "conv_pad": 0,
-    "use_noise_augment": true,
+    "use_noise_augment": false,
     "use_cache": true,
 
     "reinit_layers": [],    // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
@@ -118,17 +116,27 @@
     "noam_schedule": false,        // use noam warmup and lr schedule.
     "warmup_steps_gen": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
     "warmup_steps_disc": 4000,
-    "epochs": 100000,                // total number of epochs to train.
-    "wd": 0.000001,                // Weight decay weight.
-    "lr_gen": 0.0001,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
-    "lr_disc": 0.0001,
-    "gen_clip_grad": 10.0,
-    "disc_clip_grad": 10.0,
+    "epochs": 10000,                // total number of epochs to train.
+    "wd": 0.0,                // Weight decay weight.
+    "gen_clip_grad": -1,      // Generator gradient clipping threshold. Apply gradient clipping if > 0
+    "disc_clip_grad": -1,     // Discriminator gradient clipping threshold.
+    "lr_scheduler_gen": "MultiStepLR",   // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
+    "lr_scheduler_gen_params": {
+        "gamma": 0.5,
+        "milestones": [100000, 200000, 300000, 400000, 500000, 600000]
+    },
+    "lr_scheduler_disc": "MultiStepLR",   // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
+    "lr_scheduler_disc_params": {
+        "gamma": 0.5,
+        "milestones": [100000, 200000, 300000, 400000, 500000, 600000]
+    },
+    "lr_gen": 1e-4,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
+    "lr_disc": 1e-4,
 
     // TENSORBOARD and LOGGING
     "print_step": 25,       // Number of steps to log traning on console.
-    "print_eval": false,     // If True, it prints intermediate loss values in evalulation.
-    "save_step": 10000,      // Number of training steps expected to save traninpg stats and checkpoints.
+    "print_eval": false,     // If True, it prints loss values for each step in eval run.
+    "save_step": 25000,      // Number of training steps expected to plot training stats on TB and save model checkpoints.
     "checkpoint": true,     // If true, it saves checkpoints per "save_step"
     "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
 
@@ -138,6 +146,6 @@
     "eval_split_size": 10,
 
     // PATHS
-    "output_path": "/data/rw/home/Trainings/"
+    "output_path": "/home/erogol/Models/LJSpeech/"
 }
 
diff --git a/vocoder/train.py b/vocoder/train.py
index c563dff0..a8a8f011 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -215,7 +215,7 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
                 torch.nn.utils.clip_grad_norm_(model_D.parameters(),
                                                c.disc_clip_grad)
             optimizer_D.step()
-            if c.scheduler_D is not None:
+            if scheduler_D is not None:
                 scheduler_D.step()
 
             for key, value in loss_D_dict.items():

From 53f13461b921f861d5b07f2de28892e4704b96e1 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 10 Jun 2020 13:45:43 +0200
Subject: [PATCH 072/185] better stats logging for TTS training

---
 train.py | 132 +++++++++++++++++++------------------------------------
 1 file changed, 45 insertions(+), 87 deletions(-)

diff --git a/train.py b/train.py
index 869557e6..51c73a9f 100644
--- a/train.py
+++ b/train.py
@@ -119,21 +119,7 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
                                verbose=(epoch == 0))
     model.train()
     epoch_time = 0
-    train_values = {
-        'avg_postnet_loss': 0,
-        'avg_decoder_loss': 0,
-        'avg_stopnet_loss': 0,
-        'avg_align_error': 0,
-        'avg_step_time': 0,
-        'avg_loader_time': 0
-    }
-    if c.bidirectional_decoder:
-        train_values['avg_decoder_b_loss'] = 0  # decoder backward loss
-        train_values['avg_decoder_c_loss'] = 0  # decoder consistency loss
-    if c.ga_alpha > 0:
-        train_values['avg_ga_loss'] = 0  # guidede attention loss
     keep_avg = KeepAverage()
-    keep_avg.add_values(train_values)
     if use_cuda:
         batch_n_iter = int(
             len(data_loader.dataset) / (c.batch_size * num_gpus))
@@ -179,11 +165,6 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
                               mel_lengths, decoder_backward_output,
                               alignments, alignment_lengths, alignments_backward,
                               text_lengths)
-        if c.bidirectional_decoder:
-            keep_avg.update_values({'avg_decoder_b_loss': loss_dict['decoder_backward_loss'].item(),
-                                    'avg_decoder_c_loss': loss_dict['decoder_c_loss'].item()})
-        if c.ga_alpha > 0:
-            keep_avg.update_values({'avg_ga_loss': loss_dict['ga_loss'].item()})
 
         # backward pass
         loss_dict['loss'].backward()
@@ -193,7 +174,6 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
 
         # compute alignment error (the lower the better )
         align_error = 1 - alignment_diagonal_score(alignments)
-        keep_avg.update_value('avg_align_error', align_error)
         loss_dict['align_error'] = align_error
 
         # backpass and check the grad norm for stop loss
@@ -208,23 +188,6 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
         step_time = time.time() - start_time
         epoch_time += step_time
 
-        # update avg stats
-        update_train_values = {
-            'avg_postnet_loss': float(loss_dict['postnet_loss'].item()),
-            'avg_decoder_loss': float(loss_dict['decoder_loss'].item()),
-            'avg_stopnet_loss': loss_dict['stopnet_loss'].item() \
-                if isinstance(loss_dict['stopnet_loss'], float) else float(loss_dict['stopnet_loss'].item()),
-            'avg_step_time': step_time,
-            'avg_loader_time': loader_time
-        }
-        keep_avg.update_values(update_train_values)
-
-        if global_step % c.print_step == 0:
-            c_logger.print_train_step(batch_n_iter, num_iter, global_step,
-                                      avg_spec_length, avg_text_length,
-                                      step_time, loader_time, current_lr,
-                                      loss_dict, keep_avg.avg_values)
-
         # aggregate losses from processes
         if num_gpus > 1:
             loss_dict['postnet_loss'] = reduce_tensor(loss_dict['postnet_loss'].data, num_gpus)
@@ -232,6 +195,30 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
             loss_dict['loss'] = reduce_tensor(loss_dict['loss'] .data, num_gpus)
             loss_dict['stopnet_loss'] = reduce_tensor(loss_dict['stopnet_loss'].data, num_gpus) if c.stopnet else loss_dict['stopnet_loss']
 
+        # detach loss values
+        loss_dict_new = dict()
+        for key, value in loss_dict.items():
+            if isinstance(value, int) or isinstance(value, float):
+                loss_dict_new[key] = value
+            else:
+                loss_dict_new[key] = value.item()
+        loss_dict = loss_dict_new
+
+        # update avg stats
+        update_train_values = dict()
+        for key, value in loss_dict.items():
+            update_train_values['avg_' + key] = value
+        update_train_values['avg_loader_time'] = loader_time
+        update_train_values['avg_step_time'] = step_time
+        keep_avg.update_values(update_train_values)
+
+        # print training progress
+        if global_step % c.print_step == 0:
+            c_logger.print_train_step(batch_n_iter, num_iter, global_step,
+                                      avg_spec_length, avg_text_length,
+                                      step_time, loader_time, current_lr,
+                                      loss_dict, keep_avg.avg_values)
+
         if args.rank == 0:
             # Plot Training Iter Stats
             # reduce TB load
@@ -266,7 +253,7 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
                     "alignment": plot_alignment(align_img),
                 }
 
-                if c.bidirectional_decoder:
+                if c.bidirectional_decoder or c.double_decoder_consistency:
                     figures["alignment_backward"] = plot_alignment(alignments_backward[0].data.cpu().numpy())
 
                 tb_logger.tb_train_figures(global_step, figures)
@@ -286,16 +273,8 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
 
     # Plot Epoch Stats
     if args.rank == 0:
-        # Plot Training Epoch Stats
-        epoch_stats = {
-            "loss_postnet": keep_avg['avg_postnet_loss'],
-            "loss_decoder": keep_avg['avg_decoder_loss'],
-            "stopnet_loss": keep_avg['avg_stopnet_loss'],
-            "alignment_error": keep_avg['avg_align_error'],
-            "epoch_time": epoch_time
-        }
-        if c.ga_alpha > 0:
-            epoch_stats['guided_attention_loss'] = keep_avg['avg_ga_loss']
+        epoch_stats = {"epoch_time": epoch_time}
+        epoch_stats.update(keep_avg.avg_values)
         tb_logger.tb_train_epoch_stats(global_step, epoch_stats)
         if c.tb_model_param_stats:
             tb_logger.tb_model_weights(model, global_step)
@@ -307,20 +286,7 @@ def evaluate(model, criterion, ap, global_step, epoch):
     data_loader = setup_loader(ap, model.decoder.r, is_val=True)
     model.eval()
     epoch_time = 0
-    eval_values_dict = {
-        'avg_postnet_loss': 0,
-        'avg_decoder_loss': 0,
-        'avg_stopnet_loss': 0,
-        'avg_align_error': 0
-    }
-    if c.bidirectional_decoder:
-        eval_values_dict['avg_decoder_b_loss'] = 0  # decoder backward loss
-        eval_values_dict['avg_decoder_c_loss'] = 0  # decoder consistency loss
-    if c.ga_alpha > 0:
-        eval_values_dict['avg_ga_loss'] = 0  # guidede attention loss
     keep_avg = KeepAverage()
-    keep_avg.add_values(eval_values_dict)
-
     c_logger.print_eval_start()
     if data_loader is not None:
         for num_iter, data in enumerate(data_loader):
@@ -352,11 +318,6 @@ def evaluate(model, criterion, ap, global_step, epoch):
                                   mel_lengths, decoder_backward_output,
                                   alignments, alignment_lengths, alignments_backward,
                                   text_lengths)
-            if c.bidirectional_decoder:
-                keep_avg.update_values({'avg_decoder_b_loss': loss_dict['decoder_b_loss'].item(),
-                                        'avg_decoder_c_loss': loss_dict['decoder_c_loss'].item()})
-            if c.ga_alpha > 0:
-                keep_avg.update_values({'avg_ga_loss': loss_dict['ga_loss'].item()})
 
             # step time
             step_time = time.time() - start_time
@@ -364,7 +325,7 @@ def evaluate(model, criterion, ap, global_step, epoch):
 
             # compute alignment score
             align_error = 1 - alignment_diagonal_score(alignments)
-            keep_avg.update_value('avg_align_error', align_error)
+            loss_dict['align_error'] = align_error
 
             # aggregate losses from processes
             if num_gpus > 1:
@@ -373,14 +334,20 @@ def evaluate(model, criterion, ap, global_step, epoch):
                 if c.stopnet:
                     loss_dict['stopnet_loss'] = reduce_tensor(loss_dict['stopnet_loss'].data, num_gpus)
 
-            keep_avg.update_values({
-                'avg_postnet_loss':
-                float(loss_dict['postnet_loss'].item()),
-                'avg_decoder_loss':
-                float(loss_dict['decoder_loss'].item()),
-                'avg_stopnet_loss':
-                float(loss_dict['stopnet_loss'].item()),
-            })
+             # detach loss values
+            loss_dict_new = dict()
+            for key, value in loss_dict.items():
+                if isinstance(value, int) or isinstance(value, float):
+                    loss_dict_new[key] = value
+                else:
+                    loss_dict_new[key] = value.item()
+            loss_dict = loss_dict_new
+
+            # update avg stats
+            update_train_values = dict()
+            for key, value in loss_dict.items():
+                update_train_values['avg_' + key] = value
+            keep_avg.update_values(update_train_values)
 
             if c.print_eval:
                 c_logger.print_eval_step(num_iter, loss_dict, keep_avg.avg_values)
@@ -409,20 +376,11 @@ def evaluate(model, criterion, ap, global_step, epoch):
                                      c.audio["sample_rate"])
 
             # Plot Validation Stats
-            epoch_stats = {
-                "loss_postnet": keep_avg['avg_postnet_loss'],
-                "loss_decoder": keep_avg['avg_decoder_loss'],
-                "stopnet_loss": keep_avg['avg_stopnet_loss'],
-                "alignment_error": keep_avg['avg_align_error'],
-            }
 
-            if c.bidirectional_decoder:
-                epoch_stats['loss_decoder_backward'] = keep_avg['avg_decoder_b_loss']
+            if c.bidirectional_decoder or c.double_decoder_consistency:
                 align_b_img = alignments_backward[idx].data.cpu().numpy()
-                eval_figures['alignment_backward'] = plot_alignment(align_b_img)
-            if c.ga_alpha > 0:
-                epoch_stats['guided_attention_loss'] = keep_avg['avg_ga_loss']
-            tb_logger.tb_eval_stats(global_step, epoch_stats)
+                eval_figures['alignment2'] = plot_alignment(align_b_img)
+            tb_logger.tb_eval_stats(global_step, keep_avg.avg_values)
             tb_logger.tb_eval_figures(global_step, eval_figures)
 
     if args.rank == 0 and epoch > c.test_delay_epochs:

From 1a061c4af5a407590d922cb809906670c7510a2c Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 10 Jun 2020 13:51:59 +0200
Subject: [PATCH 073/185] new config argument to plot tb figures at training

---
 config.json | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/config.json b/config.json
index 5e10c535..53b12fe1 100644
--- a/config.json
+++ b/config.json
@@ -104,7 +104,8 @@
     "separate_stopnet": true,      // Train stopnet seperately if 'stopnet==true'. It prevents stopnet loss to influence the rest of the model. It causes a better model, but it trains SLOWER.
 
     // TENSORBOARD and LOGGING
-    "print_step": 25,       // Number of steps to log traning on console.
+    "print_step": 25,       // Number of steps to log training on console.
+    "tb_plot_step:": 100,    // Number of steps to plot TB training figures.
     "print_eval": false,     // If True, it prints intermediate loss values in evalulation.
     "save_step": 10000,      // Number of training steps expected to save traninpg stats and checkpoints.
     "checkpoint": true,     // If true, it saves checkpoints per "save_step"

From c24b57452da92f4e6bb819b38d218ed5861c5267 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 10 Jun 2020 13:52:31 +0200
Subject: [PATCH 074/185] bug fix

---
 train.py               | 5 ++---
 utils/generic_utils.py | 1 +
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/train.py b/train.py
index 51c73a9f..02f28c1d 100644
--- a/train.py
+++ b/train.py
@@ -222,15 +222,14 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
         if args.rank == 0:
             # Plot Training Iter Stats
             # reduce TB load
-            if global_step % 10 == 0:
+            if global_step % c.tb_plot_step == 0:
                 iter_stats = {
-                    "loss_posnet": loss_dict['postnet_loss'].item(),
-                    "loss_decoder": loss_dict['decoder_loss'].item(),
                     "lr": current_lr,
                     "grad_norm": grad_norm,
                     "grad_norm_st": grad_norm_st,
                     "step_time": step_time
                 }
+                iter_stats.update(loss_dict)
                 tb_logger.tb_train_iter_stats(global_step, iter_stats)
 
             if global_step % c.save_step == 0:
diff --git a/utils/generic_utils.py b/utils/generic_utils.py
index eb6d21a9..a6398771 100644
--- a/utils/generic_utils.py
+++ b/utils/generic_utils.py
@@ -327,6 +327,7 @@ def check_config(c):
 
     # tensorboard
     _check_argument('print_step', c, restricted=True, val_type=int, min_val=1)
+    _check_argument('tb_plot_step', c, restricted=True, val_type=int, min_val=1)
     _check_argument('save_step', c, restricted=True, val_type=int, min_val=1)
     _check_argument('checkpoint', c, restricted=True, val_type=bool)
     _check_argument('tb_model_param_stats', c, restricted=True, val_type=bool)

From 99420a2d9bbd91ac3ef1fe07a0b9b6af4fd40aea Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 10 Jun 2020 13:55:37 +0200
Subject: [PATCH 075/185] restore vocoder scheduler if it is in checkpoint

---
 vocoder/train.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/vocoder/train.py b/vocoder/train.py
index a8a8f011..091a6932 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -450,10 +450,14 @@ def main(args):  # pylint: disable=redefined-outer-name
                            weight_decay=0)
 
     # schedulers
-    scheduler_gen = getattr(torch.optim.lr_scheduler, c.lr_scheduler_gen)
-    scheduler_disc = getattr(torch.optim.lr_scheduler, c.lr_scheduler_disc)
-    scheduler_gen = scheduler_gen(optimizer_gen, **c.lr_scheduler_gen_params)
-    scheduler_disc = scheduler_disc(optimizer_disc, **c.lr_scheduler_disc_params)
+    scheduler_gen = None
+    scheduler_disc = None
+    if 'lr_scheduler_gen' in c:
+        scheduler_gen = getattr(torch.optim.lr_scheduler, c.lr_scheduler_gen)
+        scheduler_gen = scheduler_gen(optimizer_gen, **c.lr_scheduler_gen_params)
+    if 'lr_scheduler_disc' in c:
+        scheduler_disc = getattr(torch.optim.lr_scheduler, c.lr_scheduler_disc)
+        scheduler_disc = scheduler_disc(optimizer_disc, **c.lr_scheduler_disc_params)
 
     # setup criterion
     criterion_gen = GeneratorLoss(c)

From 6a661f98e2778befc58d1c3da229ea629abb4e15 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 10 Jun 2020 13:57:49 +0200
Subject: [PATCH 076/185] linter fix

---
 train.py         | 4 ++--
 vocoder/train.py | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/train.py b/train.py
index 02f28c1d..f6d73a4a 100644
--- a/train.py
+++ b/train.py
@@ -198,7 +198,7 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
         # detach loss values
         loss_dict_new = dict()
         for key, value in loss_dict.items():
-            if isinstance(value, int) or isinstance(value, float):
+            if isinstance(value, (int, float)):
                 loss_dict_new[key] = value
             else:
                 loss_dict_new[key] = value.item()
@@ -336,7 +336,7 @@ def evaluate(model, criterion, ap, global_step, epoch):
              # detach loss values
             loss_dict_new = dict()
             for key, value in loss_dict.items():
-                if isinstance(value, int) or isinstance(value, float):
+                if isinstance(value, (int, float)):
                     loss_dict_new[key] = value
                 else:
                     loss_dict_new[key] = value.item()
diff --git a/vocoder/train.py b/vocoder/train.py
index 091a6932..ce8f111a 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -17,7 +17,7 @@ from TTS.utils.generic_utils import (KeepAverage, count_parameters,
 from TTS.utils.io import copy_config_file, load_config
 from TTS.utils.radam import RAdam
 from TTS.utils.tensorboard_logger import TensorboardLogger
-from TTS.utils.training import setup_torch_training_env, NoamLR
+from TTS.utils.training import setup_torch_training_env
 from TTS.vocoder.datasets.gan_dataset import GANDataset
 from TTS.vocoder.datasets.preprocess import load_wav_data
 # from distribute import (DistributedSampler, apply_gradient_allreduce,

From 4d1d04a6a03a270e867c12ece86418988867bee9 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 11 Jun 2020 10:48:20 +0200
Subject: [PATCH 077/185] bug fix

---
 train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/train.py b/train.py
index f6d73a4a..8581132a 100644
--- a/train.py
+++ b/train.py
@@ -237,7 +237,7 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
                     # save model
                     save_checkpoint(model, optimizer, global_step, epoch, model.decoder.r, OUT_PATH,
                                     optimizer_st=optimizer_st,
-                                    model_loss=loss_dict['postnet_loss'].item())
+                                    model_loss=loss_dict['postnet_loss'])
 
                 # Diagnostic visualizations
                 const_spec = postnet_output[0].data.cpu().numpy()

From 92ed9b90c3de90523be1898145b99722acc80fea Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 11 Jun 2020 12:42:13 +0200
Subject: [PATCH 078/185] reformatting vocoder training and disable G_adv eval
 if step < d_training

---
 vocoder/train.py | 47 +++++++++++++++++++++++++----------------------
 1 file changed, 25 insertions(+), 22 deletions(-)

diff --git a/vocoder/train.py b/vocoder/train.py
index ce8f111a..74b3e759 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -112,7 +112,6 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
         ##############################
 
         # generator pass
-        optimizer_G.zero_grad()
         y_hat = model_G(c_G)
         y_hat_sub = None
         y_G_sub = None
@@ -121,8 +120,8 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
         # PQMF formatting
         if y_hat.shape[1] > 1:
             y_hat_sub = y_hat
-            y_hat = model_G.pqmf_synthesis(y_hat)
             y_hat_vis = y_hat
+            y_hat = model_G.pqmf_synthesis(y_hat)
             y_G_sub = model_G.pqmf_analysis(y_G)
 
         if global_step > c.steps_to_start_discriminator:
@@ -142,12 +141,11 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
             if isinstance(D_out_fake, tuple):
                 scores_fake, feats_fake = D_out_fake
                 if D_out_real is None:
-                    scores_real, feats_real = None, None
+                    feats_real = None
                 else:
-                    scores_real, feats_real = D_out_real
+                    _, feats_real = D_out_real
             else:
                 scores_fake = D_out_fake
-                scores_real = D_out_real
         else:
             scores_fake, feats_fake, feats_real = None, None, None
 
@@ -157,6 +155,7 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
         loss_G = loss_G_dict['G_loss']
 
         # optimizer generator
+        optimizer_G.zero_grad()
         loss_G.backward()
         if c.gen_clip_grad > 0:
             torch.nn.utils.clip_grad_norm_(model_G.parameters(),
@@ -184,8 +183,6 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
             if y_hat.shape[1] > 1:
                 y_hat = model_G.pqmf_synthesis(y_hat)
 
-            optimizer_D.zero_grad()
-
             # run D with or without cond. features
             if len(signature(model_D.forward).parameters) == 2:
                 D_out_fake = model_D(y_hat.detach(), c_D)
@@ -210,6 +207,7 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
             loss_D = loss_D_dict['D_loss']
 
             # optimizer discriminator
+            optimizer_D.zero_grad()
             loss_D.backward()
             if c.disc_clip_grad > 0:
                 torch.nn.utils.clip_grad_norm_(model_D.parameters(),
@@ -326,25 +324,30 @@ def evaluate(model_G, criterion_G, model_D, criterion_D, ap, global_step, epoch)
             y_hat = model_G.pqmf_synthesis(y_hat)
             y_G_sub = model_G.pqmf_analysis(y_G)
 
-        if len(signature(model_D.forward).parameters) == 2:
-            D_out_fake = model_D(y_hat, c_G)
-        else:
-            D_out_fake = model_D(y_hat)
-        D_out_real = None
 
-        if c.use_feat_match_loss:
-            with torch.no_grad():
-                D_out_real = model_D(y_G)
+        if global_step > c.steps_to_start_discriminator:
 
-        # format D outputs
-        if isinstance(D_out_fake, tuple):
-            scores_fake, feats_fake = D_out_fake
-            if D_out_real is None:
-                feats_real = None
+            if len(signature(model_D.forward).parameters) == 2:
+                D_out_fake = model_D(y_hat, c_G)
             else:
-                _, feats_real = D_out_real
+                D_out_fake = model_D(y_hat)
+            D_out_real = None
+
+            if c.use_feat_match_loss:
+                with torch.no_grad():
+                    D_out_real = model_D(y_G)
+
+            # format D outputs
+            if isinstance(D_out_fake, tuple):
+                scores_fake, feats_fake = D_out_fake
+                if D_out_real is None:
+                    feats_real = None
+                else:
+                    _, feats_real = D_out_real
+            else:
+                scores_fake = D_out_fake
         else:
-            scores_fake = D_out_fake
+            scores_fake, feats_fake, feats_real = None, None, None
 
         # compute losses
         loss_G_dict = criterion_G(y_hat, y_G, scores_fake, feats_fake,

From 7e799d58d6b8e7c56967c54f6f45c7b3d599a660 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 11 Jun 2020 15:34:38 +0200
Subject: [PATCH 079/185] bug fix

---
 utils/generic_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/utils/generic_utils.py b/utils/generic_utils.py
index 298f5970..e6466e0c 100644
--- a/utils/generic_utils.py
+++ b/utils/generic_utils.py
@@ -75,7 +75,7 @@ def split_dataset(items):
     is_multi_speaker = len(set(speakers)) > 1
     eval_split_size = 500 if len(items) * 0.01 > 500 else int(
         len(items) * 0.01)
-    assert len(eval_split_size) > 0, " [!] You do not have enough samples to train. You need at least 100 samples."
+    assert eval_split_size > 0, " [!] You do not have enough samples to train. You need at least 100 samples."
     np.random.seed(0)
     np.random.shuffle(items)
     if is_multi_speaker:

From 1c2dc9f73909590aed91734cc1ea0a83ee1fb97c Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 12 Jun 2020 11:12:57 +0200
Subject: [PATCH 080/185] enable loading precomputed vocoder dataset

---
 vocoder/datasets/gan_dataset.py | 25 +++++++++++++++++++------
 vocoder/datasets/preprocess.py  | 21 +++++++++++++++++++++
 vocoder/train.py                |  4 ++--
 3 files changed, 42 insertions(+), 8 deletions(-)

diff --git a/vocoder/datasets/gan_dataset.py b/vocoder/datasets/gan_dataset.py
index 5e410151..55513e7d 100644
--- a/vocoder/datasets/gan_dataset.py
+++ b/vocoder/datasets/gan_dataset.py
@@ -28,6 +28,7 @@ class GANDataset(Dataset):
 
         self.ap = ap
         self.item_list = items
+        self.compute_feat = not isinstance(items[0], (tuple, list))
         self.seq_len = seq_len
         self.hop_len = hop_len
         self.pad_short = pad_short
@@ -77,14 +78,26 @@ class GANDataset(Dataset):
 
     def load_item(self, idx):
         """ load (audio, feat) couple """
-        wavpath = self.item_list[idx]
-        # print(wavpath)
+        if self.compute_feat:
+            # compute features from wav
+            wavpath = self.item_list[idx]
+            # print(wavpath)
 
-        if self.use_cache and self.cache[idx] is not None:
-            audio, mel = self.cache[idx]
+            if self.use_cache and self.cache[idx] is not None:
+                audio, mel = self.cache[idx]
+            else:
+                audio = self.ap.load_wav(wavpath)
+                mel = self.ap.melspectrogram(audio)
         else:
-            audio = self.ap.load_wav(wavpath)
-            mel = self.ap.melspectrogram(audio)
+
+            # load precomputed features
+            wavpath, feat_path = self.item_list[idx]
+
+            if self.use_cache and self.cache[idx] is not None:
+                audio, mel = self.cache[idx]
+            else:
+                audio = self.ap.load_wav(wavpath)
+                mel = np.load(feat_path)
 
         if len(audio) < self.seq_len + self.pad_short:
             audio = np.pad(audio, (0, self.seq_len + self.pad_short - len(audio)), \
diff --git a/vocoder/datasets/preprocess.py b/vocoder/datasets/preprocess.py
index 01e01e3e..be60c13a 100644
--- a/vocoder/datasets/preprocess.py
+++ b/vocoder/datasets/preprocess.py
@@ -1,5 +1,6 @@
 import glob
 import os
+from pathlib import Path
 
 import numpy as np
 
@@ -9,8 +10,28 @@ def find_wav_files(data_path):
     return wav_paths
 
 
+def find_feat_files(data_path):
+    feat_paths = glob.glob(os.path.join(data_path, '**', '*.npy'), recursive=True)
+    return feat_paths
+
+
 def load_wav_data(data_path, eval_split_size):
     wav_paths = find_wav_files(data_path)
     np.random.seed(0)
     np.random.shuffle(wav_paths)
     return wav_paths[:eval_split_size], wav_paths[eval_split_size:]
+
+
+def load_wav_feat_data(data_path, feat_path, eval_split_size):
+    wav_paths = sorted(find_wav_files(data_path))
+    feat_paths = sorted(find_feat_files(feat_path))
+    assert len(wav_paths) == len(feat_paths)
+    for wav, feat in zip(wav_paths, feat_paths):
+        wav_name = Path(wav).stem
+        feat_name = Path(feat).stem
+        assert wav_name == feat_name
+
+    items = list(zip(wav_paths, feat_paths))
+    np.random.seed(0)
+    np.random.shuffle(items)
+    return items[:eval_split_size], items[eval_split_size:]
diff --git a/vocoder/train.py b/vocoder/train.py
index 74b3e759..4d1b1029 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -19,7 +19,7 @@ from TTS.utils.radam import RAdam
 from TTS.utils.tensorboard_logger import TensorboardLogger
 from TTS.utils.training import setup_torch_training_env
 from TTS.vocoder.datasets.gan_dataset import GANDataset
-from TTS.vocoder.datasets.preprocess import load_wav_data
+from TTS.vocoder.datasets.preprocess import load_wav_data, load_wav_feat_data
 # from distribute import (DistributedSampler, apply_gradient_allreduce,
 #                         init_distributed, reduce_tensor)
 from TTS.vocoder.layers.losses import DiscriminatorLoss, GeneratorLoss
@@ -543,8 +543,8 @@ def main(args):  # pylint: disable=redefined-outer-name
         best_loss = save_best_model(target_loss,
                                     best_loss,
                                     model_gen,
-                                    scheduler_gen,
                                     optimizer_gen,
+                                    scheduler_gen,
                                     model_disc,
                                     optimizer_disc,
                                     scheduler_disc,

From 9af6bb0c297511c31973c898a8cd4da263a70849 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 12 Jun 2020 11:37:32 +0200
Subject: [PATCH 081/185] bug fix

---
 vocoder/utils/console_logger.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vocoder/utils/console_logger.py b/vocoder/utils/console_logger.py
index 50882160..4f44986c 100644
--- a/vocoder/utils/console_logger.py
+++ b/vocoder/utils/console_logger.py
@@ -84,7 +84,7 @@ class ConsoleLogger():
             color = ''
             sign = '+'
             diff = 0
-            if self.old_eval_loss_dict is not None:
+            if self.old_eval_loss_dict is not None and key in self.old_eval_loss_dict:
                 diff = value - self.old_eval_loss_dict[key]
                 if diff < 0:
                     color = tcolors.OKGREEN

From 546186bbf1f62c2d36c982eb01d85bbdd8f09ec9 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 12 Jun 2020 11:54:54 +0200
Subject: [PATCH 082/185] visualization bug fix

---
 vocoder/train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vocoder/train.py b/vocoder/train.py
index 4d1b1029..326bd90e 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -120,8 +120,8 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
         # PQMF formatting
         if y_hat.shape[1] > 1:
             y_hat_sub = y_hat
-            y_hat_vis = y_hat
             y_hat = model_G.pqmf_synthesis(y_hat)
+            y_hat_vis = y_hat
             y_G_sub = model_G.pqmf_analysis(y_G)
 
         if global_step > c.steps_to_start_discriminator:

From 0b78977662bfa9d59da4c2f9ce8d4647e093c3db Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 12 Jun 2020 12:11:23 +0200
Subject: [PATCH 083/185] more comments to audio.py

---
 utils/audio.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/utils/audio.py b/utils/audio.py
index 13eab3d6..f941f609 100644
--- a/utils/audio.py
+++ b/utils/audio.py
@@ -57,8 +57,10 @@ class AudioProcessor(object):
         self.stats_path = stats_path
         # setup stft parameters
         if hop_length is None:
+            # compute stft parameters from given time values
             self.n_fft, self.hop_length, self.win_length = self._stft_parameters()
         else:
+            # use stft parameters from config file
             self.hop_length = hop_length
             self.win_length = win_length
             self.n_fft = (self.num_freq - 1) * 2

From 3eb730acf0a660d96f3af78cde0f5379b72fb3c6 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 12 Jun 2020 19:32:49 +0200
Subject: [PATCH 084/185] update vocoder loss implemenatations and fix MSEDLoss

---
 vocoder/layers/losses.py | 19 ++++++++++++++-----
 1 file changed, 14 insertions(+), 5 deletions(-)

diff --git a/vocoder/layers/losses.py b/vocoder/layers/losses.py
index a24b129c..4774ab4e 100644
--- a/vocoder/layers/losses.py
+++ b/vocoder/layers/losses.py
@@ -88,7 +88,7 @@ class MSEGLoss(nn.Module):
     """ Mean Squared Generator Loss """
     # pylint: disable=no-self-use
     def forward(self, score_fake):
-        loss_fake = torch.mean(torch.sum(torch.pow(score_fake, 2), dim=[1, 2]))
+        loss_fake = F.mse_loss(score_fake, score_fake.new_ones(score_fake.shape))
         return loss_fake
 
 
@@ -96,7 +96,8 @@ class HingeGLoss(nn.Module):
     """ Hinge Discriminator Loss """
     # pylint: disable=no-self-use
     def forward(self, score_fake):
-        loss_fake = torch.mean(F.relu(1. + score_fake))
+        # TODO: this might be wrong
+        loss_fake = torch.mean(F.relu(1. - score_fake))
         return loss_fake
 
 
@@ -107,10 +108,14 @@ class HingeGLoss(nn.Module):
 
 class MSEDLoss(nn.Module):
     """ Mean Squared Discriminator Loss """
+    def __init__(self,):
+        super(MSEDLoss, self).__init__()
+        self.loss_func = nn.MSELoss()
+
     # pylint: disable=no-self-use
     def forward(self, score_fake, score_real):
-        loss_real = torch.mean(torch.sum(torch.pow(score_real - 1.0, 2), dim=[1, 2]))
-        loss_fake = torch.mean(torch.sum(torch.pow(score_fake, 2), dim=[1, 2]))
+        loss_real = self.loss_func(score_real, score_real.new_ones(score_real.shape))
+        loss_fake = self.loss_func(score_fake, score_fake.new_zeros(score_fake.shape))
         loss_d = loss_real + loss_fake
         return loss_d, loss_real, loss_fake
 
@@ -126,11 +131,15 @@ class HingeDLoss(nn.Module):
 
 
 class MelganFeatureLoss(nn.Module):
+    def __init__(self,):
+        super(MelganFeatureLoss, self).__init__()
+        self.loss_func = nn.L1Loss()
+
     # pylint: disable=no-self-use
     def forward(self, fake_feats, real_feats):
         loss_feats = 0
         for fake_feat, real_feat in zip(fake_feats, real_feats):
-            loss_feats += torch.mean(torch.abs(fake_feat - real_feat))
+            loss_feats += self.loss_func(fake_feats, real_feats)
         loss_feats /= len(fake_feats) + len(real_feats)
         return loss_feats
 

From 773e9c7a72ab7fc930ad0e26cafbb660fcdb82b2 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 12 Jun 2020 19:50:07 +0200
Subject: [PATCH 085/185] remove redundant params in vocoder config file

---
 vocoder/configs/multiband_melgan_config.json | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/vocoder/configs/multiband_melgan_config.json b/vocoder/configs/multiband_melgan_config.json
index 3a5f0673..164fe037 100644
--- a/vocoder/configs/multiband_melgan_config.json
+++ b/vocoder/configs/multiband_melgan_config.json
@@ -20,9 +20,6 @@
         "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
         "trim_db": 60,          // threshold for timming silence. Set this according to your dataset.
 
-        // Griffin-Lim
-        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
-        "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
 
         // MelSpectrogram parameters
         "num_mels": 80,         // size of the mel spec frame.

From 3e5ce85bf7b4d84575632908c962151b355a6a25 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?J=C3=B6rg=20Thalheim?= <joerg@thalheim.io>
Date: Sat, 13 Jun 2020 19:34:26 +0100
Subject: [PATCH 086/185] load ParallelWaveGAN from PYTHONPATH if
 pwgan_lib_path is unset

---
 server/synthesizer.py | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/server/synthesizer.py b/server/synthesizer.py
index 392dcc4a..c6fde902 100644
--- a/server/synthesizer.py
+++ b/server/synthesizer.py
@@ -37,7 +37,7 @@ class Synthesizer(object):
         if self.config.wavernn_lib_path:
             self.load_wavernn(self.config.wavernn_lib_path, self.config.wavernn_file,
                               self.config.wavernn_config, self.config.use_cuda)
-        if self.config.pwgan_lib_path:
+        if self.config.pwgan_file:
             self.load_pwgan(self.config.pwgan_lib_path, self.config.pwgan_file,
                             self.config.pwgan_config, self.config.use_cuda)
 
@@ -113,9 +113,14 @@ class Synthesizer(object):
         self.wavernn.eval()
 
     def load_pwgan(self, lib_path, model_file, model_config, use_cuda):
-        sys.path.append(lib_path) # set this if ParallelWaveGAN is not installed globally
-        #pylint: disable=import-outside-toplevel
-        from parallel_wavegan.models import ParallelWaveGANGenerator
+        if lib_path:
+            # set this if ParallelWaveGAN is not installed globally
+            sys.path.append(lib_path)
+        try:
+            #pylint: disable=import-outside-toplevel
+            from parallel_wavegan.models import ParallelWaveGANGenerator
+        except ImportError as e:
+            raise RuntimeError(f"cannot import parallel-wavegan, either install it or set its directory using the --pwgan_lib_path command line argument: {e}")
         print(" > Loading PWGAN model ...")
         print(" | > model config: ", model_config)
         print(" | > model file: ", model_file)

From 9f83b6797f26be631d7717bcb8be53e59846f596 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Eren=20G=C3=B6lge?= <egolge@mozilla.com>
Date: Mon, 15 Jun 2020 11:41:42 +0200
Subject: [PATCH 087/185] handling librosa conflict with numba

https://github.com/librosa/librosa/issues/1160
---
 requirements_tests.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements_tests.txt b/requirements_tests.txt
index 1e0615b2..5aacdb56 100644
--- a/requirements_tests.txt
+++ b/requirements_tests.txt
@@ -1,4 +1,5 @@
 numpy>=1.16.0
+numba==0.48
 torch>=0.4.1
 tensorflow>=2.2
 librosa>=0.5.1

From a2e74c58f9c54b36586497e37150f58b7b8d9d8f Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sat, 30 May 2020 14:05:19 +0200
Subject: [PATCH 088/185] add a non-existing value to avg values

---
 utils/generic_utils.py | 19 ++++++++++++-------
 1 file changed, 12 insertions(+), 7 deletions(-)

diff --git a/utils/generic_utils.py b/utils/generic_utils.py
index e6466e0c..4a7aaef9 100644
--- a/utils/generic_utils.py
+++ b/utils/generic_utils.py
@@ -198,14 +198,19 @@ class KeepAverage():
         self.iters[name] = init_iter
 
     def update_value(self, name, value, weighted_avg=False):
-        if weighted_avg:
-            self.avg_values[name] = 0.99 * self.avg_values[name] + 0.01 * value
-            self.iters[name] += 1
+        if name not in self.avg_values:
+            # add value if not exist before
+            self.add_value(name, init_val=value)
         else:
-            self.avg_values[name] = self.avg_values[name] * \
-                self.iters[name] + value
-            self.iters[name] += 1
-            self.avg_values[name] /= self.iters[name]
+            # else update existing value
+            if weighted_avg:
+                self.avg_values[name] = 0.99 * self.avg_values[name] + 0.01 * value
+                self.iters[name] += 1
+            else:
+                self.avg_values[name] = self.avg_values[name] * \
+                    self.iters[name] + value
+                self.iters[name] += 1
+                self.avg_values[name] /= self.iters[name]
 
     def add_values(self, name_dict):
         for key, value in name_dict.items():

From 30347972111ff4b0c9c189e15ec037a6ae9d0fb2 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sat, 30 May 2020 18:07:01 +0200
Subject: [PATCH 089/185] differentiate modules on TB with a prefix

---
 train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/train.py b/train.py
index e4963ee7..82f91cd5 100644
--- a/train.py
+++ b/train.py
@@ -667,7 +667,7 @@ if __name__ == '__main__':
         os.chmod(OUT_PATH, 0o775)
 
         LOG_DIR = OUT_PATH
-        tb_logger = TensorboardLogger(LOG_DIR)
+        tb_logger = TensorboardLogger(LOG_DIR, model_name='TTS')
 
         # write model desc to tensorboard
         tb_logger.tb_add_text('model-description', c['run_description'], 0)

From a5fc2f578f0d570d79a67fe8a0251a2160736a67 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sat, 30 May 2020 18:08:26 +0200
Subject: [PATCH 090/185] set state dict using direct state_dict dict

---
 utils/generic_utils.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/utils/generic_utils.py b/utils/generic_utils.py
index 4a7aaef9..f7c04859 100644
--- a/utils/generic_utils.py
+++ b/utils/generic_utils.py
@@ -107,15 +107,15 @@ def sequence_mask(sequence_length, max_len=None):
     return seq_range_expand < seq_length_expand
 
 
-def set_init_dict(model_dict, checkpoint, c):
+def set_init_dict(model_dict, checkpoint_state, c):
     # Partial initialization: if there is a mismatch with new and old layer, it is skipped.
-    for k, v in checkpoint['model'].items():
+    for k, v in checkpoint_state.items():
         if k not in model_dict:
             print(" | > Layer missing in the model definition: {}".format(k))
     # 1. filter out unnecessary keys
     pretrained_dict = {
         k: v
-        for k, v in checkpoint['model'].items() if k in model_dict
+        for k, v in checkpoint_state.items() if k in model_dict
     }
     # 2. filter out different size layers
     pretrained_dict = {

From e9f3a476cfaf66b5fe9e3336d48a3f0c93d15343 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sat, 30 May 2020 18:08:56 +0200
Subject: [PATCH 091/185] use model_name prefix in tb_logger

---
 utils/tensorboard_logger.py | 21 +++++++++++----------
 1 file changed, 11 insertions(+), 10 deletions(-)

diff --git a/utils/tensorboard_logger.py b/utils/tensorboard_logger.py
index 15fe04e4..cbf68ad6 100644
--- a/utils/tensorboard_logger.py
+++ b/utils/tensorboard_logger.py
@@ -3,7 +3,8 @@ from tensorboardX import SummaryWriter
 
 
 class TensorboardLogger(object):
-    def __init__(self, log_dir):
+    def __init__(self, log_dir, model_name):
+        self.model_name = model_name
         self.writer = SummaryWriter(log_dir)
         self.train_stats = {}
         self.eval_stats = {}
@@ -50,31 +51,31 @@ class TensorboardLogger(object):
                 traceback.print_exc()
 
     def tb_train_iter_stats(self, step, stats):
-        self.dict_to_tb_scalar("TrainIterStats", stats, step)
+        self.dict_to_tb_scalar(f"{self.model_name}_TrainIterStats", stats, step)
 
     def tb_train_epoch_stats(self, step, stats):
-        self.dict_to_tb_scalar("TrainEpochStats", stats, step)
+        self.dict_to_tb_scalar(f"{self.model_name}_TrainEpochStats", stats, step)
 
     def tb_train_figures(self, step, figures):
-        self.dict_to_tb_figure("TrainFigures", figures, step)
+        self.dict_to_tb_figure(f"{self.model_name}_TrainFigures", figures, step)
 
     def tb_train_audios(self, step, audios, sample_rate):
-        self.dict_to_tb_audios("TrainAudios", audios, step, sample_rate)
+        self.dict_to_tb_audios(f"{self.model_name}_TrainAudios", audios, step, sample_rate)
 
     def tb_eval_stats(self, step, stats):
-        self.dict_to_tb_scalar("EvalStats", stats, step)
+        self.dict_to_tb_scalar(f"{self.model_name}_EvalStats", stats, step)
 
     def tb_eval_figures(self, step, figures):
-        self.dict_to_tb_figure("EvalFigures", figures, step)
+        self.dict_to_tb_figure(f"{self.model_name}_EvalFigures", figures, step)
 
     def tb_eval_audios(self, step, audios, sample_rate):
-        self.dict_to_tb_audios("EvalAudios", audios, step, sample_rate)
+        self.dict_to_tb_audios(f"{self.model_name}_EvalAudios", audios, step, sample_rate)
 
     def tb_test_audios(self, step, audios, sample_rate):
-        self.dict_to_tb_audios("TestAudios", audios, step, sample_rate)
+        self.dict_to_tb_audios(f"{self.model_name}_TestAudios", audios, step, sample_rate)
 
     def tb_test_figures(self, step, figures):
-        self.dict_to_tb_figure("TestFigures", figures, step)
+        self.dict_to_tb_figure(f"{self.model_name}_TestFigures", figures, step)
 
     def tb_add_text(self, title, text, step):
         self.writer.add_text(title, text, step)

From 0bb0ba182e24ed84880f95f5f2dc8586409c6ff9 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sat, 30 May 2020 18:09:25 +0200
Subject: [PATCH 092/185] initial commit intro. to vocoder submodule

---
 vocoder/README.md                             |  35 +
 vocoder/__init__.py                           |   0
 vocoder/compute_tts_features.py               |   0
 vocoder/configs/melgan_config.json            | 138 ++++
 vocoder/datasets/__init__.py                  |   0
 vocoder/datasets/gan_dataset.py               | 133 ++++
 vocoder/datasets/preprocess.py                |  16 +
 vocoder/layers/__init__.py                    |   0
 vocoder/layers/losses.py                      | 273 +++++++
 vocoder/layers/melgan.py                      |  48 ++
 vocoder/layers/pqmf.py                        |  55 ++
 vocoder/layers/pqmf2.py                       | 127 ++++
 vocoder/layers/qmf.dat                        | 640 +++++++++++++++++
 vocoder/models/__init__.py                    |   0
 vocoder/models/melgan_discriminator.py        |  80 +++
 vocoder/models/melgan_generator.py            |  91 +++
 .../models/melgan_multiscale_discriminator.py |  41 ++
 vocoder/models/multiband_melgan_generator.py  |  38 +
 vocoder/models/random_window_discriminator.py | 225 ++++++
 vocoder/notebooks/Untitled.ipynb              | 678 ++++++++++++++++++
 vocoder/notebooks/Untitled1.ipynb             |   6 +
 vocoder/pqmf_output.wav                       | Bin 0 -> 83812 bytes
 vocoder/tests/__init__.py                     |   1 +
 vocoder/tests/test_config.json                |  24 +
 vocoder/tests/test_datasets.py                |  95 +++
 vocoder/tests/test_losses.py                  |  62 ++
 vocoder/tests/test_melgan_discriminator.py    |  26 +
 vocoder/tests/test_melgan_generator.py        |  15 +
 vocoder/tests/test_pqmf.py                    |  33 +
 vocoder/tests/test_rwd.py                     |  21 +
 vocoder/train.py                              | 585 +++++++++++++++
 vocoder/utils/__init__.py                     |   0
 vocoder/utils/console_logger.py               |  97 +++
 vocoder/utils/generic_utils.py                | 102 +++
 vocoder/utils/io.py                           |  52 ++
 35 files changed, 3737 insertions(+)
 create mode 100644 vocoder/README.md
 create mode 100644 vocoder/__init__.py
 create mode 100644 vocoder/compute_tts_features.py
 create mode 100644 vocoder/configs/melgan_config.json
 create mode 100644 vocoder/datasets/__init__.py
 create mode 100644 vocoder/datasets/gan_dataset.py
 create mode 100644 vocoder/datasets/preprocess.py
 create mode 100644 vocoder/layers/__init__.py
 create mode 100644 vocoder/layers/losses.py
 create mode 100644 vocoder/layers/melgan.py
 create mode 100644 vocoder/layers/pqmf.py
 create mode 100644 vocoder/layers/pqmf2.py
 create mode 100644 vocoder/layers/qmf.dat
 create mode 100644 vocoder/models/__init__.py
 create mode 100644 vocoder/models/melgan_discriminator.py
 create mode 100644 vocoder/models/melgan_generator.py
 create mode 100644 vocoder/models/melgan_multiscale_discriminator.py
 create mode 100644 vocoder/models/multiband_melgan_generator.py
 create mode 100644 vocoder/models/random_window_discriminator.py
 create mode 100644 vocoder/notebooks/Untitled.ipynb
 create mode 100644 vocoder/notebooks/Untitled1.ipynb
 create mode 100644 vocoder/pqmf_output.wav
 create mode 100644 vocoder/tests/__init__.py
 create mode 100644 vocoder/tests/test_config.json
 create mode 100644 vocoder/tests/test_datasets.py
 create mode 100644 vocoder/tests/test_losses.py
 create mode 100644 vocoder/tests/test_melgan_discriminator.py
 create mode 100644 vocoder/tests/test_melgan_generator.py
 create mode 100644 vocoder/tests/test_pqmf.py
 create mode 100644 vocoder/tests/test_rwd.py
 create mode 100644 vocoder/train.py
 create mode 100644 vocoder/utils/__init__.py
 create mode 100644 vocoder/utils/console_logger.py
 create mode 100644 vocoder/utils/generic_utils.py
 create mode 100644 vocoder/utils/io.py

diff --git a/vocoder/README.md b/vocoder/README.md
new file mode 100644
index 00000000..48fc24ee
--- /dev/null
+++ b/vocoder/README.md
@@ -0,0 +1,35 @@
+# Mozilla TTS Vocoders (Experimental)
+
+We provide here different vocoder implementations which can be combined with our TTS models to enable "FASTER THAN REAL-TIME" end-to-end TTS stack.
+
+Currently, there are implementations of the following models.
+
+- Melgan
+- MultiBand-Melgan
+- GAN-TTS (Discriminator Only)
+
+It is also very easy to adapt different vocoder models as we provide here a flexible and modular (but not too modular) framework.
+
+## Training a model
+
+You can see here an example (Soon)[Colab Notebook]() training MelGAN with LJSpeech dataset.
+
+In order to train a new model, you need to collecto all your wav files under a common parent folder and give this path to `data_path` field in '''config.json'''
+
+You need to define other relevant parameters in your ```config.json``` and then start traning with the following command from Mozilla TTS root path.
+
+```CUDA_VISIBLE_DEVICES='1' python vocoder/train.py --config_path path/to/config.json```
+
+Exampled config files can be found under `vocoder/configs/` folder.
+
+You can continue a previous training by the following command.
+
+```CUDA_VISIBLE_DEVICES='1' python vocoder/train.py --continue_path path/to/your/model/folder```
+
+You can fine-tune a pre-trained model by the following command.
+
+```CUDA_VISIBLE_DEVICES='1' python vocoder/train.py --restore_path path/to/your/model.pth.tar```
+
+Restoring a model starts a new training in a different output folder. It only restores model weights with the given checkpoint file. However, continuing a training starts from the same conditions the previous training run left off.
+
+You can also follow your training runs on Tensorboard as you do with our TTS models.
\ No newline at end of file
diff --git a/vocoder/__init__.py b/vocoder/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/vocoder/compute_tts_features.py b/vocoder/compute_tts_features.py
new file mode 100644
index 00000000..e69de29b
diff --git a/vocoder/configs/melgan_config.json b/vocoder/configs/melgan_config.json
new file mode 100644
index 00000000..9a3ded37
--- /dev/null
+++ b/vocoder/configs/melgan_config.json
@@ -0,0 +1,138 @@
+{
+    "run_name": "melgan",
+    "run_description": "melgan initial run",
+
+    // AUDIO PARAMETERS
+    "audio":{
+        // stft parameters
+        "num_freq": 513,         // number of stft frequency levels. Size of the linear spectogram frame.
+        "win_length": 1024,      // stft window length in ms.
+        "hop_length": 256,       // stft window hop-lengh in ms.
+        "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
+        "frame_shift_ms": null,  // stft window hop-lengh in ms. If null, 'hop_length' is used.
+
+        // Audio processing parameters
+        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
+        "preemphasis": 0.0,     // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
+        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
+
+        // Silence trimming
+        "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
+        "trim_db": 60,          // threshold for timming silence. Set this according to your dataset.
+
+        // Griffin-Lim
+        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
+        "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
+
+        // MelSpectrogram parameters
+        "num_mels": 80,         // size of the mel spec frame.
+        "mel_fmin": 0.0,        // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
+        "mel_fmax": 8000.0,     // maximum freq level for mel-spec. Tune for dataset!!
+
+        // Normalization parameters
+        "signal_norm": true,    // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
+        "min_level_db": -100,   // lower bound for normalization
+        "symmetric_norm": true, // move normalization to range [-1, 1]
+        "max_norm": 4.0,        // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
+        "clip_norm": true,      // clip normalized values into the range.
+        "stats_path": null    // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
+    },
+
+    // DISTRIBUTED TRAINING
+    // "distributed":{
+    //     "backend": "nccl",
+    //     "url": "tcp:\/\/localhost:54321"
+    // },
+
+    // MODEL PARAMETERS
+    "use_pqmf": true,
+
+    // LOSS PARAMETERS
+    "use_stft_loss": true,
+    "use_mse_gan_loss": true,
+    "use_hinge_gan_loss": false,
+    "use_feat_match_loss": false,  // use only with melgan discriminators
+
+    "stft_loss_alpha": 1,
+    "mse_gan_loss_alpha": 1,
+    "hinge_gan_loss_alpha": 1,
+    "feat_match_loss_alpha": 10.0,
+
+    "stft_loss_params": {
+        "n_ffts": [1024, 2048, 512],
+        "hop_lengths": [120, 240, 50],
+        "win_lengths": [600, 1200, 240]
+    },
+    "target_loss": "avg_G_loss",  // loss value to pick the best model
+
+    // DISCRIMINATOR
+    "discriminator_model": "melgan_multiscale_discriminator",
+    "discriminator_model_params":{
+        "base_channels": 16,
+        "max_channels":1024,
+        "downsample_factors":[4, 4, 4, 4]
+    },
+    "steps_to_start_discriminator": 100000,      // steps required to start GAN trainining.1
+
+    // "discriminator_model": "random_window_discriminator",
+    // "discriminator_model_params":{
+    //     "uncond_disc_donwsample_factors": [8, 4],
+    //     "cond_disc_downsample_factors": [[8, 4, 2, 2, 2], [8, 4, 2, 2], [8, 4, 2], [8, 4], [4, 2, 2]],
+    //     "cond_disc_out_channels": [[128, 128, 256, 256], [128, 256, 256], [128, 256], [256], [128, 256]],
+    //     "window_sizes": [512, 1024, 2048, 4096, 8192]
+    // },
+
+
+    // GENERATOR
+    "generator_model": "multiband_melgan_generator",
+    "generator_model_params": {
+        "upsample_factors":[2 ,2, 4, 4],
+        "num_res_blocks": 4
+    },
+
+    // DATASET
+    "data_path": "/home/erogol/Data/LJSpeech-1.1/wavs/",
+    "seq_len": 16384,
+    "pad_short": 2000,
+    "conv_pad": 0,
+    "use_noise_augment": true,
+    "use_cache": true,
+
+    "reinit_layers": [],    // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
+
+    // TRAINING
+    "batch_size": 64,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
+
+    // VALIDATION
+    "run_eval": true,
+    "test_delay_epochs": 10,  //Until attention is aligned, testing only wastes computation time.
+    "test_sentences_file": null,  // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
+
+    // OPTIMIZER
+    "noam_schedule": true,        // use noam warmup and lr schedule.
+    "grad_clip": 1.0,              // upper limit for gradients for clipping.
+    "epochs": 1000,                // total number of epochs to train.
+    "wd": 0.000001,                // Weight decay weight.
+    "lr_gen": 0.0001,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
+    "lr_disc": 0.0001,
+    "warmup_steps_gen": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
+    "warmup_steps_disc": 4000,
+    "gen_clip_grad": 10.0,
+    "disc_clip_grad": 10.0,
+
+    // TENSORBOARD and LOGGING
+    "print_step": 25,       // Number of steps to log traning on console.
+    "print_eval": false,     // If True, it prints intermediate loss values in evalulation.
+    "save_step": 10000,      // Number of training steps expected to save traninpg stats and checkpoints.
+    "checkpoint": true,     // If true, it saves checkpoints per "save_step"
+    "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
+
+    // DATA LOADING
+    "num_loader_workers": 4,        // number of training data loader processes. Don't set it too big. 4-8 are good values.
+    "num_val_loader_workers": 4,    // number of evaluation data loader processes.
+    "eval_split_size": 10,
+
+    // PATHS
+    "output_path": "/home/erogol/Models/LJSpeech/"
+}
+
diff --git a/vocoder/datasets/__init__.py b/vocoder/datasets/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/vocoder/datasets/gan_dataset.py b/vocoder/datasets/gan_dataset.py
new file mode 100644
index 00000000..10d36cab
--- /dev/null
+++ b/vocoder/datasets/gan_dataset.py
@@ -0,0 +1,133 @@
+import os
+import glob
+import torch
+import random
+import numpy as np
+from torch.utils.data import Dataset, DataLoader
+from multiprocessing import Manager
+
+
+def create_dataloader(hp, args, train):
+    dataset = MelFromDisk(hp, args, train)
+
+    if train:
+        return DataLoader(dataset=dataset,
+                          batch_size=hp.train.batch_size,
+                          shuffle=True,
+                          num_workers=hp.train.num_workers,
+                          pin_memory=True,
+                          drop_last=True)
+    else:
+        return DataLoader(dataset=dataset,
+                          batch_size=1,
+                          shuffle=False,
+                          num_workers=hp.train.num_workers,
+                          pin_memory=True,
+                          drop_last=False)
+
+
+class GANDataset(Dataset):
+    """
+    GAN Dataset searchs for all the wav files under root path
+    and converts them to acoustic features on the fly and returns
+    random segments of (audio, feature) couples.
+    """
+    def __init__(self,
+                 ap,
+                 items,
+                 seq_len,
+                 hop_len,
+                 pad_short,
+                 conv_pad=2,
+                 is_training=True,
+                 return_segments=True,
+                 use_noise_augment=False,
+                 use_cache=False,
+                 verbose=False):
+
+        self.ap = ap
+        self.item_list = items
+        self.seq_len = seq_len
+        self.hop_len = hop_len
+        self.pad_short = pad_short
+        self.conv_pad = conv_pad
+        self.is_training = is_training
+        self.return_segments = return_segments
+        self.use_cache = use_cache
+        self.use_noise_augment = use_noise_augment
+
+        assert seq_len % hop_len == 0, " [!] seq_len has to be a multiple of hop_len."
+        self.feat_frame_len = seq_len // hop_len + (2 * conv_pad)
+
+        # map G and D instances
+        self.G_to_D_mappings = [i for i in range(len(self.item_list))]
+        self.shuffle_mapping()
+
+        # cache acoustic features
+        if use_cache:
+            self.create_feature_cache()
+
+
+
+    def create_feature_cache(self):
+        self.manager = Manager()
+        self.cache = self.manager.list()
+        self.cache += [None for _ in range(len(self.item_list))]
+
+    def find_wav_files(self, path):
+        return glob.glob(os.path.join(path, '**', '*.wav'), recursive=True)
+
+    def __len__(self):
+        return len(self.item_list)
+
+    def __getitem__(self, idx):
+        """ Return different items for Generator and Discriminator and
+        cache acoustic features """
+        if self.return_segments:
+            idx2 = self.G_to_D_mappings[idx]
+            item1 = self.load_item(idx)
+            item2 = self.load_item(idx2)
+            return item1, item2
+        else:
+            item1 = self.load_item(idx)
+            return item1
+
+    def shuffle_mapping(self):
+        random.shuffle(self.G_to_D_mappings)
+
+    def load_item(self, idx):
+        """ load (audio, feat) couple """
+        wavpath = self.item_list[idx]
+        # print(wavpath)
+
+        if self.use_cache and self.cache[idx] is not None:
+            audio, mel = self.cache[idx]
+        else:
+            audio = self.ap.load_wav(wavpath)
+            mel = self.ap.melspectrogram(audio)
+
+        if len(audio) < self.seq_len + self.pad_short:
+            audio = np.pad(audio, (0, self.seq_len + self.pad_short - len(audio)), \
+                    mode='constant', constant_values=0.0)
+
+        # correct the audio length wrt padding applied in stft
+        audio = np.pad(audio, (0, self.hop_len), mode="edge")
+        audio = audio[:mel.shape[-1] * self.hop_len]
+        assert mel.shape[-1] * self.hop_len == audio.shape[-1], f' [!] {mel.shape[-1] * self.hop_len} vs {audio.shape[-1]}'
+
+        audio = torch.from_numpy(audio).float().unsqueeze(0)
+        mel = torch.from_numpy(mel).float().squeeze(0)
+
+        if self.return_segments:
+            max_mel_start = mel.shape[1] - self.feat_frame_len
+            mel_start = random.randint(0, max_mel_start)
+            mel_end = mel_start + self.feat_frame_len
+            mel = mel[:, mel_start:mel_end]
+
+            audio_start = mel_start * self.hop_len
+            audio = audio[:, audio_start:audio_start +
+                          self.seq_len]
+
+        if self.use_noise_augment and self.is_training and self.return_segments:
+            audio = audio + (1 / 32768) * torch.randn_like(audio)
+        return (mel, audio)
diff --git a/vocoder/datasets/preprocess.py b/vocoder/datasets/preprocess.py
new file mode 100644
index 00000000..01e01e3e
--- /dev/null
+++ b/vocoder/datasets/preprocess.py
@@ -0,0 +1,16 @@
+import glob
+import os
+
+import numpy as np
+
+
+def find_wav_files(data_path):
+    wav_paths = glob.glob(os.path.join(data_path, '**', '*.wav'), recursive=True)
+    return wav_paths
+
+
+def load_wav_data(data_path, eval_split_size):
+    wav_paths = find_wav_files(data_path)
+    np.random.seed(0)
+    np.random.shuffle(wav_paths)
+    return wav_paths[:eval_split_size], wav_paths[eval_split_size:]
diff --git a/vocoder/layers/__init__.py b/vocoder/layers/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/vocoder/layers/losses.py b/vocoder/layers/losses.py
new file mode 100644
index 00000000..11985629
--- /dev/null
+++ b/vocoder/layers/losses.py
@@ -0,0 +1,273 @@
+import torch
+
+from torch import nn
+from torch.nn import functional as F
+
+
+class TorchSTFT():
+    def __init__(self, n_fft, hop_length, win_length, window='hann_window'):
+        self.n_fft = n_fft
+        self.hop_length = hop_length
+        self.win_length = win_length
+        self.window = getattr(torch, window)(win_length)
+
+    def __call__(self, x):
+        # B x D x T x 2
+        o = torch.stft(x,
+                       self.n_fft,
+                       self.hop_length,
+                       self.win_length,
+                       self.window,
+                       center=True,
+                       pad_mode="constant",  # compatible with audio.py
+                       normalized=False,
+                       onesided=True)
+        M = o[:, :, :, 0]
+        P = o[:, :, :, 1]
+        return torch.sqrt(torch.clamp(M ** 2 + P ** 2, min=1e-8))
+
+
+#################################
+# GENERATOR LOSSES
+#################################
+
+
+class STFTLoss(nn.Module):
+    def __init__(self, n_fft, hop_length, win_length):
+        super(STFTLoss, self).__init__()
+        self.n_fft = n_fft
+        self.hop_length = hop_length
+        self.win_length = win_length
+        self.stft = TorchSTFT(n_fft, hop_length, win_length)
+
+    def forward(self, y_hat, y):
+        y_hat_M = self.stft(y_hat)
+        y_M = self.stft(y)
+        # magnitude loss
+        loss_mag = F.l1_loss(torch.log(y_M), torch.log(y_hat_M))
+        # spectral convergence loss
+        loss_sc = torch.norm(y_M - y_hat_M, p="fro") / torch.norm(y_M, p="fro")
+        return loss_mag, loss_sc
+
+
+class MultiScaleSTFTLoss(torch.nn.Module):
+    def __init__(self,
+                 n_ffts=[1024, 2048, 512],
+                 hop_lengths=[120, 240, 50],
+                 win_lengths=[600, 1200, 240]):
+        super(MultiScaleSTFTLoss, self).__init__()
+        self.loss_funcs = torch.nn.ModuleList()
+        for idx in range(len(n_ffts)):
+            self.loss_funcs.append(STFTLoss(n_ffts[idx], hop_lengths[idx], win_lengths[idx]))
+
+    def forward(self, y_hat, y):
+        N = len(self.loss_funcs)
+        loss_sc = 0
+        loss_mag = 0
+        for f in self.loss_funcs:
+            lm, lsc = f(y_hat, y)
+            loss_mag += lm
+            loss_sc += lsc
+        loss_sc /= N
+        loss_mag /= N
+        return loss_mag, loss_sc
+
+
+class MSEGLoss(nn.Module):
+    """ Mean Squared Generator Loss """
+    def __init__(self,):
+        super(MSEGLoss, self).__init__()
+
+    def forward(self, score_fake, ):
+        loss_fake = torch.mean(torch.sum(torch.pow(score_fake, 2), dim=[1, 2]))
+        return loss_fake
+
+
+class HingeGLoss(nn.Module):
+    """ Hinge Discriminator Loss """
+    def __init__(self,):
+        super(HingeGLoss, self).__init__()
+
+    def forward(self, score_fake, score_real):
+        loss_fake = torch.mean(F.relu(1. + score_fake))
+        return loss_fake
+
+
+##################################
+# DISCRIMINATOR LOSSES
+##################################
+
+
+class MSEDLoss(nn.Module):
+    """ Mean Squared Discriminator Loss """
+    def __init__(self,):
+        super(MSEDLoss, self).__init__()
+
+    def forward(self, score_fake, score_real):
+        loss_real = torch.mean(torch.sum(torch.pow(score_real - 1.0, 2), dim=[1, 2]))
+        loss_fake = torch.mean(torch.sum(torch.pow(score_fake, 2), dim=[1, 2]))
+        loss_d = loss_real + loss_fake
+        return loss_d, loss_real, loss_fake
+
+
+class HingeDLoss(nn.Module):
+    """ Hinge Discriminator Loss """
+    def __init__(self,):
+        super(HingeDLoss, self).__init__()
+
+    def forward(self, score_fake, score_real):
+        loss_real = torch.mean(F.relu(1. - score_real))
+        loss_fake = torch.mean(F.relu(1. + score_fake))
+        loss_d = loss_real + loss_fake
+        return loss_d, loss_real, loss_fake
+
+
+class MelganFeatureLoss(nn.Module):
+    def __init__(self, ):
+        super(MelganFeatureLoss, self).__init__()
+
+    def forward(self, fake_feats, real_feats):
+        loss_feats = 0
+        for fake_feat, real_feat in zip(fake_feats, real_feats):
+            loss_feats += hp.model.feat_match * torch.mean(torch.abs(fake_feat - real_feat))
+        return loss_feats
+
+
+##################################
+# LOSS WRAPPERS
+##################################
+
+
+class GeneratorLoss(nn.Module):
+    def __init__(self, C):
+        super(GeneratorLoss, self).__init__()
+        assert C.use_mse_gan_loss and C.use_hinge_gan_loss == False,\
+            " [!] Cannot use HingeGANLoss and MSEGANLoss together."
+
+        self.use_stft_loss = C.use_stft_loss
+        self.use_mse_gan_loss = C.use_mse_gan_loss
+        self.use_hinge_gan_loss = C.use_hinge_gan_loss
+        self.use_feat_match_loss = C.use_feat_match_loss
+
+        self.stft_loss_alpha = C.stft_loss_alpha
+        self.mse_gan_loss_alpha = C.mse_gan_loss_alpha
+        self.hinge_gan_loss_alpha = C.hinge_gan_loss_alpha
+        self.feat_match_loss_alpha = C.feat_match_loss_alpha
+
+        if C.use_stft_loss:
+            self.stft_loss = MultiScaleSTFTLoss(**C.stft_loss_params)
+        if C.use_mse_gan_loss:
+            self.mse_loss = MSEGLoss()
+        if C.use_hinge_gan_loss:
+            self.hinge_loss = HingeGLoss()
+        if C.use_feat_match_loss:
+            self.feat_match_loss = MelganFeatureLoss()
+
+    def forward(self, y_hat=None, y=None, scores_fake=None, feats_fake=None, feats_real=None):
+        loss = 0
+        return_dict = {}
+
+        # STFT Loss
+        if self.use_stft_loss:
+            stft_loss_mg, stft_loss_sc = self.stft_loss(y_hat.squeeze(1), y.squeeze(1))
+            return_dict['G_stft_loss_mg'] = stft_loss_mg
+            return_dict['G_stft_loss_sc'] = stft_loss_sc
+            loss += self.stft_loss_alpha * (stft_loss_mg + stft_loss_sc)
+
+        # Fake Losses
+        if self.use_mse_gan_loss and scores_fake is not None:
+            mse_fake_loss = 0
+            if isinstance(scores_fake, list):
+                for score_fake in scores_fake:
+                    fake_loss = self.mse_loss(score_fake)
+                    mse_fake_loss += fake_loss
+            else:
+                fake_loss = self.mse_loss(scores_fake)
+                mse_fake_loss = fake_loss
+            return_dict['G_mse_fake_loss'] = mse_fake_loss
+            loss += self.mse_gan_loss_alpha * mse_fake_loss
+
+        if self.use_hinge_gan_loss and not scores_fake is not None:
+            hinge_fake_loss = 0
+            if isinstance(scores_fake, list):
+                for score_fake in scores_fake:
+                    fake_loss = self.hinge_loss(score_fake)
+                    hinge_fake_loss += fake_loss
+            else:
+                fake_loss = self.hinge_loss(scores_fake)
+                hinge_fake_loss = fake_loss
+            return_dict['G_hinge_fake_loss'] = hinge_fake_loss
+            loss += self.hinge_gan_loss_alpha * hinge_fake_loss
+
+        # Feature Matching Loss
+        if self.use_feat_match_loss and not feats_fake:
+            feat_match_loss = self.feat_match_loss(feats_fake, feats_real)
+            return_dict['G_feat_match_loss'] = feat_match_loss
+            loss += self.feat_match_loss_alpha * feat_match_loss
+        return_dict['G_loss'] = loss
+        return return_dict
+
+
+class DiscriminatorLoss(nn.Module):
+    def __init__(self, C):
+        super(DiscriminatorLoss, self).__init__()
+        assert C.use_mse_gan_loss and C.use_hinge_gan_loss == False,\
+            " [!] Cannot use HingeGANLoss and MSEGANLoss together."
+
+        self.use_mse_gan_loss = C.use_mse_gan_loss
+        self.use_hinge_gan_loss = C.use_hinge_gan_loss
+
+        self.mse_gan_loss_alpha = C.mse_gan_loss_alpha
+        self.hinge_gan_loss_alpha = C.hinge_gan_loss_alpha
+
+        if C.use_mse_gan_loss:
+            self.mse_loss = MSEDLoss()
+        if C.use_hinge_gan_loss:
+            self.hinge_loss = HingeDLoss()
+
+    def forward(self, scores_fake, scores_real):
+        loss = 0
+        return_dict = {}
+
+        if self.use_mse_gan_loss:
+            mse_gan_loss = 0
+            mse_gan_real_loss = 0
+            mse_gan_fake_loss = 0
+            if isinstance(scores_fake, list):
+                for score_fake, score_real in zip(scores_fake, scores_real):
+                    total_loss, real_loss, fake_loss = self.mse_loss(score_fake, score_real)
+                    mse_gan_loss += total_loss
+                    mse_gan_real_loss += real_loss
+                    mse_gan_fake_loss += fake_loss
+            else:
+                total_loss, real_loss, fake_loss = self.mse_loss(scores_fake, scores_real)
+                mse_gan_loss = total_loss
+                mse_gan_real_loss = real_loss
+                mse_gan_fake_loss = fake_loss
+            return_dict['D_mse_gan_loss'] = mse_gan_loss
+            return_dict['D_mse_gan_real_loss'] = mse_gan_real_loss
+            return_dict['D_mse_gan_fake_loss'] = mse_gan_fake_loss
+            loss += self.mse_gan_loss_alpha * mse_gan_loss
+
+        if self.use_hinge_gan_loss:
+            hinge_gan_loss = 0
+            hinge_gan_real_loss = 0
+            hinge_gan_fake_loss = 0
+            if isinstance(scores_fake, list):
+                for score_fake, score_real in zip(scores_fake, scores_real):
+                    total_loss, real_loss, fake_loss = self.hinge_loss(score_fake, score_real)
+                    hinge_gan_loss += total_loss
+                    hinge_gan_real_loss += real_loss
+                    hinge_gan_fake_loss += fake_loss
+            else:
+                total_loss, real_loss, fake_loss = self.hinge_loss(scores_fake, scores_real)
+                hinge_gan_loss = total_loss
+                hinge_gan_real_loss = real_loss
+                hinge_gan_fake_loss = fake_loss
+            return_dict['D_hinge_gan_loss'] = hinge_gan_loss
+            return_dict['D_hinge_gan_real_loss'] = hinge_gan_real_loss
+            return_dict['D_hinge_gan_fake_loss'] = hinge_gan_fake_loss
+            loss += self.hinge_gan_loss_alpha * hinge_gan_loss
+
+        return_dict['D_loss'] = loss
+        return return_dict
\ No newline at end of file
diff --git a/vocoder/layers/melgan.py b/vocoder/layers/melgan.py
new file mode 100644
index 00000000..cda0413c
--- /dev/null
+++ b/vocoder/layers/melgan.py
@@ -0,0 +1,48 @@
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.nn.utils import weight_norm
+
+
+class ResidualStack(nn.Module):
+    def __init__(self, channels, num_res_blocks, kernel_size):
+        super(ResidualStack, self).__init__()
+
+        assert (kernel_size - 1) % 2 == 0, " [!] kernel_size has to be odd."
+        base_padding = (kernel_size - 1) // 2
+
+        self.blocks = nn.ModuleList()
+        for idx in range(num_res_blocks):
+            layer_kernel_size = kernel_size
+            layer_dilation = layer_kernel_size**idx
+            layer_padding = base_padding * layer_dilation
+            self.blocks += [nn.Sequential(
+                nn.LeakyReLU(0.2),
+                nn.ReflectionPad1d(layer_padding),
+                weight_norm(
+                    nn.Conv1d(channels,
+                              channels,
+                              kernel_size=kernel_size,
+                              dilation=layer_padding,
+                              bias=True)),
+                nn.LeakyReLU(0.2),
+                weight_norm(
+                    nn.Conv1d(channels, channels, kernel_size=1, bias=True)),
+            )]
+
+        self.shortcuts = nn.ModuleList([
+            weight_norm(nn.Conv1d(channels, channels, kernel_size=1,
+                                  bias=True)) for i in range(num_res_blocks)
+        ])
+
+    def forward(self, x):
+        for block, shortcut in zip(self.blocks, self.shortcuts):
+            x = shortcut(x) + block(x)
+        return x
+
+    def remove_weight_norm(self):
+        for block, shortcut in zip(self.blocks, self.shortcuts):
+            nn.utils.remove_weight_norm(block[2])
+            nn.utils.remove_weight_norm(block[4])
+            nn.utils.remove_weight_norm(shortcut)
diff --git a/vocoder/layers/pqmf.py b/vocoder/layers/pqmf.py
new file mode 100644
index 00000000..f438ea00
--- /dev/null
+++ b/vocoder/layers/pqmf.py
@@ -0,0 +1,55 @@
+"""Pseudo QMF modules."""
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from scipy import signal as sig
+
+
+# adapted from
+# https://github.com/kan-bayashi/ParallelWaveGAN/tree/master/parallel_wavegan
+class PQMF(torch.nn.Module):
+    def __init__(self, N=4, taps=62, cutoff=0.15, beta=9.0):
+        super(PQMF, self).__init__()
+
+        self.N = N
+        self.taps = taps
+        self.cutoff = cutoff
+        self.beta = beta
+
+        QMF = sig.firwin(taps + 1, cutoff, window=('kaiser', beta))
+        H = np.zeros((N, len(QMF)))
+        G = np.zeros((N, len(QMF)))
+        for k in range(N):
+            constant_factor = (2 * k + 1) * (np.pi /
+                                             (2 * N)) * (np.arange(taps + 1) -
+                                                         ((taps - 1) / 2))
+            phase = (-1)**k * np.pi / 4
+            H[k] = 2 * QMF * np.cos(constant_factor + phase)
+
+            G[k] = 2 * QMF * np.cos(constant_factor - phase)
+
+        H = torch.from_numpy(H[:, None, :]).float()
+        G = torch.from_numpy(G[None, :, :]).float()
+
+        self.register_buffer("H", H)
+        self.register_buffer("G", G)
+
+        updown_filter = torch.zeros((N, N, N)).float()
+        for k in range(N):
+            updown_filter[k, k, 0] = 1.0
+        self.register_buffer("updown_filter", updown_filter)
+        self.N = N
+
+        self.pad_fn = torch.nn.ConstantPad1d(taps // 2, 0.0)
+
+    def analysis(self, x):
+        return F.conv1d(x, self.H, padding=self.taps // 2, stride=self.N)
+
+    def synthesis(self, x):
+        x = F.conv_transpose1d(x,
+                               self.updown_filter * self.N,
+                               stride=self.N)
+        x = F.conv1d(x, self.G, padding=self.taps // 2)
+        return x
diff --git a/vocoder/layers/pqmf2.py b/vocoder/layers/pqmf2.py
new file mode 100644
index 00000000..4cffb819
--- /dev/null
+++ b/vocoder/layers/pqmf2.py
@@ -0,0 +1,127 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2020 Tomoki Hayashi
+#  MIT License (https://opensource.org/licenses/MIT)
+
+"""Pseudo QMF modules."""
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from scipy.signal import kaiser
+
+
+def design_prototype_filter(taps=62, cutoff_ratio=0.15, beta=9.0):
+    """Design prototype filter for PQMF.
+
+    This method is based on `A Kaiser window approach for the design of prototype
+    filters of cosine modulated filterbanks`_.
+
+    Args:
+        taps (int): The number of filter taps.
+        cutoff_ratio (float): Cut-off frequency ratio.
+        beta (float): Beta coefficient for kaiser window.
+
+    Returns:
+        ndarray: Impluse response of prototype filter (taps + 1,).
+
+    .. _`A Kaiser window approach for the design of prototype filters of cosine modulated filterbanks`:
+        https://ieeexplore.ieee.org/abstract/document/681427
+
+    """
+    # check the arguments are valid
+    assert taps % 2 == 0, "The number of taps mush be even number."
+    assert 0.0 < cutoff_ratio < 1.0, "Cutoff ratio must be > 0.0 and < 1.0."
+
+    # make initial filter
+    omega_c = np.pi * cutoff_ratio
+    with np.errstate(invalid='ignore'):
+        h_i = np.sin(omega_c * (np.arange(taps + 1) - 0.5 * taps)) \
+            / (np.pi * (np.arange(taps + 1) - 0.5 * taps))
+    h_i[taps // 2] = np.cos(0) * cutoff_ratio  # fix nan due to indeterminate form
+
+    # apply kaiser window
+    w = kaiser(taps + 1, beta)
+    h = h_i * w
+
+    return h
+
+
+class PQMF(torch.nn.Module):
+    """PQMF module.
+
+    This module is based on `Near-perfect-reconstruction pseudo-QMF banks`_.
+
+    .. _`Near-perfect-reconstruction pseudo-QMF banks`:
+        https://ieeexplore.ieee.org/document/258122
+
+    """
+
+    def __init__(self, subbands=4, taps=62, cutoff_ratio=0.15, beta=9.0):
+        """Initilize PQMF module.
+
+        Args:
+            subbands (int): The number of subbands.
+            taps (int): The number of filter taps.
+            cutoff_ratio (float): Cut-off frequency ratio.
+            beta (float): Beta coefficient for kaiser window.
+
+        """
+        super(PQMF, self).__init__()
+
+        # define filter coefficient
+        h_proto = design_prototype_filter(taps, cutoff_ratio, beta)
+        h_analysis = np.zeros((subbands, len(h_proto)))
+        h_synthesis = np.zeros((subbands, len(h_proto)))
+        for k in range(subbands):
+            h_analysis[k] = 2 * h_proto * np.cos((2 * k + 1) * (np.pi / (2 * subbands)) * (np.arange(taps + 1) - ((taps - 1) / 2)) + (-1) ** k * np.pi / 4)
+            h_synthesis[k] = 2 * h_proto * np.cos((2 * k + 1) * (np.pi / (2 * subbands)) * (np.arange(taps + 1) - ((taps - 1) / 2)) - (-1) ** k * np.pi / 4)
+
+        # convert to tensor
+        analysis_filter = torch.from_numpy(h_analysis).float().unsqueeze(1)
+        synthesis_filter = torch.from_numpy(h_synthesis).float().unsqueeze(0)
+
+        # register coefficients as beffer
+        self.register_buffer("analysis_filter", analysis_filter)
+        self.register_buffer("synthesis_filter", synthesis_filter)
+
+        # filter for downsampling & upsampling
+        updown_filter = torch.zeros((subbands, subbands, subbands)).float()
+        for k in range(subbands):
+            updown_filter[k, k, 0] = 1.0
+        self.register_buffer("updown_filter", updown_filter)
+        self.subbands = subbands
+
+        # keep padding info
+        self.pad_fn = torch.nn.ConstantPad1d(taps // 2, 0.0)
+
+    def analysis(self, x):
+        """Analysis with PQMF.
+
+        Args:
+            x (Tensor): Input tensor (B, 1, T).
+
+        Returns:
+            Tensor: Output tensor (B, subbands, T // subbands).
+
+        """
+        x = F.conv1d(self.pad_fn(x), self.analysis_filter)
+        return F.conv1d(x, self.updown_filter, stride=self.subbands)
+
+    def synthesis(self, x):
+        """Synthesis with PQMF.
+
+        Args:
+            x (Tensor): Input tensor (B, subbands, T // subbands).
+
+        Returns:
+            Tensor: Output tensor (B, 1, T).
+
+        """
+        # NOTE(kan-bayashi): Power will be dreased so here multipy by # subbands.
+        #   Not sure this is the correct way, it is better to check again.
+        # TODO(kan-bayashi): Understand the reconstruction procedure
+        x = F.conv_transpose1d(x, self.updown_filter * self.subbands, stride=self.subbands)
+        x = F.conv1d(self.pad_fn(x), self.synthesis_filter)
+        return x
diff --git a/vocoder/layers/qmf.dat b/vocoder/layers/qmf.dat
new file mode 100644
index 00000000..17eab137
--- /dev/null
+++ b/vocoder/layers/qmf.dat
@@ -0,0 +1,640 @@
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diff --git a/vocoder/models/__init__.py b/vocoder/models/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/vocoder/models/melgan_discriminator.py b/vocoder/models/melgan_discriminator.py
new file mode 100644
index 00000000..55d3f585
--- /dev/null
+++ b/vocoder/models/melgan_discriminator.py
@@ -0,0 +1,80 @@
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.nn.utils import weight_norm
+
+
+class MelganDiscriminator(nn.Module):
+    def __init__(self,
+                 in_channels=1,
+                 out_channels=1,
+                 kernel_sizes=(5, 3),
+                 base_channels=16,
+                 max_channels=1024,
+                 downsample_factors=(4, 4, 4, 4)):
+        super(MelganDiscriminator, self).__init__()
+        self.layers = nn.ModuleList()
+
+        layer_kernel_size = np.prod(kernel_sizes)
+        layer_padding = (layer_kernel_size - 1) // 2
+
+        # initial layer
+        self.layers += [
+            nn.Sequential(
+                nn.ReflectionPad1d(layer_padding),
+                weight_norm(
+                    nn.Conv1d(in_channels,
+                              base_channels,
+                              layer_kernel_size,
+                              stride=1)), nn.LeakyReLU(0.2, inplace=True))
+        ]
+
+        # downsampling layers
+        layer_in_channels = base_channels
+        for idx, downsample_factor in enumerate(downsample_factors):
+            layer_out_channels = min(layer_in_channels * downsample_factor,
+                                     max_channels)
+            layer_kernel_size = downsample_factor * 10 + 1
+            layer_padding = (layer_kernel_size - 1) // 2
+            layer_groups = layer_in_channels // 4
+            self.layers += [
+                nn.Sequential(
+                    weight_norm(
+                        nn.Conv1d(layer_in_channels,
+                                  layer_out_channels,
+                                  kernel_size=layer_kernel_size,
+                                  stride=downsample_factor,
+                                  padding=layer_padding,
+                                  groups=layer_groups)),
+                    nn.LeakyReLU(0.2, inplace=True))
+            ]
+            layer_in_channels = layer_out_channels
+
+        # last 2 layers
+        layer_padding1 = (kernel_sizes[0] - 1) // 2
+        layer_padding2 = (kernel_sizes[1] - 1) // 2
+        self.layers += [
+            nn.Sequential(
+                weight_norm(
+                    nn.Conv1d(layer_out_channels,
+                              layer_out_channels,
+                              kernel_size=kernel_sizes[0],
+                              stride=1,
+                              padding=layer_padding1)),
+                nn.LeakyReLU(0.2, inplace=True),
+            ),
+            weight_norm(
+                nn.Conv1d(layer_out_channels,
+                          out_channels,
+                          kernel_size=kernel_sizes[1],
+                          stride=1,
+                          padding=layer_padding2)),
+        ]
+
+    def forward(self, x):
+        feats = []
+        for layer in self.layers:
+            x = layer(x)
+            feats.append(x)
+        return x, feats
diff --git a/vocoder/models/melgan_generator.py b/vocoder/models/melgan_generator.py
new file mode 100644
index 00000000..2d266f29
--- /dev/null
+++ b/vocoder/models/melgan_generator.py
@@ -0,0 +1,91 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.nn.utils import weight_norm
+
+from TTS.vocoder.layers.melgan import ResidualStack
+
+
+class MelganGenerator(nn.Module):
+    def __init__(self,
+                 in_channels=80,
+                 out_channels=1,
+                 proj_kernel=7,
+                 base_channels=512,
+                 upsample_factors=(8, 8, 2, 2),
+                 res_kernel=3,
+                 num_res_blocks=3):
+        super(MelganGenerator, self).__init__()
+
+        # assert model parameters
+        assert (proj_kernel -
+                1) % 2 == 0, " [!] proj_kernel should be an odd number."
+
+        # setup additional model parameters
+        base_padding = (proj_kernel - 1) // 2
+        act_slope = 0.2
+        self.inference_padding = 2
+
+        # initial layer
+        layers = []
+        layers += [
+            nn.ReflectionPad1d(base_padding),
+            weight_norm(
+                nn.Conv1d(in_channels,
+                          base_channels,
+                          kernel_size=proj_kernel,
+                          stride=1,
+                          bias=True))
+        ]
+
+        # upsampling layers and residual stacks
+        for idx, upsample_factor in enumerate(upsample_factors):
+            layer_in_channels = base_channels // (2**idx)
+            layer_out_channels = base_channels // (2**(idx + 1))
+            layer_filter_size = upsample_factor * 2
+            layer_stride = upsample_factor
+            layer_output_padding = upsample_factor % 2
+            layer_padding = upsample_factor // 2 + layer_output_padding
+            layers += [
+                nn.LeakyReLU(act_slope),
+                weight_norm(
+                    nn.ConvTranspose1d(layer_in_channels,
+                                       layer_out_channels,
+                                       layer_filter_size,
+                                       stride=layer_stride,
+                                       padding=layer_padding,
+                                       output_padding=layer_output_padding,
+                                       bias=True)),
+                ResidualStack(
+                    channels=layer_out_channels,
+                    num_res_blocks=num_res_blocks,
+                    kernel_size=res_kernel
+                )
+            ]
+
+        layers += [nn.LeakyReLU(act_slope)]
+
+        # final layer
+        layers += [
+            nn.ReflectionPad1d(base_padding),
+            weight_norm(
+                nn.Conv1d(layer_out_channels,
+                          out_channels,
+                          proj_kernel,
+                          stride=1,
+                          bias=True)),
+            nn.Tanh()
+        ]
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, cond_features):
+        return self.layers(cond_features)
+
+    def inference(self, cond_features):
+        cond_features = cond_features.to(self.layers[1].weight.device)
+        cond_features = torch.nn.functional.pad(
+            cond_features,
+            (self.inference_padding, self.inference_padding),
+            'replicate')
+        return self.layers(cond_features)
diff --git a/vocoder/models/melgan_multiscale_discriminator.py b/vocoder/models/melgan_multiscale_discriminator.py
new file mode 100644
index 00000000..d77b9ceb
--- /dev/null
+++ b/vocoder/models/melgan_multiscale_discriminator.py
@@ -0,0 +1,41 @@
+from torch import nn
+
+from TTS.vocoder.models.melgan_discriminator import MelganDiscriminator
+
+
+class MelganMultiscaleDiscriminator(nn.Module):
+    def __init__(self,
+                 in_channels=1,
+                 out_channels=1,
+                 num_scales=3,
+                 kernel_sizes=(5, 3),
+                 base_channels=16,
+                 max_channels=1024,
+                 downsample_factors=(4, 4, 4, 4),
+                 pooling_kernel_size=4,
+                 pooling_stride=2,
+                 pooling_padding=1):
+        super(MelganMultiscaleDiscriminator, self).__init__()
+
+        self.discriminators = nn.ModuleList([
+            MelganDiscriminator(in_channels=in_channels,
+                                out_channels=out_channels,
+                                kernel_sizes=kernel_sizes,
+                                base_channels=base_channels,
+                                max_channels=max_channels,
+                                downsample_factors=downsample_factors)
+            for _ in range(num_scales)
+        ])
+
+        self.pooling = nn.AvgPool1d(kernel_size=pooling_kernel_size, stride=pooling_stride, padding=pooling_padding, count_include_pad=False)
+
+
+    def forward(self, x):
+        scores = list()
+        feats = list()
+        for disc in self.discriminators:
+            score, feat = disc(x)
+            scores.append(score)
+            feats.append(feat)
+            x = self.pooling(x)
+        return scores, feats
\ No newline at end of file
diff --git a/vocoder/models/multiband_melgan_generator.py b/vocoder/models/multiband_melgan_generator.py
new file mode 100644
index 00000000..8feacd25
--- /dev/null
+++ b/vocoder/models/multiband_melgan_generator.py
@@ -0,0 +1,38 @@
+import torch
+
+from TTS.vocoder.models.melgan_generator import MelganGenerator
+from TTS.vocoder.layers.pqmf import PQMF
+
+
+class MultibandMelganGenerator(MelganGenerator):
+    def __init__(self,
+                 in_channels=80,
+                 out_channels=4,
+                 proj_kernel=7,
+                 base_channels=384,
+                 upsample_factors=(2, 8, 2, 2),
+                 res_kernel=3,
+                 num_res_blocks=3):
+        super(MultibandMelganGenerator,
+              self).__init__(in_channels=in_channels,
+                             out_channels=out_channels,
+                             proj_kernel=proj_kernel,
+                             base_channels=base_channels,
+                             upsample_factors=upsample_factors,
+                             res_kernel=res_kernel,
+                             num_res_blocks=num_res_blocks)
+        self.pqmf_layer = PQMF(N=4, taps=62, cutoff=0.15, beta=9.0)
+
+    def pqmf_analysis(self, x):
+        return self.pqmf_layer.analysis(x)
+
+    def pqmf_synthesis(self, x):
+        return self.pqmf_layer.synthesis(x)
+
+    def inference(self, cond_features):
+        cond_features = cond_features.to(self.layers[1].weight.device)
+        cond_features = torch.nn.functional.pad(
+            cond_features,
+            (self.inference_padding, self.inference_padding),
+            'replicate')
+        return self.pqmf.synthesis(self.layers(cond_features))
diff --git a/vocoder/models/random_window_discriminator.py b/vocoder/models/random_window_discriminator.py
new file mode 100644
index 00000000..3efd395e
--- /dev/null
+++ b/vocoder/models/random_window_discriminator.py
@@ -0,0 +1,225 @@
+import numpy as np
+from torch import nn
+
+
+class GBlock(nn.Module):
+    def __init__(self, in_channels, cond_channels, downsample_factor):
+        super(GBlock, self).__init__()
+
+        self.in_channels = in_channels
+        self.cond_channels = cond_channels
+        self.downsample_factor = downsample_factor
+
+        self.start = nn.Sequential(
+            nn.AvgPool1d(downsample_factor, stride=downsample_factor),
+            nn.ReLU(),
+            nn.Conv1d(in_channels, in_channels * 2, kernel_size=3, padding=1))
+        self.lc_conv1d = nn.Conv1d(cond_channels,
+                                   in_channels * 2,
+                                   kernel_size=1)
+        self.end = nn.Sequential(
+            nn.ReLU(),
+            nn.Conv1d(in_channels * 2,
+                      in_channels * 2,
+                      kernel_size=3,
+                      dilation=2,
+                      padding=2))
+        self.residual = nn.Sequential(
+            nn.Conv1d(in_channels, in_channels * 2, kernel_size=1),
+            nn.AvgPool1d(downsample_factor, stride=downsample_factor))
+
+    def forward(self, inputs, conditions):
+        outputs = self.start(inputs) + self.lc_conv1d(conditions)
+        outputs = self.end(outputs)
+        residual_outputs = self.residual(inputs)
+        outputs = outputs + residual_outputs
+
+        return outputs
+
+
+class DBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, downsample_factor):
+        super(DBlock, self).__init__()
+
+        self.in_channels = in_channels
+        self.downsample_factor = downsample_factor
+        self.out_channels = out_channels
+
+        self.donwsample_layer = nn.AvgPool1d(downsample_factor,
+                                             stride=downsample_factor)
+        self.layers = nn.Sequential(
+            nn.ReLU(),
+            nn.Conv1d(in_channels, out_channels, kernel_size=3, padding=1),
+            nn.ReLU(),
+            nn.Conv1d(out_channels,
+                      out_channels,
+                      kernel_size=3,
+                      dilation=2,
+                      padding=2))
+        self.residual = nn.Sequential(
+            nn.Conv1d(in_channels, out_channels, kernel_size=1), )
+
+    def forward(self, inputs):
+        if self.downsample_factor > 1:
+            outputs = self.layers(self.donwsample_layer(inputs))\
+                + self.donwsample_layer(self.residual(inputs))
+        else:
+            outputs = self.layers(inputs) + self.residual(inputs)
+        return outputs
+
+
+class ConditionalDiscriminator(nn.Module):
+    def __init__(self,
+                 in_channels,
+                 cond_channels,
+                 downsample_factors=(2, 2, 2),
+                 out_channels=(128, 256)):
+        super(ConditionalDiscriminator, self).__init__()
+
+        assert len(downsample_factors) == len(out_channels) + 1
+
+        self.in_channels = in_channels
+        self.cond_channels = cond_channels
+        self.downsample_factors = downsample_factors
+        self.out_channels = out_channels
+
+        self.pre_cond_layers = nn.ModuleList()
+        self.post_cond_layers = nn.ModuleList()
+
+        # layers before condition features
+        self.pre_cond_layers += [DBlock(in_channels, 64, 1)]
+        in_channels = 64
+        for (i, channel) in enumerate(out_channels):
+            self.pre_cond_layers.append(
+                DBlock(in_channels, channel, downsample_factors[i]))
+            in_channels = channel
+
+        # condition block
+        self.cond_block = GBlock(in_channels, cond_channels,
+                                 downsample_factors[-1])
+
+        # layers after condition block
+        self.post_cond_layers += [
+            DBlock(in_channels * 2, in_channels * 2, 1),
+            DBlock(in_channels * 2, in_channels * 2, 1),
+            nn.AdaptiveAvgPool1d(1),
+            nn.Conv1d(in_channels * 2, 1, kernel_size=1),
+        ]
+
+    def forward(self, inputs, conditions):
+        batch_size = inputs.size()[0]
+        outputs = inputs.view(batch_size, self.in_channels, -1)
+        for layer in self.pre_cond_layers:
+            outputs = layer(outputs)
+        outputs = self.cond_block(outputs, conditions)
+        for layer in self.post_cond_layers:
+            outputs = layer(outputs)
+
+        return outputs
+
+
+class UnconditionalDiscriminator(nn.Module):
+    def __init__(self,
+                 in_channels,
+                 base_channels=64,
+                 downsample_factors=(8, 4),
+                 out_channels=(128, 256)):
+        super(UnconditionalDiscriminator, self).__init__()
+
+        self.downsample_factors = downsample_factors
+        self.in_channels = in_channels
+        self.downsample_factors = downsample_factors
+        self.out_channels = out_channels
+
+        self.layers = nn.ModuleList()
+        self.layers += [DBlock(self.in_channels, base_channels, 1)]
+        in_channels = base_channels
+        for (i, factor) in enumerate(downsample_factors):
+            self.layers.append(DBlock(in_channels, out_channels[i], factor))
+            in_channels *= 2
+        self.layers += [
+            DBlock(in_channels, in_channels, 1),
+            DBlock(in_channels, in_channels, 1),
+            nn.AdaptiveAvgPool1d(1),
+            nn.Conv1d(in_channels, 1, kernel_size=1),
+        ]
+
+    def forward(self, inputs):
+        batch_size = inputs.size()[0]
+        outputs = inputs.view(batch_size, self.in_channels, -1)
+        for layer in self.layers:
+            outputs = layer(outputs)
+        return outputs
+
+
+class RandomWindowDiscriminator(nn.Module):
+    """Random Window Discriminator as described in
+    http://arxiv.org/abs/1909.11646"""
+    def __init__(self,
+                 cond_channels,
+                 hop_length,
+                 uncond_disc_donwsample_factors=(8, 4),
+                 cond_disc_downsample_factors=((8, 4, 2, 2, 2), (8, 4, 2, 2),
+                                               (8, 4, 2), (8, 4), (4, 2, 2)),
+                 cond_disc_out_channels=((128, 128, 256, 256), (128, 256, 256),
+                                         (128, 256), (256, ), (128, 256)),
+                 window_sizes=(512, 1024, 2048, 4096, 8192)):
+
+        super(RandomWindowDiscriminator, self).__init__()
+        self.cond_channels = cond_channels
+        self.window_sizes = window_sizes
+        self.hop_length = hop_length
+        self.base_window_size = self.hop_length * 2
+        self.ks = [ws // self.base_window_size for ws in window_sizes]
+
+        # check arguments
+        assert len(cond_disc_downsample_factors) == len(
+            cond_disc_out_channels) == len(window_sizes)
+        for ws in window_sizes:
+            assert ws % hop_length == 0
+
+        for idx, cf in enumerate(cond_disc_downsample_factors):
+            assert np.prod(cf) == hop_length // self.ks[idx]
+
+        # define layers
+        self.unconditional_discriminators = nn.ModuleList([])
+        for k in self.ks:
+            layer = UnconditionalDiscriminator(
+                in_channels=k,
+                base_channels=64,
+                downsample_factors=uncond_disc_donwsample_factors)
+            self.unconditional_discriminators.append(layer)
+
+        self.conditional_discriminators = nn.ModuleList([])
+        for idx, k in enumerate(self.ks):
+            layer = ConditionalDiscriminator(
+                in_channels=k,
+                cond_channels=cond_channels,
+                downsample_factors=cond_disc_downsample_factors[idx],
+                out_channels=cond_disc_out_channels[idx])
+            self.conditional_discriminators.append(layer)
+
+    def forward(self, x, c):
+        scores = []
+        feats = []
+        # unconditional pass
+        for (window_size, layer) in zip(self.window_sizes,
+                                        self.unconditional_discriminators):
+            index = np.random.randint(x.shape[-1] - window_size)
+
+            score = layer(x[:, :, index:index + window_size])
+            scores.append(score)
+
+        # conditional pass
+        for (window_size, layer) in zip(self.window_sizes,
+                                        self.conditional_discriminators):
+            frame_size = window_size // self.hop_length
+            lc_index = np.random.randint(c.shape[-1] - frame_size)
+            sample_index = lc_index * self.hop_length
+            x_sub = x[:, :,
+                      sample_index:(lc_index + frame_size) * self.hop_length]
+            c_sub = c[:, :, lc_index:lc_index + frame_size]
+
+            score = layer(x_sub, c_sub)
+            scores.append(score)
+        return scores, feats
diff --git a/vocoder/notebooks/Untitled.ipynb b/vocoder/notebooks/Untitled.ipynb
new file mode 100644
index 00000000..ce49d6fa
--- /dev/null
+++ b/vocoder/notebooks/Untitled.ipynb
@@ -0,0 +1,678 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "#function example with several unknowns (variables) for optimization\n",
+    "#Gerald Schuller, Nov. 2016\n",
+    "import numpy as np\n",
+    "\n",
+    "def functionexamp(x):\n",
+    "    #x: array with 2 variables\n",
+    "    \n",
+    "    y=np.sin(x[0])+np.cos(x[1])\n",
+    "    return y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     fun: -1.9999999999888387\n",
+      "     jac: array([4.7236681e-06, 0.0000000e+00])\n",
+      " message: 'Optimization terminated successfully.'\n",
+      "    nfev: 12\n",
+      "     nit: 2\n",
+      "    njev: 3\n",
+      "  status: 0\n",
+      " success: True\n",
+      "       x: array([-1.5707916 , -3.14159265])\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Optimization example, see also:\n",
+    "#https://docs.scipy.org/doc/scipy-0.18.1/reference/optimize.html\n",
+    "#Gerald Schuller, Nov. 2016\n",
+    "#run it with \"python optimizationExample.py\" in a termina shell\n",
+    "#or type \"ipython\" in a termina shell and copy lines below:\n",
+    "\n",
+    "import numpy as np\n",
+    "import scipy.optimize as optimize\n",
+    "\n",
+    "#Example for 2 unknowns, args: function-name, starting point, method:\n",
+    "xmin = optimize.minimize(functionexamp, [-1.0, -3.0], method='CG')\n",
+    "print(xmin)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "function [p,passedge] = opt_filter(filtorder,N)\n",
+    "\n",
+    "% opt_filter      Create Lowpass Prototype Filter for the Pseudo-QMF \n",
+    "%                 Filter Bank with N Subbands\n",
+    "%\n",
+    "% Adapted from the paper by C. D. Creusere and S. K. Mitra, titled \n",
+    "%   \"A simple method for designing high-quality prototype filters for \n",
+    "%   M-band pseudo-QMF banks,\" IEEE Trans. Signal Processing,vol. 43, \n",
+    "%   pp. 1005-1007, Apr. 1995 and the book by S. K. Mitra titled \"\n",
+    "%   Digital Signal Processing: A Computer-Based Approach, McGraw-Hill, 2001\n",
+    "%\n",
+    "% Arguments:\n",
+    "% filtorder     Filter order (i.e., filter length - 1)\n",
+    "% N             Number of subbands\n",
+    "\n",
+    "stopedge = 1/N;       % Stopband edge fixed at (1/N)pi\n",
+    "passedge = 1/(4*N);   % Start value for passband edge\n",
+    "tol = 0.000001;       % Tolerance\n",
+    "step = 0.1*passedge;  % Step size for searching the passband edge\n",
+    "way = -1;             % Search direction, increase or reduce the passband edge\n",
+    "tcost = 0;            % Current error calculated with the cost function\n",
+    "pcost = 10;           % Previous error calculated with the cost function\n",
+    "flag = 0;             % Set to 1 to stop the search\n",
+    "\n",
+    "while flag == 0\n",
+    "    \n",
+    "% Design the lowpass filter using Parks-McClellan algorithm\n",
+    " \n",
+    "    p = remez(filtorder,[0,passedge,stopedge,1],[1,1,0,0],[5,1]);\n",
+    "    \n",
+    "% Calculates the cost function according to Eq. (2.36)\n",
+    "\n",
+    "    P = fft(p,4096);\n",
+    "    OptRange = floor(2048/N);           % 0 to pi/N\n",
+    "    phi = zeros(OptRange,1);            % Initialize to zeros\n",
+    "\n",
+    "% Compute the flatness in the range from 0 to pi/N\n",
+    "\n",
+    "\tfor k = 1:OptRange\n",
+    "          phi(k) = abs(P(OptRange-k+2))^2 + abs(P(k))^2;\n",
+    "\tend\n",
+    "\ttcost = max(abs(phi - ones(max(size(phi)),1)));\n",
+    "   \t\n",
+    "\tif tcost > pcost                % If search in wrong direction\n",
+    "\t\tstep = step/2;          % Reduce step size by half \n",
+    "\t\tway = -way;             % Change the search direction \n",
+    "\tend\n",
+    "\t\n",
+    "\tif abs(pcost - tcost) < tol     % If improvement is below tol         \n",
+    "\t\tflag = 1;               % Stop the search                    \n",
+    "\tend\n",
+    "\t\n",
+    "\tpcost = tcost;\n",
+    "\tpassedge = passedge + way*step; % Adjust the passband edge\n",
+    "   \n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "sig.remez"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0125"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 / 4. / 20.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Band edges should be less than 1/2 the sampling frequency",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-90-fb4445f5709b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremez\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m16.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m4.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/scipy/signal/fir_filter_design.py\u001b[0m in \u001b[0;36mremez\u001b[0;34m(numtaps, bands, desired, weight, Hz, type, maxiter, grid_density, fs)\u001b[0m\n\u001b[1;32m    854\u001b[0m     \u001b[0mbands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    855\u001b[0m     return sigtools._remez(numtaps, bands, desired, weight, tnum, fs,\n\u001b[0;32m--> 856\u001b[0;31m                            maxiter, grid_density)\n\u001b[0m\u001b[1;32m    857\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: Band edges should be less than 1/2 the sampling frequency"
+     ]
+    }
+   ],
+   "source": [
+    "p = sig.remez(65, [0, 1/16.0, 1/4.0, 1], [1, 0], [5, 1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "def create_pqmf_filter(filter_len=64, N=4):\n",
+    "    stop_edge = 1 / N\n",
+    "    pass_edge = 1 / (4 * N)\n",
+    "    tol = 1e-8\n",
+    "    cutoff = 0.1 * pass_edge\n",
+    "    cost = 0\n",
+    "    cost_prev = float('inf')\n",
+    "    \n",
+    "    p = sig.remez(filter_len, [0, pass_edge, stop_edge, 1], [1, 1, 0, 0], [5, 1])\n",
+    "    \n",
+    "    P = sig.freqz(p, workN=2048)\n",
+    "    opt_range = 2048 // N\n",
+    "    phi = np.zeros(opt_range)\n",
+    "    \n",
+    "    H = np.abs(P)\n",
+    "    phi = H[opt_range  + 2] \n",
+    "    for i in range(opt_range):\n",
+    "        phi[i] = abs(P(opt_range - i + 2)) ** 2 + abs(P[i]) ** 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import scipy as sp\n",
+    "import scipy.signal as sig\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "\n",
+    "def optimfuncQMF(x):\n",
+    "    \"\"\"Optimization function for a PQMF Filterbank\n",
+    "    x: coefficients to optimize (first half of prototype h because of symmetry)\n",
+    "    err: resulting total error\n",
+    "    \"\"\"\n",
+    "    K = ntaps * N \n",
+    "    h = np.append(x, np.flipud(x))\n",
+    "    cutoff = 0.15\n",
+    "    \n",
+    "#     breakpoint()\n",
+    "    f, H_im = sig.freqz(h, worN=K)\n",
+    "    H = np.abs(H_im) #only keeping the real part\n",
+    "    \n",
+    "    posfreq = np.square(H[0:K//N])\n",
+    "    \n",
+    "    #Negative frequencies are symmetric around 0:\n",
+    "    negfreq = np.flipud(np.square(H[0:K//N]))\n",
+    "    \n",
+    "    #Sum of magnitude squared frequency responses should be closed to unity (or N)\n",
+    "    unitycond = np.sum(np.abs(posfreq + negfreq - 2*(N*N)*np.ones(K//N)))/K\n",
+    "    \n",
+    "    #plt.plot(posfreq+negfreq)\n",
+    "    \n",
+    "    #High attenuation after the next subband:\n",
+    "    att = np.sum(np.abs(H[int(cutoff*K//N):]))/K\n",
+    "    \n",
+    "    #Total (weighted) error:\n",
+    "    err = unitycond + 100*att\n",
+    "    return err"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(32,)"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xmin.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8.684549400499243\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import scipy.optimize as opt\n",
+    "import scipy.signal as sig\n",
+    "\n",
+    "ntaps = 64\n",
+    "N = 4\n",
+    "\n",
+    "#optimize for 16 filter coefficients:\n",
+    "xmin = opt.minimize(optimfuncQMF, ntaps*np.ones(ntaps), method='SLSQP', tol=1e-8)\n",
+    "xmin = xmin[\"x\"]\n",
+    "\n",
+    "err = optimfuncQMF(xmin)\n",
+    "print(err)\n",
+    "\n",
+    "#Restore symmetric upper half of window:\n",
+    "h = np.concatenate((xmin, np.flipud(xmin)))\n",
+    "plt.plot(h)\n",
+    "plt.title('Resulting PQMF Window Function')\n",
+    "plt.xlabel('Sample')\n",
+    "plt.ylabel('Value')\n",
+    "plt.show()\n",
+    "\n",
+    "f, H = sig.freqz(h)\n",
+    "plt.plot(f, 20*np.log10(np.abs(H)))\n",
+    "plt.title('Resulting PQMF Magnitude Response')\n",
+    "plt.xlabel('Normalized Frequency')\n",
+    "plt.ylabel('dB')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "N = 4\n",
+    "f, H_im = sig.freqz(h)\n",
+    "posfreq = np.square(H[0:512//N])\n",
+    "negfreq = np.flipud(np.square(H[0:512//N]))\n",
+    "plt.plot((np.abs(posfreq) + np.abs(negfreq)))\n",
+    "plt.xlabel('Frequency (512 is Nyquist)')\n",
+    "plt.ylabel('Magnitude')\n",
+    "plt.title('Unity Condition, Sum of Squared Magnitude of 2 Neighboring Subbands')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "b = sig.firwin(80, 0.5, window=('kaiser', 8))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, H_im = sig.freqz(h)\n",
+    "posfreq = np.square(H[0:512//N])\n",
+    "negfreq = np.flipud(np.square(H[0:512//N]))\n",
+    "plt.plot((np.abs(posfreq) + np.abs(negfreq)))\n",
+    "plt.xlabel('Frequency (512 is Nyquist)')\n",
+    "plt.ylabel('Magnitude')\n",
+    "plt.title('Unity Condition, Sum of Squared Magnitude of 2 Neighboring Subbands')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(63,)"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cutoff = 0.15\n",
+    "beta = 9\n",
+    "ntaps = 63\n",
+    "N = 4\n",
+    "\n",
+    "b = sig.firwin(ntaps, cutoff, window=('kaiser', beta))\n",
+    "w, h = sig.freqz(b)\n",
+    "\n",
+    "plt.plot(b)\n",
+    "plt.title('Resulting PQMF Window Function')\n",
+    "plt.xlabel('Sample')\n",
+    "plt.ylabel('Value')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "fig, ax1 = plt.subplots()\n",
+    "ax1.set_title('Digital filter frequency response')\n",
+    "\n",
+    "ax1.plot(w / (2 * np.pi), 20 * np.log10(abs(h)), 'b')\n",
+    "ax1.set_ylabel('Amplitude [dB]', color='b')\n",
+    "ax1.set_xlabel('Frequency [rad/sample]')\n",
+    "\n",
+    "ax2 = ax1.twinx()\n",
+    "angles = np.unwrap(np.angle(h))\n",
+    "ax2.plot(w / (2 * np.pi), angles, 'g')\n",
+    "ax2.set_ylabel('Angle (radians)', color='g')\n",
+    "ax2.grid()\n",
+    "ax2.axis('tight')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(63,)"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": [
+    "def optimfuncQMF(x):\n",
+    "    \"\"\"Optimization function for a PQMF Filterbank\n",
+    "    x: coefficients to optimize (first half of prototype h because of symmetry)\n",
+    "    err: resulting total error\n",
+    "    \"\"\"\n",
+    "    N = 2 #4 subbands\n",
+    "    cutoff = 1.5 #1.5\n",
+    "    h = np.append(x, np.flipud(x))\n",
+    "    f, H_im = sig.freqz(h)\n",
+    "    H = np.abs(H_im) #only keeping the real part\n",
+    "    \n",
+    "    posfreq = np.square(H[0:512//N])\n",
+    "    \n",
+    "    #Negative frequencies are symmetric around 0:\n",
+    "    negfreq = np.flipud(np.square(H[0:512//N]))\n",
+    "    \n",
+    "    #Sum of magnitude squared frequency responses should be closed to unity (or N)\n",
+    "    unitycond = np.sum(np.abs(posfreq + negfreq - 2*(N*N)*np.ones(512//N)))//512\n",
+    "    \n",
+    "    #plt.plot(posfreq+negfreq)\n",
+    "    \n",
+    "    #High attenuation after the next subband:\n",
+    "    att = np.sum(np.abs(H[int(cutoff*512//N):]))//512\n",
+    "    \n",
+    "    #Total (weighted) error:\n",
+    "    err = unitycond + 100*att\n",
+    "    return err"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "err = optimfuncQMF(b)\n",
+    "print(err)\n",
+    "\n",
+    "#Restore symmetric upper half of window:\n",
+    "h = np.concatenate((xmin, np.flipud(xmin)))\n",
+    "plt.plot(h)\n",
+    "plt.title('Resulting PQMF Window Function')\n",
+    "plt.xlabel('Sample')\n",
+    "plt.ylabel('Value')\n",
+    "plt.show()\n",
+    "\n",
+    "f, H = sig.freqz(h)\n",
+    "plt.plot(f, 20*np.log10(np.abs(H)))\n",
+    "plt.title('Resulting PQMF Magnitude Response')\n",
+    "plt.xlabel('Normalized Frequency')\n",
+    "plt.ylabel('dB')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "Collapsed": "false"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.7.4 64-bit ('base': conda)",
+   "language": "python",
+   "name": "python37464bitbaseconda58faf23c4b5f4fef93406f29a1005f35"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/vocoder/notebooks/Untitled1.ipynb b/vocoder/notebooks/Untitled1.ipynb
new file mode 100644
index 00000000..7fec5150
--- /dev/null
+++ b/vocoder/notebooks/Untitled1.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/vocoder/pqmf_output.wav b/vocoder/pqmf_output.wav
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diff --git a/vocoder/tests/__init__.py b/vocoder/tests/__init__.py
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/vocoder/tests/__init__.py
@@ -0,0 +1 @@
+
diff --git a/vocoder/tests/test_config.json b/vocoder/tests/test_config.json
new file mode 100644
index 00000000..08acc48c
--- /dev/null
+++ b/vocoder/tests/test_config.json
@@ -0,0 +1,24 @@
+{
+    "audio":{
+        "num_mels": 80,         // size of the mel spec frame.
+        "num_freq": 513,       // number of stft frequency levels. Size of the linear spectogram frame.
+        "sample_rate": 22050,   // wav sample-rate. If different than the original data, it is resampled.
+        "frame_length_ms": null,  // stft window length in ms.
+        "frame_shift_ms": null, // stft window hop-lengh in ms.
+        "hop_length": 256,
+        "win_length": 1024,
+        "preemphasis": 0.97,    // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
+        "min_level_db": -100,   // normalization range
+        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
+        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
+        "griffin_lim_iters": 30,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
+        "signal_norm": true,    // normalize the spec values in range [0, 1]
+        "symmetric_norm": true, // move normalization to range [-1, 1]
+        "clip_norm": true,       // clip normalized values into the range.
+        "max_norm": 4,          // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
+        "mel_fmin": 0,         // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
+        "mel_fmax": 8000,        // maximum freq level for mel-spec. Tune for dataset!!
+        "do_trim_silence": false
+    }
+}
+
diff --git a/vocoder/tests/test_datasets.py b/vocoder/tests/test_datasets.py
new file mode 100644
index 00000000..3d6280f0
--- /dev/null
+++ b/vocoder/tests/test_datasets.py
@@ -0,0 +1,95 @@
+import os
+import numpy as np
+from torch.utils.data import DataLoader
+
+from TTS.vocoder.datasets.gan_dataset import GANDataset
+from TTS.vocoder.datasets.preprocess import load_wav_data
+from TTS.utils.audio import AudioProcessor
+from TTS.utils.io import load_config
+
+
+file_path = os.path.dirname(os.path.realpath(__file__))
+OUTPATH = os.path.join(file_path, "../../tests/outputs/loader_tests/")
+os.makedirs(OUTPATH, exist_ok=True)
+
+C = load_config(os.path.join(file_path, 'test_config.json'))
+
+test_data_path = os.path.join(file_path, "../../tests/data/ljspeech/")
+ok_ljspeech = os.path.exists(test_data_path)
+
+
+
+def gan_dataset_case(batch_size, seq_len, hop_len, conv_pad, return_segments, use_noise_augment, use_cache, num_workers):
+    ''' run dataloader with given parameters and check conditions '''
+    ap = AudioProcessor(**C.audio)
+    eval_items, train_items = load_wav_data(test_data_path, 10)
+    dataset = GANDataset(ap,
+                         train_items,
+                         seq_len=seq_len,
+                         hop_len=hop_len,
+                         pad_short=2000,
+                         conv_pad=conv_pad,
+                         return_segments=return_segments,
+                         use_noise_augment=use_noise_augment,
+                         use_cache=use_cache)
+    loader = DataLoader(dataset=dataset,
+                        batch_size=batch_size,
+                        shuffle=True,
+                        num_workers=num_workers,
+                        pin_memory=True,
+                        drop_last=True)
+
+    max_iter = 10
+    count_iter = 0
+
+    # return random segments or return the whole audio
+    if return_segments:
+        for item1, item2 in loader:
+            feat1, wav1 = item1
+            feat2, wav2 = item2
+            expected_feat_shape = (batch_size, ap.num_mels, seq_len // hop_len + conv_pad * 2)
+
+            # check shapes
+            assert np.all(feat1.shape == expected_feat_shape), f" [!] {feat1.shape} vs {expected_feat_shape}"
+            assert (feat1.shape[2] - conv_pad * 2) * hop_len == wav1.shape[2]
+
+            # check feature vs audio match
+            if not use_noise_augment:
+                for idx in range(batch_size):
+                    audio = wav1[idx].squeeze()
+                    feat = feat1[idx]
+                    mel = ap.melspectrogram(audio)
+                    # the first 2 and the last frame is skipped due to the padding
+                    # applied in spec. computation.
+                    assert (feat - mel[:, :feat1.shape[-1]])[:, 2:-1].sum() == 0, f' [!] {(feat - mel[:, :feat1.shape[-1]])[:, 2:-1].sum()}'
+
+            count_iter += 1
+            # if count_iter == max_iter:
+            #     break
+    else:
+        for item in loader:
+            feat, wav = item
+            expected_feat_shape = (batch_size, ap.num_mels, (wav.shape[-1] // hop_len) + (conv_pad * 2))
+            assert np.all(feat.shape == expected_feat_shape), f" [!] {feat.shape} vs {expected_feat_shape}"
+            assert (feat.shape[2] - conv_pad * 2) * hop_len == wav.shape[2]
+            count_iter += 1
+            if count_iter == max_iter:
+                break
+
+
+def test_parametrized_gan_dataset():
+    ''' test dataloader with different parameters '''
+    params = [
+        [32, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, True, False, True, 0],
+        [32, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, True, False, True, 4],
+        [1, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, True, True, True, 0],
+        [1, C.audio['hop_length'], C.audio['hop_length'], 0, True, True, True, 0],
+        [1, C.audio['hop_length'] * 10,  C.audio['hop_length'], 2, True, True, True, 0],
+        [1, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, False, True, True, 0],
+        [1, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, True, False, True, 0],
+        [1, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, True, True, False, 0],
+        [1, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, False, False, False, 0],
+    ]
+    for param in params:
+        print(param)
+        gan_dataset_case(*param)
diff --git a/vocoder/tests/test_losses.py b/vocoder/tests/test_losses.py
new file mode 100644
index 00000000..83314832
--- /dev/null
+++ b/vocoder/tests/test_losses.py
@@ -0,0 +1,62 @@
+import os
+import unittest
+import torch
+
+from TTS.vocoder.layers.losses import TorchSTFT, STFTLoss, MultiScaleSTFTLoss
+
+from TTS.tests import get_tests_path, get_tests_input_path, get_tests_output_path
+from TTS.utils.audio import AudioProcessor
+from TTS.utils.io import load_config
+
+TESTS_PATH = get_tests_path()
+
+OUT_PATH = os.path.join(get_tests_output_path(), "audio_tests")
+os.makedirs(OUT_PATH, exist_ok=True)
+
+WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
+
+file_path = os.path.dirname(os.path.realpath(__file__))
+C = load_config(os.path.join(file_path, 'test_config.json'))
+ap = AudioProcessor(**C.audio)
+
+
+def test_torch_stft():
+    torch_stft = TorchSTFT(ap.n_fft, ap.hop_length, ap.win_length)
+    # librosa stft
+    wav = ap.load_wav(WAV_FILE)
+    M_librosa = abs(ap._stft(wav))
+    # torch stft
+    wav = torch.from_numpy(wav[None, :]).float()
+    M_torch = torch_stft(wav)
+    # check the difference b/w librosa and torch outputs
+    assert (M_librosa - M_torch[0].data.numpy()).max() < 1e-5
+
+
+def test_stft_loss():
+    stft_loss = STFTLoss(ap.n_fft, ap.hop_length, ap.win_length)
+    wav = ap.load_wav(WAV_FILE)
+    wav = torch.from_numpy(wav[None, :]).float()
+    loss_m, loss_sc = stft_loss(wav, wav)
+    assert loss_m + loss_sc == 0
+    loss_m, loss_sc = stft_loss(wav, torch.rand_like(wav))
+    assert loss_sc < 1.0
+    assert loss_m + loss_sc > 0
+
+
+def test_multiscale_stft_loss():
+    stft_loss = MultiScaleSTFTLoss([ap.n_fft//2, ap.n_fft, ap.n_fft*2],
+                                   [ap.hop_length // 2, ap.hop_length, ap.hop_length * 2],
+                                   [ap.win_length // 2, ap.win_length, ap.win_length * 2])
+    wav = ap.load_wav(WAV_FILE)
+    wav = torch.from_numpy(wav[None, :]).float()
+    loss_m, loss_sc = stft_loss(wav, wav)
+    assert loss_m + loss_sc == 0
+    loss_m, loss_sc = stft_loss(wav, torch.rand_like(wav))
+    assert loss_sc < 1.0
+    assert loss_m + loss_sc > 0
+
+
+
+
+
+
diff --git a/vocoder/tests/test_melgan_discriminator.py b/vocoder/tests/test_melgan_discriminator.py
new file mode 100644
index 00000000..83acec8f
--- /dev/null
+++ b/vocoder/tests/test_melgan_discriminator.py
@@ -0,0 +1,26 @@
+import numpy as np
+import torch
+
+from TTS.vocoder.models.melgan_discriminator import MelganDiscriminator
+from TTS.vocoder.models.melgan_multiscale_discriminator import MelganMultiscaleDiscriminator
+
+
+def test_melgan_discriminator():
+    model = MelganDiscriminator()
+    print(model)
+    dummy_input = torch.rand((4, 1, 256 * 10))
+    output, _ = model(dummy_input)
+    assert np.all(output.shape == (4, 1, 10))
+
+
+def test_melgan_multi_scale_discriminator():
+    model = MelganMultiscaleDiscriminator()
+    print(model)
+    dummy_input = torch.rand((4, 1, 256 * 16))
+    scores, feats = model(dummy_input)
+    assert len(scores) == 3
+    assert len(scores) == len(feats)
+    assert np.all(scores[0].shape == (4, 1, 16))
+    assert np.all(feats[0][0].shape == (4, 16, 4096))
+    assert np.all(feats[0][1].shape == (4, 64, 1024))
+    assert np.all(feats[0][2].shape == (4, 256, 256))
diff --git a/vocoder/tests/test_melgan_generator.py b/vocoder/tests/test_melgan_generator.py
new file mode 100644
index 00000000..e9c4ad60
--- /dev/null
+++ b/vocoder/tests/test_melgan_generator.py
@@ -0,0 +1,15 @@
+import numpy as np
+import unittest
+import torch
+
+from TTS.vocoder.models.melgan_generator import MelganGenerator
+
+def test_melgan_generator():
+    model = MelganGenerator()
+    print(model)
+    dummy_input = torch.rand((4, 80, 64))
+    output = model(dummy_input)
+    assert np.all(output.shape == (4, 1, 64 * 256))
+    output = model.inference(dummy_input)
+    assert np.all(output.shape == (4, 1, (64 + 4) * 256))
+
diff --git a/vocoder/tests/test_pqmf.py b/vocoder/tests/test_pqmf.py
new file mode 100644
index 00000000..8444fc5a
--- /dev/null
+++ b/vocoder/tests/test_pqmf.py
@@ -0,0 +1,33 @@
+import os
+import torch
+import unittest
+
+import numpy as np
+import soundfile as sf
+from librosa.core import load
+
+from TTS.tests import get_tests_path, get_tests_input_path, get_tests_output_path
+from TTS.utils.audio import AudioProcessor
+from TTS.utils.io import load_config
+from TTS.vocoder.layers.pqmf import PQMF
+from TTS.vocoder.layers.pqmf2 import PQMF as PQMF2
+
+
+TESTS_PATH = get_tests_path()
+WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
+
+
+def test_pqmf():
+    w, sr = load(WAV_FILE)
+
+    layer = PQMF(N=4, taps=62, cutoff=0.15, beta=9.0)
+    w, sr = load(WAV_FILE)
+    w2 = torch.from_numpy(w[None, None, :])
+    b2 = layer.analysis(w2)
+    w2_ = layer.synthesis(b2)
+
+    print(w2_.max())
+    print(w2_.min())
+    print(w2_.mean())
+    sf.write('pqmf_output.wav', w2_.flatten().detach(), sr)
+
diff --git a/vocoder/tests/test_rwd.py b/vocoder/tests/test_rwd.py
new file mode 100644
index 00000000..424d3b49
--- /dev/null
+++ b/vocoder/tests/test_rwd.py
@@ -0,0 +1,21 @@
+import torch
+import numpy as np
+
+from TTS.vocoder.models.random_window_discriminator import RandomWindowDiscriminator
+
+
+def test_rwd():
+    layer = RandomWindowDiscriminator(cond_channels=80,
+                                      window_sizes=(512, 1024, 2048, 4096,
+                                                    8192),
+                                      cond_disc_downsample_factors=[
+                                          (8, 4, 2, 2, 2), (8, 4, 2, 2),
+                                          (8, 4, 2), (8, 4), (4, 2, 2)
+                                      ],
+                                      hop_length=256)
+    x = torch.rand([4, 1, 22050])
+    c = torch.rand([4, 80, 22050 // 256])
+
+    scores, _ = layer(x, c)
+    assert len(scores) == 10
+    assert np.all(scores[0].shape == (4, 1, 1))
diff --git a/vocoder/train.py b/vocoder/train.py
new file mode 100644
index 00000000..b0b4833a
--- /dev/null
+++ b/vocoder/train.py
@@ -0,0 +1,585 @@
+import argparse
+import glob
+import os
+import sys
+import time
+import traceback
+
+import torch
+from torch.utils.data import DataLoader
+
+from inspect import signature
+
+from TTS.utils.audio import AudioProcessor
+from TTS.utils.generic_utils import (KeepAverage, count_parameters,
+                                     create_experiment_folder, get_git_branch,
+                                     remove_experiment_folder, set_init_dict)
+from TTS.utils.io import copy_config_file, load_config
+from TTS.utils.radam import RAdam
+from TTS.utils.tensorboard_logger import TensorboardLogger
+from TTS.utils.training import NoamLR
+from TTS.vocoder.datasets.gan_dataset import GANDataset
+from TTS.vocoder.datasets.preprocess import load_wav_data
+# from distribute import (DistributedSampler, apply_gradient_allreduce,
+#                         init_distributed, reduce_tensor)
+from TTS.vocoder.layers.losses import DiscriminatorLoss, GeneratorLoss
+from TTS.vocoder.utils.io import save_checkpoint, save_best_model
+from TTS.vocoder.utils.console_logger import ConsoleLogger
+from TTS.vocoder.utils.generic_utils import (check_config, plot_results,
+                                             setup_discriminator,
+                                             setup_generator)
+
+torch.backends.cudnn.enabled = True
+torch.backends.cudnn.benchmark = True
+torch.manual_seed(54321)
+use_cuda = torch.cuda.is_available()
+num_gpus = torch.cuda.device_count()
+print(" > Using CUDA: ", use_cuda)
+print(" > Number of GPUs: ", num_gpus)
+
+
+def setup_loader(ap, is_val=False, verbose=False):
+    if is_val and not c.run_eval:
+        loader = None
+    else:
+        dataset = GANDataset(ap=ap,
+                             items=eval_data if is_val else train_data,
+                             seq_len=c.seq_len,
+                             hop_len=ap.hop_length,
+                             pad_short=c.pad_short,
+                             conv_pad=c.conv_pad,
+                             is_training=not is_val,
+                             return_segments=False if is_val else True,
+                             use_noise_augment=c.use_noise_augment,
+                             use_cache=c.use_cache,
+                             verbose=verbose)
+        # sampler = DistributedSampler(dataset) if num_gpus > 1 else None
+        loader = DataLoader(dataset,
+                            batch_size=1 if is_val else c.batch_size,
+                            shuffle=False,
+                            drop_last=False,
+                            sampler=None,
+                            num_workers=c.num_val_loader_workers
+                            if is_val else c.num_loader_workers,
+                            pin_memory=False)
+    return loader
+
+
+def format_data(data):
+    if isinstance(data[0], list):
+        # setup input data
+        c_G, x_G = data[0]
+        c_D, x_D = data[1]
+
+        # dispatch data to GPU
+        if use_cuda:
+            c_G = c_G.cuda(non_blocking=True)
+            x_G = x_G.cuda(non_blocking=True)
+            c_D = c_D.cuda(non_blocking=True)
+            x_D = x_D.cuda(non_blocking=True)
+
+        return c_G, x_G, c_D, x_D
+
+    # return a whole audio segment
+    c, x = data
+    if use_cuda:
+        c = c.cuda(non_blocking=True)
+        x = x.cuda(non_blocking=True)
+    return c, x
+
+
+def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
+          scheduler_G, scheduler_D, ap, global_step, epoch):
+    data_loader = setup_loader(ap, is_val=False, verbose=(epoch == 0))
+    model_G.train()
+    model_D.train()
+    epoch_time = 0
+    keep_avg = KeepAverage()
+    if use_cuda:
+        batch_n_iter = int(
+            len(data_loader.dataset) / (c.batch_size * num_gpus))
+    else:
+        batch_n_iter = int(len(data_loader.dataset) / c.batch_size)
+    end_time = time.time()
+    c_logger.print_train_start()
+    for num_iter, data in enumerate(data_loader):
+        start_time = time.time()
+
+        # format data
+        c_G, y_G, c_D, y_D = format_data(data)
+        loader_time = time.time() - end_time
+
+        global_step += 1
+
+        # get current learning rates
+        current_lr_G = list(optimizer_G.param_groups)[0]['lr']
+        current_lr_D = list(optimizer_D.param_groups)[0]['lr']
+
+        ##############################
+        # GENERATOR
+        ##############################
+
+        # generator pass
+        optimizer_G.zero_grad()
+        y_hat = model_G(c_G)
+
+        in_real_D = y_hat
+        in_fake_D = y_G
+
+        # PQMF formatting
+        if y_hat.shape[1] > 1:
+            in_real_D = y_G
+            in_fake_D = model_G.pqmf_synthesis(y_hat)
+            y_G = model_G.pqmf_analysis(y_G)
+            y_hat = y_hat.view(-1, 1, y_hat.shape[2])
+            y_G = y_G.view(-1, 1, y_G.shape[2])
+
+        if global_step > c.steps_to_start_discriminator:
+
+            # run D with or without cond. features
+            if len(signature(model_D).parameters) == 2:
+                D_out_fake = model_D(in_fake_D, c_G)
+            else:
+                D_out_fake = model_D(in_fake_D)
+            D_out_real = None
+
+            if c.use_feat_match_loss:
+                with torch.no_grad():
+                    D_out_real = model_D(in_real_D)
+
+            # format D outputs
+            if isinstance(D_out_fake, tuple):
+                scores_fake, feats_fake = D_out_fake
+                if D_out_real is None:
+                    scores_real, feats_real = None, None
+                else:
+                    scores_real, feats_real = D_out_real
+            else:
+                scores_fake = D_out_fake
+                scores_real = D_out_real
+        else:
+            scores_fake, feats_fake, feats_real = None, None, None
+
+        # compute losses
+        loss_G_dict = criterion_G(y_hat, y_G, scores_fake, feats_fake,
+                                  feats_real)
+        loss_G = loss_G_dict['G_loss']
+
+        # optimizer generator
+        loss_G.backward()
+        if c.gen_clip_grad > 0:
+            torch.nn.utils.clip_grad_norm_(model_G.parameters(),
+                                           c.gen_clip_grad)
+        optimizer_G.step()
+
+        # setup lr
+        if c.noam_schedule:
+            scheduler_G.step()
+
+        loss_dict = dict()
+        for key, value in loss_G_dict.items():
+            loss_dict[key] = value.item()
+
+        ##############################
+        # DISCRIMINATOR
+        ##############################
+        if global_step > c.steps_to_start_discriminator:
+            # discriminator pass
+            with torch.no_grad():
+                y_hat = model_G(c_D)
+
+            # PQMF formatting
+            if y_hat.shape[1] > 1:
+                y_hat = model_G.pqmf_synthesis(y_hat)
+
+            optimizer_D.zero_grad()
+
+            # run D with or without cond. features
+            if len(signature(model_D).parameters) == 2:
+                D_out_fake = model_D(y_hat.detach(), c_D)
+                D_out_real = model_D(y_D, c_D)
+            else:
+                D_out_fake = model_D(y_hat.detach())
+                D_out_real = model_D(y_D)
+
+            # format D outputs
+            if isinstance(D_out_fake, tuple):
+                scores_fake, feats_fake = D_out_fake
+                if D_out_real is None:
+                    scores_real, feats_real = None, None
+                else:
+                    scores_real, feats_real = D_out_real
+            else:
+                scores_fake = D_out_fake
+                scores_real = D_out_real
+
+            # compute losses
+            loss_D_dict = criterion_D(scores_fake, scores_real)
+            loss_D = loss_D_dict['D_loss']
+
+            # optimizer discriminator
+            loss_D.backward()
+            if c.disc_clip_grad > 0:
+                torch.nn.utils.clip_grad_norm_(model_D.parameters(),
+                                               c.disc_clip_grad)
+            optimizer_D.step()
+
+            # setup lr
+            if c.noam_schedule:
+                scheduler_D.step()
+
+            for key, value in loss_D_dict.items():
+                loss_dict[key] = value.item()
+
+        step_time = time.time() - start_time
+        epoch_time += step_time
+
+        # update avg stats
+        update_train_values = dict()
+        for key, value in loss_dict.items():
+            update_train_values['avg_' + key] = value
+        update_train_values['avg_loader_time'] = loader_time
+        update_train_values['avg_step_time'] = step_time
+        keep_avg.update_values(update_train_values)
+
+        # print training stats
+        if global_step % c.print_step == 0:
+            c_logger.print_train_step(batch_n_iter, num_iter, global_step,
+                                      step_time, loader_time, current_lr_G,
+                                      loss_dict, keep_avg.avg_values)
+
+        # plot step stats
+        if global_step % 10 == 0:
+            iter_stats = {
+                "lr_G": current_lr_G,
+                "lr_D": current_lr_D,
+                "step_time": step_time
+            }
+            iter_stats.update(loss_dict)
+            tb_logger.tb_train_iter_stats(global_step, iter_stats)
+
+        # save checkpoint
+        if global_step % c.save_step == 0:
+            if c.checkpoint:
+                # save model
+                save_checkpoint(model_G,
+                                optimizer_G,
+                                model_D,
+                                optimizer_D,
+                                global_step,
+                                epoch,
+                                OUT_PATH,
+                                model_losses=loss_dict)
+
+            # compute spectrograms
+            figures = plot_results(in_fake_D, in_real_D, ap, global_step,
+                                   'train')
+            tb_logger.tb_train_figures(global_step, figures)
+
+            # Sample audio
+            sample_voice = in_fake_D[0].squeeze(0).detach().cpu().numpy()
+            tb_logger.tb_train_audios(global_step,
+                                      {'train/audio': sample_voice},
+                                      c.audio["sample_rate"])
+        end_time = time.time()
+
+    # print epoch stats
+    c_logger.print_train_epoch_end(global_step, epoch, epoch_time, keep_avg)
+
+    # Plot Training Epoch Stats
+    epoch_stats = {"epoch_time": epoch_time}
+    epoch_stats.update(keep_avg.avg_values)
+    tb_logger.tb_train_epoch_stats(global_step, epoch_stats)
+    # TODO: plot model stats
+    # if c.tb_model_param_stats:
+    # tb_logger.tb_model_weights(model, global_step)
+    return keep_avg.avg_values, global_step
+
+
+@torch.no_grad()
+def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
+    data_loader = setup_loader(ap, is_val=True, verbose=(epoch == 0))
+    model_G.eval()
+    model_D.eval()
+    epoch_time = 0
+    keep_avg = KeepAverage()
+    end_time = time.time()
+    c_logger.print_eval_start()
+    for num_iter, data in enumerate(data_loader):
+        start_time = time.time()
+
+        # format data
+        c_G, y_G = format_data(data)
+        loader_time = time.time() - end_time
+
+        global_step += 1
+
+        ##############################
+        # GENERATOR
+        ##############################
+
+        # generator pass
+        y_hat = model_G(c_G)
+
+        in_real_D = y_hat
+        in_fake_D = y_G
+
+        # PQMF formatting
+        if y_hat.shape[1] > 1:
+            in_real_D = y_G
+            in_fake_D = model_G.pqmf_synthesis(y_hat)
+            y_G = model_G.pqmf_analysis(y_G)
+            y_hat = y_hat.view(-1, 1, y_hat.shape[2])
+            y_G = y_G.view(-1, 1, y_G.shape[2])
+
+        D_out_fake = model_D(in_fake_D)
+        D_out_real = None
+        if c.use_feat_match_loss:
+            with torch.no_grad():
+                D_out_real = model_D(in_real_D)
+
+        # format D outputs
+        if isinstance(D_out_fake, tuple):
+            scores_fake, feats_fake = D_out_fake
+            if D_out_real is None:
+                feats_real = None
+            else:
+                _, feats_real = D_out_real
+        else:
+            scores_fake = D_out_fake
+
+        # compute losses
+        loss_G_dict = criterion_G(y_hat, y_G, scores_fake, feats_fake,
+                                  feats_real)
+
+        loss_dict = dict()
+        for key, value in loss_G_dict.items():
+            loss_dict[key] = value.item()
+
+        step_time = time.time() - start_time
+        epoch_time += step_time
+
+        # update avg stats
+        update_eval_values = dict()
+        for key, value in loss_G_dict.items():
+            update_eval_values['avg_' + key] = value.item()
+        update_eval_values['avg_loader_time'] = loader_time
+        update_eval_values['avg_step_time'] = step_time
+        keep_avg.update_values(update_eval_values)
+
+        # print eval stats
+        if c.print_eval:
+            c_logger.print_eval_step(num_iter, loss_dict, keep_avg.avg_values)
+
+    # compute spectrograms
+    figures = plot_results(y_hat, y_G, ap, global_step, 'eval')
+    tb_logger.tb_eval_figures(global_step, figures)
+
+    # Sample audio
+    sample_voice = y_hat[0].squeeze(0).detach().cpu().numpy()
+    tb_logger.tb_eval_audios(global_step, {'eval/audio': sample_voice},
+                             c.audio["sample_rate"])
+
+    # synthesize a full voice
+    data_loader.return_segments = False
+
+    return keep_avg.avg_values
+
+
+# FIXME: move args definition/parsing inside of main?
+def main(args):  # pylint: disable=redefined-outer-name
+    # pylint: disable=global-variable-undefined
+    global train_data, eval_data
+    eval_data, train_data = load_wav_data(c.data_path, c.eval_split_size)
+
+    # setup audio processor
+    ap = AudioProcessor(**c.audio)
+    # DISTRUBUTED
+    # if num_gpus > 1:
+    # init_distributed(args.rank, num_gpus, args.group_id,
+    #  c.distributed["backend"], c.distributed["url"])
+
+    # setup models
+    model_gen = setup_generator(c)
+    model_disc = setup_discriminator(c)
+
+    # setup optimizers
+    optimizer_gen = RAdam(model_gen.parameters(), lr=c.lr_gen, weight_decay=0)
+    optimizer_disc = RAdam(model_disc.parameters(),
+                           lr=c.lr_disc,
+                           weight_decay=0)
+
+    # setup criterion
+    criterion_gen = GeneratorLoss(c)
+    criterion_disc = DiscriminatorLoss(c)
+
+    if args.restore_path:
+        checkpoint = torch.load(args.restore_path, map_location='cpu')
+        try:
+            model_gen.load_state_dict(checkpoint['model'])
+            optimizer_gen.load_state_dict(checkpoint['optimizer'])
+            model_disc.load_state_dict(checkpoint['model_disc'])
+            optimizer_disc.load_state_dict(checkpoint['optimizer_disc'])
+        except:
+            print(" > Partial model initialization.")
+            model_dict = model_gen.state_dict()
+            model_dict = set_init_dict(model_dict, checkpoint['model'], c)
+            model_gen.load_state_dict(model_dict)
+
+            model_dict = model_disc.state_dict()
+            model_dict = set_init_dict(model_dict, checkpoint['model_disc'], c)
+            model_disc.load_state_dict(model_dict)
+            del model_dict
+
+        # reset lr if not countinuining training.
+        for group in optimizer_gen.param_groups:
+            group['lr'] = c.lr_gen
+
+        for group in optimizer_disc.param_groups:
+            group['lr'] = c.lr_disc
+
+        print(" > Model restored from step %d" % checkpoint['step'],
+              flush=True)
+        args.restore_step = checkpoint['step']
+    else:
+        args.restore_step = 0
+
+    if use_cuda:
+        model_gen.cuda()
+        criterion_gen.cuda()
+        model_disc.cuda()
+        criterion_disc.cuda()
+
+    # DISTRUBUTED
+    # if num_gpus > 1:
+    #     model = apply_gradient_allreduce(model)
+
+    if c.noam_schedule:
+        scheduler_gen = NoamLR(optimizer_gen,
+                               warmup_steps=c.warmup_steps_gen,
+                               last_epoch=args.restore_step - 1)
+        scheduler_disc = NoamLR(optimizer_disc,
+                                warmup_steps=c.warmup_steps_gen,
+                                last_epoch=args.restore_step - 1)
+    else:
+        scheduler_gen, scheduler_disc = None, None
+
+    num_params = count_parameters(model_gen)
+    print(" > Generator has {} parameters".format(num_params), flush=True)
+    num_params = count_parameters(model_disc)
+    print(" > Discriminator has {} parameters".format(num_params), flush=True)
+
+    if 'best_loss' not in locals():
+        best_loss = float('inf')
+
+    global_step = args.restore_step
+    for epoch in range(0, c.epochs):
+        c_logger.print_epoch_start(epoch, c.epochs)
+        _, global_step = train(model_gen, criterion_gen, optimizer_gen,
+                               model_disc, criterion_disc, optimizer_disc,
+                               scheduler_gen, scheduler_disc, ap, global_step,
+                               epoch)
+        eval_avg_loss_dict = evaluate(model_gen, criterion_gen, model_disc, ap,
+                                      global_step, epoch)
+        c_logger.print_epoch_end(epoch, eval_avg_loss_dict)
+        target_loss = eval_avg_loss_dict[c.target_loss]
+        best_loss = save_best_model(target_loss,
+                                    best_loss,
+                                    model_gen,
+                                    optimizer_gen,
+                                    model_disc,
+                                    optimizer_disc,
+                                    global_step,
+                                    epoch,
+                                    OUT_PATH,
+                                    model_losses=eval_avg_loss_dict)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--continue_path',
+        type=str,
+        help=
+        'Training output folder to continue training. Use to continue a training. If it is used, "config_path" is ignored.',
+        default='',
+        required='--config_path' not in sys.argv)
+    parser.add_argument(
+        '--restore_path',
+        type=str,
+        help='Model file to be restored. Use to finetune a model.',
+        default='')
+    parser.add_argument('--config_path',
+                        type=str,
+                        help='Path to config file for training.',
+                        required='--continue_path' not in sys.argv)
+    parser.add_argument('--debug',
+                        type=bool,
+                        default=False,
+                        help='Do not verify commit integrity to run training.')
+
+    # DISTRUBUTED
+    parser.add_argument(
+        '--rank',
+        type=int,
+        default=0,
+        help='DISTRIBUTED: process rank for distributed training.')
+    parser.add_argument('--group_id',
+                        type=str,
+                        default="",
+                        help='DISTRIBUTED: process group id.')
+    args = parser.parse_args()
+
+    if args.continue_path != '':
+        args.output_path = args.continue_path
+        args.config_path = os.path.join(args.continue_path, 'config.json')
+        list_of_files = glob.glob(
+            args.continue_path +
+            "/*.pth.tar")  # * means all if need specific format then *.csv
+        latest_model_file = max(list_of_files, key=os.path.getctime)
+        args.restore_path = latest_model_file
+        print(f" > Training continues for {args.restore_path}")
+
+    # setup output paths and read configs
+    c = load_config(args.config_path)
+    check_config(c)
+    _ = os.path.dirname(os.path.realpath(__file__))
+
+    OUT_PATH = args.continue_path
+    if args.continue_path == '':
+        OUT_PATH = create_experiment_folder(c.output_path, c.run_name,
+                                            args.debug)
+
+    AUDIO_PATH = os.path.join(OUT_PATH, 'test_audios')
+
+    c_logger = ConsoleLogger()
+
+    if args.rank == 0:
+        os.makedirs(AUDIO_PATH, exist_ok=True)
+        new_fields = {}
+        if args.restore_path:
+            new_fields["restore_path"] = args.restore_path
+        new_fields["github_branch"] = get_git_branch()
+        copy_config_file(args.config_path,
+                         os.path.join(OUT_PATH, 'config.json'), new_fields)
+        os.chmod(AUDIO_PATH, 0o775)
+        os.chmod(OUT_PATH, 0o775)
+
+        LOG_DIR = OUT_PATH
+        tb_logger = TensorboardLogger(LOG_DIR, model_name='VOCODER')
+
+        # write model desc to tensorboard
+        tb_logger.tb_add_text('model-description', c['run_description'], 0)
+
+    try:
+        main(args)
+    except KeyboardInterrupt:
+        remove_experiment_folder(OUT_PATH)
+        try:
+            sys.exit(0)
+        except SystemExit:
+            os._exit(0)  # pylint: disable=protected-access
+    except Exception:  # pylint: disable=broad-except
+        remove_experiment_folder(OUT_PATH)
+        traceback.print_exc()
+        sys.exit(1)
diff --git a/vocoder/utils/__init__.py b/vocoder/utils/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/vocoder/utils/console_logger.py b/vocoder/utils/console_logger.py
new file mode 100644
index 00000000..4fe132bb
--- /dev/null
+++ b/vocoder/utils/console_logger.py
@@ -0,0 +1,97 @@
+import datetime
+from TTS.utils.io import AttrDict
+
+
+tcolors = AttrDict({
+    'OKBLUE': '\033[94m',
+    'HEADER': '\033[95m',
+    'OKGREEN': '\033[92m',
+    'WARNING': '\033[93m',
+    'FAIL': '\033[91m',
+    'ENDC': '\033[0m',
+    'BOLD': '\033[1m',
+    'UNDERLINE': '\033[4m'
+})
+
+
+class ConsoleLogger():
+    def __init__(self):
+        # TODO: color code for value changes
+        # use these to compare values between iterations
+        self.old_train_loss_dict = None
+        self.old_epoch_loss_dict = None
+        self.old_eval_loss_dict = None
+
+    # pylint: disable=no-self-use
+    def get_time(self):
+        now = datetime.datetime.now()
+        return now.strftime("%Y-%m-%d %H:%M:%S")
+
+    def print_epoch_start(self, epoch, max_epoch):
+        print("\n{}{} > EPOCH: {}/{}{}".format(tcolors.UNDERLINE, tcolors.BOLD,
+                                               epoch, max_epoch, tcolors.ENDC),
+              flush=True)
+
+    def print_train_start(self):
+        print(f"\n{tcolors.BOLD} > TRAINING ({self.get_time()}) {tcolors.ENDC}")
+
+    def print_train_step(self, batch_steps, step, global_step,
+                         step_time, loader_time, lr,
+                         loss_dict, avg_loss_dict):
+        indent = "     | > "
+        print()
+        log_text = "{}   --> STEP: {}/{} -- GLOBAL_STEP: {}{}\n".format(
+            tcolors.BOLD, step, batch_steps, global_step, tcolors.ENDC)
+        for key, value in loss_dict.items():
+            # print the avg value if given
+            if f'avg_{key}' in avg_loss_dict.keys():
+                log_text += "{}{}: {:.5f}  ({:.5f})\n".format(indent, key, value, avg_loss_dict[f'avg_{key}'])
+            else:
+                log_text += "{}{}: {:.5f} \n".format(indent, key, value)
+        log_text += f"{indent}step_time: {step_time:.2f}\n{indent}loader_time: {loader_time:.2f}\n{indent}lr: {lr:.5f}"
+        print(log_text, flush=True)
+
+    # pylint: disable=unused-argument
+    def print_train_epoch_end(self, global_step, epoch, epoch_time,
+                              print_dict):
+        indent = "     | > "
+        log_text = f"\n{tcolors.BOLD}   --> TRAIN PERFORMACE -- EPOCH TIME: {epoch} sec -- GLOBAL_STEP: {global_step}{tcolors.ENDC}\n"
+        for key, value in print_dict.items():
+            log_text += "{}{}: {:.5f}\n".format(indent, key, value)
+        print(log_text, flush=True)
+
+    def print_eval_start(self):
+        print(f"{tcolors.BOLD} > EVALUATION {tcolors.ENDC}\n")
+
+    def print_eval_step(self, step, loss_dict, avg_loss_dict):
+        indent = "     | > "
+        print()
+        log_text = f"{tcolors.BOLD}   --> STEP: {step}{tcolors.ENDC}\n"
+        for key, value in loss_dict.items():
+            # print the avg value if given
+            if f'avg_{key}' in avg_loss_dict.keys():
+                log_text += "{}{}: {:.5f}  ({:.5f})\n".format(indent, key, value, avg_loss_dict[f'avg_{key}'])
+            else:
+                log_text += "{}{}: {:.5f} \n".format(indent, key, value)
+        print(log_text, flush=True)
+
+    def print_epoch_end(self, epoch, avg_loss_dict):
+        indent = "     | > "
+        log_text = "  {}--> EVAL PERFORMANCE{}\n".format(
+            tcolors.BOLD, tcolors.ENDC)
+        for key, value in avg_loss_dict.items():
+            # print the avg value if given
+            color = ''
+            sign = '+'
+            diff = 0
+            if self.old_eval_loss_dict is not None:
+                diff = value - self.old_eval_loss_dict[key]
+                if diff < 0:
+                    color = tcolors.OKGREEN
+                    sign = ''
+                elif diff > 0:
+                    color = tcolors.FAIL
+                    sing = '+'
+            log_text += "{}{}:{} {:.5f} {}({}{:.5f})\n".format(indent, key, color, value, tcolors.ENDC, sign, diff)
+        self.old_eval_loss_dict = avg_loss_dict
+        print(log_text, flush=True)
diff --git a/vocoder/utils/generic_utils.py b/vocoder/utils/generic_utils.py
new file mode 100644
index 00000000..062de160
--- /dev/null
+++ b/vocoder/utils/generic_utils.py
@@ -0,0 +1,102 @@
+import re
+import importlib
+import numpy as np
+from matplotlib import pyplot as plt
+
+from TTS.utils.visual import plot_spectrogram
+
+
+def plot_results(y_hat, y, ap, global_step, name_prefix):
+    """ Plot vocoder model results """
+
+    # select an instance from batch
+    y_hat = y_hat[0].squeeze(0).detach().cpu().numpy()
+    y = y[0].squeeze(0).detach().cpu().numpy()
+
+    spec_fake = ap.spectrogram(y_hat).T
+    spec_real = ap.spectrogram(y).T
+    spec_diff = np.abs(spec_fake - spec_real)
+
+    # plot figure and save it
+    fig_wave = plt.figure()
+    plt.subplot(2, 1, 1)
+    plt.plot(y)
+    plt.title("groundtruth speech")
+    plt.subplot(2, 1, 2)
+    plt.plot(y_hat)
+    plt.title(f"generated speech @ {global_step} steps")
+    plt.tight_layout()
+    plt.close()
+
+    figures = {
+        name_prefix + "/spectrogram/fake": plot_spectrogram(spec_fake, ap),
+        name_prefix + "spectrogram/real": plot_spectrogram(spec_real, ap),
+        name_prefix + "spectrogram/diff": plot_spectrogram(spec_diff, ap),
+        name_prefix + "speech_comparison": fig_wave,
+    }
+    return figures
+
+
+def to_camel(text):
+    text = text.capitalize()
+    return re.sub(r'(?!^)_([a-zA-Z])', lambda m: m.group(1).upper(), text)
+
+
+def setup_generator(c):
+    print(" > Generator Model: {}".format(c.generator_model))
+    MyModel = importlib.import_module('TTS.vocoder.models.' +
+                                      c.generator_model.lower())
+    MyModel = getattr(MyModel, to_camel(c.generator_model))
+    if c.generator_model in 'melgan_generator':
+        model = MyModel(
+            in_channels=c.audio['num_mels'],
+            out_channels=1,
+            proj_kernel=7,
+            base_channels=512,
+            upsample_factors=c.generator_model_params['upsample_factors'],
+            res_kernel=3,
+            num_res_blocks=c.generator_model_params['num_res_blocks'])
+    if c.generator_model in 'melgan_fb_generator':
+        pass
+    if c.generator_model in 'multiband_melgan_generator':
+        model = MyModel(
+            in_channels=c.audio['num_mels'],
+            out_channels=4,
+            proj_kernel=7,
+            base_channels=384,
+            upsample_factors=c.generator_model_params['upsample_factors'],
+            res_kernel=3,
+            num_res_blocks=c.generator_model_params['num_res_blocks'])
+    return model
+
+
+def setup_discriminator(c):
+    print(" > Discriminator Model: {}".format(c.discriminator_model))
+    MyModel = importlib.import_module('TTS.vocoder.models.' +
+                                      c.discriminator_model.lower())
+    MyModel = getattr(MyModel, to_camel(c.discriminator_model))
+    if c.discriminator_model in 'random_window_discriminator':
+        model = MyModel(
+            cond_channels=c.audio['num_mels'],
+            hop_length=c.audio['hop_length'],
+            uncond_disc_donwsample_factors=c.
+            discriminator_model_params['uncond_disc_donwsample_factors'],
+            cond_disc_downsample_factors=c.
+            discriminator_model_params['cond_disc_downsample_factors'],
+            cond_disc_out_channels=c.
+            discriminator_model_params['cond_disc_out_channels'],
+            window_sizes=c.discriminator_model_params['window_sizes'])
+    if c.discriminator_model in 'melgan_multiscale_discriminator':
+        model = MyModel(
+            in_channels=1,
+            out_channels=1,
+            kernel_sizes=(5, 3),
+            base_channels=c.discriminator_model_params['base_channels'],
+            max_channels=c.discriminator_model_params['max_channels'],
+            downsample_factors=c.
+            discriminator_model_params['downsample_factors'])
+    return model
+
+
+# def check_config(c):
+#     pass
\ No newline at end of file
diff --git a/vocoder/utils/io.py b/vocoder/utils/io.py
new file mode 100644
index 00000000..9526b9db
--- /dev/null
+++ b/vocoder/utils/io.py
@@ -0,0 +1,52 @@
+import os
+import torch
+import datetime
+
+
+def save_model(model, optimizer, model_disc, optimizer_disc, current_step,
+               epoch, output_path, **kwargs):
+    model_state = model.state_dict()
+    model_disc_state = model_disc.state_dict()
+    optimizer_state = optimizer.state_dict() if optimizer is not None else None
+    optimizer_disc_state = optimizer_disc.state_dict(
+    ) if optimizer_disc is not None else None
+    state = {
+        'model': model_state,
+        'optimizer': optimizer_state,
+        'model_disc': model_disc_state,
+        'optimizer_disc': optimizer_disc_state,
+        'step': current_step,
+        'epoch': epoch,
+        'date': datetime.date.today().strftime("%B %d, %Y"),
+    }
+    state.update(kwargs)
+    torch.save(state, output_path)
+
+
+def save_checkpoint(model, optimizer, model_disc, optimizer_disc, current_step,
+                    epoch, output_folder, **kwargs):
+    file_name = 'checkpoint_{}.pth.tar'.format(current_step)
+    checkpoint_path = os.path.join(output_folder, file_name)
+    print(" > CHECKPOINT : {}".format(checkpoint_path))
+    save_model(model, optimizer, model_disc, optimizer_disc, current_step,
+               epoch, checkpoint_path, **kwargs)
+
+
+def save_best_model(target_loss, best_loss, model, optimizer, model_disc,
+                    optimizer_disc, current_step, epoch, output_folder,
+                    **kwargs):
+    if target_loss < best_loss:
+        file_name = 'best_model.pth.tar'
+        checkpoint_path = os.path.join(output_folder, file_name)
+        print(" > BEST MODEL : {}".format(checkpoint_path))
+        save_model(model,
+                   optimizer,
+                   model_disc,
+                   optimizer_disc,
+                   current_step,
+                   epoch,
+                   checkpoint_path,
+                   model_loss=target_loss,
+                   **kwargs)
+        best_loss = target_loss
+    return best_loss
\ No newline at end of file

From a361df3186827d3e84bc30b8e6b41a31c11c50e8 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sun, 31 May 2020 03:31:42 +0200
Subject: [PATCH 093/185] plot fix

---
 vocoder/configs/melgan_config.json | 7 +++----
 vocoder/train.py                   | 4 ++--
 vocoder/utils/generic_utils.py     | 8 ++++----
 3 files changed, 9 insertions(+), 10 deletions(-)

diff --git a/vocoder/configs/melgan_config.json b/vocoder/configs/melgan_config.json
index 9a3ded37..7da42f75 100644
--- a/vocoder/configs/melgan_config.json
+++ b/vocoder/configs/melgan_config.json
@@ -109,14 +109,13 @@
     "test_sentences_file": null,  // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
 
     // OPTIMIZER
-    "noam_schedule": true,        // use noam warmup and lr schedule.
-    "grad_clip": 1.0,              // upper limit for gradients for clipping.
+    "noam_schedule": false,        // use noam warmup and lr schedule.
+    "warmup_steps_gen": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
+    "warmup_steps_disc": 4000,
     "epochs": 1000,                // total number of epochs to train.
     "wd": 0.000001,                // Weight decay weight.
     "lr_gen": 0.0001,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
     "lr_disc": 0.0001,
-    "warmup_steps_gen": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
-    "warmup_steps_disc": 4000,
     "gen_clip_grad": 10.0,
     "disc_clip_grad": 10.0,
 
diff --git a/vocoder/train.py b/vocoder/train.py
index b0b4833a..0d039e73 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -372,11 +372,11 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
             c_logger.print_eval_step(num_iter, loss_dict, keep_avg.avg_values)
 
     # compute spectrograms
-    figures = plot_results(y_hat, y_G, ap, global_step, 'eval')
+    figures = plot_results(in_fake_D, in_real_D, ap, global_step, 'eval')
     tb_logger.tb_eval_figures(global_step, figures)
 
     # Sample audio
-    sample_voice = y_hat[0].squeeze(0).detach().cpu().numpy()
+    sample_voice = in_fake_D[0].squeeze(0).detach().cpu().numpy()
     tb_logger.tb_eval_audios(global_step, {'eval/audio': sample_voice},
                              c.audio["sample_rate"])
 
diff --git a/vocoder/utils/generic_utils.py b/vocoder/utils/generic_utils.py
index 062de160..179c4064 100644
--- a/vocoder/utils/generic_utils.py
+++ b/vocoder/utils/generic_utils.py
@@ -13,8 +13,8 @@ def plot_results(y_hat, y, ap, global_step, name_prefix):
     y_hat = y_hat[0].squeeze(0).detach().cpu().numpy()
     y = y[0].squeeze(0).detach().cpu().numpy()
 
-    spec_fake = ap.spectrogram(y_hat).T
-    spec_real = ap.spectrogram(y).T
+    spec_fake = ap.melspectrogram(y_hat).T
+    spec_real = ap.melspectrogram(y).T
     spec_diff = np.abs(spec_fake - spec_real)
 
     # plot figure and save it
@@ -98,5 +98,5 @@ def setup_discriminator(c):
     return model
 
 
-# def check_config(c):
-#     pass
\ No newline at end of file
+def check_config(c):
+    pass
\ No newline at end of file

From f675fca345a48d20ea2a6fc80488652488fc847e Mon Sep 17 00:00:00 2001
From: ForceCore <ssanzing@gmail.com>
Date: Sun, 31 May 2020 19:51:16 +0900
Subject: [PATCH 094/185] Fix phoneme cache file name aliasing problem

When the wav file has multiple dots in the file name,
_load_or_generate_phoneme_sequence would only use only the first segment
of the file name and cause overwrite of *_phoneme.npy
---
 datasets/TTSDataset.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/datasets/TTSDataset.py b/datasets/TTSDataset.py
index 0d884c00..7fe966d7 100644
--- a/datasets/TTSDataset.py
+++ b/datasets/TTSDataset.py
@@ -92,7 +92,7 @@ class MyDataset(Dataset):
         return phonemes
 
     def _load_or_generate_phoneme_sequence(self, wav_file, text):
-        file_name = os.path.basename(wav_file).split('.')[0]
+        file_name = os.path.splitext(os.path.basename(wav_file))[0]
         cache_path = os.path.join(self.phoneme_cache_path,
                                   file_name + '_phoneme.npy')
         try:

From 1a49ce895652ef9815e45b584f2c4c1ead5d1b09 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sun, 31 May 2020 14:18:29 +0200
Subject: [PATCH 095/185] bug fix

---
 vocoder/train.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/vocoder/train.py b/vocoder/train.py
index 0d039e73..a30e3bff 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -137,7 +137,7 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
         if global_step > c.steps_to_start_discriminator:
 
             # run D with or without cond. features
-            if len(signature(model_D).parameters) == 2:
+            if len(signature(model_D.forward).parameters) == 2:
                 D_out_fake = model_D(in_fake_D, c_G)
             else:
                 D_out_fake = model_D(in_fake_D)
@@ -195,7 +195,7 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
             optimizer_D.zero_grad()
 
             # run D with or without cond. features
-            if len(signature(model_D).parameters) == 2:
+            if len(signature(model_D.forward).parameters) == 2:
                 D_out_fake = model_D(y_hat.detach(), c_D)
                 D_out_real = model_D(y_D, c_D)
             else:

From 604453bcb770149a54a0deabcc00baa888a5dd02 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sun, 31 May 2020 18:56:57 +0200
Subject: [PATCH 096/185] add multiband melgan config and rename loss weights
 for vocoder

---
 vocoder/configs/melgan_config.json |  8 ++++----
 vocoder/layers/losses.py           | 24 ++++++++++++------------
 2 files changed, 16 insertions(+), 16 deletions(-)

diff --git a/vocoder/configs/melgan_config.json b/vocoder/configs/melgan_config.json
index 7da42f75..b97206fa 100644
--- a/vocoder/configs/melgan_config.json
+++ b/vocoder/configs/melgan_config.json
@@ -53,10 +53,10 @@
     "use_hinge_gan_loss": false,
     "use_feat_match_loss": false,  // use only with melgan discriminators
 
-    "stft_loss_alpha": 1,
-    "mse_gan_loss_alpha": 1,
-    "hinge_gan_loss_alpha": 1,
-    "feat_match_loss_alpha": 10.0,
+    "stft_loss_weight": 1,
+    "mse_gan_loss_weight": 2.5,
+    "hinge_gan_loss_weight": 2.5,
+    "feat_match_loss_weight": 10.0,
 
     "stft_loss_params": {
         "n_ffts": [1024, 2048, 512],
diff --git a/vocoder/layers/losses.py b/vocoder/layers/losses.py
index 11985629..27091c0a 100644
--- a/vocoder/layers/losses.py
+++ b/vocoder/layers/losses.py
@@ -149,10 +149,10 @@ class GeneratorLoss(nn.Module):
         self.use_hinge_gan_loss = C.use_hinge_gan_loss
         self.use_feat_match_loss = C.use_feat_match_loss
 
-        self.stft_loss_alpha = C.stft_loss_alpha
-        self.mse_gan_loss_alpha = C.mse_gan_loss_alpha
-        self.hinge_gan_loss_alpha = C.hinge_gan_loss_alpha
-        self.feat_match_loss_alpha = C.feat_match_loss_alpha
+        self.stft_loss_weight = C.stft_loss_weight
+        self.mse_gan_loss_weight = C.mse_gan_loss_weight
+        self.hinge_gan_loss_weight = C.hinge_gan_loss_weight
+        self.feat_match_loss_weight = C.feat_match_loss_weight
 
         if C.use_stft_loss:
             self.stft_loss = MultiScaleSTFTLoss(**C.stft_loss_params)
@@ -172,7 +172,7 @@ class GeneratorLoss(nn.Module):
             stft_loss_mg, stft_loss_sc = self.stft_loss(y_hat.squeeze(1), y.squeeze(1))
             return_dict['G_stft_loss_mg'] = stft_loss_mg
             return_dict['G_stft_loss_sc'] = stft_loss_sc
-            loss += self.stft_loss_alpha * (stft_loss_mg + stft_loss_sc)
+            loss += self.stft_loss_weight * (stft_loss_mg + stft_loss_sc)
 
         # Fake Losses
         if self.use_mse_gan_loss and scores_fake is not None:
@@ -185,7 +185,7 @@ class GeneratorLoss(nn.Module):
                 fake_loss = self.mse_loss(scores_fake)
                 mse_fake_loss = fake_loss
             return_dict['G_mse_fake_loss'] = mse_fake_loss
-            loss += self.mse_gan_loss_alpha * mse_fake_loss
+            loss += self.mse_gan_loss_weight * mse_fake_loss
 
         if self.use_hinge_gan_loss and not scores_fake is not None:
             hinge_fake_loss = 0
@@ -197,13 +197,13 @@ class GeneratorLoss(nn.Module):
                 fake_loss = self.hinge_loss(scores_fake)
                 hinge_fake_loss = fake_loss
             return_dict['G_hinge_fake_loss'] = hinge_fake_loss
-            loss += self.hinge_gan_loss_alpha * hinge_fake_loss
+            loss += self.hinge_gan_loss_weight * hinge_fake_loss
 
         # Feature Matching Loss
         if self.use_feat_match_loss and not feats_fake:
             feat_match_loss = self.feat_match_loss(feats_fake, feats_real)
             return_dict['G_feat_match_loss'] = feat_match_loss
-            loss += self.feat_match_loss_alpha * feat_match_loss
+            loss += self.feat_match_loss_weight * feat_match_loss
         return_dict['G_loss'] = loss
         return return_dict
 
@@ -217,8 +217,8 @@ class DiscriminatorLoss(nn.Module):
         self.use_mse_gan_loss = C.use_mse_gan_loss
         self.use_hinge_gan_loss = C.use_hinge_gan_loss
 
-        self.mse_gan_loss_alpha = C.mse_gan_loss_alpha
-        self.hinge_gan_loss_alpha = C.hinge_gan_loss_alpha
+        self.mse_gan_loss_weight = C.mse_gan_loss_weight
+        self.hinge_gan_loss_weight = C.hinge_gan_loss_weight
 
         if C.use_mse_gan_loss:
             self.mse_loss = MSEDLoss()
@@ -247,7 +247,7 @@ class DiscriminatorLoss(nn.Module):
             return_dict['D_mse_gan_loss'] = mse_gan_loss
             return_dict['D_mse_gan_real_loss'] = mse_gan_real_loss
             return_dict['D_mse_gan_fake_loss'] = mse_gan_fake_loss
-            loss += self.mse_gan_loss_alpha * mse_gan_loss
+            loss += self.mse_gan_loss_weight * mse_gan_loss
 
         if self.use_hinge_gan_loss:
             hinge_gan_loss = 0
@@ -267,7 +267,7 @@ class DiscriminatorLoss(nn.Module):
             return_dict['D_hinge_gan_loss'] = hinge_gan_loss
             return_dict['D_hinge_gan_real_loss'] = hinge_gan_real_loss
             return_dict['D_hinge_gan_fake_loss'] = hinge_gan_fake_loss
-            loss += self.hinge_gan_loss_alpha * hinge_gan_loss
+            loss += self.hinge_gan_loss_weight * hinge_gan_loss
 
         return_dict['D_loss'] = loss
         return return_dict
\ No newline at end of file

From 78cb7478d15108a004384f849ac17d494673a0bf Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sun, 31 May 2020 19:03:07 +0200
Subject: [PATCH 097/185] multiband rwd D config

---
 .../multiband-melgan_and_rwd_config.json      | 136 ++++++++++++++++++
 1 file changed, 136 insertions(+)
 create mode 100644 vocoder/configs/multiband-melgan_and_rwd_config.json

diff --git a/vocoder/configs/multiband-melgan_and_rwd_config.json b/vocoder/configs/multiband-melgan_and_rwd_config.json
new file mode 100644
index 00000000..9aeb7933
--- /dev/null
+++ b/vocoder/configs/multiband-melgan_and_rwd_config.json
@@ -0,0 +1,136 @@
+{
+    "run_name": "multiband-melgan-rwd",
+    "run_description": "multibadn melgan with random window discriminator",
+
+    // AUDIO PARAMETERS
+    "audio":{
+        // stft parameters
+        "num_freq": 513,         // number of stft frequency levels. Size of the linear spectogram frame.
+        "win_length": 1024,      // stft window length in ms.
+        "hop_length": 256,       // stft window hop-lengh in ms.
+        "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
+        "frame_shift_ms": null,  // stft window hop-lengh in ms. If null, 'hop_length' is used.
+
+        // Audio processing parameters
+        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
+        "preemphasis": 0.0,     // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
+        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
+
+        // Silence trimming
+        "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
+        "trim_db": 60,          // threshold for timming silence. Set this according to your dataset.
+
+        // Griffin-Lim
+        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
+        "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
+
+        // MelSpectrogram parameters
+        "num_mels": 80,         // size of the mel spec frame.
+        "mel_fmin": 0.0,        // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
+        "mel_fmax": 8000.0,     // maximum freq level for mel-spec. Tune for dataset!!
+
+        // Normalization parameters
+        "signal_norm": true,    // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
+        "min_level_db": -100,   // lower bound for normalization
+        "symmetric_norm": true, // move normalization to range [-1, 1]
+        "max_norm": 4.0,        // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
+        "clip_norm": true,      // clip normalized values into the range.
+        "stats_path": null    // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
+    },
+
+    // DISTRIBUTED TRAINING
+    // "distributed":{
+    //     "backend": "nccl",
+    //     "url": "tcp:\/\/localhost:54321"
+    // },
+
+    // MODEL PARAMETERS
+    "use_pqmf": true,
+
+    // LOSS PARAMETERS
+    "use_stft_loss": true,
+    "use_mse_gan_loss": true,
+    "use_hinge_gan_loss": false,
+    "use_feat_match_loss": false,  // use only with melgan discriminators
+
+    "stft_loss_weight": 1,
+    "mse_gan_loss_weight": 2.5,
+    "hinge_gan_loss_weight": 1,
+    "feat_match_loss_weight": 10.0,
+
+    "stft_loss_params": {
+        "n_ffts": [1024, 2048, 512],
+        "hop_lengths": [120, 240, 50],
+        "win_lengths": [600, 1200, 240]
+    },
+    "target_loss": "avg_G_loss",  // loss value to pick the best model
+
+    // DISCRIMINATOR
+    // "discriminator_model": "melgan_multiscale_discriminator",
+    // "discriminator_model_params":{
+    //     "base_channels": 16,
+    //     "max_channels":1024,
+    //     "downsample_factors":[4, 4, 4, 4]
+    // },
+    "steps_to_start_discriminator": 100000,      // steps required to start GAN trainining.1
+
+    "discriminator_model": "random_window_discriminator",
+    "discriminator_model_params":{
+        "uncond_disc_donwsample_factors": [8, 4],
+        "cond_disc_downsample_factors": [[8, 4, 2, 2, 2], [8, 4, 2, 2], [8, 4, 2], [8, 4], [4, 2, 2]],
+        "cond_disc_out_channels": [[128, 128, 256, 256], [128, 256, 256], [128, 256], [256], [128, 256]],
+        "window_sizes": [512, 1024, 2048, 4096, 8192]
+    },
+
+    // GENERATOR
+    "generator_model": "multiband_melgan_generator",
+    "generator_model_params": {
+        "upsample_factors":[2 ,2, 4, 4],
+        "num_res_blocks": 4
+    },
+
+    // DATASET
+    "data_path": "/root/LJSpeech-1.1/wavs/",
+    "seq_len": 16384,
+    "pad_short": 2000,
+    "conv_pad": 0,
+    "use_noise_augment": true,
+    "use_cache": true,
+
+    "reinit_layers": [],    // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
+
+    // TRAINING
+    "batch_size": 64,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
+
+    // VALIDATION
+    "run_eval": true,
+    "test_delay_epochs": 10,  //Until attention is aligned, testing only wastes computation time.
+    "test_sentences_file": null,  // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
+
+    // OPTIMIZER
+    "noam_schedule": false,        // use noam warmup and lr schedule.
+    "warmup_steps_gen": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
+    "warmup_steps_disc": 4000,
+    "epochs": 1000,                // total number of epochs to train.
+    "wd": 0.000001,                // Weight decay weight.
+    "lr_gen": 0.0001,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
+    "lr_disc": 0.0001,
+    "gen_clip_grad": 10.0,
+    "disc_clip_grad": 10.0,
+
+    // TENSORBOARD and LOGGING
+    "print_step": 25,       // Number of steps to log traning on console.
+    "print_eval": false,     // If True, it prints intermediate loss values in evalulation.
+    "save_step": 10000,      // Number of training steps expected to save traninpg stats and checkpoints.
+    "checkpoint": true,     // If true, it saves checkpoints per "save_step"
+    "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
+
+    // DATA LOADING
+    "num_loader_workers": 4,        // number of training data loader processes. Don't set it too big. 4-8 are good values.
+    "num_val_loader_workers": 4,    // number of evaluation data loader processes.
+    "eval_split_size": 10,
+
+    // PATHS
+    "output_path": "/home/erogol/Models/LJSpeech/"
+}
+

From d425daad5b383007b3e9d40bf591ebe2f75ea008 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Mon, 1 Jun 2020 03:02:51 +0200
Subject: [PATCH 098/185] tb eval print

---
 vocoder/train.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/vocoder/train.py b/vocoder/train.py
index a30e3bff..27e14e8a 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -383,6 +383,7 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
     # synthesize a full voice
     data_loader.return_segments = False
 
+    tb_logger.tb_eval_stats(global_step, keep_avg.avg_values)
     return keep_avg.avg_values
 
 

From 3dff81e7e228e1d20e6ed8669c4eb5bbcea26709 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 2 Jun 2020 17:07:15 +0200
Subject: [PATCH 099/185] multiscale subband stft loss and missing tb_logger
 for eval stats

---
 vocoder/configs/melgan_config.json |  8 +++++
 vocoder/layers/losses.py           | 21 ++++++++++--
 vocoder/train.py                   | 53 ++++++++++++++----------------
 3 files changed, 51 insertions(+), 31 deletions(-)

diff --git a/vocoder/configs/melgan_config.json b/vocoder/configs/melgan_config.json
index b97206fa..4aeba82e 100644
--- a/vocoder/configs/melgan_config.json
+++ b/vocoder/configs/melgan_config.json
@@ -49,11 +49,13 @@
 
     // LOSS PARAMETERS
     "use_stft_loss": true,
+    "use_subband_stft_loss": true,
     "use_mse_gan_loss": true,
     "use_hinge_gan_loss": false,
     "use_feat_match_loss": false,  // use only with melgan discriminators
 
     "stft_loss_weight": 1,
+    "subband_stft_loss_weight": 1,
     "mse_gan_loss_weight": 2.5,
     "hinge_gan_loss_weight": 2.5,
     "feat_match_loss_weight": 10.0,
@@ -63,6 +65,12 @@
         "hop_lengths": [120, 240, 50],
         "win_lengths": [600, 1200, 240]
     },
+    "subband_stft_loss_params":{
+        "n_ffts": [384, 683, 171],
+        "hop_lengths": [30, 60, 10],
+        "win_lengths": [150, 300, 60]
+    },
+
     "target_loss": "avg_G_loss",  // loss value to pick the best model
 
     // DISCRIMINATOR
diff --git a/vocoder/layers/losses.py b/vocoder/layers/losses.py
index 27091c0a..e242d209 100644
--- a/vocoder/layers/losses.py
+++ b/vocoder/layers/losses.py
@@ -49,7 +49,6 @@ class STFTLoss(nn.Module):
         loss_sc = torch.norm(y_M - y_hat_M, p="fro") / torch.norm(y_M, p="fro")
         return loss_mag, loss_sc
 
-
 class MultiScaleSTFTLoss(torch.nn.Module):
     def __init__(self,
                  n_ffts=[1024, 2048, 512],
@@ -73,6 +72,13 @@ class MultiScaleSTFTLoss(torch.nn.Module):
         return loss_mag, loss_sc
 
 
+class MultiScaleSubbandSTFTLoss(MultiScaleSTFTLoss):
+    def forward(self, y_hat, y):
+        y_hat = y_hat.view(-1, 1, y_hat.shape[2])
+        y = y.view(-1, 1, y.shape[2])
+        return super().forward(y_hat.squeeze(1), y.squeeze(1))
+
+
 class MSEGLoss(nn.Module):
     """ Mean Squared Generator Loss """
     def __init__(self,):
@@ -145,17 +151,21 @@ class GeneratorLoss(nn.Module):
             " [!] Cannot use HingeGANLoss and MSEGANLoss together."
 
         self.use_stft_loss = C.use_stft_loss
+        self.use_subband_stft_loss = C.use_subband_stft_loss
         self.use_mse_gan_loss = C.use_mse_gan_loss
         self.use_hinge_gan_loss = C.use_hinge_gan_loss
         self.use_feat_match_loss = C.use_feat_match_loss
 
         self.stft_loss_weight = C.stft_loss_weight
+        self.subband_stft_loss_weight = C.subband_stft_loss_weight
         self.mse_gan_loss_weight = C.mse_gan_loss_weight
         self.hinge_gan_loss_weight = C.hinge_gan_loss_weight
         self.feat_match_loss_weight = C.feat_match_loss_weight
 
         if C.use_stft_loss:
             self.stft_loss = MultiScaleSTFTLoss(**C.stft_loss_params)
+        if C.use_subband_stft_loss:
+            self.subband_stft_loss = MultiScaleSubbandSTFTLoss(**C.subband_stft_loss_params)
         if C.use_mse_gan_loss:
             self.mse_loss = MSEGLoss()
         if C.use_hinge_gan_loss:
@@ -163,7 +173,7 @@ class GeneratorLoss(nn.Module):
         if C.use_feat_match_loss:
             self.feat_match_loss = MelganFeatureLoss()
 
-    def forward(self, y_hat=None, y=None, scores_fake=None, feats_fake=None, feats_real=None):
+    def forward(self, y_hat=None, y=None, scores_fake=None, feats_fake=None, feats_real=None, y_hat_sub=None, y_sub=None):
         loss = 0
         return_dict = {}
 
@@ -174,6 +184,13 @@ class GeneratorLoss(nn.Module):
             return_dict['G_stft_loss_sc'] = stft_loss_sc
             loss += self.stft_loss_weight * (stft_loss_mg + stft_loss_sc)
 
+        # subband STFT Loss
+        if self.use_subband_stft_loss:
+            subband_stft_loss_mg, subband_stft_loss_sc = self.subband_stft_loss(y_hat_sub, y_sub)
+            return_dict['G_subband_stft_loss_mg'] = subband_stft_loss_mg
+            return_dict['G_subband_stft_loss_sc'] = subband_stft_loss_sc
+            loss += self.subband_stft_loss_weight * (subband_stft_loss_mg + subband_stft_loss_sc)
+
         # Fake Losses
         if self.use_mse_gan_loss and scores_fake is not None:
             mse_fake_loss = 0
diff --git a/vocoder/train.py b/vocoder/train.py
index 27e14e8a..6341bb6c 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -122,30 +122,27 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
         # generator pass
         optimizer_G.zero_grad()
         y_hat = model_G(c_G)
-
-        in_real_D = y_hat
-        in_fake_D = y_G
+        y_hat_sub = None
+        y_G_sub = None
 
         # PQMF formatting
         if y_hat.shape[1] > 1:
-            in_real_D = y_G
-            in_fake_D = model_G.pqmf_synthesis(y_hat)
-            y_G = model_G.pqmf_analysis(y_G)
-            y_hat = y_hat.view(-1, 1, y_hat.shape[2])
-            y_G = y_G.view(-1, 1, y_G.shape[2])
+            y_hat_sub = y_hat
+            y_hat = model_G.pqmf_synthesis(y_hat)
+            y_G_sub = model_G.pqmf_analysis(y_G)
 
         if global_step > c.steps_to_start_discriminator:
 
             # run D with or without cond. features
             if len(signature(model_D.forward).parameters) == 2:
-                D_out_fake = model_D(in_fake_D, c_G)
+                D_out_fake = model_D(y_hat, c_G)
             else:
-                D_out_fake = model_D(in_fake_D)
+                D_out_fake = model_D(y_hat)
             D_out_real = None
 
             if c.use_feat_match_loss:
                 with torch.no_grad():
-                    D_out_real = model_D(in_real_D)
+                    D_out_real = model_D(y_G)
 
             # format D outputs
             if isinstance(D_out_fake, tuple):
@@ -162,7 +159,7 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
 
         # compute losses
         loss_G_dict = criterion_G(y_hat, y_G, scores_fake, feats_fake,
-                                  feats_real)
+                                  feats_real, y_hat_sub, y_G_sub)
         loss_G = loss_G_dict['G_loss']
 
         # optimizer generator
@@ -272,12 +269,12 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
                                 model_losses=loss_dict)
 
             # compute spectrograms
-            figures = plot_results(in_fake_D, in_real_D, ap, global_step,
+            figures = plot_results(y_hat, y_G, ap, global_step,
                                    'train')
             tb_logger.tb_train_figures(global_step, figures)
 
             # Sample audio
-            sample_voice = in_fake_D[0].squeeze(0).detach().cpu().numpy()
+            sample_voice = y_hat[0].squeeze(0).detach().cpu().numpy()
             tb_logger.tb_train_audios(global_step,
                                       {'train/audio': sample_voice},
                                       c.audio["sample_rate"])
@@ -320,23 +317,20 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
 
         # generator pass
         y_hat = model_G(c_G)
-
-        in_real_D = y_hat
-        in_fake_D = y_G
+        y_hat_sub = None
+        y_G_sub = None
 
         # PQMF formatting
         if y_hat.shape[1] > 1:
-            in_real_D = y_G
-            in_fake_D = model_G.pqmf_synthesis(y_hat)
-            y_G = model_G.pqmf_analysis(y_G)
-            y_hat = y_hat.view(-1, 1, y_hat.shape[2])
-            y_G = y_G.view(-1, 1, y_G.shape[2])
+            y_hat_sub = y_hat
+            y_hat = model_G.pqmf_synthesis(y_hat)
+            y_G_sub = model_G.pqmf_analysis(y_G)
 
-        D_out_fake = model_D(in_fake_D)
+        D_out_fake = model_D(y_hat)
         D_out_real = None
         if c.use_feat_match_loss:
             with torch.no_grad():
-                D_out_real = model_D(in_real_D)
+                D_out_real = model_D(y_G)
 
         # format D outputs
         if isinstance(D_out_fake, tuple):
@@ -350,7 +344,7 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
 
         # compute losses
         loss_G_dict = criterion_G(y_hat, y_G, scores_fake, feats_fake,
-                                  feats_real)
+                                  feats_real, y_hat_sub, y_G_sub)
 
         loss_dict = dict()
         for key, value in loss_G_dict.items():
@@ -364,7 +358,7 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
         for key, value in loss_G_dict.items():
             update_eval_values['avg_' + key] = value.item()
         update_eval_values['avg_loader_time'] = loader_time
-        update_eval_values['avg_step_time'] = step_time
+        update_eval_values['avgP_step_time'] = step_time
         keep_avg.update_values(update_eval_values)
 
         # print eval stats
@@ -372,18 +366,19 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
             c_logger.print_eval_step(num_iter, loss_dict, keep_avg.avg_values)
 
     # compute spectrograms
-    figures = plot_results(in_fake_D, in_real_D, ap, global_step, 'eval')
+    figures = plot_results(y_hat, y_G, ap, global_step, 'eval')
     tb_logger.tb_eval_figures(global_step, figures)
 
     # Sample audio
-    sample_voice = in_fake_D[0].squeeze(0).detach().cpu().numpy()
+    sample_voice = y_hat[0].squeeze(0).detach().cpu().numpy()
     tb_logger.tb_eval_audios(global_step, {'eval/audio': sample_voice},
                              c.audio["sample_rate"])
 
     # synthesize a full voice
     data_loader.return_segments = False
 
-    tb_logger.tb_eval_stats(global_step, keep_avg.avg_values)
+    tb_logger.tb_evals_stats(global_step, keep_avg.avg_values)
+
     return keep_avg.avg_values
 
 

From 3a2e501b7a4d8f04d7d13474a8c153f16a8165dc Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 2 Jun 2020 17:09:05 +0200
Subject: [PATCH 100/185] bug fix

---
 vocoder/train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vocoder/train.py b/vocoder/train.py
index 6341bb6c..398051a9 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -377,7 +377,7 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
     # synthesize a full voice
     data_loader.return_segments = False
 
-    tb_logger.tb_evals_stats(global_step, keep_avg.avg_values)
+    tb_logger.tb_eval_stats(global_step, keep_avg.avg_values)
 
     return keep_avg.avg_values
 

From b7b0c58e3f0c6c6ad1c815044f6d32aee4908956 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 2 Jun 2020 17:28:52 +0200
Subject: [PATCH 101/185] update travis script

---
 .travis/script         | 4 +++-
 requirements_tests.txt | 1 +
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/.travis/script b/.travis/script
index ca6f4cd3..278ba4f2 100755
--- a/.travis/script
+++ b/.travis/script
@@ -12,7 +12,9 @@ fi
 if [[ "$TEST_SUITE" == "unittest" ]]; then
   # Run tests on all pushes
   pushd tts_namespace
-  python -m unittest
+  nosetests speaker_encoder.tests --nocapture
+  nosetests vocoder.tests --nocapture
+  nosetests tests --nocapture
   popd
   # Test server package
   ./tests/test_server_package.sh
diff --git a/requirements_tests.txt b/requirements_tests.txt
index 5aacdb56..36243398 100644
--- a/requirements_tests.txt
+++ b/requirements_tests.txt
@@ -14,3 +14,4 @@ tqdm
 soundfile
 phonemizer
 bokeh==1.4.0
+nose

From 502ba2d5c4ea5b80e91c6dffc16a5a57d9070528 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 2 Jun 2020 18:54:03 +0200
Subject: [PATCH 102/185] cardboard config update

---
 .cardboardlint.yml | 3 +++
 .pylintrc          | 3 ++-
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/.cardboardlint.yml b/.cardboardlint.yml
index 464ea733..4a115a37 100644
--- a/.cardboardlint.yml
+++ b/.cardboardlint.yml
@@ -1,2 +1,5 @@
 linters:
 - pylint:
+    # pylintrc: pylintrc
+    filefilter: ['- test_*.py', '+ *.py', '- *.npy']
+    # exclude:
\ No newline at end of file
diff --git a/.pylintrc b/.pylintrc
index b6e04944..a78b521e 100644
--- a/.pylintrc
+++ b/.pylintrc
@@ -157,7 +157,8 @@ disable=missing-docstring,
         xreadlines-attribute,
         deprecated-sys-function,
         exception-escape,
-        comprehension-escape
+        comprehension-escape,
+        duplicate-code
 
 # Enable the message, report, category or checker with the given id(s). You can
 # either give multiple identifier separated by comma (,) or put this option

From 58117c0c6e3d38c9ccf4c2e193a8afd299aa799d Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 2 Jun 2020 18:54:29 +0200
Subject: [PATCH 103/185] travis script for nose

---
 .travis/script | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.travis/script b/.travis/script
index 278ba4f2..ce886fc2 100755
--- a/.travis/script
+++ b/.travis/script
@@ -12,9 +12,9 @@ fi
 if [[ "$TEST_SUITE" == "unittest" ]]; then
   # Run tests on all pushes
   pushd tts_namespace
-  nosetests speaker_encoder.tests --nocapture
-  nosetests vocoder.tests --nocapture
-  nosetests tests --nocapture
+  nosetests TTS.speaker_encoder.tests --nocapture
+  nosetests TTS.vocoder.tests --nocapture
+  nosetests TTS.tests --nocapture
   popd
   # Test server package
   ./tests/test_server_package.sh

From d6b9a5738af8c6dbe642b2464b4fec31c306aaa7 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 2 Jun 2020 18:58:10 +0200
Subject: [PATCH 104/185] Ton of linter fixes

---
 train.py                               |  12 +--
 utils/training.py                      |  11 +++
 vocoder/datasets/gan_dataset.py        |  34 ++-----
 vocoder/layers/losses.py               |  35 ++-----
 vocoder/layers/melgan.py               |   3 -
 vocoder/layers/pqmf.py                 |   3 +
 vocoder/layers/pqmf2.py                | 127 -------------------------
 vocoder/models/melgan_discriminator.py |   4 +-
 vocoder/models/melgan_generator.py     |   2 -
 vocoder/tests/test_datasets.py         |  22 ++---
 vocoder/tests/test_losses.py           |   3 +-
 vocoder/tests/test_melgan_generator.py |   1 -
 vocoder/tests/test_pqmf.py             |   7 +-
 vocoder/train.py                       |  24 ++---
 vocoder/utils/console_logger.py        |   2 +-
 vocoder/utils/io.py                    |   1 -
 16 files changed, 60 insertions(+), 231 deletions(-)
 delete mode 100644 vocoder/layers/pqmf2.py

diff --git a/train.py b/train.py
index 82f91cd5..5a345b59 100644
--- a/train.py
+++ b/train.py
@@ -20,7 +20,8 @@ from TTS.utils.generic_utils import (count_parameters, create_experiment_folder,
 from TTS.utils.io import (save_best_model, save_checkpoint,
                           load_config, copy_config_file)
 from TTS.utils.training import (NoamLR, check_update, adam_weight_decay,
-                                gradual_training_scheduler, set_weight_decay)
+                                gradual_training_scheduler, set_weight_decay,
+                                setup_torch_training_env)
 from TTS.utils.tensorboard_logger import TensorboardLogger
 from TTS.utils.console_logger import ConsoleLogger
 from TTS.utils.speakers import load_speaker_mapping, save_speaker_mapping, \
@@ -32,13 +33,8 @@ from TTS.datasets.preprocess import load_meta_data
 from TTS.utils.radam import RAdam
 from TTS.utils.measures import alignment_diagonal_score
 
-torch.backends.cudnn.enabled = True
-torch.backends.cudnn.benchmark = False
-torch.manual_seed(54321)
-use_cuda = torch.cuda.is_available()
-num_gpus = torch.cuda.device_count()
-print(" > Using CUDA: ", use_cuda)
-print(" > Number of GPUs: ", num_gpus)
+
+use_cuda, num_gpus = setup_torch_training_env(True, False)
 
 
 def setup_loader(ap, r, is_val=False, verbose=False):
diff --git a/utils/training.py b/utils/training.py
index 5ce7948b..9046f9e0 100644
--- a/utils/training.py
+++ b/utils/training.py
@@ -2,6 +2,17 @@ import torch
 import numpy as np
 
 
+def setup_torch_training_env(cudnn_enable, cudnn_benchmark):
+    torch.backends.cudnn.enabled = cudnn_enable
+    torch.backends.cudnn.benchmark = cudnn_benchmark
+    torch.manual_seed(54321)
+    use_cuda = torch.cuda.is_available()
+    num_gpus = torch.cuda.device_count()
+    print(" > Using CUDA: ", use_cuda)
+    print(" > Number of GPUs: ", num_gpus)
+    return use_cuda, num_gpus
+
+
 def check_update(model, grad_clip, ignore_stopnet=False):
     r'''Check model gradient against unexpected jumps and failures'''
     skip_flag = False
diff --git a/vocoder/datasets/gan_dataset.py b/vocoder/datasets/gan_dataset.py
index 10d36cab..5e410151 100644
--- a/vocoder/datasets/gan_dataset.py
+++ b/vocoder/datasets/gan_dataset.py
@@ -3,29 +3,10 @@ import glob
 import torch
 import random
 import numpy as np
-from torch.utils.data import Dataset, DataLoader
+from torch.utils.data import Dataset
 from multiprocessing import Manager
 
 
-def create_dataloader(hp, args, train):
-    dataset = MelFromDisk(hp, args, train)
-
-    if train:
-        return DataLoader(dataset=dataset,
-                          batch_size=hp.train.batch_size,
-                          shuffle=True,
-                          num_workers=hp.train.num_workers,
-                          pin_memory=True,
-                          drop_last=True)
-    else:
-        return DataLoader(dataset=dataset,
-                          batch_size=1,
-                          shuffle=False,
-                          num_workers=hp.train.num_workers,
-                          pin_memory=True,
-                          drop_last=False)
-
-
 class GANDataset(Dataset):
     """
     GAN Dataset searchs for all the wav files under root path
@@ -55,26 +36,26 @@ class GANDataset(Dataset):
         self.return_segments = return_segments
         self.use_cache = use_cache
         self.use_noise_augment = use_noise_augment
+        self.verbose = verbose
 
         assert seq_len % hop_len == 0, " [!] seq_len has to be a multiple of hop_len."
         self.feat_frame_len = seq_len // hop_len + (2 * conv_pad)
 
         # map G and D instances
-        self.G_to_D_mappings = [i for i in range(len(self.item_list))]
+        self.G_to_D_mappings = list(range(len(self.item_list)))
         self.shuffle_mapping()
 
         # cache acoustic features
         if use_cache:
             self.create_feature_cache()
 
-
-
     def create_feature_cache(self):
         self.manager = Manager()
         self.cache = self.manager.list()
         self.cache += [None for _ in range(len(self.item_list))]
 
-    def find_wav_files(self, path):
+    @staticmethod
+    def find_wav_files(path):
         return glob.glob(os.path.join(path, '**', '*.wav'), recursive=True)
 
     def __len__(self):
@@ -88,9 +69,8 @@ class GANDataset(Dataset):
             item1 = self.load_item(idx)
             item2 = self.load_item(idx2)
             return item1, item2
-        else:
-            item1 = self.load_item(idx)
-            return item1
+        item1 = self.load_item(idx)
+        return item1
 
     def shuffle_mapping(self):
         random.shuffle(self.G_to_D_mappings)
diff --git a/vocoder/layers/losses.py b/vocoder/layers/losses.py
index e242d209..1c3d4442 100644
--- a/vocoder/layers/losses.py
+++ b/vocoder/layers/losses.py
@@ -51,13 +51,13 @@ class STFTLoss(nn.Module):
 
 class MultiScaleSTFTLoss(torch.nn.Module):
     def __init__(self,
-                 n_ffts=[1024, 2048, 512],
-                 hop_lengths=[120, 240, 50],
-                 win_lengths=[600, 1200, 240]):
+                 n_ffts=(1024, 2048, 512),
+                 hop_lengths=(120, 240, 50),
+                 win_lengths=(600, 1200, 240)):
         super(MultiScaleSTFTLoss, self).__init__()
         self.loss_funcs = torch.nn.ModuleList()
-        for idx in range(len(n_ffts)):
-            self.loss_funcs.append(STFTLoss(n_ffts[idx], hop_lengths[idx], win_lengths[idx]))
+        for n_fft, hop_length, win_length in zip(n_ffts, hop_lengths, win_lengths):
+            self.loss_funcs.append(STFTLoss(n_fft, hop_length, win_length))
 
     def forward(self, y_hat, y):
         N = len(self.loss_funcs)
@@ -81,20 +81,14 @@ class MultiScaleSubbandSTFTLoss(MultiScaleSTFTLoss):
 
 class MSEGLoss(nn.Module):
     """ Mean Squared Generator Loss """
-    def __init__(self,):
-        super(MSEGLoss, self).__init__()
-
-    def forward(self, score_fake, ):
+    def forward(self, score_fake):
         loss_fake = torch.mean(torch.sum(torch.pow(score_fake, 2), dim=[1, 2]))
         return loss_fake
 
 
 class HingeGLoss(nn.Module):
     """ Hinge Discriminator Loss """
-    def __init__(self,):
-        super(HingeGLoss, self).__init__()
-
-    def forward(self, score_fake, score_real):
+    def forward(self, score_fake):
         loss_fake = torch.mean(F.relu(1. + score_fake))
         return loss_fake
 
@@ -106,9 +100,6 @@ class HingeGLoss(nn.Module):
 
 class MSEDLoss(nn.Module):
     """ Mean Squared Discriminator Loss """
-    def __init__(self,):
-        super(MSEDLoss, self).__init__()
-
     def forward(self, score_fake, score_real):
         loss_real = torch.mean(torch.sum(torch.pow(score_real - 1.0, 2), dim=[1, 2]))
         loss_fake = torch.mean(torch.sum(torch.pow(score_fake, 2), dim=[1, 2]))
@@ -118,9 +109,6 @@ class MSEDLoss(nn.Module):
 
 class HingeDLoss(nn.Module):
     """ Hinge Discriminator Loss """
-    def __init__(self,):
-        super(HingeDLoss, self).__init__()
-
     def forward(self, score_fake, score_real):
         loss_real = torch.mean(F.relu(1. - score_real))
         loss_fake = torch.mean(F.relu(1. + score_fake))
@@ -129,13 +117,10 @@ class HingeDLoss(nn.Module):
 
 
 class MelganFeatureLoss(nn.Module):
-    def __init__(self, ):
-        super(MelganFeatureLoss, self).__init__()
-
     def forward(self, fake_feats, real_feats):
         loss_feats = 0
         for fake_feat, real_feat in zip(fake_feats, real_feats):
-            loss_feats += hp.model.feat_match * torch.mean(torch.abs(fake_feat - real_feat))
+            loss_feats += torch.mean(torch.abs(fake_feat - real_feat))
         return loss_feats
 
 
@@ -147,7 +132,7 @@ class MelganFeatureLoss(nn.Module):
 class GeneratorLoss(nn.Module):
     def __init__(self, C):
         super(GeneratorLoss, self).__init__()
-        assert C.use_mse_gan_loss and C.use_hinge_gan_loss == False,\
+        assert not(C.use_mse_gan_loss and C.use_hinge_gan_loss),\
             " [!] Cannot use HingeGANLoss and MSEGANLoss together."
 
         self.use_stft_loss = C.use_stft_loss
@@ -228,7 +213,7 @@ class GeneratorLoss(nn.Module):
 class DiscriminatorLoss(nn.Module):
     def __init__(self, C):
         super(DiscriminatorLoss, self).__init__()
-        assert C.use_mse_gan_loss and C.use_hinge_gan_loss == False,\
+        assert not(C.use_mse_gan_loss and C.use_hinge_gan_loss),\
             " [!] Cannot use HingeGANLoss and MSEGANLoss together."
 
         self.use_mse_gan_loss = C.use_mse_gan_loss
diff --git a/vocoder/layers/melgan.py b/vocoder/layers/melgan.py
index cda0413c..61f7a96b 100644
--- a/vocoder/layers/melgan.py
+++ b/vocoder/layers/melgan.py
@@ -1,7 +1,4 @@
-import numpy as np
-import torch
 from torch import nn
-from torch.nn import functional as F
 from torch.nn.utils import weight_norm
 
 
diff --git a/vocoder/layers/pqmf.py b/vocoder/layers/pqmf.py
index f438ea00..3985aad4 100644
--- a/vocoder/layers/pqmf.py
+++ b/vocoder/layers/pqmf.py
@@ -44,6 +44,9 @@ class PQMF(torch.nn.Module):
 
         self.pad_fn = torch.nn.ConstantPad1d(taps // 2, 0.0)
 
+    def forward(self, x):
+        return self.analysis(x)
+
     def analysis(self, x):
         return F.conv1d(x, self.H, padding=self.taps // 2, stride=self.N)
 
diff --git a/vocoder/layers/pqmf2.py b/vocoder/layers/pqmf2.py
deleted file mode 100644
index 4cffb819..00000000
--- a/vocoder/layers/pqmf2.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Copyright 2020 Tomoki Hayashi
-#  MIT License (https://opensource.org/licenses/MIT)
-
-"""Pseudo QMF modules."""
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-from scipy.signal import kaiser
-
-
-def design_prototype_filter(taps=62, cutoff_ratio=0.15, beta=9.0):
-    """Design prototype filter for PQMF.
-
-    This method is based on `A Kaiser window approach for the design of prototype
-    filters of cosine modulated filterbanks`_.
-
-    Args:
-        taps (int): The number of filter taps.
-        cutoff_ratio (float): Cut-off frequency ratio.
-        beta (float): Beta coefficient for kaiser window.
-
-    Returns:
-        ndarray: Impluse response of prototype filter (taps + 1,).
-
-    .. _`A Kaiser window approach for the design of prototype filters of cosine modulated filterbanks`:
-        https://ieeexplore.ieee.org/abstract/document/681427
-
-    """
-    # check the arguments are valid
-    assert taps % 2 == 0, "The number of taps mush be even number."
-    assert 0.0 < cutoff_ratio < 1.0, "Cutoff ratio must be > 0.0 and < 1.0."
-
-    # make initial filter
-    omega_c = np.pi * cutoff_ratio
-    with np.errstate(invalid='ignore'):
-        h_i = np.sin(omega_c * (np.arange(taps + 1) - 0.5 * taps)) \
-            / (np.pi * (np.arange(taps + 1) - 0.5 * taps))
-    h_i[taps // 2] = np.cos(0) * cutoff_ratio  # fix nan due to indeterminate form
-
-    # apply kaiser window
-    w = kaiser(taps + 1, beta)
-    h = h_i * w
-
-    return h
-
-
-class PQMF(torch.nn.Module):
-    """PQMF module.
-
-    This module is based on `Near-perfect-reconstruction pseudo-QMF banks`_.
-
-    .. _`Near-perfect-reconstruction pseudo-QMF banks`:
-        https://ieeexplore.ieee.org/document/258122
-
-    """
-
-    def __init__(self, subbands=4, taps=62, cutoff_ratio=0.15, beta=9.0):
-        """Initilize PQMF module.
-
-        Args:
-            subbands (int): The number of subbands.
-            taps (int): The number of filter taps.
-            cutoff_ratio (float): Cut-off frequency ratio.
-            beta (float): Beta coefficient for kaiser window.
-
-        """
-        super(PQMF, self).__init__()
-
-        # define filter coefficient
-        h_proto = design_prototype_filter(taps, cutoff_ratio, beta)
-        h_analysis = np.zeros((subbands, len(h_proto)))
-        h_synthesis = np.zeros((subbands, len(h_proto)))
-        for k in range(subbands):
-            h_analysis[k] = 2 * h_proto * np.cos((2 * k + 1) * (np.pi / (2 * subbands)) * (np.arange(taps + 1) - ((taps - 1) / 2)) + (-1) ** k * np.pi / 4)
-            h_synthesis[k] = 2 * h_proto * np.cos((2 * k + 1) * (np.pi / (2 * subbands)) * (np.arange(taps + 1) - ((taps - 1) / 2)) - (-1) ** k * np.pi / 4)
-
-        # convert to tensor
-        analysis_filter = torch.from_numpy(h_analysis).float().unsqueeze(1)
-        synthesis_filter = torch.from_numpy(h_synthesis).float().unsqueeze(0)
-
-        # register coefficients as beffer
-        self.register_buffer("analysis_filter", analysis_filter)
-        self.register_buffer("synthesis_filter", synthesis_filter)
-
-        # filter for downsampling & upsampling
-        updown_filter = torch.zeros((subbands, subbands, subbands)).float()
-        for k in range(subbands):
-            updown_filter[k, k, 0] = 1.0
-        self.register_buffer("updown_filter", updown_filter)
-        self.subbands = subbands
-
-        # keep padding info
-        self.pad_fn = torch.nn.ConstantPad1d(taps // 2, 0.0)
-
-    def analysis(self, x):
-        """Analysis with PQMF.
-
-        Args:
-            x (Tensor): Input tensor (B, 1, T).
-
-        Returns:
-            Tensor: Output tensor (B, subbands, T // subbands).
-
-        """
-        x = F.conv1d(self.pad_fn(x), self.analysis_filter)
-        return F.conv1d(x, self.updown_filter, stride=self.subbands)
-
-    def synthesis(self, x):
-        """Synthesis with PQMF.
-
-        Args:
-            x (Tensor): Input tensor (B, subbands, T // subbands).
-
-        Returns:
-            Tensor: Output tensor (B, 1, T).
-
-        """
-        # NOTE(kan-bayashi): Power will be dreased so here multipy by # subbands.
-        #   Not sure this is the correct way, it is better to check again.
-        # TODO(kan-bayashi): Understand the reconstruction procedure
-        x = F.conv_transpose1d(x, self.updown_filter * self.subbands, stride=self.subbands)
-        x = F.conv1d(self.pad_fn(x), self.synthesis_filter)
-        return x
diff --git a/vocoder/models/melgan_discriminator.py b/vocoder/models/melgan_discriminator.py
index 55d3f585..3847babb 100644
--- a/vocoder/models/melgan_discriminator.py
+++ b/vocoder/models/melgan_discriminator.py
@@ -1,7 +1,5 @@
 import numpy as np
-import torch
 from torch import nn
-from torch.nn import functional as F
 from torch.nn.utils import weight_norm
 
 
@@ -32,7 +30,7 @@ class MelganDiscriminator(nn.Module):
 
         # downsampling layers
         layer_in_channels = base_channels
-        for idx, downsample_factor in enumerate(downsample_factors):
+        for downsample_factor in downsample_factors:
             layer_out_channels = min(layer_in_channels * downsample_factor,
                                      max_channels)
             layer_kernel_size = downsample_factor * 10 + 1
diff --git a/vocoder/models/melgan_generator.py b/vocoder/models/melgan_generator.py
index 2d266f29..1e47816d 100644
--- a/vocoder/models/melgan_generator.py
+++ b/vocoder/models/melgan_generator.py
@@ -1,7 +1,5 @@
-import math
 import torch
 from torch import nn
-from torch.nn import functional as F
 from torch.nn.utils import weight_norm
 
 from TTS.vocoder.layers.melgan import ResidualStack
diff --git a/vocoder/tests/test_datasets.py b/vocoder/tests/test_datasets.py
index 3d6280f0..5d409b3f 100644
--- a/vocoder/tests/test_datasets.py
+++ b/vocoder/tests/test_datasets.py
@@ -22,7 +22,7 @@ ok_ljspeech = os.path.exists(test_data_path)
 def gan_dataset_case(batch_size, seq_len, hop_len, conv_pad, return_segments, use_noise_augment, use_cache, num_workers):
     ''' run dataloader with given parameters and check conditions '''
     ap = AudioProcessor(**C.audio)
-    eval_items, train_items = load_wav_data(test_data_path, 10)
+    _, train_items = load_wav_data(test_data_path, 10)
     dataset = GANDataset(ap,
                          train_items,
                          seq_len=seq_len,
@@ -44,9 +44,9 @@ def gan_dataset_case(batch_size, seq_len, hop_len, conv_pad, return_segments, us
 
     # return random segments or return the whole audio
     if return_segments:
-        for item1, item2 in loader:
+        for item1, _ in loader:
             feat1, wav1 = item1
-            feat2, wav2 = item2
+            # feat2, wav2 = item2
             expected_feat_shape = (batch_size, ap.num_mels, seq_len // hop_len + conv_pad * 2)
 
             # check shapes
@@ -80,15 +80,15 @@ def gan_dataset_case(batch_size, seq_len, hop_len, conv_pad, return_segments, us
 def test_parametrized_gan_dataset():
     ''' test dataloader with different parameters '''
     params = [
-        [32, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, True, False, True, 0],
-        [32, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, True, False, True, 4],
-        [1, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, True, True, True, 0],
+        [32, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, True, False, True, 0],
+        [32, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, True, False, True, 4],
+        [1, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, True, True, True, 0],
         [1, C.audio['hop_length'], C.audio['hop_length'], 0, True, True, True, 0],
-        [1, C.audio['hop_length'] * 10,  C.audio['hop_length'], 2, True, True, True, 0],
-        [1, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, False, True, True, 0],
-        [1, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, True, False, True, 0],
-        [1, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, True, True, False, 0],
-        [1, C.audio['hop_length'] * 10,  C.audio['hop_length'], 0, False, False, False, 0],
+        [1, C.audio['hop_length'] * 10, C.audio['hop_length'], 2, True, True, True, 0],
+        [1, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, False, True, True, 0],
+        [1, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, True, False, True, 0],
+        [1, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, True, True, False, 0],
+        [1, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, False, False, False, 0],
     ]
     for param in params:
         print(param)
diff --git a/vocoder/tests/test_losses.py b/vocoder/tests/test_losses.py
index 83314832..44a1321d 100644
--- a/vocoder/tests/test_losses.py
+++ b/vocoder/tests/test_losses.py
@@ -1,5 +1,4 @@
 import os
-import unittest
 import torch
 
 from TTS.vocoder.layers.losses import TorchSTFT, STFTLoss, MultiScaleSTFTLoss
@@ -24,7 +23,7 @@ def test_torch_stft():
     torch_stft = TorchSTFT(ap.n_fft, ap.hop_length, ap.win_length)
     # librosa stft
     wav = ap.load_wav(WAV_FILE)
-    M_librosa = abs(ap._stft(wav))
+    M_librosa = abs(ap._stft(wav)) # pylint: disable=protected-access
     # torch stft
     wav = torch.from_numpy(wav[None, :]).float()
     M_torch = torch_stft(wav)
diff --git a/vocoder/tests/test_melgan_generator.py b/vocoder/tests/test_melgan_generator.py
index e9c4ad60..e03da31b 100644
--- a/vocoder/tests/test_melgan_generator.py
+++ b/vocoder/tests/test_melgan_generator.py
@@ -1,5 +1,4 @@
 import numpy as np
-import unittest
 import torch
 
 from TTS.vocoder.models.melgan_generator import MelganGenerator
diff --git a/vocoder/tests/test_pqmf.py b/vocoder/tests/test_pqmf.py
index 8444fc5a..a26bdd59 100644
--- a/vocoder/tests/test_pqmf.py
+++ b/vocoder/tests/test_pqmf.py
@@ -1,16 +1,11 @@
 import os
 import torch
-import unittest
 
-import numpy as np
 import soundfile as sf
 from librosa.core import load
 
-from TTS.tests import get_tests_path, get_tests_input_path, get_tests_output_path
-from TTS.utils.audio import AudioProcessor
-from TTS.utils.io import load_config
+from TTS.tests import get_tests_path, get_tests_input_path
 from TTS.vocoder.layers.pqmf import PQMF
-from TTS.vocoder.layers.pqmf2 import PQMF as PQMF2
 
 
 TESTS_PATH = get_tests_path()
diff --git a/vocoder/train.py b/vocoder/train.py
index 398051a9..d401e72e 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -17,7 +17,7 @@ from TTS.utils.generic_utils import (KeepAverage, count_parameters,
 from TTS.utils.io import copy_config_file, load_config
 from TTS.utils.radam import RAdam
 from TTS.utils.tensorboard_logger import TensorboardLogger
-from TTS.utils.training import NoamLR
+from TTS.utils.training import setup_torch_training_env, NoamLR
 from TTS.vocoder.datasets.gan_dataset import GANDataset
 from TTS.vocoder.datasets.preprocess import load_wav_data
 # from distribute import (DistributedSampler, apply_gradient_allreduce,
@@ -29,13 +29,8 @@ from TTS.vocoder.utils.generic_utils import (check_config, plot_results,
                                              setup_discriminator,
                                              setup_generator)
 
-torch.backends.cudnn.enabled = True
-torch.backends.cudnn.benchmark = True
-torch.manual_seed(54321)
-use_cuda = torch.cuda.is_available()
-num_gpus = torch.cuda.device_count()
-print(" > Using CUDA: ", use_cuda)
-print(" > Number of GPUs: ", num_gpus)
+
+use_cuda, num_gpus = setup_torch_training_env(True, True)
 
 
 def setup_loader(ap, is_val=False, verbose=False):
@@ -49,10 +44,11 @@ def setup_loader(ap, is_val=False, verbose=False):
                              pad_short=c.pad_short,
                              conv_pad=c.conv_pad,
                              is_training=not is_val,
-                             return_segments=False if is_val else True,
+                             return_segments=not is_val,
                              use_noise_augment=c.use_noise_augment,
                              use_cache=c.use_cache,
                              verbose=verbose)
+        dataset.shuffle_mapping()
         # sampler = DistributedSampler(dataset) if num_gpus > 1 else None
         loader = DataLoader(dataset,
                             batch_size=1 if is_val else c.batch_size,
@@ -81,11 +77,11 @@ def format_data(data):
         return c_G, x_G, c_D, x_D
 
     # return a whole audio segment
-    c, x = data
+    co, x = data
     if use_cuda:
-        c = c.cuda(non_blocking=True)
+        co = co.cuda(non_blocking=True)
         x = x.cuda(non_blocking=True)
-    return c, x
+    return co, x, None, None
 
 
 def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
@@ -306,7 +302,7 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
         start_time = time.time()
 
         # format data
-        c_G, y_G = format_data(data)
+        c_G, y_G, _, _ = format_data(data)
         loader_time = time.time() - end_time
 
         global_step += 1
@@ -416,7 +412,7 @@ def main(args):  # pylint: disable=redefined-outer-name
             optimizer_gen.load_state_dict(checkpoint['optimizer'])
             model_disc.load_state_dict(checkpoint['model_disc'])
             optimizer_disc.load_state_dict(checkpoint['optimizer_disc'])
-        except:
+        except KeyError:
             print(" > Partial model initialization.")
             model_dict = model_gen.state_dict()
             model_dict = set_init_dict(model_dict, checkpoint['model'], c)
diff --git a/vocoder/utils/console_logger.py b/vocoder/utils/console_logger.py
index 4fe132bb..ff7edfd0 100644
--- a/vocoder/utils/console_logger.py
+++ b/vocoder/utils/console_logger.py
@@ -91,7 +91,7 @@ class ConsoleLogger():
                     sign = ''
                 elif diff > 0:
                     color = tcolors.FAIL
-                    sing = '+'
+                    sign = '+'
             log_text += "{}{}:{} {:.5f} {}({}{:.5f})\n".format(indent, key, color, value, tcolors.ENDC, sign, diff)
         self.old_eval_loss_dict = avg_loss_dict
         print(log_text, flush=True)
diff --git a/vocoder/utils/io.py b/vocoder/utils/io.py
index 9526b9db..b6ea7484 100644
--- a/vocoder/utils/io.py
+++ b/vocoder/utils/io.py
@@ -2,7 +2,6 @@ import os
 import torch
 import datetime
 
-
 def save_model(model, optimizer, model_disc, optimizer_disc, current_step,
                epoch, output_path, **kwargs):
     model_state = model.state_dict()

From dfce9ab9b72344eab6269515de3e66d943603557 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 2 Jun 2020 23:58:18 +0200
Subject: [PATCH 105/185] espeak install travis

---
 .travis.yml | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/.travis.yml b/.travis.yml
index 645f9861..83ba25a3 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -3,6 +3,10 @@ language: python
 git:
   quiet: true
 
+before_install:
+  - sudo apt-get update
+  - sudo apt-get -y install espeak
+
 matrix:
   include:
   - name: "Lint check"

From 34eacb6383cbf185ecd70e1679a8bb5e0956973e Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 3 Jun 2020 12:15:48 +0200
Subject: [PATCH 106/185] reset loss weights

---
 vocoder/configs/melgan_config.json | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/vocoder/configs/melgan_config.json b/vocoder/configs/melgan_config.json
index 4aeba82e..bdfdf88a 100644
--- a/vocoder/configs/melgan_config.json
+++ b/vocoder/configs/melgan_config.json
@@ -54,11 +54,11 @@
     "use_hinge_gan_loss": false,
     "use_feat_match_loss": false,  // use only with melgan discriminators
 
-    "stft_loss_weight": 1,
-    "subband_stft_loss_weight": 1,
+    "stft_loss_weight": 0.5,
+    "subband_stft_loss_weight": 0.5,
     "mse_gan_loss_weight": 2.5,
     "hinge_gan_loss_weight": 2.5,
-    "feat_match_loss_weight": 10.0,
+    "feat_match_loss_weight": 25,
 
     "stft_loss_params": {
         "n_ffts": [1024, 2048, 512],

From 6b1de26869251a3f0af0d22f9f1fe23d0556cc76 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 3 Jun 2020 12:16:08 +0200
Subject: [PATCH 107/185] correct loss normalization and function refactoring

---
 vocoder/layers/losses.py | 141 +++++++++++++++++++++------------------
 1 file changed, 77 insertions(+), 64 deletions(-)

diff --git a/vocoder/layers/losses.py b/vocoder/layers/losses.py
index 1c3d4442..fb4e85d4 100644
--- a/vocoder/layers/losses.py
+++ b/vocoder/layers/losses.py
@@ -6,6 +6,7 @@ from torch.nn import functional as F
 
 class TorchSTFT():
     def __init__(self, n_fft, hop_length, win_length, window='hann_window'):
+        """ Torch based STFT operation """
         self.n_fft = n_fft
         self.hop_length = hop_length
         self.win_length = win_length
@@ -33,6 +34,7 @@ class TorchSTFT():
 
 
 class STFTLoss(nn.Module):
+    """ Single scale  STFT Loss """
     def __init__(self, n_fft, hop_length, win_length):
         super(STFTLoss, self).__init__()
         self.n_fft = n_fft
@@ -50,6 +52,7 @@ class STFTLoss(nn.Module):
         return loss_mag, loss_sc
 
 class MultiScaleSTFTLoss(torch.nn.Module):
+    """ Multi scale STFT loss """
     def __init__(self,
                  n_ffts=(1024, 2048, 512),
                  hop_lengths=(120, 240, 50),
@@ -73,6 +76,7 @@ class MultiScaleSTFTLoss(torch.nn.Module):
 
 
 class MultiScaleSubbandSTFTLoss(MultiScaleSTFTLoss):
+    """ Multiscale STFT loss for multi band model outputs """
     def forward(self, y_hat, y):
         y_hat = y_hat.view(-1, 1, y_hat.shape[2])
         y = y.view(-1, 1, y.shape[2])
@@ -121,16 +125,62 @@ class MelganFeatureLoss(nn.Module):
         loss_feats = 0
         for fake_feat, real_feat in zip(fake_feats, real_feats):
             loss_feats += torch.mean(torch.abs(fake_feat - real_feat))
+        loss_feats /= len(fake_feats) + len(real_feats)
         return loss_feats
 
 
-##################################
+#####################################
 # LOSS WRAPPERS
+#####################################
+
+
+def _apply_G_adv_loss(scores_fake, loss_func):
+    """ Compute G adversarial loss function
+    and normalize values """
+    adv_loss = 0
+    if isinstance(scores_fake, list):
+        for score_fake in scores_fake:
+            fake_loss = loss_func(score_fake)
+            adv_loss += fake_loss
+        adv_loss /= len(scores_fake)
+    else:
+        fake_loss = loss_func(scores_fake)
+        adv_loss = fake_loss
+    return adv_loss
+
+
+def _apply_D_loss(scores_fake, scores_real, loss_func):
+    """ Compute D loss func and normalize loss values """
+    loss = 0
+    real_loss = 0
+    fake_loss = 0
+    if isinstance(scores_fake, list):
+        # multi-scale loss
+        for score_fake, score_real in zip(scores_fake, scores_real):
+            total_loss, real_loss, fake_loss = loss_func(score_fake, score_real)
+            loss += total_loss
+            real_loss += real_loss
+            fake_loss += fake_loss
+        # normalize loss values with number of scales
+        loss /= len(scores_fake)
+        real_loss /= len(scores_real)
+        fake_loss /= len(scores_fake)
+    else:
+        # single scale loss
+        total_loss, real_loss, fake_loss = loss_func(scores_fake, scores_real)
+        loss = total_loss
+    return loss, real_loss, fake_loss
+
+
+##################################
+# MODEL LOSSES
 ##################################
 
 
 class GeneratorLoss(nn.Module):
     def __init__(self, C):
+        """ Compute Generator Loss values depending on training
+        configuration """
         super(GeneratorLoss, self).__init__()
         assert not(C.use_mse_gan_loss and C.use_hinge_gan_loss),\
             " [!] Cannot use HingeGANLoss and MSEGANLoss together."
@@ -159,7 +209,8 @@ class GeneratorLoss(nn.Module):
             self.feat_match_loss = MelganFeatureLoss()
 
     def forward(self, y_hat=None, y=None, scores_fake=None, feats_fake=None, feats_real=None, y_hat_sub=None, y_sub=None):
-        loss = 0
+        gen_loss = 0
+        adv_loss = 0
         return_dict = {}
 
         # STFT Loss
@@ -167,50 +218,41 @@ class GeneratorLoss(nn.Module):
             stft_loss_mg, stft_loss_sc = self.stft_loss(y_hat.squeeze(1), y.squeeze(1))
             return_dict['G_stft_loss_mg'] = stft_loss_mg
             return_dict['G_stft_loss_sc'] = stft_loss_sc
-            loss += self.stft_loss_weight * (stft_loss_mg + stft_loss_sc)
+            gen_loss += self.stft_loss_weight * (stft_loss_mg + stft_loss_sc)
 
         # subband STFT Loss
         if self.use_subband_stft_loss:
             subband_stft_loss_mg, subband_stft_loss_sc = self.subband_stft_loss(y_hat_sub, y_sub)
             return_dict['G_subband_stft_loss_mg'] = subband_stft_loss_mg
             return_dict['G_subband_stft_loss_sc'] = subband_stft_loss_sc
-            loss += self.subband_stft_loss_weight * (subband_stft_loss_mg + subband_stft_loss_sc)
+            gen_loss += self.subband_stft_loss_weight * (subband_stft_loss_mg + subband_stft_loss_sc)
 
-        # Fake Losses
+        # multiscale MSE adversarial loss
         if self.use_mse_gan_loss and scores_fake is not None:
-            mse_fake_loss = 0
-            if isinstance(scores_fake, list):
-                for score_fake in scores_fake:
-                    fake_loss = self.mse_loss(score_fake)
-                    mse_fake_loss += fake_loss
-            else:
-                fake_loss = self.mse_loss(scores_fake)
-                mse_fake_loss = fake_loss
+            mse_fake_loss = _apply_G_adv_loss(scores_fake, self.mse_loss)
             return_dict['G_mse_fake_loss'] = mse_fake_loss
-            loss += self.mse_gan_loss_weight * mse_fake_loss
+            adv_loss += self.mse_gan_loss_weight * mse_fake_loss
 
+        # multiscale Hinge adversarial loss
         if self.use_hinge_gan_loss and not scores_fake is not None:
-            hinge_fake_loss = 0
-            if isinstance(scores_fake, list):
-                for score_fake in scores_fake:
-                    fake_loss = self.hinge_loss(score_fake)
-                    hinge_fake_loss += fake_loss
-            else:
-                fake_loss = self.hinge_loss(scores_fake)
-                hinge_fake_loss = fake_loss
+            hinge_fake_loss = _apply_G_adv_loss(scores_fake, self.hinge_loss)
             return_dict['G_hinge_fake_loss'] = hinge_fake_loss
-            loss += self.hinge_gan_loss_weight * hinge_fake_loss
+            adv_loss += self.hinge_gan_loss_weight * hinge_fake_loss
 
         # Feature Matching Loss
         if self.use_feat_match_loss and not feats_fake:
             feat_match_loss = self.feat_match_loss(feats_fake, feats_real)
             return_dict['G_feat_match_loss'] = feat_match_loss
-            loss += self.feat_match_loss_weight * feat_match_loss
-        return_dict['G_loss'] = loss
+            adv_loss += self.feat_match_loss_weight * feat_match_loss
+        return_dict['G_loss'] = gen_loss + adv_loss
+        return_dict['G_gen_loss'] = gen_loss
+        return_dict['G_adv_loss'] = adv_loss
         return return_dict
 
 
 class DiscriminatorLoss(nn.Module):
+    """ Compute Discriminator Loss values depending on training
+    configuration """
     def __init__(self, C):
         super(DiscriminatorLoss, self).__init__()
         assert not(C.use_mse_gan_loss and C.use_hinge_gan_loss),\
@@ -219,9 +261,6 @@ class DiscriminatorLoss(nn.Module):
         self.use_mse_gan_loss = C.use_mse_gan_loss
         self.use_hinge_gan_loss = C.use_hinge_gan_loss
 
-        self.mse_gan_loss_weight = C.mse_gan_loss_weight
-        self.hinge_gan_loss_weight = C.hinge_gan_loss_weight
-
         if C.use_mse_gan_loss:
             self.mse_loss = MSEDLoss()
         if C.use_hinge_gan_loss:
@@ -232,44 +271,18 @@ class DiscriminatorLoss(nn.Module):
         return_dict = {}
 
         if self.use_mse_gan_loss:
-            mse_gan_loss = 0
-            mse_gan_real_loss = 0
-            mse_gan_fake_loss = 0
-            if isinstance(scores_fake, list):
-                for score_fake, score_real in zip(scores_fake, scores_real):
-                    total_loss, real_loss, fake_loss = self.mse_loss(score_fake, score_real)
-                    mse_gan_loss += total_loss
-                    mse_gan_real_loss += real_loss
-                    mse_gan_fake_loss += fake_loss
-            else:
-                total_loss, real_loss, fake_loss = self.mse_loss(scores_fake, scores_real)
-                mse_gan_loss = total_loss
-                mse_gan_real_loss = real_loss
-                mse_gan_fake_loss = fake_loss
-            return_dict['D_mse_gan_loss'] = mse_gan_loss
-            return_dict['D_mse_gan_real_loss'] = mse_gan_real_loss
-            return_dict['D_mse_gan_fake_loss'] = mse_gan_fake_loss
-            loss += self.mse_gan_loss_weight * mse_gan_loss
+            mse_D_loss, mse_D_real_loss, mse_D_fake_loss = _apply_D_loss(scores_fake, scores_real, self.mse_loss)
+            return_dict['D_mse_gan_loss'] = mse_D_loss
+            return_dict['D_mse_gan_real_loss'] = mse_D_real_loss
+            return_dict['D_mse_gan_fake_loss'] = mse_D_fake_loss
+            loss += mse_D_loss
 
         if self.use_hinge_gan_loss:
-            hinge_gan_loss = 0
-            hinge_gan_real_loss = 0
-            hinge_gan_fake_loss = 0
-            if isinstance(scores_fake, list):
-                for score_fake, score_real in zip(scores_fake, scores_real):
-                    total_loss, real_loss, fake_loss = self.hinge_loss(score_fake, score_real)
-                    hinge_gan_loss += total_loss
-                    hinge_gan_real_loss += real_loss
-                    hinge_gan_fake_loss += fake_loss
-            else:
-                total_loss, real_loss, fake_loss = self.hinge_loss(scores_fake, scores_real)
-                hinge_gan_loss = total_loss
-                hinge_gan_real_loss = real_loss
-                hinge_gan_fake_loss = fake_loss
-            return_dict['D_hinge_gan_loss'] = hinge_gan_loss
-            return_dict['D_hinge_gan_real_loss'] = hinge_gan_real_loss
-            return_dict['D_hinge_gan_fake_loss'] = hinge_gan_fake_loss
-            loss += self.hinge_gan_loss_weight * hinge_gan_loss
+            hinge_D_loss, hinge_D_real_loss, hinge_D_fake_loss = _apply_D_loss(scores_fake, scores_real, self.hinge_loss)
+            return_dict['D_hinge_gan_loss'] = hinge_D_loss
+            return_dict['D_hinge_gan_real_loss'] = hinge_D_real_loss
+            return_dict['D_hinge_gan_fake_loss'] = hinge_D_fake_loss
+            loss += hinge_D_loss
 
         return_dict['D_loss'] = loss
         return return_dict
\ No newline at end of file

From 6f4c1108f07ead33bf6da21675fdb2765e0fc133 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 3 Jun 2020 12:33:53 +0200
Subject: [PATCH 108/185] suppress pylint no-self-use for loss layers

---
 vocoder/layers/losses.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/vocoder/layers/losses.py b/vocoder/layers/losses.py
index fb4e85d4..22e2fb54 100644
--- a/vocoder/layers/losses.py
+++ b/vocoder/layers/losses.py
@@ -77,6 +77,7 @@ class MultiScaleSTFTLoss(torch.nn.Module):
 
 class MultiScaleSubbandSTFTLoss(MultiScaleSTFTLoss):
     """ Multiscale STFT loss for multi band model outputs """
+    # pylint: disable=no-self-use
     def forward(self, y_hat, y):
         y_hat = y_hat.view(-1, 1, y_hat.shape[2])
         y = y.view(-1, 1, y.shape[2])
@@ -85,6 +86,7 @@ class MultiScaleSubbandSTFTLoss(MultiScaleSTFTLoss):
 
 class MSEGLoss(nn.Module):
     """ Mean Squared Generator Loss """
+    # pylint: disable=no-self-use
     def forward(self, score_fake):
         loss_fake = torch.mean(torch.sum(torch.pow(score_fake, 2), dim=[1, 2]))
         return loss_fake
@@ -92,6 +94,7 @@ class MSEGLoss(nn.Module):
 
 class HingeGLoss(nn.Module):
     """ Hinge Discriminator Loss """
+    # pylint: disable=no-self-use
     def forward(self, score_fake):
         loss_fake = torch.mean(F.relu(1. + score_fake))
         return loss_fake
@@ -104,6 +107,7 @@ class HingeGLoss(nn.Module):
 
 class MSEDLoss(nn.Module):
     """ Mean Squared Discriminator Loss """
+    # pylint: disable=no-self-use
     def forward(self, score_fake, score_real):
         loss_real = torch.mean(torch.sum(torch.pow(score_real - 1.0, 2), dim=[1, 2]))
         loss_fake = torch.mean(torch.sum(torch.pow(score_fake, 2), dim=[1, 2]))
@@ -113,6 +117,7 @@ class MSEDLoss(nn.Module):
 
 class HingeDLoss(nn.Module):
     """ Hinge Discriminator Loss """
+    # pylint: disable=no-self-use
     def forward(self, score_fake, score_real):
         loss_real = torch.mean(F.relu(1. - score_real))
         loss_fake = torch.mean(F.relu(1. + score_fake))
@@ -121,6 +126,7 @@ class HingeDLoss(nn.Module):
 
 
 class MelganFeatureLoss(nn.Module):
+    # pylint: disable=no-self-use
     def forward(self, fake_feats, real_feats):
         loss_feats = 0
         for fake_feat, real_feat in zip(fake_feats, real_feats):

From 8dc1d6a208e19b919da76fcef75632b29a4bcaf4 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 02:58:25 +0200
Subject: [PATCH 109/185] update README.md

---
 README.md        | 8 +++++---
 vocoder/train.py | 2 +-
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 7d9884b0..20799943 100644
--- a/README.md
+++ b/README.md
@@ -17,9 +17,11 @@ If you are new, you can also find [here](http://www.erogol.com/text-speech-deep-
 [Details...](https://github.com/mozilla/TTS/wiki/Mean-Opinion-Score-Results)
 
 ## Features
-- High performance Text2Speech models on Torch and Tensorflow 2.0.
-- High performance Speaker Encoder to compute speaker embeddings efficiently. 
-- Integration with various Neural Vocoders (PWGAN, MelGAN, WaveRNN)
+- High performance Deep Learning models for Text2Speech related tasks.
+    - Text2Speech models (Tacotron, Tacotron2).
+    - Speaker Encoder to compute speaker embeddings efficiently. 
+    - Vocoder models (MelGAN, Multiband-MelGAN, GAN-TTS)
+- Ability to convert Torch models to Tensorflow 2.0 for inference.
 - Released trained models.
 - Efficient training codes for PyTorch. (soon for Tensorflow 2.0)
 - Codes to convert Torch models to Tensorflow 2.0.
diff --git a/vocoder/train.py b/vocoder/train.py
index d401e72e..03e14f4a 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -52,7 +52,7 @@ def setup_loader(ap, is_val=False, verbose=False):
         # sampler = DistributedSampler(dataset) if num_gpus > 1 else None
         loader = DataLoader(dataset,
                             batch_size=1 if is_val else c.batch_size,
-                            shuffle=False,
+                            shuffle=True,
                             drop_last=False,
                             sampler=None,
                             num_workers=c.num_val_loader_workers

From 1b05bfce2e1649ead3d07dbd9b0756258c4fbb5e Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 09:51:20 +0200
Subject: [PATCH 110/185] visualization fix

---
 vocoder/train.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/vocoder/train.py b/vocoder/train.py
index 03e14f4a..a75c6a12 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -120,11 +120,13 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
         y_hat = model_G(c_G)
         y_hat_sub = None
         y_G_sub = None
+        y_hat_vis = y_hat  # for visualization
 
         # PQMF formatting
         if y_hat.shape[1] > 1:
             y_hat_sub = y_hat
             y_hat = model_G.pqmf_synthesis(y_hat)
+            y_hat_vis = y_hat
             y_G_sub = model_G.pqmf_analysis(y_G)
 
         if global_step > c.steps_to_start_discriminator:
@@ -265,12 +267,12 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
                                 model_losses=loss_dict)
 
             # compute spectrograms
-            figures = plot_results(y_hat, y_G, ap, global_step,
+            figures = plot_results(y_hat_vis, y_G, ap, global_step,
                                    'train')
             tb_logger.tb_train_figures(global_step, figures)
 
             # Sample audio
-            sample_voice = y_hat[0].squeeze(0).detach().cpu().numpy()
+            sample_voice = y_hat_vis[0].squeeze(0).detach().cpu().numpy()
             tb_logger.tb_train_audios(global_step,
                                       {'train/audio': sample_voice},
                                       c.audio["sample_rate"])

From aa06a0609d0a8d5e5b2780ab0b6f059ae7b0398f Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 14:26:30 +0200
Subject: [PATCH 111/185] tacotron abstract class

---
 models/tacotron_abstract.py | 160 ++++++++++++++++++++++++++++++++++++
 1 file changed, 160 insertions(+)
 create mode 100644 models/tacotron_abstract.py

diff --git a/models/tacotron_abstract.py b/models/tacotron_abstract.py
new file mode 100644
index 00000000..db6cbdee
--- /dev/null
+++ b/models/tacotron_abstract.py
@@ -0,0 +1,160 @@
+import copy
+from abc import ABC, abstractmethod
+
+import torch
+from torch import nn
+
+from TTS.layers.gst_layers import GST
+from TTS.utils.generic_utils import sequence_mask
+
+
+class TacotronAbstract(ABC, nn.Module):
+    def __init__(self, num_chars,
+                 num_speakers,
+                 r,
+                 postnet_output_dim=80,
+                 decoder_output_dim=80,
+                 attn_type='original',
+                 attn_win=False,
+                 attn_norm="softmax",
+                 prenet_type="original",
+                 prenet_dropout=True,
+                 forward_attn=False,
+                 trans_agent=False,
+                 forward_attn_mask=False,
+                 location_attn=True,
+                 attn_K=5,
+                 separate_stopnet=True,
+                 bidirectional_decoder=False,
+                 double_decoder_consistency=False,
+                 ddc_r=None,
+                 gst=False):
+        """ Abstract Tacotron class """
+        super().__init__()
+        self.r = r
+        self.decoder_output_dim = decoder_output_dim
+        self.postnet_output_dim = postnet_output_dim
+        self.gst = gst
+        self.num_speakers = num_speakers
+        self.bidirectional_decoder = bidirectional_decoder
+        self.double_decoder_consistency = double_decoder_consistency
+        self.ddc_r = ddc_r
+
+        # layers
+        self.embedding = None
+        self.encoder = None
+        self.decoder = None
+        self.postnet = None
+
+        # global style token
+        if self.gst:
+            gst_embedding_dim = None
+            self.gst_layer = None
+
+    #############################
+    # INIT FUNCTIONS
+    #############################
+
+    def _init_states(self):
+        self.speaker_embeddings = None
+        self.speaker_embeddings_projected = None
+
+    def _init_backward_decoder(self):
+        self.decoder_backward = copy.deepcopy(self.decoder)
+
+    def _init_coarse_decoder(self):
+        self.coarse_decoder = copy.deepcopy(self.decoder)
+        self.coarse_decoder.r_init = self.ddc_r
+        self.coarse_decoder.set_r(self.ddc_r)
+
+    #############################
+    # CORE FUNCTIONS
+    #############################
+
+    @abstractmethod
+    def forward(self):
+        pass
+
+    @abstractmethod
+    def inference(self):
+        pass
+
+    #############################
+    # COMMON COMPUTE FUNCTIONS
+    #############################
+
+    def compute_masks(self, text_lengths, mel_lengths):
+        """Compute masks  against sequence paddings."""
+        # B x T_in_max (boolean)
+        device = text_lengths.device
+        input_mask = sequence_mask(text_lengths).to(device)
+        output_mask = None
+        if mel_lengths is not None:
+            max_len = mel_lengths.max()
+            r = self.decoder.r
+            max_len = max_len + (r - (max_len % r)) if max_len % r > 0 else max_len
+            output_mask = sequence_mask(mel_lengths, max_len=max_len).to(device)
+        return input_mask, output_mask
+
+    def _backward_pass(self, mel_specs, encoder_outputs, mask):
+        """ Run backwards decoder """
+        decoder_outputs_b, alignments_b, _ = self.decoder_backward(
+            encoder_outputs, torch.flip(mel_specs, dims=(1,)), mask,
+            self.speaker_embeddings_projected)
+        decoder_outputs_b = decoder_outputs_b.transpose(1, 2).contiguous()
+        return decoder_outputs_b, alignments_b
+
+    def _coarse_decoder_pass(self, mel_specs, encoder_outputs, alignments,
+                             input_mask):
+        """ Double Decoder Consistency """
+        T = mel_specs.shape[1]
+        if T % self.coarse_decoder.r > 0:
+            padding_size = self.coarse_decoder.r - (T % self.coarse_decoder.r)
+            mel_specs = torch.nn.functional.pad(mel_specs,
+                                                (0, 0, 0, padding_size, 0, 0))
+        decoder_outputs_backward, alignments_backward, _ = self.coarse_decoder(
+            encoder_outputs.detach(), mel_specs, input_mask)
+        scale_factor = self.decoder.r_init / self.decoder.r
+        alignments_backward = torch.nn.functional.interpolate(
+            alignments_backward.transpose(1, 2),
+            size=alignments.shape[1],
+            mode='nearest').transpose(1, 2)
+        decoder_outputs_backward = decoder_outputs_backward.transpose(1, 2)
+        decoder_outputs_backward = decoder_outputs_backward[:, :T, :]
+        return decoder_outputs_backward, alignments_backward
+
+    #############################
+    # EMBEDDING FUNCTIONS
+    #############################
+
+    def compute_speaker_embedding(self, speaker_ids):
+        """ Compute speaker embedding vectors """
+        if hasattr(self, "speaker_embedding") and speaker_ids is None:
+            raise RuntimeError(
+                " [!] Model has speaker embedding layer but speaker_id is not provided"
+            )
+        if hasattr(self, "speaker_embedding") and speaker_ids is not None:
+            self.speaker_embeddings = self.speaker_embedding(speaker_ids).unsqueeze(1)
+        if hasattr(self, "speaker_project_mel") and speaker_ids is not None:
+            self.speaker_embeddings_projected = self.speaker_project_mel(
+                self.speaker_embeddings).squeeze(1)
+
+    def compute_gst(self, inputs, mel_specs):
+        """ Compute global style token """
+        gst_outputs = self.gst_layer(mel_specs)
+        inputs = self._add_speaker_embedding(inputs, gst_outputs)
+        return inputs
+
+    @staticmethod
+    def _add_speaker_embedding(outputs, speaker_embeddings):
+        speaker_embeddings_ = speaker_embeddings.expand(
+            outputs.size(0), outputs.size(1), -1)
+        outputs = outputs + speaker_embeddings_
+        return outputs
+
+    @staticmethod
+    def _concat_speaker_embedding(outputs, speaker_embeddings):
+        speaker_embeddings_ = speaker_embeddings.expand(
+            outputs.size(0), outputs.size(1), -1)
+        outputs = torch.cat([outputs, speaker_embeddings_], dim=-1)
+        return outputs

From ac24bdfddda30b2b35cd860be6073d412cb2e96d Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 14:26:51 +0200
Subject: [PATCH 112/185] update README.md

---
 README.md | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/README.md b/README.md
index 20799943..f6ee1ade 100644
--- a/README.md
+++ b/README.md
@@ -11,7 +11,7 @@ If you are new, you can also find [here](http://www.erogol.com/text-speech-deep-
 
 [![](https://sourcerer.io/fame/erogol/erogol/TTS/images/0)](https://sourcerer.io/fame/erogol/erogol/TTS/links/0)[![](https://sourcerer.io/fame/erogol/erogol/TTS/images/1)](https://sourcerer.io/fame/erogol/erogol/TTS/links/1)[![](https://sourcerer.io/fame/erogol/erogol/TTS/images/2)](https://sourcerer.io/fame/erogol/erogol/TTS/links/2)[![](https://sourcerer.io/fame/erogol/erogol/TTS/images/3)](https://sourcerer.io/fame/erogol/erogol/TTS/links/3)[![](https://sourcerer.io/fame/erogol/erogol/TTS/images/4)](https://sourcerer.io/fame/erogol/erogol/TTS/links/4)[![](https://sourcerer.io/fame/erogol/erogol/TTS/images/5)](https://sourcerer.io/fame/erogol/erogol/TTS/links/5)[![](https://sourcerer.io/fame/erogol/erogol/TTS/images/6)](https://sourcerer.io/fame/erogol/erogol/TTS/links/6)[![](https://sourcerer.io/fame/erogol/erogol/TTS/images/7)](https://sourcerer.io/fame/erogol/erogol/TTS/links/7)
 
-## TTS Performance 
+## TTS Performance
 <p align="center"><img src="https://camo.githubusercontent.com/9fa79f977015e55eb9ec7aa32045555f60d093d3/68747470733a2f2f646973636f757273652d706161732d70726f64756374696f6e2d636f6e74656e742e73332e6475616c737461636b2e75732d656173742d312e616d617a6f6e6177732e636f6d2f6f7074696d697a65642f33582f362f342f363432386639383065396563373531633234386535393134363038393566373838316165633063365f325f363930783339342e706e67"/></p>
 
 [Details...](https://github.com/mozilla/TTS/wiki/Mean-Opinion-Score-Results)
@@ -19,8 +19,9 @@ If you are new, you can also find [here](http://www.erogol.com/text-speech-deep-
 ## Features
 - High performance Deep Learning models for Text2Speech related tasks.
     - Text2Speech models (Tacotron, Tacotron2).
-    - Speaker Encoder to compute speaker embeddings efficiently. 
+    - Speaker Encoder to compute speaker embeddings efficiently.
     - Vocoder models (MelGAN, Multiband-MelGAN, GAN-TTS)
+- Support for multi-speaker TTS training.
 - Ability to convert Torch models to Tensorflow 2.0 for inference.
 - Released trained models.
 - Efficient training codes for PyTorch. (soon for Tensorflow 2.0)
@@ -86,7 +87,7 @@ Audio length is approximately 6 secs.
 
 
 ## Datasets and Data-Loading
-TTS provides a generic dataloder easy to use for new datasets. You need to write an preprocessor function to integrate your own dataset.Check ```datasets/preprocess.py``` to see some examples. After the function, you need to set ```dataset``` field in ```config.json```. Do not forget other data related fields too.  
+TTS provides a generic dataloder easy to use for new datasets. You need to write an preprocessor function to integrate your own dataset.Check ```datasets/preprocess.py``` to see some examples. After the function, you need to set ```dataset``` field in ```config.json```. Do not forget other data related fields too.
 
 Some of the open-sourced datasets that we successfully applied TTS, are linked below.
 
@@ -98,9 +99,9 @@ Some of the open-sourced datasets that we successfully applied TTS, are linked b
 - [Spanish](https://drive.google.com/file/d/1Sm_zyBo67XHkiFhcRSQ4YaHPYM0slO_e/view?usp=sharing) - thx! @carlfm01
 
 ## Training and Fine-tuning LJ-Speech
-Here you can find a [CoLab](https://gist.github.com/erogol/97516ad65b44dbddb8cd694953187c5b) notebook for a hands-on example, training LJSpeech. Or you can manually follow the guideline below. 
+Here you can find a [CoLab](https://gist.github.com/erogol/97516ad65b44dbddb8cd694953187c5b) notebook for a hands-on example, training LJSpeech. Or you can manually follow the guideline below.
 
-To start with, split ```metadata.csv``` into train and validation subsets respectively ```metadata_train.csv``` and ```metadata_val.csv```. Note that for text-to-speech, validation performance might be misleading since the loss value does not directly measure the voice quality to the human ear and it also does not measure the attention module performance. Therefore, running the model with new sentences and listening to the results is the best way to go. 
+To start with, split ```metadata.csv``` into train and validation subsets respectively ```metadata_train.csv``` and ```metadata_val.csv```. Note that for text-to-speech, validation performance might be misleading since the loss value does not directly measure the voice quality to the human ear and it also does not measure the attention module performance. Therefore, running the model with new sentences and listening to the results is the best way to go.
 
 ```
 shuf metadata.csv > metadata_shuf.csv
@@ -139,10 +140,10 @@ cardboardlinter --refspec master
 ```
 
 ## Collaborative Experimentation Guide
-If you like to use TTS to try a new idea and like to share your experiments with the community, we urge you to use the following guideline for a better collaboration. 
+If you like to use TTS to try a new idea and like to share your experiments with the community, we urge you to use the following guideline for a better collaboration.
 (If you have an idea for better collaboration, let us know)
 - Create a new branch.
-- Open an issue pointing your branch. 
+- Open an issue pointing your branch.
 - Explain your experiment.
 - Share your results as you proceed. (Tensorboard log files, audio results, visuals etc.)
 - Use LJSpeech dataset (for English) if you like to compare results with the released models. (It is the most open scalable dataset for quick experimentation)

From cd93b7b3515f6c1a3f47055df5b5018e6f9b0de8 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 14:27:06 +0200
Subject: [PATCH 113/185] update model config.json with double decoder
 consistency

---
 config.json | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/config.json b/config.json
index 32debf86..74021970 100644
--- a/config.json
+++ b/config.json
@@ -96,6 +96,8 @@
     "transition_agent": false,     // enable/disable transition agent of forward attention.
     "location_attn": true,         // enable_disable location sensitive attention. It is enabled for TACOTRON by default.
     "bidirectional_decoder": false,  // use https://arxiv.org/abs/1907.09006. Use it, if attention does not work well with your dataset.
+    "double_decoder_consistency": true,  // use DDC explained here https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency-draft/
+    "ddc_r": 7,                           // reduction rate for coarse decoder.
 
     // STOPNET
     "stopnet": true,               // Train stopnet predicting the end of synthesis.

From 23f65df6c6cbe9ee20547841c8d40e0a87b2c736 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 14:27:40 +0200
Subject: [PATCH 114/185] add DDC loss

---
 layers/losses.py | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/layers/losses.py b/layers/losses.py
index 608e247d..f7745b6e 100644
--- a/layers/losses.py
+++ b/layers/losses.py
@@ -184,7 +184,7 @@ class TacotronLoss(torch.nn.Module):
 
     def forward(self, postnet_output, decoder_output, mel_input, linear_input,
                 stopnet_output, stopnet_target, output_lens, decoder_b_output,
-                alignments, alignment_lens, input_lens):
+                alignments, alignment_lens, alignments_backwards, input_lens):
 
         return_dict = {}
         # decoder and postnet losses
@@ -226,6 +226,15 @@ class TacotronLoss(torch.nn.Module):
             return_dict['decoder_b_loss'] = decoder_b_loss
             return_dict['decoder_c_loss'] = decoder_c_loss
 
+        # double decoder consistency loss (if enabled)
+        if self.config.double_decoder_consistency:
+            decoder_b_loss = self.criterion(decoder_b_output, mel_input, output_lens)
+            # decoder_c_loss = torch.nn.functional.l1_loss(decoder_b_output, decoder_output)
+            attention_c_loss = torch.nn.functional.l1_loss(alignments, alignments_backwards)
+            loss += decoder_b_loss + attention_c_loss
+            return_dict['decoder_coarse_loss'] = decoder_b_loss
+            return_dict['decoder_ddc_loss'] = attention_c_loss
+
         # guided attention loss (if enabled)
         if self.config.ga_alpha > 0:
             ga_loss = self.criterion_ga(alignments, input_lens, alignment_lens)

From 9559e3fc308d70c029b1d703d1eeb06295d4bee3 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 14:28:16 +0200
Subject: [PATCH 115/185] inherit TacotronAbstact with both tacotron and
 tacotron2

---
 models/tacotron.py  | 135 ++++++++++++++++++-------------------------
 models/tacotron2.py | 136 ++++++++++++++++++++++++++++----------------
 2 files changed, 142 insertions(+), 129 deletions(-)

diff --git a/models/tacotron.py b/models/tacotron.py
index fba82b1b..c526374a 100644
--- a/models/tacotron.py
+++ b/models/tacotron.py
@@ -1,23 +1,21 @@
 # coding: utf-8
 import torch
-import copy
 from torch import nn
-from TTS.layers.tacotron import Encoder, Decoder, PostCBHG
-from TTS.utils.generic_utils import sequence_mask
+
 from TTS.layers.gst_layers import GST
+from TTS.layers.tacotron import Decoder, Encoder, PostCBHG
+from TTS.models.tacotron_abstract import TacotronAbstract
 
 
-class Tacotron(nn.Module):
+class Tacotron(TacotronAbstract):
     def __init__(self,
                  num_chars,
                  num_speakers,
                  r=5,
                  postnet_output_dim=1025,
                  decoder_output_dim=80,
-                 memory_size=5,
                  attn_type='original',
                  attn_win=False,
-                 gst=False,
                  attn_norm="sigmoid",
                  prenet_type="original",
                  prenet_dropout=True,
@@ -27,38 +25,41 @@ class Tacotron(nn.Module):
                  location_attn=True,
                  attn_K=5,
                  separate_stopnet=True,
-                 bidirectional_decoder=False):
-        super(Tacotron, self).__init__()
-        self.r = r
-        self.decoder_output_dim = decoder_output_dim
-        self.postnet_output_dim = postnet_output_dim
-        self.gst = gst
-        self.num_speakers = num_speakers
-        self.bidirectional_decoder = bidirectional_decoder
-        decoder_dim = 512 if num_speakers > 1 else 256
-        encoder_dim = 512 if num_speakers > 1 else 256
+                 bidirectional_decoder=False,
+                 double_decoder_consistency=False,
+                 ddc_r=None,
+                 gst=False,
+                 memory_size=5):
+        super(Tacotron,
+              self).__init__(num_chars, num_speakers, r, postnet_output_dim,
+                             decoder_output_dim, attn_type, attn_win,
+                             attn_norm, prenet_type, prenet_dropout,
+                             forward_attn, trans_agent, forward_attn_mask,
+                             location_attn, attn_K, separate_stopnet,
+                             bidirectional_decoder, double_decoder_consistency,
+                             ddc_r, gst)
+        decoder_in_features = 512 if num_speakers > 1 else 256
+        encoder_in_features = 512 if num_speakers > 1 else 256
+        speaker_embedding_dim = 256
         proj_speaker_dim = 80 if num_speakers > 1 else 0
-        # embedding layer
+        # base model layers
         self.embedding = nn.Embedding(num_chars, 256, padding_idx=0)
         self.embedding.weight.data.normal_(0, 0.3)
-        # boilerplate model
-        self.encoder = Encoder(encoder_dim)
-        self.decoder = Decoder(decoder_dim, decoder_output_dim, r, memory_size, attn_type, attn_win,
+        self.encoder = Encoder(encoder_in_features)
+        self.decoder = Decoder(decoder_in_features, decoder_output_dim, r, memory_size, attn_type, attn_win,
                                attn_norm, prenet_type, prenet_dropout,
                                forward_attn, trans_agent, forward_attn_mask,
                                location_attn, attn_K, separate_stopnet,
                                proj_speaker_dim)
-        if self.bidirectional_decoder:
-            self.decoder_backward = copy.deepcopy(self.decoder)
         self.postnet = PostCBHG(decoder_output_dim)
         self.last_linear = nn.Linear(self.postnet.cbhg.gru_features * 2,
                                      postnet_output_dim)
         # speaker embedding layers
         if num_speakers > 1:
-            self.speaker_embedding = nn.Embedding(num_speakers, 256)
+            self.speaker_embedding = nn.Embedding(num_speakers, speaker_embedding_dim)
             self.speaker_embedding.weight.data.normal_(0, 0.3)
             self.speaker_project_mel = nn.Sequential(
-                nn.Linear(256, proj_speaker_dim), nn.Tanh())
+                nn.Linear(speaker_embedding_dim, proj_speaker_dim), nn.Tanh())
             self.speaker_embeddings = None
             self.speaker_embeddings_projected = None
         # global style token layers
@@ -68,28 +69,15 @@ class Tacotron(nn.Module):
                                  num_heads=4,
                                  num_style_tokens=10,
                                  embedding_dim=gst_embedding_dim)
+        # backward pass decoder
+        if self.bidirectional_decoder:
+            self._init_backward_decoder()
+        # setup DDC
+        if self.double_decoder_consistency:
+            self._init_coarse_decoder()
 
-    def _init_states(self):
-        self.speaker_embeddings = None
-        self.speaker_embeddings_projected = None
 
-    def compute_speaker_embedding(self, speaker_ids):
-        if hasattr(self, "speaker_embedding") and speaker_ids is None:
-            raise RuntimeError(
-                " [!] Model has speaker embedding layer but speaker_id is not provided"
-            )
-        if hasattr(self, "speaker_embedding") and speaker_ids is not None:
-            self.speaker_embeddings = self._compute_speaker_embedding(
-                speaker_ids)
-            self.speaker_embeddings_projected = self.speaker_project_mel(
-                self.speaker_embeddings).squeeze(1)
-
-    def compute_gst(self, inputs, mel_specs):
-        gst_outputs = self.gst_layer(mel_specs)
-        inputs = self._add_speaker_embedding(inputs, gst_outputs)
-        return inputs
-
-    def forward(self, characters, text_lengths, mel_specs, speaker_ids=None):
+    def forward(self, characters, text_lengths, mel_specs, mel_lengths=None, speaker_ids=None):
         """
         Shapes:
             - characters: B x T_in
@@ -98,45 +86,59 @@ class Tacotron(nn.Module):
             - speaker_ids: B x 1
         """
         self._init_states()
-        mask = sequence_mask(text_lengths).to(characters.device)
+        input_mask, output_mask = self.compute_masks(text_lengths, mel_lengths)
         # B x T_in x embed_dim
         inputs = self.embedding(characters)
         # B x speaker_embed_dim
-        self.compute_speaker_embedding(speaker_ids)
+        if speaker_ids is not None:
+            self.compute_speaker_embedding(speaker_ids)
         if self.num_speakers > 1:
             # B x T_in x embed_dim + speaker_embed_dim
             inputs = self._concat_speaker_embedding(inputs,
                                                     self.speaker_embeddings)
-        # B x T_in x encoder_dim
+        # B x T_in x encoder_in_features
         encoder_outputs = self.encoder(inputs)
+        # sequence masking
+        encoder_outputs = encoder_outputs * input_mask.unsqueeze(2).expand_as(encoder_outputs)
+        # global style token
         if self.gst:
             # B x gst_dim
             encoder_outputs = self.compute_gst(encoder_outputs, mel_specs)
         if self.num_speakers > 1:
             encoder_outputs = self._concat_speaker_embedding(
                 encoder_outputs, self.speaker_embeddings)
-        # decoder_outputs: B x decoder_dim x T_out
-        # alignments: B x T_in x encoder_dim
+        # decoder_outputs: B x decoder_in_features x T_out
+        # alignments: B x T_in x encoder_in_features
         # stop_tokens: B x T_in
         decoder_outputs, alignments, stop_tokens = self.decoder(
-            encoder_outputs, mel_specs, mask,
+            encoder_outputs, mel_specs, input_mask,
             self.speaker_embeddings_projected)
-        # B x T_out x decoder_dim
+        # sequence masking
+        if output_mask is not None:
+            decoder_outputs = decoder_outputs * output_mask.unsqueeze(1).expand_as(decoder_outputs)
+        # B x T_out x decoder_in_features
         postnet_outputs = self.postnet(decoder_outputs)
+        # sequence masking
+        if output_mask is not None:
+            postnet_outputs = postnet_outputs * output_mask.unsqueeze(2).expand_as(postnet_outputs)
         # B x T_out x posnet_dim
         postnet_outputs = self.last_linear(postnet_outputs)
-        # B x T_out x decoder_dim
+        # B x T_out x decoder_in_features
         decoder_outputs = decoder_outputs.transpose(1, 2).contiguous()
         if self.bidirectional_decoder:
-            decoder_outputs_backward, alignments_backward = self._backward_inference(mel_specs, encoder_outputs, mask)
+            decoder_outputs_backward, alignments_backward = self._backward_pass(mel_specs, encoder_outputs, input_mask)
             return decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward
+        if self.double_decoder_consistency:
+            decoder_outputs_backward, alignments_backward = self._coarse_decoder_pass(mel_specs, encoder_outputs, alignments, input_mask)
+            return  decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward
         return decoder_outputs, postnet_outputs, alignments, stop_tokens
 
     @torch.no_grad()
     def inference(self, characters, speaker_ids=None, style_mel=None):
         inputs = self.embedding(characters)
         self._init_states()
-        self.compute_speaker_embedding(speaker_ids)
+        if speaker_ids is not None:
+            self.compute_speaker_embedding(speaker_ids)
         if self.num_speakers > 1:
             inputs = self._concat_speaker_embedding(inputs,
                                                     self.speaker_embeddings)
@@ -152,28 +154,3 @@ class Tacotron(nn.Module):
         postnet_outputs = self.last_linear(postnet_outputs)
         decoder_outputs = decoder_outputs.transpose(1, 2)
         return decoder_outputs, postnet_outputs, alignments, stop_tokens
-
-    def _backward_inference(self, mel_specs, encoder_outputs, mask):
-        decoder_outputs_b, alignments_b, _ = self.decoder_backward(
-            encoder_outputs, torch.flip(mel_specs, dims=(1,)), mask,
-            self.speaker_embeddings_projected)
-        decoder_outputs_b = decoder_outputs_b.transpose(1, 2).contiguous()
-        return decoder_outputs_b, alignments_b
-
-    def _compute_speaker_embedding(self, speaker_ids):
-        speaker_embeddings = self.speaker_embedding(speaker_ids)
-        return speaker_embeddings.unsqueeze_(1)
-
-    @staticmethod
-    def _add_speaker_embedding(outputs, speaker_embeddings):
-        speaker_embeddings_ = speaker_embeddings.expand(
-            outputs.size(0), outputs.size(1), -1)
-        outputs = outputs + speaker_embeddings_
-        return outputs
-
-    @staticmethod
-    def _concat_speaker_embedding(outputs, speaker_embeddings):
-        speaker_embeddings_ = speaker_embeddings.expand(
-            outputs.size(0), outputs.size(1), -1)
-        outputs = torch.cat([outputs, speaker_embeddings_], dim=-1)
-        return outputs
diff --git a/models/tacotron2.py b/models/tacotron2.py
index 3e7adfca..bbce4be9 100644
--- a/models/tacotron2.py
+++ b/models/tacotron2.py
@@ -1,13 +1,15 @@
-import copy
-import torch
 from math import sqrt
+
+import torch
 from torch import nn
-from TTS.layers.tacotron2 import Encoder, Decoder, Postnet
-from TTS.utils.generic_utils import sequence_mask
+
+from TTS.layers.gst_layers import GST
+from TTS.layers.tacotron2 import Decoder, Encoder, Postnet
+from TTS.models.tacotron_abstract import TacotronAbstract
 
 
 # TODO: match function arguments with tacotron
-class Tacotron2(nn.Module):
+class Tacotron2(TacotronAbstract):
     def __init__(self,
                  num_chars,
                  num_speakers,
@@ -25,16 +27,22 @@ class Tacotron2(nn.Module):
                  location_attn=True,
                  attn_K=5,
                  separate_stopnet=True,
-                 bidirectional_decoder=False):
-        super(Tacotron2, self).__init__()
-        self.postnet_output_dim = postnet_output_dim
-        self.decoder_output_dim = decoder_output_dim
-        self.r = r
-        self.bidirectional_decoder = bidirectional_decoder
-        decoder_dim = 512 if num_speakers > 1 else 512
-        encoder_dim = 512 if num_speakers > 1 else 512
+                 bidirectional_decoder=False,
+                 double_decoder_consistency=False,
+                 ddc_r=None,
+                 gst=False):
+        super(Tacotron2,
+              self).__init__(num_chars, num_speakers, r, postnet_output_dim,
+                             decoder_output_dim, attn_type, attn_win,
+                             attn_norm, prenet_type, prenet_dropout,
+                             forward_attn, trans_agent, forward_attn_mask,
+                             location_attn, attn_K, separate_stopnet,
+                             bidirectional_decoder, double_decoder_consistency,
+                             ddc_r, gst)
+        decoder_in_features = 512 if num_speakers > 1 else 512
+        encoder_in_features = 512 if num_speakers > 1 else 512
         proj_speaker_dim = 80 if num_speakers > 1 else 0
-        # embedding layer
+        # base layers
         self.embedding = nn.Embedding(num_chars, 512, padding_idx=0)
         std = sqrt(2.0 / (num_chars + 512))
         val = sqrt(3.0) * std  # uniform bounds for std
@@ -42,20 +50,25 @@ class Tacotron2(nn.Module):
         if num_speakers > 1:
             self.speaker_embedding = nn.Embedding(num_speakers, 512)
             self.speaker_embedding.weight.data.normal_(0, 0.3)
-            self.speaker_embeddings = None
-            self.speaker_embeddings_projected = None
-        self.encoder = Encoder(encoder_dim)
-        self.decoder = Decoder(decoder_dim, self.decoder_output_dim, r, attn_type, attn_win,
+        self.encoder = Encoder(encoder_in_features)
+        self.decoder = Decoder(decoder_in_features, self.decoder_output_dim, r, attn_type, attn_win,
                                attn_norm, prenet_type, prenet_dropout,
                                forward_attn, trans_agent, forward_attn_mask,
                                location_attn, attn_K, separate_stopnet, proj_speaker_dim)
-        if self.bidirectional_decoder:
-            self.decoder_backward = copy.deepcopy(self.decoder)
         self.postnet = Postnet(self.postnet_output_dim)
-
-    def _init_states(self):
-        self.speaker_embeddings = None
-        self.speaker_embeddings_projected = None
+        # global style token layers
+        if self.gst:
+            gst_embedding_dim = encoder_in_features
+            self.gst_layer = GST(num_mel=80,
+                                 num_heads=4,
+                                 num_style_tokens=10,
+                                 embedding_dim=gst_embedding_dim)
+        # backward pass decoder
+        if self.bidirectional_decoder:
+            self._init_backward_decoder()
+        # setup DDC
+        if self.double_decoder_consistency:
+            self._init_coarse_decoder()
 
     @staticmethod
     def shape_outputs(mel_outputs, mel_outputs_postnet, alignments):
@@ -63,31 +76,60 @@ class Tacotron2(nn.Module):
         mel_outputs_postnet = mel_outputs_postnet.transpose(1, 2)
         return mel_outputs, mel_outputs_postnet, alignments
 
-    def forward(self, text, text_lengths, mel_specs=None, speaker_ids=None):
+    def forward(self, text, text_lengths, mel_specs=None, mel_lengths=None, speaker_ids=None):
         self._init_states()
         # compute mask for padding
-        mask = sequence_mask(text_lengths).to(text.device)
+        # B x T_in_max (boolean)
+        input_mask, output_mask = self.compute_masks(text_lengths, mel_lengths)
+        # B x D_embed x T_in_max
         embedded_inputs = self.embedding(text).transpose(1, 2)
+        # B x T_in_max x D_en
         encoder_outputs = self.encoder(embedded_inputs, text_lengths)
-        encoder_outputs = self._add_speaker_embedding(encoder_outputs,
-                                                      speaker_ids)
+        # adding speaker embeddding to encoder output
+        # TODO: multi-speaker
+        # B x speaker_embed_dim
+        if speaker_ids is not None:
+            self.compute_speaker_embedding(speaker_ids)
+        if self.num_speakers > 1:
+            # B x T_in x embed_dim + speaker_embed_dim
+            encoder_outputs = self._add_speaker_embedding(encoder_outputs,
+                                                          self.speaker_embeddings)
+        encoder_outputs = encoder_outputs * input_mask.unsqueeze(2).expand_as(encoder_outputs)
+        # global style token
+        if self.gst:
+            # B x gst_dim
+            encoder_outputs = self.compute_gst(encoder_outputs, mel_specs)
+        # B x mel_dim x T_out -- B x T_out//r x T_in -- B x T_out//r
         decoder_outputs, alignments, stop_tokens = self.decoder(
-            encoder_outputs, mel_specs, mask)
+            encoder_outputs, mel_specs, input_mask)
+        # sequence masking
+        if mel_lengths is not None:
+            decoder_outputs = decoder_outputs * output_mask.unsqueeze(1).expand_as(decoder_outputs)
+        # B x mel_dim x T_out
         postnet_outputs = self.postnet(decoder_outputs)
-        postnet_outputs = decoder_outputs + postnet_outputs
+        # sequence masking
+        if output_mask is not None:
+            postnet_outputs = postnet_outputs * output_mask.unsqueeze(1).expand_as(postnet_outputs)
+        # B x T_out x mel_dim -- B x T_out x mel_dim -- B x T_out//r x T_in
         decoder_outputs, postnet_outputs, alignments = self.shape_outputs(
             decoder_outputs, postnet_outputs, alignments)
         if self.bidirectional_decoder:
-            decoder_outputs_backward, alignments_backward = self._backward_inference(mel_specs, encoder_outputs, mask)
+            decoder_outputs_backward, alignments_backward = self._backward_pass(mel_specs, encoder_outputs, input_mask)
             return decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward
+        if self.double_decoder_consistency:
+            decoder_outputs_backward, alignments_backward = self._coarse_decoder_pass(mel_specs, encoder_outputs, alignments, input_mask)
+            return  decoder_outputs, postnet_outputs, alignments, stop_tokens, decoder_outputs_backward, alignments_backward
         return decoder_outputs, postnet_outputs, alignments, stop_tokens
 
     @torch.no_grad()
     def inference(self, text, speaker_ids=None):
         embedded_inputs = self.embedding(text).transpose(1, 2)
         encoder_outputs = self.encoder.inference(embedded_inputs)
-        encoder_outputs = self._add_speaker_embedding(encoder_outputs,
-                                                      speaker_ids)
+        if speaker_ids is not None:
+            self.compute_speaker_embedding(speaker_ids)
+        if self.num_speakers > 1:
+            encoder_outputs = self._add_speaker_embedding(encoder_outputs,
+                                                    self.speaker_embeddings)
         mel_outputs, alignments, stop_tokens = self.decoder.inference(
             encoder_outputs)
         mel_outputs_postnet = self.postnet(mel_outputs)
@@ -112,22 +154,16 @@ class Tacotron2(nn.Module):
             mel_outputs, mel_outputs_postnet, alignments)
         return mel_outputs, mel_outputs_postnet, alignments, stop_tokens
 
-    def _backward_inference(self, mel_specs, encoder_outputs, mask):
-        decoder_outputs_b, alignments_b, _ = self.decoder_backward(
-            encoder_outputs, torch.flip(mel_specs, dims=(1,)), mask,
-            self.speaker_embeddings_projected)
-        decoder_outputs_b = decoder_outputs_b.transpose(1, 2)
-        return decoder_outputs_b, alignments_b
 
-    def _add_speaker_embedding(self, encoder_outputs, speaker_ids):
-        if hasattr(self, "speaker_embedding") and speaker_ids is None:
-            raise RuntimeError(" [!] Model has speaker embedding layer but speaker_id is not provided")
-        if hasattr(self, "speaker_embedding") and speaker_ids is not None:
-            speaker_embeddings = self.speaker_embedding(speaker_ids)
+    def _speaker_embedding_pass(self, encoder_outputs, speaker_ids):
+        # TODO: multi-speaker
+        # if hasattr(self, "speaker_embedding") and speaker_ids is None:
+        #     raise RuntimeError(" [!] Model has speaker embedding layer but speaker_id is not provided")
+        # if hasattr(self, "speaker_embedding") and speaker_ids is not None:
 
-            speaker_embeddings.unsqueeze_(1)
-            speaker_embeddings = speaker_embeddings.expand(encoder_outputs.size(0),
-                                                           encoder_outputs.size(1),
-                                                           -1)
-            encoder_outputs = encoder_outputs + speaker_embeddings
-        return encoder_outputs
+        #     speaker_embeddings = speaker_embeddings.expand(encoder_outputs.size(0),
+        #                                                    encoder_outputs.size(1),
+        #                                                    -1)
+        #     encoder_outputs = encoder_outputs + speaker_embeddings
+        # return encoder_outputs
+        pass

From 596251558175aa8d7f57e1983430e8c9ed172fe3 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 14:28:55 +0200
Subject: [PATCH 116/185] update dummy model config

---
 tests/outputs/dummy_model_config.json | 20 +++++++++++---------
 1 file changed, 11 insertions(+), 9 deletions(-)

diff --git a/tests/outputs/dummy_model_config.json b/tests/outputs/dummy_model_config.json
index 2f56c6ce..d301b61a 100644
--- a/tests/outputs/dummy_model_config.json
+++ b/tests/outputs/dummy_model_config.json
@@ -4,7 +4,7 @@
 
     "audio":{
         // Audio processing parameters
-        "num_mels": 80,         // size of the mel spec frame. 
+        "num_mels": 80,         // size of the mel spec frame.
         "num_freq": 1025,       // number of stft frequency levels. Size of the linear spectogram frame.
         "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
         "frame_length_ms": 50,  // stft window length in ms.
@@ -31,19 +31,19 @@
 
     "reinit_layers": [],
 
-    "model": "Tacotron2",          // one of the model in models/    
+    "model": "Tacotron2",          // one of the model in models/
     "grad_clip": 1,                // upper limit for gradients for clipping.
     "epochs": 1000,                // total number of epochs to train.
     "lr": 0.0001,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
     "lr_decay": false,             // if true, Noam learning rate decaying is applied through training.
     "warmup_steps": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
     "windowing": false,            // Enables attention windowing. Used only in eval mode.
-    "memory_size": 5,              // ONLY TACOTRON - memory queue size used to queue network predictions to feed autoregressive connection. Useful if r < 5. 
+    "memory_size": 5,              // ONLY TACOTRON - memory queue size used to queue network predictions to feed autoregressive connection. Useful if r < 5.
     "attention_norm": "sigmoid",   // softmax or sigmoid. Suggested to use softmax for Tacotron2 and sigmoid for Tacotron.
     "prenet_type": "original",     // ONLY TACOTRON2 - "original" or "bn".
-    "prenet_dropout": true,        // ONLY TACOTRON2 - enable/disable dropout at prenet. 
+    "prenet_dropout": true,        // ONLY TACOTRON2 - enable/disable dropout at prenet.
     "use_forward_attn": true,      // ONLY TACOTRON2 - if it uses forward attention. In general, it aligns faster.
-    "forward_attn_mask": false, 
+    "forward_attn_mask": false,
     "attention_type": "original",
     "attention_heads": 5,
     "bidirectional_decoder": false,
@@ -51,13 +51,15 @@
     "location_attn": false,        // ONLY TACOTRON2 - enable_disable location sensitive attention. It is enabled for TACOTRON by default.
     "loss_masking": true,         // enable / disable loss masking against the sequence padding.
     "enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.
-    "stopnet": true,               // Train stopnet predicting the end of synthesis. 
+    "stopnet": true,               // Train stopnet predicting the end of synthesis.
     "separate_stopnet": true,     // Train stopnet seperately if 'stopnet==true'. It prevents stopnet loss to influence the rest of the model. It causes a better model, but it trains SLOWER.
     "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
-    "use_gst": false, 
-    
+    "use_gst": false,
+    "double_decoder_consistency": true,  // use DDC explained here https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency-draft/
+    "ddc_r": 7,                           // reduction rate for coarse decoder.
+
     "batch_size": 32,       // Batch size for training. Lower values than 32 might cause hard to learn attention.
-    "eval_batch_size":16,   
+    "eval_batch_size":16,
     "r": 1,                 // Number of frames to predict for step.
     "wd": 0.000001,         // Weight decay weight.
     "checkpoint": true,     // If true, it saves checkpoints per "save_step"

From fbf1689d18adb28215c85d69a3a8e239740bdfc1 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 14:29:37 +0200
Subject: [PATCH 117/185] update model tests for ddc

---
 tests/test_tacotron2_model.py | 2 +-
 tests/test_tacotron_model.py  | 5 +++--
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/tests/test_tacotron2_model.py b/tests/test_tacotron2_model.py
index eb91b3cc..cf1d0778 100644
--- a/tests/test_tacotron2_model.py
+++ b/tests/test_tacotron2_model.py
@@ -51,7 +51,7 @@ class TacotronTrainTest(unittest.TestCase):
         optimizer = optim.Adam(model.parameters(), lr=c.lr)
         for i in range(5):
             mel_out, mel_postnet_out, align, stop_tokens = model.forward(
-                input, input_lengths, mel_spec, speaker_ids)
+                input, input_lengths, mel_spec, mel_lengths, speaker_ids)
             assert torch.sigmoid(stop_tokens).data.max() <= 1.0
             assert torch.sigmoid(stop_tokens).data.min() >= 0.0
             optimizer.zero_grad()
diff --git a/tests/test_tacotron_model.py b/tests/test_tacotron_model.py
index 7053a580..2bbb3c8d 100644
--- a/tests/test_tacotron_model.py
+++ b/tests/test_tacotron_model.py
@@ -66,7 +66,7 @@ class TacotronTrainTest(unittest.TestCase):
         optimizer = optim.Adam(model.parameters(), lr=c.lr)
         for _ in range(5):
             mel_out, linear_out, align, stop_tokens = model.forward(
-                input_dummy, input_lengths, mel_spec, speaker_ids)
+                input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids)
             optimizer.zero_grad()
             loss = criterion(mel_out, mel_spec, mel_lengths)
             stop_loss = criterion_st(stop_tokens, stop_targets)
@@ -95,6 +95,7 @@ class TacotronGSTTrainTest(unittest.TestCase):
         mel_spec = torch.rand(8, 120, c.audio['num_mels']).to(device)
         linear_spec = torch.rand(8, 120, c.audio['num_freq']).to(device)
         mel_lengths = torch.randint(20, 120, (8, )).long().to(device)
+        mel_lengths[-1] = 120
         stop_targets = torch.zeros(8, 120, 1).float().to(device)
         speaker_ids = torch.randint(0, 5, (8, )).long().to(device)
 
@@ -130,7 +131,7 @@ class TacotronGSTTrainTest(unittest.TestCase):
         optimizer = optim.Adam(model.parameters(), lr=c.lr)
         for _ in range(10):
             mel_out, linear_out, align, stop_tokens = model.forward(
-                input_dummy, input_lengths, mel_spec, speaker_ids)
+                input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids)
             optimizer.zero_grad()
             loss = criterion(mel_out, mel_spec, mel_lengths)
             stop_loss = criterion_st(stop_tokens, stop_targets)

From 4aaf57b50b7d806dfddc22d49d8ffc0ff3d9f137 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 14:30:12 +0200
Subject: [PATCH 118/185] train.py update for DDC

---
 train.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/train.py b/train.py
index 5a345b59..612ee8a6 100644
--- a/train.py
+++ b/train.py
@@ -158,13 +158,14 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
             optimizer_st.zero_grad()
 
         # forward pass model
-        if c.bidirectional_decoder:
+        if c.bidirectional_decoder or c.double_decoder_consistency:
             decoder_output, postnet_output, alignments, stop_tokens, decoder_backward_output, alignments_backward = model(
-                text_input, text_lengths, mel_input, speaker_ids=speaker_ids)
+                text_input, text_lengths, mel_input, mel_lengths, speaker_ids=speaker_ids)
         else:
             decoder_output, postnet_output, alignments, stop_tokens = model(
-                text_input, text_lengths, mel_input, speaker_ids=speaker_ids)
+                text_input, text_lengths, mel_input, mel_lengths, speaker_ids=speaker_ids)
             decoder_backward_output = None
+            alignments_backward = None
 
         # set the alignment lengths wrt reduction factor for guided attention
         if mel_lengths.max() % model.decoder.r != 0:
@@ -176,7 +177,8 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
         loss_dict = criterion(postnet_output, decoder_output, mel_input,
                               linear_input, stop_tokens, stop_targets,
                               mel_lengths, decoder_backward_output,
-                              alignments, alignment_lengths, text_lengths)
+                              alignments, alignment_lengths, alignments_backward,
+                              text_lengths)
         if c.bidirectional_decoder:
             keep_avg.update_values({'avg_decoder_b_loss': loss_dict['decoder_backward_loss'].item(),
                                     'avg_decoder_c_loss': loss_dict['decoder_c_loss'].item()})

From 01339e94a5e43275875db2823dfad206346dd996 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 14:30:43 +0200
Subject: [PATCH 119/185] setup_model update following TacotronAbstract

---
 utils/generic_utils.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/utils/generic_utils.py b/utils/generic_utils.py
index f7c04859..6d347bcd 100644
--- a/utils/generic_utils.py
+++ b/utils/generic_utils.py
@@ -161,13 +161,16 @@ def setup_model(num_chars, num_speakers, c):
                         location_attn=c.location_attn,
                         attn_K=c.attention_heads,
                         separate_stopnet=c.separate_stopnet,
-                        bidirectional_decoder=c.bidirectional_decoder)
+                        bidirectional_decoder=c.bidirectional_decoder,
+                        double_decoder_consistency=c.double_decoder_consistency,
+                        ddc_r=c.ddc_r)
     elif c.model.lower() == "tacotron2":
         model = MyModel(num_chars=num_chars,
                         num_speakers=num_speakers,
                         r=c.r,
                         postnet_output_dim=c.audio['num_mels'],
                         decoder_output_dim=c.audio['num_mels'],
+                        gst=c.use_gst,
                         attn_type=c.attention_type,
                         attn_win=c.windowing,
                         attn_norm=c.attention_norm,
@@ -179,7 +182,9 @@ def setup_model(num_chars, num_speakers, c):
                         location_attn=c.location_attn,
                         attn_K=c.attention_heads,
                         separate_stopnet=c.separate_stopnet,
-                        bidirectional_decoder=c.bidirectional_decoder)
+                        bidirectional_decoder=c.bidirectional_decoder,
+                        double_decoder_consistency=c.double_decoder_consistency,
+                        ddc_r=c.ddc_r)
     return model
 
 class KeepAverage():

From 9f5f3910e4d1f4882d625fa4e7faf6716a73182c Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 14:31:17 +0200
Subject: [PATCH 120/185] check DDC parameters in config.json

---
 utils/generic_utils.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/utils/generic_utils.py b/utils/generic_utils.py
index 6d347bcd..e3daf574 100644
--- a/utils/generic_utils.py
+++ b/utils/generic_utils.py
@@ -319,6 +319,8 @@ def check_config(c):
     _check_argument('transition_agent', c, restricted=True, val_type=bool)
     _check_argument('location_attn', c, restricted=True, val_type=bool)
     _check_argument('bidirectional_decoder', c, restricted=True, val_type=bool)
+    _check_argument('double_decoder_consistency', c, restricted=True, val_type=bool)
+    _check_argument('ddc_r', c, restricted='double_decoder_consistency' in c.keys(), min_val=1, max_val=7, val_type=int)
 
     # stopnet
     _check_argument('stopnet', c, restricted=True, val_type=bool)

From 16eb15a5fff436dd0f48fb96cc11dd252301a4b1 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 4 Jun 2020 14:58:18 +0200
Subject: [PATCH 121/185] lint updates

---
 models/tacotron_abstract.py | 28 ++++++++++++++++++++++++----
 1 file changed, 24 insertions(+), 4 deletions(-)

diff --git a/models/tacotron_abstract.py b/models/tacotron_abstract.py
index db6cbdee..75a1a5cd 100644
--- a/models/tacotron_abstract.py
+++ b/models/tacotron_abstract.py
@@ -4,12 +4,12 @@ from abc import ABC, abstractmethod
 import torch
 from torch import nn
 
-from TTS.layers.gst_layers import GST
 from TTS.utils.generic_utils import sequence_mask
 
 
 class TacotronAbstract(ABC, nn.Module):
-    def __init__(self, num_chars,
+    def __init__(self,
+                 num_chars,
                  num_speakers,
                  r,
                  postnet_output_dim=80,
@@ -31,6 +31,7 @@ class TacotronAbstract(ABC, nn.Module):
                  gst=False):
         """ Abstract Tacotron class """
         super().__init__()
+        self.num_chars = num_chars
         self.r = r
         self.decoder_output_dim = decoder_output_dim
         self.postnet_output_dim = postnet_output_dim
@@ -39,6 +40,17 @@ class TacotronAbstract(ABC, nn.Module):
         self.bidirectional_decoder = bidirectional_decoder
         self.double_decoder_consistency = double_decoder_consistency
         self.ddc_r = ddc_r
+        self.attn_type = attn_type
+        self.attn_win = attn_win
+        self.attn_norm = attn_norm
+        self.prenet_type = prenet_type
+        self.prenet_dropout = prenet_dropout
+        self.forward_attn = forward_attn
+        self.trans_agent = trans_agent
+        self.forward_attn_mask = forward_attn_mask
+        self.location_attn = location_attn
+        self.attn_K = attn_K
+        self.separate_stopnet = separate_stopnet
 
         # layers
         self.embedding = None
@@ -48,9 +60,16 @@ class TacotronAbstract(ABC, nn.Module):
 
         # global style token
         if self.gst:
-            gst_embedding_dim = None
             self.gst_layer = None
 
+        # model states
+        self.speaker_embeddings = None
+        self.speaker_embeddings_projected = None
+
+        # additional layers
+        self.decoder_backward = None
+        self.coarse_decoder = None
+
     #############################
     # INIT FUNCTIONS
     #############################
@@ -114,7 +133,7 @@ class TacotronAbstract(ABC, nn.Module):
                                                 (0, 0, 0, padding_size, 0, 0))
         decoder_outputs_backward, alignments_backward, _ = self.coarse_decoder(
             encoder_outputs.detach(), mel_specs, input_mask)
-        scale_factor = self.decoder.r_init / self.decoder.r
+        # scale_factor = self.decoder.r_init / self.decoder.r
         alignments_backward = torch.nn.functional.interpolate(
             alignments_backward.transpose(1, 2),
             size=alignments.shape[1],
@@ -141,6 +160,7 @@ class TacotronAbstract(ABC, nn.Module):
 
     def compute_gst(self, inputs, mel_specs):
         """ Compute global style token """
+        # pylint: disable=not-callable
         gst_outputs = self.gst_layer(mel_specs)
         inputs = self._add_speaker_embedding(inputs, gst_outputs)
         return inputs

From acb367be263e40ebbc6771ed7ed3ac37fd4f92ae Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 5 Jun 2020 13:23:11 +0200
Subject: [PATCH 122/185] bug fix vocoder training

---
 vocoder/layers/losses.py | 4 ++--
 vocoder/train.py         | 5 ++++-
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/vocoder/layers/losses.py b/vocoder/layers/losses.py
index 22e2fb54..a24b129c 100644
--- a/vocoder/layers/losses.py
+++ b/vocoder/layers/losses.py
@@ -199,8 +199,8 @@ class GeneratorLoss(nn.Module):
 
         self.stft_loss_weight = C.stft_loss_weight
         self.subband_stft_loss_weight = C.subband_stft_loss_weight
-        self.mse_gan_loss_weight = C.mse_gan_loss_weight
-        self.hinge_gan_loss_weight = C.hinge_gan_loss_weight
+        self.mse_gan_loss_weight = C.mse_G_loss_weight
+        self.hinge_gan_loss_weight = C.hinge_G_loss_weight
         self.feat_match_loss_weight = C.feat_match_loss_weight
 
         if C.use_stft_loss:
diff --git a/vocoder/train.py b/vocoder/train.py
index a75c6a12..d1e6befe 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -173,7 +173,10 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
 
         loss_dict = dict()
         for key, value in loss_G_dict.items():
-            loss_dict[key] = value.item()
+            if isinstance(value, int):
+                loss_dict[key] = value
+            else:
+                loss_dict[key] = value.item()
 
         ##############################
         # DISCRIMINATOR

From 06d3eb42cbe7607fedd744ad5e3d6988045a102b Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 5 Jun 2020 13:25:53 +0200
Subject: [PATCH 123/185] bug fix for rwd discriminator

---
 vocoder/train.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/vocoder/train.py b/vocoder/train.py
index d1e6befe..82f3bee6 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -327,8 +327,12 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
             y_hat = model_G.pqmf_synthesis(y_hat)
             y_G_sub = model_G.pqmf_analysis(y_G)
 
-        D_out_fake = model_D(y_hat)
+        if len(signature(model_D.forward).parameters) == 2:
+            D_out_fake = model_D(y_hat, c_G)
+        else:
+            D_out_fake = model_D(y_hat)
         D_out_real = None
+
         if c.use_feat_match_loss:
             with torch.no_grad():
                 D_out_real = model_D(y_G)

From d7022a33b3a528117dba5c87820fbe67fff0e37e Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 5 Jun 2020 13:27:09 +0200
Subject: [PATCH 124/185] correct name

---
 vocoder/train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vocoder/train.py b/vocoder/train.py
index 82f3bee6..2c3a2e0a 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -363,7 +363,7 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
         for key, value in loss_G_dict.items():
             update_eval_values['avg_' + key] = value.item()
         update_eval_values['avg_loader_time'] = loader_time
-        update_eval_values['avgP_step_time'] = step_time
+        update_eval_values['avg_step_time'] = step_time
         keep_avg.update_values(update_eval_values)
 
         # print eval stats

From 2b377b548758939f2158c746d58ae16a7ff1c465 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 5 Jun 2020 13:28:16 +0200
Subject: [PATCH 125/185] model config update

---
 .../multiband-melgan_and_rwd_config.json      | 21 ++++++++++++-------
 1 file changed, 14 insertions(+), 7 deletions(-)

diff --git a/vocoder/configs/multiband-melgan_and_rwd_config.json b/vocoder/configs/multiband-melgan_and_rwd_config.json
index 9aeb7933..c783877d 100644
--- a/vocoder/configs/multiband-melgan_and_rwd_config.json
+++ b/vocoder/configs/multiband-melgan_and_rwd_config.json
@@ -49,21 +49,28 @@
 
     // LOSS PARAMETERS
     "use_stft_loss": true,
+    "use_subband_stft_loss": true,
     "use_mse_gan_loss": true,
     "use_hinge_gan_loss": false,
     "use_feat_match_loss": false,  // use only with melgan discriminators
 
-    "stft_loss_weight": 1,
+    "stft_loss_weight": 0.5,
+    "subband_stft_loss_weight": 0.5,
     "mse_gan_loss_weight": 2.5,
-    "hinge_gan_loss_weight": 1,
-    "feat_match_loss_weight": 10.0,
+    "hinge_gan_loss_weight": 2.5,
+    "feat_match_loss_weight": 25.0,
 
     "stft_loss_params": {
         "n_ffts": [1024, 2048, 512],
         "hop_lengths": [120, 240, 50],
         "win_lengths": [600, 1200, 240]
     },
-    "target_loss": "avg_G_loss",  // loss value to pick the best model
+    "subband_stft_loss_params":{
+        "n_ffts": [384, 683, 171],
+        "hop_lengths": [30, 60, 10],
+        "win_lengths": [150, 300, 60]
+    },
+    "target_loss": "avg_G_loss",
 
     // DISCRIMINATOR
     // "discriminator_model": "melgan_multiscale_discriminator",
@@ -85,7 +92,7 @@
     // GENERATOR
     "generator_model": "multiband_melgan_generator",
     "generator_model_params": {
-        "upsample_factors":[2 ,2, 4, 4],
+        "upsample_factors":[8, 4, 2],
         "num_res_blocks": 4
     },
 
@@ -111,7 +118,7 @@
     "noam_schedule": false,        // use noam warmup and lr schedule.
     "warmup_steps_gen": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
     "warmup_steps_disc": 4000,
-    "epochs": 1000,                // total number of epochs to train.
+    "epochs": 100000,                // total number of epochs to train.
     "wd": 0.000001,                // Weight decay weight.
     "lr_gen": 0.0001,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
     "lr_disc": 0.0001,
@@ -131,6 +138,6 @@
     "eval_split_size": 10,
 
     // PATHS
-    "output_path": "/home/erogol/Models/LJSpeech/"
+    "output_path": "/data/rw/home/Trainings/"
 }
 

From d2cfab71d3fa8beca43904cf9380e7386a15f253 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 5 Jun 2020 14:47:25 +0200
Subject: [PATCH 126/185] bug fix for DDC model eval run

---
 train.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/train.py b/train.py
index 612ee8a6..13bda5ef 100644
--- a/train.py
+++ b/train.py
@@ -331,13 +331,14 @@ def evaluate(model, criterion, ap, global_step, epoch):
             assert mel_input.shape[1] % model.decoder.r == 0
 
             # forward pass model
-            if c.bidirectional_decoder:
+            if c.bidirectional_decoder or c.double_decoder_consistency:
                 decoder_output, postnet_output, alignments, stop_tokens, decoder_backward_output, alignments_backward = model(
                     text_input, text_lengths, mel_input, speaker_ids=speaker_ids)
             else:
                 decoder_output, postnet_output, alignments, stop_tokens = model(
                     text_input, text_lengths, mel_input, speaker_ids=speaker_ids)
                 decoder_backward_output = None
+                alignments_backward = None
 
             # set the alignment lengths wrt reduction factor for guided attention
             if mel_lengths.max() % model.decoder.r != 0:
@@ -349,7 +350,8 @@ def evaluate(model, criterion, ap, global_step, epoch):
             loss_dict = criterion(postnet_output, decoder_output, mel_input,
                                   linear_input, stop_tokens, stop_targets,
                                   mel_lengths, decoder_backward_output,
-                                  alignments, alignment_lengths, text_lengths)
+                                  alignments, alignment_lengths, alignments_backward, 
+                                  text_lengths)
             if c.bidirectional_decoder:
                 keep_avg.update_values({'avg_decoder_b_loss': loss_dict['decoder_b_loss'].item(),
                                         'avg_decoder_c_loss': loss_dict['decoder_c_loss'].item()})

From b736c6cdacf5e41ec78fb9c847e91975aebfa26c Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sat, 6 Jun 2020 12:59:59 +0200
Subject: [PATCH 127/185] bug fix for TTS model restore

---
 train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/train.py b/train.py
index 13bda5ef..a47828e0 100644
--- a/train.py
+++ b/train.py
@@ -542,7 +542,7 @@ def main(args):  # pylint: disable=redefined-outer-name
         except:
             print(" > Partial model initialization.")
             model_dict = model.state_dict()
-            model_dict = set_init_dict(model_dict, checkpoint, c)
+            model_dict = set_init_dict(model_dict, checkpoint['model'], c)
             model.load_state_dict(model_dict)
             del model_dict
         for group in optimizer.param_groups:

From 1daba4371dc0b29e907e43f88ad372b87d9f510b Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Sat, 6 Jun 2020 13:00:46 +0200
Subject: [PATCH 128/185] bug fix vocoder model restore

---
 vocoder/configs/multiband-melgan_and_rwd_config.json | 4 ++--
 vocoder/train.py                                     | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/vocoder/configs/multiband-melgan_and_rwd_config.json b/vocoder/configs/multiband-melgan_and_rwd_config.json
index c783877d..736f3459 100644
--- a/vocoder/configs/multiband-melgan_and_rwd_config.json
+++ b/vocoder/configs/multiband-melgan_and_rwd_config.json
@@ -56,8 +56,8 @@
 
     "stft_loss_weight": 0.5,
     "subband_stft_loss_weight": 0.5,
-    "mse_gan_loss_weight": 2.5,
-    "hinge_gan_loss_weight": 2.5,
+    "mse_G_loss_weight": 2.5,
+    "hinge_G_loss_weight": 2.5,
     "feat_match_loss_weight": 25.0,
 
     "stft_loss_params": {
diff --git a/vocoder/train.py b/vocoder/train.py
index 2c3a2e0a..05805c68 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -421,7 +421,7 @@ def main(args):  # pylint: disable=redefined-outer-name
             optimizer_gen.load_state_dict(checkpoint['optimizer'])
             model_disc.load_state_dict(checkpoint['model_disc'])
             optimizer_disc.load_state_dict(checkpoint['optimizer_disc'])
-        except KeyError:
+        except RuntimeError:
             print(" > Partial model initialization.")
             model_dict = model_gen.state_dict()
             model_dict = set_init_dict(model_dict, checkpoint['model'], c)

From 78d43664a151a3ef53fdd16315dced3661ea1080 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Mon, 8 Jun 2020 03:22:17 +0200
Subject: [PATCH 129/185] bug fix and var renaming

---
 models/tacotron2.py | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/models/tacotron2.py b/models/tacotron2.py
index bbce4be9..bce21e9e 100644
--- a/models/tacotron2.py
+++ b/models/tacotron2.py
@@ -107,6 +107,7 @@ class Tacotron2(TacotronAbstract):
             decoder_outputs = decoder_outputs * output_mask.unsqueeze(1).expand_as(decoder_outputs)
         # B x mel_dim x T_out
         postnet_outputs = self.postnet(decoder_outputs)
+        postnet_outputs = decoder_outputs + postnet_outputs
         # sequence masking
         if output_mask is not None:
             postnet_outputs = postnet_outputs * output_mask.unsqueeze(1).expand_as(postnet_outputs)
@@ -130,13 +131,13 @@ class Tacotron2(TacotronAbstract):
         if self.num_speakers > 1:
             encoder_outputs = self._add_speaker_embedding(encoder_outputs,
                                                     self.speaker_embeddings)
-        mel_outputs, alignments, stop_tokens = self.decoder.inference(
+        decoder_outputs, alignments, stop_tokens = self.decoder.inference(
             encoder_outputs)
-        mel_outputs_postnet = self.postnet(mel_outputs)
-        mel_outputs_postnet = mel_outputs + mel_outputs_postnet
-        mel_outputs, mel_outputs_postnet, alignments = self.shape_outputs(
-            mel_outputs, mel_outputs_postnet, alignments)
-        return mel_outputs, mel_outputs_postnet, alignments, stop_tokens
+        postnet_outputs = self.postnet(decoder_outputs)
+        postnet_outputs = decoder_outputs + postnet_outputs
+        decoder_outputs, postnet_outputs, alignments = self.shape_outputs(
+            decoder_outputs, postnet_outputs, alignments)
+        return decoder_outputs, postnet_outputs, alignments, stop_tokens
 
     def inference_truncated(self, text, speaker_ids=None):
         """

From 55b8952c3d216fbf644e850fb319adfd89b56c13 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Mon, 8 Jun 2020 10:43:09 +0200
Subject: [PATCH 130/185] run D in eval

---
 vocoder/configs/melgan_config.json |  4 +--
 vocoder/train.py                   | 47 +++++++++++++++++++++++++++---
 2 files changed, 45 insertions(+), 6 deletions(-)

diff --git a/vocoder/configs/melgan_config.json b/vocoder/configs/melgan_config.json
index bdfdf88a..0b6b16db 100644
--- a/vocoder/configs/melgan_config.json
+++ b/vocoder/configs/melgan_config.json
@@ -56,8 +56,8 @@
 
     "stft_loss_weight": 0.5,
     "subband_stft_loss_weight": 0.5,
-    "mse_gan_loss_weight": 2.5,
-    "hinge_gan_loss_weight": 2.5,
+    "mse_G_loss_weight": 2.5,
+    "hinge_G_loss_weight": 2.5,
     "feat_match_loss_weight": 25,
 
     "stft_loss_params": {
diff --git a/vocoder/train.py b/vocoder/train.py
index 05805c68..54b36e6b 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -181,7 +181,7 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
         ##############################
         # DISCRIMINATOR
         ##############################
-        if global_step > c.steps_to_start_discriminator:
+        if global_step >= c.steps_to_start_discriminator:
             # discriminator pass
             with torch.no_grad():
                 y_hat = model_G(c_D)
@@ -295,7 +295,7 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
 
 
 @torch.no_grad()
-def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
+def evaluate(model_G, criterion_G, model_D, criterion_D, ap, global_step, epoch):
     data_loader = setup_loader(ap, is_val=True, verbose=(epoch == 0))
     model_G.eval()
     model_D.eval()
@@ -355,13 +355,52 @@ def evaluate(model_G, criterion_G, model_D, ap, global_step, epoch):
         for key, value in loss_G_dict.items():
             loss_dict[key] = value.item()
 
+        ##############################
+        # DISCRIMINATOR
+        ##############################
+
+        if global_step >= c.steps_to_start_discriminator:
+            # discriminator pass
+            with torch.no_grad():
+                y_hat = model_G(c_G)
+
+            # PQMF formatting
+            if y_hat.shape[1] > 1:
+                y_hat = model_G.pqmf_synthesis(y_hat)
+
+            # run D with or without cond. features
+            if len(signature(model_D.forward).parameters) == 2:
+                D_out_fake = model_D(y_hat.detach(), c_G)
+                D_out_real = model_D(y_G, c_G)
+            else:
+                D_out_fake = model_D(y_hat.detach())
+                D_out_real = model_D(y_G)
+
+            # format D outputs
+            if isinstance(D_out_fake, tuple):
+                scores_fake, feats_fake = D_out_fake
+                if D_out_real is None:
+                    scores_real, feats_real = None, None
+                else:
+                    scores_real, feats_real = D_out_real
+            else:
+                scores_fake = D_out_fake
+                scores_real = D_out_real
+
+            # compute losses
+            loss_D_dict = criterion_D(scores_fake, scores_real)
+
+            for key, value in loss_D_dict.items():
+                loss_dict[key] = value.item()
+
+
         step_time = time.time() - start_time
         epoch_time += step_time
 
         # update avg stats
         update_eval_values = dict()
-        for key, value in loss_G_dict.items():
-            update_eval_values['avg_' + key] = value.item()
+        for key, value in loss_dict.items():
+            update_eval_values['avg_' + key] = value
         update_eval_values['avg_loader_time'] = loader_time
         update_eval_values['avg_step_time'] = step_time
         keep_avg.update_values(update_eval_values)

From f0616a3b2473c3d0196289cd0a1c5be9a120845a Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Mon, 8 Jun 2020 10:45:38 +0200
Subject: [PATCH 131/185] linter fix

---
 models/tacotron2.py | 2 +-
 train.py            | 2 +-
 vocoder/train.py    | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/models/tacotron2.py b/models/tacotron2.py
index bce21e9e..46b915b5 100644
--- a/models/tacotron2.py
+++ b/models/tacotron2.py
@@ -130,7 +130,7 @@ class Tacotron2(TacotronAbstract):
             self.compute_speaker_embedding(speaker_ids)
         if self.num_speakers > 1:
             encoder_outputs = self._add_speaker_embedding(encoder_outputs,
-                                                    self.speaker_embeddings)
+                                                          self.speaker_embeddings)
         decoder_outputs, alignments, stop_tokens = self.decoder.inference(
             encoder_outputs)
         postnet_outputs = self.postnet(decoder_outputs)
diff --git a/train.py b/train.py
index a47828e0..a5f3d2e7 100644
--- a/train.py
+++ b/train.py
@@ -350,7 +350,7 @@ def evaluate(model, criterion, ap, global_step, epoch):
             loss_dict = criterion(postnet_output, decoder_output, mel_input,
                                   linear_input, stop_tokens, stop_targets,
                                   mel_lengths, decoder_backward_output,
-                                  alignments, alignment_lengths, alignments_backward, 
+                                  alignments, alignment_lengths, alignments_backward,
                                   text_lengths)
             if c.bidirectional_decoder:
                 keep_avg.update_values({'avg_decoder_b_loss': loss_dict['decoder_b_loss'].item(),
diff --git a/vocoder/train.py b/vocoder/train.py
index 54b36e6b..259c5eb7 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -519,7 +519,7 @@ def main(args):  # pylint: disable=redefined-outer-name
                                model_disc, criterion_disc, optimizer_disc,
                                scheduler_gen, scheduler_disc, ap, global_step,
                                epoch)
-        eval_avg_loss_dict = evaluate(model_gen, criterion_gen, model_disc, ap,
+        eval_avg_loss_dict = evaluate(model_gen, criterion_gen, model_disc, criterion_disc, ap,
                                       global_step, epoch)
         c_logger.print_epoch_end(epoch, eval_avg_loss_dict)
         target_loss = eval_avg_loss_dict[c.target_loss]

From 9913724a70ae66b109f2ac83ca6e42a2545536f8 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 9 Jun 2020 23:01:13 +0200
Subject: [PATCH 132/185] TTS config update for using DDC

---
 config.json | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/config.json b/config.json
index 74021970..5e10c535 100644
--- a/config.json
+++ b/config.json
@@ -1,7 +1,7 @@
 {
     "model": "Tacotron2",
-    "run_name": "ljspeech",
-    "run_description": "tacotron2",
+    "run_name": "ljspeech-ddc-bn",
+    "run_description": "tacotron2 with ddc and batch-normalization",
 
     // AUDIO PARAMETERS
     "audio":{
@@ -83,8 +83,8 @@
 
     // TACOTRON PRENET
     "memory_size": -1,             // ONLY TACOTRON - size of the memory queue used fro storing last decoder predictions for auto-regression. If < 0, memory queue is disabled and decoder only uses the last prediction frame.
-    "prenet_type": "original",     // "original" or "bn".
-    "prenet_dropout": true,        // enable/disable dropout at prenet.
+    "prenet_type": "bn",     // "original" or "bn".
+    "prenet_dropout": false,        // enable/disable dropout at prenet.
 
     // ATTENTION
     "attention_type": "original",  // 'original' or 'graves'
@@ -124,7 +124,7 @@
 
     // PHONEMES
     "phoneme_cache_path": "/media/erogol/data_ssd2/mozilla_us_phonemes_3",  // phoneme computation is slow, therefore, it caches results in the given folder.
-    "use_phonemes": false,           // use phonemes instead of raw characters. It is suggested for better pronounciation.
+    "use_phonemes": true,           // use phonemes instead of raw characters. It is suggested for better pronounciation.
     "phoneme_language": "en-us",     // depending on your target language, pick one from  https://github.com/bootphon/phonemizer#languages
 
     // MULTI-SPEAKER and GST

From 0245a2e25891b16e3be11630ec0d74459f9c5768 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 9 Jun 2020 23:01:34 +0200
Subject: [PATCH 133/185] change alignment_score to alignment_error

---
 train.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/train.py b/train.py
index a5f3d2e7..869557e6 100644
--- a/train.py
+++ b/train.py
@@ -291,7 +291,7 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
             "loss_postnet": keep_avg['avg_postnet_loss'],
             "loss_decoder": keep_avg['avg_decoder_loss'],
             "stopnet_loss": keep_avg['avg_stopnet_loss'],
-            "alignment_score": keep_avg['avg_align_error'],
+            "alignment_error": keep_avg['avg_align_error'],
             "epoch_time": epoch_time
         }
         if c.ga_alpha > 0:
@@ -413,7 +413,7 @@ def evaluate(model, criterion, ap, global_step, epoch):
                 "loss_postnet": keep_avg['avg_postnet_loss'],
                 "loss_decoder": keep_avg['avg_decoder_loss'],
                 "stopnet_loss": keep_avg['avg_stopnet_loss'],
-                "alignment_score": keep_avg['avg_align_error'],
+                "alignment_error": keep_avg['avg_align_error'],
             }
 
             if c.bidirectional_decoder:

From f9698af06ec037ef61e6ecbabed396db5b60b24a Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 9 Jun 2020 23:02:05 +0200
Subject: [PATCH 134/185] correction for vocoder configs

---
 vocoder/configs/melgan_config.json            | 145 ------------------
 .../multiband-melgan_and_rwd_config.json      |   2 +-
 2 files changed, 1 insertion(+), 146 deletions(-)
 delete mode 100644 vocoder/configs/melgan_config.json

diff --git a/vocoder/configs/melgan_config.json b/vocoder/configs/melgan_config.json
deleted file mode 100644
index 0b6b16db..00000000
--- a/vocoder/configs/melgan_config.json
+++ /dev/null
@@ -1,145 +0,0 @@
-{
-    "run_name": "melgan",
-    "run_description": "melgan initial run",
-
-    // AUDIO PARAMETERS
-    "audio":{
-        // stft parameters
-        "num_freq": 513,         // number of stft frequency levels. Size of the linear spectogram frame.
-        "win_length": 1024,      // stft window length in ms.
-        "hop_length": 256,       // stft window hop-lengh in ms.
-        "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
-        "frame_shift_ms": null,  // stft window hop-lengh in ms. If null, 'hop_length' is used.
-
-        // Audio processing parameters
-        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
-        "preemphasis": 0.0,     // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
-        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
-
-        // Silence trimming
-        "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
-        "trim_db": 60,          // threshold for timming silence. Set this according to your dataset.
-
-        // Griffin-Lim
-        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
-        "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
-
-        // MelSpectrogram parameters
-        "num_mels": 80,         // size of the mel spec frame.
-        "mel_fmin": 0.0,        // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
-        "mel_fmax": 8000.0,     // maximum freq level for mel-spec. Tune for dataset!!
-
-        // Normalization parameters
-        "signal_norm": true,    // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
-        "min_level_db": -100,   // lower bound for normalization
-        "symmetric_norm": true, // move normalization to range [-1, 1]
-        "max_norm": 4.0,        // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
-        "clip_norm": true,      // clip normalized values into the range.
-        "stats_path": null    // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
-    },
-
-    // DISTRIBUTED TRAINING
-    // "distributed":{
-    //     "backend": "nccl",
-    //     "url": "tcp:\/\/localhost:54321"
-    // },
-
-    // MODEL PARAMETERS
-    "use_pqmf": true,
-
-    // LOSS PARAMETERS
-    "use_stft_loss": true,
-    "use_subband_stft_loss": true,
-    "use_mse_gan_loss": true,
-    "use_hinge_gan_loss": false,
-    "use_feat_match_loss": false,  // use only with melgan discriminators
-
-    "stft_loss_weight": 0.5,
-    "subband_stft_loss_weight": 0.5,
-    "mse_G_loss_weight": 2.5,
-    "hinge_G_loss_weight": 2.5,
-    "feat_match_loss_weight": 25,
-
-    "stft_loss_params": {
-        "n_ffts": [1024, 2048, 512],
-        "hop_lengths": [120, 240, 50],
-        "win_lengths": [600, 1200, 240]
-    },
-    "subband_stft_loss_params":{
-        "n_ffts": [384, 683, 171],
-        "hop_lengths": [30, 60, 10],
-        "win_lengths": [150, 300, 60]
-    },
-
-    "target_loss": "avg_G_loss",  // loss value to pick the best model
-
-    // DISCRIMINATOR
-    "discriminator_model": "melgan_multiscale_discriminator",
-    "discriminator_model_params":{
-        "base_channels": 16,
-        "max_channels":1024,
-        "downsample_factors":[4, 4, 4, 4]
-    },
-    "steps_to_start_discriminator": 100000,      // steps required to start GAN trainining.1
-
-    // "discriminator_model": "random_window_discriminator",
-    // "discriminator_model_params":{
-    //     "uncond_disc_donwsample_factors": [8, 4],
-    //     "cond_disc_downsample_factors": [[8, 4, 2, 2, 2], [8, 4, 2, 2], [8, 4, 2], [8, 4], [4, 2, 2]],
-    //     "cond_disc_out_channels": [[128, 128, 256, 256], [128, 256, 256], [128, 256], [256], [128, 256]],
-    //     "window_sizes": [512, 1024, 2048, 4096, 8192]
-    // },
-
-
-    // GENERATOR
-    "generator_model": "multiband_melgan_generator",
-    "generator_model_params": {
-        "upsample_factors":[2 ,2, 4, 4],
-        "num_res_blocks": 4
-    },
-
-    // DATASET
-    "data_path": "/home/erogol/Data/LJSpeech-1.1/wavs/",
-    "seq_len": 16384,
-    "pad_short": 2000,
-    "conv_pad": 0,
-    "use_noise_augment": true,
-    "use_cache": true,
-
-    "reinit_layers": [],    // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
-
-    // TRAINING
-    "batch_size": 64,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
-
-    // VALIDATION
-    "run_eval": true,
-    "test_delay_epochs": 10,  //Until attention is aligned, testing only wastes computation time.
-    "test_sentences_file": null,  // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
-
-    // OPTIMIZER
-    "noam_schedule": false,        // use noam warmup and lr schedule.
-    "warmup_steps_gen": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
-    "warmup_steps_disc": 4000,
-    "epochs": 1000,                // total number of epochs to train.
-    "wd": 0.000001,                // Weight decay weight.
-    "lr_gen": 0.0001,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
-    "lr_disc": 0.0001,
-    "gen_clip_grad": 10.0,
-    "disc_clip_grad": 10.0,
-
-    // TENSORBOARD and LOGGING
-    "print_step": 25,       // Number of steps to log traning on console.
-    "print_eval": false,     // If True, it prints intermediate loss values in evalulation.
-    "save_step": 10000,      // Number of training steps expected to save traninpg stats and checkpoints.
-    "checkpoint": true,     // If true, it saves checkpoints per "save_step"
-    "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
-
-    // DATA LOADING
-    "num_loader_workers": 4,        // number of training data loader processes. Don't set it too big. 4-8 are good values.
-    "num_val_loader_workers": 4,    // number of evaluation data loader processes.
-    "eval_split_size": 10,
-
-    // PATHS
-    "output_path": "/home/erogol/Models/LJSpeech/"
-}
-
diff --git a/vocoder/configs/multiband-melgan_and_rwd_config.json b/vocoder/configs/multiband-melgan_and_rwd_config.json
index 736f3459..f4b91aae 100644
--- a/vocoder/configs/multiband-melgan_and_rwd_config.json
+++ b/vocoder/configs/multiband-melgan_and_rwd_config.json
@@ -79,7 +79,7 @@
     //     "max_channels":1024,
     //     "downsample_factors":[4, 4, 4, 4]
     // },
-    "steps_to_start_discriminator": 100000,      // steps required to start GAN trainining.1
+    "steps_to_start_discriminator": 200000,      // steps required to start GAN trainining.1
 
     "discriminator_model": "random_window_discriminator",
     "discriminator_model_params":{

From c866f23af654ef7c874977b94365556093baffcf Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 9 Jun 2020 23:02:28 +0200
Subject: [PATCH 135/185] remove weight norm for melgan generator

---
 vocoder/models/melgan_generator.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/vocoder/models/melgan_generator.py b/vocoder/models/melgan_generator.py
index 1e47816d..e69e6ef3 100644
--- a/vocoder/models/melgan_generator.py
+++ b/vocoder/models/melgan_generator.py
@@ -87,3 +87,12 @@ class MelganGenerator(nn.Module):
             (self.inference_padding, self.inference_padding),
             'replicate')
         return self.layers(cond_features)
+
+    def remove_weight_norm(self):
+        for _, layer in enumerate(self.layers):
+            if len(layer.state_dict()) != 0:
+                try:
+                    nn.utils.remove_weight_norm(layer)
+                except ValueError:
+                    layer.remove_weight_norm()
+

From 1dfafe003da30679887b523f4a76635d256b969a Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 9 Jun 2020 23:02:55 +0200
Subject: [PATCH 136/185] fix multiband melgan inference

---
 vocoder/models/multiband_melgan_generator.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/vocoder/models/multiband_melgan_generator.py b/vocoder/models/multiband_melgan_generator.py
index 8feacd25..15e7426e 100644
--- a/vocoder/models/multiband_melgan_generator.py
+++ b/vocoder/models/multiband_melgan_generator.py
@@ -29,10 +29,11 @@ class MultibandMelganGenerator(MelganGenerator):
     def pqmf_synthesis(self, x):
         return self.pqmf_layer.synthesis(x)
 
+    @torch.no_grad()
     def inference(self, cond_features):
         cond_features = cond_features.to(self.layers[1].weight.device)
         cond_features = torch.nn.functional.pad(
             cond_features,
             (self.inference_padding, self.inference_padding),
             'replicate')
-        return self.pqmf.synthesis(self.layers(cond_features))
+        return self.pqmf_synthesis(self.layers(cond_features))

From 74e6e29479fe7a05ade55441ef939d593651aef2 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 9 Jun 2020 23:03:37 +0200
Subject: [PATCH 137/185] add lr schedulers for generator and discriminator

---
 vocoder/train.py | 56 +++++++++++++++++++++++++++++-------------------
 1 file changed, 34 insertions(+), 22 deletions(-)

diff --git a/vocoder/train.py b/vocoder/train.py
index 259c5eb7..c563dff0 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -107,10 +107,6 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
 
         global_step += 1
 
-        # get current learning rates
-        current_lr_G = list(optimizer_G.param_groups)[0]['lr']
-        current_lr_D = list(optimizer_D.param_groups)[0]['lr']
-
         ##############################
         # GENERATOR
         ##############################
@@ -166,9 +162,7 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
             torch.nn.utils.clip_grad_norm_(model_G.parameters(),
                                            c.gen_clip_grad)
         optimizer_G.step()
-
-        # setup lr
-        if c.noam_schedule:
+        if scheduler_G is not None:
             scheduler_G.step()
 
         loss_dict = dict()
@@ -221,9 +215,7 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
                 torch.nn.utils.clip_grad_norm_(model_D.parameters(),
                                                c.disc_clip_grad)
             optimizer_D.step()
-
-            # setup lr
-            if c.noam_schedule:
+            if c.scheduler_D is not None:
                 scheduler_D.step()
 
             for key, value in loss_D_dict.items():
@@ -232,6 +224,10 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
         step_time = time.time() - start_time
         epoch_time += step_time
 
+        # get current learning rates
+        current_lr_G = list(optimizer_G.param_groups)[0]['lr']
+        current_lr_D = list(optimizer_D.param_groups)[0]['lr']
+
         # update avg stats
         update_train_values = dict()
         for key, value in loss_dict.items():
@@ -244,7 +240,8 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
         if global_step % c.print_step == 0:
             c_logger.print_train_step(batch_n_iter, num_iter, global_step,
                                       step_time, loader_time, current_lr_G,
-                                      loss_dict, keep_avg.avg_values)
+                                      current_lr_D, loss_dict,
+                                      keep_avg.avg_values)
 
         # plot step stats
         if global_step % 10 == 0:
@@ -262,8 +259,10 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
                 # save model
                 save_checkpoint(model_G,
                                 optimizer_G,
+                                scheduler_G,
                                 model_D,
                                 optimizer_D,
+                                scheduler_D,
                                 global_step,
                                 epoch,
                                 OUT_PATH,
@@ -434,6 +433,7 @@ def main(args):  # pylint: disable=redefined-outer-name
 
     # setup audio processor
     ap = AudioProcessor(**c.audio)
+
     # DISTRUBUTED
     # if num_gpus > 1:
     # init_distributed(args.rank, num_gpus, args.group_id,
@@ -449,6 +449,12 @@ def main(args):  # pylint: disable=redefined-outer-name
                            lr=c.lr_disc,
                            weight_decay=0)
 
+    # schedulers
+    scheduler_gen = getattr(torch.optim.lr_scheduler, c.lr_scheduler_gen)
+    scheduler_disc = getattr(torch.optim.lr_scheduler, c.lr_scheduler_disc)
+    scheduler_gen = scheduler_gen(optimizer_gen, **c.lr_scheduler_gen_params)
+    scheduler_disc = scheduler_disc(optimizer_disc, **c.lr_scheduler_disc_params)
+
     # setup criterion
     criterion_gen = GeneratorLoss(c)
     criterion_disc = DiscriminatorLoss(c)
@@ -456,12 +462,26 @@ def main(args):  # pylint: disable=redefined-outer-name
     if args.restore_path:
         checkpoint = torch.load(args.restore_path, map_location='cpu')
         try:
+            print(" > Restoring Generator Model...")
             model_gen.load_state_dict(checkpoint['model'])
+            print(" > Restoring Generator Optimizer...")
             optimizer_gen.load_state_dict(checkpoint['optimizer'])
+            print(" > Restoring Discriminator Model...")
             model_disc.load_state_dict(checkpoint['model_disc'])
+            print(" > Restoring Discriminator Optimizer...")
             optimizer_disc.load_state_dict(checkpoint['optimizer_disc'])
+            if 'scheduler' in checkpoint:
+                print(" > Restoring Generator LR Scheduler...")
+                scheduler_gen.load_state_dict(checkpoint['scheduler'])
+                # NOTE: Not sure if necessary
+                scheduler_gen.optimizer = optimizer_gen
+            if 'scheduler_disc' in checkpoint:
+                print(" > Restoring Discriminator LR Scheduler...")
+                scheduler_disc.load_state_dict(checkpoint['scheduler_disc'])
+                scheduler_disc.optimizer = optimizer_disc
         except RuntimeError:
-            print(" > Partial model initialization.")
+            # retore only matching layers.
+            print(" > Partial model initialization...")
             model_dict = model_gen.state_dict()
             model_dict = set_init_dict(model_dict, checkpoint['model'], c)
             model_gen.load_state_dict(model_dict)
@@ -494,16 +514,6 @@ def main(args):  # pylint: disable=redefined-outer-name
     # if num_gpus > 1:
     #     model = apply_gradient_allreduce(model)
 
-    if c.noam_schedule:
-        scheduler_gen = NoamLR(optimizer_gen,
-                               warmup_steps=c.warmup_steps_gen,
-                               last_epoch=args.restore_step - 1)
-        scheduler_disc = NoamLR(optimizer_disc,
-                                warmup_steps=c.warmup_steps_gen,
-                                last_epoch=args.restore_step - 1)
-    else:
-        scheduler_gen, scheduler_disc = None, None
-
     num_params = count_parameters(model_gen)
     print(" > Generator has {} parameters".format(num_params), flush=True)
     num_params = count_parameters(model_disc)
@@ -526,9 +536,11 @@ def main(args):  # pylint: disable=redefined-outer-name
         best_loss = save_best_model(target_loss,
                                     best_loss,
                                     model_gen,
+                                    scheduler_gen,
                                     optimizer_gen,
                                     model_disc,
                                     optimizer_disc,
+                                    scheduler_disc,
                                     global_step,
                                     epoch,
                                     OUT_PATH,

From ecff45369b852579bddb53f7b1b540587e8786b3 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 9 Jun 2020 23:04:02 +0200
Subject: [PATCH 138/185] print lrg and lrD

---
 vocoder/utils/console_logger.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/vocoder/utils/console_logger.py b/vocoder/utils/console_logger.py
index ff7edfd0..50882160 100644
--- a/vocoder/utils/console_logger.py
+++ b/vocoder/utils/console_logger.py
@@ -15,8 +15,8 @@ tcolors = AttrDict({
 
 
 class ConsoleLogger():
+    # TODO: merge this with TTS ConsoleLogger
     def __init__(self):
-        # TODO: color code for value changes
         # use these to compare values between iterations
         self.old_train_loss_dict = None
         self.old_epoch_loss_dict = None
@@ -36,7 +36,7 @@ class ConsoleLogger():
         print(f"\n{tcolors.BOLD} > TRAINING ({self.get_time()}) {tcolors.ENDC}")
 
     def print_train_step(self, batch_steps, step, global_step,
-                         step_time, loader_time, lr,
+                         step_time, loader_time, lrG, lrD,
                          loss_dict, avg_loss_dict):
         indent = "     | > "
         print()
@@ -48,7 +48,7 @@ class ConsoleLogger():
                 log_text += "{}{}: {:.5f}  ({:.5f})\n".format(indent, key, value, avg_loss_dict[f'avg_{key}'])
             else:
                 log_text += "{}{}: {:.5f} \n".format(indent, key, value)
-        log_text += f"{indent}step_time: {step_time:.2f}\n{indent}loader_time: {loader_time:.2f}\n{indent}lr: {lr:.5f}"
+        log_text += f"{indent}step_time: {step_time:.2f}\n{indent}loader_time: {loader_time:.2f}\n{indent}lrG: {lrG}\n{indent}lrD: {lrD}"
         print(log_text, flush=True)
 
     # pylint: disable=unused-argument

From 0fa17bd9bb60d02b5156f853dff3785d2b1a0cae Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 9 Jun 2020 23:04:16 +0200
Subject: [PATCH 139/185] save schedulers with checkpoints

---
 vocoder/utils/io.py | 38 +++++++++++++++++++++++++-------------
 1 file changed, 25 insertions(+), 13 deletions(-)

diff --git a/vocoder/utils/io.py b/vocoder/utils/io.py
index b6ea7484..9d350238 100644
--- a/vocoder/utils/io.py
+++ b/vocoder/utils/io.py
@@ -2,18 +2,27 @@ import os
 import torch
 import datetime
 
-def save_model(model, optimizer, model_disc, optimizer_disc, current_step,
-               epoch, output_path, **kwargs):
+
+def save_model(model, optimizer, scheduler, model_disc, optimizer_disc,
+               scheduler_disc, current_step, epoch, output_path, **kwargs):
     model_state = model.state_dict()
-    model_disc_state = model_disc.state_dict()
-    optimizer_state = optimizer.state_dict() if optimizer is not None else None
-    optimizer_disc_state = optimizer_disc.state_dict(
-    ) if optimizer_disc is not None else None
+    model_disc_state = model_disc.state_dict()\
+         if model_disc is not None else None
+    optimizer_state = optimizer.state_dict()\
+         if optimizer is not None else None
+    optimizer_disc_state = optimizer_disc.state_dict()\
+        if optimizer_disc is not None else None
+    scheduler_state = scheduler.state_dict()\
+        if scheduler is not None else None
+    scheduler_disc_state = scheduler_disc.state_dict()\
+        if scheduler_disc is not None else None
     state = {
         'model': model_state,
         'optimizer': optimizer_state,
+        'scheduler': scheduler_state,
         'model_disc': model_disc_state,
         'optimizer_disc': optimizer_disc_state,
+        'scheduler_disc': scheduler_disc_state,
         'step': current_step,
         'epoch': epoch,
         'date': datetime.date.today().strftime("%B %d, %Y"),
@@ -22,26 +31,29 @@ def save_model(model, optimizer, model_disc, optimizer_disc, current_step,
     torch.save(state, output_path)
 
 
-def save_checkpoint(model, optimizer, model_disc, optimizer_disc, current_step,
-                    epoch, output_folder, **kwargs):
+def save_checkpoint(model, optimizer, scheduler, model_disc, optimizer_disc,
+                    scheduler_disc, current_step, epoch, output_folder,
+                    **kwargs):
     file_name = 'checkpoint_{}.pth.tar'.format(current_step)
     checkpoint_path = os.path.join(output_folder, file_name)
     print(" > CHECKPOINT : {}".format(checkpoint_path))
-    save_model(model, optimizer, model_disc, optimizer_disc, current_step,
-               epoch, checkpoint_path, **kwargs)
+    save_model(model, optimizer, scheduler, model_disc, optimizer_disc,
+               scheduler_disc, current_step, epoch, checkpoint_path, **kwargs)
 
 
-def save_best_model(target_loss, best_loss, model, optimizer, model_disc,
-                    optimizer_disc, current_step, epoch, output_folder,
-                    **kwargs):
+def save_best_model(target_loss, best_loss, model, optimizer, scheduler,
+                    model_disc, optimizer_disc, scheduler_disc, current_step,
+                    epoch, output_folder, **kwargs):
     if target_loss < best_loss:
         file_name = 'best_model.pth.tar'
         checkpoint_path = os.path.join(output_folder, file_name)
         print(" > BEST MODEL : {}".format(checkpoint_path))
         save_model(model,
                    optimizer,
+                   scheduler,
                    model_disc,
                    optimizer_disc,
+                   scheduler_disc,
                    current_step,
                    epoch,
                    checkpoint_path,

From 8a201c92c6a86915a36febca586d991cf5db3f22 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 9 Jun 2020 23:05:02 +0200
Subject: [PATCH 140/185] multiband melga nconfig

---
 vocoder/configs/multiband_melgan_config.json | 150 +++++++++++++++++++
 1 file changed, 150 insertions(+)
 create mode 100644 vocoder/configs/multiband_melgan_config.json

diff --git a/vocoder/configs/multiband_melgan_config.json b/vocoder/configs/multiband_melgan_config.json
new file mode 100644
index 00000000..3a5f0673
--- /dev/null
+++ b/vocoder/configs/multiband_melgan_config.json
@@ -0,0 +1,150 @@
+{
+    "run_name": "multiband-melgan",
+    "run_description": "multiband melgan",
+
+    // AUDIO PARAMETERS
+    "audio":{
+        // stft parameters
+        "num_freq": 513,         // number of stft frequency levels. Size of the linear spectogram frame.
+        "win_length": 1024,      // stft window length in ms.
+        "hop_length": 256,       // stft window hop-lengh in ms.
+        "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
+        "frame_shift_ms": null,  // stft window hop-lengh in ms. If null, 'hop_length' is used.
+
+        // Audio processing parameters
+        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
+        "preemphasis": 0.0,     // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
+        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
+
+        // Silence trimming
+        "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
+        "trim_db": 60,          // threshold for timming silence. Set this according to your dataset.
+
+        // Griffin-Lim
+        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
+        "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
+
+        // MelSpectrogram parameters
+        "num_mels": 80,         // size of the mel spec frame.
+        "mel_fmin": 0.0,        // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
+        "mel_fmax": 8000.0,     // maximum freq level for mel-spec. Tune for dataset!!
+
+        // Normalization parameters
+        "signal_norm": true,    // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
+        "min_level_db": -100,   // lower bound for normalization
+        "symmetric_norm": true, // move normalization to range [-1, 1]
+        "max_norm": 4.0,        // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
+        "clip_norm": true,      // clip normalized values into the range.
+        "stats_path": null    // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
+    },
+
+    // DISTRIBUTED TRAINING
+    // "distributed":{
+    //     "backend": "nccl",
+    //     "url": "tcp:\/\/localhost:54321"
+    // },
+
+    // MODEL PARAMETERS
+    "use_pqmf": true,
+
+    // LOSS PARAMETERS
+    "use_stft_loss": true,
+    "use_subband_stft_loss": true,
+    "use_mse_gan_loss": true,
+    "use_hinge_gan_loss": false,
+    "use_feat_match_loss": false,  // use only with melgan discriminators
+
+    // loss weights
+    "stft_loss_weight": 0.5,
+    "subband_stft_loss_weight": 0.5,
+    "mse_G_loss_weight": 2.5,
+    "hinge_G_loss_weight": 2.5,
+    "feat_match_loss_weight": 25,
+
+    // multiscale stft loss parameters
+    "stft_loss_params": {
+        "n_ffts": [1024, 2048, 512],
+        "hop_lengths": [120, 240, 50],
+        "win_lengths": [600, 1200, 240]
+    },
+
+    // subband multiscale stft loss parameters
+    "subband_stft_loss_params":{
+        "n_ffts": [384, 683, 171],
+        "hop_lengths": [30, 60, 10],
+        "win_lengths": [150, 300, 60]
+    },
+
+    "target_loss": "avg_G_loss",  // loss value to pick the best model to save after each epoch
+
+    // DISCRIMINATOR
+    "discriminator_model": "melgan_multiscale_discriminator",
+    "discriminator_model_params":{
+        "base_channels": 16,
+        "max_channels":1024,
+        "downsample_factors":[4, 4, 4, 4]
+    },
+    "steps_to_start_discriminator": 200000,      // steps required to start GAN trainining.1
+
+    // GENERATOR
+    "generator_model": "multiband_melgan_generator",
+    "generator_model_params": {
+        "upsample_factors":[8, 4, 2],
+        "num_res_blocks": 4
+    },
+
+    // DATASET
+    "data_path": "/home/erogol/Data/LJSpeech-1.1/wavs/",
+    "seq_len": 16384,
+    "pad_short": 2000,
+    "conv_pad": 0,
+    "use_noise_augment": false,
+    "use_cache": true,
+
+    "reinit_layers": [],    // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
+
+    // TRAINING
+    "batch_size": 64,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
+
+    // VALIDATION
+    "run_eval": true,
+    "test_delay_epochs": 10,  //Until attention is aligned, testing only wastes computation time.
+    "test_sentences_file": null,  // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
+
+    // OPTIMIZER
+    "noam_schedule": false,        // use noam warmup and lr schedule.
+    "warmup_steps_gen": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
+    "warmup_steps_disc": 4000,
+    "epochs": 10000,                // total number of epochs to train.
+    "wd": 0.0,                // Weight decay weight.
+    "gen_clip_grad": -1,      // Generator gradient clipping threshold. Apply gradient clipping if > 0
+    "disc_clip_grad": -1,     // Discriminator gradient clipping threshold.
+    "lr_scheduler_gen": "MultiStepLR",   // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
+    "lr_scheduler_gen_params": {
+        "gamma": 0.5,
+        "milestones": [100000, 200000, 300000, 400000, 500000, 600000]
+    },
+    "lr_scheduler_disc": "MultiStepLR",   // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
+    "lr_scheduler_disc_params": {
+        "gamma": 0.5,
+        "milestones": [100000, 200000, 300000, 400000, 500000, 600000]
+    },
+    "lr_gen": 1e-4,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
+    "lr_disc": 1e-4,
+
+    // TENSORBOARD and LOGGING
+    "print_step": 25,       // Number of steps to log traning on console.
+    "print_eval": false,     // If True, it prints loss values for each step in eval run.
+    "save_step": 25000,      // Number of training steps expected to plot training stats on TB and save model checkpoints.
+    "checkpoint": true,     // If true, it saves checkpoints per "save_step"
+    "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
+
+    // DATA LOADING
+    "num_loader_workers": 4,        // number of training data loader processes. Don't set it too big. 4-8 are good values.
+    "num_val_loader_workers": 4,    // number of evaluation data loader processes.
+    "eval_split_size": 10,
+
+    // PATHS
+    "output_path": "/home/erogol/Models/LJSpeech/"
+}
+

From 8a931e705cf7ba8bdb13e505df8ba9705f3a423a Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 9 Jun 2020 23:14:10 +0200
Subject: [PATCH 141/185] q

---
 .../multiband-melgan_and_rwd_config.json      | 52 +++++++++++--------
 vocoder/train.py                              |  2 +-
 2 files changed, 31 insertions(+), 23 deletions(-)

diff --git a/vocoder/configs/multiband-melgan_and_rwd_config.json b/vocoder/configs/multiband-melgan_and_rwd_config.json
index f4b91aae..0b751854 100644
--- a/vocoder/configs/multiband-melgan_and_rwd_config.json
+++ b/vocoder/configs/multiband-melgan_and_rwd_config.json
@@ -1,6 +1,6 @@
 {
     "run_name": "multiband-melgan-rwd",
-    "run_description": "multibadn melgan with random window discriminator",
+    "run_description": "multiband melgan with random window discriminator from https://arxiv.org/pdf/1909.11646.pdf",
 
     // AUDIO PARAMETERS
     "audio":{
@@ -54,33 +54,30 @@
     "use_hinge_gan_loss": false,
     "use_feat_match_loss": false,  // use only with melgan discriminators
 
+    // loss weights
     "stft_loss_weight": 0.5,
     "subband_stft_loss_weight": 0.5,
     "mse_G_loss_weight": 2.5,
     "hinge_G_loss_weight": 2.5,
-    "feat_match_loss_weight": 25.0,
+    "feat_match_loss_weight": 25,
 
+    // multiscale stft loss parameters
     "stft_loss_params": {
         "n_ffts": [1024, 2048, 512],
         "hop_lengths": [120, 240, 50],
         "win_lengths": [600, 1200, 240]
     },
+
+    // subband multiscale stft loss parameters
     "subband_stft_loss_params":{
         "n_ffts": [384, 683, 171],
         "hop_lengths": [30, 60, 10],
         "win_lengths": [150, 300, 60]
     },
-    "target_loss": "avg_G_loss",
+
+    "target_loss": "avg_G_loss",  // loss value to pick the best model to save after each epoch
 
     // DISCRIMINATOR
-    // "discriminator_model": "melgan_multiscale_discriminator",
-    // "discriminator_model_params":{
-    //     "base_channels": 16,
-    //     "max_channels":1024,
-    //     "downsample_factors":[4, 4, 4, 4]
-    // },
-    "steps_to_start_discriminator": 200000,      // steps required to start GAN trainining.1
-
     "discriminator_model": "random_window_discriminator",
     "discriminator_model_params":{
         "uncond_disc_donwsample_factors": [8, 4],
@@ -88,6 +85,7 @@
         "cond_disc_out_channels": [[128, 128, 256, 256], [128, 256, 256], [128, 256], [256], [128, 256]],
         "window_sizes": [512, 1024, 2048, 4096, 8192]
     },
+    "steps_to_start_discriminator": 200000,      // steps required to start GAN trainining.1
 
     // GENERATOR
     "generator_model": "multiband_melgan_generator",
@@ -97,11 +95,11 @@
     },
 
     // DATASET
-    "data_path": "/root/LJSpeech-1.1/wavs/",
+    "data_path": "/home/erogol/Data/LJSpeech-1.1/wavs/",
     "seq_len": 16384,
     "pad_short": 2000,
     "conv_pad": 0,
-    "use_noise_augment": true,
+    "use_noise_augment": false,
     "use_cache": true,
 
     "reinit_layers": [],    // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
@@ -118,17 +116,27 @@
     "noam_schedule": false,        // use noam warmup and lr schedule.
     "warmup_steps_gen": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
     "warmup_steps_disc": 4000,
-    "epochs": 100000,                // total number of epochs to train.
-    "wd": 0.000001,                // Weight decay weight.
-    "lr_gen": 0.0001,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
-    "lr_disc": 0.0001,
-    "gen_clip_grad": 10.0,
-    "disc_clip_grad": 10.0,
+    "epochs": 10000,                // total number of epochs to train.
+    "wd": 0.0,                // Weight decay weight.
+    "gen_clip_grad": -1,      // Generator gradient clipping threshold. Apply gradient clipping if > 0
+    "disc_clip_grad": -1,     // Discriminator gradient clipping threshold.
+    "lr_scheduler_gen": "MultiStepLR",   // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
+    "lr_scheduler_gen_params": {
+        "gamma": 0.5,
+        "milestones": [100000, 200000, 300000, 400000, 500000, 600000]
+    },
+    "lr_scheduler_disc": "MultiStepLR",   // one of the schedulers from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
+    "lr_scheduler_disc_params": {
+        "gamma": 0.5,
+        "milestones": [100000, 200000, 300000, 400000, 500000, 600000]
+    },
+    "lr_gen": 1e-4,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
+    "lr_disc": 1e-4,
 
     // TENSORBOARD and LOGGING
     "print_step": 25,       // Number of steps to log traning on console.
-    "print_eval": false,     // If True, it prints intermediate loss values in evalulation.
-    "save_step": 10000,      // Number of training steps expected to save traninpg stats and checkpoints.
+    "print_eval": false,     // If True, it prints loss values for each step in eval run.
+    "save_step": 25000,      // Number of training steps expected to plot training stats on TB and save model checkpoints.
     "checkpoint": true,     // If true, it saves checkpoints per "save_step"
     "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
 
@@ -138,6 +146,6 @@
     "eval_split_size": 10,
 
     // PATHS
-    "output_path": "/data/rw/home/Trainings/"
+    "output_path": "/home/erogol/Models/LJSpeech/"
 }
 
diff --git a/vocoder/train.py b/vocoder/train.py
index c563dff0..a8a8f011 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -215,7 +215,7 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
                 torch.nn.utils.clip_grad_norm_(model_D.parameters(),
                                                c.disc_clip_grad)
             optimizer_D.step()
-            if c.scheduler_D is not None:
+            if scheduler_D is not None:
                 scheduler_D.step()
 
             for key, value in loss_D_dict.items():

From 9b15331eeb4fbe70ef169ada72c59ebb3be67f4e Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 10 Jun 2020 13:45:43 +0200
Subject: [PATCH 142/185] better stats logging for TTS training

---
 train.py | 132 +++++++++++++++++++------------------------------------
 1 file changed, 45 insertions(+), 87 deletions(-)

diff --git a/train.py b/train.py
index 869557e6..51c73a9f 100644
--- a/train.py
+++ b/train.py
@@ -119,21 +119,7 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
                                verbose=(epoch == 0))
     model.train()
     epoch_time = 0
-    train_values = {
-        'avg_postnet_loss': 0,
-        'avg_decoder_loss': 0,
-        'avg_stopnet_loss': 0,
-        'avg_align_error': 0,
-        'avg_step_time': 0,
-        'avg_loader_time': 0
-    }
-    if c.bidirectional_decoder:
-        train_values['avg_decoder_b_loss'] = 0  # decoder backward loss
-        train_values['avg_decoder_c_loss'] = 0  # decoder consistency loss
-    if c.ga_alpha > 0:
-        train_values['avg_ga_loss'] = 0  # guidede attention loss
     keep_avg = KeepAverage()
-    keep_avg.add_values(train_values)
     if use_cuda:
         batch_n_iter = int(
             len(data_loader.dataset) / (c.batch_size * num_gpus))
@@ -179,11 +165,6 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
                               mel_lengths, decoder_backward_output,
                               alignments, alignment_lengths, alignments_backward,
                               text_lengths)
-        if c.bidirectional_decoder:
-            keep_avg.update_values({'avg_decoder_b_loss': loss_dict['decoder_backward_loss'].item(),
-                                    'avg_decoder_c_loss': loss_dict['decoder_c_loss'].item()})
-        if c.ga_alpha > 0:
-            keep_avg.update_values({'avg_ga_loss': loss_dict['ga_loss'].item()})
 
         # backward pass
         loss_dict['loss'].backward()
@@ -193,7 +174,6 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
 
         # compute alignment error (the lower the better )
         align_error = 1 - alignment_diagonal_score(alignments)
-        keep_avg.update_value('avg_align_error', align_error)
         loss_dict['align_error'] = align_error
 
         # backpass and check the grad norm for stop loss
@@ -208,23 +188,6 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
         step_time = time.time() - start_time
         epoch_time += step_time
 
-        # update avg stats
-        update_train_values = {
-            'avg_postnet_loss': float(loss_dict['postnet_loss'].item()),
-            'avg_decoder_loss': float(loss_dict['decoder_loss'].item()),
-            'avg_stopnet_loss': loss_dict['stopnet_loss'].item() \
-                if isinstance(loss_dict['stopnet_loss'], float) else float(loss_dict['stopnet_loss'].item()),
-            'avg_step_time': step_time,
-            'avg_loader_time': loader_time
-        }
-        keep_avg.update_values(update_train_values)
-
-        if global_step % c.print_step == 0:
-            c_logger.print_train_step(batch_n_iter, num_iter, global_step,
-                                      avg_spec_length, avg_text_length,
-                                      step_time, loader_time, current_lr,
-                                      loss_dict, keep_avg.avg_values)
-
         # aggregate losses from processes
         if num_gpus > 1:
             loss_dict['postnet_loss'] = reduce_tensor(loss_dict['postnet_loss'].data, num_gpus)
@@ -232,6 +195,30 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
             loss_dict['loss'] = reduce_tensor(loss_dict['loss'] .data, num_gpus)
             loss_dict['stopnet_loss'] = reduce_tensor(loss_dict['stopnet_loss'].data, num_gpus) if c.stopnet else loss_dict['stopnet_loss']
 
+        # detach loss values
+        loss_dict_new = dict()
+        for key, value in loss_dict.items():
+            if isinstance(value, int) or isinstance(value, float):
+                loss_dict_new[key] = value
+            else:
+                loss_dict_new[key] = value.item()
+        loss_dict = loss_dict_new
+
+        # update avg stats
+        update_train_values = dict()
+        for key, value in loss_dict.items():
+            update_train_values['avg_' + key] = value
+        update_train_values['avg_loader_time'] = loader_time
+        update_train_values['avg_step_time'] = step_time
+        keep_avg.update_values(update_train_values)
+
+        # print training progress
+        if global_step % c.print_step == 0:
+            c_logger.print_train_step(batch_n_iter, num_iter, global_step,
+                                      avg_spec_length, avg_text_length,
+                                      step_time, loader_time, current_lr,
+                                      loss_dict, keep_avg.avg_values)
+
         if args.rank == 0:
             # Plot Training Iter Stats
             # reduce TB load
@@ -266,7 +253,7 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
                     "alignment": plot_alignment(align_img),
                 }
 
-                if c.bidirectional_decoder:
+                if c.bidirectional_decoder or c.double_decoder_consistency:
                     figures["alignment_backward"] = plot_alignment(alignments_backward[0].data.cpu().numpy())
 
                 tb_logger.tb_train_figures(global_step, figures)
@@ -286,16 +273,8 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
 
     # Plot Epoch Stats
     if args.rank == 0:
-        # Plot Training Epoch Stats
-        epoch_stats = {
-            "loss_postnet": keep_avg['avg_postnet_loss'],
-            "loss_decoder": keep_avg['avg_decoder_loss'],
-            "stopnet_loss": keep_avg['avg_stopnet_loss'],
-            "alignment_error": keep_avg['avg_align_error'],
-            "epoch_time": epoch_time
-        }
-        if c.ga_alpha > 0:
-            epoch_stats['guided_attention_loss'] = keep_avg['avg_ga_loss']
+        epoch_stats = {"epoch_time": epoch_time}
+        epoch_stats.update(keep_avg.avg_values)
         tb_logger.tb_train_epoch_stats(global_step, epoch_stats)
         if c.tb_model_param_stats:
             tb_logger.tb_model_weights(model, global_step)
@@ -307,20 +286,7 @@ def evaluate(model, criterion, ap, global_step, epoch):
     data_loader = setup_loader(ap, model.decoder.r, is_val=True)
     model.eval()
     epoch_time = 0
-    eval_values_dict = {
-        'avg_postnet_loss': 0,
-        'avg_decoder_loss': 0,
-        'avg_stopnet_loss': 0,
-        'avg_align_error': 0
-    }
-    if c.bidirectional_decoder:
-        eval_values_dict['avg_decoder_b_loss'] = 0  # decoder backward loss
-        eval_values_dict['avg_decoder_c_loss'] = 0  # decoder consistency loss
-    if c.ga_alpha > 0:
-        eval_values_dict['avg_ga_loss'] = 0  # guidede attention loss
     keep_avg = KeepAverage()
-    keep_avg.add_values(eval_values_dict)
-
     c_logger.print_eval_start()
     if data_loader is not None:
         for num_iter, data in enumerate(data_loader):
@@ -352,11 +318,6 @@ def evaluate(model, criterion, ap, global_step, epoch):
                                   mel_lengths, decoder_backward_output,
                                   alignments, alignment_lengths, alignments_backward,
                                   text_lengths)
-            if c.bidirectional_decoder:
-                keep_avg.update_values({'avg_decoder_b_loss': loss_dict['decoder_b_loss'].item(),
-                                        'avg_decoder_c_loss': loss_dict['decoder_c_loss'].item()})
-            if c.ga_alpha > 0:
-                keep_avg.update_values({'avg_ga_loss': loss_dict['ga_loss'].item()})
 
             # step time
             step_time = time.time() - start_time
@@ -364,7 +325,7 @@ def evaluate(model, criterion, ap, global_step, epoch):
 
             # compute alignment score
             align_error = 1 - alignment_diagonal_score(alignments)
-            keep_avg.update_value('avg_align_error', align_error)
+            loss_dict['align_error'] = align_error
 
             # aggregate losses from processes
             if num_gpus > 1:
@@ -373,14 +334,20 @@ def evaluate(model, criterion, ap, global_step, epoch):
                 if c.stopnet:
                     loss_dict['stopnet_loss'] = reduce_tensor(loss_dict['stopnet_loss'].data, num_gpus)
 
-            keep_avg.update_values({
-                'avg_postnet_loss':
-                float(loss_dict['postnet_loss'].item()),
-                'avg_decoder_loss':
-                float(loss_dict['decoder_loss'].item()),
-                'avg_stopnet_loss':
-                float(loss_dict['stopnet_loss'].item()),
-            })
+             # detach loss values
+            loss_dict_new = dict()
+            for key, value in loss_dict.items():
+                if isinstance(value, int) or isinstance(value, float):
+                    loss_dict_new[key] = value
+                else:
+                    loss_dict_new[key] = value.item()
+            loss_dict = loss_dict_new
+
+            # update avg stats
+            update_train_values = dict()
+            for key, value in loss_dict.items():
+                update_train_values['avg_' + key] = value
+            keep_avg.update_values(update_train_values)
 
             if c.print_eval:
                 c_logger.print_eval_step(num_iter, loss_dict, keep_avg.avg_values)
@@ -409,20 +376,11 @@ def evaluate(model, criterion, ap, global_step, epoch):
                                      c.audio["sample_rate"])
 
             # Plot Validation Stats
-            epoch_stats = {
-                "loss_postnet": keep_avg['avg_postnet_loss'],
-                "loss_decoder": keep_avg['avg_decoder_loss'],
-                "stopnet_loss": keep_avg['avg_stopnet_loss'],
-                "alignment_error": keep_avg['avg_align_error'],
-            }
 
-            if c.bidirectional_decoder:
-                epoch_stats['loss_decoder_backward'] = keep_avg['avg_decoder_b_loss']
+            if c.bidirectional_decoder or c.double_decoder_consistency:
                 align_b_img = alignments_backward[idx].data.cpu().numpy()
-                eval_figures['alignment_backward'] = plot_alignment(align_b_img)
-            if c.ga_alpha > 0:
-                epoch_stats['guided_attention_loss'] = keep_avg['avg_ga_loss']
-            tb_logger.tb_eval_stats(global_step, epoch_stats)
+                eval_figures['alignment2'] = plot_alignment(align_b_img)
+            tb_logger.tb_eval_stats(global_step, keep_avg.avg_values)
             tb_logger.tb_eval_figures(global_step, eval_figures)
 
     if args.rank == 0 and epoch > c.test_delay_epochs:

From 0a8e4cf9d6a902dc386d3f8fa170836ec8ccd187 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 10 Jun 2020 13:51:59 +0200
Subject: [PATCH 143/185] new config argument to plot tb figures at training

---
 config.json | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/config.json b/config.json
index 5e10c535..53b12fe1 100644
--- a/config.json
+++ b/config.json
@@ -104,7 +104,8 @@
     "separate_stopnet": true,      // Train stopnet seperately if 'stopnet==true'. It prevents stopnet loss to influence the rest of the model. It causes a better model, but it trains SLOWER.
 
     // TENSORBOARD and LOGGING
-    "print_step": 25,       // Number of steps to log traning on console.
+    "print_step": 25,       // Number of steps to log training on console.
+    "tb_plot_step:": 100,    // Number of steps to plot TB training figures.
     "print_eval": false,     // If True, it prints intermediate loss values in evalulation.
     "save_step": 10000,      // Number of training steps expected to save traninpg stats and checkpoints.
     "checkpoint": true,     // If true, it saves checkpoints per "save_step"

From b3efbbf54b9a1ad84bbd73f9270847e838f2c6a8 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 10 Jun 2020 13:52:31 +0200
Subject: [PATCH 144/185] bug fix

---
 train.py               | 5 ++---
 utils/generic_utils.py | 1 +
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/train.py b/train.py
index 51c73a9f..02f28c1d 100644
--- a/train.py
+++ b/train.py
@@ -222,15 +222,14 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
         if args.rank == 0:
             # Plot Training Iter Stats
             # reduce TB load
-            if global_step % 10 == 0:
+            if global_step % c.tb_plot_step == 0:
                 iter_stats = {
-                    "loss_posnet": loss_dict['postnet_loss'].item(),
-                    "loss_decoder": loss_dict['decoder_loss'].item(),
                     "lr": current_lr,
                     "grad_norm": grad_norm,
                     "grad_norm_st": grad_norm_st,
                     "step_time": step_time
                 }
+                iter_stats.update(loss_dict)
                 tb_logger.tb_train_iter_stats(global_step, iter_stats)
 
             if global_step % c.save_step == 0:
diff --git a/utils/generic_utils.py b/utils/generic_utils.py
index e3daf574..c50f8060 100644
--- a/utils/generic_utils.py
+++ b/utils/generic_utils.py
@@ -328,6 +328,7 @@ def check_config(c):
 
     # tensorboard
     _check_argument('print_step', c, restricted=True, val_type=int, min_val=1)
+    _check_argument('tb_plot_step', c, restricted=True, val_type=int, min_val=1)
     _check_argument('save_step', c, restricted=True, val_type=int, min_val=1)
     _check_argument('checkpoint', c, restricted=True, val_type=bool)
     _check_argument('tb_model_param_stats', c, restricted=True, val_type=bool)

From ddfede1f52191fd9568a9fe5c178b2147b6e0b3e Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 10 Jun 2020 13:55:37 +0200
Subject: [PATCH 145/185] restore vocoder scheduler if it is in checkpoint

---
 vocoder/train.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/vocoder/train.py b/vocoder/train.py
index a8a8f011..091a6932 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -450,10 +450,14 @@ def main(args):  # pylint: disable=redefined-outer-name
                            weight_decay=0)
 
     # schedulers
-    scheduler_gen = getattr(torch.optim.lr_scheduler, c.lr_scheduler_gen)
-    scheduler_disc = getattr(torch.optim.lr_scheduler, c.lr_scheduler_disc)
-    scheduler_gen = scheduler_gen(optimizer_gen, **c.lr_scheduler_gen_params)
-    scheduler_disc = scheduler_disc(optimizer_disc, **c.lr_scheduler_disc_params)
+    scheduler_gen = None
+    scheduler_disc = None
+    if 'lr_scheduler_gen' in c:
+        scheduler_gen = getattr(torch.optim.lr_scheduler, c.lr_scheduler_gen)
+        scheduler_gen = scheduler_gen(optimizer_gen, **c.lr_scheduler_gen_params)
+    if 'lr_scheduler_disc' in c:
+        scheduler_disc = getattr(torch.optim.lr_scheduler, c.lr_scheduler_disc)
+        scheduler_disc = scheduler_disc(optimizer_disc, **c.lr_scheduler_disc_params)
 
     # setup criterion
     criterion_gen = GeneratorLoss(c)

From e8a59db839acc151ac2e20e5cdbd9bf6dc494dda Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 10 Jun 2020 13:57:49 +0200
Subject: [PATCH 146/185] linter fix

---
 train.py         | 4 ++--
 vocoder/train.py | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/train.py b/train.py
index 02f28c1d..f6d73a4a 100644
--- a/train.py
+++ b/train.py
@@ -198,7 +198,7 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
         # detach loss values
         loss_dict_new = dict()
         for key, value in loss_dict.items():
-            if isinstance(value, int) or isinstance(value, float):
+            if isinstance(value, (int, float)):
                 loss_dict_new[key] = value
             else:
                 loss_dict_new[key] = value.item()
@@ -336,7 +336,7 @@ def evaluate(model, criterion, ap, global_step, epoch):
              # detach loss values
             loss_dict_new = dict()
             for key, value in loss_dict.items():
-                if isinstance(value, int) or isinstance(value, float):
+                if isinstance(value, (int, float)):
                     loss_dict_new[key] = value
                 else:
                     loss_dict_new[key] = value.item()
diff --git a/vocoder/train.py b/vocoder/train.py
index 091a6932..ce8f111a 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -17,7 +17,7 @@ from TTS.utils.generic_utils import (KeepAverage, count_parameters,
 from TTS.utils.io import copy_config_file, load_config
 from TTS.utils.radam import RAdam
 from TTS.utils.tensorboard_logger import TensorboardLogger
-from TTS.utils.training import setup_torch_training_env, NoamLR
+from TTS.utils.training import setup_torch_training_env
 from TTS.vocoder.datasets.gan_dataset import GANDataset
 from TTS.vocoder.datasets.preprocess import load_wav_data
 # from distribute import (DistributedSampler, apply_gradient_allreduce,

From b8766c05cea3c3767157ed7540a12f9f572aee55 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 11 Jun 2020 10:48:20 +0200
Subject: [PATCH 147/185] bug fix

---
 train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/train.py b/train.py
index f6d73a4a..8581132a 100644
--- a/train.py
+++ b/train.py
@@ -237,7 +237,7 @@ def train(model, criterion, optimizer, optimizer_st, scheduler,
                     # save model
                     save_checkpoint(model, optimizer, global_step, epoch, model.decoder.r, OUT_PATH,
                                     optimizer_st=optimizer_st,
-                                    model_loss=loss_dict['postnet_loss'].item())
+                                    model_loss=loss_dict['postnet_loss'])
 
                 # Diagnostic visualizations
                 const_spec = postnet_output[0].data.cpu().numpy()

From 9e71d4d66d5126a13f881f42f6e80504ca0ee560 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Thu, 11 Jun 2020 12:42:13 +0200
Subject: [PATCH 148/185] reformatting vocoder training and disable G_adv eval
 if step < d_training

---
 vocoder/train.py | 47 +++++++++++++++++++++++++----------------------
 1 file changed, 25 insertions(+), 22 deletions(-)

diff --git a/vocoder/train.py b/vocoder/train.py
index ce8f111a..74b3e759 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -112,7 +112,6 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
         ##############################
 
         # generator pass
-        optimizer_G.zero_grad()
         y_hat = model_G(c_G)
         y_hat_sub = None
         y_G_sub = None
@@ -121,8 +120,8 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
         # PQMF formatting
         if y_hat.shape[1] > 1:
             y_hat_sub = y_hat
-            y_hat = model_G.pqmf_synthesis(y_hat)
             y_hat_vis = y_hat
+            y_hat = model_G.pqmf_synthesis(y_hat)
             y_G_sub = model_G.pqmf_analysis(y_G)
 
         if global_step > c.steps_to_start_discriminator:
@@ -142,12 +141,11 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
             if isinstance(D_out_fake, tuple):
                 scores_fake, feats_fake = D_out_fake
                 if D_out_real is None:
-                    scores_real, feats_real = None, None
+                    feats_real = None
                 else:
-                    scores_real, feats_real = D_out_real
+                    _, feats_real = D_out_real
             else:
                 scores_fake = D_out_fake
-                scores_real = D_out_real
         else:
             scores_fake, feats_fake, feats_real = None, None, None
 
@@ -157,6 +155,7 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
         loss_G = loss_G_dict['G_loss']
 
         # optimizer generator
+        optimizer_G.zero_grad()
         loss_G.backward()
         if c.gen_clip_grad > 0:
             torch.nn.utils.clip_grad_norm_(model_G.parameters(),
@@ -184,8 +183,6 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
             if y_hat.shape[1] > 1:
                 y_hat = model_G.pqmf_synthesis(y_hat)
 
-            optimizer_D.zero_grad()
-
             # run D with or without cond. features
             if len(signature(model_D.forward).parameters) == 2:
                 D_out_fake = model_D(y_hat.detach(), c_D)
@@ -210,6 +207,7 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
             loss_D = loss_D_dict['D_loss']
 
             # optimizer discriminator
+            optimizer_D.zero_grad()
             loss_D.backward()
             if c.disc_clip_grad > 0:
                 torch.nn.utils.clip_grad_norm_(model_D.parameters(),
@@ -326,25 +324,30 @@ def evaluate(model_G, criterion_G, model_D, criterion_D, ap, global_step, epoch)
             y_hat = model_G.pqmf_synthesis(y_hat)
             y_G_sub = model_G.pqmf_analysis(y_G)
 
-        if len(signature(model_D.forward).parameters) == 2:
-            D_out_fake = model_D(y_hat, c_G)
-        else:
-            D_out_fake = model_D(y_hat)
-        D_out_real = None
 
-        if c.use_feat_match_loss:
-            with torch.no_grad():
-                D_out_real = model_D(y_G)
+        if global_step > c.steps_to_start_discriminator:
 
-        # format D outputs
-        if isinstance(D_out_fake, tuple):
-            scores_fake, feats_fake = D_out_fake
-            if D_out_real is None:
-                feats_real = None
+            if len(signature(model_D.forward).parameters) == 2:
+                D_out_fake = model_D(y_hat, c_G)
             else:
-                _, feats_real = D_out_real
+                D_out_fake = model_D(y_hat)
+            D_out_real = None
+
+            if c.use_feat_match_loss:
+                with torch.no_grad():
+                    D_out_real = model_D(y_G)
+
+            # format D outputs
+            if isinstance(D_out_fake, tuple):
+                scores_fake, feats_fake = D_out_fake
+                if D_out_real is None:
+                    feats_real = None
+                else:
+                    _, feats_real = D_out_real
+            else:
+                scores_fake = D_out_fake
         else:
-            scores_fake = D_out_fake
+            scores_fake, feats_fake, feats_real = None, None, None
 
         # compute losses
         loss_G_dict = criterion_G(y_hat, y_G, scores_fake, feats_fake,

From 28ee6af06c4e7cac09f6bd9fa8b34db57acc1746 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 12 Jun 2020 11:12:57 +0200
Subject: [PATCH 149/185] enable loading precomputed vocoder dataset

---
 vocoder/datasets/gan_dataset.py | 25 +++++++++++++++++++------
 vocoder/datasets/preprocess.py  | 21 +++++++++++++++++++++
 vocoder/train.py                |  4 ++--
 3 files changed, 42 insertions(+), 8 deletions(-)

diff --git a/vocoder/datasets/gan_dataset.py b/vocoder/datasets/gan_dataset.py
index 5e410151..55513e7d 100644
--- a/vocoder/datasets/gan_dataset.py
+++ b/vocoder/datasets/gan_dataset.py
@@ -28,6 +28,7 @@ class GANDataset(Dataset):
 
         self.ap = ap
         self.item_list = items
+        self.compute_feat = not isinstance(items[0], (tuple, list))
         self.seq_len = seq_len
         self.hop_len = hop_len
         self.pad_short = pad_short
@@ -77,14 +78,26 @@ class GANDataset(Dataset):
 
     def load_item(self, idx):
         """ load (audio, feat) couple """
-        wavpath = self.item_list[idx]
-        # print(wavpath)
+        if self.compute_feat:
+            # compute features from wav
+            wavpath = self.item_list[idx]
+            # print(wavpath)
 
-        if self.use_cache and self.cache[idx] is not None:
-            audio, mel = self.cache[idx]
+            if self.use_cache and self.cache[idx] is not None:
+                audio, mel = self.cache[idx]
+            else:
+                audio = self.ap.load_wav(wavpath)
+                mel = self.ap.melspectrogram(audio)
         else:
-            audio = self.ap.load_wav(wavpath)
-            mel = self.ap.melspectrogram(audio)
+
+            # load precomputed features
+            wavpath, feat_path = self.item_list[idx]
+
+            if self.use_cache and self.cache[idx] is not None:
+                audio, mel = self.cache[idx]
+            else:
+                audio = self.ap.load_wav(wavpath)
+                mel = np.load(feat_path)
 
         if len(audio) < self.seq_len + self.pad_short:
             audio = np.pad(audio, (0, self.seq_len + self.pad_short - len(audio)), \
diff --git a/vocoder/datasets/preprocess.py b/vocoder/datasets/preprocess.py
index 01e01e3e..be60c13a 100644
--- a/vocoder/datasets/preprocess.py
+++ b/vocoder/datasets/preprocess.py
@@ -1,5 +1,6 @@
 import glob
 import os
+from pathlib import Path
 
 import numpy as np
 
@@ -9,8 +10,28 @@ def find_wav_files(data_path):
     return wav_paths
 
 
+def find_feat_files(data_path):
+    feat_paths = glob.glob(os.path.join(data_path, '**', '*.npy'), recursive=True)
+    return feat_paths
+
+
 def load_wav_data(data_path, eval_split_size):
     wav_paths = find_wav_files(data_path)
     np.random.seed(0)
     np.random.shuffle(wav_paths)
     return wav_paths[:eval_split_size], wav_paths[eval_split_size:]
+
+
+def load_wav_feat_data(data_path, feat_path, eval_split_size):
+    wav_paths = sorted(find_wav_files(data_path))
+    feat_paths = sorted(find_feat_files(feat_path))
+    assert len(wav_paths) == len(feat_paths)
+    for wav, feat in zip(wav_paths, feat_paths):
+        wav_name = Path(wav).stem
+        feat_name = Path(feat).stem
+        assert wav_name == feat_name
+
+    items = list(zip(wav_paths, feat_paths))
+    np.random.seed(0)
+    np.random.shuffle(items)
+    return items[:eval_split_size], items[eval_split_size:]
diff --git a/vocoder/train.py b/vocoder/train.py
index 74b3e759..4d1b1029 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -19,7 +19,7 @@ from TTS.utils.radam import RAdam
 from TTS.utils.tensorboard_logger import TensorboardLogger
 from TTS.utils.training import setup_torch_training_env
 from TTS.vocoder.datasets.gan_dataset import GANDataset
-from TTS.vocoder.datasets.preprocess import load_wav_data
+from TTS.vocoder.datasets.preprocess import load_wav_data, load_wav_feat_data
 # from distribute import (DistributedSampler, apply_gradient_allreduce,
 #                         init_distributed, reduce_tensor)
 from TTS.vocoder.layers.losses import DiscriminatorLoss, GeneratorLoss
@@ -543,8 +543,8 @@ def main(args):  # pylint: disable=redefined-outer-name
         best_loss = save_best_model(target_loss,
                                     best_loss,
                                     model_gen,
-                                    scheduler_gen,
                                     optimizer_gen,
+                                    scheduler_gen,
                                     model_disc,
                                     optimizer_disc,
                                     scheduler_disc,

From b62ad06f4cf67cb1db5da20c72ea1bda7819f33a Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 12 Jun 2020 11:37:32 +0200
Subject: [PATCH 150/185] bug fix

---
 vocoder/utils/console_logger.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vocoder/utils/console_logger.py b/vocoder/utils/console_logger.py
index 50882160..4f44986c 100644
--- a/vocoder/utils/console_logger.py
+++ b/vocoder/utils/console_logger.py
@@ -84,7 +84,7 @@ class ConsoleLogger():
             color = ''
             sign = '+'
             diff = 0
-            if self.old_eval_loss_dict is not None:
+            if self.old_eval_loss_dict is not None and key in self.old_eval_loss_dict:
                 diff = value - self.old_eval_loss_dict[key]
                 if diff < 0:
                     color = tcolors.OKGREEN

From bc69a54f44f066589b460a5e8def4a57acb921db Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 12 Jun 2020 11:54:54 +0200
Subject: [PATCH 151/185] visualization bug fix

---
 vocoder/train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vocoder/train.py b/vocoder/train.py
index 4d1b1029..326bd90e 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -120,8 +120,8 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
         # PQMF formatting
         if y_hat.shape[1] > 1:
             y_hat_sub = y_hat
-            y_hat_vis = y_hat
             y_hat = model_G.pqmf_synthesis(y_hat)
+            y_hat_vis = y_hat
             y_G_sub = model_G.pqmf_analysis(y_G)
 
         if global_step > c.steps_to_start_discriminator:

From 369a91291469a6284b921fb4f4b9a66a3095136c Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 12 Jun 2020 12:11:23 +0200
Subject: [PATCH 152/185] more comments to audio.py

---
 utils/audio.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/utils/audio.py b/utils/audio.py
index 13eab3d6..f941f609 100644
--- a/utils/audio.py
+++ b/utils/audio.py
@@ -57,8 +57,10 @@ class AudioProcessor(object):
         self.stats_path = stats_path
         # setup stft parameters
         if hop_length is None:
+            # compute stft parameters from given time values
             self.n_fft, self.hop_length, self.win_length = self._stft_parameters()
         else:
+            # use stft parameters from config file
             self.hop_length = hop_length
             self.win_length = win_length
             self.n_fft = (self.num_freq - 1) * 2

From 5547ddeb618eb9d8b3dbbee5b4313dc697d78b09 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 12 Jun 2020 19:32:49 +0200
Subject: [PATCH 153/185] update vocoder loss implemenatations and fix MSEDLoss

---
 vocoder/layers/losses.py | 19 ++++++++++++++-----
 1 file changed, 14 insertions(+), 5 deletions(-)

diff --git a/vocoder/layers/losses.py b/vocoder/layers/losses.py
index a24b129c..4774ab4e 100644
--- a/vocoder/layers/losses.py
+++ b/vocoder/layers/losses.py
@@ -88,7 +88,7 @@ class MSEGLoss(nn.Module):
     """ Mean Squared Generator Loss """
     # pylint: disable=no-self-use
     def forward(self, score_fake):
-        loss_fake = torch.mean(torch.sum(torch.pow(score_fake, 2), dim=[1, 2]))
+        loss_fake = F.mse_loss(score_fake, score_fake.new_ones(score_fake.shape))
         return loss_fake
 
 
@@ -96,7 +96,8 @@ class HingeGLoss(nn.Module):
     """ Hinge Discriminator Loss """
     # pylint: disable=no-self-use
     def forward(self, score_fake):
-        loss_fake = torch.mean(F.relu(1. + score_fake))
+        # TODO: this might be wrong
+        loss_fake = torch.mean(F.relu(1. - score_fake))
         return loss_fake
 
 
@@ -107,10 +108,14 @@ class HingeGLoss(nn.Module):
 
 class MSEDLoss(nn.Module):
     """ Mean Squared Discriminator Loss """
+    def __init__(self,):
+        super(MSEDLoss, self).__init__()
+        self.loss_func = nn.MSELoss()
+
     # pylint: disable=no-self-use
     def forward(self, score_fake, score_real):
-        loss_real = torch.mean(torch.sum(torch.pow(score_real - 1.0, 2), dim=[1, 2]))
-        loss_fake = torch.mean(torch.sum(torch.pow(score_fake, 2), dim=[1, 2]))
+        loss_real = self.loss_func(score_real, score_real.new_ones(score_real.shape))
+        loss_fake = self.loss_func(score_fake, score_fake.new_zeros(score_fake.shape))
         loss_d = loss_real + loss_fake
         return loss_d, loss_real, loss_fake
 
@@ -126,11 +131,15 @@ class HingeDLoss(nn.Module):
 
 
 class MelganFeatureLoss(nn.Module):
+    def __init__(self,):
+        super(MelganFeatureLoss, self).__init__()
+        self.loss_func = nn.L1Loss()
+
     # pylint: disable=no-self-use
     def forward(self, fake_feats, real_feats):
         loss_feats = 0
         for fake_feat, real_feat in zip(fake_feats, real_feats):
-            loss_feats += torch.mean(torch.abs(fake_feat - real_feat))
+            loss_feats += self.loss_func(fake_feats, real_feats)
         loss_feats /= len(fake_feats) + len(real_feats)
         return loss_feats
 

From 8f72ad900ad1d1727a119326cd21ea4a84b3074d Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 12 Jun 2020 19:50:07 +0200
Subject: [PATCH 154/185] remove redundant params in vocoder config file

---
 vocoder/configs/multiband_melgan_config.json | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/vocoder/configs/multiband_melgan_config.json b/vocoder/configs/multiband_melgan_config.json
index 3a5f0673..164fe037 100644
--- a/vocoder/configs/multiband_melgan_config.json
+++ b/vocoder/configs/multiband_melgan_config.json
@@ -20,9 +20,6 @@
         "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
         "trim_db": 60,          // threshold for timming silence. Set this according to your dataset.
 
-        // Griffin-Lim
-        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
-        "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
 
         // MelSpectrogram parameters
         "num_mels": 80,         // size of the mel spec frame.

From 3c20afa1c98beb292055a493b8957526346cebf3 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 12 Jun 2020 20:55:06 +0200
Subject: [PATCH 155/185] do not call .item() if returned loss is not torch
 type

---
 vocoder/train.py | 15 ++++++++++++---
 1 file changed, 12 insertions(+), 3 deletions(-)

diff --git a/vocoder/train.py b/vocoder/train.py
index 326bd90e..41b3c1ec 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -217,7 +217,10 @@ def train(model_G, criterion_G, optimizer_G, model_D, criterion_D, optimizer_D,
                 scheduler_D.step()
 
             for key, value in loss_D_dict.items():
-                loss_dict[key] = value.item()
+                if isinstance(value, (int, float)):
+                    loss_dict[key] = value
+                else:
+                    loss_dict[key] = value.item()
 
         step_time = time.time() - start_time
         epoch_time += step_time
@@ -355,7 +358,10 @@ def evaluate(model_G, criterion_G, model_D, criterion_D, ap, global_step, epoch)
 
         loss_dict = dict()
         for key, value in loss_G_dict.items():
-            loss_dict[key] = value.item()
+            if isinstance(value, (int, float)):
+                loss_dict[key] = value
+            else:
+                loss_dict[key] = value.item()
 
         ##############################
         # DISCRIMINATOR
@@ -393,7 +399,10 @@ def evaluate(model_G, criterion_G, model_D, criterion_D, ap, global_step, epoch)
             loss_D_dict = criterion_D(scores_fake, scores_real)
 
             for key, value in loss_D_dict.items():
-                loss_dict[key] = value.item()
+                if isinstance(value, (int, float)):
+                    loss_dict[key] = value
+                else:
+                    loss_dict[key] = value.item()
 
 
         step_time = time.time() - start_time

From 98fd3b53f293e6908509982f22b4ad8479a566b5 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Mon, 15 Jun 2020 11:28:27 +0200
Subject: [PATCH 156/185] load mel features if 'feature_path' is provided

---
 vocoder/configs/multiband_melgan_config.json | 2 +-
 vocoder/train.py                             | 7 ++++++-
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/vocoder/configs/multiband_melgan_config.json b/vocoder/configs/multiband_melgan_config.json
index 164fe037..81048e31 100644
--- a/vocoder/configs/multiband_melgan_config.json
+++ b/vocoder/configs/multiband_melgan_config.json
@@ -111,7 +111,7 @@
     // OPTIMIZER
     "noam_schedule": false,        // use noam warmup and lr schedule.
     "warmup_steps_gen": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
-    "warmup_steps_disc": 4000,
+    "warmup_steps_disc": 4000,         // Noam decay steps to increase the learning rate from 0 to "lr"
     "epochs": 10000,                // total number of epochs to train.
     "wd": 0.0,                // Weight decay weight.
     "gen_clip_grad": -1,      // Generator gradient clipping threshold. Apply gradient clipping if > 0
diff --git a/vocoder/train.py b/vocoder/train.py
index 41b3c1ec..fd44c470 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -441,7 +441,12 @@ def evaluate(model_G, criterion_G, model_D, criterion_D, ap, global_step, epoch)
 def main(args):  # pylint: disable=redefined-outer-name
     # pylint: disable=global-variable-undefined
     global train_data, eval_data
-    eval_data, train_data = load_wav_data(c.data_path, c.eval_split_size)
+    print(f" > Loading wavs from: {c.data_path}")
+    if c.feature_path is not None:
+        print(f" > Loading features from: {c.feature_path}")
+        eval_data, train_data = load_wav_feat_data(c.data_path, c.feature_path, c.eval_split_size)
+    else:
+        eval_data, train_data = load_wav_data(c.data_path, c.eval_split_size)
 
     # setup audio processor
     ap = AudioProcessor(**c.audio)

From 460d0675a6657f4037d38340ed7b997eb90ef593 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Mon, 15 Jun 2020 11:47:59 +0200
Subject: [PATCH 157/185] bug fix for vocoder feature macthing loss

---
 vocoder/layers/losses.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vocoder/layers/losses.py b/vocoder/layers/losses.py
index 4774ab4e..22237077 100644
--- a/vocoder/layers/losses.py
+++ b/vocoder/layers/losses.py
@@ -139,7 +139,7 @@ class MelganFeatureLoss(nn.Module):
     def forward(self, fake_feats, real_feats):
         loss_feats = 0
         for fake_feat, real_feat in zip(fake_feats, real_feats):
-            loss_feats += self.loss_func(fake_feats, real_feats)
+            loss_feats += self.loss_func(fake_feat, real_feat)
         loss_feats /= len(fake_feats) + len(real_feats)
         return loss_feats
 

From c90145eef91350e476d64070fd63001ea646c097 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Mon, 15 Jun 2020 14:16:31 +0200
Subject: [PATCH 158/185] add feature path to vocder config

---
 vocoder/configs/multiband_melgan_config.json | 1 +
 1 file changed, 1 insertion(+)

diff --git a/vocoder/configs/multiband_melgan_config.json b/vocoder/configs/multiband_melgan_config.json
index 81048e31..7ba48854 100644
--- a/vocoder/configs/multiband_melgan_config.json
+++ b/vocoder/configs/multiband_melgan_config.json
@@ -92,6 +92,7 @@
 
     // DATASET
     "data_path": "/home/erogol/Data/LJSpeech-1.1/wavs/",
+    "feature_path": null,
     "seq_len": 16384,
     "pad_short": 2000,
     "conv_pad": 0,

From f3d4d1f83178e6c8519905400c58b1f52dda1d58 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Mon, 15 Jun 2020 19:26:26 +0200
Subject: [PATCH 159/185] bug fix init AudioProcessor in train.py

---
 vocoder/train.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/vocoder/train.py b/vocoder/train.py
index 144d63c3..fd44c470 100644
--- a/vocoder/train.py
+++ b/vocoder/train.py
@@ -448,6 +448,9 @@ def main(args):  # pylint: disable=redefined-outer-name
     else:
         eval_data, train_data = load_wav_data(c.data_path, c.eval_split_size)
 
+    # setup audio processor
+    ap = AudioProcessor(**c.audio)
+
     # DISTRUBUTED
     # if num_gpus > 1:
     # init_distributed(args.rank, num_gpus, args.group_id,

From f18f6e6d3e8473d0ffcceb3a7108e1cdfa197558 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 16 Jun 2020 12:33:36 +0200
Subject: [PATCH 160/185] plot spectrograms undenormalized if no ap is provided

---
 utils/visual.py | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/utils/visual.py b/utils/visual.py
index 87fbc8e4..3d95c2e3 100644
--- a/utils/visual.py
+++ b/utils/visual.py
@@ -27,14 +27,15 @@ def plot_alignment(alignment, info=None, fig_size=(16, 10), title=None):
     return fig
 
 
-def plot_spectrogram(linear_output, audio, fig_size=(16, 10)):
-    if isinstance(linear_output, torch.Tensor):
-        linear_output_ = linear_output.detach().cpu().numpy().squeeze()
+def plot_spectrogram(spectrogram, ap=None, fig_size=(16, 10)):
+    if isinstance(spectrogram, torch.Tensor):
+        spectrogram_ = spectrogram.detach().cpu().numpy().squeeze()
     else:
-        linear_output_ = linear_output
-    spectrogram = audio._denormalize(linear_output_.T)  # pylint: disable=protected-access
+        spectrogram_ = spectrogram
+    if ap is not None:
+        spectrogram_ = ap._denormalize(spectrogram_.T)  # pylint: disable=protected-access
     fig = plt.figure(figsize=fig_size)
-    plt.imshow(spectrogram, aspect="auto", origin="lower")
+    plt.imshow(spectrogram_, aspect="auto", origin="lower")
     plt.colorbar()
     plt.tight_layout()
     return fig

From 0de38c261769a1f184c906589882a8fe79951a4a Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 16 Jun 2020 12:34:10 +0200
Subject: [PATCH 161/185] fixing naming convention in vocoder losses

---
 vocoder/layers/losses.py       | 14 +++++++-------
 vocoder/utils/generic_utils.py |  6 +++---
 2 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/vocoder/layers/losses.py b/vocoder/layers/losses.py
index 22237077..fe13fa8a 100644
--- a/vocoder/layers/losses.py
+++ b/vocoder/layers/losses.py
@@ -87,17 +87,17 @@ class MultiScaleSubbandSTFTLoss(MultiScaleSTFTLoss):
 class MSEGLoss(nn.Module):
     """ Mean Squared Generator Loss """
     # pylint: disable=no-self-use
-    def forward(self, score_fake):
-        loss_fake = F.mse_loss(score_fake, score_fake.new_ones(score_fake.shape))
+    def forward(self, score_real):
+        loss_fake = F.mse_loss(score_real, score_real.new_ones(score_real.shape))
         return loss_fake
 
 
 class HingeGLoss(nn.Module):
     """ Hinge Discriminator Loss """
     # pylint: disable=no-self-use
-    def forward(self, score_fake):
+    def forward(self, score_real):
         # TODO: this might be wrong
-        loss_fake = torch.mean(F.relu(1. - score_fake))
+        loss_fake = torch.mean(F.relu(1. - score_real))
         return loss_fake
 
 
@@ -172,7 +172,7 @@ def _apply_D_loss(scores_fake, scores_real, loss_func):
     if isinstance(scores_fake, list):
         # multi-scale loss
         for score_fake, score_real in zip(scores_fake, scores_real):
-            total_loss, real_loss, fake_loss = loss_func(score_fake, score_real)
+            total_loss, real_loss, fake_loss = loss_func(score_fake=score_fake, score_real=score_real)
             loss += total_loss
             real_loss += real_loss
             fake_loss += fake_loss
@@ -286,14 +286,14 @@ class DiscriminatorLoss(nn.Module):
         return_dict = {}
 
         if self.use_mse_gan_loss:
-            mse_D_loss, mse_D_real_loss, mse_D_fake_loss = _apply_D_loss(scores_fake, scores_real, self.mse_loss)
+            mse_D_loss, mse_D_real_loss, mse_D_fake_loss = _apply_D_loss(scores_fake=scores_fake, scores_real=scores_real, self.mse_loss)
             return_dict['D_mse_gan_loss'] = mse_D_loss
             return_dict['D_mse_gan_real_loss'] = mse_D_real_loss
             return_dict['D_mse_gan_fake_loss'] = mse_D_fake_loss
             loss += mse_D_loss
 
         if self.use_hinge_gan_loss:
-            hinge_D_loss, hinge_D_real_loss, hinge_D_fake_loss = _apply_D_loss(scores_fake, scores_real, self.hinge_loss)
+            hinge_D_loss, hinge_D_real_loss, hinge_D_fake_loss = _apply_D_loss(scores_fake=scores_fake, scores_real=scores_real, self.hinge_loss)
             return_dict['D_hinge_gan_loss'] = hinge_D_loss
             return_dict['D_hinge_gan_real_loss'] = hinge_D_real_loss
             return_dict['D_hinge_gan_fake_loss'] = hinge_D_fake_loss
diff --git a/vocoder/utils/generic_utils.py b/vocoder/utils/generic_utils.py
index 179c4064..80c97f1a 100644
--- a/vocoder/utils/generic_utils.py
+++ b/vocoder/utils/generic_utils.py
@@ -29,9 +29,9 @@ def plot_results(y_hat, y, ap, global_step, name_prefix):
     plt.close()
 
     figures = {
-        name_prefix + "/spectrogram/fake": plot_spectrogram(spec_fake, ap),
-        name_prefix + "spectrogram/real": plot_spectrogram(spec_real, ap),
-        name_prefix + "spectrogram/diff": plot_spectrogram(spec_diff, ap),
+        name_prefix + "spectrogram/fake": plot_spectrogram(spec_fake),
+        name_prefix + "spectrogram/real": plot_spectrogram(spec_real),
+        name_prefix + "spectrogram/diff": plot_spectrogram(spec_diff),
         name_prefix + "speech_comparison": fig_wave,
     }
     return figures

From 6948e4ce386a3e7215adb7b2e4be0aa5dfb1bf28 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 16 Jun 2020 12:35:28 +0200
Subject: [PATCH 162/185] change sample data

---
 tests/data/ljspeech/wavs/LJ001-0001.wav | Bin 309040 -> 425830 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/tests/data/ljspeech/wavs/LJ001-0001.wav b/tests/data/ljspeech/wavs/LJ001-0001.wav
index a4662ab0cb12f959da2c50d7f3c273d583fc6128..a274be89422809113adc336e624afeb255cdc67a 100644
GIT binary patch
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z3N6rqHVbq?H}s+sf?>1~u>3qaDlAkuXk!B|-~j>PgJh8O=RFHB0qxH@sDc0A3qzmi
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From b2e9c05acc7fc5f934d5b5785b13ed1855543207 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 16 Jun 2020 13:24:59 +0200
Subject: [PATCH 163/185] losses bug fix

---
 vocoder/layers/losses.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/vocoder/layers/losses.py b/vocoder/layers/losses.py
index fe13fa8a..d6ffe9fe 100644
--- a/vocoder/layers/losses.py
+++ b/vocoder/layers/losses.py
@@ -286,14 +286,20 @@ class DiscriminatorLoss(nn.Module):
         return_dict = {}
 
         if self.use_mse_gan_loss:
-            mse_D_loss, mse_D_real_loss, mse_D_fake_loss = _apply_D_loss(scores_fake=scores_fake, scores_real=scores_real, self.mse_loss)
+            mse_D_loss, mse_D_real_loss, mse_D_fake_loss = _apply_D_loss(
+                scores_fake=scores_fake,
+                scores_real=scores_real,
+                loss_func=self.mse_loss)
             return_dict['D_mse_gan_loss'] = mse_D_loss
             return_dict['D_mse_gan_real_loss'] = mse_D_real_loss
             return_dict['D_mse_gan_fake_loss'] = mse_D_fake_loss
             loss += mse_D_loss
 
         if self.use_hinge_gan_loss:
-            hinge_D_loss, hinge_D_real_loss, hinge_D_fake_loss = _apply_D_loss(scores_fake=scores_fake, scores_real=scores_real, self.hinge_loss)
+            hinge_D_loss, hinge_D_real_loss, hinge_D_fake_loss = _apply_D_loss(
+                scores_fake=scores_fake,
+                scores_real=scores_real,
+                loss_func=self.hinge_loss)
             return_dict['D_hinge_gan_loss'] = hinge_D_loss
             return_dict['D_hinge_gan_real_loss'] = hinge_D_real_loss
             return_dict['D_hinge_gan_fake_loss'] = hinge_D_fake_loss

From 55230bb317a8e5fd67b547b9a3a3dda3c6a71692 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 16 Jun 2020 18:53:02 +0200
Subject: [PATCH 164/185] plot spectrogram transpose fix

---
 utils/visual.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/utils/visual.py b/utils/visual.py
index 3d95c2e3..b4ebec9a 100644
--- a/utils/visual.py
+++ b/utils/visual.py
@@ -29,11 +29,11 @@ def plot_alignment(alignment, info=None, fig_size=(16, 10), title=None):
 
 def plot_spectrogram(spectrogram, ap=None, fig_size=(16, 10)):
     if isinstance(spectrogram, torch.Tensor):
-        spectrogram_ = spectrogram.detach().cpu().numpy().squeeze()
+        spectrogram_ = spectrogram.detach().cpu().numpy().squeeze().T
     else:
-        spectrogram_ = spectrogram
+        spectrogram_ = spectrogram.T
     if ap is not None:
-        spectrogram_ = ap._denormalize(spectrogram_.T)  # pylint: disable=protected-access
+        spectrogram_ = ap._denormalize(spectrogram_)  # pylint: disable=protected-access
     fig = plt.figure(figsize=fig_size)
     plt.imshow(spectrogram_, aspect="auto", origin="lower")
     plt.colorbar()

From 82f8e9cb123abadb4dcdebc108d2f1b2f4daf6f9 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?J=C3=B6rg=20Thalheim?= <joerg@thalheim.io>
Date: Sat, 13 Jun 2020 00:34:51 +0100
Subject: [PATCH 165/185] add tts-server executable

This makes the project easier to use when installing via package manager.
---
 server/server.py | 6 +++++-
 setup.py         | 5 +++++
 2 files changed, 10 insertions(+), 1 deletion(-)

diff --git a/server/server.py b/server/server.py
index 705937e2..593eeb18 100644
--- a/server/server.py
+++ b/server/server.py
@@ -76,5 +76,9 @@ def tts():
     return send_file(data, mimetype='audio/wav')
 
 
-if __name__ == '__main__':
+def main():
     app.run(debug=args.debug, host='0.0.0.0', port=args.port)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/setup.py b/setup.py
index a7840568..96032de0 100644
--- a/setup.py
+++ b/setup.py
@@ -75,6 +75,11 @@ setup(
     url='https://github.com/mozilla/TTS',
     description='Text to Speech with Deep Learning',
     license='MPL-2.0',
+    entry_points={
+        'console_scripts': [
+            'tts-server = TTS.server.server:main'
+        ]
+    },
     package_dir={'': 'tts_namespace'},
     packages=find_packages('tts_namespace'),
     package_data={

From df10e2a92af5fd30a4dd84b3338e38a8a9a30f10 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 17 Jun 2020 11:13:16 +0200
Subject: [PATCH 166/185] fixinf discriminator configuration

---
 vocoder/configs/multiband_melgan_config.json      | 7 ++-----
 vocoder/models/melgan_multiscale_discriminator.py | 2 +-
 2 files changed, 3 insertions(+), 6 deletions(-)

diff --git a/vocoder/configs/multiband_melgan_config.json b/vocoder/configs/multiband_melgan_config.json
index 7ba48854..d54c64aa 100644
--- a/vocoder/configs/multiband_melgan_config.json
+++ b/vocoder/configs/multiband_melgan_config.json
@@ -78,8 +78,8 @@
     "discriminator_model": "melgan_multiscale_discriminator",
     "discriminator_model_params":{
         "base_channels": 16,
-        "max_channels":1024,
-        "downsample_factors":[4, 4, 4, 4]
+        "max_channels":512,
+        "downsample_factors":[4, 4, 4]
     },
     "steps_to_start_discriminator": 200000,      // steps required to start GAN trainining.1
 
@@ -110,9 +110,6 @@
     "test_sentences_file": null,  // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
 
     // OPTIMIZER
-    "noam_schedule": false,        // use noam warmup and lr schedule.
-    "warmup_steps_gen": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
-    "warmup_steps_disc": 4000,         // Noam decay steps to increase the learning rate from 0 to "lr"
     "epochs": 10000,                // total number of epochs to train.
     "wd": 0.0,                // Weight decay weight.
     "gen_clip_grad": -1,      // Generator gradient clipping threshold. Apply gradient clipping if > 0
diff --git a/vocoder/models/melgan_multiscale_discriminator.py b/vocoder/models/melgan_multiscale_discriminator.py
index d77b9ceb..dbcc1f30 100644
--- a/vocoder/models/melgan_multiscale_discriminator.py
+++ b/vocoder/models/melgan_multiscale_discriminator.py
@@ -11,7 +11,7 @@ class MelganMultiscaleDiscriminator(nn.Module):
                  kernel_sizes=(5, 3),
                  base_channels=16,
                  max_channels=1024,
-                 downsample_factors=(4, 4, 4, 4),
+                 downsample_factors=(4, 4, 4),
                  pooling_kernel_size=4,
                  pooling_stride=2,
                  pooling_padding=1):

From 25aec41626286cde412643f63068f16612db3393 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 17 Jun 2020 13:35:37 +0200
Subject: [PATCH 167/185] README update

---
 vocoder/README.md | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/vocoder/README.md b/vocoder/README.md
index 48fc24ee..1b65f929 100644
--- a/vocoder/README.md
+++ b/vocoder/README.md
@@ -32,4 +32,7 @@ You can fine-tune a pre-trained model by the following command.
 
 Restoring a model starts a new training in a different output folder. It only restores model weights with the given checkpoint file. However, continuing a training starts from the same conditions the previous training run left off.
 
-You can also follow your training runs on Tensorboard as you do with our TTS models.
\ No newline at end of file
+You can also follow your training runs on Tensorboard as you do with our TTS models.
+
+## Acknowledgement
+Thanks to @kan-bayashi for his [repository](https://github.com/kan-bayashi/ParallelWaveGAN) being the start point of our work.
\ No newline at end of file

From 1611a7182df8efc7843ca0a4c9d4b39aeeb3a085 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Eren=20G=C3=B6lge?= <egolge@mozilla.com>
Date: Thu, 18 Jun 2020 16:15:57 +0200
Subject: [PATCH 168/185] Update README.md
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: Jörg Thalheim <Mic92@users.noreply.github.com>
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index f6ee1ade..a211764c 100644
--- a/README.md
+++ b/README.md
@@ -87,7 +87,7 @@ Audio length is approximately 6 secs.
 
 
 ## Datasets and Data-Loading
-TTS provides a generic dataloder easy to use for new datasets. You need to write an preprocessor function to integrate your own dataset.Check ```datasets/preprocess.py``` to see some examples. After the function, you need to set ```dataset``` field in ```config.json```. Do not forget other data related fields too.
+TTS provides a generic dataloader easy to use for new datasets. You need to write an preprocessor function to integrate your own dataset.Check ```datasets/preprocess.py``` to see some examples. After the function, you need to set ```dataset``` field in ```config.json```. Do not forget other data related fields too.
 
 Some of the open-sourced datasets that we successfully applied TTS, are linked below.
 

From 58784ad09c05a5d07d3b86cd53b89e34972e34bc Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 19 Jun 2020 12:25:03 +0200
Subject: [PATCH 169/185] renaming for melgan generator

---
 vocoder/layers/melgan.py           |  2 +-
 vocoder/layers/pqmf.py             |  2 --
 vocoder/models/melgan_generator.py | 14 +++++++-------
 3 files changed, 8 insertions(+), 10 deletions(-)

diff --git a/vocoder/layers/melgan.py b/vocoder/layers/melgan.py
index 61f7a96b..58c12a2e 100644
--- a/vocoder/layers/melgan.py
+++ b/vocoder/layers/melgan.py
@@ -21,7 +21,7 @@ class ResidualStack(nn.Module):
                     nn.Conv1d(channels,
                               channels,
                               kernel_size=kernel_size,
-                              dilation=layer_padding,
+                              dilation=layer_dilation,
                               bias=True)),
                 nn.LeakyReLU(0.2),
                 weight_norm(
diff --git a/vocoder/layers/pqmf.py b/vocoder/layers/pqmf.py
index 3985aad4..ef5a3507 100644
--- a/vocoder/layers/pqmf.py
+++ b/vocoder/layers/pqmf.py
@@ -1,5 +1,3 @@
-"""Pseudo QMF modules."""
-
 import numpy as np
 import torch
 import torch.nn.functional as F
diff --git a/vocoder/models/melgan_generator.py b/vocoder/models/melgan_generator.py
index e69e6ef3..01b52ea8 100644
--- a/vocoder/models/melgan_generator.py
+++ b/vocoder/models/melgan_generator.py
@@ -77,16 +77,16 @@ class MelganGenerator(nn.Module):
         ]
         self.layers = nn.Sequential(*layers)
 
-    def forward(self, cond_features):
-        return self.layers(cond_features)
+    def forward(self, c):
+        return self.layers(c)
 
-    def inference(self, cond_features):
-        cond_features = cond_features.to(self.layers[1].weight.device)
-        cond_features = torch.nn.functional.pad(
-            cond_features,
+    def inference(self, c):
+        c = c.to(self.layers[1].weight.device)
+        c = torch.nn.functional.pad(
+            c,
             (self.inference_padding, self.inference_padding),
             'replicate')
-        return self.layers(cond_features)
+        return self.layers(c)
 
     def remove_weight_norm(self):
         for _, layer in enumerate(self.layers):

From 6b2ff0823924d5a5a197f4f7a07b252590826c29 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 19 Jun 2020 12:25:27 +0200
Subject: [PATCH 170/185] Issue #435 - Convert melgan vocoder models to TF2.0

---
 vocoder/tf/convert_melgan_torch_to_tf.py      | 115 ++++++++++++++++++
 vocoder/tf/layers/melgan.py                   |  52 ++++++++
 vocoder/tf/layers/pqmf.py                     |  66 ++++++++++
 vocoder/tf/models/melgan_generator.py         | 106 ++++++++++++++++
 .../tf/models/multiband_melgan_generator.py   |  46 +++++++
 vocoder/tf/utils/__init__.py                  |   0
 vocoder/tf/utils/convert_torch_to_tf_utils.py |  48 ++++++++
 vocoder/tf/utils/generic_utils.py             |  37 ++++++
 vocoder/tf/utils/io.py                        |  27 ++++
 9 files changed, 497 insertions(+)
 create mode 100644 vocoder/tf/convert_melgan_torch_to_tf.py
 create mode 100644 vocoder/tf/layers/melgan.py
 create mode 100644 vocoder/tf/layers/pqmf.py
 create mode 100644 vocoder/tf/models/melgan_generator.py
 create mode 100644 vocoder/tf/models/multiband_melgan_generator.py
 create mode 100644 vocoder/tf/utils/__init__.py
 create mode 100644 vocoder/tf/utils/convert_torch_to_tf_utils.py
 create mode 100644 vocoder/tf/utils/generic_utils.py
 create mode 100644 vocoder/tf/utils/io.py

diff --git a/vocoder/tf/convert_melgan_torch_to_tf.py b/vocoder/tf/convert_melgan_torch_to_tf.py
new file mode 100644
index 00000000..b767f268
--- /dev/null
+++ b/vocoder/tf/convert_melgan_torch_to_tf.py
@@ -0,0 +1,115 @@
+import argparse
+import os
+import sys
+from pprint import pprint
+
+import numpy as np
+import tensorflow as tf
+import torch
+from fuzzywuzzy import fuzz
+
+from TTS.utils.io import load_config
+from TTS.vocoder.tf.models.multiband_melgan_generator import \
+    MultibandMelganGenerator
+from TTS.vocoder.tf.utils.convert_torch_to_tf_utils import (
+    compare_torch_tf, convert_tf_name, transfer_weights_torch_to_tf)
+from TTS.vocoder.tf.utils.generic_utils import \
+    setup_generator as setup_tf_generator
+from TTS.vocoder.tf.utils.io import save_checkpoint
+from TTS.vocoder.utils.generic_utils import setup_generator
+
+# prevent GPU use
+os.environ['CUDA_VISIBLE_DEVICES'] = ''
+
+# define args
+parser = argparse.ArgumentParser()
+parser.add_argument('--torch_model_path',
+                    type=str,
+                    help='Path to target torch model to be converted to TF.')
+parser.add_argument('--config_path',
+                    type=str,
+                    help='Path to config file of torch model.')
+parser.add_argument(
+    '--output_path',
+    type=str,
+    help='path to output file including file name to save TF model.')
+args = parser.parse_args()
+
+# load model config
+config_path = args.config_path
+c = load_config(config_path)
+num_speakers = 0
+
+# init torch model
+model = setup_generator(c)
+checkpoint = torch.load(args.torch_model_path,
+                        map_location=torch.device('cpu'))
+state_dict = checkpoint['model']
+model.load_state_dict(state_dict)
+model.remove_weight_norm()
+state_dict = model.state_dict()
+
+# init tf model
+model_tf = setup_tf_generator(c)
+
+common_sufix = '/.ATTRIBUTES/VARIABLE_VALUE'
+# get tf_model graph by passing an input
+# B x D x T
+dummy_input = tf.random.uniform((7, 80, 64))
+mel_pred = model_tf(dummy_input, training=False)
+
+# get tf variables
+tf_vars = model_tf.weights
+
+# match variable names with fuzzy logic
+torch_var_names = list(state_dict.keys())
+tf_var_names = [we.name for we in model_tf.weights]
+for tf_name in tf_var_names:
+    # skip re-mapped layer names
+    if tf_name in [name[0] for name in var_map]:
+        continue
+    tf_name_edited = convert_tf_name(tf_name)
+    ratios = [
+        fuzz.ratio(torch_name, tf_name_edited)
+        for torch_name in torch_var_names
+    ]
+    max_idx = np.argmax(ratios)
+    matching_name = torch_var_names[max_idx]
+    del torch_var_names[max_idx]
+    var_map.append((tf_name, matching_name))
+
+# pass weights
+tf_vars = transfer_weights_torch_to_tf(tf_vars, dict(var_map), state_dict)
+
+# Compare TF and TORCH models
+# check embedding outputs
+model.eval()
+dummy_input_torch = torch.ones((1, 80, 10))
+dummy_input_tf = tf.convert_to_tensor(dummy_input_torch.numpy())
+dummy_input_tf = tf.transpose(dummy_input_tf, perm=[0, 2, 1])
+dummy_input_tf = tf.expand_dims(dummy_input_tf, 2)
+
+out_torch = model.layers[0](dummy_input_torch)
+out_tf = model_tf.model_layers[0](dummy_input_tf)
+out_tf_ = tf.transpose(out_tf, perm=[0, 3, 2, 1])[:, :, 0, :]
+
+assert compare_torch_tf(out_torch, out_tf_) < 1e-5
+
+for i in range(1, len(model.layers)):
+    print(f"{i} -> {model.layers[i]} vs {model_tf.model_layers[i]}")
+    out_torch = model.layers[i](out_torch)
+    out_tf = model_tf.model_layers[i](out_tf)
+    out_tf_ = tf.transpose(out_tf, perm=[0, 3, 2, 1])[:, :, 0, :]
+    diff = compare_torch_tf(out_torch, out_tf_)
+    assert diff < 1e-5, diff
+
+dummy_input_torch = torch.ones((1, 80, 10))
+dummy_input_tf = tf.convert_to_tensor(dummy_input_torch.numpy())
+output_torch = model.inference(dummy_input_torch)
+output_tf = model_tf(dummy_input_tf, training=False)
+assert compare_torch_tf(output_torch, output_tf) < 1e-5, compare_torch_tf(
+    output_torch, output_tf)
+# save tf model
+save_checkpoint(model_tf, checkpoint['step'], checkpoint['epoch'],
+                args.output_path)
+print(' > Model conversion is successfully completed :).')
diff --git a/vocoder/tf/layers/melgan.py b/vocoder/tf/layers/melgan.py
new file mode 100644
index 00000000..8625bbab
--- /dev/null
+++ b/vocoder/tf/layers/melgan.py
@@ -0,0 +1,52 @@
+import tensorflow as tf
+
+
+class ReflectionPad1d(tf.keras.layers.Layer):
+    def __init__(self, padding):
+        super(ReflectionPad1d, self).__init__()
+        self.padding = padding
+
+    def call(self, x):
+        print(x.shape)
+        return tf.pad(x, [[0, 0], [self.padding, self.padding], [0, 0], [0, 0]], "REFLECT")
+
+
+class ResidualStack(tf.keras.layers.Layer):
+    def __init__(self, channels, num_res_blocks, kernel_size, name):
+        super(ResidualStack, self).__init__(name=name)
+
+        assert (kernel_size - 1) % 2 == 0, " [!] kernel_size has to be odd."
+        base_padding = (kernel_size - 1) // 2
+
+        self.blocks = []
+        num_layers = 2
+        for idx in range(num_res_blocks):
+            layer_kernel_size = kernel_size
+            layer_dilation = layer_kernel_size**idx
+            layer_padding = base_padding * layer_dilation
+            block = [
+                tf.keras.layers.LeakyReLU(0.2),
+                ReflectionPad1d(layer_padding),
+                tf.keras.layers.Conv2D(filters=channels,
+                            kernel_size=(kernel_size, 1),
+                            dilation_rate=(layer_dilation, 1),
+                            use_bias=True,
+                            padding='valid',
+                            name=f'blocks.{idx}.{num_layers}'),
+                tf.keras.layers.LeakyReLU(0.2),
+                tf.keras.layers.Conv2D(filters=channels, kernel_size=(1, 1), use_bias=True, name=f'blocks.{idx}.{num_layers + 2}')
+            ]
+            self.blocks.append(block)
+        self.shortcuts = [
+            tf.keras.layers.Conv2D(channels, kernel_size=1, use_bias=True, name=f'shortcuts.{i}')
+                                 for i in range(num_res_blocks)
+        ]
+
+    def call(self, x):
+        # breakpoint()
+        for block, shortcut in zip(self.blocks, self.shortcuts):
+            res = shortcut(x)
+            for layer in block:
+                 x = layer(x)
+            x += res
+        return x
\ No newline at end of file
diff --git a/vocoder/tf/layers/pqmf.py b/vocoder/tf/layers/pqmf.py
new file mode 100644
index 00000000..474b6e7f
--- /dev/null
+++ b/vocoder/tf/layers/pqmf.py
@@ -0,0 +1,66 @@
+import numpy as np
+import tensorflow as tf
+
+from scipy import signal as sig
+
+
+class PQMF(tf.keras.layers.Layer):
+    def __init__(self, N=4, taps=62, cutoff=0.15, beta=9.0):
+        super(PQMF, self).__init__()
+        # define filter coefficient
+        self.N = N
+        self.taps = taps
+        self.cutoff = cutoff
+        self.beta = beta
+
+        QMF = sig.firwin(taps + 1, cutoff, window=('kaiser', beta))
+        H = np.zeros((N, len(QMF)))
+        G = np.zeros((N, len(QMF)))
+        for k in range(N):
+            constant_factor = (2 * k + 1) * (np.pi /
+                                             (2 * N)) * (np.arange(taps + 1) -
+                                                         ((taps - 1) / 2))
+            phase = (-1)**k * np.pi / 4
+            H[k] = 2 * QMF * np.cos(constant_factor + phase)
+
+            G[k] = 2 * QMF * np.cos(constant_factor - phase)
+
+        # [N, 1, taps + 1] == [filter_width, in_channels, out_channels]
+        self.H = np.transpose(H[:, None, :], (2, 1, 0)).astype('float32')
+        self.G = np.transpose(G[None, :, :], (2, 1, 0)).astype('float32')
+
+        # filter for downsampling & upsampling
+        updown_filter = np.zeros((N, N, N), dtype=np.float32)
+        for k in range(N):
+            updown_filter[0, k, k] = 1.0
+        self.updown_filter = updown_filter.astype(np.float32)
+
+    def analysis(self, x):
+        """
+        x : B x 1 x T
+        """
+        x = tf.transpose(x, perm=[0, 2, 1])
+        x = tf.pad(x, [[0, 0], [self.taps // 2, self.taps // 2], [0, 0]])
+        x = tf.nn.conv1d(x, self.H, stride=1, padding='VALID')
+        x = tf.nn.conv1d(x,
+                         self.updown_filter,
+                         stride=self.N,
+                         padding='VALID')
+        x = tf.transpose(x, perm=[0, 2, 1])
+        return x
+
+    def synthesis(self, x):
+        """
+        x : B x 1 x T
+        """
+        x = tf.transpose(x, perm=[0, 2, 1])
+        x = tf.nn.conv1d_transpose(
+            x,
+            self.updown_filter * self.N,
+            strides=self.N,
+            output_shape=(tf.shape(x)[0], tf.shape(x)[1] * self.N,
+                          self.N))
+        x = tf.pad(x, [[0, 0], [self.taps // 2, self.taps // 2], [0, 0]])
+        x = tf.nn.conv1d(x, self.G, stride=1, padding="VALID")
+        x = tf.transpose(x, perm=[0, 2, 1])
+        return x
\ No newline at end of file
diff --git a/vocoder/tf/models/melgan_generator.py b/vocoder/tf/models/melgan_generator.py
new file mode 100644
index 00000000..db0a9675
--- /dev/null
+++ b/vocoder/tf/models/melgan_generator.py
@@ -0,0 +1,106 @@
+import logging
+import os
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'  # FATAL
+logging.getLogger('tensorflow').setLevel(logging.FATAL)
+
+import tensorflow as tf
+from TTS.vocoder.tf.layers.melgan import ResidualStack, ReflectionPad1d
+
+
+class MelganGenerator(tf.keras.models.Model):
+    """ Melgan Generator TF implementation dedicated for inference with no
+    weight norm """
+    def __init__(self,
+                 in_channels=80,
+                 out_channels=1,
+                 proj_kernel=7,
+                 base_channels=512,
+                 upsample_factors=(8, 8, 2, 2),
+                 res_kernel=3,
+                 num_res_blocks=3):
+        super(MelganGenerator, self).__init__()
+
+        # assert model parameters
+        assert (proj_kernel -
+                1) % 2 == 0, " [!] proj_kernel should be an odd number."
+
+        # setup additional model parameters
+        base_padding = (proj_kernel - 1) // 2
+        act_slope = 0.2
+        self.inference_padding = 2
+
+        # initial layer
+        self.initial_layer = [
+            ReflectionPad1d(base_padding),
+            tf.keras.layers.Conv2D(filters=base_channels,
+                        kernel_size=(proj_kernel, 1),
+                        strides=1,
+                        padding='valid',
+                        use_bias=True,
+                        name="1")
+        ]
+        num_layers = 3  # count number of layers for layer naming
+
+        # upsampling layers and residual stacks
+        self.upsample_layers = []
+        for idx, upsample_factor in enumerate(upsample_factors):
+            layer_out_channels = base_channels // (2**(idx + 1))
+            layer_filter_size = upsample_factor * 2
+            layer_stride = upsample_factor
+            layer_output_padding = upsample_factor % 2
+            self.upsample_layers += [
+                tf.keras.layers.LeakyReLU(act_slope),
+                tf.keras.layers.Conv2DTranspose(
+                                    filters=layer_out_channels,
+                                    kernel_size=(layer_filter_size, 1),
+                                    strides=(layer_stride, 1),
+                                    padding='same',
+                                    # output_padding=layer_output_padding,
+                                    use_bias=True,
+                                    name=f'{num_layers}'),
+                ResidualStack(
+                    channels=layer_out_channels,
+                    num_res_blocks=num_res_blocks,
+                    kernel_size=res_kernel,
+                    name=f'layers.{num_layers + 1}'
+                )
+            ]
+            num_layers += num_res_blocks - 1
+
+        self.upsample_layers += [tf.keras.layers.LeakyReLU(act_slope)]
+
+        # final layer
+        self.final_layers = [
+            ReflectionPad1d(base_padding),
+            tf.keras.layers.Conv2D(filters=out_channels,
+                            kernel_size=(proj_kernel, 1),
+                            use_bias=True,
+                            name=f'layers.{num_layers + 1}'),
+            tf.keras.layers.Activation("tanh")
+        ]
+
+        # self.initial_layer = tf.keras.models.Sequential(self.initial_layer)
+        # self.upsample_layers = tf.keras.models.Sequential(self.upsample_layers)
+        # self.final_layers = tf.keras.models.Sequential(self.final_layers)
+        # self.model_layers = tf.keras.models.Sequential(self.initial_layer + self.upsample_layers + self.final_layers, name="layers")
+        self.model_layers = self.initial_layer + self.upsample_layers + self.final_layers
+
+    def call(self, c, training=False):
+        """
+        c : B x C x T
+        """
+        if training:
+            raise NotImplementedError()
+        return self.inference(c)
+
+    def inference(self, c):
+        c = tf.transpose(c, perm=[0, 2, 1])
+        c = tf.expand_dims(c, 2)
+        c = tf.pad(c, [[0, 0], [self.inference_padding, self.inference_padding], [0, 0], [0, 0]], "REFLECT")
+        o = c
+        for layer in self.model_layers:
+            o = layer(o)
+        # o = self.model_layers(c)
+        o = tf.transpose(o, perm=[0, 3, 2, 1])
+        return o[:, :, 0, :]
\ No newline at end of file
diff --git a/vocoder/tf/models/multiband_melgan_generator.py b/vocoder/tf/models/multiband_melgan_generator.py
new file mode 100644
index 00000000..e8599760
--- /dev/null
+++ b/vocoder/tf/models/multiband_melgan_generator.py
@@ -0,0 +1,46 @@
+import tensorflow as tf
+
+from TTS.vocoder.tf.models.melgan_generator import MelganGenerator
+from TTS.vocoder.tf.layers.pqmf import PQMF
+
+
+class MultibandMelganGenerator(MelganGenerator):
+    def __init__(self,
+                 in_channels=80,
+                 out_channels=4,
+                 proj_kernel=7,
+                 base_channels=384,
+                 upsample_factors=(2, 8, 2, 2),
+                 res_kernel=3,
+                 num_res_blocks=3):
+        super(MultibandMelganGenerator,
+              self).__init__(in_channels=in_channels,
+                             out_channels=out_channels,
+                             proj_kernel=proj_kernel,
+                             base_channels=base_channels,
+                             upsample_factors=upsample_factors,
+                             res_kernel=res_kernel,
+                             num_res_blocks=num_res_blocks)
+        self.pqmf_layer = PQMF(N=4, taps=62, cutoff=0.15, beta=9.0)
+
+    def pqmf_analysis(self, x):
+        return self.pqmf_layer.analysis(x)
+
+    def pqmf_synthesis(self, x):
+        return self.pqmf_layer.synthesis(x)
+
+    # def call(self, c, training=False):
+    #     if training:
+    #         raise NotImplementedError()
+    #     return self.inference(c)
+
+    def inference(self, c):
+        c = tf.transpose(c, perm=[0, 2, 1])
+        c = tf.expand_dims(c, 2)
+        c = tf.pad(c, [[0, 0], [self.inference_padding, self.inference_padding], [0, 0], [0, 0]], "REFLECT")
+        o = c
+        for layer in self.model_layers:
+            o = layer(o)
+        o = tf.transpose(o, perm=[0, 3, 2, 1])
+        o = self.pqmf_layer.synthesis(o[:, :, 0, :])
+        return o
diff --git a/vocoder/tf/utils/__init__.py b/vocoder/tf/utils/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/vocoder/tf/utils/convert_torch_to_tf_utils.py b/vocoder/tf/utils/convert_torch_to_tf_utils.py
new file mode 100644
index 00000000..799235e3
--- /dev/null
+++ b/vocoder/tf/utils/convert_torch_to_tf_utils.py
@@ -0,0 +1,48 @@
+import numpy as np
+import tensorflow as tf
+
+
+def compare_torch_tf(torch_tensor, tf_tensor):
+    """ Compute the average absolute difference b/w torch and tf tensors """
+    return abs(torch_tensor.detach().numpy() - tf_tensor.numpy()).mean()
+
+
+def convert_tf_name(tf_name):
+    """ Convert certain patterns in TF layer names to Torch patterns """
+    tf_name_tmp = tf_name
+    tf_name_tmp = tf_name_tmp.replace(':0', '')
+    tf_name_tmp = tf_name_tmp.replace('/forward_lstm/lstm_cell_1/recurrent_kernel', '/weight_hh_l0')
+    tf_name_tmp = tf_name_tmp.replace('/forward_lstm/lstm_cell_2/kernel', '/weight_ih_l1')
+    tf_name_tmp = tf_name_tmp.replace('/recurrent_kernel', '/weight_hh')
+    tf_name_tmp = tf_name_tmp.replace('/kernel', '/weight')
+    tf_name_tmp = tf_name_tmp.replace('/gamma', '/weight')
+    tf_name_tmp = tf_name_tmp.replace('/beta', '/bias')
+    tf_name_tmp = tf_name_tmp.replace('/', '.')
+    return tf_name_tmp
+
+
+def transfer_weights_torch_to_tf(tf_vars, var_map_dict, state_dict):
+    """ Transfer weigths from torch state_dict to TF variables """
+    print(" > Passing weights from Torch to TF ...")
+    for tf_var in tf_vars:
+        torch_var_name = var_map_dict[tf_var.name]
+        print(f' | > {tf_var.name} <-- {torch_var_name}')
+        # if tuple, it is a bias variable
+        if 'kernel' in tf_var.name:
+            torch_weight = state_dict[torch_var_name]
+            try:
+                numpy_weight = torch_weight.permute([2, 1, 0]).numpy()[:, None, :, :]
+            except:
+                breakpoint()
+        if 'bias' in tf_var.name:
+            torch_weight = state_dict[torch_var_name]
+            numpy_weight = torch_weight
+        assert np.all(tf_var.shape == numpy_weight.shape), f" [!] weight shapes does not match: {tf_var.name} vs {torch_var_name} --> {tf_var.shape} vs {numpy_weight.shape}"
+        tf.keras.backend.set_value(tf_var, numpy_weight)
+    return tf_vars
+
+
+def load_tf_vars(model_tf, tf_vars):
+    for tf_var in tf_vars:
+        model_tf.get_layer(tf_var.name).set_weights(tf_var)
+    return model_tf
diff --git a/vocoder/tf/utils/generic_utils.py b/vocoder/tf/utils/generic_utils.py
new file mode 100644
index 00000000..b17db596
--- /dev/null
+++ b/vocoder/tf/utils/generic_utils.py
@@ -0,0 +1,37 @@
+import re
+import importlib
+import numpy as np
+from matplotlib import pyplot as plt
+
+
+def to_camel(text):
+    text = text.capitalize()
+    return re.sub(r'(?!^)_([a-zA-Z])', lambda m: m.group(1).upper(), text)
+
+
+def setup_generator(c):
+    print(" > Generator Model: {}".format(c.generator_model))
+    MyModel = importlib.import_module('TTS.vocoder.tf.models.' +
+                                      c.generator_model.lower())
+    MyModel = getattr(MyModel, to_camel(c.generator_model))
+    if c.generator_model in 'melgan_generator':
+        model = MyModel(
+            in_channels=c.audio['num_mels'],
+            out_channels=1,
+            proj_kernel=7,
+            base_channels=512,
+            upsample_factors=c.generator_model_params['upsample_factors'],
+            res_kernel=3,
+            num_res_blocks=c.generator_model_params['num_res_blocks'])
+    if c.generator_model in 'melgan_fb_generator':
+        pass
+    if c.generator_model in 'multiband_melgan_generator':
+        model = MyModel(
+            in_channels=c.audio['num_mels'],
+            out_channels=4,
+            proj_kernel=7,
+            base_channels=384,
+            upsample_factors=c.generator_model_params['upsample_factors'],
+            res_kernel=3,
+            num_res_blocks=c.generator_model_params['num_res_blocks'])
+    return model
\ No newline at end of file
diff --git a/vocoder/tf/utils/io.py b/vocoder/tf/utils/io.py
new file mode 100644
index 00000000..d95d972c
--- /dev/null
+++ b/vocoder/tf/utils/io.py
@@ -0,0 +1,27 @@
+import datetime
+import pickle
+import tensorflow as tf
+
+
+def save_checkpoint(model, current_step, epoch, output_path, **kwargs):
+    """ Save TF Vocoder model """
+    state = {
+        'model': model.weights,
+        'step': current_step,
+        'epoch': epoch,
+        'date': datetime.date.today().strftime("%B %d, %Y"),
+    }
+    state.update(kwargs)
+    pickle.dump(state, open(output_path, 'wb'))
+
+
+def load_checkpoint(model, checkpoint_path):
+    """ Load TF Vocoder model """
+    checkpoint = pickle.load(open(checkpoint_path, 'rb'))
+    chkp_var_dict = {var.name: var.numpy() for var in checkpoint['model']}
+    tf_vars = model.weights
+    for tf_var in tf_vars:
+        layer_name = tf_var.name
+        chkp_var_value = chkp_var_dict[layer_name]
+        tf.keras.backend.set_value(tf_var, chkp_var_value)
+    return model
\ No newline at end of file

From ce076330bf817d44bb464e66af9b020a083591f7 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 19 Jun 2020 14:46:13 +0200
Subject: [PATCH 171/185] linter fixes

---
 vocoder/tests/test_melgan_discriminator.py    |  2 +-
 vocoder/tf/convert_melgan_torch_to_tf.py      |  7 +--
 vocoder/tf/layers/melgan.py                   | 24 +++++----
 vocoder/tf/layers/pqmf.py                     |  6 +--
 vocoder/tf/models/melgan_generator.py         | 49 +++++++++----------
 .../tf/models/multiband_melgan_generator.py   |  2 +-
 vocoder/tf/utils/convert_torch_to_tf_utils.py |  5 +-
 vocoder/tf/utils/generic_utils.py             |  2 -
 8 files changed, 46 insertions(+), 51 deletions(-)

diff --git a/vocoder/tests/test_melgan_discriminator.py b/vocoder/tests/test_melgan_discriminator.py
index 83acec8f..a4564b56 100644
--- a/vocoder/tests/test_melgan_discriminator.py
+++ b/vocoder/tests/test_melgan_discriminator.py
@@ -20,7 +20,7 @@ def test_melgan_multi_scale_discriminator():
     scores, feats = model(dummy_input)
     assert len(scores) == 3
     assert len(scores) == len(feats)
-    assert np.all(scores[0].shape == (4, 1, 16))
+    assert np.all(scores[0].shape == (4, 1, 64))
     assert np.all(feats[0][0].shape == (4, 16, 4096))
     assert np.all(feats[0][1].shape == (4, 64, 1024))
     assert np.all(feats[0][2].shape == (4, 256, 256))
diff --git a/vocoder/tf/convert_melgan_torch_to_tf.py b/vocoder/tf/convert_melgan_torch_to_tf.py
index b767f268..1b3d687d 100644
--- a/vocoder/tf/convert_melgan_torch_to_tf.py
+++ b/vocoder/tf/convert_melgan_torch_to_tf.py
@@ -1,7 +1,5 @@
 import argparse
 import os
-import sys
-from pprint import pprint
 
 import numpy as np
 import tensorflow as tf
@@ -9,8 +7,6 @@ import torch
 from fuzzywuzzy import fuzz
 
 from TTS.utils.io import load_config
-from TTS.vocoder.tf.models.multiband_melgan_generator import \
-    MultibandMelganGenerator
 from TTS.vocoder.tf.utils.convert_torch_to_tf_utils import (
     compare_torch_tf, convert_tf_name, transfer_weights_torch_to_tf)
 from TTS.vocoder.tf.utils.generic_utils import \
@@ -55,7 +51,7 @@ model_tf = setup_tf_generator(c)
 common_sufix = '/.ATTRIBUTES/VARIABLE_VALUE'
 # get tf_model graph by passing an input
 # B x D x T
-dummy_input = tf.random.uniform((7, 80, 64))
+dummy_input = tf.random.uniform((7, 80, 64), dtype=tf.float32)
 mel_pred = model_tf(dummy_input, training=False)
 
 # get tf variables
@@ -64,6 +60,7 @@ tf_vars = model_tf.weights
 # match variable names with fuzzy logic
 torch_var_names = list(state_dict.keys())
 tf_var_names = [we.name for we in model_tf.weights]
+var_map = []
 for tf_name in tf_var_names:
     # skip re-mapped layer names
     if tf_name in [name[0] for name in var_map]:
diff --git a/vocoder/tf/layers/melgan.py b/vocoder/tf/layers/melgan.py
index 8625bbab..4f195d3f 100644
--- a/vocoder/tf/layers/melgan.py
+++ b/vocoder/tf/layers/melgan.py
@@ -28,18 +28,24 @@ class ResidualStack(tf.keras.layers.Layer):
                 tf.keras.layers.LeakyReLU(0.2),
                 ReflectionPad1d(layer_padding),
                 tf.keras.layers.Conv2D(filters=channels,
-                            kernel_size=(kernel_size, 1),
-                            dilation_rate=(layer_dilation, 1),
-                            use_bias=True,
-                            padding='valid',
-                            name=f'blocks.{idx}.{num_layers}'),
+                                       kernel_size=(kernel_size, 1),
+                                       dilation_rate=(layer_dilation, 1),
+                                       use_bias=True,
+                                       padding='valid',
+                                       name=f'blocks.{idx}.{num_layers}'),
                 tf.keras.layers.LeakyReLU(0.2),
-                tf.keras.layers.Conv2D(filters=channels, kernel_size=(1, 1), use_bias=True, name=f'blocks.{idx}.{num_layers + 2}')
+                tf.keras.layers.Conv2D(filters=channels,
+                                       kernel_size=(1, 1),
+                                       use_bias=True,
+                                       name=f'blocks.{idx}.{num_layers + 2}')
             ]
             self.blocks.append(block)
         self.shortcuts = [
-            tf.keras.layers.Conv2D(channels, kernel_size=1, use_bias=True, name=f'shortcuts.{i}')
-                                 for i in range(num_res_blocks)
+            tf.keras.layers.Conv2D(channels,
+                                   kernel_size=1,
+                                   use_bias=True,
+                                   name=f'shortcuts.{i}')
+            for i in range(num_res_blocks)
         ]
 
     def call(self, x):
@@ -47,6 +53,6 @@ class ResidualStack(tf.keras.layers.Layer):
         for block, shortcut in zip(self.blocks, self.shortcuts):
             res = shortcut(x)
             for layer in block:
-                 x = layer(x)
+                x = layer(x)
             x += res
         return x
\ No newline at end of file
diff --git a/vocoder/tf/layers/pqmf.py b/vocoder/tf/layers/pqmf.py
index 474b6e7f..6c47dfc4 100644
--- a/vocoder/tf/layers/pqmf.py
+++ b/vocoder/tf/layers/pqmf.py
@@ -40,7 +40,7 @@ class PQMF(tf.keras.layers.Layer):
         x : B x 1 x T
         """
         x = tf.transpose(x, perm=[0, 2, 1])
-        x = tf.pad(x, [[0, 0], [self.taps // 2, self.taps // 2], [0, 0]])
+        x = tf.pad(x, [[0, 0], [self.taps // 2, self.taps // 2], [0, 0]], constant_values=0.0)
         x = tf.nn.conv1d(x, self.H, stride=1, padding='VALID')
         x = tf.nn.conv1d(x,
                          self.updown_filter,
@@ -60,7 +60,7 @@ class PQMF(tf.keras.layers.Layer):
             strides=self.N,
             output_shape=(tf.shape(x)[0], tf.shape(x)[1] * self.N,
                           self.N))
-        x = tf.pad(x, [[0, 0], [self.taps // 2, self.taps // 2], [0, 0]])
+        x = tf.pad(x, [[0, 0], [self.taps // 2, self.taps // 2], [0, 0]], constant_values=0.0)
         x = tf.nn.conv1d(x, self.G, stride=1, padding="VALID")
         x = tf.transpose(x, perm=[0, 2, 1])
-        return x
\ No newline at end of file
+        return x
diff --git a/vocoder/tf/models/melgan_generator.py b/vocoder/tf/models/melgan_generator.py
index db0a9675..8a2a49be 100644
--- a/vocoder/tf/models/melgan_generator.py
+++ b/vocoder/tf/models/melgan_generator.py
@@ -8,7 +8,7 @@ import tensorflow as tf
 from TTS.vocoder.tf.layers.melgan import ResidualStack, ReflectionPad1d
 
 
-class MelganGenerator(tf.keras.models.Model):
+class MelganGenerator(tf.keras.models.Model):  # pylint: disable=too-many-ancestors
     """ Melgan Generator TF implementation dedicated for inference with no
     weight norm """
     def __init__(self,
@@ -21,6 +21,8 @@ class MelganGenerator(tf.keras.models.Model):
                  num_res_blocks=3):
         super(MelganGenerator, self).__init__()
 
+        self.in_channels = in_channels
+
         # assert model parameters
         assert (proj_kernel -
                 1) % 2 == 0, " [!] proj_kernel should be an odd number."
@@ -34,11 +36,11 @@ class MelganGenerator(tf.keras.models.Model):
         self.initial_layer = [
             ReflectionPad1d(base_padding),
             tf.keras.layers.Conv2D(filters=base_channels,
-                        kernel_size=(proj_kernel, 1),
-                        strides=1,
-                        padding='valid',
-                        use_bias=True,
-                        name="1")
+                                   kernel_size=(proj_kernel, 1),
+                                   strides=1,
+                                   padding='valid',
+                                   use_bias=True,
+                                   name="1")
         ]
         num_layers = 3  # count number of layers for layer naming
 
@@ -48,23 +50,21 @@ class MelganGenerator(tf.keras.models.Model):
             layer_out_channels = base_channels // (2**(idx + 1))
             layer_filter_size = upsample_factor * 2
             layer_stride = upsample_factor
-            layer_output_padding = upsample_factor % 2
+            # layer_output_padding = upsample_factor % 2
             self.upsample_layers += [
                 tf.keras.layers.LeakyReLU(act_slope),
                 tf.keras.layers.Conv2DTranspose(
-                                    filters=layer_out_channels,
-                                    kernel_size=(layer_filter_size, 1),
-                                    strides=(layer_stride, 1),
-                                    padding='same',
-                                    # output_padding=layer_output_padding,
-                                    use_bias=True,
-                                    name=f'{num_layers}'),
-                ResidualStack(
-                    channels=layer_out_channels,
-                    num_res_blocks=num_res_blocks,
-                    kernel_size=res_kernel,
-                    name=f'layers.{num_layers + 1}'
-                )
+                    filters=layer_out_channels,
+                    kernel_size=(layer_filter_size, 1),
+                    strides=(layer_stride, 1),
+                    padding='same',
+                    # output_padding=layer_output_padding,
+                    use_bias=True,
+                    name=f'{num_layers}'),
+                ResidualStack(channels=layer_out_channels,
+                              num_res_blocks=num_res_blocks,
+                              kernel_size=res_kernel,
+                              name=f'layers.{num_layers + 1}')
             ]
             num_layers += num_res_blocks - 1
 
@@ -74,15 +74,12 @@ class MelganGenerator(tf.keras.models.Model):
         self.final_layers = [
             ReflectionPad1d(base_padding),
             tf.keras.layers.Conv2D(filters=out_channels,
-                            kernel_size=(proj_kernel, 1),
-                            use_bias=True,
-                            name=f'layers.{num_layers + 1}'),
+                                   kernel_size=(proj_kernel, 1),
+                                   use_bias=True,
+                                   name=f'layers.{num_layers + 1}'),
             tf.keras.layers.Activation("tanh")
         ]
 
-        # self.initial_layer = tf.keras.models.Sequential(self.initial_layer)
-        # self.upsample_layers = tf.keras.models.Sequential(self.upsample_layers)
-        # self.final_layers = tf.keras.models.Sequential(self.final_layers)
         # self.model_layers = tf.keras.models.Sequential(self.initial_layer + self.upsample_layers + self.final_layers, name="layers")
         self.model_layers = self.initial_layer + self.upsample_layers + self.final_layers
 
diff --git a/vocoder/tf/models/multiband_melgan_generator.py b/vocoder/tf/models/multiband_melgan_generator.py
index e8599760..d3e1c754 100644
--- a/vocoder/tf/models/multiband_melgan_generator.py
+++ b/vocoder/tf/models/multiband_melgan_generator.py
@@ -4,7 +4,7 @@ from TTS.vocoder.tf.models.melgan_generator import MelganGenerator
 from TTS.vocoder.tf.layers.pqmf import PQMF
 
 
-class MultibandMelganGenerator(MelganGenerator):
+class MultibandMelganGenerator(MelganGenerator):  # pylint: disable=too-many-ancestors
     def __init__(self,
                  in_channels=80,
                  out_channels=4,
diff --git a/vocoder/tf/utils/convert_torch_to_tf_utils.py b/vocoder/tf/utils/convert_torch_to_tf_utils.py
index 799235e3..25139cc3 100644
--- a/vocoder/tf/utils/convert_torch_to_tf_utils.py
+++ b/vocoder/tf/utils/convert_torch_to_tf_utils.py
@@ -30,10 +30,7 @@ def transfer_weights_torch_to_tf(tf_vars, var_map_dict, state_dict):
         # if tuple, it is a bias variable
         if 'kernel' in tf_var.name:
             torch_weight = state_dict[torch_var_name]
-            try:
-                numpy_weight = torch_weight.permute([2, 1, 0]).numpy()[:, None, :, :]
-            except:
-                breakpoint()
+            numpy_weight = torch_weight.permute([2, 1, 0]).numpy()[:, None, :, :]
         if 'bias' in tf_var.name:
             torch_weight = state_dict[torch_var_name]
             numpy_weight = torch_weight
diff --git a/vocoder/tf/utils/generic_utils.py b/vocoder/tf/utils/generic_utils.py
index b17db596..580a3738 100644
--- a/vocoder/tf/utils/generic_utils.py
+++ b/vocoder/tf/utils/generic_utils.py
@@ -1,7 +1,5 @@
 import re
 import importlib
-import numpy as np
-from matplotlib import pyplot as plt
 
 
 def to_camel(text):

From aad51bc4f9f3f9970a8773c1fc8416c418f33574 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 19 Jun 2020 14:52:43 +0200
Subject: [PATCH 172/185] readme update

---
 README.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index a211764c..57a979d7 100644
--- a/README.md
+++ b/README.md
@@ -22,11 +22,11 @@ If you are new, you can also find [here](http://www.erogol.com/text-speech-deep-
     - Speaker Encoder to compute speaker embeddings efficiently.
     - Vocoder models (MelGAN, Multiband-MelGAN, GAN-TTS)
 - Support for multi-speaker TTS training.
+- Support for Multi-GPUs training.
 - Ability to convert Torch models to Tensorflow 2.0 for inference.
-- Released trained models.
-- Efficient training codes for PyTorch. (soon for Tensorflow 2.0)
-- Codes to convert Torch models to Tensorflow 2.0.
-- Detailed training anlaysis on console and Tensorboard.
+- Released pre-trained models.
+- Fast and efficient model training.
+- Detailed training logs on console and Tensorboard.
 - Tools to curate Text2Speech datasets under```dataset_analysis```.
 - Demo server for model testing.
 - Notebooks for extensive model benchmarking.

From de7ed4176c190d54fc3d68ec4624e8664fdd55f3 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 19 Jun 2020 15:56:02 +0200
Subject: [PATCH 173/185] README directory structure

---
 README.md | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/README.md b/README.md
index 57a979d7..df143174 100644
--- a/README.md
+++ b/README.md
@@ -50,6 +50,22 @@ Or you can use ```requirements.txt``` to install the requirements only.
 
 ```pip install -r requirements.txt```
 
+### Directory Structure
+```
+|- TTS/
+|   |- train.py         (train your TTS model.)
+|   |- distribute.py    (train your TTS model using Multiple GPUs)
+|   |- config.json      (TTS model configuration file)
+|   |- tf               (Tensorflow 2 utilities and model implementations)
+|   |- layers/          (model layer definitions)
+|   |- models/          (model definitions)
+|   |- notebooks/       (Jupyter Notebooks for model evaluation and parameter selection)
+|   |- data_analysis/   (TTS Dataset analysis tools and notebooks.)
+|   |- utils/           (TTS utilities -io, visualization, data processing etc.-)
+|   |- speaker_encoder/ (Speaker Encoder implementation with the same folder structure.)
+|   |- vocoder/         (Vocoder implementations with the same folder structure.)
+```
+
 ### Docker
 A barebone `Dockerfile` exists at the root of the project, which should let you quickly setup the environment. By default, it will start the server and let you query it. Make sure to use `nvidia-docker` to use your GPUs. Make sure you follow the instructions in the [`server README`](server/README.md) before you build your image so that the server can find the model within the image.
 

From 4b99eacb38d9983271610947267063cfd39606a6 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 19 Jun 2020 16:51:48 +0200
Subject: [PATCH 174/185] README update add models and method section

---
 speaker_encoder/README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/speaker_encoder/README.md b/speaker_encoder/README.md
index 38b4bb1b..b6f541f8 100644
--- a/speaker_encoder/README.md
+++ b/speaker_encoder/README.md
@@ -1,16 +1,16 @@
-### Speaker embedding (Experimental)
+### Speaker Encoder
 
 This is an implementation of https://arxiv.org/abs/1710.10467. This model can be used for voice and speaker embedding.
 
 With the code here you can generate d-vectors for both multi-speaker and single-speaker TTS datasets, then visualise and explore them along with the associated audio files in an interactive chart.
 
-Below is an example showing embedding results of various speakers. You can generate the same plot with the provided notebook as demonstrated in [this video](https://youtu.be/KW3oO7JVa7Q). 
+Below is an example showing embedding results of various speakers. You can generate the same plot with the provided notebook as demonstrated in [this video](https://youtu.be/KW3oO7JVa7Q).
 
 ![](umap.png)
 
 Download a pretrained model from [Released Models](https://github.com/mozilla/TTS/wiki/Released-Models) page.
 
-To run the code, you need to follow the same flow as in TTS. 
+To run the code, you need to follow the same flow as in TTS.
 
 - Define 'config.json' for your needs. Note that, audio parameters should match your TTS model.
 - Example training call ```python speaker_encoder/train.py --config_path speaker_encoder/config.json --data_path ~/Data/Libri-TTS/train-clean-360```

From ec7aa4496eb83f1a9d6acef0f8f4e183dbaa4aa8 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 19 Jun 2020 16:53:37 +0200
Subject: [PATCH 175/185] README updates added models and method ssection

---
 README.md | 27 +++++++++++++++++++++++----
 1 file changed, 23 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index df143174..bf25c558 100644
--- a/README.md
+++ b/README.md
@@ -3,9 +3,7 @@
 
 <img src="https://travis-ci.org/mozilla/TTS.svg?branch=dev"/>
 
-This project is a part of [Mozilla Common Voice](https://voice.mozilla.org/en). TTS aims a deep learning based Text2Speech engine, low in cost and high in quality. To begin with, you can hear a sample generated voice from [here](https://soundcloud.com/user-565970875/commonvoice-loc-sens-attn).
-
-TTS includes two different model implementations which are based on [Tacotron](https://arxiv.org/abs/1703.10135) and [Tacotron2](https://arxiv.org/abs/1712.05884). Tacotron is smaller, efficient and easier to train but Tacotron2 provides better results, especially when it is combined with a Neural vocoder. Therefore, choose depending on your project requirements.
+This project is a part of [Mozilla Common Voice](https://voice.mozilla.org/en). TTS aims a deep learning based Text2Speech engine, low in cost and high in quality. To begin with, you can hear a sample synthesized voice from [here](https://soundcloud.com/user-565970875/commonvoice-loc-sens-attn).
 
 If you are new, you can also find [here](http://www.erogol.com/text-speech-deep-learning-architectures/) a brief post about TTS architectures and their comparisons.
 
@@ -16,6 +14,27 @@ If you are new, you can also find [here](http://www.erogol.com/text-speech-deep-
 
 [Details...](https://github.com/mozilla/TTS/wiki/Mean-Opinion-Score-Results)
 
+## Provided Models and Methods
+Text-to-Spectrogram:
+- Tacotron: [paper](https://arxiv.org/abs/1703.10135)
+- Tacotron2: [paper](https://arxiv.org/abs/1712.05884)
+
+Attention Methods:
+- Guided Attention [paper](https://arxiv.org/abs/1710.08969)
+- Forward Backward Decoding [paper](https://arxiv.org/abs/1907.09006)
+- Graves Attention [paper](https://arxiv.org/abs/1907.09006)
+- Double Decoder Consistency [blog](https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency/)
+
+Speaker Encoder:
+- GE2E: [paper](https://arxiv.org/abs/1710.10467)
+
+Vocoders:
+- MelGAN: [paper](https://arxiv.org/abs/1710.10467)
+- MultiBandMelGAN: [paper](https://arxiv.org/abs/2005.05106)
+- GAN-TTS discriminators: [paper](https://arxiv.org/abs/1909.11646)
+
+You can also help us implement more models. Some TTS related work can be found [here](https://github.com/erogol/TTS-papers).
+
 ## Features
 - High performance Deep Learning models for Text2Speech related tasks.
     - Text2Speech models (Tacotron, Tacotron2).
@@ -56,7 +75,7 @@ Or you can use ```requirements.txt``` to install the requirements only.
 |   |- train.py         (train your TTS model.)
 |   |- distribute.py    (train your TTS model using Multiple GPUs)
 |   |- config.json      (TTS model configuration file)
-|   |- tf               (Tensorflow 2 utilities and model implementations)
+|   |- tf/              (Tensorflow 2 utilities and model implementations)
 |   |- layers/          (model layer definitions)
 |   |- models/          (model definitions)
 |   |- notebooks/       (Jupyter Notebooks for model evaluation and parameter selection)

From d587094dc522aa420f309831346133b593ae8766 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 19 Jun 2020 16:55:44 +0200
Subject: [PATCH 176/185] formatting fix

---
 README.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index bf25c558..13d6bcfc 100644
--- a/README.md
+++ b/README.md
@@ -20,10 +20,10 @@ Text-to-Spectrogram:
 - Tacotron2: [paper](https://arxiv.org/abs/1712.05884)
 
 Attention Methods:
-- Guided Attention [paper](https://arxiv.org/abs/1710.08969)
-- Forward Backward Decoding [paper](https://arxiv.org/abs/1907.09006)
-- Graves Attention [paper](https://arxiv.org/abs/1907.09006)
-- Double Decoder Consistency [blog](https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency/)
+- Guided Attention: [paper](https://arxiv.org/abs/1710.08969)
+- Forward Backward Decoding: [paper](https://arxiv.org/abs/1907.09006)
+- Graves Attention: [paper](https://arxiv.org/abs/1907.09006)
+- Double Decoder Consistency: [blog](https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency/)
 
 Speaker Encoder:
 - GE2E: [paper](https://arxiv.org/abs/1710.10467)

From dbb4bd884a8745181dee3111d0ad1ab48cd82050 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Mon, 22 Jun 2020 14:52:23 +0200
Subject: [PATCH 177/185] print more info in compute_statistics.py

---
 compute_statistics.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/compute_statistics.py b/compute_statistics.py
index 9cd073cd..f718cde3 100755
--- a/compute_statistics.py
+++ b/compute_statistics.py
@@ -63,6 +63,11 @@ def main():
     stats['linear_mean'] = linear_mean
     stats['linear_std'] = linear_scale
 
+    print(f' > Avg mel spec mean: {mel_mean.mean()}')
+    print(f' > Avg mel spec scale: {mel_scale.mean()}')
+    print(f' > Avg linear spec mean: {linear_mean.mean()}')
+    print(f' > Avg lienar spec scale: {linear_scale.mean()}')
+
     # set default config values for mean-var scaling
     CONFIG.audio['stats_path'] = output_file_path
     CONFIG.audio['signal_norm'] = True
@@ -73,6 +78,7 @@ def main():
     del CONFIG.audio['clip_norm']
     stats['audio_config'] = CONFIG.audio
     np.save(output_file_path, stats, allow_pickle=True)
+    print(f' > scale_stats.npy is saved to {output_file_path}')
 
 
 if __name__ == "__main__":

From 18d4ed3dc42184295e269f9acb7567b94d312921 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Mon, 22 Jun 2020 14:52:45 +0200
Subject: [PATCH 178/185] replace n_fft with fft_size

---
 config.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/config.json b/config.json
index 53b12fe1..bdeecdea 100644
--- a/config.json
+++ b/config.json
@@ -6,7 +6,7 @@
     // AUDIO PARAMETERS
     "audio":{
         // stft parameters
-        "num_freq": 513,         // number of stft frequency levels. Size of the linear spectogram frame.
+        "fft_size": 1024,         // number of stft frequency levels. Size of the linear spectogram frame.
         "win_length": 1024,      // stft window length in ms.
         "hop_length": 256,       // stft window hop-lengh in ms.
         "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.

From 70c83671e68b0b73bf41605ed91b7134e1cba159 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Mon, 22 Jun 2020 14:54:23 +0200
Subject: [PATCH 179/185] init coarse decoder with argument list

---
 models/tacotron.py  | 16 ++++++++++------
 models/tacotron2.py |  5 ++++-
 2 files changed, 14 insertions(+), 7 deletions(-)

diff --git a/models/tacotron.py b/models/tacotron.py
index c526374a..ba42610c 100644
--- a/models/tacotron.py
+++ b/models/tacotron.py
@@ -46,11 +46,11 @@ class Tacotron(TacotronAbstract):
         self.embedding = nn.Embedding(num_chars, 256, padding_idx=0)
         self.embedding.weight.data.normal_(0, 0.3)
         self.encoder = Encoder(encoder_in_features)
-        self.decoder = Decoder(decoder_in_features, decoder_output_dim, r, memory_size, attn_type, attn_win,
-                               attn_norm, prenet_type, prenet_dropout,
-                               forward_attn, trans_agent, forward_attn_mask,
-                               location_attn, attn_K, separate_stopnet,
-                               proj_speaker_dim)
+        self.decoder = Decoder(decoder_in_features, decoder_output_dim, r,
+                               memory_size, attn_type, attn_win, attn_norm,
+                               prenet_type, prenet_dropout, forward_attn,
+                               trans_agent, forward_attn_mask, location_attn,
+                               attn_K, separate_stopnet, proj_speaker_dim)
         self.postnet = PostCBHG(decoder_output_dim)
         self.last_linear = nn.Linear(self.postnet.cbhg.gru_features * 2,
                                      postnet_output_dim)
@@ -74,7 +74,11 @@ class Tacotron(TacotronAbstract):
             self._init_backward_decoder()
         # setup DDC
         if self.double_decoder_consistency:
-            self._init_coarse_decoder()
+            self.coarse_decoder = Decoder(
+                decoder_in_features, decoder_output_dim, ddc_r, memory_size,
+                attn_type, attn_win, attn_norm, prenet_type, prenet_dropout,
+                forward_attn, trans_agent, forward_attn_mask, location_attn,
+                attn_K, separate_stopnet, proj_speaker_dim)
 
 
     def forward(self, characters, text_lengths, mel_specs, mel_lengths=None, speaker_ids=None):
diff --git a/models/tacotron2.py b/models/tacotron2.py
index 46b915b5..7a56212d 100644
--- a/models/tacotron2.py
+++ b/models/tacotron2.py
@@ -68,7 +68,10 @@ class Tacotron2(TacotronAbstract):
             self._init_backward_decoder()
         # setup DDC
         if self.double_decoder_consistency:
-            self._init_coarse_decoder()
+            self.coarse_decoder = Decoder(decoder_in_features, self.decoder_output_dim, ddc_r, attn_type, attn_win,
+                               attn_norm, prenet_type, prenet_dropout,
+                               forward_attn, trans_agent, forward_attn_mask,
+                               location_attn, attn_K, separate_stopnet, proj_speaker_dim)
 
     @staticmethod
     def shape_outputs(mel_outputs, mel_outputs_postnet, alignments):

From 2f3f43844e80306a5d83e42101cfa5e937ef4583 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Mon, 22 Jun 2020 14:55:11 +0200
Subject: [PATCH 180/185] spec_gain fft_size and stft_pad_mode parameters for
 audio_processor

---
 train.py               |  3 ---
 utils/audio.py         | 28 +++++++++++++++-------------
 utils/generic_utils.py |  5 +++--
 3 files changed, 18 insertions(+), 18 deletions(-)

diff --git a/train.py b/train.py
index 8581132a..bdafaeba 100644
--- a/train.py
+++ b/train.py
@@ -473,8 +473,6 @@ def main(args):  # pylint: disable=redefined-outer-name
 
     model = setup_model(num_chars, num_speakers, c)
 
-    print(" | > Num output units : {}".format(ap.num_freq), flush=True)
-
     params = set_weight_decay(model, c.wd)
     optimizer = RAdam(params, lr=c.lr, weight_decay=0)
     if c.stopnet and c.separate_stopnet:
@@ -542,7 +540,6 @@ def main(args):  # pylint: disable=redefined-outer-name
             if c.bidirectional_decoder:
                 model.decoder_backward.set_r(r)
             print("\n > Number of output frames:", model.decoder.r)
-
         train_avg_loss_dict, global_step = train(model, criterion, optimizer,
                                                  optimizer_st, scheduler, ap,
                                                  global_step, epoch)
diff --git a/utils/audio.py b/utils/audio.py
index f941f609..31c065ff 100644
--- a/utils/audio.py
+++ b/utils/audio.py
@@ -17,7 +17,7 @@ class AudioProcessor(object):
                  hop_length=None,
                  win_length=None,
                  ref_level_db=None,
-                 num_freq=None,
+                 fft_size=1024,
                  power=None,
                  preemphasis=0.0,
                  signal_norm=None,
@@ -25,6 +25,8 @@ class AudioProcessor(object):
                  max_norm=None,
                  mel_fmin=None,
                  mel_fmax=None,
+                 spec_gain=20,
+                 stft_pad_mode='reflect',
                  clip_norm=True,
                  griffin_lim_iters=None,
                  do_trim_silence=False,
@@ -41,7 +43,7 @@ class AudioProcessor(object):
         self.frame_shift_ms = frame_shift_ms
         self.frame_length_ms = frame_length_ms
         self.ref_level_db = ref_level_db
-        self.num_freq = num_freq
+        self.fft_size = fft_size
         self.power = power
         self.preemphasis = preemphasis
         self.griffin_lim_iters = griffin_lim_iters
@@ -49,6 +51,8 @@ class AudioProcessor(object):
         self.symmetric_norm = symmetric_norm
         self.mel_fmin = mel_fmin or 0
         self.mel_fmax = mel_fmax
+        self.spec_gain = float(spec_gain)
+        self.stft_pad_mode = 'reflect'
         self.max_norm = 1.0 if max_norm is None else float(max_norm)
         self.clip_norm = clip_norm
         self.do_trim_silence = do_trim_silence
@@ -58,12 +62,11 @@ class AudioProcessor(object):
         # setup stft parameters
         if hop_length is None:
             # compute stft parameters from given time values
-            self.n_fft, self.hop_length, self.win_length = self._stft_parameters()
+            self.hop_length, self.win_length = self._stft_parameters()
         else:
             # use stft parameters from config file
             self.hop_length = hop_length
             self.win_length = win_length
-            self.n_fft = (self.num_freq - 1) * 2
         assert min_level_db != 0.0, " [!] min_level_db is 0"
         members = vars(self)
         for key, value in members.items():
@@ -86,19 +89,18 @@ class AudioProcessor(object):
             assert self.mel_fmax <= self.sample_rate // 2
         return librosa.filters.mel(
             self.sample_rate,
-            self.n_fft,
+            self.fft_size,
             n_mels=self.num_mels,
             fmin=self.mel_fmin,
             fmax=self.mel_fmax)
 
     def _stft_parameters(self, ):
         """Compute necessary stft parameters with given time values"""
-        n_fft = (self.num_freq - 1) * 2
         factor = self.frame_length_ms / self.frame_shift_ms
         assert (factor).is_integer(), " [!] frame_shift_ms should divide frame_length_ms"
         hop_length = int(self.frame_shift_ms / 1000.0 * self.sample_rate)
         win_length = int(hop_length * factor)
-        return n_fft, hop_length, win_length
+        return hop_length, win_length
 
     ### normalization ###
     def _normalize(self, S):
@@ -110,7 +112,7 @@ class AudioProcessor(object):
             if hasattr(self, 'mel_scaler'):
                 if S.shape[0] == self.num_mels:
                     return self.mel_scaler.transform(S.T).T
-                elif S.shape[0] == self.n_fft / 2:
+                elif S.shape[0] == self.fft_size / 2:
                     return self.linear_scaler.transform(S.T).T
                 else:
                     raise RuntimeError(' [!] Mean-Var stats does not match the given feature dimensions.')
@@ -139,7 +141,7 @@ class AudioProcessor(object):
             if hasattr(self, 'mel_scaler'):
                 if S_denorm.shape[0] == self.num_mels:
                     return self.mel_scaler.inverse_transform(S_denorm.T).T
-                elif S_denorm.shape[0] == self.n_fft / 2:
+                elif S_denorm.shape[0] == self.fft_size / 2:
                     return self.linear_scaler.inverse_transform(S_denorm.T).T
                 else:
                     raise RuntimeError(' [!] Mean-Var stats does not match the given feature dimensions.')
@@ -184,11 +186,11 @@ class AudioProcessor(object):
     ### DB and AMP conversion ###
     # pylint: disable=no-self-use
     def _amp_to_db(self, x):
-        return 20 * np.log10(np.maximum(1e-5, x))
+        return self.spec_gain * np.log10(np.maximum(1e-5, x))
 
     # pylint: disable=no-self-use
     def _db_to_amp(self, x):
-        return np.power(10.0, x * 0.05)
+        return np.power(10.0, x / self.spec_gain)
 
     ### Preemphasis ###
     def apply_preemphasis(self, x):
@@ -254,10 +256,10 @@ class AudioProcessor(object):
     def _stft(self, y):
         return librosa.stft(
             y=y,
-            n_fft=self.n_fft,
+            n_fft=self.fft_size,
             hop_length=self.hop_length,
             win_length=self.win_length,
-            pad_mode='constant'
+            pad_mode=self.stft_pad_mode,
         )
 
     def _istft(self, y):
diff --git a/utils/generic_utils.py b/utils/generic_utils.py
index c50f8060..c806bdf3 100644
--- a/utils/generic_utils.py
+++ b/utils/generic_utils.py
@@ -146,7 +146,7 @@ def setup_model(num_chars, num_speakers, c):
         model = MyModel(num_chars=num_chars,
                         num_speakers=num_speakers,
                         r=c.r,
-                        postnet_output_dim=c.audio['num_freq'],
+                        postnet_output_dim=int(c.audio['fft_size'] / 2 + 1),
                         decoder_output_dim=c.audio['num_mels'],
                         gst=c.use_gst,
                         memory_size=c.memory_size,
@@ -252,7 +252,7 @@ def check_config(c):
 
     # audio processing parameters
     _check_argument('num_mels', c['audio'], restricted=True, val_type=int, min_val=10, max_val=2056)
-    _check_argument('num_freq', c['audio'], restricted=True, val_type=int, min_val=128, max_val=4058)
+    _check_argument('fft_size', c['audio'], restricted=True, val_type=int, min_val=128, max_val=4058)
     _check_argument('sample_rate', c['audio'], restricted=True, val_type=int, min_val=512, max_val=100000)
     _check_argument('frame_length_ms', c['audio'], restricted=True, val_type=float, min_val=10, max_val=1000, alternative='win_length')
     _check_argument('frame_shift_ms', c['audio'], restricted=True, val_type=float, min_val=1, max_val=1000, alternative='hop_length')
@@ -278,6 +278,7 @@ def check_config(c):
     _check_argument('clip_norm', c['audio'], restricted=True, val_type=bool)
     _check_argument('mel_fmin', c['audio'], restricted=True, val_type=float, min_val=0.0, max_val=1000)
     _check_argument('mel_fmax', c['audio'], restricted=True, val_type=float, min_val=500.0)
+    _check_argument('spec_gain', c['audio'], restricted=True, val_type=float, min_val=1, max_val=100)
     _check_argument('do_trim_silence', c['audio'], restricted=True, val_type=bool)
     _check_argument('trim_db', c['audio'], restricted=True, val_type=int)
 

From 1324117703da659316163109556a5016862a8b07 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Mon, 22 Jun 2020 14:56:12 +0200
Subject: [PATCH 181/185] update multiband melgan for mean-scale normalization
 and new audio parameters

---
 vocoder/configs/multiband_melgan_config.json | 15 +++++++--------
 vocoder/layers/losses.py                     |  2 +-
 2 files changed, 8 insertions(+), 9 deletions(-)

diff --git a/vocoder/configs/multiband_melgan_config.json b/vocoder/configs/multiband_melgan_config.json
index d54c64aa..a89d43bb 100644
--- a/vocoder/configs/multiband_melgan_config.json
+++ b/vocoder/configs/multiband_melgan_config.json
@@ -1,11 +1,10 @@
 {
     "run_name": "multiband-melgan",
-    "run_description": "multiband melgan",
+    "run_description": "multiband melgan mean-var scaling",
 
     // AUDIO PARAMETERS
     "audio":{
-        // stft parameters
-        "num_freq": 513,         // number of stft frequency levels. Size of the linear spectogram frame.
+        "fft_size": 1024,         // number of stft frequency levels. Size of the linear spectogram frame.
         "win_length": 1024,      // stft window length in ms.
         "hop_length": 256,       // stft window hop-lengh in ms.
         "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
@@ -14,17 +13,17 @@
         // Audio processing parameters
         "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
         "preemphasis": 0.0,     // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
-        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
+        "ref_level_db": 0,     // reference level db, theoretically 20db is the sound of air.
 
         // Silence trimming
         "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
         "trim_db": 60,          // threshold for timming silence. Set this according to your dataset.
 
-
         // MelSpectrogram parameters
         "num_mels": 80,         // size of the mel spec frame.
-        "mel_fmin": 0.0,        // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
-        "mel_fmax": 8000.0,     // maximum freq level for mel-spec. Tune for dataset!!
+        "mel_fmin": 50.0,        // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
+        "mel_fmax": 7600.0,     // maximum freq level for mel-spec. Tune for dataset!!
+        "spec_gain": 1.0,         // scaler value appplied after log transform of spectrogram.
 
         // Normalization parameters
         "signal_norm": true,    // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
@@ -32,7 +31,7 @@
         "symmetric_norm": true, // move normalization to range [-1, 1]
         "max_norm": 4.0,        // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
         "clip_norm": true,      // clip normalized values into the range.
-        "stats_path": null    // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
+        "stats_path": "/home/erogol/Data/LJSpeech-1.1/scale_stats.npy"    // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
     },
 
     // DISTRIBUTED TRAINING
diff --git a/vocoder/layers/losses.py b/vocoder/layers/losses.py
index d6ffe9fe..431f7f45 100644
--- a/vocoder/layers/losses.py
+++ b/vocoder/layers/losses.py
@@ -20,7 +20,7 @@ class TorchSTFT():
                        self.win_length,
                        self.window,
                        center=True,
-                       pad_mode="constant",  # compatible with audio.py
+                       pad_mode="reflect",  # compatible with audio.py
                        normalized=False,
                        onesided=True)
         M = o[:, :, :, 0]

From ccba431cdac1e6f44bd39b83d6a8ebdd166992a5 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 23 Jun 2020 13:29:55 +0200
Subject: [PATCH 182/185] handling dates better with the text cleaner

---
 models/tacotron2.py                        |   3 -
 utils/text/__init__.py                     |   1 -
 utils/text/cleaners.py                     |   2 +-
 utils/text/number_norm.py                  | 149 +++++++--------------
 vocoder/tests/test_melgan_discriminator.py |   2 +-
 5 files changed, 49 insertions(+), 108 deletions(-)

diff --git a/models/tacotron2.py b/models/tacotron2.py
index 7a56212d..d9570f8d 100644
--- a/models/tacotron2.py
+++ b/models/tacotron2.py
@@ -44,9 +44,6 @@ class Tacotron2(TacotronAbstract):
         proj_speaker_dim = 80 if num_speakers > 1 else 0
         # base layers
         self.embedding = nn.Embedding(num_chars, 512, padding_idx=0)
-        std = sqrt(2.0 / (num_chars + 512))
-        val = sqrt(3.0) * std  # uniform bounds for std
-        self.embedding.weight.data.uniform_(-val, val)
         if num_speakers > 1:
             self.speaker_embedding = nn.Embedding(num_speakers, 512)
             self.speaker_embedding.weight.data.normal_(0, 0.3)
diff --git a/utils/text/__init__.py b/utils/text/__init__.py
index 79069192..41aa6778 100644
--- a/utils/text/__init__.py
+++ b/utils/text/__init__.py
@@ -77,7 +77,6 @@ def phoneme_to_sequence(text, cleaner_names, language, enable_eos_bos=False, tp=
         _phonemes_to_id = {s: i for i, s in enumerate(_phonemes)}
 
     sequence = []
-    text = text.replace(":", "")
     clean_text = _clean_text(text, cleaner_names)
     to_phonemes = text2phone(clean_text, language)
     if to_phonemes is None:
diff --git a/utils/text/cleaners.py b/utils/text/cleaners.py
index 35da8aef..f0a66f57 100644
--- a/utils/text/cleaners.py
+++ b/utils/text/cleaners.py
@@ -71,7 +71,7 @@ def remove_aux_symbols(text):
 def replace_symbols(text):
     text = text.replace(';', ',')
     text = text.replace('-', ' ')
-    text = text.replace(':', ' ')
+    text = text.replace(':', ',')
     text = text.replace('&', 'and')
     return text
 
diff --git a/utils/text/number_norm.py b/utils/text/number_norm.py
index d3d9a46b..54fa1093 100644
--- a/utils/text/number_norm.py
+++ b/utils/text/number_norm.py
@@ -1,127 +1,72 @@
+# -*- coding: utf-8 -*-
+""" from https://github.com/keithito/tacotron """
+
+import inflect
 import re
 
+
+_inflect = inflect.engine()
 _comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
 _decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
 _pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
 _dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
-_ordinal_re = re.compile(r'([0-9]+)(st|nd|rd|th)')
+_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
 _number_re = re.compile(r'[0-9]+')
 
-_units = [
-    '', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine',
-    'ten', 'eleven', 'twelve', 'thirteen', 'fourteen', 'fifteen', 'sixteen',
-    'seventeen', 'eighteen', 'nineteen'
-]
-
-_tens = [
-    '',
-    'ten',
-    'twenty',
-    'thirty',
-    'forty',
-    'fifty',
-    'sixty',
-    'seventy',
-    'eighty',
-    'ninety',
-]
-
-_digit_groups = [
-    '',
-    'thousand',
-    'million',
-    'billion',
-    'trillion',
-    'quadrillion',
-]
-
-_ordinal_suffixes = [
-    ('one', 'first'),
-    ('two', 'second'),
-    ('three', 'third'),
-    ('five', 'fifth'),
-    ('eight', 'eighth'),
-    ('nine', 'ninth'),
-    ('twelve', 'twelfth'),
-    ('ty', 'tieth'),
-]
-
 
 def _remove_commas(m):
-    return m.group(1).replace(',', '')
+  return m.group(1).replace(',', '')
 
 
 def _expand_decimal_point(m):
-    return m.group(1).replace('.', ' point ')
+  return m.group(1).replace('.', ' point ')
 
 
 def _expand_dollars(m):
-    match = m.group(1)
-    parts = match.split('.')
-    if len(parts) > 2:
-        return match + ' dollars'  # Unexpected format
-    dollars = int(parts[0]) if parts[0] else 0
-    cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
-    if dollars and cents:
-        dollar_unit = 'dollar' if dollars == 1 else 'dollars'
-        cent_unit = 'cent' if cents == 1 else 'cents'
-        return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
-    if dollars:
-        dollar_unit = 'dollar' if dollars == 1 else 'dollars'
-        return '%s %s' % (dollars, dollar_unit)
-    if cents:
-        cent_unit = 'cent' if cents == 1 else 'cents'
-        return '%s %s' % (cents, cent_unit)
+  match = m.group(1)
+  parts = match.split('.')
+  if len(parts) > 2:
+    return match + ' dollars'  # Unexpected format
+  dollars = int(parts[0]) if parts[0] else 0
+  cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
+  if dollars and cents:
+    dollar_unit = 'dollar' if dollars == 1 else 'dollars'
+    cent_unit = 'cent' if cents == 1 else 'cents'
+    return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
+  elif dollars:
+    dollar_unit = 'dollar' if dollars == 1 else 'dollars'
+    return '%s %s' % (dollars, dollar_unit)
+  elif cents:
+    cent_unit = 'cent' if cents == 1 else 'cents'
+    return '%s %s' % (cents, cent_unit)
+  else:
     return 'zero dollars'
 
 
-def _standard_number_to_words(n, digit_group):
-    parts = []
-    if n >= 1000:
-        # Format next higher digit group.
-        parts.append(_standard_number_to_words(n // 1000, digit_group + 1))
-        n = n % 1000
-
-    if n >= 100:
-        parts.append('%s hundred' % _units[n // 100])
-    if n % 100 >= len(_units):
-        parts.append(_tens[(n % 100) // 10])
-        parts.append(_units[(n % 100) % 10])
-    else:
-        parts.append(_units[n % 100])
-    if n > 0:
-        parts.append(_digit_groups[digit_group])
-    return ' '.join([x for x in parts if x])
-
-
-def _number_to_words(n):
-    # Handle special cases first, then go to the standard case:
-    if n >= 1000000000000000000:
-        return str(n)  # Too large, just return the digits
-    if n == 0:
-        return 'zero'
-    if n % 100 == 0 and n % 1000 != 0 and n < 3000:
-        return _standard_number_to_words(n // 100, 0) + ' hundred'
-    return _standard_number_to_words(n, 0)
+def _expand_ordinal(m):
+  return _inflect.number_to_words(m.group(0))
 
 
 def _expand_number(m):
-    return _number_to_words(int(m.group(0)))
-
-
-def _expand_ordinal(m):
-    num = _number_to_words(int(m.group(1)))
-    for suffix, replacement in _ordinal_suffixes:
-        if num.endswith(suffix):
-            return num[:-len(suffix)] + replacement
-    return num + 'th'
+  num = int(m.group(0))
+  if num > 1000 and num < 3000:
+    if num == 2000:
+      return 'two thousand'
+    elif num > 2000 and num < 2010:
+      return 'two thousand ' + _inflect.number_to_words(num % 100)
+    elif num % 100 == 0:
+      return _inflect.number_to_words(num // 100) + ' hundred'
+    else:
+      return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ')
+  else:
+    return _inflect.number_to_words(num, andword='')
 
 
 def normalize_numbers(text):
-    text = re.sub(_comma_number_re, _remove_commas, text)
-    text = re.sub(_pounds_re, r'\1 pounds', text)
-    text = re.sub(_dollars_re, _expand_dollars, text)
-    text = re.sub(_decimal_number_re, _expand_decimal_point, text)
-    text = re.sub(_ordinal_re, _expand_ordinal, text)
-    text = re.sub(_number_re, _expand_number, text)
-    return text
+  text = re.sub(_comma_number_re, _remove_commas, text)
+  text = re.sub(_pounds_re, r'\1 pounds', text)
+  text = re.sub(_dollars_re, _expand_dollars, text)
+  text = re.sub(_decimal_number_re, _expand_decimal_point, text)
+  text = re.sub(_ordinal_re, _expand_ordinal, text)
+  text = re.sub(_number_re, _expand_number, text)
+  return text
\ No newline at end of file
diff --git a/vocoder/tests/test_melgan_discriminator.py b/vocoder/tests/test_melgan_discriminator.py
index a4564b56..a89dad5e 100644
--- a/vocoder/tests/test_melgan_discriminator.py
+++ b/vocoder/tests/test_melgan_discriminator.py
@@ -23,4 +23,4 @@ def test_melgan_multi_scale_discriminator():
     assert np.all(scores[0].shape == (4, 1, 64))
     assert np.all(feats[0][0].shape == (4, 16, 4096))
     assert np.all(feats[0][1].shape == (4, 64, 1024))
-    assert np.all(feats[0][2].shape == (4, 256, 256))
+    assert np.all(feats[0][2].shape == (4, 256, 256))
\ No newline at end of file

From 216bba97599af72e3e2b8dabf58fe865ed30fd0b Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 23 Jun 2020 21:55:51 +0200
Subject: [PATCH 183/185] add new test sentence

---
 layers/custom_layers.py | 24 ------------------------
 train.py                |  5 +++--
 2 files changed, 3 insertions(+), 26 deletions(-)
 delete mode 100644 layers/custom_layers.py

diff --git a/layers/custom_layers.py b/layers/custom_layers.py
deleted file mode 100644
index 72668c97..00000000
--- a/layers/custom_layers.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# coding: utf-8
-# import torch
-# from torch import nn
-
-# class StopProjection(nn.Module):
-#     r""" Simple projection layer to predict the "stop token"
-
-#     Args:
-#         in_features (int): size of the input vector
-#         out_features (int or list): size of each output vector. aka number
-#             of predicted frames.
-#     """
-
-#     def __init__(self, in_features, out_features):
-#         super(StopProjection, self).__init__()
-#         self.linear = nn.Linear(in_features, out_features)
-#         self.dropout = nn.Dropout(0.5)
-#         self.sigmoid = nn.Sigmoid()
-
-#     def forward(self, inputs):
-#         out = self.dropout(inputs)
-#         out = self.linear(out)
-#         out = self.sigmoid(out)
-#         return out
diff --git a/train.py b/train.py
index bdafaeba..189a6baa 100644
--- a/train.py
+++ b/train.py
@@ -333,7 +333,7 @@ def evaluate(model, criterion, ap, global_step, epoch):
                 if c.stopnet:
                     loss_dict['stopnet_loss'] = reduce_tensor(loss_dict['stopnet_loss'].data, num_gpus)
 
-             # detach loss values
+            # detach loss values
             loss_dict_new = dict()
             for key, value in loss_dict.items():
                 if isinstance(value, (int, float)):
@@ -388,7 +388,8 @@ def evaluate(model, criterion, ap, global_step, epoch):
                 "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
                 "Be a voice, not an echo.",
                 "I'm sorry Dave. I'm afraid I can't do that.",
-                "This cake is great. It's so delicious and moist."
+                "This cake is great. It's so delicious and moist.",
+                "Prior to November 22, 1963."
             ]
         else:
             with open(c.test_sentences_file, "r") as f:

From 77c962561ec8d68b5690756d3301438f966e3e4d Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Wed, 24 Jun 2020 12:18:13 +0200
Subject: [PATCH 184/185] resolve config comments

---
 config.json | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/config.json b/config.json
index bdeecdea..77a223dc 100644
--- a/config.json
+++ b/config.json
@@ -13,12 +13,12 @@
         "frame_shift_ms": null,  // stft window hop-lengh in ms. If null, 'hop_length' is used.
 
         // Audio processing parameters
-        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
+        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate.
         "preemphasis": 0.0,     // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
         "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
 
         // Silence trimming
-        "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
+        "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (true), TWEB (false), Nancy (true)
         "trim_db": 60,          // threshold for timming silence. Set this according to your dataset.
 
         // Griffin-Lim
@@ -89,7 +89,7 @@
     // ATTENTION
     "attention_type": "original",  // 'original' or 'graves'
     "attention_heads": 4,          // number of attention heads (only for 'graves')
-    "attention_norm": "sigmoid",   // softmax or sigmoid. Suggested to use softmax for Tacotron2 and sigmoid for Tacotron.
+    "attention_norm": "sigmoid",   // softmax or sigmoid.
     "windowing": false,            // Enables attention windowing. Used only in eval mode.
     "use_forward_attn": false,     // if it uses forward attention. In general, it aligns faster.
     "forward_attn_mask": false,    // Additional masking forcing monotonicity only in eval mode.

From b1935c97fa1175908c579a4db06214174253f5f4 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Fri, 26 Jun 2020 14:35:40 +0200
Subject: [PATCH 185/185] update server to enable native vocoder inference and
 remove pwgan support

---
 server/server.py            | 12 ++++++++++
 server/synthesizer.py       | 46 ++++++++++++++++++++++++++-----------
 server/templates/index.html |  8 +++----
 3 files changed, 49 insertions(+), 17 deletions(-)

diff --git a/server/server.py b/server/server.py
index 593eeb18..43f1b3c4 100644
--- a/server/server.py
+++ b/server/server.py
@@ -21,6 +21,8 @@ def create_argparser():
     parser.add_argument('--pwgan_lib_path', type=str, default=None, help='path to ParallelWaveGAN project folder to be imported. If this is not passed, model uses Griffin-Lim for synthesis.')
     parser.add_argument('--pwgan_file', type=str, default=None, help='path to ParallelWaveGAN checkpoint file.')
     parser.add_argument('--pwgan_config', type=str, default=None, help='path to ParallelWaveGAN config file.')
+    parser.add_argument('--vocoder_config', type=str, default=None, help='path to TTS.vocoder config file.')
+    parser.add_argument('--vocoder_checkpoint', type=str, default=None, help='path to TTS.vocoder checkpoint file.')
     parser.add_argument('--port', type=int, default=5002, help='port to listen on.')
     parser.add_argument('--use_cuda', type=convert_boolean, default=False, help='true to use CUDA.')
     parser.add_argument('--debug', type=convert_boolean, default=False, help='true to enable Flask debug mode.')
@@ -35,6 +37,11 @@ embedded_tts_folder = os.path.join(embedded_models_folder, 'tts')
 tts_checkpoint_file = os.path.join(embedded_tts_folder, 'checkpoint.pth.tar')
 tts_config_file = os.path.join(embedded_tts_folder, 'config.json')
 
+embedded_vocoder_folder = os.path.join(embedded_models_folder, 'vocoder')
+vocoder_checkpoint_file = os.path.join(embedded_vocoder_folder, 'checkpoint.pth.tar')
+vocoder_config_file = os.path.join(embedded_vocoder_folder, 'config.json')
+
+# These models are soon to be deprecated
 embedded_wavernn_folder = os.path.join(embedded_models_folder, 'wavernn')
 wavernn_checkpoint_file = os.path.join(embedded_wavernn_folder, 'checkpoint.pth.tar')
 wavernn_config_file = os.path.join(embedded_wavernn_folder, 'config.json')
@@ -50,6 +57,11 @@ if not args.tts_checkpoint and os.path.isfile(tts_checkpoint_file):
     args.tts_checkpoint = tts_checkpoint_file
 if not args.tts_config and os.path.isfile(tts_config_file):
     args.tts_config = tts_config_file
+if not args.vocoder_checkpoint and os.path.isfile(tts_checkpoint_file):
+    args.tts_checkpoint = tts_checkpoint_file
+if not args.vocoder_config and os.path.isfile(tts_config_file):
+    args.tts_config = tts_config_file
+
 if not args.wavernn_file and os.path.isfile(wavernn_checkpoint_file):
     args.wavernn_file = wavernn_checkpoint_file
 if not args.wavernn_config and os.path.isfile(wavernn_config_file):
diff --git a/server/synthesizer.py b/server/synthesizer.py
index c6fde902..d85bbebc 100644
--- a/server/synthesizer.py
+++ b/server/synthesizer.py
@@ -1,6 +1,7 @@
 import io
 import re
 import sys
+import time
 
 import numpy as np
 import torch
@@ -10,6 +11,7 @@ from TTS.utils.audio import AudioProcessor
 from TTS.utils.io import load_config
 from TTS.utils.generic_utils import setup_model
 from TTS.utils.speakers import load_speaker_mapping
+from TTS.vocoder.utils.generic_utils import setup_generator
 # pylint: disable=unused-wildcard-import
 # pylint: disable=wildcard-import
 from TTS.utils.synthesis import *
@@ -34,6 +36,8 @@ class Synthesizer(object):
             assert torch.cuda.is_available(), "CUDA is not availabe on this machine."
         self.load_tts(self.config.tts_checkpoint, self.config.tts_config,
                       self.config.use_cuda)
+        if self.config.vocoder_checkpoint:
+            self.load_vocoder(self.config.vocoder_checkpoint, self.config.vocoder_config, self.config.use_cuda)
         if self.config.wavernn_lib_path:
             self.load_wavernn(self.config.wavernn_lib_path, self.config.wavernn_file,
                               self.config.wavernn_config, self.config.use_cuda)
@@ -77,6 +81,19 @@ class Synthesizer(object):
         self.tts_model.decoder.max_decoder_steps = 3000
         if 'r' in cp:
             self.tts_model.decoder.set_r(cp['r'])
+            print(f" > model reduction factor: {cp['r']}")
+
+    def load_vocoder(self, model_file, model_config, use_cuda):
+        self.vocoder_config = load_config(model_config)
+        self.vocoder_model = setup_generator(self.vocoder_config)
+        self.vocoder_model.load_state_dict(torch.load(model_file, map_location="cpu")["model"])
+        self.vocoder_model.remove_weight_norm()
+        self.vocoder_model.inference_padding = 0
+        self.vocoder_config = load_config(model_config)
+
+        if use_cuda:
+            self.vocoder_model.cuda()
+        self.vocoder_model.eval()
 
     def load_wavernn(self, lib_path, model_file, model_config, use_cuda):
         # TODO: set a function in wavernn code base for model setup and call it here.
@@ -171,6 +188,7 @@ class Synthesizer(object):
         return sentences
 
     def tts(self, text, speaker_id=None):
+        start_time = time.time()
         wavs = []
         sens = self.split_into_sentences(text)
         print(sens)
@@ -184,29 +202,25 @@ class Synthesizer(object):
             inputs = numpy_to_torch(inputs, torch.long, cuda=self.use_cuda)
             inputs = inputs.unsqueeze(0)
             # synthesize voice
-            decoder_output, postnet_output, alignments, stop_tokens = run_model_torch(
-                self.tts_model, inputs, self.tts_config, False, speaker_id, None)
+            decoder_output, postnet_output, alignments, stop_tokens = run_model_torch(self.tts_model, inputs, self.tts_config, False, speaker_id, None)
             # convert outputs to numpy
-            postnet_output, decoder_output, _, _ = parse_outputs_torch(
-                postnet_output, decoder_output, alignments, stop_tokens)
-
-            if self.pwgan:
-                vocoder_input = torch.FloatTensor(postnet_output.T).unsqueeze(0)
+            if self.vocoder_model:
+                vocoder_input = postnet_output[0].transpose(0, 1).unsqueeze(0)
+                wav = self.vocoder_model.inference(vocoder_input)
                 if self.use_cuda:
-                    vocoder_input.cuda()
-                wav = self.pwgan.inference(vocoder_input, hop_size=self.ap.hop_length)
+                    wav = wav.cpu().numpy()
+                else:
+                    wav = wav.numpy()
+                wav = wav.flatten()
             elif self.wavernn:
                 vocoder_input = None
                 if self.tts_config.model == "Tacotron":
                     vocoder_input = torch.FloatTensor(self.ap.out_linear_to_mel(linear_spec=postnet_output.T).T).T.unsqueeze(0)
                 else:
-                    vocoder_input = torch.FloatTensor(postnet_output.T).unsqueeze(0)
-
+                    vocoder_input = postnet_output[0].transpose(0, 1).unsqueeze(0)
                 if self.use_cuda:
                     vocoder_input.cuda()
                 wav = self.wavernn.generate(vocoder_input, batched=self.config.is_wavernn_batched, target=11000, overlap=550)
-            else:
-                wav = inv_spectrogram(postnet_output, self.ap, self.tts_config)
             # trim silence
             wav = trim_silence(wav, self.ap)
 
@@ -215,4 +229,10 @@ class Synthesizer(object):
 
         out = io.BytesIO()
         self.save_wav(wavs, out)
+
+        # compute stats
+        process_time = time.time() - start_time
+        audio_time = len(wavs) / self.tts_config.audio['sample_rate']
+        print(f" > Processing time: {process_time}")
+        print(f" > Real-time factor: {process_time / audio_time}")
         return out
diff --git a/server/templates/index.html b/server/templates/index.html
index d1bde024..45b874a9 100644
--- a/server/templates/index.html
+++ b/server/templates/index.html
@@ -8,10 +8,10 @@
     <meta name="description" content="">
     <meta name="author" content="">
 
-    <title>Mozillia - Text2Speech engine</title>
+    <title>Mozilla - Text2Speech engine</title>
 
     <!-- Bootstrap core CSS -->
-    <link href="https://stackpath.bootstrapcdn.com/bootstrap/4.1.1/css/bootstrap.min.css" 
+    <link href="https://stackpath.bootstrapcdn.com/bootstrap/4.1.1/css/bootstrap.min.css"
      integrity="sha384-WskhaSGFgHYWDcbwN70/dfYBj47jz9qbsMId/iRN3ewGhXQFZCSftd1LZCfmhktB" crossorigin="anonymous" rel="stylesheet">
 
     <!-- Custom styles for this template -->
@@ -27,7 +27,7 @@
 
     </style>
   </head>
-  
+
   <body>
     <a href="https://github.com/mozilla/TTS"><img style="position: absolute; z-index:1000; top: 0; left: 0; border: 0;" src="https://s3.amazonaws.com/github/ribbons/forkme_left_darkblue_121621.png" alt="Fork me on GitHub"></a>
 
@@ -60,7 +60,7 @@
           <h1 class="mt-5">Mozilla TTS</h1>
           <ul class="list-unstyled">
           </ul>
-          <input id="text" placeholder="Type here..." size=45 type="text" name="text"> 
+          <input id="text" placeholder="Type here..." size=45 type="text" name="text">
           <button id="speak-button" name="speak">Speak</button><br/><br/>
           <audio id="audio" controls autoplay hidden></audio>
           <p id="message"></p>