Merge remote-tracking branch 'TTS/dev' into dev

README.md

@@ -1,9 +1,14 @@
 <p align="center"><img src="https://user-images.githubusercontent.com/1402048/52643646-c2102980-2edd-11e9-8c37-b72f3c89a640.png" data-canonical-src="![TTS banner](https://user-images.githubusercontent.com/1402048/52643646-c2102980-2edd-11e9-8c37-b72f3c89a640.png =250x250)
 " width="320" height="95" /></p>
 
-<img src="https://travis-ci.org/mozilla/TTS.svg?branch=dev"/>
+<p align='center'>
+    <img src="https://travis-ci.org/mozilla/TTS.svg?branch=dev"/>
+    <a href='https://discourse.mozilla.org/c/tts'><img src="https://img.shields.io/badge/discourse-online-green.svg"/></a>
+</p>
 
-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.
+This project is a part of [Mozilla Common Voice](https://voice.mozilla.org/en). 
+
+Mozilla TTS aims a deep learning based Text2Speech engine, low in cost and high in quality.
 
 You can check some of synthesized voice samples from [here](https://erogol.github.io/ddc-samples/).
 
@@ -38,25 +43,26 @@ Vocoders:
 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).
+- High performance Deep Learning models for Text2Speech tasks.
+    - Text2Spec models (Tacotron, Tacotron2).
     - 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 pre-trained models.
+    - Vocoder models (MelGAN, Multiband-MelGAN, GAN-TTS, ParallelWaveGAN)
 - Fast and efficient model training.
 - Detailed training logs on console and Tensorboard.
+- Support for multi-speaker TTS.
+- Efficient Multi-GPUs training.
+- Ability to convert PyTorch models to Tensorflow 2.0 and TFLite for inference.
+- Released models in PyTorch, Tensorflow and TFLite.
 - Tools to curate Text2Speech datasets under```dataset_analysis```.
 - Demo server for model testing.
 - Notebooks for extensive model benchmarking.
 - Modular (but not too much) code base enabling easy testing for new ideas.
 
-## Requirements and Installation
+## Main Requirements and Installation
 Highly recommended to use [miniconda](https://conda.io/miniconda.html) for easier installation.
   * python>=3.6
-  * pytorch>=0.4.1
+  * pytorch>=1.4.1
+  * tensorflow>=2.2
   * librosa
   * tensorboard
   * tensorboardX
@@ -107,26 +113,14 @@ Audio examples: [soundcloud](https://soundcloud.com/user-565970875/pocket-articl
 
 <img src="images/example_model_output.png?raw=true" alt="example_output" width="400"/>
 
-## Runtime
-The most time-consuming part is the vocoder algorithm (Griffin-Lim) which runs on CPU. By setting its number of iterations lower, you might have faster execution with a small loss of quality. Some of the experimental values are below.
-
-Sentence: "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."
-
-Audio length is approximately 6 secs.
-
-| Time (secs) | System | # GL iters | Model
-| ---- |:-------|:-----------| ---- |
-|2.00|GTX1080Ti|30|Tacotron|
-|3.01|GTX1080Ti|60|Tacotron|
-|3.57|CPU|60|Tacotron|
-|5.27|GTX1080Ti|60|Tacotron2|
-|6.50|CPU|60|Tacotron2|
-
+## [Mozilla TTS Tutorials and Notebooks](https://github.com/mozilla/TTS/wiki/TTS-Notebooks-and-Tutorials)
 
 ## Datasets and Data-Loading
-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.
+TTS provides a generic dataloader easy to use for your custom dataset. 
+You just need to write a simple function to format the dataset. Check ```datasets/preprocess.py``` to see some examples. 
+After that, you need to set ```dataset``` fields in ```config.json```.
 
-Some of the open-sourced datasets that we successfully applied TTS, are linked below.
+Some of the public datasets that we successfully applied TTS:
 
 - [LJ Speech](https://keithito.com/LJ-Speech-Dataset/)
 - [Nancy](http://www.cstr.ed.ac.uk/projects/blizzard/2011/lessac_blizzard2011/)
@@ -164,8 +158,6 @@ In case of any error or intercepted execution, if there is no checkpoint yet und
 
 You can also enjoy Tensorboard,  if you point Tensorboard argument```--logdir``` to the experiment folder.
 
-## [Testing and Examples](https://github.com/mozilla/TTS/wiki/Examples-using-TTS)
-
 ## Contribution guidelines
 This repository is governed by Mozilla's code of conduct and etiquette guidelines. For more details, please read the [Mozilla Community Participation Guidelines.](https://www.mozilla.org/about/governance/policies/participation/)
 

requirements.txt

@@ -15,7 +15,10 @@ tqdm
 inflect
 pysbd
 bokeh==1.4.0
+pysbd
 soundfile
 nose==1.3.7
 cardboardlint==1.3.0
 pylint==2.5.3
+fuzzywuzzy
+gdown

server/synthesizer.py

@@ -1,5 +1,4 @@
 import io
-import re
 import sys
 import time
 

setup.py

@@ -93,11 +93,14 @@ requirements = {
         "inflect",
         "pysbd",
         "bokeh==1.4.0",
+        "pysbd",
         "soundfile",
         "phonemizer>=2.2.0",
         "nose==1.3.7",
         "cardboardlint==1.3.0",
         "pylint==2.5.3",
+        'fuzzywuzzy',
+        'gdown'
     ],
     'pip_install':[
         'tensorflow>=2.2.0',

tf/convert_tacotron2_tflite.py

@@ -34,6 +34,4 @@ model = load_checkpoint(model, args.tf_model)
 model.decoder.set_max_decoder_steps(1000)
 
 # create tflite model
-tflite_model = convert_tacotron2_to_tflite(model, output_path=args.output_path)
-
-print(f'Tflite Model size is {len(tflite_model) / (1024.0 * 1024.0)} MBs.')
+tflite_model = convert_tacotron2_to_tflite(model, output_path=args.output_path)

tf/utils/tflite.py

@@ -16,6 +16,7 @@ def convert_tacotron2_to_tflite(model,
         tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS
     ]
     tflite_model = converter.convert()
+    print(f'Tflite Model size is {len(tflite_model) / (1024.0 * 1024.0)} MBs.')
     if output_path is not None:
         # same model binary if outputpath is provided
         with open(output_path, 'wb') as f:

vocoder/tf/convert_melgan_tflite.py

@@ -0,0 +1,33 @@
+# Convert Tensorflow Tacotron2 model to TF-Lite binary
+
+import argparse
+
+from TTS.utils.io import load_config
+from TTS.vocoder.tf.utils.generic_utils import setup_generator
+from TTS.vocoder.tf.utils.io import load_checkpoint
+from TTS.vocoder.tf.utils.tflite import convert_melgan_to_tflite
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--tf_model',
+                    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 tflite output binary.')
+args = parser.parse_args()
+
+# Set constants
+CONFIG = load_config(args.config_path)
+
+# load the model
+model = setup_generator(CONFIG)
+model.build_inference()
+model = load_checkpoint(model, args.tf_model)
+
+# create tflite model
+tflite_model = convert_melgan_to_tflite(model, output_path=args.output_path)
+

vocoder/tf/layers/pqmf.py

@@ -51,7 +51,7 @@ class PQMF(tf.keras.layers.Layer):
 
     def synthesis(self, x):
         """
-        x : B x 1 x T
+        x : B x D x T
         """
         x = tf.transpose(x, perm=[0, 2, 1])
         x = tf.nn.conv1d_transpose(

vocoder/tf/models/melgan_generator.py

@@ -108,4 +108,21 @@ class MelganGenerator(tf.keras.models.Model):
 
     def build_inference(self):
         x = tf.random.uniform((1, self.in_channels, 4), dtype=tf.float32)
-        self(x, training=False)
+        self(x, training=False)
+
+    @tf.function(
+        experimental_relax_shapes=True,
+        input_signature=[
+            tf.TensorSpec([1, None, None], dtype=tf.float32),
+        ],)
+    def inference_tflite(self, c):
+        c = tf.transpose(c, perm=[0, 2, 1])
+        c = tf.expand_dims(c, 2)
+        # FIXME: TF had no replicate padding as in Torch
+        # 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, :]

vocoder/tf/models/multiband_melgan_generator.py

@@ -30,11 +30,6 @@ class MultibandMelganGenerator(MelganGenerator):
     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)
@@ -46,3 +41,20 @@ class MultibandMelganGenerator(MelganGenerator):
         o = tf.transpose(o, perm=[0, 3, 2, 1])
         o = self.pqmf_layer.synthesis(o[:, :, 0, :])
         return o
+
+    @tf.function(
+        experimental_relax_shapes=True,
+        input_signature=[
+            tf.TensorSpec([1, 80, None], dtype=tf.float32),
+        ],)
+    def inference_tflite(self, c):
+        c = tf.transpose(c, perm=[0, 2, 1])
+        c = tf.expand_dims(c, 2)
+        # FIXME: TF had no replicate padding as in Torch
+        # 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

vocoder/tf/utils/tflite.py

@@ -0,0 +1,31 @@
+import tensorflow as tf
+
+
+def convert_melgan_to_tflite(model,
+                             output_path=None,
+                             experimental_converter=True):
+    """Convert Tensorflow MelGAN model to TFLite. Save a binary file if output_path is
+    provided, else return TFLite model."""
+
+    concrete_function = model.inference_tflite.get_concrete_function()
+    converter = tf.lite.TFLiteConverter.from_concrete_functions(
+        [concrete_function])
+    converter.experimental_new_converter = experimental_converter
+    converter.optimizations = []
+    converter.target_spec.supported_ops = [
+        tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS
+    ]
+    tflite_model = converter.convert()
+    print(f'Tflite Model size is {len(tflite_model) / (1024.0 * 1024.0)} MBs.')
+    if output_path is not None:
+        # same model binary if outputpath is provided
+        with open(output_path, 'wb') as f:
+            f.write(tflite_model)
+        return None
+    return tflite_model
+
+
+def load_tflite_model(tflite_path):
+    tflite_model = tf.lite.Interpreter(model_path=tflite_path)
+    tflite_model.allocate_tensors()
+    return tflite_model