styling

TTS/_version.py

@@ -1 +1 @@
-__version__ = '0.0.13.2'
+__version__ = "0.0.13.2"

TTS/bin/extract_tts_spectrograms.py

@@ -1,25 +1,26 @@
 #!/usr/bin/env python3
 """Extract Mel spectrograms with teacher forcing."""
 
-import os
 import argparse
+import os
+
 import numpy as np
-from tqdm import tqdm
 import torch
-
 from torch.utils.data import DataLoader
+from tqdm import tqdm
 
+from TTS.config import load_config
 from TTS.tts.datasets.preprocess import load_meta_data
 from TTS.tts.datasets.TTSDataset import MyDataset
 from TTS.tts.utils.generic_utils import setup_model
 from TTS.tts.utils.speakers import parse_speakers
 from TTS.tts.utils.text.symbols import make_symbols, phonemes, symbols
-from TTS.config import load_config
 from TTS.utils.audio import AudioProcessor
 from TTS.utils.generic_utils import count_parameters
 
 use_cuda = torch.cuda.is_available()
 
+
 def setup_loader(ap, r, verbose=False):
     dataset = MyDataset(
         r,
@@ -38,9 +39,7 @@ def setup_loader(ap, r, verbose=False):
         enable_eos_bos=c.enable_eos_bos_chars,
         use_noise_augment=False,
         verbose=verbose,
-        speaker_mapping=speaker_mapping
+        speaker_mapping=speaker_mapping if c.use_speaker_embedding and c.use_external_speaker_embedding_file else None,
-        if c.use_speaker_embedding and c.use_external_speaker_embedding_file
-        else None,
     )
 
     if c.use_phonemes and c.compute_input_seq_cache:
@@ -60,19 +59,21 @@ def setup_loader(ap, r, verbose=False):
     )
     return loader
 
+
 def set_filename(wav_path, out_path):
     wav_file = os.path.basename(wav_path)
-    file_name = wav_file.split('.')[0]
+    file_name = wav_file.split(".")[0]
     os.makedirs(os.path.join(out_path, "quant"), exist_ok=True)
     os.makedirs(os.path.join(out_path, "mel"), exist_ok=True)
     os.makedirs(os.path.join(out_path, "wav_gl"), exist_ok=True)
     os.makedirs(os.path.join(out_path, "wav"), exist_ok=True)
     wavq_path = os.path.join(out_path, "quant", file_name)
     mel_path = os.path.join(out_path, "mel", file_name)
-    wav_gl_path = os.path.join(out_path, "wav_gl", file_name+'.wav')
+    wav_gl_path = os.path.join(out_path, "wav_gl", file_name + ".wav")
-    wav_path = os.path.join(out_path, "wav", file_name+'.wav')
+    wav_path = os.path.join(out_path, "wav", file_name + ".wav")
     return file_name, wavq_path, mel_path, wav_gl_path, wav_path
 
+
 def format_data(data):
     # setup input data
     text_input = data[0]
@@ -123,8 +124,20 @@ def format_data(data):
         item_idx,
     )
 
+
 @torch.no_grad()
-def inference(model_name, model, ap, text_input, text_lengths, mel_input, mel_lengths, attn_mask=None, speaker_ids=None, speaker_embeddings=None):
+def inference(
+    model_name,
+    model,
+    ap,
+    text_input,
+    text_lengths,
+    mel_input,
+    mel_lengths,
+    attn_mask=None,
+    speaker_ids=None,
+    speaker_embeddings=None,
+):
     if model_name == "glow_tts":
         mel_input = mel_input.permute(0, 2, 1)  # B x D x T
         speaker_c = None
@@ -140,7 +153,13 @@ def inference(model_name, model, ap, text_input, text_lengths, mel_input, mel_le
 
     elif "tacotron" in model_name:
         _, postnet_outputs, *_ = model(
-                text_input, text_lengths, mel_input, mel_lengths, speaker_ids=speaker_ids, speaker_embeddings=speaker_embeddings)
+            text_input,
+            text_lengths,
+            mel_input,
+            mel_lengths,
+            speaker_ids=speaker_ids,
+            speaker_embeddings=speaker_embeddings,
+        )
         # normalize tacotron output
         if model_name == "tacotron":
             mel_specs = []
@@ -154,7 +173,10 @@ def inference(model_name, model, ap, text_input, text_lengths, mel_input, mel_le
             model_output = postnet_outputs.detach().cpu().numpy()
     return model_output
 
-def extract_spectrograms(data_loader, model, ap, output_path, quantized_wav=False, save_audio=False, debug=False, metada_name="metada.txt"):
+
+def extract_spectrograms(
+    data_loader, model, ap, output_path, quantized_wav=False, save_audio=False, debug=False, metada_name="metada.txt"
+):
     model.eval()
     export_metadata = []
     for _, data in tqdm(enumerate(data_loader), total=len(data_loader)):
@@ -173,7 +195,18 @@ def extract_spectrograms(data_loader, model, ap, output_path, quantized_wav=Fals
             item_idx,
         ) = format_data(data)
 
-        model_output = inference(c.model.lower(), model, ap, text_input, text_lengths, mel_input, mel_lengths, attn_mask, speaker_ids, speaker_embeddings)
+        model_output = inference(
+            c.model.lower(),
+            model,
+            ap,
+            text_input,
+            text_lengths,
+            mel_input,
+            mel_lengths,
+            attn_mask,
+            speaker_ids,
+            speaker_embeddings,
+        )
 
         for idx in range(text_input.shape[0]):
             wav_file_path = item_idx[idx]
@@ -204,13 +237,14 @@ def extract_spectrograms(data_loader, model, ap, output_path, quantized_wav=Fals
         for data in export_metadata:
             f.write(f"{data[0]}|{data[1]+'.npy'}\n")
 
+
 def main(args):  # pylint: disable=redefined-outer-name
     # pylint: disable=global-variable-undefined
     global meta_data, symbols, phonemes, model_characters, speaker_mapping
 
     # Audio processor
     ap = AudioProcessor(**c.audio)
-    if "characters" in c.keys() and c['characters']:
+    if "characters" in c.keys() and c["characters"]:
         symbols, phonemes = make_symbols(**c.characters)
 
     # set model characters
@@ -242,37 +276,26 @@ def main(args):  # pylint: disable=redefined-outer-name
     r = 1 if c.model.lower() == "glow_tts" else model.decoder.r
     own_loader = setup_loader(ap, r, verbose=True)
 
-    extract_spectrograms(own_loader, model, ap, args.output_path, quantized_wav=args.quantized, save_audio=args.save_audio, debug=args.debug, metada_name="metada.txt")
+    extract_spectrograms(
+        own_loader,
+        model,
+        ap,
+        args.output_path,
+        quantized_wav=args.quantized,
+        save_audio=args.save_audio,
+        debug=args.debug,
+        metada_name="metada.txt",
+    )
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument(
+    parser.add_argument("--config_path", type=str, help="Path to config file for training.", required=True)
-        '--config_path',
+    parser.add_argument("--checkpoint_path", type=str, help="Model file to be restored.", required=True)
-        type=str,
+    parser.add_argument("--output_path", type=str, help="Path to save mel specs", required=True)
-        help='Path to config file for training.',
+    parser.add_argument("--debug", default=False, action="store_true", help="Save audio files for debug")
-        required=True)
+    parser.add_argument("--save_audio", default=False, action="store_true", help="Save audio files")
-    parser.add_argument(
+    parser.add_argument("--quantized", action="store_true", help="Save quantized audio files")
-        '--checkpoint_path',
-        type=str,
-        help='Model file to be restored.',
-        required=True)
-    parser.add_argument(
-        '--output_path',
-        type=str,
-        help='Path to save mel specs',
-        required=True)
-    parser.add_argument('--debug',
-                        default=False,
-                        action='store_true',
-                        help='Save audio files for debug')
-    parser.add_argument('--save_audio',
-                        default=False,
-                        action='store_true',
-                        help='Save audio files')
-    parser.add_argument('--quantized',
-                        action='store_true',
-                        help='Save quantized audio files')
     args = parser.parse_args()
 
     c = load_config(args.config_path)

TTS/bin/train_vocoder_gan.py

@@ -2,10 +2,10 @@
 # TODO: mixed precision training
 """Trains GAN based vocoder model."""
 
+import itertools
 import os
 import sys
 import time
-import itertools
 import traceback
 from inspect import signature
 
@@ -496,8 +496,12 @@ def main(args):  # pylint: disable=redefined-outer-name
     optimizer_gen = optimizer_gen(model_gen.parameters(), lr=c.lr_gen, **c.optimizer_params)
     optimizer_disc = getattr(torch.optim, c.optimizer)
 
-    if c.discriminator_model == 'hifigan_discriminator':
+    if c.discriminator_model == "hifigan_discriminator":
-        optimizer_disc = optimizer_disc(itertools.chain(model_disc.msd.parameters(), model_disc.mpd.parameters()), lr=c.lr_disc, **c.optimizer_params)
+        optimizer_disc = optimizer_disc(
+            itertools.chain(model_disc.msd.parameters(), model_disc.mpd.parameters()),
+            lr=c.lr_disc,
+            **c.optimizer_params,
+        )
     else:
         optimizer_disc = optimizer_disc(model_disc.parameters(), lr=c.lr_disc, **c.optimizer_params)
 

TTS/config/shared_configs.py

@@ -1,5 +1,6 @@
 from dataclasses import asdict, dataclass
 from typing import List, Union
+
 from coqpit import MISSING, Coqpit, check_argument
 
 
@@ -151,11 +152,14 @@ class BaseDatasetConfig(Coqpit):
             Path to the file that lists the attention mask files used with models that require attention masks to
             train the duration predictor.
     """
-    name: str = ''
+
-    path: str = ''
+    name: str = ""
-    meta_file_train: Union[str, List] = ''  # TODO: don't take ignored speakers for multi-speaker datasets over this. This is Union for SC-Glow compat.
+    path: str = ""
-    meta_file_val: str = ''
+    meta_file_train: Union[
-    meta_file_attn_mask: str = ''
+        str, List
+    ] = ""  # TODO: don't take ignored speakers for multi-speaker datasets over this. This is Union for SC-Glow compat.
+    meta_file_val: str = ""
+    meta_file_attn_mask: str = ""
 
     def check_values(
         self,

TTS/tts/configs/glow_tts_config.py

@@ -59,7 +59,6 @@ class GlowTTSConfig(BaseTTSConfig):
             Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
     """
 
-
     model: str = "glow_tts"
 
     # model params

TTS/tts/configs/shared_configs.py

@@ -22,6 +22,7 @@ class GSTConfig(Coqpit):
         gst_num_style_tokens (int):
             Number of style token vectors. Defaults to 10.
     """
+
     gst_style_input_wav: str = None
     gst_style_input_weights: dict = None
     gst_embedding_dim: int = 256

TTS/tts/configs/speedy_speech_config.py

@@ -113,4 +113,4 @@ class SpeedySpeechConfig(BaseTTSConfig):
     # overrides
     min_seq_len: int = 13
     max_seq_len: int = 200
-    r: int = 1  #DO NOT CHANGE
+    r: int = 1  # DO NOT CHANGE

TTS/vocoder/configs/multiband_melgan_config.py

@@ -90,7 +90,6 @@ class MultibandMelganConfig(BaseGANVocoderConfig):
             L1 spectrogram loss weight that multiplies the computed loss before summing up the total loss. Defaults to 0.
     """
 
-
     model: str = "multiband_melgan"
 
     # Model specific params

TTS/vocoder/configs/wavegrad_config.py

@@ -66,6 +66,7 @@ class WavegradConfig(BaseVocoderConfig):
         lr_scheduler_params (dict):
             kwargs for the scheduler. Defaults to `{"gamma": 0.5, "milestones": [100000, 200000, 300000, 400000, 500000, 600000]}`
     """
+
     model: str = "wavegrad"
     # Model specific params
     generator_model: str = "wavegrad"

tests/test_extract_tts_spectrograms.py

@@ -3,13 +3,9 @@ import unittest
 
 import torch
 
-from tests import get_tests_input_path
+from tests import get_tests_input_path, get_tests_output_path, run_cli
-
-from tests import get_tests_output_path, run_cli
-
-from TTS.tts.utils.generic_utils import setup_model
-
 from TTS.config import load_config
+from TTS.tts.utils.generic_utils import setup_model
 from TTS.tts.utils.text.symbols import phonemes, symbols
 
 torch.manual_seed(1)
@@ -20,8 +16,8 @@ class TestExtractTTSSpectrograms(unittest.TestCase):
     def test_GlowTTS():
         # set paths
         config_path = os.path.join(get_tests_input_path(), "test_glow_tts.json")
-        checkpoint_path = os.path.join(get_tests_output_path(), 'checkpoint_test.pth.tar')
+        checkpoint_path = os.path.join(get_tests_output_path(), "checkpoint_test.pth.tar")
-        output_path = os.path.join(get_tests_output_path(), 'output_extract_tts_spectrograms/')
+        output_path = os.path.join(get_tests_output_path(), "output_extract_tts_spectrograms/")
         # load config
         c = load_config(config_path)
         # create model
@@ -30,14 +26,17 @@ class TestExtractTTSSpectrograms(unittest.TestCase):
         # save model
         torch.save({"model": model.state_dict()}, checkpoint_path)
         # run test
-        run_cli(f'CUDA_VISIBLE_DEVICES="" python TTS/bin/extract_tts_spectrograms.py --config_path "{config_path}" --checkpoint_path "{checkpoint_path}" --output_path "{output_path}"')
+        run_cli(
+            f'CUDA_VISIBLE_DEVICES="" python TTS/bin/extract_tts_spectrograms.py --config_path "{config_path}" --checkpoint_path "{checkpoint_path}" --output_path "{output_path}"'
+        )
         run_cli(f'rm -rf "{output_path}" "{checkpoint_path}"')
+
     @staticmethod
     def test_Tacotron2():
         # set paths
         config_path = os.path.join(get_tests_input_path(), "test_tacotron2_config.json")
-        checkpoint_path = os.path.join(get_tests_output_path(), 'checkpoint_test.pth.tar')
+        checkpoint_path = os.path.join(get_tests_output_path(), "checkpoint_test.pth.tar")
-        output_path = os.path.join(get_tests_output_path(), 'output_extract_tts_spectrograms/')
+        output_path = os.path.join(get_tests_output_path(), "output_extract_tts_spectrograms/")
         # load config
         c = load_config(config_path)
         # create model
@@ -46,14 +45,17 @@ class TestExtractTTSSpectrograms(unittest.TestCase):
         # save model
         torch.save({"model": model.state_dict()}, checkpoint_path)
         # run test
-        run_cli(f'CUDA_VISIBLE_DEVICES="" python TTS/bin/extract_tts_spectrograms.py --config_path "{config_path}" --checkpoint_path "{checkpoint_path}" --output_path "{output_path}"')
+        run_cli(
+            f'CUDA_VISIBLE_DEVICES="" python TTS/bin/extract_tts_spectrograms.py --config_path "{config_path}" --checkpoint_path "{checkpoint_path}" --output_path "{output_path}"'
+        )
         run_cli(f'rm -rf "{output_path}" "{checkpoint_path}"')
+
     @staticmethod
     def test_Tacotron():
         # set paths
         config_path = os.path.join(get_tests_input_path(), "test_tacotron_config.json")
-        checkpoint_path = os.path.join(get_tests_output_path(), 'checkpoint_test.pth.tar')
+        checkpoint_path = os.path.join(get_tests_output_path(), "checkpoint_test.pth.tar")
-        output_path = os.path.join(get_tests_output_path(), 'output_extract_tts_spectrograms/')
+        output_path = os.path.join(get_tests_output_path(), "output_extract_tts_spectrograms/")
         # load config
         c = load_config(config_path)
         # create model
@@ -62,5 +64,7 @@ class TestExtractTTSSpectrograms(unittest.TestCase):
         # save model
         torch.save({"model": model.state_dict()}, checkpoint_path)
         # run test
-        run_cli(f'CUDA_VISIBLE_DEVICES="" python TTS/bin/extract_tts_spectrograms.py --config_path "{config_path}" --checkpoint_path "{checkpoint_path}" --output_path "{output_path}"')
+        run_cli(
+            f'CUDA_VISIBLE_DEVICES="" python TTS/bin/extract_tts_spectrograms.py --config_path "{config_path}" --checkpoint_path "{checkpoint_path}" --output_path "{output_path}"'
+        )
         run_cli(f'rm -rf "{output_path}" "{checkpoint_path}"')

tests/test_glow_tts.py

@@ -130,6 +130,7 @@ class GlowTTSTrainTest(unittest.TestCase):
             )
             count += 1
 
+
 class GlowTTSInferenceTest(unittest.TestCase):
     @staticmethod
     def test_inference():
@@ -174,13 +175,12 @@ class GlowTTSInferenceTest(unittest.TestCase):
         print(" > Num parameters for GlowTTS model:%s" % (count_parameters(model)))
 
         # inference encoder and decoder with MAS
-        y, *_ = model.inference_with_MAS(
+        y, *_ = model.inference_with_MAS(input_dummy, input_lengths, mel_spec, mel_lengths, None)
-                input_dummy, input_lengths, mel_spec, mel_lengths, None
-            )
 
-        y_dec, _ = model.decoder_inference(mel_spec, mel_lengths
+        y_dec, _ = model.decoder_inference(mel_spec, mel_lengths)
-            )
 
-        assert (y_dec.shape == y.shape), "Difference between the shapes of the glowTTS inference with MAS ({}) and the inference using only the decoder ({}) !!".format(
+        assert (
+            y_dec.shape == y.shape
+        ), "Difference between the shapes of the glowTTS inference with MAS ({}) and the inference using only the decoder ({}) !!".format(
             y.shape, y_dec.shape
         )