Make style

TTS/bin/extract_tts_spectrograms.py

@@ -242,7 +242,6 @@ def main(args):  # pylint: disable=redefined-outer-name
     else:
         speaker_manager = None
 
-
     # setup model
     model = setup_model(c)
 

TTS/bin/find_unique_phonemes.py

@@ -19,6 +19,7 @@ def compute_phonemes(item):
         return []
     return list(set(ph))
 
+
 def main():
     # pylint: disable=W0601
     global c

TTS/bin/remove_silence_using_vad.py

@@ -1,12 +1,13 @@
-import os
-import glob
-import pathlib
 import argparse
+import glob
 import multiprocessing
+import os
+import pathlib
 
 from tqdm.contrib.concurrent import process_map
 
-from TTS.utils.vad import  read_wave, write_wave, get_vad_speech_segments
+from TTS.utils.vad import get_vad_speech_segments, read_wave, write_wave
+
 
 def remove_silence(filepath):
     output_path = filepath.replace(os.path.join(args.input_dir, ""), os.path.join(args.output_dir, ""))
@@ -69,10 +70,7 @@ if __name__ == "__main__":
     parser.add_argument(
         "-o", "--output_dir", type=str, default="../VCTK-Corpus-removed-silence", help="Output Dataset dir"
     )
-    parser.add_argument("-f", "--force",
+    parser.add_argument("-f", "--force", default=False, action="store_true", help="Force the replace of exists files")
-                        default=False,
-                        action='store_true',
-                        help='Force the replace of exists files')
     parser.add_argument(
         "-g",
         "--glob",

TTS/bin/train_tts.py

@@ -4,8 +4,8 @@ from TTS.config import load_config, register_config
 from TTS.trainer import Trainer, TrainingArgs
 from TTS.tts.datasets import load_tts_samples
 from TTS.tts.models import setup_model
-from TTS.tts.utils.speakers import SpeakerManager
 from TTS.tts.utils.languages import LanguageManager
+from TTS.tts.utils.speakers import SpeakerManager
 from TTS.utils.audio import AudioProcessor
 
 

TTS/server/server.py

@@ -100,7 +100,7 @@ if args.vocoder_path is not None:
 
 # load models
 synthesizer = Synthesizer(
-    model_path, config_path, speakers_file_path, vocoder_path, vocoder_config_path, use_cuda=args.use_cuda
+    model_path, config_path, speakers_file_path, None, vocoder_path, vocoder_config_path, use_cuda=args.use_cuda
 )
 
 use_multi_speaker = hasattr(synthesizer.tts_model, "num_speakers") and synthesizer.tts_model.num_speakers > 1

TTS/speaker_encoder/models/resnet.py

@@ -2,11 +2,12 @@ import numpy as np
 import torch
 from torch import nn
 
+from TTS.utils.audio import TorchSTFT
+from TTS.utils.io import load_fsspec
+
 # import torchaudio
 
-from TTS.utils.audio import TorchSTFT
 
-from TTS.utils.io import load_fsspec
 
 
 class PreEmphasis(torch.nn.Module):
@@ -126,16 +127,16 @@ class ResNetSpeakerEncoder(nn.Module):
                     n_mels=audio_config["num_mels"],
                     power=2.0,
                     use_mel=True,
-                    mel_norm=None
+                    mel_norm=None,
                 ),
-                '''torchaudio.transforms.MelSpectrogram(
+                """torchaudio.transforms.MelSpectrogram(
                     sample_rate=audio_config["sample_rate"],
                     n_fft=audio_config["fft_size"],
                     win_length=audio_config["win_length"],
                     hop_length=audio_config["hop_length"],
                     window_fn=torch.hamming_window,
                     n_mels=audio_config["num_mels"],
-                ),'''
+                ),""",
             )
         else:
             self.torch_spec = None

TTS/tts/datasets/dataset.py

@@ -531,7 +531,7 @@ class TTSDataset(Dataset):
                 "waveform": wav_padded,
                 "raw_text": batch["raw_text"],
                 "pitch": pitch,
-                "language_ids": language_ids
+                "language_ids": language_ids,
             }
 
         raise TypeError(

TTS/tts/layers/losses.py

@@ -588,7 +588,7 @@ class VitsGeneratorLoss(nn.Module):
 
     @staticmethod
     def cosine_similarity_loss(gt_spk_emb, syn_spk_emb):
-        l = - torch.nn.functional.cosine_similarity(gt_spk_emb, syn_spk_emb).mean()
+        l = -torch.nn.functional.cosine_similarity(gt_spk_emb, syn_spk_emb).mean()
         return l
 
     def forward(

TTS/tts/models/__init__.py

@@ -2,11 +2,7 @@ from TTS.tts.utils.text.symbols import make_symbols, parse_symbols
 from TTS.utils.generic_utils import find_module
 
 
-def setup_model(
+def setup_model(config, speaker_manager: "SpeakerManager" = None, language_manager: "LanguageManager" = None):
-        config,
-        speaker_manager: "SpeakerManager" = None,
-        language_manager: "LanguageManager" = None
-    ):
     print(" > Using model: {}".format(config.model))
     # fetch the right model implementation.
     if "base_model" in config and config["base_model"] is not None:
@@ -35,7 +31,7 @@ def setup_model(
         config.model_params.num_chars = num_chars
     if "model_args" in config:
         config.model_args.num_chars = num_chars
-    if config.model.lower() in ["vits"]: # If model supports multiple languages
+    if config.model.lower() in ["vits"]:  # If model supports multiple languages
         model = MyModel(config, speaker_manager=speaker_manager, language_manager=language_manager)
     else:
         model = MyModel(config, speaker_manager=speaker_manager)

TTS/tts/models/base_tts.py

@@ -12,8 +12,8 @@ from torch.utils.data.distributed import DistributedSampler
 from TTS.model import BaseModel
 from TTS.tts.configs.shared_configs import CharactersConfig
 from TTS.tts.datasets.dataset import TTSDataset
-from TTS.tts.utils.speakers import SpeakerManager, get_speaker_manager, get_speaker_weighted_sampler
 from TTS.tts.utils.languages import LanguageManager, get_language_weighted_sampler
+from TTS.tts.utils.speakers import SpeakerManager, get_speaker_manager, get_speaker_weighted_sampler
 from TTS.tts.utils.synthesis import synthesis
 from TTS.tts.utils.text import make_symbols
 from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
@@ -150,7 +150,13 @@ class BaseTTS(BaseModel):
         if hasattr(self, "language_manager") and config.use_language_embedding and language_name is not None:
             language_id = self.language_manager.language_id_mapping[language_name]
 
-        return {"text": text, "speaker_id": speaker_id, "style_wav": style_wav, "d_vector": d_vector, "language_id": language_id}
+        return {
+            "text": text,
+            "speaker_id": speaker_id,
+            "style_wav": style_wav,
+            "d_vector": d_vector,
+            "language_id": language_id,
+        }
 
     def format_batch(self, batch: Dict) -> Dict:
         """Generic batch formatting for `TTSDataset`.
@@ -337,14 +343,16 @@ class BaseTTS(BaseModel):
             if config.compute_f0:
                 dataset.pitch_extractor.load_pitch_stats(config.get("f0_cache_path", None))
 
-
-
             # sampler for DDP
             sampler = DistributedSampler(dataset) if num_gpus > 1 else None
 
             # Weighted samplers
-            assert not (num_gpus > 1 and getattr(config, "use_language_weighted_sampler", False)), "language_weighted_sampler is not supported with DistributedSampler"
+            assert not (
-            assert not (num_gpus > 1 and getattr(config, "use_speaker_weighted_sampler", False)), "speaker_weighted_sampler is not supported with DistributedSampler"
+                num_gpus > 1 and getattr(config, "use_language_weighted_sampler", False)
+            ), "language_weighted_sampler is not supported with DistributedSampler"
+            assert not (
+                num_gpus > 1 and getattr(config, "use_speaker_weighted_sampler", False)
+            ), "speaker_weighted_sampler is not supported with DistributedSampler"
 
             if sampler is None:
                 if getattr(config, "use_language_weighted_sampler", False):
@@ -354,7 +362,6 @@ class BaseTTS(BaseModel):
                     print(" > Using Language weighted sampler")
                     sampler = get_speaker_weighted_sampler(dataset.items)
 
-
             loader = DataLoader(
                 dataset,
                 batch_size=config.eval_batch_size if is_eval else config.batch_size,

TTS/tts/models/vits.py

@@ -4,6 +4,7 @@ from itertools import chain
 from typing import Dict, List, Tuple
 
 import torch
+
 # import torchaudio
 from coqpit import Coqpit
 from torch import nn
@@ -420,8 +421,9 @@ class Vits(BaseTTS):
             ):
                 # TODO: change this with torchaudio Resample
                 raise RuntimeError(
-                    ' [!] To use the speaker consistency loss (SCL) you need to have matching sample rates between the TTS model ({}) and the speaker encoder ({})!'
+                    " [!] To use the speaker consistency loss (SCL) you need to have matching sample rates between the TTS model ({}) and the speaker encoder ({})!".format(
-                        .format(self.config.audio["sample_rate"], self.speaker_encoder.audio_config["sample_rate"])
+                        self.config.audio["sample_rate"], self.speaker_encoder.audio_config["sample_rate"]
+                    )
                 )
                 # pylint: disable=W0101,W0105
                 """ self.audio_transform = torchaudio.transforms.Resample(
@@ -675,7 +677,6 @@ class Vits(BaseTTS):
         )
         return outputs
 
-
     def inference(self, x, aux_input={"d_vectors": None, "speaker_ids": None, "language_ids": None}):
         """
         Shapes:

TTS/utils/audio.py

@@ -88,7 +88,7 @@ class TorchSTFT(nn.Module):  # pylint: disable=abstract-method
         spec_gain=1.0,
         power=None,
         use_htk=False,
-        mel_norm="slaney"
+        mel_norm="slaney",
     ):
         super().__init__()
         self.n_fft = n_fft
@@ -155,7 +155,13 @@ class TorchSTFT(nn.Module):  # pylint: disable=abstract-method
 
     def _build_mel_basis(self):
         mel_basis = librosa.filters.mel(
-            self.sample_rate, self.n_fft, n_mels=self.n_mels, fmin=self.mel_fmin, fmax=self.mel_fmax, htk=self.use_htk, norm=self.mel_norm
+            self.sample_rate,
+            self.n_fft,
+            n_mels=self.n_mels,
+            fmin=self.mel_fmin,
+            fmax=self.mel_fmax,
+            htk=self.use_htk,
+            norm=self.mel_norm,
         )
         self.mel_basis = torch.from_numpy(mel_basis).float()
 

TTS/utils/synthesizer.py

@@ -7,8 +7,8 @@ import torch
 
 from TTS.config import load_config
 from TTS.tts.models import setup_model as setup_tts_model
-from TTS.tts.utils.speakers import SpeakerManager
 from TTS.tts.utils.languages import LanguageManager
+from TTS.tts.utils.speakers import SpeakerManager
 
 # pylint: disable=unused-wildcard-import
 # pylint: disable=wildcard-import
@@ -200,12 +200,7 @@ class Synthesizer(object):
         self.ap.save_wav(wav, path, self.output_sample_rate)
 
     def tts(
-        self,
+        self, text: str, speaker_idx: str = "", language_idx: str = "", speaker_wav=None, style_wav=None
-        text: str,
-        speaker_idx: str = "",
-        language_idx: str = "",
-        speaker_wav=None,
-        style_wav=None
     ) -> List[int]:
         """🐸 TTS magic. Run all the models and generate speech.
 
@@ -254,7 +249,9 @@ class Synthesizer(object):
 
         # handle multi-lingaul
         language_id = None
-        if self.tts_languages_file or (hasattr(self.tts_model, "language_manager") and self.tts_model.language_manager is not None):
+        if self.tts_languages_file or (
+            hasattr(self.tts_model, "language_manager") and self.tts_model.language_manager is not None
+        ):
             if language_idx and isinstance(language_idx, str):
                 language_id = self.tts_model.language_manager.language_id_mapping[language_idx]
 

TTS/utils/vad.py

@@ -1,8 +1,9 @@
 # This code is adpated from: https://github.com/wiseman/py-webrtcvad/blob/master/example.py
-import wave
-import webrtcvad
-import contextlib
 import collections
+import contextlib
+import wave
+
+import webrtcvad
 
 
 def read_wave(path):
@@ -37,7 +38,7 @@ class Frame(object):
     """Represents a "frame" of audio data."""
 
     def __init__(self, _bytes, timestamp, duration):
-        self.bytes =_bytes
+        self.bytes = _bytes
         self.timestamp = timestamp
         self.duration = duration
 
@@ -133,6 +134,7 @@ def vad_collector(sample_rate, frame_duration_ms, padding_duration_ms, vad, fram
     if voiced_frames:
         yield b"".join([f.bytes for f in voiced_frames])
 
+
 def get_vad_speech_segments(audio, sample_rate, aggressiveness=2, padding_duration_ms=300):
 
     vad = webrtcvad.Vad(int(aggressiveness))

recipes/multilingual/vits_tts/train_vits_tts.py

@@ -7,15 +7,18 @@ from TTS.tts.configs.shared_configs import BaseDatasetConfig
 from TTS.tts.configs.vits_config import VitsConfig
 from TTS.tts.datasets import load_tts_samples
 from TTS.tts.models.vits import Vits, VitsArgs
-from TTS.tts.utils.speakers import SpeakerManager
 from TTS.tts.utils.languages import LanguageManager
+from TTS.tts.utils.speakers import SpeakerManager
 from TTS.utils.audio import AudioProcessor
 
 output_path = os.path.dirname(os.path.abspath(__file__))
 
-mailabs_path = '/home/julian/workspace/mailabs/**'
+mailabs_path = "/home/julian/workspace/mailabs/**"
 dataset_paths = glob(mailabs_path)
-dataset_config = [BaseDatasetConfig(name="mailabs", meta_file_train=None, path=path, language=path.split('/')[-1]) for path in dataset_paths]
+dataset_config = [
+    BaseDatasetConfig(name="mailabs", meta_file_train=None, path=path, language=path.split("/")[-1])
+    for path in dataset_paths
+]
 
 audio_config = BaseAudioConfig(
     sample_rate=16000,
@@ -61,7 +64,7 @@ config = VitsConfig(
     phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
     compute_input_seq_cache=True,
     print_step=25,
-    use_language_weighted_sampler= True,
+    use_language_weighted_sampler=True,
     print_eval=False,
     mixed_precision=False,
     sort_by_audio_len=True,
@@ -69,21 +72,31 @@ config = VitsConfig(
     max_seq_len=160000,
     output_path=output_path,
     datasets=dataset_config,
-    characters= {
+    characters={
         "pad": "_",
         "eos": "&",
         "bos": "*",
         "characters": "!¡'(),-.:;¿?abcdefghijklmnopqrstuvwxyzµßàáâäåæçèéêëìíîïñòóôöùúûüąćęłńœśşźżƒабвгдежзийклмнопрстуфхцчшщъыьэюяёєіїґӧ «°±µ»$%&‘’‚“`”„",
         "punctuations": "!¡'(),-.:;¿? ",
         "phonemes": None,
-        "unique": True
+        "unique": True,
     },
     test_sentences=[
-        ["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.", 'mary_ann', None, 'en_US'],
+        [
-        ["Il m'a fallu beaucoup de temps pour d\u00e9velopper une voix, et maintenant que je l'ai, je ne vais pas me taire.", "ezwa", None, 'fr_FR'],
+            "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
-        ["Ich finde, dieses Startup ist wirklich unglaublich.", "eva_k", None, 'de_DE'],
+            "mary_ann",
-        ["Я думаю, что этот стартап действительно удивительный.", "oblomov", None, 'ru_RU'],
+            None,
-    ]
+            "en_US",
+        ],
+        [
+            "Il m'a fallu beaucoup de temps pour d\u00e9velopper une voix, et maintenant que je l'ai, je ne vais pas me taire.",
+            "ezwa",
+            None,
+            "fr_FR",
+        ],
+        ["Ich finde, dieses Startup ist wirklich unglaublich.", "eva_k", None, "de_DE"],
+        ["Я думаю, что этот стартап действительно удивительный.", "oblomov", None, "ru_RU"],
+    ],
 )
 
 # init audio processor

requirements.txt

@@ -26,3 +26,4 @@ unidic-lite==1.0.8
 gruut[cs,de,es,fr,it,nl,pt,ru,sv]~=2.0.0
 fsspec>=2021.04.0
 pyworld
+webrtcvad

tests/aux_tests/test_find_unique_phonemes.py

@@ -31,7 +31,7 @@ dataset_config_pt = BaseDatasetConfig(
 class TestFindUniquePhonemes(unittest.TestCase):
     @staticmethod
     def test_espeak_phonemes():
-       # prepare the config
+        # prepare the config
         config = VitsConfig(
             batch_size=2,
             eval_batch_size=2,
@@ -52,9 +52,7 @@ class TestFindUniquePhonemes(unittest.TestCase):
         config.save_json(config_path)
 
         # run test
-        run_cli(
+        run_cli(f'CUDA_VISIBLE_DEVICES="" python TTS/bin/find_unique_phonemes.py --config_path "{config_path}"')
-            f'CUDA_VISIBLE_DEVICES="" python TTS/bin/find_unique_phonemes.py --config_path "{config_path}"'
-        )
 
     @staticmethod
     def test_no_espeak_phonemes():
@@ -79,6 +77,4 @@ class TestFindUniquePhonemes(unittest.TestCase):
         config.save_json(config_path)
 
         # run test
-        run_cli(
+        run_cli(f'CUDA_VISIBLE_DEVICES="" python TTS/bin/find_unique_phonemes.py --config_path "{config_path}"')
-            f'CUDA_VISIBLE_DEVICES="" python TTS/bin/find_unique_phonemes.py --config_path "{config_path}"'
-        )

tests/data_tests/test_samplers.py

@@ -1,9 +1,11 @@
-from TTS.tts.datasets import load_tts_samples
-from TTS.config.shared_configs import BaseDatasetConfig
-from TTS.tts.utils.languages import get_language_weighted_sampler
-import torch
 import functools
 
+import torch
+
+from TTS.config.shared_configs import BaseDatasetConfig
+from TTS.tts.datasets import load_tts_samples
+from TTS.tts.utils.languages import get_language_weighted_sampler
+
 # Fixing random state to avoid random fails
 torch.manual_seed(0)
 
@@ -25,18 +27,19 @@ dataset_config_pt = BaseDatasetConfig(
 
 # Adding the EN samples twice to create an unbalanced dataset
 train_samples, eval_samples = load_tts_samples(
-    [dataset_config_en, dataset_config_en, dataset_config_pt],
+    [dataset_config_en, dataset_config_en, dataset_config_pt], eval_split=True
-    eval_split=True
 )
 
+
 def is_balanced(lang_1, lang_2):
-    return 0.85 < lang_1/lang_2 < 1.2
+    return 0.85 < lang_1 / lang_2 < 1.2
+
 
 random_sampler = torch.utils.data.RandomSampler(train_samples)
 ids = functools.reduce(lambda a, b: a + b, [list(random_sampler) for i in range(100)])
 en, pt = 0, 0
 for index in ids:
-    if train_samples[index][3] == 'en':
+    if train_samples[index][3] == "en":
         en += 1
     else:
         pt += 1
@@ -47,7 +50,7 @@ weighted_sampler = get_language_weighted_sampler(train_samples)
 ids = functools.reduce(lambda a, b: a + b, [list(weighted_sampler) for i in range(100)])
 en, pt = 0, 0
 for index in ids:
-    if train_samples[index][3] == 'en':
+    if train_samples[index][3] == "en":
         en += 1
     else:
         pt += 1