make style

TTS/bin/find_unique_phonemes.py

@@ -1,14 +1,15 @@
 """Find all the unique characters in a dataset"""
 import argparse
+import multiprocessing
 from argparse import RawTextHelpFormatter
 
+import numpy
+from tqdm.contrib.concurrent import process_map
+
 from TTS.config import load_config
 from TTS.tts.datasets import load_meta_data
-
-import numpy
-import multiprocessing
 from TTS.tts.utils.text import text2phone
-from tqdm.contrib.concurrent import process_map
+
 
 def compute_phonemes(item):
     try:
@@ -19,6 +20,7 @@ def compute_phonemes(item):
         return []
     return list(set(ph))
 
+
 def main():
     global c
     # pylint: disable=bad-option-value
@@ -51,8 +53,6 @@ def main():
     phones_force_lower = [c.lower() for c in phones]
     phones_force_lower = set(phones_force_lower)
 
-    
-
     print(f" > Number of unique phonemes: {len(phones)}")
     print(f" > Unique phonemes: {''.join(sorted(phones))}")
     print(f" > Unique lower phonemes: {''.join(sorted(lower_phones))}")

TTS/bin/remove_silence_using_vad.py

@@ -1,26 +1,27 @@
 # This code is adpated from: https://github.com/wiseman/py-webrtcvad/blob/master/example.py
-import os
-import tqdm
-import glob
 import argparse
-import pathlib
-
 import collections
 import contextlib
+import glob
+import multiprocessing
+import os
+import pathlib
 import sys
 import wave
+from itertools import chain
+
 import numpy as np
+import tqdm
 import webrtcvad
 from tqdm.contrib.concurrent import process_map
-import multiprocessing
-from itertools import chain
+
 
 def read_wave(path):
     """Reads a .wav file.
 
     Takes the path, and returns (PCM audio data, sample rate).
     """
-    with contextlib.closing(wave.open(path, 'rb')) as wf:
+    with contextlib.closing(wave.open(path, "rb")) as wf:
         num_channels = wf.getnchannels()
         assert num_channels == 1
         sample_width = wf.getsampwidth()
@@ -36,7 +37,7 @@ def write_wave(path, audio, sample_rate):
 
     Takes path, PCM audio data, and sample rate.
     """
-    with contextlib.closing(wave.open(path, 'wb')) as wf:
+    with contextlib.closing(wave.open(path, "wb")) as wf:
         wf.setnchannels(1)
         wf.setsampwidth(2)
         wf.setframerate(sample_rate)
@@ -45,6 +46,7 @@ def write_wave(path, audio, sample_rate):
 
 class Frame(object):
     """Represents a "frame" of audio data."""
+
     def __init__(self, bytes, timestamp, duration):
         self.bytes = bytes
         self.timestamp = timestamp
@@ -64,13 +66,12 @@ def frame_generator(frame_duration_ms, audio, sample_rate):
     timestamp = 0.0
     duration = (float(n) / sample_rate) / 2.0
     while offset + n < len(audio):
-        yield Frame(audio[offset:offset + n], timestamp, duration)
+        yield Frame(audio[offset : offset + n], timestamp, duration)
         timestamp += duration
         offset += n
 
 
-def vad_collector(sample_rate, frame_duration_ms,
-                  padding_duration_ms, vad, frames):
+def vad_collector(sample_rate, frame_duration_ms, padding_duration_ms, vad, frames):
     """Filters out non-voiced audio frames.
 
     Given a webrtcvad.Vad and a source of audio frames, yields only
@@ -133,25 +134,26 @@ def vad_collector(sample_rate, frame_duration_ms,
             # unvoiced, then enter NOTTRIGGERED and yield whatever
             # audio we've collected.
             if num_unvoiced > 0.9 * ring_buffer.maxlen:
-                #sys.stdout.write('-(%s)' % (frame.timestamp + frame.duration))
+                # sys.stdout.write('-(%s)' % (frame.timestamp + frame.duration))
                 triggered = False
-                yield b''.join([f.bytes for f in voiced_frames])
+                yield b"".join([f.bytes for f in voiced_frames])
                 ring_buffer.clear()
                 voiced_frames = []
     # If we have any leftover voiced audio when we run out of input,
     # yield it.
     if voiced_frames:
-        yield b''.join([f.bytes for f in voiced_frames])
+        yield b"".join([f.bytes for f in voiced_frames])
+
 
 def remove_silence(filepath):
     filename = os.path.basename(filepath)
-    output_path = filepath.replace(os.path.join(args.input_dir, ''),os.path.join(args.output_dir, ''))
+    output_path = filepath.replace(os.path.join(args.input_dir, ""), os.path.join(args.output_dir, ""))
     # ignore if the file exists
     if os.path.exists(output_path) and not args.force:
         return False
     # create all directory structure
     pathlib.Path(output_path).parent.mkdir(parents=True, exist_ok=True)
-    padding_duration_ms = 300 # default 300
+    padding_duration_ms = 300  # default 300
     audio, sample_rate = read_wave(filepath)
     vad = webrtcvad.Vad(int(args.aggressiveness))
     frames = frame_generator(30, audio, sample_rate)
@@ -180,6 +182,7 @@ def remove_silence(filepath):
         # if fail to remove silence just write the file
         write_wave(output_path, audio, sample_rate)
 
+
 def preprocess_audios():
     files = sorted(glob.glob(os.path.join(args.input_dir, args.glob), recursive=True))
     print("> Number of files: ", len(files))
@@ -193,21 +196,31 @@ def preprocess_audios():
     else:
         print("> No files Found !")
 
+
 if __name__ == "__main__":
     """
     usage
     python remove_silence.py -i=VCTK-Corpus-bk/ -o=../VCTK-Corpus-removed-silence -g=wav48/*/*.wav -a=2
     """
     parser = argparse.ArgumentParser()
-    parser.add_argument('-i', '--input_dir', type=str, default='../VCTK-Corpus',
-                        help='Dataset root dir')
-    parser.add_argument('-o', '--output_dir', type=str, default='../VCTK-Corpus-removed-silence',
-                        help='Output Dataset dir')
-    parser.add_argument('-f', '--force', type=bool, default=True,
-                        help='Force the replace of exists files')
-    parser.add_argument('-g', '--glob', type=str, default='**/*.wav',
-                        help='path in glob format for acess wavs from input_dir. ex: wav48/*/*.wav')
-    parser.add_argument('-a', '--aggressiveness', type=int, default=2,
-                        help='set its aggressiveness mode, which is an integer between 0 and 3. 0 is the least aggressive about filtering out non-speech, 3 is the most aggressive.')
+    parser.add_argument("-i", "--input_dir", type=str, default="../VCTK-Corpus", help="Dataset root dir")
+    parser.add_argument(
+        "-o", "--output_dir", type=str, default="../VCTK-Corpus-removed-silence", help="Output Dataset dir"
+    )
+    parser.add_argument("-f", "--force", type=bool, default=True, help="Force the replace of exists files")
+    parser.add_argument(
+        "-g",
+        "--glob",
+        type=str,
+        default="**/*.wav",
+        help="path in glob format for acess wavs from input_dir. ex: wav48/*/*.wav",
+    )
+    parser.add_argument(
+        "-a",
+        "--aggressiveness",
+        type=int,
+        default=2,
+        help="set its aggressiveness mode, which is an integer between 0 and 3. 0 is the least aggressive about filtering out non-speech, 3 is the most aggressive.",
+    )
     args = parser.parse_args()
     preprocess_audios()

TTS/speaker_encoder/models/resnet.py

@@ -5,20 +5,20 @@ import torch.nn as nn
 
 from TTS.utils.io import load_fsspec
 
+
 class PreEmphasis(torch.nn.Module):
     def __init__(self, coefficient=0.97):
         super().__init__()
         self.coefficient = coefficient
-        self.register_buffer(
-            'filter', torch.FloatTensor([-self.coefficient, 1.]).unsqueeze(0).unsqueeze(0)
-        )
+        self.register_buffer("filter", torch.FloatTensor([-self.coefficient, 1.0]).unsqueeze(0).unsqueeze(0))
 
     def forward(self, x):
         assert len(x.size()) == 2
 
-        x = torch.nn.functional.pad(x.unsqueeze(1), (1, 0), 'reflect')
+        x = torch.nn.functional.pad(x.unsqueeze(1), (1, 0), "reflect")
         return torch.nn.functional.conv1d(x, self.filter).squeeze(1)
 
+
 class SELayer(nn.Module):
     def __init__(self, channel, reduction=8):
         super(SELayer, self).__init__()
@@ -110,8 +110,15 @@ class ResNetSpeakerEncoder(nn.Module):
         if self.use_torch_spec:
             self.torch_spec = torch.nn.Sequential(
                 PreEmphasis(audio_config["preemphasis"]),
-                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"])
-                )
+                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
 
@@ -213,7 +220,7 @@ class ResNetSpeakerEncoder(nn.Module):
         """
         # map to the waveform size
         if self.use_torch_spec:
-            num_frames = num_frames * self.audio_config['hop_length']
+            num_frames = num_frames * self.audio_config["hop_length"]
 
         max_len = x.shape[1]
 

TTS/speaker_encoder/utils/generic_utils.py

@@ -179,10 +179,12 @@ def setup_model(c):
             c.model_params["num_lstm_layers"],
         )
     elif c.model_params["model_name"].lower() == "resnet":
-        model = ResNetSpeakerEncoder(input_dim=c.model_params["input_dim"], proj_dim=c.model_params["proj_dim"],
+        model = ResNetSpeakerEncoder(
+            input_dim=c.model_params["input_dim"],
+            proj_dim=c.model_params["proj_dim"],
             log_input=c.model_params.get("log_input", False),
             use_torch_spec=c.model_params.get("use_torch_spec", False),
-            audio_config=c.audio
+            audio_config=c.audio,
         )
     return model
 

TTS/trainer.py

@@ -265,7 +265,9 @@ class Trainer:
             config = self.config.model_args if hasattr(self.config, "model_args") else self.config
             # save speakers json
             if config.use_language_embedding and self.model.language_manager.num_languages > 1:
-                self.model.language_manager.save_language_ids_to_file(os.path.join(self.output_path, "language_ids.json"))
+                self.model.language_manager.save_language_ids_to_file(
+                    os.path.join(self.output_path, "language_ids.json")
+                )
             if hasattr(self.config, "model_args"):
                 self.config.model_args["num_languages"] = self.model.language_manager.num_languages
             else:

TTS/tts/datasets/dataset.py

@@ -542,6 +542,7 @@ class TTSDataset(Dataset):
             )
         )
 
+
 class PitchExtractor:
     """Pitch Extractor for computing F0 from wav files.
     Args:

TTS/tts/datasets/formatters.py

@@ -304,7 +304,7 @@ def vctk(root_path, meta_files=None, wavs_path="wav48", ununsed_speakers=None):
     return items
 
 
-def vctk_slim(root_path, meta_files=None, wavs_path="wav48", ununsed_speakers=None):   # pylint: disable=unused-argument
+def vctk_slim(root_path, meta_files=None, wavs_path="wav48", ununsed_speakers=None):  # pylint: disable=unused-argument
     """homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz"""
     items = []
     txt_files = glob(f"{os.path.join(root_path,'txt')}/**/*.txt", recursive=True)

TTS/tts/layers/losses.py

@@ -602,7 +602,7 @@ class VitsGeneratorLoss(nn.Module):
         fine_tuning_mode=0,
         use_speaker_encoder_as_loss=False,
         gt_spk_emb=None,
-        syn_spk_emb=None
+        syn_spk_emb=None,
     ):
         """
         Shapes:
@@ -638,7 +638,9 @@ class VitsGeneratorLoss(nn.Module):
         loss = loss_kl + loss_feat + loss_mel + loss_gen + loss_duration
 
         if use_speaker_encoder_as_loss:
-            loss_se = - torch.nn.functional.cosine_similarity(gt_spk_emb, syn_spk_emb).mean() * self.spk_encoder_loss_alpha
+            loss_se = (
+                -torch.nn.functional.cosine_similarity(gt_spk_emb, syn_spk_emb).mean() * self.spk_encoder_loss_alpha
+            )
             loss += loss_se
             return_dict["loss_spk_encoder"] = loss_se
 

TTS/tts/layers/vits/stochastic_duration_predictor.py

@@ -178,7 +178,14 @@ class StochasticDurationPredictor(nn.Module):
     """
 
     def __init__(
-        self, in_channels: int, hidden_channels: int, kernel_size: int, dropout_p: float, num_flows=4, cond_channels=0, language_emb_dim=None
+        self,
+        in_channels: int,
+        hidden_channels: int,
+        kernel_size: int,
+        dropout_p: float,
+        num_flows=4,
+        cond_channels=0,
+        language_emb_dim=None,
     ):
         super().__init__()
 

TTS/tts/models/base_tts.py

@@ -246,7 +246,9 @@ class BaseTTS(BaseModel):
             # setup multi-speaker attributes
             if hasattr(self, "speaker_manager") and self.speaker_manager is not None:
                 if hasattr(config, "model_args"):
-                    speaker_id_mapping = self.speaker_manager.speaker_ids if config.model_args.use_speaker_embedding else None
+                    speaker_id_mapping = (
+                        self.speaker_manager.speaker_ids if config.model_args.use_speaker_embedding else None
+                    )
                     d_vector_mapping = self.speaker_manager.d_vectors if config.model_args.use_d_vector_file else None
                     config.use_d_vector_file = config.model_args.use_d_vector_file
                 else:
@@ -262,7 +264,9 @@ class BaseTTS(BaseModel):
                 custom_symbols = self.make_symbols(self.config)
 
             if hasattr(self, "language_manager"):
-                language_id_mapping = self.language_manager.language_id_mapping if self.args.use_language_embedding else None
+                language_id_mapping = (
+                    self.language_manager.language_id_mapping if self.args.use_language_embedding else None
+                )
             else:
                 language_id_mapping = None
 

TTS/tts/models/vits.py

@@ -229,7 +229,6 @@ class VitsArgs(Coqpit):
     freeze_waveform_decoder: bool = False
 
 
-
 class Vits(BaseTTS):
     """VITS TTS model
 
@@ -306,7 +305,7 @@ class Vits(BaseTTS):
             args.num_layers_text_encoder,
             args.kernel_size_text_encoder,
             args.dropout_p_text_encoder,
-            language_emb_dim=self.embedded_language_dim
+            language_emb_dim=self.embedded_language_dim,
         )
 
         self.posterior_encoder = PosteriorEncoder(
@@ -389,16 +388,26 @@ class Vits(BaseTTS):
         # TODO: make this a function
         if config.use_speaker_encoder_as_loss:
             if not config.speaker_encoder_model_path or not config.speaker_encoder_config_path:
-                raise RuntimeError(" [!] To use the speaker encoder loss you need to specify speaker_encoder_model_path and speaker_encoder_config_path !!")
-            self.speaker_manager.init_speaker_encoder(config.speaker_encoder_model_path, config.speaker_encoder_config_path)
+                raise RuntimeError(
+                    " [!] To use the speaker encoder loss you need to specify speaker_encoder_model_path and speaker_encoder_config_path !!"
+                )
+            self.speaker_manager.init_speaker_encoder(
+                config.speaker_encoder_model_path, config.speaker_encoder_config_path
+            )
             self.speaker_encoder = self.speaker_manager.speaker_encoder.train()
             for param in self.speaker_encoder.parameters():
                 param.requires_grad = False
 
             print(" > External Speaker Encoder Loaded !!")
 
-            if hasattr(self.speaker_encoder, "audio_config") and self.audio_config["sample_rate"] != self.speaker_encoder.audio_config["sample_rate"]:
-                self.audio_transform = torchaudio.transforms.Resample(orig_freq=self.audio_config["sample_rate"], new_freq=self.speaker_encoder.audio_config["sample_rate"])
+            if (
+                hasattr(self.speaker_encoder, "audio_config")
+                and self.audio_config["sample_rate"] != self.speaker_encoder.audio_config["sample_rate"]
+            ):
+                self.audio_transform = torchaudio.transforms.Resample(
+                    orig_freq=self.audio_config["sample_rate"],
+                    new_freq=self.speaker_encoder.audio_config["sample_rate"],
+                )
             else:
                 self.audio_transform = None
         else:
@@ -529,7 +538,13 @@ class Vits(BaseTTS):
         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 forward(
         self,
@@ -567,7 +582,7 @@ class Vits(BaseTTS):
             g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
 
         # language embedding
-        lang_emb=None
+        lang_emb = None
         if self.args.use_language_embedding and lid is not None:
             lang_emb = self.emb_l(lid).unsqueeze(-1)
 
@@ -621,9 +636,9 @@ class Vits(BaseTTS):
         o = self.waveform_decoder(z_slice, g=g)
 
         wav_seg = segment(
-                waveform.transpose(1, 2),
-                slice_ids * self.config.audio.hop_length,
-                self.args.spec_segment_size * self.config.audio.hop_length,
+            waveform.transpose(1, 2),
+            slice_ids * self.config.audio.hop_length,
+            self.args.spec_segment_size * self.config.audio.hop_length,
         )
 
         if self.args.use_speaker_encoder_as_loss and self.speaker_encoder is not None:
@@ -653,7 +668,7 @@ class Vits(BaseTTS):
                 "logs_q": logs_q,
                 "waveform_seg": wav_seg,
                 "gt_spk_emb": gt_spk_emb,
-                "syn_spk_emb": syn_spk_emb
+                "syn_spk_emb": syn_spk_emb,
             }
         )
         return outputs
@@ -695,7 +710,7 @@ class Vits(BaseTTS):
                 g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
 
             # language embedding
-            lang_emb=None
+            lang_emb = None
             if self.args.use_language_embedding and lid is not None:
                 lang_emb = self.emb_l(lid).unsqueeze(-1)
 
@@ -737,9 +752,9 @@ class Vits(BaseTTS):
         o = self.waveform_decoder(z_slice, g=g)
 
         wav_seg = segment(
-                waveform.transpose(1, 2),
-                slice_ids * self.config.audio.hop_length,
-                self.args.spec_segment_size * self.config.audio.hop_length,
+            waveform.transpose(1, 2),
+            slice_ids * self.config.audio.hop_length,
+            self.args.spec_segment_size * self.config.audio.hop_length,
         )
 
         if self.args.use_speaker_encoder_as_loss and self.speaker_encoder is not None:
@@ -770,7 +785,7 @@ class Vits(BaseTTS):
                 "logs_q": logs_q,
                 "waveform_seg": wav_seg,
                 "gt_spk_emb": gt_spk_emb,
-                "syn_spk_emb": syn_spk_emb
+                "syn_spk_emb": syn_spk_emb,
             }
         )
         return outputs
@@ -790,14 +805,16 @@ class Vits(BaseTTS):
             g = self.emb_g(sid).unsqueeze(-1)
 
         # language embedding
-        lang_emb=None
+        lang_emb = None
         if self.args.use_language_embedding and lid is not None:
             lang_emb = self.emb_l(lid).unsqueeze(-1)
 
         x, m_p, logs_p, x_mask = self.text_encoder(x, x_lengths, lang_emb=lang_emb)
 
         if self.args.use_sdp:
-            logw = self.duration_predictor(x, x_mask, g=g, reverse=True, noise_scale=self.inference_noise_scale_dp, lang_emb=lang_emb)
+            logw = self.duration_predictor(
+                x, x_mask, g=g, reverse=True, noise_scale=self.inference_noise_scale_dp, lang_emb=lang_emb
+            )
         else:
             logw = self.duration_predictor(x, x_mask, g=g, lang_emb=lang_emb)
 
@@ -866,7 +883,7 @@ class Vits(BaseTTS):
             for param in self.text_encoder.parameters():
                 param.requires_grad = False
 
-            if hasattr(self, 'emb_l'):
+            if hasattr(self, "emb_l"):
                 for param in self.emb_l.parameters():
                     param.requires_grad = False
 
@@ -932,7 +949,7 @@ class Vits(BaseTTS):
             with autocast(enabled=False):  # use float32 for the criterion
                 loss_dict = criterion[optimizer_idx](
                     waveform_hat=outputs["model_outputs"].float(),
-                    waveform= outputs["waveform_seg"].float(),
+                    waveform=outputs["waveform_seg"].float(),
                     z_p=outputs["z_p"].float(),
                     logs_q=outputs["logs_q"].float(),
                     m_p=outputs["m_p"].float(),
@@ -945,7 +962,7 @@ class Vits(BaseTTS):
                     fine_tuning_mode=self.args.fine_tuning_mode,
                     use_speaker_encoder_as_loss=self.args.use_speaker_encoder_as_loss,
                     gt_spk_emb=outputs["gt_spk_emb"],
-                    syn_spk_emb=outputs["syn_spk_emb"]
+                    syn_spk_emb=outputs["syn_spk_emb"],
                 )
             # ignore duration loss if fine tuning mode is on
             if not self.args.fine_tuning_mode:

TTS/tts/utils/languages.py

@@ -1,13 +1,14 @@
-import os
 import json
-import torch
+import os
+from typing import Dict, List, Tuple
+
 import fsspec
 import numpy as np
-from typing import Dict, Tuple, List
+import torch
 from coqpit import Coqpit
-
 from torch.utils.data.sampler import WeightedRandomSampler
 
+
 class LanguageManager:
     """Manage the languages for multi-lingual 🐸TTS models. Load a datafile and parse the information
     in a way that can be queried by language.
@@ -20,7 +21,9 @@ class LanguageManager:
         >>> manager = LanguageManager(language_id_file_path=language_id_file_path)
         >>> language_id_mapper = manager.language_ids
     """
+
     language_id_mapping: Dict = {}
+
     def __init__(
         self,
         language_id_file_path: str = "",
@@ -85,6 +88,7 @@ class LanguageManager:
         """
         self._save_json(file_path, self.language_id_mapping)
 
+
 def _set_file_path(path):
     """Find the language_ids.json under the given path or the above it.
     Intended to band aid the different paths returned in restored and continued training."""
@@ -97,6 +101,7 @@ def _set_file_path(path):
         return path_continue
     return None
 
+
 def get_language_manager(c: Coqpit, data: List = None, restore_path: str = None) -> LanguageManager:
     """Initiate a `LanguageManager` instance by the provided config.
 
@@ -118,7 +123,7 @@ def get_language_manager(c: Coqpit, data: List = None, restore_path: str = None)
             # restoring language manager from a previous run.
             if language_file:
                 language_manager.set_language_ids_from_file(language_file)
-        if  language_manager.num_languages > 0:
+        if language_manager.num_languages > 0:
             print(
                 " > Language manager is loaded with {} languages: {}".format(
                     language_manager.num_languages, ", ".join(language_manager.language_names)
@@ -126,11 +131,12 @@ def get_language_manager(c: Coqpit, data: List = None, restore_path: str = None)
             )
     return language_manager
 
+
 def get_language_weighted_sampler(items: list):
     language_names = np.array([item[3] for item in items])
     unique_language_names = np.unique(language_names).tolist()
     language_ids = [unique_language_names.index(l) for l in language_names]
     language_count = np.array([len(np.where(language_names == l)[0]) for l in unique_language_names])
-    weight_language = 1. / language_count
+    weight_language = 1.0 / language_count
     dataset_samples_weight = torch.from_numpy(np.array([weight_language[l] for l in language_ids])).double()
     return WeightedRandomSampler(dataset_samples_weight, len(dataset_samples_weight))

TTS/tts/utils/speakers.py

@@ -432,11 +432,12 @@ def get_speaker_manager(c: Coqpit, data: List = None, restore_path: str = None,
                 speaker_manager.save_speaker_ids_to_file(out_file_path)
     return speaker_manager
 
+
 def get_speaker_weighted_sampler(items: list):
     speaker_names = np.array([item[2] for item in items])
     unique_speaker_names = np.unique(speaker_names).tolist()
     speaker_ids = [unique_speaker_names.index(l) for l in speaker_names]
     speaker_count = np.array([len(np.where(speaker_names == l)[0]) for l in unique_speaker_names])
-    weight_speaker = 1. / speaker_count
+    weight_speaker = 1.0 / speaker_count
     dataset_samples_weight = torch.from_numpy(np.array([weight_speaker[l] for l in speaker_ids])).double()
     return WeightedRandomSampler(dataset_samples_weight, len(dataset_samples_weight))

TTS/tts/utils/text/cleaners.py

@@ -136,8 +136,9 @@ def phoneme_cleaners(text):
     text = collapse_whitespace(text)
     return text
 
+
 def multilingual_cleaners(text):
-    '''Pipeline for multilingual text'''
+    """Pipeline for multilingual text"""
     text = lowercase(text)
     text = replace_symbols(text, lang=None)
     text = remove_aux_symbols(text)

tests/tts_tests/test_vits_multilingual_train.py

@@ -3,19 +3,27 @@ import os
 import shutil
 
 from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.tts.configs.vits_config import VitsConfig
 from TTS.config.shared_configs import BaseDatasetConfig
+from TTS.tts.configs.vits_config import VitsConfig
 
 config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
 output_path = os.path.join(get_tests_output_path(), "train_outputs")
 
 
 dataset_config1 = BaseDatasetConfig(
-    name="ljspeech", meta_file_train="metadata.csv", meta_file_val="metadata.csv", path="tests/data/ljspeech", language="en"
+    name="ljspeech",
+    meta_file_train="metadata.csv",
+    meta_file_val="metadata.csv",
+    path="tests/data/ljspeech",
+    language="en",
 )
 
 dataset_config2 = BaseDatasetConfig(
-    name="ljspeech", meta_file_train="metadata.csv", meta_file_val="metadata.csv", path="tests/data/ljspeech", language="en2"
+    name="ljspeech",
+    meta_file_train="metadata.csv",
+    meta_file_val="metadata.csv",
+    path="tests/data/ljspeech",
+    language="en2",
 )
 
 config = VitsConfig(