Make style and lint

TTS/bin/extract_tts_spectrograms.py

@@ -229,7 +229,9 @@ def main(args):  # pylint: disable=redefined-outer-name
     ap = AudioProcessor(**c.audio)
 
     # load data instances
-    meta_data_train, meta_data_eval = load_tts_samples(c.datasets, eval_split=args.eval, eval_split_max_size=c.eval_split_max_size, eval_split_size=c.eval_split_size)
+    meta_data_train, meta_data_eval = load_tts_samples(
+        c.datasets, eval_split=args.eval, eval_split_max_size=c.eval_split_max_size, eval_split_size=c.eval_split_size
+    )
 
     # use eval and training partitions
     meta_data = meta_data_train + meta_data_eval

TTS/bin/find_unique_chars.py

@@ -23,7 +23,9 @@ def main():
     c = load_config(args.config_path)
 
     # load all datasets
-    train_items, eval_items = load_tts_samples(c.datasets, eval_split=True, eval_split_max_size=c.eval_split_max_size, eval_split_size=c.eval_split_size)
+    train_items, eval_items = load_tts_samples(
+        c.datasets, eval_split=True, eval_split_max_size=c.eval_split_max_size, eval_split_size=c.eval_split_size
+    )
 
     items = train_items + eval_items
 

TTS/bin/find_unique_phonemes.py

@@ -40,7 +40,9 @@ def main():
     c = load_config(args.config_path)
 
     # load all datasets
-    train_items, eval_items = load_tts_samples(c.datasets, eval_split=True, eval_split_max_size=c.eval_split_max_size, eval_split_size=c.eval_split_size)
+    train_items, eval_items = load_tts_samples(
+        c.datasets, eval_split=True, eval_split_max_size=c.eval_split_max_size, eval_split_size=c.eval_split_size
+    )
     items = train_items + eval_items
     print("Num items:", len(items))
 

TTS/bin/train_tts.py

@@ -44,7 +44,12 @@ def main():
             config = register_config(config_base.model)()
 
     # load training samples
-    train_samples, eval_samples = load_tts_samples(config.datasets, eval_split=True, eval_split_max_size=config.eval_split_max_size, eval_split_size=config.eval_split_size)
+    train_samples, eval_samples = load_tts_samples(
+        config.datasets,
+        eval_split=True,
+        eval_split_max_size=config.eval_split_max_size,
+        eval_split_size=config.eval_split_size,
+    )
 
     # init the model from config
     model = setup_model(config, train_samples + eval_samples)

TTS/tts/datasets/__init__.py

@@ -12,20 +12,20 @@ from TTS.tts.datasets.formatters import *
 def split_dataset(items, eval_split_max_size=None, eval_split_size=0.01):
     """Split a dataset into train and eval. Consider speaker distribution in multi-speaker training.
 
-    Args:
-<<<<<<< HEAD
-        items (List[List]):
-            A list of samples. Each sample is a list of `[audio_path, text, speaker_id]`.
+        Args:
+    <<<<<<< HEAD
+            items (List[List]):
+                A list of samples. Each sample is a list of `[audio_path, text, speaker_id]`.
 
-        eval_split_max_size (int):
-            Number maximum of samples to be used for evaluation in proportion split. Defaults to None (Disabled).
+            eval_split_max_size (int):
+                Number maximum of samples to be used for evaluation in proportion split. Defaults to None (Disabled).
 
-        eval_split_size (float):
-            If between 0.0 and 1.0 represents the proportion of the dataset to include in the evaluation set.
-            If > 1, represents the absolute number of evaluation samples. Defaults to 0.01 (1%).
-=======
-        items (List[List]): A list of samples. Each sample is a list of `[text, audio_path, speaker_id]`.
->>>>>>> Fix docstring
+            eval_split_size (float):
+                If between 0.0 and 1.0 represents the proportion of the dataset to include in the evaluation set.
+                If > 1, represents the absolute number of evaluation samples. Defaults to 0.01 (1%).
+    =======
+            items (List[List]): A list of samples. Each sample is a list of `[text, audio_path, speaker_id]`.
+    >>>>>>> Fix docstring
     """
     speakers = [item["speaker_name"] for item in items]
     is_multi_speaker = len(set(speakers)) > 1
@@ -37,7 +37,11 @@ def split_dataset(items, eval_split_max_size=None, eval_split_size=0.01):
         else:
             eval_split_size = int(len(items) * eval_split_size)
 
-    assert eval_split_size > 0, " [!] You do not have enough samples for the evaluation set. You can work around this setting the 'eval_split_size' parameter to a minimum of {}".format(1/len(items))
+    assert (
+        eval_split_size > 0
+    ), " [!] You do not have enough samples for the evaluation set. You can work around this setting the 'eval_split_size' parameter to a minimum of {}".format(
+        1 / len(items)
+    )
     np.random.seed(0)
     np.random.shuffle(items)
     if is_multi_speaker:
@@ -56,8 +60,11 @@ def split_dataset(items, eval_split_max_size=None, eval_split_size=0.01):
 
 
 def load_tts_samples(
-    datasets: Union[List[Dict], Dict], eval_split=True, formatter: Callable = None,
-    eval_split_max_size=None, eval_split_size=0.01
+    datasets: Union[List[Dict], Dict],
+    eval_split=True,
+    formatter: Callable = None,
+    eval_split_max_size=None,
+    eval_split_size=0.01,
 ) -> Tuple[List[List], List[List]]:
     """Parse the dataset from the datasets config, load the samples as a List and load the attention alignments if provided.
     If `formatter` is not None, apply the formatter to the samples else pick the formatter from the available ones based

TTS/tts/datasets/formatters.py

@@ -132,7 +132,7 @@ def ljspeech_test(root_path, meta_file, **kwargs):  # pylint: disable=unused-arg
         speaker_id = 0
         for idx, line in enumerate(ttf):
             # 2 samples per speaker to avoid eval split issues
-            if idx%2 == 0:
+            if idx % 2 == 0:
                 speaker_id += 1
             cols = line.split("|")
             wav_file = os.path.join(root_path, "wavs", cols[0] + ".wav")

TTS/tts/models/glow_tts.py

@@ -183,8 +183,8 @@ class GlowTTS(BaseTTS):
         if g is not None:
             if hasattr(self, "emb_g"):
                 # use speaker embedding layer
-                if not g.size(): # if is a scalar
-                    g = g.unsqueeze(0) # unsqueeze
+                if not g.size():  # if is a scalar
+                    g = g.unsqueeze(0)  # unsqueeze
                 g = F.normalize(self.emb_g(g)).unsqueeze(-1)  # [b, h, 1]
             else:
                 # use d-vector

TTS/tts/models/vits.py

@@ -14,6 +14,7 @@ from torch.cuda.amp.autocast_mode import autocast
 from torch.nn import functional as F
 from torch.utils.data import DataLoader
 from torch.utils.data.distributed import DistributedSampler
+from trainer.trainer_utils import get_optimizer, get_scheduler
 
 from TTS.tts.configs.shared_configs import CharactersConfig
 from TTS.tts.datasets.dataset import TTSDataset, _parse_sample
@@ -29,7 +30,6 @@ from TTS.tts.utils.synthesis import synthesis
 from TTS.tts.utils.text.characters import BaseCharacters, _characters, _pad, _phonemes, _punctuations
 from TTS.tts.utils.text.tokenizer import TTSTokenizer
 from TTS.tts.utils.visual import plot_alignment
-from trainer.trainer_utils import get_optimizer, get_scheduler
 from TTS.vocoder.models.hifigan_generator import HifiganGenerator
 from TTS.vocoder.utils.generic_utils import plot_results
 
@@ -1481,10 +1481,12 @@ class Vits(BaseTTS):
         language_manager = LanguageManager.init_from_config(config)
 
         if config.model_args.speaker_encoder_model_path is not None:
-            speaker_manager.init_speaker_encoder(config.model_args.speaker_encoder_model_path,
-                                                                   config.model_args.speaker_encoder_config_path)
+            speaker_manager.init_speaker_encoder(
+                config.model_args.speaker_encoder_model_path, config.model_args.speaker_encoder_config_path
+            )
         return Vits(new_config, ap, tokenizer, speaker_manager, language_manager)
 
+
 ##################################
 # VITS CHARACTERS
 ##################################

TTS/vocoder/models/gan.py

@@ -7,10 +7,10 @@ from coqpit import Coqpit
 from torch import nn
 from torch.utils.data import DataLoader
 from torch.utils.data.distributed import DistributedSampler
+from trainer.trainer_utils import get_optimizer, get_scheduler
 
 from TTS.utils.audio import AudioProcessor
 from TTS.utils.io import load_fsspec
-from trainer.trainer_utils import get_optimizer, get_scheduler
 from TTS.vocoder.datasets.gan_dataset import GANDataset
 from TTS.vocoder.layers.losses import DiscriminatorLoss, GeneratorLoss
 from TTS.vocoder.models import setup_discriminator, setup_generator

TTS/vocoder/models/wavegrad.py

@@ -8,9 +8,9 @@ from torch import nn
 from torch.nn.utils import weight_norm
 from torch.utils.data import DataLoader
 from torch.utils.data.distributed import DistributedSampler
+from trainer.trainer_utils import get_optimizer, get_scheduler
 
 from TTS.utils.io import load_fsspec
-from trainer.trainer_utils import get_optimizer, get_scheduler
 from TTS.vocoder.datasets import WaveGradDataset
 from TTS.vocoder.layers.wavegrad import Conv1d, DBlock, FiLM, UBlock
 from TTS.vocoder.models.base_vocoder import BaseVocoder

tests/data_tests/test_loader.py

@@ -7,7 +7,7 @@ import torch
 from torch.utils.data import DataLoader
 
 from tests import get_tests_output_path
-from TTS.tts.configs.shared_configs import BaseTTSConfig, BaseDatasetConfig
+from TTS.tts.configs.shared_configs import BaseDatasetConfig, BaseTTSConfig
 from TTS.tts.datasets import TTSDataset, load_tts_samples
 from TTS.tts.utils.text.tokenizer import TTSTokenizer
 from TTS.utils.audio import AudioProcessor
@@ -24,7 +24,7 @@ c.data_path = "tests/data/ljspeech/"
 ok_ljspeech = os.path.exists(c.data_path)
 
 dataset_config = BaseDatasetConfig(
-    name="ljspeech_test", # ljspeech_test to multi-speaker
+    name="ljspeech_test",  # ljspeech_test to multi-speaker
     meta_file_train="metadata.csv",
     meta_file_val=None,
     path=c.data_path,
@@ -106,9 +106,9 @@ class TestTTSDataset(unittest.TestCase):
                 # make sure that the computed mels and the waveform match and correctly computed
                 mel_new = self.ap.melspectrogram(wavs[0].squeeze().numpy())
                 # remove padding in mel-spectrogram
-                mel_dataloader = mel_input[0].T.numpy()[:, :mel_lengths[0]]
+                mel_dataloader = mel_input[0].T.numpy()[:, : mel_lengths[0]]
                 # guarantee that both mel-spectrograms have the same size and that we will remove waveform padding
-                mel_new = mel_new[:, :mel_lengths[0]]
+                mel_new = mel_new[:, : mel_lengths[0]]
                 ignore_seg = -(1 + c.audio.win_length // c.audio.hop_length)
                 mel_diff = (mel_new[:, : mel_input.shape[1]] - mel_input[0].T.numpy())[:, 0:ignore_seg]
                 self.assertLess(abs(mel_diff.sum()), 1e-5)

tests/inference_tests/test_synthesizer.py

@@ -1,13 +1,12 @@
 import os
 import unittest
 
+from tests import get_tests_output_path
 from TTS.config import load_config
 from TTS.tts.models import setup_model
 from TTS.utils.io import save_checkpoint
 from TTS.utils.synthesizer import Synthesizer
 
-from tests import get_tests_output_path
-
 
 class SynthesizerTest(unittest.TestCase):
     # pylint: disable=R0201

tests/text_tests/test_characters.py

@@ -1,13 +1,20 @@
 import unittest
 
-from TTS.tts.utils.text.characters import BaseCharacters, Graphemes, IPAPhonemes, BaseVocabulary
+from TTS.tts.utils.text.characters import BaseCharacters, BaseVocabulary, Graphemes, IPAPhonemes
 
 # pylint: disable=protected-access
 
+
 class BaseVocabularyTest(unittest.TestCase):
     def setUp(self):
         self.phonemes = IPAPhonemes()
-        self.base_vocab = BaseVocabulary(vocab=self.phonemes._vocab, pad=self.phonemes.pad,  blank=self.phonemes.blank, bos=self.phonemes.bos, eos=self.phonemes.eos)
+        self.base_vocab = BaseVocabulary(
+            vocab=self.phonemes._vocab,
+            pad=self.phonemes.pad,
+            blank=self.phonemes.blank,
+            bos=self.phonemes.bos,
+            eos=self.phonemes.eos,
+        )
         self.empty_vocab = BaseVocabulary({})
 
     def test_pad_id(self):
@@ -22,8 +29,8 @@ class BaseVocabularyTest(unittest.TestCase):
         self.assertEqual(self.empty_vocab.vocab, {})
         self.assertEqual(self.base_vocab.vocab, self.phonemes._vocab)
 
-    def test_init_from_config(self):
-        ...
+    # def test_init_from_config(self):
+    #     ...
 
     def test_num_chars(self):
         self.assertEqual(self.empty_vocab.num_chars, 0)

tests/tts_tests/test_align_tts_train.py

@@ -2,9 +2,10 @@ import glob
 import os
 import shutil
 
+from trainer import get_last_checkpoint
+
 from tests import get_device_id, get_tests_output_path, run_cli
 from TTS.tts.configs.align_tts_config import AlignTTSConfig
-from trainer import get_last_checkpoint
 
 config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
 output_path = os.path.join(get_tests_output_path(), "train_outputs")
@@ -51,7 +52,7 @@ continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getm
 # Inference using TTS API
 continue_config_path = os.path.join(continue_path, "config.json")
 continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), 'output.wav')
+out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
 
 inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
 run_cli(inference_command)

tests/tts_tests/test_fast_pitch_speaker_emb_train.py

@@ -2,10 +2,11 @@ import glob
 import os
 import shutil
 
+from trainer import get_last_checkpoint
+
 from tests import get_device_id, get_tests_output_path, run_cli
 from TTS.config.shared_configs import BaseAudioConfig
 from TTS.tts.configs.fast_pitch_config import FastPitchConfig
-from trainer import get_last_checkpoint
 
 config_path = os.path.join(get_tests_output_path(), "fast_pitch_speaker_emb_config.json")
 output_path = os.path.join(get_tests_output_path(), "train_outputs")
@@ -69,7 +70,7 @@ continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getm
 # Inference using TTS API
 continue_config_path = os.path.join(continue_path, "config.json")
 continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), 'output.wav')
+out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
 speaker_id = "ljspeech-1"
 continue_speakers_path = os.path.join(continue_path, "speakers.json")
 

tests/tts_tests/test_fast_pitch_train.py

@@ -2,10 +2,11 @@ import glob
 import os
 import shutil
 
+from trainer import get_last_checkpoint
+
 from tests import get_device_id, get_tests_output_path, run_cli
 from TTS.config.shared_configs import BaseAudioConfig
 from TTS.tts.configs.fast_pitch_config import FastPitchConfig
-from trainer import get_last_checkpoint
 
 config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
 output_path = os.path.join(get_tests_output_path(), "train_outputs")
@@ -70,7 +71,7 @@ continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getm
 # Inference using TTS API
 continue_config_path = os.path.join(continue_path, "config.json")
 continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), 'output.wav')
+out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
 
 inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
 run_cli(inference_command)

tests/tts_tests/test_glow_tts_d-vectors_train.py

@@ -2,9 +2,10 @@ import glob
 import os
 import shutil
 
+from trainer import get_last_checkpoint
+
 from tests import get_device_id, get_tests_output_path, run_cli
 from TTS.tts.configs.glow_tts_config import GlowTTSConfig
-from trainer import get_last_checkpoint
 
 config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
 output_path = os.path.join(get_tests_output_path(), "train_outputs")
@@ -56,7 +57,7 @@ continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getm
 # Inference using TTS API
 continue_config_path = os.path.join(continue_path, "config.json")
 continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), 'output.wav')
+out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
 speaker_id = "ljspeech-1"
 continue_speakers_path = config.d_vector_file
 

tests/tts_tests/test_glow_tts_speaker_emb_train.py

@@ -2,9 +2,10 @@ import glob
 import os
 import shutil
 
+from trainer import get_last_checkpoint
+
 from tests import get_device_id, get_tests_output_path, run_cli
 from TTS.tts.configs.glow_tts_config import GlowTTSConfig
-from trainer import get_last_checkpoint
 
 config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
 output_path = os.path.join(get_tests_output_path(), "train_outputs")
@@ -53,7 +54,7 @@ continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getm
 # Inference using TTS API
 continue_config_path = os.path.join(continue_path, "config.json")
 continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), 'output.wav')
+out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
 speaker_id = "ljspeech-1"
 continue_speakers_path = os.path.join(continue_path, "speakers.json")
 

tests/tts_tests/test_glow_tts_train.py

@@ -2,9 +2,10 @@ import glob
 import os
 import shutil
 
+from trainer import get_last_checkpoint
+
 from tests import get_device_id, get_tests_output_path, run_cli
 from TTS.tts.configs.glow_tts_config import GlowTTSConfig
-from trainer import get_last_checkpoint
 
 config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
 output_path = os.path.join(get_tests_output_path(), "train_outputs")
@@ -52,7 +53,7 @@ continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getm
 # Inference using TTS API
 continue_config_path = os.path.join(continue_path, "config.json")
 continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), 'output.wav')
+out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
 
 inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
 run_cli(inference_command)

tests/tts_tests/test_speedy_speech_train.py

@@ -2,9 +2,10 @@ import glob
 import os
 import shutil
 
+from trainer import get_last_checkpoint
+
 from tests import get_device_id, get_tests_output_path, run_cli
 from TTS.tts.configs.speedy_speech_config import SpeedySpeechConfig
-from trainer import get_last_checkpoint
 
 config_path = os.path.join(get_tests_output_path(), "test_speedy_speech_config.json")
 output_path = os.path.join(get_tests_output_path(), "train_outputs")
@@ -51,7 +52,7 @@ continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getm
 # Inference using TTS API
 continue_config_path = os.path.join(continue_path, "config.json")
 continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), 'output.wav')
+out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
 
 inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example for it.' --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
 run_cli(inference_command)

tests/tts_tests/test_tacotron2_d-vectors_train.py

@@ -2,9 +2,10 @@ import glob
 import os
 import shutil
 
+from trainer import get_last_checkpoint
+
 from tests import get_device_id, get_tests_output_path, run_cli
 from TTS.tts.configs.tacotron2_config import Tacotron2Config
-from trainer import get_last_checkpoint
 
 config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
 output_path = os.path.join(get_tests_output_path(), "train_outputs")
@@ -56,7 +57,7 @@ continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getm
 # Inference using TTS API
 continue_config_path = os.path.join(continue_path, "config.json")
 continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), 'output.wav')
+out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
 speaker_id = "ljspeech-1"
 continue_speakers_path = config.d_vector_file
 

tests/tts_tests/test_tacotron2_speaker_emb_train.py

@@ -2,9 +2,10 @@ import glob
 import os
 import shutil
 
+from trainer import get_last_checkpoint
+
 from tests import get_device_id, get_tests_output_path, run_cli
 from TTS.tts.configs.tacotron2_config import Tacotron2Config
-from trainer import get_last_checkpoint
 
 config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
 output_path = os.path.join(get_tests_output_path(), "train_outputs")
@@ -54,7 +55,7 @@ continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getm
 # Inference using TTS API
 continue_config_path = os.path.join(continue_path, "config.json")
 continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), 'output.wav')
+out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
 speaker_id = "ljspeech-1"
 continue_speakers_path = os.path.join(continue_path, "speakers.json")
 

tests/tts_tests/test_tacotron2_train.py

@@ -2,9 +2,10 @@ import glob
 import os
 import shutil
 
+from trainer import get_last_checkpoint
+
 from tests import get_device_id, get_tests_output_path, run_cli
 from TTS.tts.configs.tacotron2_config import Tacotron2Config
-from trainer import get_last_checkpoint
 
 config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
 output_path = os.path.join(get_tests_output_path(), "train_outputs")
@@ -51,7 +52,7 @@ continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getm
 # Inference using TTS API
 continue_config_path = os.path.join(continue_path, "config.json")
 continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), 'output.wav')
+out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
 
 inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
 run_cli(inference_command)

tests/tts_tests/test_tacotron_train.py

@@ -2,9 +2,10 @@ import glob
 import os
 import shutil
 
+from trainer import get_last_checkpoint
+
 from tests import get_device_id, get_tests_output_path, run_cli
 from TTS.tts.configs.tacotron_config import TacotronConfig
-from trainer import get_last_checkpoint
 
 config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
 output_path = os.path.join(get_tests_output_path(), "train_outputs")
@@ -52,7 +53,7 @@ continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getm
 # Inference using TTS API
 continue_config_path = os.path.join(continue_path, "config.json")
 continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), 'output.wav')
+out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
 
 inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
 run_cli(inference_command)

tests/tts_tests/test_vits_multilingual_speaker_emb_train.py

@@ -2,10 +2,11 @@ import glob
 import os
 import shutil
 
+from trainer import get_last_checkpoint
+
 from tests import get_device_id, get_tests_output_path, run_cli
 from TTS.config.shared_configs import BaseDatasetConfig
 from TTS.tts.configs.vits_config import VitsConfig
-from trainer import get_last_checkpoint
 
 config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
 output_path = os.path.join(get_tests_output_path(), "train_outputs")
@@ -85,7 +86,7 @@ continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getm
 # Inference using TTS API
 continue_config_path = os.path.join(continue_path, "config.json")
 continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), 'output.wav')
+out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
 speaker_id = "ljspeech"
 languae_id = "en"
 continue_speakers_path = os.path.join(continue_path, "speakers.json")

tests/tts_tests/test_vits_multilingual_train-d_vectors.py

@@ -2,10 +2,11 @@ import glob
 import os
 import shutil
 
+from trainer import get_last_checkpoint
+
 from tests import get_device_id, get_tests_output_path, run_cli
 from TTS.config.shared_configs import BaseDatasetConfig
 from TTS.tts.configs.vits_config import VitsConfig
-from trainer import get_last_checkpoint
 
 config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
 output_path = os.path.join(get_tests_output_path(), "train_outputs")
@@ -89,7 +90,7 @@ continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getm
 # Inference using TTS API
 continue_config_path = os.path.join(continue_path, "config.json")
 continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), 'output.wav')
+out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
 speaker_id = "ljspeech-1"
 languae_id = "en"
 continue_speakers_path = config.d_vector_file

tests/tts_tests/test_vits_speaker_emb_train.py

@@ -2,9 +2,10 @@ import glob
 import os
 import shutil
 
+from trainer import get_last_checkpoint
+
 from tests import get_device_id, get_tests_output_path, run_cli
 from TTS.tts.configs.vits_config import VitsConfig
-from trainer import get_last_checkpoint
 
 config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
 output_path = os.path.join(get_tests_output_path(), "train_outputs")
@@ -60,7 +61,7 @@ continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getm
 # Inference using TTS API
 continue_config_path = os.path.join(continue_path, "config.json")
 continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), 'output.wav')
+out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
 speaker_id = "ljspeech-1"
 continue_speakers_path = os.path.join(continue_path, "speakers.json")
 

tests/tts_tests/test_vits_train.py

@@ -2,9 +2,10 @@ import glob
 import os
 import shutil
 
+from trainer import get_last_checkpoint
+
 from tests import get_device_id, get_tests_output_path, run_cli
 from TTS.tts.configs.vits_config import VitsConfig
-from trainer import get_last_checkpoint
 
 config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
 output_path = os.path.join(get_tests_output_path(), "train_outputs")
@@ -51,7 +52,7 @@ continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getm
 # Inference using TTS API
 continue_config_path = os.path.join(continue_path, "config.json")
 continue_restore_path, _ = get_last_checkpoint(continue_path)
-out_wav_path = os.path.join(get_tests_output_path(), 'output.wav')
+out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
 
 inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
 run_cli(inference_command)