mirror of https://github.com/coqui-ai/TTS.git
Update TTSDataset
This commit is contained in:
Eren Gölge
parent
750903d2ba
commit
ff23dce081
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@ -37,10 +37,10 @@ def noise_augment_audio(wav):
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class TTSDataset(Dataset):
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class TTSDataset(Dataset):
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def __init__(
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def __init__(
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self,
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self,
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outputs_per_step: int,
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outputs_per_step: int = 1,
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compute_linear_spec: bool,
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compute_linear_spec: bool = False,
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ap: AudioProcessor,
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ap: AudioProcessor = None,
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samples: List[Dict],
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samples: List[Dict] = None,
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tokenizer: "TTSTokenizer" = None,
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tokenizer: "TTSTokenizer" = None,
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compute_f0: bool = False,
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compute_f0: bool = False,
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f0_cache_path: str = None,
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f0_cache_path: str = None,
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@ -118,7 +118,6 @@ class TTSDataset(Dataset):
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self.batch_group_size = batch_group_size
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self.batch_group_size = batch_group_size
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self._samples = samples
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self._samples = samples
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self.outputs_per_step = outputs_per_step
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self.outputs_per_step = outputs_per_step
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self.sample_rate = ap.sample_rate
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self.compute_linear_spec = compute_linear_spec
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self.compute_linear_spec = compute_linear_spec
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self.return_wav = return_wav
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self.return_wav = return_wav
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self.compute_f0 = compute_f0
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self.compute_f0 = compute_f0
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@ -153,6 +152,15 @@ class TTSDataset(Dataset):
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if self.verbose:
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if self.verbose:
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self.print_logs()
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self.print_logs()
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@property
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def lengths(self):
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lens = []
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for item in self.samples:
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_, wav_file, *_ = _parse_sample(item)
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audio_len = os.path.getsize(wav_file) / 16 * 8 # assuming 16bit audio
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lens.append(audio_len)
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return lens
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@property
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@property
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def samples(self):
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def samples(self):
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return self._samples
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return self._samples
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@ -763,80 +771,3 @@ class F0Dataset:
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print(f"{indent}| > Number of instances : {len(self.samples)}")
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print(f"{indent}| > Number of instances : {len(self.samples)}")
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# if __name__ == "__main__":
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# from torch.utils.data import DataLoader
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# from TTS.config.shared_configs import BaseAudioConfig, BaseDatasetConfig
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# from TTS.tts.datasets import load_tts_samples
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# from TTS.tts.utils.text.characters import IPAPhonemes
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# from TTS.tts.utils.text.phonemizers import ESpeak
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# dataset_config = BaseDatasetConfig(
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# name="ljspeech",
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# meta_file_train="metadata.csv",
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# path="/Users/erengolge/Projects/TTS/recipes/ljspeech/LJSpeech-1.1",
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# )
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# train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
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# samples = train_samples + eval_samples
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# phonemizer = ESpeak(language="en-us")
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# tokenizer = TTSTokenizer(use_phonemes=True, characters=IPAPhonemes(), phonemizer=phonemizer)
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# # ph_dataset = PhonemeDataset(samples, tokenizer, phoneme_cache_path="/Users/erengolge/Projects/TTS/phonemes_tests")
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# # ph_dataset.precompute(num_workers=4)
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# # dataloader = DataLoader(ph_dataset, batch_size=4, shuffle=False, num_workers=4, collate_fn=ph_dataset.collate_fn)
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# # for batch in dataloader:
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# # print(batch)
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# # break
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# audio_config = BaseAudioConfig(
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# sample_rate=22050,
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# win_length=1024,
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# hop_length=256,
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# num_mels=80,
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# preemphasis=0.0,
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# ref_level_db=20,
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# log_func="np.log",
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# do_trim_silence=True,
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# trim_db=45,
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# mel_fmin=0,
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# mel_fmax=8000,
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# spec_gain=1.0,
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# signal_norm=False,
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# do_amp_to_db_linear=False,
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# )
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# ap = AudioProcessor.init_from_config(audio_config)
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# # f0_dataset = F0Dataset(samples, ap, cache_path="/Users/erengolge/Projects/TTS/f0_tests", verbose=False, precompute_num_workers=4)
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# # dataloader = DataLoader(f0_dataset, batch_size=4, shuffle=False, num_workers=4, collate_fn=f0_dataset.collate_fn)
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# # for batch in dataloader:
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# # print(batch)
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# # breakpoint()
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# # break
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# dataset = TTSDataset(
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# outputs_per_step=1,
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# compute_linear_spec=False,
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# samples=samples,
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# ap=ap,
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# return_wav=False,
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# batch_group_size=0,
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# min_seq_len=0,
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# max_seq_len=500,
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# use_noise_augment=False,
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# verbose=True,
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# speaker_id_mapping=None,
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# d_vector_mapping=None,
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# compute_f0=True,
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# f0_cache_path="/Users/erengolge/Projects/TTS/f0_tests",
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# tokenizer=tokenizer,
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# phoneme_cache_path="/Users/erengolge/Projects/TTS/phonemes_tests",
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# precompute_num_workers=4,
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# )
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# dataloader = DataLoader(dataset, batch_size=4, shuffle=False, num_workers=0, collate_fn=dataset.collate_fn)
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# for batch in dataloader:
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# print(batch)
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# break
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