forked from mrq/DL-Art-School
grand: support validation mode
This commit is contained in:
James Betker
parent
e55d949855
commit
5bc9772cb0
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@ -44,10 +44,12 @@ class GrandConjoinedDataset(torch.utils.data.Dataset):
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Performs tokenization at this level, ignoring any tokenization performed by upstream datasets.
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"""
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def __init__(self, opt):
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sample_rate = 22050 # Fixed.
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paired_dataset_args = opt['paired_dataset_args']
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unsupervised_audio_args = opt['unsupervised_audio_args']
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text_corpus_args = opt['text_corpus_args']
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sample_rate = 22050
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self.only_paired = opt_get(opt, ['only_paired'], False)
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if not self.only_paired:
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unsupervised_audio_args = opt['unsupervised_audio_args']
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text_corpus_args = opt['text_corpus_args']
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self.max_paired_audio_length = opt['max_paired_audio_length']
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self.max_paired_text_length = opt['max_paired_text_length']
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@ -61,14 +63,15 @@ class GrandConjoinedDataset(torch.utils.data.Dataset):
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paired_dataset_args['sample_rate'] = sample_rate
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paired_dataset_args['max_wav_length'] = self.max_paired_audio_length
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paired_dataset_args['max_text_length'] = self.max_paired_text_length
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unsupervised_audio_args['sampling_rate'] = sample_rate
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unsupervised_audio_args['do_augmentation'] = False
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unsupervised_audio_args['resample_clip'] = False
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unsupervised_audio_args['pad_to_samples'] = self.max_solo_audio_length
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self.speech_and_text = build_paired_voice_dataset(paired_dataset_args)
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self.speech = UnsupervisedAudioDataset(unsupervised_audio_args)
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self.text = HfDataset(**text_corpus_args)
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if not self.only_paired:
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unsupervised_audio_args['sampling_rate'] = sample_rate
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unsupervised_audio_args['do_augmentation'] = False
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unsupervised_audio_args['resample_clip'] = False
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unsupervised_audio_args['pad_to_samples'] = self.max_solo_audio_length
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self.speech = UnsupervisedAudioDataset(unsupervised_audio_args)
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self.text = HfDataset(**text_corpus_args)
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def fetch_text_at(self, i):
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try:
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@ -76,7 +79,7 @@ class GrandConjoinedDataset(torch.utils.data.Dataset):
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tok = self.speech_and_text.get_text(txt)
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padding_required = self.max_solo_text_length - tok.shape[0]
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if padding_required < 0:
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# Just truncate since there is no conditioning requried.
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# Just truncate since there is no conditioning required.
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tok = tok[:self.max_solo_text_length]
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elif padding_required > 0:
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tok = F.pad(tok, (0, padding_required))
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@ -88,29 +91,45 @@ class GrandConjoinedDataset(torch.utils.data.Dataset):
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def __getitem__(self, i):
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snt = self.speech_and_text[i % len(self.speech_and_text)]
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sp = self.speech[i % len(self.speech)]
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txt, txt_tok = self.fetch_text_at(i % len(self.text))
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return {
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'paired_audio': snt['wav'],
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'paired_audio_lengths': snt['wav_lengths'],
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'paired_text': snt['real_text'],
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'paired_text_tokens': snt['padded_text'],
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'paired_file': snt['filenames'],
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'speech_audio': sp['clip'],
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'speech_lengths': clamp(sp['clip_lengths'], 0, self.max_solo_audio_length),
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'speech_file': sp['path'],
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'text_text': txt,
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'text_tokens': txt_tok,
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}
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if self.only_paired:
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return {
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'paired_audio': snt['wav'],
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'paired_audio_lengths': snt['wav_lengths'],
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'paired_text': snt['real_text'],
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'paired_text_tokens': snt['padded_text'],
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'paired_file': snt['filenames'],
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'speech_audio': snt['wav'],
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'speech_lengths': snt['wav_lengths'],
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'speech_file': snt['filenames'],
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'text_text': snt['real_text'],
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'text_tokens': snt['padded_text'],
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}
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else:
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sp = self.speech[i % len(self.speech)]
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txt, txt_tok = self.fetch_text_at(i % len(self.text))
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return {
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'paired_audio': snt['wav'],
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'paired_audio_lengths': snt['wav_lengths'],
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'paired_text': snt['real_text'],
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'paired_text_tokens': snt['padded_text'],
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'paired_file': snt['filenames'],
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'speech_audio': sp['clip'],
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'speech_lengths': clamp(sp['clip_lengths'], 0, self.max_solo_audio_length),
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'speech_file': sp['path'],
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'text_text': txt,
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'text_tokens': txt_tok,
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}
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def __len__(self):
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return max(len(self.speech), len(self.speech_and_text), len(self.text))
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if self.only_paired:
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return len(self.speech_and_text)
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else:
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return max(len(self.speech), len(self.speech_and_text), len(self.text))
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if __name__ == '__main__':
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batch_sz = 8
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params = {
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train_params = {
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'mode': 'grand_conjoined_voice',
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'phase': 'train',
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'n_workers': 0,
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@ -133,10 +152,26 @@ if __name__ == '__main__':
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'cache_path': 'Z:\\huggingface_datasets\\cache',
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},
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}
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val_params = {
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'mode': 'grand_conjoined_voice',
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'phase': 'val',
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'n_workers': 0,
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'batch_size': batch_sz,
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'max_paired_audio_length': 255995,
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'max_paired_text_length': 80,
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'max_solo_text_length': 330,
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'max_solo_audio_length': 300000,
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'only_paired': True,
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'paired_dataset_args': {
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'path': ['Z:\\bigasr_dataset\\libritts\\test-clean_list.txt'],
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'fetcher_mode': ['libritts'],
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},
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}
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from data import create_dataset, create_dataloader
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ds = create_dataset(params)
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dl = create_dataloader(ds, params)
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ds = create_dataset(val_params)
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dl = create_dataloader(ds, val_params)
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def save(b, i, ib, key):
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torchaudio.save(f'{i}_clip_{ib}_{key}.wav', b[key][ib], 22050)
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