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Remove collating from paired_voice_audio_dataset

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This will now be done at the model level, which is more efficient
This commit is contained in:
James Betker 2022-01-06 10:29:39 -07:00
parent e7a705fe6e
commit 06c1093090
1 changed files with 16 additions and 77 deletions
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93
codes/data/audio/paired_voice_audio_dataset.py
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@ -89,10 +89,7 @@ class TextWavLoader(torch.utils.data.Dataset):
random.shuffle(self.audiopaths_and_text)
self.max_wav_len = opt_get(hparams, ['max_wav_length'], None)
self.max_text_len = opt_get(hparams, ['max_text_length'], None)
# If needs_collate=False, all outputs will be aligned and padded at maximum length.
self.needs_collate = opt_get(hparams, ['needs_collate'], True)
if not self.needs_collate:
assert self.max_wav_len is not None and self.max_text_len is not None
assert self.max_wav_len is not None and self.max_text_len is not None
self.use_bpe_tokenizer = opt_get(hparams, ['use_bpe_tokenizer'], True)
if self.use_bpe_tokenizer:
from data.audio.voice_tokenizer import VoiceBpeTokenizer
@ -137,83 +134,26 @@ class TextWavLoader(torch.utils.data.Dataset):
return self[rv]
orig_output = wav.shape[-1]
orig_text_len = tseq.shape[0]
if not self.needs_collate:
if wav.shape[-1] != self.max_wav_len:
wav = F.pad(wav, (0, self.max_wav_len - wav.shape[-1]))
if tseq.shape[0] != self.max_text_len:
tseq = F.pad(tseq, (0, self.max_text_len - tseq.shape[0]))
res = {
'real_text': text,
'padded_text': tseq,
'text_lengths': torch.tensor(orig_text_len, dtype=torch.long),
'wav': wav,
'wav_lengths': torch.tensor(orig_output, dtype=torch.long),
'filenames': path
}
if self.load_conditioning:
res['conditioning'] = cond
return res
return tseq, wav, path, text, cond
if wav.shape[-1] != self.max_wav_len:
wav = F.pad(wav, (0, self.max_wav_len - wav.shape[-1]))
if tseq.shape[0] != self.max_text_len:
tseq = F.pad(tseq, (0, self.max_text_len - tseq.shape[0]))
res = {
'real_text': text,
'padded_text': tseq,
'text_lengths': torch.tensor(orig_text_len, dtype=torch.long),
'wav': wav,
'wav_lengths': torch.tensor(orig_output, dtype=torch.long),
'filenames': path
}
if self.load_conditioning:
res['conditioning'] = cond
return res
def __len__(self):
return len(self.audiopaths_and_text)
class TextMelCollate():
""" Zero-pads model inputs and targets based on number of frames per step
"""
def __call__(self, batch):
"""Collate's training batch from normalized text and wav
PARAMS
------
batch: [text_normalized, wav, filename, text]
"""
# Right zero-pad all one-hot text sequences to max input length
input_lengths, ids_sorted_decreasing = torch.sort(
torch.LongTensor([len(x[0]) for x in batch]),
dim=0, descending=True)
max_input_len = input_lengths[0]
text_padded = torch.LongTensor(len(batch), max_input_len)
text_padded.zero_()
filenames = []
real_text = []
conds = []
for i in range(len(ids_sorted_decreasing)):
text = batch[ids_sorted_decreasing[i]][0]
text_padded[i, :text.size(0)] = text
filenames.append(batch[ids_sorted_decreasing[i]][2])
real_text.append(batch[ids_sorted_decreasing[i]][3])
c = batch[ids_sorted_decreasing[i]][4]
if c is not None:
conds.append(c)
# Right zero-pad wav
num_wavs = batch[0][1].size(0)
max_target_len = max([x[1].size(1) for x in batch])
# include mel padded and gate padded
wav_padded = torch.FloatTensor(len(batch), num_wavs, max_target_len)
wav_padded.zero_()
output_lengths = torch.LongTensor(len(batch))
for i in range(len(ids_sorted_decreasing)):
wav = batch[ids_sorted_decreasing[i]][1]
wav_padded[i, :, :wav.size(1)] = wav
output_lengths[i] = wav.size(1)
res = {
'padded_text': text_padded,
'text_lengths': input_lengths,
'wav': wav_padded,
'wav_lengths': output_lengths,
'filenames': filenames,
'real_text': real_text,
}
if len(conds) > 0:
res['conditioning'] = torch.stack(conds)
return res
if __name__ == '__main__':
batch_sz = 8
params = {
@ -223,7 +163,6 @@ if __name__ == '__main__':
'phase': 'train',
'n_workers': 0,
'batch_size': batch_sz,
'needs_collate': True,
'max_wav_length': 255995,
'max_text_length': 200,
'sample_rate': 22050,