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Introduce clip resampling

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This commit is contained in:
James Betker 2021-10-26 10:42:23 -06:00
parent 43e389aac6
commit 21b6daa0ed
1 changed files with 24 additions and 9 deletions
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25
codes/data/audio/unsupervised_audio_dataset.py
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@ -60,6 +60,11 @@ class UnsupervisedAudioDataset(torch.utils.data.Dataset):
self.pad_to *= self.sampling_rate self.pad_to *= self.sampling_rate
self.pad_to = opt_get(opt, ['pad_to_samples'], self.pad_to) self.pad_to = opt_get(opt, ['pad_to_samples'], self.pad_to)
# "Resampled clip" is audio data pulled from the basis of "clip" but with randomly different bounds. There are no
# guarantees that "clip_resampled" is different from "clip": in fact, if "clip" is less than pad_to_seconds/samples,
self.should_resample_clip = opt_get(opt, ['resample_clip'], False)
# "Extra samples" are other audio clips pulled from wav files in the same directory as the 'clip' wav file.
self.extra_samples = opt_get(opt, ['extra_samples'], 0) self.extra_samples = opt_get(opt, ['extra_samples'], 0)
self.extra_sample_len = opt_get(opt, ['extra_sample_length'], 2) self.extra_sample_len = opt_get(opt, ['extra_sample_length'], 2)
self.extra_sample_len *= self.sampling_rate self.extra_sample_len *= self.sampling_rate
@ -109,19 +114,27 @@ class UnsupervisedAudioDataset(torch.utils.data.Dataset):
print(f"Error loading audio for file {self.audiopaths[index]} {sys.exc_info()}") print(f"Error loading audio for file {self.audiopaths[index]} {sys.exc_info()}")
return self[index+1] return self[index+1]
# This is required when training to make sure all clips align. # When generating resampled clips, skew is a bias that tries to spread them out from each other, reducing their
# influence on one another.
skew = [-1, 1] if self.should_resample_clip else [0]
# To increase variability, which skew is applied to the clip and resampled_clip is randomized.
random.shuffle(skew)
clips = []
for sk in skew:
if self.pad_to is not None: if self.pad_to is not None:
if audio_norm.shape[-1] <= self.pad_to: if audio_norm.shape[-1] <= self.pad_to:
audio_norm = torch.nn.functional.pad(audio_norm, (0, self.pad_to - audio_norm.shape[-1])) clips.append(torch.nn.functional.pad(audio_norm, (0, self.pad_to - audio_norm.shape[-1])))
else: else:
gap = audio_norm.shape[-1] - self.pad_to gap = audio_norm.shape[-1] - self.pad_to
start = random.randint(0, gap-1) start = min(max(random.randint(0, gap-1) + sk * gap // 2, 0), gap-1)
audio_norm = audio_norm[:, start:start+self.pad_to] clips.append(audio_norm[:, start:start+self.pad_to])
output = { output = {
'clip': audio_norm, 'clip': clips[0],
'path': filename, 'path': filename,
} }
if self.should_resample_clip:
output['resampled_clip'] = clips[1]
if self.extra_samples > 0: if self.extra_samples > 0:
output['alt_clips'] = alt_files output['alt_clips'] = alt_files
output['num_alt_clips'] = actual_samples output['num_alt_clips'] = actual_samples
@ -142,6 +155,7 @@ if __name__ == '__main__':
'n_workers': 1, 'n_workers': 1,
'batch_size': 16, 'batch_size': 16,
'extra_samples': 4, 'extra_samples': 4,
'resample_clip': True,
} }
from data import create_dataset, create_dataloader, util from data import create_dataset, create_dataloader, util
@ -152,4 +166,5 @@ if __name__ == '__main__':
for b_ in range(b['clip'].shape[0]): for b_ in range(b['clip'].shape[0]):
#pass #pass
torchaudio.save(f'{i}_clip_{b_}.wav', b['clip'][b_], ds.sampling_rate) torchaudio.save(f'{i}_clip_{b_}.wav', b['clip'][b_], ds.sampling_rate)
torchaudio.save(f'{i}_resampled_clip_{b_}.wav', b['resampled_clip'][b_], ds.sampling_rate)
i += 1 i += 1