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misc

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James Betker 2022-04-16 20:28:04 -06:00
parent 147478a148
commit 48cb6a5abd
3 changed files with 6 additions and 19 deletions

10
codes/data/audio/unsupervised_audio_dataset.py
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@ -176,15 +176,15 @@ class UnsupervisedAudioDataset(torch.utils.data.Dataset):
if __name__ == '__main__': if __name__ == '__main__':
params = { params = {
'mode': 'unsupervised_audio', 'mode': 'unsupervised_audio',
'path': ['\\\\192.168.5.3\\rtx3080_audio\\split\\cleaned\\books0'], 'path': ['Y:\\split\\yt-music'],
'cache_path': 'E:\\audio\\remote-cache3.pth', 'cache_path': 'Y:\\split\\yt-music\\cache-windows.pth',
'sampling_rate': 22050, 'sampling_rate': 22050,
'pad_to_samples': 40960, 'pad_to_samples': 22050,
'phase': 'train', 'phase': 'train',
'n_workers': 1, 'n_workers': 1,
'batch_size': 16, 'batch_size': 16,
'extra_samples': 4, 'extra_samples': 4,
'resample_clip': True, 'resample_clip': False,
} }
from data import create_dataset, create_dataloader from data import create_dataset, create_dataloader
@ -195,5 +195,5 @@ 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) #torchaudio.save(f'{i}_resampled_clip_{b_}.wav', b['resampled_clip'][b_], ds.sampling_rate)
i += 1 i += 1

13
codes/models/clip/clvp.py
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@ -86,7 +86,6 @@ class CLVP(nn.Module):
speech_enc_depth=6, speech_enc_depth=6,
speech_mask_percentage=0, speech_mask_percentage=0,
latent_multiplier=4, latent_multiplier=4,
is_distributed=False,
): ):
super().__init__() super().__init__()
latent_dim = latent_multiplier*model_dim latent_dim = latent_multiplier*model_dim
@ -102,8 +101,6 @@ class CLVP(nn.Module):
self.text_transformer = CollapsingTransformer(model_dim, latent_dim, transformer_heads, dropout, text_enc_depth, text_mask_percentage, use_rms_scaleshift_norm=True) self.text_transformer = CollapsingTransformer(model_dim, latent_dim, transformer_heads, dropout, text_enc_depth, text_mask_percentage, use_rms_scaleshift_norm=True)
self.to_text_latent = nn.Linear(latent_dim, latent_dim, bias=False) self.to_text_latent = nn.Linear(latent_dim, latent_dim, bias=False)
self.distributed = is_distributed
if mel_codes is None: if mel_codes is None:
self.speech_emb = nn.Conv1d(mel_channels, model_dim, kernel_size=5, padding=2) self.speech_emb = nn.Conv1d(mel_channels, model_dim, kernel_size=5, padding=2)
else: else:
@ -143,16 +140,6 @@ class CLVP(nn.Module):
text_latents = self.to_text_latent(enc_text) text_latents = self.to_text_latent(enc_text)
speech_latents = self.to_speech_latent(enc_speech) speech_latents = self.to_speech_latent(enc_speech)
if self.distributed:
ws = get_world_size()
text_gather_cells = [torch.zeros_like(text_latents) for _ in range(ws)]
speech_gather_cells = [torch.zeros_like(speech_latents) for _ in range(ws)]
distributed.all_gather(text_gather_cells, text_latents)
text_gather_cells[distributed.get_rank()] = text_latents # Propagate gradients in this way.
text_latents = torch.cat(text_gather_cells, dim=0)
distributed.all_gather(speech_gather_cells, speech_latents)
speech_gather_cells[distributed.get_rank()] = speech_latents
speech_latents = torch.cat(speech_gather_cells, dim=0)
text_latents, speech_latents = map(lambda t: F.normalize(t, p=2, dim=-1), (text_latents, speech_latents)) text_latents, speech_latents = map(lambda t: F.normalize(t, p=2, dim=-1), (text_latents, speech_latents))
temp = self.temperature.exp() temp = self.temperature.exp()

2
codes/train.py
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@ -327,7 +327,7 @@ class Trainer:
if __name__ == '__main__': if __name__ == '__main__':
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_clip_text_to_voice.yml') parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_cvvp_codes.yml')
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher') parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
args = parser.parse_args() args = parser.parse_args()
opt = option.parse(args.opt, is_train=True) opt = option.parse(args.opt, is_train=True)