Jarod/tortoise-tts
1
1
Fork 0
forked from mrq/tortoise-tts
Code Pull Requests Activity

Optimizing graphics card memory

Browse Source 
During inference it does not store gradients, which take up most of the video memory
This commit is contained in:
Mark Baushenko 2022-05-11 16:35:11 +03:00 committed by GitHub
parent ea8c825ee0
commit cc38333249
1 changed files with 22 additions and 21 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

43
tortoise/api.py
View File

@ -225,30 +225,31 @@ class TextToSpeech:
properties.
:param voice_samples: List of 2 or more ~10 second reference clips, which should be torch tensors containing 22.05kHz waveform data.
"""
voice_samples = [v.to('cuda') for v in voice_samples]
with torch.no_grad():
voice_samples = [v.to('cuda') for v in voice_samples]
auto_conds = []
if not isinstance(voice_samples, list):
voice_samples = [voice_samples]
for vs in voice_samples:
auto_conds.append(format_conditioning(vs))
auto_conds = torch.stack(auto_conds, dim=1)
self.autoregressive = self.autoregressive.cuda()
auto_latent = self.autoregressive.get_conditioning(auto_conds)
self.autoregressive = self.autoregressive.cpu()
auto_conds = []
if not isinstance(voice_samples, list):
voice_samples = [voice_samples]
for vs in voice_samples:
auto_conds.append(format_conditioning(vs))
auto_conds = torch.stack(auto_conds, dim=1)
self.autoregressive = self.autoregressive.cuda()
auto_latent = self.autoregressive.get_conditioning(auto_conds)
self.autoregressive = self.autoregressive.cpu()
diffusion_conds = []
for sample in voice_samples:
# The diffuser operates at a sample rate of 24000 (except for the latent inputs)
sample = torchaudio.functional.resample(sample, 22050, 24000)
sample = pad_or_truncate(sample, 102400)
cond_mel = wav_to_univnet_mel(sample.to('cuda'), do_normalization=False)
diffusion_conds.append(cond_mel)
diffusion_conds = torch.stack(diffusion_conds, dim=1)
diffusion_conds = []
for sample in voice_samples:
# The diffuser operates at a sample rate of 24000 (except for the latent inputs)
sample = torchaudio.functional.resample(sample, 22050, 24000)
sample = pad_or_truncate(sample, 102400)
cond_mel = wav_to_univnet_mel(sample.to('cuda'), do_normalization=False)
diffusion_conds.append(cond_mel)
diffusion_conds = torch.stack(diffusion_conds, dim=1)
self.diffusion = self.diffusion.cuda()
diffusion_latent = self.diffusion.get_conditioning(diffusion_conds)
self.diffusion = self.diffusion.cpu()
self.diffusion = self.diffusion.cuda()
diffusion_latent = self.diffusion.get_conditioning(diffusion_conds)
self.diffusion = self.diffusion.cpu()
if return_mels:
return auto_latent, diffusion_latent, auto_conds, diffusion_conds