fixed up the computing conditional latents

This commit is contained in:
mrq 2023-02-06 03:44:34 +00:00
parent 2cfd3bc213
commit a1f3b6a4da
3 changed files with 32 additions and 13 deletions

View File

@ -284,7 +284,7 @@ class TextToSpeech:
if self.minor_optimizations:
self.cvvp = self.cvvp.to(self.device)
def get_conditioning_latents(self, voice_samples, return_mels=False, verbose=False, progress=None, enforced_length=102400):
def get_conditioning_latents(self, voice_samples, return_mels=False, verbose=False, progress=None, enforced_length=None, chunk_tensors=False):
"""
Transforms one or more voice_samples into a tuple (autoregressive_conditioning_latent, diffusion_conditioning_latent).
These are expressive learned latents that encode aspects of the provided clips like voice, intonation, and acoustic
@ -303,16 +303,34 @@ class TextToSpeech:
auto_conds = torch.stack(auto_conds, dim=1)
diffusion_conds = []
for sample in tqdm_override(voice_samples, verbose=verbose, progress=progress, desc="Computing conditioning latents..."):
samples = [] # resample in its own pass to make things easier
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)
chunks = torch.chunk(sample, int(sample.shape[-1] / enforced_length) + 1, dim=1)
samples.append(torchaudio.functional.resample(sample, 22050, 24000))
if enforced_length is None:
for sample in tqdm_override(samples, verbose=verbose and len(samples) > 1, progress=progress if len(samples) > 1 else None, desc="Calculating size of best fit..."):
if chunk_tensors:
enforced_length = sample.shape[-1] if enforced_length is None else min( enforced_length, sample.shape[-1] )
else:
enforced_length = sample.shape[-1] if enforced_length is None else max( enforced_length, sample.shape[-1] )
print(f"Size of best fit: {enforced_length}")
chunks = []
if chunk_tensors:
for sample in tqdm_override(samples, verbose=verbose, progress=progress, desc="Slicing samples into chunks..."):
sliced = torch.chunk(sample, int(sample.shape[-1] / enforced_length) + 1, dim=1)
for s in sliced:
chunks.append(s)
else:
chunks = samples
for chunk in chunks:
chunk = pad_or_truncate(chunk, enforced_length)
cond_mel = wav_to_univnet_mel(chunk.to(self.device), do_normalization=False, device=self.device)
diffusion_conds.append(cond_mel)
for chunk in tqdm_override(chunks, verbose=verbose, progress=progress, desc="Computing conditioning latents..."):
chunk = pad_or_truncate(chunk, enforced_length)
cond_mel = wav_to_univnet_mel(chunk.to(self.device), do_normalization=False, device=self.device)
diffusion_conds.append(cond_mel)
diffusion_conds = torch.stack(diffusion_conds, dim=1)
@ -424,6 +442,7 @@ class TextToSpeech:
:return: Generated audio clip(s) as a torch tensor. Shape 1,S if k=1 else, (k,1,S) where S is the sample length.
Sample rate is 24kHz.
"""
self.diffusion.enable_fp16 = half_p
deterministic_seed = self.deterministic_state(seed=use_deterministic_seed)
text_tokens = torch.IntTensor(self.tokenizer.encode(text)).unsqueeze(0).to(self.device)
@ -432,7 +451,7 @@ class TextToSpeech:
auto_conds = None
if voice_samples is not None:
auto_conditioning, diffusion_conditioning, auto_conds, _ = self.get_conditioning_latents(voice_samples, return_mels=True)
auto_conditioning, diffusion_conditioning, auto_conds, _ = self.get_conditioning_latents(voice_samples, return_mels=True, verbose=True)
elif conditioning_latents is not None:
auto_conditioning, diffusion_conditioning = conditioning_latents
else:
@ -441,7 +460,7 @@ class TextToSpeech:
diffusion_conditioning = diffusion_conditioning.to(self.device)
diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=diffusion_iterations, cond_free=cond_free, cond_free_k=cond_free_k)
with torch.no_grad():
samples = []
num_batches = num_autoregressive_samples // self.autoregressive_batch_size

2
tortoise/get_conditioning_latents.py Normal file → Executable file
View File

@ -25,6 +25,6 @@ if __name__ == '__main__':
for cond_path in cond_paths:
c = load_audio(cond_path, 22050)
conds.append(c)
conditioning_latents = tts.get_conditioning_latents(conds)
conditioning_latents = tts.get_conditioning_latents(conds, verbose=True)
torch.save(conditioning_latents, os.path.join(args.output_path, f'{voice}.pth'))

2
tortoise/models/diffusion_decoder.py Normal file → Executable file
View File

@ -141,7 +141,7 @@ class DiffusionTts(nn.Module):
in_tokens=8193,
out_channels=200, # mean and variance
dropout=0,
use_fp16=False,
use_fp16=True,
num_heads=16,
# Parameters for regularization.
layer_drop=.1,