fixed up the computing conditional latents
This commit is contained in:
parent
2cfd3bc213
commit
a1f3b6a4da
|
@ -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
2
tortoise/get_conditioning_latents.py
Normal file → Executable 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
2
tortoise/models/diffusion_decoder.py
Normal file → Executable 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,
|
||||
|
|
Loading…
Reference in New Issue
Block a user