mrq/tortoise-tts
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added flag (--cond-latent-max-chunk-size) that should restrict the maximum chunk size when chunking for calculating conditional latents, to avoid OOMing on VRAM

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mrq 2023-02-06 05:10:07 +00:00
parent a1f3b6a4da
commit b441a84615
2 changed files with 13 additions and 9 deletions
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3
app.py
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@ -27,7 +27,7 @@ def generate(text, delimiter, emotion, prompt, voice, mic_audio, preset, seed, c
if voice_samples is not None:
sample_voice = voice_samples[0]
conditioning_latents = tts.get_conditioning_latents(voice_samples, progress=progress)
conditioning_latents = tts.get_conditioning_latents(voice_samples, progress=progress, max_chunk_size=args.cond_latent_max_chunk_size)
torch.save(conditioning_latents, os.path.join(f'./tortoise/voices/{voice}/', f'cond_latents.pth'))
voice_samples = None
else:
@ -265,6 +265,7 @@ if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--share", action='store_true', help="Lets Gradio return a public URL to use anywhere")
parser.add_argument("--low-vram", action='store_true', help="Disables some optimizations that increases VRAM usage")
parser.add_argument("--cond-latent-max-chunk-size", type=int, default=None, help="Sets an upper limit to audio chunk size when computing conditioning latents")
args = parser.parse_args()
tts = TextToSpeech(minor_optimizations=not args.low_vram)

19
tortoise/api.py
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=None, chunk_tensors=False):
def get_conditioning_latents(self, voice_samples, return_mels=False, verbose=False, progress=None, chunk_size=None, max_chunk_size=None, chunk_tensors=True):
"""
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
@ -309,26 +309,29 @@ class TextToSpeech:
# The diffuser operates at a sample rate of 24000 (except for the latent inputs)
samples.append(torchaudio.functional.resample(sample, 22050, 24000))
if enforced_length is None:
if chunk_size 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] )
chunk_size = sample.shape[-1] if chunk_size is None else min( chunk_size, sample.shape[-1] )
else:
enforced_length = sample.shape[-1] if enforced_length is None else max( enforced_length, sample.shape[-1] )
chunk_size = sample.shape[-1] if chunk_size is None else max( chunk_size, sample.shape[-1] )
print(f"Size of best fit: {enforced_length}")
print(f"Size of best fit: {chunk_size}")
if max_chunk_size is not None and chunk_size > max_chunk_size:
chunk_size = max_chunk_size
print(f"Chunk size exceeded, clamping to: {max_chunk_size}")
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)
sliced = torch.chunk(sample, int(sample.shape[-1] / chunk_size) + 1, dim=1)
for s in sliced:
chunks.append(s)
else:
chunks = samples
for chunk in tqdm_override(chunks, verbose=verbose, progress=progress, desc="Computing conditioning latents..."):
chunk = pad_or_truncate(chunk, enforced_length)
chunk = pad_or_truncate(chunk, chunk_size)
cond_mel = wav_to_univnet_mel(chunk.to(self.device), do_normalization=False, device=self.device)
diffusion_conds.append(cond_mel)
@ -460,7 +463,7 @@ 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