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tortoise-tts
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removed kludgy wrappers for passing progress when I was a pythonlet and didn't know gradio can hook into tqdm outputs anyways

remotes/1710274000886183304/main
mrq 2023-05-04 23:39:39 +07:00
parent 086aad5b49
commit c90ee7c529
2 changed files with 11 additions and 40 deletions
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28
tortoise/api.py
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@ -83,16 +83,6 @@ def check_for_kill_signal():
STOP_SIGNAL = False
raise Exception("Kill signal detected")
def tqdm_override(arr, verbose=False, progress=None, desc=None):
check_for_kill_signal()
if verbose and desc is not None:
print(desc)
if progress is None:
return tqdm(arr, disable=not verbose)
return progress.tqdm(arr, desc=f'{progress.msg_prefix} {desc}' if hasattr(progress, 'msg_prefix') else desc)
def download_models(specific_models=None):
"""
Call to download all the models that Tortoise uses.
@ -205,7 +195,7 @@ def fix_autoregressive_output(codes, stop_token, complain=True):
return codes
def do_spectrogram_diffusion(diffusion_model, diffuser, latents, conditioning_latents, temperature=1, verbose=True, progress=None, desc=None, sampler="P", input_sample_rate=22050, output_sample_rate=24000):
def do_spectrogram_diffusion(diffusion_model, diffuser, latents, conditioning_latents, temperature=1, verbose=True, desc=None, sampler="P", input_sample_rate=22050, output_sample_rate=24000):
"""
Uses the specified diffusion model to convert discrete codes into a spectrogram.
"""
@ -218,8 +208,7 @@ def do_spectrogram_diffusion(diffusion_model, diffuser, latents, conditioning_la
diffuser.sampler = sampler.lower()
mel = diffuser.sample_loop(diffusion_model, output_shape, noise=noise,
model_kwargs={'precomputed_aligned_embeddings': precomputed_embeddings},
verbose=verbose, progress=progress, desc=desc)
model_kwargs={'precomputed_aligned_embeddings': precomputed_embeddings}, desc=desc)
mel = denormalize_tacotron_mel(mel)[:,:,:output_seq_len]
if get_device_name() == "dml":
@ -459,7 +448,7 @@ class TextToSpeech:
if self.preloaded_tensors:
self.cvvp = migrate_to_device( self.cvvp, self.device )
def get_conditioning_latents(self, voice_samples, return_mels=False, verbose=False, progress=None, slices=1, max_chunk_size=None, force_cpu=False):
def get_conditioning_latents(self, voice_samples, return_mels=False, verbose=False, slices=1, max_chunk_size=None, force_cpu=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
@ -503,7 +492,7 @@ class TextToSpeech:
chunk_size = chunks[0].shape[-1]
auto_conds = []
for chunk in tqdm_override(chunks, verbose=verbose, progress=progress, desc="Computing AR conditioning latents..."):
for chunk in tqdm(chunks, desc="Computing AR conditioning latents..."):
auto_conds.append(format_conditioning(chunk, device=device, sampling_rate=self.input_sample_rate, cond_length=chunk_size))
auto_conds = torch.stack(auto_conds, dim=1)
@ -512,7 +501,7 @@ class TextToSpeech:
self.autoregressive = migrate_to_device( self.autoregressive, self.device if self.preloaded_tensors else 'cpu' )
diffusion_conds = []
for chunk in tqdm_override(chunks, verbose=verbose, progress=progress, desc="Computing diffusion conditioning latents..."):
for chunk in tqdm(chunks, desc="Computing diffusion conditioning latents..."):
check_for_kill_signal()
chunk = pad_or_truncate(chunk, chunk_size)
cond_mel = wav_to_univnet_mel(migrate_to_device( chunk, device ), do_normalization=False, device=device)
@ -576,7 +565,6 @@ class TextToSpeech:
diffusion_sampler="P",
breathing_room=8,
half_p=False,
progress=None,
**hf_generate_kwargs):
"""
Produces an audio clip of the given text being spoken with the given reference voice.
@ -681,7 +669,7 @@ class TextToSpeech:
text_tokens = migrate_to_device( text_tokens, self.device )
with torch.autocast(device_type='cuda', dtype=torch.float16, enabled=half_p):
for b in tqdm_override(range(num_batches), verbose=verbose, progress=progress, desc="Generating autoregressive samples"):
for b in tqdm(range(num_batches), desc="Generating autoregressive samples"):
check_for_kill_signal()
codes = self.autoregressive.inference_speech(auto_conditioning, text_tokens,
do_sample=True,
@ -730,7 +718,7 @@ class TextToSpeech:
desc = f"Computing best candidates using CLVP {((1-cvvp_amount) * 100):2.0f}% and CVVP {(cvvp_amount * 100):2.0f}%"
for batch in tqdm_override(samples, verbose=verbose, progress=progress, desc=desc):
for batch in tqdm(samples, desc=desc):
check_for_kill_signal()
for i in range(batch.shape[0]):
batch[i] = fix_autoregressive_output(batch[i], stop_mel_token)
@ -815,7 +803,7 @@ class TextToSpeech:
break
mel = do_spectrogram_diffusion(self.diffusion, diffuser, latents, diffusion_conditioning,
temperature=diffusion_temperature, verbose=verbose, progress=progress, desc="Transforming autoregressive outputs into audio..", sampler=diffusion_sampler,
temperature=diffusion_temperature, desc="Transforming autoregressive outputs into audio..", sampler=diffusion_sampler,
input_sample_rate=self.input_sample_rate, output_sample_rate=self.output_sample_rate)
wav = self.vocoder.inference(mel)

23
tortoise/utils/diffusion.py
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@ -13,15 +13,7 @@ import math
import numpy as np
import torch
import torch as th
from tqdm import tqdm
def tqdm_override(arr, verbose=False, progress=None, desc=None):
if verbose and desc is not None:
print(desc)
if progress is None:
return tqdm(arr, disable=not verbose)
return progress.tqdm(arr, desc=f'{progress.msg_prefix} {desc}' if hasattr(progress, 'msg_prefix') else desc)
from tqdm.auto import tqdm
def normal_kl(mean1, logvar1, mean2, logvar2):
"""
@ -556,7 +548,6 @@ class GaussianDiffusion:
model_kwargs=None,
device=None,
verbose=False,
progress=None,
desc=None
):
"""
@ -589,7 +580,6 @@ class GaussianDiffusion:
model_kwargs=model_kwargs,
device=device,
verbose=verbose,
progress=progress,
desc=desc
):
final = sample
@ -606,7 +596,6 @@ class GaussianDiffusion:
model_kwargs=None,
device=None,
verbose=False,
progress=None,
desc=None
):
"""
@ -626,7 +615,7 @@ class GaussianDiffusion:
img = th.randn(*shape, device=device)
indices = list(range(self.num_timesteps))[::-1]
for i in tqdm_override(indices, verbose=verbose, desc=desc, progress=progress):
for i in tqdm(indices, desc=desc):
t = th.tensor([i] * shape[0], device=device)
with th.no_grad():
out = self.p_sample(
@ -741,7 +730,6 @@ class GaussianDiffusion:
device=None,
verbose=False,
eta=0.0,
progress=None,
desc=None,
):
"""
@ -761,7 +749,6 @@ class GaussianDiffusion:
device=device,
verbose=verbose,
eta=eta,
progress=progress,
desc=desc
):
final = sample
@ -779,7 +766,6 @@ class GaussianDiffusion:
device=None,
verbose=False,
eta=0.0,
progress=None,
desc=None,
):
"""
@ -798,10 +784,7 @@ class GaussianDiffusion:
indices = list(range(self.num_timesteps))[::-1]
if verbose:
# Lazy import so that we don't depend on tqdm.
from tqdm.auto import tqdm
indices = tqdm_override(indices, verbose=verbose, desc=desc, progress=progress)
indices = tqdm(indices, desc=desc)
for i in indices:
t = th.tensor([i] * shape[0], device=device)