removed kludgy wrappers for passing progress when I was a pythonlet and didn't know gradio can hook into tqdm outputs anyways
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086aad5b49
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@ -83,16 +83,6 @@ def check_for_kill_signal():
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STOP_SIGNAL = False
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raise Exception("Kill signal detected")
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def tqdm_override(arr, verbose=False, progress=None, desc=None):
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check_for_kill_signal()
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if verbose and desc is not None:
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print(desc)
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if progress is None:
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return tqdm(arr, disable=not verbose)
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return progress.tqdm(arr, desc=f'{progress.msg_prefix} {desc}' if hasattr(progress, 'msg_prefix') else desc)
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def download_models(specific_models=None):
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"""
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Call to download all the models that Tortoise uses.
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@ -205,7 +195,7 @@ def fix_autoregressive_output(codes, stop_token, complain=True):
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return codes
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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):
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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):
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"""
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Uses the specified diffusion model to convert discrete codes into a spectrogram.
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"""
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@ -218,8 +208,7 @@ def do_spectrogram_diffusion(diffusion_model, diffuser, latents, conditioning_la
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diffuser.sampler = sampler.lower()
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mel = diffuser.sample_loop(diffusion_model, output_shape, noise=noise,
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model_kwargs={'precomputed_aligned_embeddings': precomputed_embeddings},
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verbose=verbose, progress=progress, desc=desc)
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model_kwargs={'precomputed_aligned_embeddings': precomputed_embeddings}, desc=desc)
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mel = denormalize_tacotron_mel(mel)[:,:,:output_seq_len]
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if get_device_name() == "dml":
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@ -459,7 +448,7 @@ class TextToSpeech:
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if self.preloaded_tensors:
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self.cvvp = migrate_to_device( self.cvvp, self.device )
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def get_conditioning_latents(self, voice_samples, return_mels=False, verbose=False, progress=None, slices=1, max_chunk_size=None, force_cpu=False):
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def get_conditioning_latents(self, voice_samples, return_mels=False, verbose=False, slices=1, max_chunk_size=None, force_cpu=False):
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"""
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Transforms one or more voice_samples into a tuple (autoregressive_conditioning_latent, diffusion_conditioning_latent).
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These are expressive learned latents that encode aspects of the provided clips like voice, intonation, and acoustic
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@ -503,7 +492,7 @@ class TextToSpeech:
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chunk_size = chunks[0].shape[-1]
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auto_conds = []
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for chunk in tqdm_override(chunks, verbose=verbose, progress=progress, desc="Computing AR conditioning latents..."):
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for chunk in tqdm(chunks, desc="Computing AR conditioning latents..."):
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auto_conds.append(format_conditioning(chunk, device=device, sampling_rate=self.input_sample_rate, cond_length=chunk_size))
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auto_conds = torch.stack(auto_conds, dim=1)
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@ -512,7 +501,7 @@ class TextToSpeech:
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self.autoregressive = migrate_to_device( self.autoregressive, self.device if self.preloaded_tensors else 'cpu' )
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diffusion_conds = []
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for chunk in tqdm_override(chunks, verbose=verbose, progress=progress, desc="Computing diffusion conditioning latents..."):
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for chunk in tqdm(chunks, desc="Computing diffusion conditioning latents..."):
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check_for_kill_signal()
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chunk = pad_or_truncate(chunk, chunk_size)
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cond_mel = wav_to_univnet_mel(migrate_to_device( chunk, device ), do_normalization=False, device=device)
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@ -576,7 +565,6 @@ class TextToSpeech:
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diffusion_sampler="P",
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breathing_room=8,
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half_p=False,
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progress=None,
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**hf_generate_kwargs):
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"""
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Produces an audio clip of the given text being spoken with the given reference voice.
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@ -681,7 +669,7 @@ class TextToSpeech:
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text_tokens = migrate_to_device( text_tokens, self.device )
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with torch.autocast(device_type='cuda', dtype=torch.float16, enabled=half_p):
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for b in tqdm_override(range(num_batches), verbose=verbose, progress=progress, desc="Generating autoregressive samples"):
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for b in tqdm(range(num_batches), desc="Generating autoregressive samples"):
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check_for_kill_signal()
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codes = self.autoregressive.inference_speech(auto_conditioning, text_tokens,
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do_sample=True,
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@ -730,7 +718,7 @@ class TextToSpeech:
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desc = f"Computing best candidates using CLVP {((1-cvvp_amount) * 100):2.0f}% and CVVP {(cvvp_amount * 100):2.0f}%"
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for batch in tqdm_override(samples, verbose=verbose, progress=progress, desc=desc):
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for batch in tqdm(samples, desc=desc):
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check_for_kill_signal()
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for i in range(batch.shape[0]):
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batch[i] = fix_autoregressive_output(batch[i], stop_mel_token)
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@ -815,7 +803,7 @@ class TextToSpeech:
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break
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mel = do_spectrogram_diffusion(self.diffusion, diffuser, latents, diffusion_conditioning,
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temperature=diffusion_temperature, verbose=verbose, progress=progress, desc="Transforming autoregressive outputs into audio..", sampler=diffusion_sampler,
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temperature=diffusion_temperature, desc="Transforming autoregressive outputs into audio..", sampler=diffusion_sampler,
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input_sample_rate=self.input_sample_rate, output_sample_rate=self.output_sample_rate)
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wav = self.vocoder.inference(mel)
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@ -13,15 +13,7 @@ import math
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import numpy as np
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import torch
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import torch as th
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from tqdm import tqdm
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def tqdm_override(arr, verbose=False, progress=None, desc=None):
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if verbose and desc is not None:
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print(desc)
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if progress is None:
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return tqdm(arr, disable=not verbose)
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return progress.tqdm(arr, desc=f'{progress.msg_prefix} {desc}' if hasattr(progress, 'msg_prefix') else desc)
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from tqdm.auto import tqdm
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def normal_kl(mean1, logvar1, mean2, logvar2):
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"""
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@ -556,7 +548,6 @@ class GaussianDiffusion:
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model_kwargs=None,
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device=None,
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verbose=False,
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progress=None,
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desc=None
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):
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"""
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@ -589,7 +580,6 @@ class GaussianDiffusion:
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model_kwargs=model_kwargs,
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device=device,
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verbose=verbose,
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progress=progress,
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desc=desc
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):
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final = sample
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@ -606,7 +596,6 @@ class GaussianDiffusion:
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model_kwargs=None,
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device=None,
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verbose=False,
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progress=None,
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desc=None
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):
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"""
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@ -626,7 +615,7 @@ class GaussianDiffusion:
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img = th.randn(*shape, device=device)
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indices = list(range(self.num_timesteps))[::-1]
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for i in tqdm_override(indices, verbose=verbose, desc=desc, progress=progress):
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for i in tqdm(indices, desc=desc):
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t = th.tensor([i] * shape[0], device=device)
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with th.no_grad():
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out = self.p_sample(
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@ -741,7 +730,6 @@ class GaussianDiffusion:
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device=None,
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verbose=False,
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eta=0.0,
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progress=None,
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desc=None,
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):
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"""
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@ -761,7 +749,6 @@ class GaussianDiffusion:
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device=device,
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verbose=verbose,
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eta=eta,
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progress=progress,
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desc=desc
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):
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final = sample
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@ -779,7 +766,6 @@ class GaussianDiffusion:
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device=None,
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verbose=False,
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eta=0.0,
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progress=None,
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desc=None,
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):
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"""
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@ -798,10 +784,7 @@ class GaussianDiffusion:
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indices = list(range(self.num_timesteps))[::-1]
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if verbose:
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# Lazy import so that we don't depend on tqdm.
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from tqdm.auto import tqdm
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indices = tqdm_override(indices, verbose=verbose, desc=desc, progress=progress)
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indices = tqdm(indices, desc=desc)
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for i in indices:
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t = th.tensor([i] * shape[0], device=device)
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