forked from mrq/tortoise-tts
286 lines
14 KiB
Python
286 lines
14 KiB
Python
import argparse
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import os
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import random
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from urllib import request
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import torch
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import torch.nn.functional as F
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import progressbar
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from models.diffusion_decoder import DiffusionTts
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from models.autoregressive import UnifiedVoice
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from tqdm import tqdm
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from models.arch_util import TorchMelSpectrogram
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from models.text_voice_clip import VoiceCLIP
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from models.vocoder import UnivNetGenerator
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from utils.audio import load_audio, wav_to_univnet_mel, denormalize_tacotron_mel
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from utils.diffusion import SpacedDiffusion, space_timesteps, get_named_beta_schedule
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from utils.tokenizer import VoiceBpeTokenizer, lev_distance
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pbar = None
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def download_models():
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MODELS = {
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'clip.pth': 'https://huggingface.co/jbetker/tortoise-tts-clip/resolve/main/pytorch-model.bin',
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'diffusion.pth': 'https://huggingface.co/jbetker/tortoise-tts-diffusion-v1/resolve/main/pytorch-model.bin',
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'autoregressive.pth': 'https://huggingface.co/jbetker/tortoise-tts-autoregressive/resolve/main/pytorch-model.bin'
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}
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os.makedirs('.models', exist_ok=True)
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def show_progress(block_num, block_size, total_size):
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global pbar
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if pbar is None:
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pbar = progressbar.ProgressBar(maxval=total_size)
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pbar.start()
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downloaded = block_num * block_size
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if downloaded < total_size:
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pbar.update(downloaded)
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else:
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pbar.finish()
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pbar = None
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for model_name, url in MODELS.items():
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if os.path.exists(f'.models/{model_name}'):
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continue
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print(f'Downloading {model_name} from {url}...')
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request.urlretrieve(url, f'.models/{model_name}', show_progress)
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print('Done.')
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def pad_or_truncate(t, length):
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if t.shape[-1] == length:
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return t
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elif t.shape[-1] < length:
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return F.pad(t, (0, length-t.shape[-1]))
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else:
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return t[..., :length]
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def load_discrete_vocoder_diffuser(trained_diffusion_steps=4000, desired_diffusion_steps=200, cond_free=True, cond_free_k=1):
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"""
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Helper function to load a GaussianDiffusion instance configured for use as a vocoder.
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"""
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return SpacedDiffusion(use_timesteps=space_timesteps(trained_diffusion_steps, [desired_diffusion_steps]), model_mean_type='epsilon',
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model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', trained_diffusion_steps),
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conditioning_free=cond_free, conditioning_free_k=cond_free_k)
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def load_conditioning(clip, cond_length=132300):
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gap = clip.shape[-1] - cond_length
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if gap < 0:
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clip = F.pad(clip, pad=(0, abs(gap)))
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elif gap > 0:
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rand_start = random.randint(0, gap)
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clip = clip[:, rand_start:rand_start + cond_length]
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mel_clip = TorchMelSpectrogram()(clip.unsqueeze(0)).squeeze(0)
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return mel_clip.unsqueeze(0).cuda()
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def clip_guided_generation(autoregressive_model, clip_model, conditioning_input, text_input, num_batches, stop_mel_token,
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tokens_per_clip_inference=10, clip_results_to_reduce_to=8, **generation_kwargs):
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"""
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Uses a CLVP model trained to associate full text with **partial** audio clips to pick the best generation candidates
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every few iterations. The top results are then propagated forward through the generation process. Rinse and repeat.
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This is a hybrid between beam search and sampling.
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"""
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token_goal = tokens_per_clip_inference
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finished = False
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while not finished and token_goal < autoregressive_model.max_mel_tokens:
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samples = []
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for b in tqdm(range(num_batches)):
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codes = autoregressive_model.inference_speech(conditioning_input, text_input, **generation_kwargs)
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samples.append(codes)
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for batch in samples:
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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, complain=False)
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clip_results.append(clip_model(text_input.repeat(batch.shape[0], 1), batch, return_loss=False))
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clip_results = torch.cat(clip_results, dim=0)
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samples = torch.cat(samples, dim=0)
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best_results = samples[torch.topk(clip_results, k=clip_results_to_reduce_to).indices]
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def fix_autoregressive_output(codes, stop_token, complain=True):
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"""
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This function performs some padding on coded audio that fixes a mismatch issue between what the diffusion model was
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trained on and what the autoregressive code generator creates (which has no padding or end).
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This is highly specific to the DVAE being used, so this particular coding will not necessarily work if used with
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a different DVAE. This can be inferred by feeding a audio clip padded with lots of zeros on the end through the DVAE
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and copying out the last few codes.
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Failing to do this padding will produce speech with a harsh end that sounds like "BLAH" or similar.
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"""
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# Strip off the autoregressive stop token and add padding.
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stop_token_indices = (codes == stop_token).nonzero()
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if len(stop_token_indices) == 0:
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if complain:
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print("No stop tokens found, enjoy that output of yours!")
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return codes
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else:
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codes[stop_token_indices] = 83
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stm = stop_token_indices.min().item()
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codes[stm:] = 83
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if stm - 3 < codes.shape[0]:
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codes[-3] = 45
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codes[-2] = 45
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codes[-1] = 248
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return codes
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def do_spectrogram_diffusion(diffusion_model, diffuser, mel_codes, conditioning_samples, temperature=1):
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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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with torch.no_grad():
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cond_mels = []
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for sample in conditioning_samples:
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sample = pad_or_truncate(sample, 102400)
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cond_mel = wav_to_univnet_mel(sample.to(mel_codes.device), do_normalization=False)
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cond_mels.append(cond_mel)
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cond_mels = torch.stack(cond_mels, dim=1)
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output_seq_len = mel_codes.shape[1]*4*24000//22050 # This diffusion model converts from 22kHz spectrogram codes to a 24kHz spectrogram signal.
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output_shape = (mel_codes.shape[0], 100, output_seq_len)
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precomputed_embeddings = diffusion_model.timestep_independent(mel_codes, cond_mels, output_seq_len, False)
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noise = torch.randn(output_shape, device=mel_codes.device) * temperature
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mel = diffuser.p_sample_loop(diffusion_model, output_shape, noise=noise,
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model_kwargs={'precomputed_aligned_embeddings': precomputed_embeddings})
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return denormalize_tacotron_mel(mel)[:,:,:output_seq_len]
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class TextToSpeech:
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def __init__(self, autoregressive_batch_size=16):
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self.autoregressive_batch_size = autoregressive_batch_size
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self.tokenizer = VoiceBpeTokenizer()
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download_models()
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self.autoregressive = UnifiedVoice(max_mel_tokens=604, max_text_tokens=402, max_conditioning_inputs=2, layers=30,
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model_dim=1024,
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heads=16, number_text_tokens=256, start_text_token=255, checkpointing=False,
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train_solo_embeddings=False,
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average_conditioning_embeddings=True).cpu().eval()
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self.autoregressive.load_state_dict(torch.load('.models/autoregressive.pth'))
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self.clip = VoiceCLIP(dim_text=512, dim_speech=512, dim_latent=512, num_text_tokens=256, text_enc_depth=12,
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text_seq_len=350, text_heads=8,
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num_speech_tokens=8192, speech_enc_depth=12, speech_heads=8, speech_seq_len=430,
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use_xformers=True).cpu().eval()
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self.clip.load_state_dict(torch.load('.models/clip.pth'))
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self.diffusion = DiffusionTts(model_channels=1024, num_layers=10, in_channels=100, out_channels=200,
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in_latent_channels=1024, in_tokens=8193, dropout=0, use_fp16=False, num_heads=16,
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layer_drop=0, unconditioned_percentage=0).cpu().eval()
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self.diffusion.load_state_dict(torch.load('.models/diffusion_decoder.pth'))
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self.vocoder = UnivNetGenerator().cpu()
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self.vocoder.load_state_dict(torch.load('.models/vocoder.pth')['model_g'])
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self.vocoder.eval(inference=True)
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def tts_with_preset(self, text, voice_samples, preset='fast', **kwargs):
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"""
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Calls TTS with one of a set of preset generation parameters. Options:
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'ultra_fast': Produces speech at a speed which belies the name of this repo. (Not really, but it's definitely fastest).
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'fast': Decent quality speech at a decent inference rate. A good choice for mass inference.
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'standard': Very good quality. This is generally about as good as you are going to get.
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'high_quality': Use if you want the absolute best. This is not really worth the compute, though.
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"""
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# Use generally found best tuning knobs for generation.
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kwargs.update({'temperature': .8, 'length_penalty': 1.0, 'repetition_penalty': 2.0, 'top_p': .8,
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'cond_free_k': 2.0, 'diffusion_temperature': 1.0})
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# Presets are defined here.
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presets = {
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'ultra_fast': {'num_autoregressive_samples': 32, 'diffusion_iterations': 16, 'cond_free': False},
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'fast': {'num_autoregressive_samples': 96, 'diffusion_iterations': 32},
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'standard': {'num_autoregressive_samples': 256, 'diffusion_iterations': 128},
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'high_quality': {'num_autoregressive_samples': 512, 'diffusion_iterations': 2048},
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}
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kwargs.update(presets[preset])
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return self.tts(text, voice_samples, **kwargs)
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def tts(self, text, voice_samples, k=1,
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# autoregressive generation parameters follow
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num_autoregressive_samples=512, temperature=.8, length_penalty=1, repetition_penalty=2.0, top_p=.8,
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# diffusion generation parameters follow
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diffusion_iterations=100, cond_free=True, cond_free_k=2, diffusion_temperature=1.0,):
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text = torch.IntTensor(self.tokenizer.encode(text)).unsqueeze(0).cuda()
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text = F.pad(text, (0, 1)) # This may not be necessary.
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conds = []
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if not isinstance(voice_samples, list):
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voice_samples = [voice_samples]
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for vs in voice_samples:
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conds.append(load_conditioning(vs))
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conds = torch.stack(conds, dim=1)
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diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=diffusion_iterations, cond_free=cond_free, cond_free_k=cond_free_k)
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with torch.no_grad():
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samples = []
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num_batches = num_autoregressive_samples // self.autoregressive_batch_size
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stop_mel_token = self.autoregressive.stop_mel_token
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calm_token = 83 # This is the token for coding silence, which is fixed in place with "fix_autoregressive_output"
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self.autoregressive = self.autoregressive.cuda()
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for b in tqdm(range(num_batches)):
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codes = self.autoregressive.inference_speech(conds, text,
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do_sample=True,
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top_p=top_p,
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temperature=temperature,
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num_return_sequences=self.autoregressive_batch_size,
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length_penalty=length_penalty,
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repetition_penalty=repetition_penalty)
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padding_needed = self.autoregressive.max_mel_tokens - codes.shape[1]
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codes = F.pad(codes, (0, padding_needed), value=stop_mel_token)
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samples.append(codes)
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self.autoregressive = self.autoregressive.cpu()
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clip_results = []
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self.clip = self.clip.cuda()
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for batch in samples:
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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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clip_results.append(self.clip(text.repeat(batch.shape[0], 1), batch, return_loss=False))
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clip_results = torch.cat(clip_results, dim=0)
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samples = torch.cat(samples, dim=0)
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best_results = samples[torch.topk(clip_results, k=k).indices]
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self.clip = self.clip.cpu()
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del samples
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# The diffusion model actually wants the last hidden layer from the autoregressive model as conditioning
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# inputs. Re-produce those for the top results. This could be made more efficient by storing all of these
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# results, but will increase memory usage.
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self.autoregressive = self.autoregressive.cuda()
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best_latents = self.autoregressive(conds, text, torch.tensor([text.shape[-1]], device=conds.device), best_results,
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torch.tensor([best_results.shape[-1]*self.autoregressive.mel_length_compression], device=conds.device),
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return_latent=True, clip_inputs=False)
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self.autoregressive = self.autoregressive.cpu()
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print("Performing vocoding..")
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wav_candidates = []
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self.diffusion = self.diffusion.cuda()
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self.vocoder = self.vocoder.cuda()
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for b in range(best_results.shape[0]):
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codes = best_results[b].unsqueeze(0)
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latents = best_latents[b].unsqueeze(0)
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# Find the first occurrence of the "calm" token and trim the codes to that.
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ctokens = 0
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for k in range(codes.shape[-1]):
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if codes[0, k] == calm_token:
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ctokens += 1
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else:
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ctokens = 0
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if ctokens > 8: # 8 tokens gives the diffusion model some "breathing room" to terminate speech.
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latents = latents[:, :k]
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break
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mel = do_spectrogram_diffusion(self.diffusion, diffuser, latents, voice_samples, temperature=diffusion_temperature)
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wav = self.vocoder.inference(mel)
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wav_candidates.append(wav.cpu())
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self.diffusion = self.diffusion.cpu()
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self.vocoder = self.vocoder.cpu()
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if len(wav_candidates) > 1:
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return wav_candidates
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return wav_candidates[0]
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