forked from mrq/tortoise-tts
246 lines
12 KiB
Python
246 lines
12 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 torchaudio
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import progressbar
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import ocotillo
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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 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 fix_autoregressive_output(codes, stop_token):
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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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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_input, temperature=1):
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"""
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Uses the specified diffusion model and DVAE model to convert the provided MEL & conditioning inputs into an audio clip.
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"""
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with torch.no_grad():
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cond_mel = wav_to_univnet_mel(conditioning_input.squeeze(1), do_normalization=False)
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# Pad MEL to multiples of 32
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msl = mel_codes.shape[-1]
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dsl = 32
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gap = dsl - (msl % dsl)
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if gap > 0:
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mel = torch.nn.functional.pad(mel_codes, (0, gap))
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output_shape = (mel.shape[0], 100, mel.shape[-1]*4)
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precomputed_embeddings = diffusion_model.timestep_independent(mel_codes, cond_mel)
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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)[:,:,:msl*4]
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class TextToSpeech:
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def __init__(self, autoregressive_batch_size=32):
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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=300, max_text_tokens=200, 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=512, in_channels=100, out_channels=200, in_latent_channels=1024,
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channel_mult=[1, 2, 3, 4], num_res_blocks=[3, 3, 3, 3],
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token_conditioning_resolutions=[1, 4, 8],
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dropout=0, attention_resolutions=[4, 8], num_heads=8, kernel_size=3, scale_factor=2,
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time_embed_dim_multiplier=4, unconditioned_percentage=0, conditioning_dim_factor=2,
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conditioning_expansion=1).cpu().eval()
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self.diffusion.load_state_dict(torch.load('.models/diffusion.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(self, text, voice_samples, k=1,
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# autoregressive generation parameters follow
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num_autoregressive_samples=512, temperature=.5, length_penalty=2, repetition_penalty=2.0, top_p=.5,
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typical_sampling=False, typical_mass=.9,
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# diffusion generation parameters follow
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diffusion_iterations=100, cond_free=True, cond_free_k=2, diffusion_temperature=.7,):
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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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cond_diffusion = voice_samples[0].cuda()
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# The diffusion model expects = 88200 conditioning samples.
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if cond_diffusion.shape[-1] < 88200:
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cond_diffusion = F.pad(cond_diffusion, (0, 88200-cond_diffusion.shape[-1]))
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else:
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cond_diffusion = cond_diffusion[:, :88200]
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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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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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typical_sampling=typical_sampling,
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typical_mass=typical_mass)
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padding_needed = 250 - 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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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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code = best_results[b].unsqueeze(0)
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mel = do_spectrogram_diffusion(self.diffusion, diffuser, code, cond_diffusion, 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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def refine_for_intellibility(self, wav_candidates, corresponding_codes, output_path):
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"""
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Further refine the remaining candidates using a ASR model to pick out the ones that are the most understandable.
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TODO: finish this function
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:param wav_candidates:
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:return:
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"""
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transcriber = ocotillo.Transcriber(on_cuda=True)
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transcriptions = transcriber.transcribe_batch(torch.cat(wav_candidates, dim=0).squeeze(1), 24000)
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best = 99999999
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for i, transcription in enumerate(transcriptions):
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dist = lev_distance(transcription, args.text.lower())
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if dist < best:
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best = dist
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best_codes = corresponding_codes[i].unsqueeze(0)
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best_wav = wav_candidates[i]
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del transcriber
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torchaudio.save(os.path.join(output_path, f'{voice}_poor.wav'), best_wav.squeeze(0).cpu(), 24000)
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# Perform diffusion again with the high-quality diffuser.
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mel = do_spectrogram_diffusion(diffusion, final_diffuser, best_codes, cond_diffusion, mean=False)
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wav = vocoder.inference(mel)
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torchaudio.save(os.path.join(args.output_path, f'{voice}.wav'), wav.squeeze(0).cpu(), 24000) |