8fdf516e62
After training a similar model for a different purpose, I realized that this model is faulty: the contrastive loss it uses only pays attention to high-frequency details which do not contribute meaningfully to output quality. I validated this by comparing a no-CVVP output with a baseline using tts-scores and found no differences.
473 lines
26 KiB
Python
473 lines
26 KiB
Python
import os
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import random
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import uuid
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from time import time
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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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import torchaudio
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from tortoise.models.classifier import AudioMiniEncoderWithClassifierHead
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from tortoise.models.diffusion_decoder import DiffusionTts
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from tortoise.models.autoregressive import UnifiedVoice
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from tqdm import tqdm
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from tortoise.models.arch_util import TorchMelSpectrogram
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from tortoise.models.clvp import CLVP
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from tortoise.models.random_latent_generator import RandomLatentConverter
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from tortoise.models.vocoder import UnivNetGenerator
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from tortoise.utils.audio import wav_to_univnet_mel, denormalize_tacotron_mel
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from tortoise.utils.diffusion import SpacedDiffusion, space_timesteps, get_named_beta_schedule
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from tortoise.utils.tokenizer import VoiceBpeTokenizer
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from tortoise.utils.wav2vec_alignment import Wav2VecAlignment
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pbar = None
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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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"""
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MODELS = {
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'autoregressive.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/autoregressive.pth',
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'classifier.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/classifier.pth',
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'clvp2.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/clvp2.pth',
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'diffusion_decoder.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/diffusion_decoder.pth',
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'vocoder.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/vocoder.pth',
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'rlg_auto.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/rlg_auto.pth',
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'rlg_diffuser.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/rlg_diffuser.pth',
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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 specific_models is not None and model_name not in specific_models:
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continue
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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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"""
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Utility function for forcing <t> to have the specified sequence length, whether by clipping it or padding it with 0s.
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"""
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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 format_conditioning(clip, cond_length=132300):
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"""
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Converts the given conditioning signal to a MEL spectrogram and clips it as expected by the models.
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"""
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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, 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 in one of the generated voice clips. This typically means the spoken audio is "
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"too long. In some cases, the output will still be good, though. Listen to it and if it is missing words, "
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"try breaking up your input text.")
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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, latents, conditioning_latents, temperature=1, verbose=True):
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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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output_seq_len = latents.shape[1] * 4 * 24000 // 22050 # This diffusion model converts from 22kHz spectrogram codes to a 24kHz spectrogram signal.
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output_shape = (latents.shape[0], 100, output_seq_len)
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precomputed_embeddings = diffusion_model.timestep_independent(latents, conditioning_latents, output_seq_len, False)
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noise = torch.randn(output_shape, device=latents.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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progress=verbose)
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return denormalize_tacotron_mel(mel)[:,:,:output_seq_len]
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def classify_audio_clip(clip):
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"""
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Returns whether or not Tortoises' classifier thinks the given clip came from Tortoise.
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:param clip: torch tensor containing audio waveform data (get it from load_audio)
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:return: True if the clip was classified as coming from Tortoise and false if it was classified as real.
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"""
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download_models(['classifier.pth'])
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classifier = AudioMiniEncoderWithClassifierHead(2, spec_dim=1, embedding_dim=512, depth=5, downsample_factor=4,
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resnet_blocks=2, attn_blocks=4, num_attn_heads=4, base_channels=32,
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dropout=0, kernel_size=5, distribute_zero_label=False)
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classifier.load_state_dict(torch.load('.models/classifier.pth', map_location=torch.device('cpu')))
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clip = clip.cpu().unsqueeze(0)
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results = F.softmax(classifier(clip), dim=-1)
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return results[0][0]
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def pick_best_batch_size_for_gpu():
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"""
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Tries to pick a batch size that will fit in your GPU. These sizes aren't guaranteed to work, but they should give
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you a good shot.
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"""
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free, available = torch.cuda.mem_get_info()
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availableGb = available / (1024 ** 3)
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if availableGb > 14:
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return 16
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elif availableGb > 10:
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return 8
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elif availableGb > 7:
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return 4
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return 1
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class TextToSpeech:
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"""
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Main entry point into Tortoise.
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"""
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def __init__(self, autoregressive_batch_size=None, models_dir='.models', enable_redaction=True):
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"""
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Constructor
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:param autoregressive_batch_size: Specifies how many samples to generate per batch. Lower this if you are seeing
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GPU OOM errors. Larger numbers generates slightly faster.
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:param models_dir: Where model weights are stored. This should only be specified if you are providing your own
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models, otherwise use the defaults.
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:param enable_redaction: When true, text enclosed in brackets are automatically redacted from the spoken output
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(but are still rendered by the model). This can be used for prompt engineering.
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Default is true.
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"""
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self.autoregressive_batch_size = pick_best_batch_size_for_gpu() if autoregressive_batch_size is None else autoregressive_batch_size
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self.enable_redaction = enable_redaction
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if self.enable_redaction:
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self.aligner = Wav2VecAlignment()
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self.tokenizer = VoiceBpeTokenizer()
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download_models()
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if os.path.exists(f'{models_dir}/autoregressive.ptt'):
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# Assume this is a traced directory.
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self.autoregressive = torch.jit.load(f'{models_dir}/autoregressive.ptt')
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self.diffusion = torch.jit.load(f'{models_dir}/diffusion_decoder.ptt')
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else:
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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=255, start_text_token=255, checkpointing=False,
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train_solo_embeddings=False).cpu().eval()
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self.autoregressive.load_state_dict(torch.load(f'{models_dir}/autoregressive.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(f'{models_dir}/diffusion_decoder.pth'))
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self.clvp = CLVP(dim_text=768, dim_speech=768, dim_latent=768, num_text_tokens=256, text_enc_depth=20,
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text_seq_len=350, text_heads=12,
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num_speech_tokens=8192, speech_enc_depth=20, speech_heads=12, speech_seq_len=430,
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use_xformers=True).cpu().eval()
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self.clvp.load_state_dict(torch.load(f'{models_dir}/clvp2.pth'))
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self.vocoder = UnivNetGenerator().cpu()
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self.vocoder.load_state_dict(torch.load(f'{models_dir}/vocoder.pth')['model_g'])
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self.vocoder.eval(inference=True)
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# Random latent generators (RLGs) are loaded lazily.
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self.rlg_auto = None
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self.rlg_diffusion = None
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def get_conditioning_latents(self, voice_samples, return_mels=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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properties.
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:param voice_samples: List of 2 or more ~10 second reference clips, which should be torch tensors containing 22.05kHz waveform data.
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"""
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with torch.no_grad():
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voice_samples = [v.to('cuda') for v in voice_samples]
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auto_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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auto_conds.append(format_conditioning(vs))
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auto_conds = torch.stack(auto_conds, dim=1)
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self.autoregressive = self.autoregressive.cuda()
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auto_latent = self.autoregressive.get_conditioning(auto_conds)
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self.autoregressive = self.autoregressive.cpu()
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diffusion_conds = []
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for sample in voice_samples:
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# The diffuser operates at a sample rate of 24000 (except for the latent inputs)
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sample = torchaudio.functional.resample(sample, 22050, 24000)
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sample = pad_or_truncate(sample, 102400)
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cond_mel = wav_to_univnet_mel(sample.to('cuda'), do_normalization=False)
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diffusion_conds.append(cond_mel)
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diffusion_conds = torch.stack(diffusion_conds, dim=1)
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self.diffusion = self.diffusion.cuda()
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diffusion_latent = self.diffusion.get_conditioning(diffusion_conds)
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self.diffusion = self.diffusion.cpu()
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if return_mels:
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return auto_latent, diffusion_latent, auto_conds, diffusion_conds
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else:
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return auto_latent, diffusion_latent
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def get_random_conditioning_latents(self):
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# Lazy-load the RLG models.
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if self.rlg_auto is None:
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self.rlg_auto = RandomLatentConverter(1024).eval()
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self.rlg_auto.load_state_dict(torch.load('.models/rlg_auto.pth', map_location=torch.device('cpu')))
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self.rlg_diffusion = RandomLatentConverter(2048).eval()
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self.rlg_diffusion.load_state_dict(torch.load('.models/rlg_diffuser.pth', map_location=torch.device('cpu')))
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with torch.no_grad():
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return self.rlg_auto(torch.tensor([0.0])), self.rlg_diffusion(torch.tensor([0.0]))
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def tts_with_preset(self, text, 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,
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'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': 16, 'diffusion_iterations': 30, 'cond_free': False},
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'fast': {'num_autoregressive_samples': 96, 'diffusion_iterations': 80},
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'standard': {'num_autoregressive_samples': 256, 'diffusion_iterations': 200},
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'high_quality': {'num_autoregressive_samples': 256, 'diffusion_iterations': 400},
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}
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kwargs.update(presets[preset])
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return self.tts(text, **kwargs)
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def tts(self, text, voice_samples=None, conditioning_latents=None, k=1, verbose=True, use_deterministic_seed=None,
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return_deterministic_state=False,
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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, max_mel_tokens=500,
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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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**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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:param text: Text to be spoken.
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:param voice_samples: List of 2 or more ~10 second reference clips which should be torch tensors containing 22.05kHz waveform data.
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:param conditioning_latents: A tuple of (autoregressive_conditioning_latent, diffusion_conditioning_latent), which
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can be provided in lieu of voice_samples. This is ignored unless voice_samples=None.
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Conditioning latents can be retrieved via get_conditioning_latents().
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:param k: The number of returned clips. The most likely (as determined by Tortoises' CLVP model) clips are returned.
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:param verbose: Whether or not to print log messages indicating the progress of creating a clip. Default=true.
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~~AUTOREGRESSIVE KNOBS~~
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:param num_autoregressive_samples: Number of samples taken from the autoregressive model, all of which are filtered using CLVP.
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As Tortoise is a probabilistic model, more samples means a higher probability of creating something "great".
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:param temperature: The softmax temperature of the autoregressive model.
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:param length_penalty: A length penalty applied to the autoregressive decoder. Higher settings causes the model to produce more terse outputs.
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:param repetition_penalty: A penalty that prevents the autoregressive decoder from repeating itself during decoding. Can be used to reduce the incidence
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of long silences or "uhhhhhhs", etc.
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:param top_p: P value used in nucleus sampling. (0,1]. Lower values mean the decoder produces more "likely" (aka boring) outputs.
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:param max_mel_tokens: Restricts the output length. (0,600] integer. Each unit is 1/20 of a second.
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:param typical_sampling: Turns typical sampling on or off. This sampling mode is discussed in this paper: https://arxiv.org/abs/2202.00666
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I was interested in the premise, but the results were not as good as I was hoping. This is off by default, but
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could use some tuning.
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:param typical_mass: The typical_mass parameter from the typical_sampling algorithm.
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~~DIFFUSION KNOBS~~
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:param diffusion_iterations: Number of diffusion steps to perform. [0,4000]. More steps means the network has more chances to iteratively refine
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the output, which should theoretically mean a higher quality output. Generally a value above 250 is not noticeably better,
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however.
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:param cond_free: Whether or not to perform conditioning-free diffusion. Conditioning-free diffusion performs two forward passes for
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each diffusion step: one with the outputs of the autoregressive model and one with no conditioning priors. The output
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of the two is blended according to the cond_free_k value below. Conditioning-free diffusion is the real deal, and
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dramatically improves realism.
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:param cond_free_k: Knob that determines how to balance the conditioning free signal with the conditioning-present signal. [0,inf].
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As cond_free_k increases, the output becomes dominated by the conditioning-free signal.
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Formula is: output=cond_present_output*(cond_free_k+1)-cond_absenct_output*cond_free_k
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:param diffusion_temperature: Controls the variance of the noise fed into the diffusion model. [0,1]. Values at 0
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are the "mean" prediction of the diffusion network and will sound bland and smeared.
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~~OTHER STUFF~~
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:param hf_generate_kwargs: The huggingface Transformers generate API is used for the autoregressive transformer.
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Extra keyword args fed to this function get forwarded directly to that API. Documentation
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here: https://huggingface.co/docs/transformers/internal/generation_utils
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:return: Generated audio clip(s) as a torch tensor. Shape 1,S if k=1 else, (k,1,S) where S is the sample length.
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Sample rate is 24kHz.
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"""
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deterministic_seed = self.deterministic_state(seed=use_deterministic_seed)
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text_tokens = torch.IntTensor(self.tokenizer.encode(text)).unsqueeze(0).cuda()
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text_tokens = F.pad(text_tokens, (0, 1)) # This may not be necessary.
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assert text_tokens.shape[-1] < 400, 'Too much text provided. Break the text up into separate segments and re-try inference.'
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auto_conds = None
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if voice_samples is not None:
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auto_conditioning, diffusion_conditioning, auto_conds, _ = self.get_conditioning_latents(voice_samples, return_mels=True)
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elif conditioning_latents is not None:
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auto_conditioning, diffusion_conditioning = conditioning_latents
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else:
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auto_conditioning, diffusion_conditioning = self.get_random_conditioning_latents()
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auto_conditioning = auto_conditioning.cuda()
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diffusion_conditioning = diffusion_conditioning.cuda()
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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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if verbose:
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|
print("Generating autoregressive samples..")
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|
for b in tqdm(range(num_batches), disable=not verbose):
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|
codes = self.autoregressive.inference_speech(auto_conditioning, text_tokens,
|
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do_sample=True,
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|
top_p=top_p,
|
|
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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|
max_generate_length=max_mel_tokens,
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|
**hf_generate_kwargs)
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|
padding_needed = max_mel_tokens - codes.shape[1]
|
|
codes = F.pad(codes, (0, padding_needed), value=stop_mel_token)
|
|
samples.append(codes)
|
|
self.autoregressive = self.autoregressive.cpu()
|
|
|
|
clvp_results = []
|
|
self.clvp = self.clvp.cuda()
|
|
if verbose:
|
|
print("Computing best candidates using CLVP")
|
|
for batch in tqdm(samples, disable=not verbose):
|
|
for i in range(batch.shape[0]):
|
|
batch[i] = fix_autoregressive_output(batch[i], stop_mel_token)
|
|
clvp = self.clvp(text_tokens.repeat(batch.shape[0], 1), batch, return_loss=False)
|
|
clvp_results.append(clvp)
|
|
clvp_results = torch.cat(clvp_results, dim=0)
|
|
samples = torch.cat(samples, dim=0)
|
|
best_results = samples[torch.topk(clvp_results, k=k).indices]
|
|
self.clvp = self.clvp.cpu()
|
|
del samples
|
|
|
|
# The diffusion model actually wants the last hidden layer from the autoregressive model as conditioning
|
|
# inputs. Re-produce those for the top results. This could be made more efficient by storing all of these
|
|
# results, but will increase memory usage.
|
|
self.autoregressive = self.autoregressive.cuda()
|
|
best_latents = self.autoregressive(auto_conditioning.repeat(k, 1), text_tokens.repeat(k, 1),
|
|
torch.tensor([text_tokens.shape[-1]], device=text_tokens.device), best_results,
|
|
torch.tensor([best_results.shape[-1]*self.autoregressive.mel_length_compression], device=text_tokens.device),
|
|
return_latent=True, clip_inputs=False)
|
|
self.autoregressive = self.autoregressive.cpu()
|
|
del auto_conditioning
|
|
|
|
if verbose:
|
|
print("Transforming autoregressive outputs into audio..")
|
|
wav_candidates = []
|
|
self.diffusion = self.diffusion.cuda()
|
|
self.vocoder = self.vocoder.cuda()
|
|
for b in range(best_results.shape[0]):
|
|
codes = best_results[b].unsqueeze(0)
|
|
latents = best_latents[b].unsqueeze(0)
|
|
|
|
# Find the first occurrence of the "calm" token and trim the codes to that.
|
|
ctokens = 0
|
|
for k in range(codes.shape[-1]):
|
|
if codes[0, k] == calm_token:
|
|
ctokens += 1
|
|
else:
|
|
ctokens = 0
|
|
if ctokens > 8: # 8 tokens gives the diffusion model some "breathing room" to terminate speech.
|
|
latents = latents[:, :k]
|
|
break
|
|
|
|
mel = do_spectrogram_diffusion(self.diffusion, diffuser, latents, diffusion_conditioning,
|
|
temperature=diffusion_temperature, verbose=verbose)
|
|
wav = self.vocoder.inference(mel)
|
|
wav_candidates.append(wav.cpu())
|
|
self.diffusion = self.diffusion.cpu()
|
|
self.vocoder = self.vocoder.cpu()
|
|
|
|
def potentially_redact(clip, text):
|
|
if self.enable_redaction:
|
|
return self.aligner.redact(clip.squeeze(1), text).unsqueeze(1)
|
|
return clip
|
|
wav_candidates = [potentially_redact(wav_candidate, text) for wav_candidate in wav_candidates]
|
|
|
|
if len(wav_candidates) > 1:
|
|
res = wav_candidates
|
|
else:
|
|
res = wav_candidates[0]
|
|
|
|
if return_deterministic_state:
|
|
return res, (deterministic_seed, text, voice_samples, conditioning_latents)
|
|
else:
|
|
return res
|
|
|
|
def deterministic_state(self, seed=None):
|
|
"""
|
|
Sets the random seeds that tortoise uses to the current time() and returns that seed so results can be
|
|
reproduced.
|
|
"""
|
|
seed = int(time()) if seed is None else seed
|
|
torch.manual_seed(seed)
|
|
random.seed(seed)
|
|
# Can't currently set this because of CUBLAS. TODO: potentially enable it if necessary.
|
|
# torch.use_deterministic_algorithms(True)
|
|
|
|
return seed
|