diff --git a/api_new_autoregressive.py b/api_new_autoregressive.py deleted file mode 100644 index cd5cd89..0000000 --- a/api_new_autoregressive.py +++ /dev/null @@ -1,245 +0,0 @@ -import argparse -import os -import random -from urllib import request - -import torch -import torch.nn.functional as F -import torchaudio -import progressbar -import ocotillo - -from models.diffusion_decoder import DiffusionTts -from models.autoregressive import UnifiedVoice -from tqdm import tqdm - -from models.arch_util import TorchMelSpectrogram -from models.new_autoregressive import AutoregressiveCodegen -from models.text_voice_clip import VoiceCLIP -from models.vocoder import UnivNetGenerator -from utils.audio import load_audio, wav_to_univnet_mel, denormalize_tacotron_mel -from utils.diffusion import SpacedDiffusion, space_timesteps, get_named_beta_schedule -from utils.tokenizer import VoiceBpeTokenizer, lev_distance - - -pbar = None -def download_models(): - MODELS = { - 'clip.pth': 'https://huggingface.co/jbetker/tortoise-tts-clip/resolve/main/pytorch-model.bin', - 'diffusion.pth': 'https://huggingface.co/jbetker/tortoise-tts-diffusion-v1/resolve/main/pytorch-model.bin', - 'autoregressive.pth': 'https://huggingface.co/jbetker/tortoise-tts-autoregressive/resolve/main/pytorch-model.bin' - } - os.makedirs('.models', exist_ok=True) - def show_progress(block_num, block_size, total_size): - global pbar - if pbar is None: - pbar = progressbar.ProgressBar(maxval=total_size) - pbar.start() - - downloaded = block_num * block_size - if downloaded < total_size: - pbar.update(downloaded) - else: - pbar.finish() - pbar = None - for model_name, url in MODELS.items(): - if os.path.exists(f'.models/{model_name}'): - continue - print(f'Downloading {model_name} from {url}...') - request.urlretrieve(url, f'.models/{model_name}', show_progress) - print('Done.') - - -def pad_or_truncate(t, length): - if t.shape[-1] == length: - return t - elif t.shape[-1] < length: - return F.pad(t, (0, length-t.shape[-1])) - else: - return t[..., :length] - - -def load_discrete_vocoder_diffuser(trained_diffusion_steps=4000, desired_diffusion_steps=200, cond_free=True, cond_free_k=1): - """ - Helper function to load a GaussianDiffusion instance configured for use as a vocoder. - """ - return SpacedDiffusion(use_timesteps=space_timesteps(trained_diffusion_steps, [desired_diffusion_steps]), model_mean_type='epsilon', - model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', trained_diffusion_steps), - conditioning_free=cond_free, conditioning_free_k=cond_free_k) - - -def load_conditioning(clip, cond_length=132300): - gap = clip.shape[-1] - cond_length - if gap < 0: - clip = F.pad(clip, pad=(0, abs(gap))) - elif gap > 0: - rand_start = random.randint(0, gap) - clip = clip[:, rand_start:rand_start + cond_length] - mel_clip = TorchMelSpectrogram()(clip.unsqueeze(0)).squeeze(0) - return mel_clip.unsqueeze(0).cuda() - - -def fix_autoregressive_output(codes, stop_token): - """ - This function performs some padding on coded audio that fixes a mismatch issue between what the diffusion model was - trained on and what the autoregressive code generator creates (which has no padding or end). - This is highly specific to the DVAE being used, so this particular coding will not necessarily work if used with - a different DVAE. This can be inferred by feeding a audio clip padded with lots of zeros on the end through the DVAE - and copying out the last few codes. - - Failing to do this padding will produce speech with a harsh end that sounds like "BLAH" or similar. - """ - # Strip off the autoregressive stop token and add padding. - stop_token_indices = (codes == stop_token).nonzero() - if len(stop_token_indices) == 0: - print("No stop tokens found, enjoy that output of yours!") - return codes - else: - codes[stop_token_indices] = 83 - stm = stop_token_indices.min().item() - codes[stm:] = 83 - if stm - 3 < codes.shape[0]: - codes[-3] = 45 - codes[-2] = 45 - codes[-1] = 248 - - return codes - - -def do_spectrogram_diffusion(diffusion_model, diffuser, mel_codes, conditioning_samples, temperature=1): - """ - Uses the specified diffusion model to convert discrete codes into a spectrogram. - """ - with torch.no_grad(): - cond_mels = [] - for sample in conditioning_samples: - sample = pad_or_truncate(sample, 102400) - cond_mel = wav_to_univnet_mel(sample.to(mel_codes.device), do_normalization=False) - cond_mels.append(cond_mel) - cond_mels = torch.stack(cond_mels, dim=1) - - output_seq_len = mel_codes.shape[-1]*4*24000//22050 # This diffusion model converts from 22kHz spectrogram codes to a 24kHz spectrogram signal. - output_shape = (mel_codes.shape[0], 100, output_seq_len) - precomputed_embeddings = diffusion_model.timestep_independent(mel_codes, cond_mels, output_seq_len, False) - - noise = torch.randn(output_shape, device=mel_codes.device) * temperature - mel = diffuser.p_sample_loop(diffusion_model, output_shape, noise=noise, - model_kwargs={'precomputed_aligned_embeddings': precomputed_embeddings}) - return denormalize_tacotron_mel(mel)[:,:,:output_seq_len] - - -class TextToSpeech: - def __init__(self, autoregressive_batch_size=32): - self.autoregressive_batch_size = autoregressive_batch_size - self.tokenizer = VoiceBpeTokenizer() - download_models() - - self.autoregressive = AutoregressiveCodegen(1024, 16).cpu().eval() - self.autoregressive.load_state_dict(torch.load('X:\\dlas\\experiments\\train_autoregressive_codegen\\models\\20750_codegen_ema.pth')) - - self.clip = VoiceCLIP(dim_text=512, dim_speech=512, dim_latent=512, num_text_tokens=256, text_enc_depth=12, - text_seq_len=350, text_heads=8, - num_speech_tokens=8192, speech_enc_depth=12, speech_heads=8, speech_seq_len=430, - use_xformers=True).cpu().eval() - self.clip.load_state_dict(torch.load('.models/clip.pth')) - - self.diffusion = DiffusionTts(model_channels=1024, num_layers=10, in_channels=100, out_channels=200, - in_latent_channels=1024, in_tokens=8193, dropout=0, use_fp16=False, num_heads=16, - layer_drop=0, unconditioned_percentage=0).cpu().eval() - self.diffusion.load_state_dict(torch.load('.models/diffusion.pth')) - - self.vocoder = UnivNetGenerator().cpu() - self.vocoder.load_state_dict(torch.load('.models/vocoder.pth')['model_g']) - self.vocoder.eval(inference=True) - - def tts(self, text, voice_samples, k=1, - # autoregressive generation parameters follow - num_autoregressive_samples=512, temperature=.5, length_penalty=2, repetition_penalty=2.0, top_p=.5, - typical_sampling=False, typical_mass=.9, - # diffusion generation parameters follow - diffusion_iterations=100, cond_free=True, cond_free_k=2, diffusion_temperature=.7,): - text = torch.IntTensor(self.tokenizer.encode(text)).unsqueeze(0).cuda() - text = F.pad(text, (0, 1)) # This may not be necessary. - - conds = [] - if not isinstance(voice_samples, list): - voice_samples = [voice_samples] - for vs in voice_samples: - conds.append(load_conditioning(vs)) - conds = torch.stack(conds, dim=1) - - diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=diffusion_iterations, cond_free=cond_free, cond_free_k=cond_free_k) - - with torch.no_grad(): - samples = [] - num_batches = num_autoregressive_samples // self.autoregressive_batch_size - stop_mel_token = self.autoregressive.STOP_TOKEN - self.autoregressive = self.autoregressive.cuda() - for _ in tqdm(range(num_batches)): - codes = self.autoregressive.generate(conds, text, - do_sample=True, - top_p=top_p, - temperature=temperature, - num_return_sequences=self.autoregressive_batch_size, - length_penalty=length_penalty, - repetition_penalty=repetition_penalty, - typical_sampling=typical_sampling, - typical_mass=typical_mass) - padding_needed = 250 - codes.shape[1] - codes = F.pad(codes, (0, padding_needed), value=stop_mel_token) - samples.append(codes) - #self.autoregressive = self.autoregressive.cpu() - - clip_results = [] - self.clip = self.clip.cuda() - for batch in samples: - for i in range(batch.shape[0]): - batch[i] = fix_autoregressive_output(batch[i], stop_mel_token) - bad_toks = batch >= 8192 - batch = batch * bad_toks.logical_not() - clip_results.append(self.clip(text.repeat(batch.shape[0], 1), batch, return_loss=False)) - clip_results = torch.cat(clip_results, dim=0) - samples = torch.cat(samples, dim=0) - best_results = samples[torch.topk(clip_results, k=k).indices] - self.clip = self.clip.cpu() - del samples - - print("Performing vocoding..") - wav_candidates = [] - self.diffusion = self.diffusion.cuda() - self.vocoder = self.vocoder.cuda() - for b in range(best_results.shape[0]): - code = best_results[b].unsqueeze(0) - mel = do_spectrogram_diffusion(self.diffusion, diffuser, code, voice_samples, temperature=diffusion_temperature) - wav = self.vocoder.inference(mel) - wav_candidates.append(wav.cpu()) - self.diffusion = self.diffusion.cpu() - self.vocoder = self.vocoder.cpu() - - if len(wav_candidates) > 1: - return wav_candidates - return wav_candidates[0] - - def refine_for_intellibility(self, wav_candidates, corresponding_codes, output_path): - """ - Further refine the remaining candidates using a ASR model to pick out the ones that are the most understandable. - TODO: finish this function - :param wav_candidates: - :return: - """ - transcriber = ocotillo.Transcriber(on_cuda=True) - transcriptions = transcriber.transcribe_batch(torch.cat(wav_candidates, dim=0).squeeze(1), 24000) - best = 99999999 - for i, transcription in enumerate(transcriptions): - dist = lev_distance(transcription, args.text.lower()) - if dist < best: - best = dist - best_codes = corresponding_codes[i].unsqueeze(0) - best_wav = wav_candidates[i] - del transcriber - torchaudio.save(os.path.join(output_path, f'{voice}_poor.wav'), best_wav.squeeze(0).cpu(), 24000) - - # Perform diffusion again with the high-quality diffuser. - mel = do_spectrogram_diffusion(diffusion, final_diffuser, best_codes, cond_diffusion, mean=False) - wav = vocoder.inference(mel) - torchaudio.save(os.path.join(args.output_path, f'{voice}.wav'), wav.squeeze(0).cpu(), 24000) \ No newline at end of file