forked from mrq/tortoise-tts
213 lines
11 KiB
Python
213 lines
11 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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from progressbar import progressbar
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from models.dvae import DiscreteVAE
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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.discrete_diffusion_vocoder import DiscreteDiffusionVocoder
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from models.text_voice_clip import VoiceCLIP
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from utils.audio import load_audio
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from utils.diffusion import SpacedDiffusion, space_timesteps, get_named_beta_schedule
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from utils.tokenizer import VoiceBpeTokenizer
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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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'dvae.pth': 'https://huggingface.co/jbetker/voice-dvae/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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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):
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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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def load_conditioning(path, sample_rate=22050, cond_length=132300):
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rel_clip = load_audio(path, sample_rate)
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gap = rel_clip.shape[-1] - cond_length
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if gap < 0:
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rel_clip = F.pad(rel_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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rel_clip = rel_clip[:, rand_start:rand_start + cond_length]
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mel_clip = TorchMelSpectrogram()(rel_clip.unsqueeze(0)).squeeze(0)
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return mel_clip.unsqueeze(0).cuda(), rel_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
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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, dvae_model, diffuser, mel_codes, conditioning_input, spectrogram_compression_factor=128, mean=False):
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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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mel = dvae_model.decode(mel_codes)[0]
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# Pad MEL to multiples of 2048//spectrogram_compression_factor
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msl = mel.shape[-1]
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dsl = 2048 // spectrogram_compression_factor
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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, (0, gap))
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output_shape = (mel.shape[0], 1, mel.shape[-1] * spectrogram_compression_factor)
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if mean:
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return diffuser.p_sample_loop(diffusion_model, output_shape, noise=torch.zeros(output_shape, device=mel_codes.device),
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model_kwargs={'spectrogram': mel, 'conditioning_input': conditioning_input})
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else:
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return diffuser.p_sample_loop(diffusion_model, output_shape, model_kwargs={'spectrogram': mel, 'conditioning_input': conditioning_input})
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if __name__ == '__main__':
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# These are voices drawn randomly from the training set. You are free to substitute your own voices in, but testing
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# has shown that the model does not generalize to new voices very well.
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preselected_cond_voices = {
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# Male voices
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'dotrice': ['voices/dotrice/1.wav', 'voices/dotrice/2.wav'],
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'harris': ['voices/male_harris1.wav', 'voices/male_harris2.wav'],
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'lescault': ['voices/male_lescault1.wav', 'voices/male_lescault2.wav'],
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'otto': ['voices/male_otto1.wav', 'voices/male_otto2.wav'],
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# Female voices
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'atkins': ['voices/female_atkins1.wav', 'voices/female_atkins2.wav'],
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'grace': ['voices/female_grace1.wav', 'voices/female_grace2.wav'],
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'kennard': ['voices/female_kennard1.wav', 'voices/female_kennard2.wav'],
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'mol': ['voices/female_mol1.wav', 'voices/female_mol2.wav'],
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}
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parser = argparse.ArgumentParser()
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parser.add_argument('-text', type=str, help='Text to speak.', default="I am a language model that has learned to speak.")
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parser.add_argument('-voice', type=str, help='Use a preset conditioning voice (defined above). Overrides cond_path.', default='dotrice,harris,lescault,otto,atkins,grace,kennard,mol')
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parser.add_argument('-num_samples', type=int, help='How many total outputs the autoregressive transformer should produce.', default=512)
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parser.add_argument('-num_batches', type=int, help='How many batches those samples should be produced over.', default=16)
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parser.add_argument('-num_outputs', type=int, help='Number of outputs to produce.', default=2)
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parser.add_argument('-output_path', type=str, help='Where to store outputs.', default='results/')
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args = parser.parse_args()
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os.makedirs(args.output_path, exist_ok=True)
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download_models()
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for voice in args.voice.split(','):
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print("Loading GPT TTS..")
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autoregressive = UnifiedVoice(max_mel_tokens=300, max_text_tokens=200, max_conditioning_inputs=2, layers=30, model_dim=1024,
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heads=16, number_text_tokens=256, start_text_token=255, checkpointing=False, train_solo_embeddings=False).cuda().eval()
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autoregressive.load_state_dict(torch.load('.models/autoregressive.pth'))
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stop_mel_token = autoregressive.stop_mel_token
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print("Loading data..")
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tokenizer = VoiceBpeTokenizer()
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text = torch.IntTensor(tokenizer.encode(args.text)).unsqueeze(0).cuda()
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text = F.pad(text, (0,1)) # This may not be necessary.
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cond_paths = preselected_cond_voices[voice]
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conds = []
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for cond_path in cond_paths:
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c, cond_wav = load_conditioning(cond_path)
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conds.append(c)
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conds = torch.stack(conds, dim=1) # And just use the last cond_wav for the diffusion model.
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with torch.no_grad():
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print("Performing autoregressive inference..")
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samples = []
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for b in tqdm(range(args.num_batches)):
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codes = autoregressive.inference_speech(conds, text, num_beams=1, repetition_penalty=1.0, do_sample=True, top_k=50, top_p=.95,
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temperature=.9, num_return_sequences=args.num_samples//args.num_batches, length_penalty=1)
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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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del autoregressive
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print("Loading CLIP..")
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clip = VoiceCLIP(dim_text=512, dim_speech=512, dim_latent=512, num_text_tokens=256, text_enc_depth=8, text_seq_len=120, text_heads=8,
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num_speech_tokens=8192, speech_enc_depth=10, speech_heads=8, speech_seq_len=250).cuda().eval()
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clip.load_state_dict(torch.load('.models/clip.pth'))
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print("Performing CLIP filtering..")
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clip_results = []
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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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text = text[:, :120] # Ugly hack to fix the fact that I didn't train CLIP to handle long enough text.
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clip_results.append(clip(text.repeat(batch.shape[0], 1),
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torch.full((batch.shape[0],), fill_value=text.shape[1]-1, dtype=torch.long, device='cuda'),
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batch, torch.full((batch.shape[0],), fill_value=batch.shape[1]*1024, dtype=torch.long, device='cuda'),
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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=args.num_outputs).indices]
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# Delete the autoregressive and clip models to free up GPU memory
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del samples, clip
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print("Loading DVAE..")
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dvae = DiscreteVAE(positional_dims=1, channels=80, hidden_dim=512, num_resnet_blocks=3, codebook_dim=512, num_tokens=8192, num_layers=2,
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record_codes=True, kernel_size=3, use_transposed_convs=False).cuda().eval()
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dvae.load_state_dict(torch.load('.models/dvae.pth'))
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print("Loading Diffusion Model..")
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diffusion = DiscreteDiffusionVocoder(model_channels=128, dvae_dim=80, channel_mult=[1, 1, 1.5, 2, 3, 4, 6, 8, 8, 8, 8], num_res_blocks=[1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1],
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spectrogram_conditioning_resolutions=[2,512], attention_resolutions=[512,1024], num_heads=4, kernel_size=3, scale_factor=2,
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conditioning_inputs_provided=True, time_embed_dim_multiplier=4).cuda().eval()
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diffusion.load_state_dict(torch.load('.models/diffusion.pth'))
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diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=100)
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print("Performing vocoding..")
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# Perform vocoding on each batch element separately: The diffusion model is very memory (and compute!) intensive.
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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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wav = do_spectrogram_diffusion(diffusion, dvae, diffuser, code, cond_wav, spectrogram_compression_factor=256, mean=True)
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torchaudio.save(os.path.join(args.output_path, f'{voice}_{b}.wav'), wav.squeeze(0).cpu(), 22050)
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