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port do_tts to use the API

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This commit is contained in:
James Betker 2022-04-01 11:55:07 -06:00
parent 9db06e139b
commit 287debd1d3
2 changed files with 36 additions and 201 deletions
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29
api.py
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@ -151,10 +151,10 @@ class TextToSpeech:
def tts(self, text, voice_samples, k=1,
# autoregressive generation parameters follow
num_autoregressive_samples=512, temperature=.9, length_penalty=1, repetition_penalty=1.0, top_k=50, top_p=.95,
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=1, diffusion_temperature=1,):
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.
@ -181,7 +181,6 @@ class TextToSpeech:
for b in tqdm(range(num_batches)):
codes = self.autoregressive.inference_speech(conds, text,
do_sample=True,
top_k=top_k,
top_p=top_p,
temperature=temperature,
num_return_sequences=self.autoregressive_batch_size,
@ -221,3 +220,27 @@ class TextToSpeech:
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)

206
do_tts.py
View File

@ -1,123 +1,13 @@
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.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 load_discrete_vocoder_diffuser(trained_diffusion_steps=4000, desired_diffusion_steps=200, cond_free=True):
"""
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=1)
def load_conditioning(path, sample_rate=22050, cond_length=132300):
rel_clip = load_audio(path, sample_rate)
gap = rel_clip.shape[-1] - cond_length
if gap < 0:
rel_clip = F.pad(rel_clip, pad=(0, abs(gap)))
elif gap > 0:
rand_start = random.randint(0, gap)
rel_clip = rel_clip[:, rand_start:rand_start + cond_length]
mel_clip = TorchMelSpectrogram()(rel_clip.unsqueeze(0)).squeeze(0)
return mel_clip.unsqueeze(0).cuda(), rel_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
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_input, mean=False):
"""
Uses the specified diffusion model and DVAE model to convert the provided MEL & conditioning inputs into an audio clip.
"""
with torch.no_grad():
cond_mel = wav_to_univnet_mel(conditioning_input.squeeze(1), do_normalization=False)
# Pad MEL to multiples of 32
msl = mel_codes.shape[-1]
dsl = 32
gap = dsl - (msl % dsl)
if gap > 0:
mel = torch.nn.functional.pad(mel_codes, (0, gap))
output_shape = (mel.shape[0], 100, mel.shape[-1]*4)
precomputed_embeddings = diffusion_model.timestep_independent(mel_codes, cond_mel)
if mean:
mel = diffuser.p_sample_loop(diffusion_model, output_shape, noise=torch.zeros(output_shape, device=mel_codes.device),
model_kwargs={'precomputed_aligned_embeddings': precomputed_embeddings})
else:
mel = diffuser.p_sample_loop(diffusion_model, output_shape, model_kwargs={'precomputed_aligned_embeddings': precomputed_embeddings})
return denormalize_tacotron_mel(mel)[:,:,:msl*4]
from api import TextToSpeech, load_conditioning
from utils.audio import load_audio
from utils.tokenizer import VoiceBpeTokenizer
if __name__ == '__main__':
# These are voices drawn randomly from the training set. You are free to substitute your own voices in, but testing
@ -139,101 +29,23 @@ if __name__ == '__main__':
parser.add_argument('-text', type=str, help='Text to speak.', default="I am a language model that has learned to speak.")
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')
parser.add_argument('-num_samples', type=int, help='How many total outputs the autoregressive transformer should produce.', default=512)
parser.add_argument('-num_batches', type=int, help='How many batches those samples should be produced over.', default=16)
parser.add_argument('-batch_size', type=int, help='How many samples to process at once in the autoregressive model.', default=16)
parser.add_argument('-num_diffusion_samples', type=int, help='Number of outputs that progress to the diffusion stage.', default=16)
parser.add_argument('-output_path', type=str, help='Where to store outputs.', default='results/')
args = parser.parse_args()
os.makedirs(args.output_path, exist_ok=True)
download_models()
tts = TextToSpeech(autoregressive_batch_size=args.batch_size)
for voice in args.voice.split(','):
print("Loading data..")
tokenizer = VoiceBpeTokenizer()
text = torch.IntTensor(tokenizer.encode(args.text)).unsqueeze(0).cuda()
text = F.pad(text, (0,1)) # This may not be necessary.
cond_paths = preselected_cond_voices[voice]
conds = []
for cond_path in cond_paths:
c, cond_wav = load_conditioning(cond_path)
c = load_audio(cond_path, 22050)
conds.append(c)
conds = torch.stack(conds, dim=1)
cond_diffusion = cond_wav[:, :88200] # The diffusion model expects <= 88200 conditioning samples.
print("Loading GPT TTS..")
autoregressive = UnifiedVoice(max_mel_tokens=300, max_text_tokens=200, max_conditioning_inputs=2, layers=30, model_dim=1024,
heads=16, number_text_tokens=256, start_text_token=255, checkpointing=False, train_solo_embeddings=False,
average_conditioning_embeddings=True).cuda().eval()
autoregressive.load_state_dict(torch.load('.models/autoregressive.pth'))
stop_mel_token = autoregressive.stop_mel_token
with torch.no_grad():
print("Performing autoregressive inference..")
samples = []
for b in tqdm(range(args.num_batches)):
codes = autoregressive.inference_speech(conds, text, num_beams=1, repetition_penalty=1.0, do_sample=True, top_k=50, top_p=.95,
temperature=.9, num_return_sequences=args.num_samples//args.num_batches, length_penalty=1)
padding_needed = 250 - codes.shape[1]
codes = F.pad(codes, (0, padding_needed), value=stop_mel_token)
samples.append(codes)
del autoregressive
print("Loading CLIP..")
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).cuda().eval()
clip.load_state_dict(torch.load('.models/clip.pth'))
print("Performing CLIP filtering..")
clip_results = []
for batch in samples:
for i in range(batch.shape[0]):
batch[i] = fix_autoregressive_output(batch[i], stop_mel_token)
clip_results.append(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=args.num_diffusion_samples).indices]
# Delete the autoregressive and clip models to free up GPU memory
del samples, clip
print("Loading Diffusion Model..")
diffusion = DiffusionTts(model_channels=512, in_channels=100, out_channels=200, in_latent_channels=1024,
channel_mult=[1, 2, 3, 4], num_res_blocks=[3, 3, 3, 3], token_conditioning_resolutions=[1,4,8],
dropout=0, attention_resolutions=[4,8], num_heads=8, kernel_size=3, scale_factor=2,
time_embed_dim_multiplier=4, unconditioned_percentage=0, conditioning_dim_factor=2,
conditioning_expansion=1)
diffusion.load_state_dict(torch.load('.models/diffusion.pth'))
diffusion = diffusion.cuda().eval()
print("Loading vocoder..")
vocoder = UnivNetGenerator()
vocoder.load_state_dict(torch.load('.models/vocoder.pth')['model_g'])
vocoder = vocoder.cuda()
vocoder.eval(inference=True)
initial_diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=40, cond_free=False)
final_diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=500)
print("Performing vocoding..")
wav_candidates = []
for b in range(best_results.shape[0]):
code = best_results[b].unsqueeze(0)
mel = do_spectrogram_diffusion(diffusion, initial_diffuser, code, cond_diffusion, mean=False)
wav = vocoder.inference(mel)
wav_candidates.append(wav.cpu())
# Further refine the remaining candidates using a ASR model to pick out the ones that are the most understandable.
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 = best_results[i].unsqueeze(0)
best_wav = wav_candidates[i]
del transcriber
torchaudio.save(os.path.join(args.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)
gen = tts.tts(args.text, conds, num_autoregressive_samples=args.num_samples)
torchaudio.save(os.path.join(args.output_path, f'{voice}.wav'), gen.squeeze(0).cpu(), 24000)