mrq/tortoise-tts
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James Betker 2022-04-15 08:26:11 -06:00
parent 4aab81b074
commit 904561d250
5 changed files with 81 additions and 60 deletions
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1
.gitignore vendored
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@ -20,6 +20,7 @@ parts/
sdist/
var/
wheels/
results/*
pip-wheel-metadata/
share/python-wheels/
*.egg-info/

43
api.py
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@ -150,7 +150,7 @@ def do_spectrogram_diffusion(diffusion_model, diffuser, mel_codes, conditioning_
class TextToSpeech:
def __init__(self, autoregressive_batch_size=32):
def __init__(self, autoregressive_batch_size=16):
self.autoregressive_batch_size = autoregressive_batch_size
self.tokenizer = VoiceBpeTokenizer()
download_models()
@ -160,14 +160,7 @@ class TextToSpeech:
heads=16, number_text_tokens=256, start_text_token=255, checkpointing=False,
train_solo_embeddings=False,
average_conditioning_embeddings=True).cpu().eval()
self.autoregressive.load_state_dict(torch.load('.models/autoregressive_audiobooks.pth'))
self.autoregressive_for_latents = UnifiedVoice(max_mel_tokens=604, max_text_tokens=402, 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).cpu().eval()
self.autoregressive_for_latents.load_state_dict(torch.load('.models/autoregressive_audiobooks.pth'))
self.autoregressive.load_state_dict(torch.load('.models/autoregressive.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,
@ -178,32 +171,38 @@ class TextToSpeech:
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_decoder_audiobooks.pth'))
self.diffusion.load_state_dict(torch.load('.models/diffusion_decoder.pth'))
self.vocoder = UnivNetGenerator().cpu()
self.vocoder.load_state_dict(torch.load('.models/vocoder.pth')['model_g'])
self.vocoder.eval(inference=True)
def tts_with_preset(self, text, voice_samples, preset='intelligible', **kwargs):
def tts_with_preset(self, text, voice_samples, preset='fast', **kwargs):
"""
Calls TTS with one of a set of preset generation parameters. Options:
'intelligible': Maximizes the probability of understandable words at the cost of diverse voices, intonation and prosody.
'realistic': Increases the diversity of spoken voices and improves realism of vocal characteristics at the cost of intelligibility.
'mid': Somewhere between 'intelligible' and 'realistic'.
'ultra_fast': Produces speech at a speed which belies the name of this repo. (Not really, but it's definitely fastest).
'fast': Decent quality speech at a decent inference rate. A good choice for mass inference.
'standard': Very good quality. This is generally about as good as you are going to get.
'high_quality': Use if you want the absolute best. This is not really worth the compute, though.
"""
# Use generally found best tuning knobs for generation.
kwargs.update({'temperature': .8, 'length_penalty': 1.0, 'repetition_penalty': 2.0, 'top_p': .8,
'cond_free_k': 2.0, 'diffusion_temperature': 1.0})
# Presets are defined here.
presets = {
'intelligible': {'temperature': .5, 'length_penalty': 2.0, 'repetition_penalty': 2.0, 'top_p': .5, 'diffusion_iterations': 100, 'cond_free': True, 'cond_free_k': .7, 'diffusion_temperature': .7},
'mid': {'temperature': .7, 'length_penalty': 1.0, 'repetition_penalty': 2.0, 'top_p': .7, 'diffusion_iterations': 100, 'cond_free': True, 'cond_free_k': 1.5, 'diffusion_temperature': .8},
'realistic': {'temperature': 1.0, 'length_penalty': 1.0, 'repetition_penalty': 2.0, 'top_p': .9, 'diffusion_iterations': 100, 'cond_free': True, 'cond_free_k': 2, 'diffusion_temperature': 1},
'ultra_fast': {'num_autoregressive_samples': 32, 'diffusion_iterations': 16, 'cond_free': False},
'fast': {'num_autoregressive_samples': 96, 'diffusion_iterations': 32},
'standard': {'num_autoregressive_samples': 256, 'diffusion_iterations': 128},
'high_quality': {'num_autoregressive_samples': 512, 'diffusion_iterations': 2048},
}
kwargs.update(presets[preset])
return self.tts(text, voice_samples, **kwargs)
def tts(self, text, voice_samples, k=1,
# autoregressive generation parameters follow
num_autoregressive_samples=512, temperature=.5, length_penalty=1, repetition_penalty=2.0, top_p=.5,
num_autoregressive_samples=512, temperature=.8, length_penalty=1, repetition_penalty=2.0, top_p=.8,
# diffusion generation parameters follow
diffusion_iterations=100, cond_free=True, cond_free_k=2, diffusion_temperature=.7,):
diffusion_iterations=100, cond_free=True, cond_free_k=2, diffusion_temperature=1.0,):
text = torch.IntTensor(self.tokenizer.encode(text)).unsqueeze(0).cuda()
text = F.pad(text, (0, 1)) # This may not be necessary.
@ -250,11 +249,11 @@ class TextToSpeech:
# 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_for_latents = self.autoregressive_for_latents.cuda()
best_latents = self.autoregressive_for_latents(conds, text, torch.tensor([text.shape[-1]], device=conds.device), best_results,
self.autoregressive = self.autoregressive.cuda()
best_latents = self.autoregressive(conds, text, torch.tensor([text.shape[-1]], device=conds.device), best_results,
torch.tensor([best_results.shape[-1]*self.autoregressive.mel_length_compression], device=conds.device),
return_latent=True, clip_inputs=False)
self.autoregressive_for_latents = self.autoregressive_for_latents.cpu()
self.autoregressive = self.autoregressive.cpu()
print("Performing vocoding..")
wav_candidates = []

12
do_tts.py
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@ -27,12 +27,12 @@ if __name__ == '__main__':
}
parser = argparse.ArgumentParser()
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='obama,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=128)
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/')
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='obama,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=128)
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)

48
read.py
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@ -6,7 +6,7 @@ import torch.nn.functional as F
import torchaudio
from api import TextToSpeech, load_conditioning
from utils.audio import load_audio
from utils.audio import load_audio, get_voices
from utils.tokenizer import VoiceBpeTokenizer
def split_and_recombine_text(texts, desired_length=200, max_len=300):
@ -27,39 +27,47 @@ def split_and_recombine_text(texts, desired_length=200, max_len=300):
return texts
if __name__ == '__main__':
# These are voices drawn randomly from the training set. You are free to substitute your own voices in, but testing
# has shown that the model does not generalize to new voices very well.
preselected_cond_voices = {
'emma_stone': ['voices/emma_stone/1.wav','voices/emma_stone/2.wav','voices/emma_stone/3.wav'],
'tom_hanks': ['voices/tom_hanks/1.wav','voices/tom_hanks/2.wav','voices/tom_hanks/3.wav'],
'patrick_stewart': ['voices/patrick_stewart/1.wav','voices/patrick_stewart/2.wav','voices/patrick_stewart/3.wav','voices/patrick_stewart/4.wav'],
}
parser = argparse.ArgumentParser()
parser.add_argument('-textfile', type=str, help='A file containing the text to read.', default="data/riding_hood.txt")
parser.add_argument('-voice', type=str, help='Use a preset conditioning voice (defined above). Overrides cond_path.', default='patrick_stewart')
parser.add_argument('-num_samples', type=int, help='How many total outputs the autoregressive transformer should produce.', default=128)
parser.add_argument('-batch_size', type=int, help='How many samples to process at once in the autoregressive model.', default=16)
parser.add_argument('-output_path', type=str, help='Where to store outputs.', default='results/longform/')
parser.add_argument('-generation_preset', type=str, help='Preset to use for generation', default='realistic')
parser.add_argument('--textfile', type=str, help='A file containing the text to read.', default="data/riding_hood.txt")
parser.add_argument('--voice', type=str, help='Selects the voice to use for generation. See options in voices/ directory (and add your own!) '
'Use the & character to join two voices together. Use a comma to perform inference on multiple voices.', default='patrick_stewart')
parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='results/longform/')
parser.add_argument('--generation_preset', type=str, help='Preset to use for generation', default='standard')
args = parser.parse_args()
os.makedirs(args.output_path, exist_ok=True)
outpath = args.output_path
voices = get_voices()
selected_voices = args.voice.split(',')
for selected_voice in selected_voices:
voice_outpath = os.path.join(outpath, selected_voice)
os.makedirs(voice_outpath, exist_ok=True)
with open(args.textfile, 'r', encoding='utf-8') as f:
text = ''.join([l for l in f.readlines()])
texts = split_and_recombine_text(text)
tts = TextToSpeech()
tts = TextToSpeech(autoregressive_batch_size=args.batch_size)
if '&' in selected_voice:
voice_sel = selected_voice.split('&')
else:
voice_sel = [selected_voice]
cond_paths = []
for vsel in voice_sel:
if vsel not in voices.keys():
print(f'Error: voice {vsel} not available. Skipping.')
continue
cond_paths.extend(voices[vsel])
if not cond_paths:
print('Error: no valid voices specified. Try again.')
priors = []
for j, text in enumerate(texts):
cond_paths = preselected_cond_voices[args.voice]
conds = priors.copy()
for cond_path in cond_paths:
c = load_audio(cond_path, 22050)
conds.append(c)
gen = tts.tts_with_preset(text, conds, preset=args.generation_preset, num_autoregressive_samples=args.num_samples)
torchaudio.save(os.path.join(args.output_path, f'{j}.wav'), gen.squeeze(0).cpu(), 24000)
gen = tts.tts_with_preset(text, conds, preset=args.generation_preset)
torchaudio.save(os.path.join(voice_outpath, f'{j}.wav'), gen.squeeze(0).cpu(), 24000)
priors.append(torchaudio.functional.resample(gen, 24000, 22050).squeeze(0))
while len(priors) > 2:

13
utils/audio.py
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@ -1,3 +1,6 @@
import os
from glob import glob
import torch
import torchaudio
import numpy as np
@ -78,6 +81,16 @@ def dynamic_range_decompression(x, C=1):
return torch.exp(x) / C
def get_voices():
subs = os.listdir('voices')
voices = {}
for sub in subs:
subj = os.path.join('voices', sub)
if os.path.isdir(subj):
voices[sub] = glob(f'{subj}/*.wav')
return voices
class TacotronSTFT(torch.nn.Module):
def __init__(self, filter_length=1024, hop_length=256, win_length=1024,
n_mel_channels=80, sampling_rate=22050, mel_fmin=0.0,