mrq/tortoise-tts
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misc fixes

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This commit is contained in:
James Betker 2022-05-02 18:00:57 -06:00
parent e00606a601
commit 5663e98904
6 changed files with 53 additions and 51 deletions
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68
tortoise/api.py
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@ -37,6 +37,8 @@ def download_models(specific_models=None):
'cvvp.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/hf/.models/cvvp.pth',
'diffusion_decoder.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/hf/.models/diffusion_decoder.pth',
'vocoder.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/hf/.models/vocoder.pth',
'rlg_auto.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/hf/.models/rlg_auto.pth',
'rlg_diffuser.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/hf/.models/rlg_diffuser.pth',
}
os.makedirs('.models', exist_ok=True)
def show_progress(block_num, block_size, total_size):
@ -110,9 +112,9 @@ def fix_autoregressive_output(codes, stop_token, complain=True):
stop_token_indices = (codes == stop_token).nonzero()
if len(stop_token_indices) == 0:
if complain:
print("No stop tokens found. This typically means the spoken audio is too long. In some cases, the output "
"will still be good, though. Listen to it and if it is missing words, try breaking up your input "
"text.")
print("No stop tokens found in one of the generated voice clips. This typically means the spoken audio is "
"too long. In some cases, the output will still be good, though. Listen to it and if it is missing words, "
"try breaking up your input text.")
return codes
else:
codes[stop_token_indices] = 83
@ -163,8 +165,7 @@ class TextToSpeech:
Main entry point into Tortoise.
"""
def __init__(self, autoregressive_batch_size=16, models_dir='.models', enable_redaction=True,
save_random_voices=False):
def __init__(self, autoregressive_batch_size=16, models_dir='.models', enable_redaction=True):
"""
Constructor
:param autoregressive_batch_size: Specifies how many samples to generate per batch. Lower this if you are seeing
@ -174,14 +175,11 @@ class TextToSpeech:
:param enable_redaction: When true, text enclosed in brackets are automatically redacted from the spoken output
(but are still rendered by the model). This can be used for prompt engineering.
Default is true.
:param save_random_voices: When true, voices that are randomly generated are saved to the `random_voices`
directory. Default is false.
"""
self.autoregressive_batch_size = autoregressive_batch_size
self.enable_redaction = enable_redaction
if self.enable_redaction:
self.aligner = Wav2VecAlignment()
self.save_random_voices = save_random_voices
self.tokenizer = VoiceBpeTokenizer()
download_models()
@ -220,29 +218,6 @@ class TextToSpeech:
self.rlg_auto = None
self.rlg_diffusion = None
def tts_with_preset(self, text, preset='fast', **kwargs):
"""
Calls TTS with one of a set of preset generation parameters. Options:
'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,
#'typical_sampling': True,
'top_p': .8,
'cond_free_k': 2.0, 'diffusion_temperature': 1.0})
# Presets are defined here.
presets = {
'ultra_fast': {'num_autoregressive_samples': 16, 'diffusion_iterations': 32, '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': 1024},
}
kwargs.update(presets[preset])
return self.tts(text, **kwargs)
def get_conditioning_latents(self, voice_samples, return_mels=False):
"""
Transforms one or more voice_samples into a tuple (autoregressive_conditioning_latent, diffusion_conditioning_latent).
@ -288,11 +263,30 @@ class TextToSpeech:
self.rlg_diffusion = RandomLatentConverter(2048).eval()
self.rlg_diffusion.load_state_dict(torch.load('.models/rlg_diffuser.pth', map_location=torch.device('cpu')))
with torch.no_grad():
latents = self.rlg_auto(torch.tensor([0.0])), self.rlg_diffusion(torch.tensor([0.0]))
if self.save_random_voices:
os.makedirs('random_voices', exist_ok=True)
torch.save(latents, f'random_voices/{str(uuid.uuid4())}.pth')
return latents
return self.rlg_auto(torch.tensor([0.0])), self.rlg_diffusion(torch.tensor([0.0]))
def tts_with_preset(self, text, preset='fast', **kwargs):
"""
Calls TTS with one of a set of preset generation parameters. Options:
'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,
#'typical_sampling': True,
'top_p': .8,
'cond_free_k': 2.0, 'diffusion_temperature': 1.0})
# Presets are defined here.
presets = {
'ultra_fast': {'num_autoregressive_samples': 16, 'diffusion_iterations': 32, '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': 1024},
}
kwargs.update(presets[preset])
return self.tts(text, **kwargs)
def tts(self, text, voice_samples=None, conditioning_latents=None, k=1, verbose=True,
# autoregressive generation parameters follow
@ -452,7 +446,7 @@ class TextToSpeech:
def potentially_redact(clip, text):
if self.enable_redaction:
return self.aligner.redact(clip, text)
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:

4
tortoise/do_tts.py
View File

@ -8,7 +8,7 @@ from tortoise.utils.audio import load_audio, get_voices, load_voice
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('--text', type=str, help='Text to speak.', default="The expressiveness of autoregressive transformers is literally nuts! I absolutely adore them.")
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='random')
parser.add_argument('--preset', type=str, help='Which voice preset to use.', default='fast')
@ -21,7 +21,7 @@ if __name__ == '__main__':
args = parser.parse_args()
os.makedirs(args.output_path, exist_ok=True)
tts = TextToSpeech(models_dir=args.model_dir, save_random_voices=True)
tts = TextToSpeech(models_dir=args.model_dir)
selected_voices = args.voice.split(',')
for k, voice in enumerate(selected_voices):

2
tortoise/is_this_from_tortoise.py
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@ -5,7 +5,7 @@ from tortoise.utils.audio import load_audio
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--clip', type=str, help='Path to an audio clip to classify.', default="results/favorite_riding_hood.mp3")
parser.add_argument('--clip', type=str, help='Path to an audio clip to classify.', default="../examples/favorite_riding_hood.mp3")
args = parser.parse_args()
clip = load_audio(args.clip, 24000)

2
tortoise/read.py
View File

@ -40,7 +40,7 @@ if __name__ == '__main__':
parser.add_argument('--model_dir', type=str, help='Where to find pretrained model checkpoints. Tortoise automatically downloads these to .models, so this'
'should only be specified if you have custom checkpoints.', default='.models')
args = parser.parse_args()
tts = TextToSpeech(models_dir=args.model_dir, save_random_voices=True)
tts = TextToSpeech(models_dir=args.model_dir)
outpath = args.output_path
selected_voices = args.voice.split(',')

2
tortoise/utils/audio.py
View File

@ -114,7 +114,7 @@ def load_voices(voices):
if voice == 'random':
print("Cannot combine a random voice with a non-random voice. Just using a random voice.")
return None, None
latent, clip = load_voice(voice)
clip, latent = load_voice(voice)
if latent is None:
assert len(latents) == 0, "Can only combine raw audio voices or latent voices, not both. Do it yourself if you want this."
clips.extend(clip)

26
tortoise/utils/wav2vec_alignment.py
View File

@ -1,3 +1,5 @@
import re
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2FeatureExtractor, Wav2Vec2CTCTokenizer, Wav2Vec2Processor
@ -11,7 +13,7 @@ class Wav2VecAlignment:
self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(f"facebook/wav2vec2-large-960h")
self.tokenizer = Wav2Vec2CTCTokenizer.from_pretrained('jbetker/tacotron_symbols')
def align(self, audio, expected_text, audio_sample_rate=24000, topk=3):
def align(self, audio, expected_text, audio_sample_rate=24000, topk=3, return_partial=False):
orig_len = audio.shape[-1]
with torch.no_grad():
@ -41,8 +43,10 @@ class Wav2VecAlignment:
if len(expected_tokens) > 0:
print(f"Alignment did not work. {len(expected_tokens)} were not found, with the following string un-aligned:"
f" {self.tokenizer.decode(expected_tokens)}")
return None
f" `{self.tokenizer.decode(expected_tokens)}`. Here's what wav2vec thought it heard:"
f"`{self.tokenizer.decode(logits.argmax(-1).tolist())}`")
if not return_partial:
return None
return alignments
@ -54,6 +58,8 @@ class Wav2VecAlignment:
for spl in splitted[1:]:
assert ']' in spl, 'Every "[" character must be paired with a "]" with no nesting.'
fully_split.extend(spl.split(']'))
# Remove any non-alphabetic character in the input text. This makes matching more likely.
fully_split = [re.sub(r'[^a-zA-Z ]', '', s) for s in fully_split]
# At this point, fully_split is a list of strings, with every other string being something that should be redacted.
non_redacted_intervals = []
last_point = 0
@ -63,20 +69,22 @@ class Wav2VecAlignment:
last_point += len(fully_split[i])
bare_text = ''.join(fully_split)
alignments = self.align(audio, bare_text, audio_sample_rate, topk)
if alignments is None:
return audio # Cannot redact because alignment did not succeed.
alignments = self.align(audio, bare_text, audio_sample_rate, topk, return_partial=True)
# If alignment fails, we will attempt to recover by assuming the remaining alignments consume the rest of the string.
def get_alignment(i):
if i >= len(alignments):
return audio.shape[-1]
output_audio = []
for nri in non_redacted_intervals:
start, stop = nri
output_audio.append(audio[:, alignments[start]:alignments[stop]])
output_audio.append(audio[:, get_alignment(start):get_alignment(stop)])
return torch.cat(output_audio, dim=-1)
if __name__ == '__main__':
some_audio = load_audio('../../results/favorites/morgan_freeman_metallic_hydrogen.mp3', 24000)
some_audio = load_audio('../../results/train_dotrice_0.wav', 24000)
aligner = Wav2VecAlignment()
text = "instead of molten iron, jupiter [and brown dwaves] have hydrogen, which [is under so much pressure that it] develops metallic properties"
text = "[God fucking damn it I'm so angry] The expressiveness of autoregressive transformers is literally nuts! I absolutely adore them."
redact = aligner.redact(some_audio, text)
torchaudio.save(f'test_output.wav', redact, 24000)