File size: 14,603 Bytes
3e423f6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 |
#!/usr/bin/env python3
# AGPL: a notification must be added stating that changes have been made to that file.
import os
import sys
import tempfile
from dataclasses import dataclass
from pathlib import Path
from typing import Literal, Optional
import torch
import torchaudio
from simple_parsing import ArgumentParser, field
from tortoise.api import MODELS_DIR, TextToSpeech
from tortoise.utils.audio import load_audio
from tortoise.utils.diffusion import SAMPLERS
from tortoise.models.vocoder import VocConf
@dataclass
class General:
"""General options"""
text: str = field(positional=True, nargs="*", metavar="text")
"""Text to speak. If omitted, text is read from stdin."""
voice: str = field(default="random", alias=["-v"])
"""Selects the voice to use for generation. Use the & character to join two voices together.
Use a comma to perform inference on multiple voices. Set to "all" to use all available voices.
Note that multiple voices require the --output-dir option to be set."""
voices_dir: Optional[str] = field(default=None, alias=["-V"])
"""Path to directory containing extra voices to be loaded. Use a comma to specify multiple directories."""
preset: Literal["ultra_fast", "fast", "standard", "high_quality"] = field(
default="fast", alias=["-p"]
)
"""Which voice quality preset to use."""
quiet: bool = field(default=False, alias=["-q"])
"""Suppress all output."""
voicefixer: bool = field(default=True)
"""Enable/Disable voicefixer"""
@dataclass
class Output:
"""Output options"""
list_voices: bool = field(default=False, alias=["-l"])
"""List available voices and exit."""
play: bool = field(default=False, alias=["-P"])
"""Play the audio (requires pydub)."""
output: Optional[Path] = field(default=None, alias=["-o"])
"""Save the audio to a file."""
output_dir: Path = field(default=Path("results/"), alias=["-O"])
"""Save the audio to a directory as individual segments."""
@dataclass
class MultiOutput:
"""Multi-output options"""
candidates: int = 1
"""How many output candidates to produce per-voice. Note that only the first candidate is used in the combined output."""
regenerate: Optional[str] = None
"""Comma-separated list of clip numbers to re-generate."""
skip_existing: bool = False
"""Set to skip re-generating existing clips."""
@dataclass
class Advanced:
"""Advanced options"""
produce_debug_state: bool = False
"""Whether or not to produce debug_states in current directory, which can aid in reproducing problems."""
seed: Optional[int] = None
"""Random seed which can be used to reproduce results."""
models_dir: str = MODELS_DIR
"""Where to find pretrained model checkpoints. Tortoise automatically downloads these to
~/.cache/tortoise/.models, so this should only be specified if you have custom checkpoints."""
text_split: Optional[str] = None
"""How big chunks to split the text into, in the format <desired_length>,<max_length>."""
disable_redaction: bool = False
"""Normally 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.
Set this to disable this behavior."""
device: Optional[str] = None
"""Device to use for inference."""
batch_size: Optional[int] = None
"""Batch size to use for inference. If omitted, the batch size is set based on available GPU memory."""
vocoder: Literal["Univnet", "BigVGAN", "BigVGAN_Base"] = "BigVGAN_Base"
"""Pretrained vocoder to be used.
Univnet - tortoise original
BigVGAN - 112M model
BigVGAN_Base - 14M model
"""
ar_checkpoint: Optional[str] = None
"""Path to a checkpoint to use for the autoregressive model. If omitted, the default checkpoint is used."""
clvp_checkpoint: Optional[str] = None
"""Path to a checkpoint to use for the CLVP model. If omitted, the default checkpoint is used."""
diff_checkpoint: Optional[str] = None
"""Path to a checkpoint to use for the diffusion model. If omitted, the default checkpoint is used."""
@dataclass
class Tuning:
"""Tuning options (overrides preset settings)"""
num_autoregressive_samples: Optional[int] = None
"""Number of samples taken from the autoregressive model, all of which are filtered using CLVP.
As TorToiSe is a probabilistic model, more samples means a higher probability of creating something "great"."""
temperature: Optional[float] = None
"""The softmax temperature of the autoregressive model."""
length_penalty: Optional[float] = None
"""A length penalty applied to the autoregressive decoder. Higher settings causes the model to produce more terse outputs."""
repetition_penalty: Optional[float] = None
"""A penalty that prevents the autoregressive decoder from repeating itself during decoding.
Can be used to reduce the incidence of long silences or "uhhhhhhs", etc."""
top_p: Optional[float] = None
"""P value used in nucleus sampling. 0 to 1. Lower values mean the decoder produces more "likely" (aka boring) outputs."""
max_mel_tokens: Optional[int] = None
"""Restricts the output length. 1 to 600. Each unit is 1/20 of a second."""
cvvp_amount: Optional[float] = None
"""How much the CVVP model should influence the output.
Increasing this can in some cases reduce the likelihood of multiple speakers."""
diffusion_iterations: Optional[int] = None
"""Number of diffusion steps to perform. More steps means the network has more chances to iteratively
refine the output, which should theoretically mean a higher quality output.
Generally a value above 250 is not noticeably better, however."""
cond_free: Optional[bool] = None
"""Whether or not to perform conditioning-free diffusion. Conditioning-free diffusion performs two forward passes for
each diffusion step: one with the outputs of the autoregressive model and one with no conditioning priors. The output
of the two is blended according to the cond_free_k value below. Conditioning-free diffusion is the real deal, and
dramatically improves realism."""
cond_free_k: Optional[float] = None
"""Knob that determines how to balance the conditioning free signal with the conditioning-present signal. [0,inf].
As cond_free_k increases, the output becomes dominated by the conditioning-free signal.
Formula is: output=cond_present_output*(cond_free_k+1)-cond_absenct_output*cond_free_k"""
diffusion_temperature: Optional[float] = None
"""Controls the variance of the noise fed into the diffusion model. [0,1]. Values at 0
are the "mean" prediction of the diffusion network and will sound bland and smeared."""
@dataclass
class Speed:
"""New/speed options"""
low_vram: bool = False
"""re-enable default offloading behaviour of tortoise"""
half: bool = False
"""enable autocast to half precision for autoregressive model"""
no_cache: bool = False
"""disable kv_cache usage. This should really only be used if you are very low on vram."""
sampler: Optional[str] = field(default=None, choices=SAMPLERS)
"""override the sampler used for diffusion (default depends on --preset)"""
original_tortoise: bool = False
"""ensure results are identical to original tortoise-tts repo"""
if __name__ == "__main__":
parser = ArgumentParser(
description="TorToiSe is a text-to-speech program that is capable of synthesizing speech "
"in multiple voices with realistic prosody and intonation."
)
# bugs out for some reason
# parser.add_argument(
# "--web",
# action="store_true",
# help="launch the webui (doesn't pass it the other arguments)",
# )
parser.add_arguments(General, "general")
parser.add_arguments(Output, "output")
parser.add_arguments(MultiOutput, "multi_output")
parser.add_arguments(Advanced, "advanced")
parser.add_arguments(Tuning, "tuning")
parser.add_arguments(Speed, "speed")
usage_examples = f"""
Examples:
Read text using random voice and place it in a file:
{parser.prog} -o hello.wav "Hello, how are you?"
Read text from stdin and play it using the tom voice:
echo "Say it like you mean it!" | {parser.prog} -P -v tom
Read a text file using multiple voices and save the audio clips to a directory:
{parser.prog} -O /tmp/tts-results -v tom,emma <textfile.txt
"""
# show usage even when Ctrl+C is pressed early
try:
args = parser.parse_args()
except SystemExit as e:
if e.code == 0:
print(usage_examples)
sys.exit(e.code)
# bugs out for some reason
# if args.web:
# from importlib import import_module
# app = import_module("app")
# sys.exit(app.main())
from tortoise.inference import (
check_pydub,
get_all_voices,
get_seed,
parse_multiarg_text,
parse_voice_str,
split_text,
validate_output_dir,
voice_loader,
save_gen_with_voicefix
)
# get voices
all_voices, extra_voice_dirs = get_all_voices(args.general.voices_dir)
if args.output.list_voices:
for v in all_voices:
print(v)
sys.exit(0)
selected_voices = parse_voice_str(args.general.voice, all_voices)
voice_generator = voice_loader(selected_voices, extra_voice_dirs)
# parse text
if not args.general.text:
print("reading text from stdin!")
text = parse_multiarg_text(args.general.text)
texts = split_text(text, args.advanced.text_split)
output_dir = validate_output_dir(
args.output.output_dir, selected_voices, args.multi_output.candidates
)
# error out early if pydub isn't installed
pydub = check_pydub(args.output.play)
seed = get_seed(args.advanced.seed)
verbose = not args.general.quiet
vocoder = getattr(VocConf, args.advanced.vocoder)
if verbose:
print("Loading tts...")
tts = TextToSpeech(
models_dir=args.advanced.models_dir,
enable_redaction=not args.advanced.disable_redaction,
device=args.advanced.device,
autoregressive_batch_size=args.advanced.batch_size,
high_vram=not args.speed.low_vram,
kv_cache=not args.speed.no_cache,
ar_checkpoint=args.advanced.ar_checkpoint,
clvp_checkpoint=args.advanced.clvp_checkpoint,
diff_checkpoint=args.advanced.diff_checkpoint,
vocoder=vocoder,
)
gen_settings = {
"use_deterministic_seed": seed,
"verbose": verbose,
"k": args.multi_output.candidates,
"preset": args.general.preset,
}
tuning_options = [
"num_autoregressive_samples",
"temperature",
"length_penalty",
"repetition_penalty",
"top_p",
"max_mel_tokens",
"cvvp_amount",
"diffusion_iterations",
"cond_free",
"cond_free_k",
"diffusion_temperature",
]
for option in tuning_options:
if getattr(args.tuning, option) is not None:
gen_settings[option] = getattr(args.tuning, option)
speed_options = [
"sampler",
"original_tortoise",
"half",
]
for option in speed_options:
if getattr(args.speed, option) is not None:
gen_settings[option] = getattr(args.speed, option)
total_clips = len(texts) * len(selected_voices)
regenerate_clips = (
[int(x) for x in args.multi_output.regenerate.split(",")]
if args.multi_output.regenerate
else None
)
for voice_idx, (voice, voice_samples, conditioning_latents) in enumerate(
voice_generator
):
audio_parts = []
for text_idx, text in enumerate(texts):
clip_name = f'{"-".join(voice)}_{text_idx:02d}'
if args.output.output_dir:
first_clip = os.path.join(args.output.output_dir, f"{clip_name}_00.wav")
if (
args.multi_output.skip_existing
or (regenerate_clips and text_idx not in regenerate_clips)
) and os.path.exists(first_clip):
audio_parts.append(load_audio(first_clip, 24000))
if verbose:
print(f"Skipping {clip_name}")
continue
if verbose:
print(
f"Rendering {clip_name} ({(voice_idx * len(texts) + text_idx + 1)} of {total_clips})..."
)
print(" " + text)
gen = tts.tts_with_preset(
text,
voice_samples=voice_samples,
conditioning_latents=conditioning_latents,
**gen_settings,
)
gen = gen if args.multi_output.candidates > 1 else [gen]
for candidate_idx, audio in enumerate(gen):
audio = audio.squeeze(0).cpu()
if candidate_idx == 0:
audio_parts.append(audio)
if args.output.output_dir:
filename = f"{clip_name}_{candidate_idx:02d}.wav"
save_gen_with_voicefix(audio, os.path.join(args.output.output_dir, filename), squeeze=False, voicefixer=args.general.voicefixer)
audio = torch.cat(audio_parts, dim=-1)
if args.output.output_dir:
filename = f'{"-".join(voice)}_combined.wav'
save_gen_with_voicefix(
audio,
os.path.join(args.output.output_dir, filename),
squeeze=False,
voicefixer=args.general.voicefixer,
)
elif args.output.output:
filename = args.output.output or os.tmp
save_gen_with_voicefix(audio, filename, squeeze=False, voicefixer=args.general.voicefixer)
elif args.output.play:
print("WARNING: cannot use voicefixer with --play")
f = tempfile.NamedTemporaryFile(suffix=".wav", delete=True)
torchaudio.save(f.name, audio, 24000)
pydub.playback.play(pydub.AudioSegment.from_wav(f.name))
if args.advanced.produce_debug_state:
os.makedirs("debug_states", exist_ok=True)
dbg_state = (seed, texts, voice_samples, conditioning_latents, args)
torch.save(
dbg_state, os.path.join("debug_states", f'debug_{"-".join(voice)}.pth')
)
|