Spaces:
Runtime error
Runtime error
File size: 19,712 Bytes
c968fc3 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 |
# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import sys
import time
import torch
import json
import itertools
import accelerate
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from torch.utils.tensorboard import SummaryWriter
from torch.optim import AdamW
from torch.optim.lr_scheduler import ExponentialLR
from librosa.filters import mel as librosa_mel_fn
from accelerate.logging import get_logger
from pathlib import Path
from utils.io import save_audio
from utils.data_utils import *
from utils.util import (
Logger,
ValueWindow,
remove_older_ckpt,
set_all_random_seed,
save_config,
)
from utils.mel import extract_mel_features
from models.vocoders.vocoder_trainer import VocoderTrainer
from models.vocoders.diffusion.diffusion_vocoder_dataset import (
DiffusionVocoderDataset,
DiffusionVocoderCollator,
)
from models.vocoders.diffusion.diffwave.diffwave import DiffWave
from models.vocoders.diffusion.diffusion_vocoder_inference import vocoder_inference
supported_models = {
"diffwave": DiffWave,
}
class DiffusionVocoderTrainer(VocoderTrainer):
def __init__(self, args, cfg):
super().__init__()
self.args = args
self.cfg = cfg
cfg.exp_name = args.exp_name
# Diffusion
self.cfg.model.diffwave.noise_schedule = np.linspace(
self.cfg.model.diffwave.noise_schedule_factors[0],
self.cfg.model.diffwave.noise_schedule_factors[1],
self.cfg.model.diffwave.noise_schedule_factors[2],
)
beta = np.array(self.cfg.model.diffwave.noise_schedule)
noise_level = np.cumprod(1 - beta)
self.noise_level = torch.tensor(noise_level.astype(np.float32))
# Init accelerator
self._init_accelerator()
self.accelerator.wait_for_everyone()
# Init logger
with self.accelerator.main_process_first():
self.logger = get_logger(args.exp_name, log_level=args.log_level)
self.logger.info("=" * 56)
self.logger.info("||\t\t" + "New training process started." + "\t\t||")
self.logger.info("=" * 56)
self.logger.info("\n")
self.logger.debug(f"Using {args.log_level.upper()} logging level.")
self.logger.info(f"Experiment name: {args.exp_name}")
self.logger.info(f"Experiment directory: {self.exp_dir}")
self.checkpoint_dir = os.path.join(self.exp_dir, "checkpoint")
if self.accelerator.is_main_process:
os.makedirs(self.checkpoint_dir, exist_ok=True)
self.logger.debug(f"Checkpoint directory: {self.checkpoint_dir}")
# Init training status
self.batch_count: int = 0
self.step: int = 0
self.epoch: int = 0
self.max_epoch = (
self.cfg.train.max_epoch if self.cfg.train.max_epoch > 0 else float("inf")
)
self.logger.info(
"Max epoch: {}".format(
self.max_epoch if self.max_epoch < float("inf") else "Unlimited"
)
)
# Check potential erorrs
if self.accelerator.is_main_process:
self._check_basic_configs()
self.save_checkpoint_stride = self.cfg.train.save_checkpoint_stride
self.checkpoints_path = [
[] for _ in range(len(self.save_checkpoint_stride))
]
self.run_eval = self.cfg.train.run_eval
# Set random seed
with self.accelerator.main_process_first():
start = time.monotonic_ns()
self._set_random_seed(self.cfg.train.random_seed)
end = time.monotonic_ns()
self.logger.debug(
f"Setting random seed done in {(end - start) / 1e6:.2f}ms"
)
self.logger.debug(f"Random seed: {self.cfg.train.random_seed}")
# Build dataloader
with self.accelerator.main_process_first():
self.logger.info("Building dataset...")
start = time.monotonic_ns()
self.train_dataloader, self.valid_dataloader = self._build_dataloader()
end = time.monotonic_ns()
self.logger.info(f"Building dataset done in {(end - start) / 1e6:.2f}ms")
# Build model
with self.accelerator.main_process_first():
self.logger.info("Building model...")
start = time.monotonic_ns()
self.model = self._build_model()
end = time.monotonic_ns()
self.logger.debug(self.model)
self.logger.info(f"Building model done in {(end - start) / 1e6:.2f}ms")
self.logger.info(f"Model parameters: {self._count_parameters()/1e6:.2f}M")
# Build optimizers and schedulers
with self.accelerator.main_process_first():
self.logger.info("Building optimizer and scheduler...")
start = time.monotonic_ns()
self.optimizer = self._build_optimizer()
self.scheduler = self._build_scheduler()
end = time.monotonic_ns()
self.logger.info(
f"Building optimizer and scheduler done in {(end - start) / 1e6:.2f}ms"
)
# Accelerator preparing
self.logger.info("Initializing accelerate...")
start = time.monotonic_ns()
(
self.train_dataloader,
self.valid_dataloader,
self.model,
self.optimizer,
self.scheduler,
) = self.accelerator.prepare(
self.train_dataloader,
self.valid_dataloader,
self.model,
self.optimizer,
self.scheduler,
)
end = time.monotonic_ns()
self.logger.info(f"Initializing accelerate done in {(end - start) / 1e6:.2f}ms")
# Build criterions
with self.accelerator.main_process_first():
self.logger.info("Building criterion...")
start = time.monotonic_ns()
self.criterion = self._build_criterion()
end = time.monotonic_ns()
self.logger.info(f"Building criterion done in {(end - start) / 1e6:.2f}ms")
# Resume checkpoints
with self.accelerator.main_process_first():
if args.resume_type:
self.logger.info("Resuming from checkpoint...")
start = time.monotonic_ns()
ckpt_path = Path(args.checkpoint)
if self._is_valid_pattern(ckpt_path.parts[-1]):
ckpt_path = self._load_model(
None, args.checkpoint, args.resume_type
)
else:
ckpt_path = self._load_model(
args.checkpoint, resume_type=args.resume_type
)
end = time.monotonic_ns()
self.logger.info(
f"Resuming from checkpoint done in {(end - start) / 1e6:.2f}ms"
)
self.checkpoints_path = json.load(
open(os.path.join(ckpt_path, "ckpts.json"), "r")
)
self.checkpoint_dir = os.path.join(self.exp_dir, "checkpoint")
if self.accelerator.is_main_process:
os.makedirs(self.checkpoint_dir, exist_ok=True)
self.logger.debug(f"Checkpoint directory: {self.checkpoint_dir}")
# Save config
self.config_save_path = os.path.join(self.exp_dir, "args.json")
# Device
self.device = next(self.model.parameters()).device
self.noise_level = self.noise_level.to(self.device)
def _build_dataset(self):
return DiffusionVocoderDataset, DiffusionVocoderCollator
def _build_criterion(self):
criterion = nn.L1Loss()
return criterion
def _build_model(self):
model = supported_models[self.cfg.model.generator](self.cfg)
return model
def _build_optimizer(self):
optimizer = AdamW(
self.model.parameters(),
lr=self.cfg.train.adamw.lr,
betas=(self.cfg.train.adamw.adam_b1, self.cfg.train.adamw.adam_b2),
)
return optimizer
def _build_scheduler(self):
scheduler = ExponentialLR(
self.optimizer,
gamma=self.cfg.train.exponential_lr.lr_decay,
last_epoch=self.epoch - 1,
)
return scheduler
def train_loop(self):
"""Training process"""
self.accelerator.wait_for_everyone()
# Dump config
if self.accelerator.is_main_process:
self._dump_cfg(self.config_save_path)
self.model.train()
self.optimizer.zero_grad()
# Sync and start training
self.accelerator.wait_for_everyone()
while self.epoch < self.max_epoch:
self.logger.info("\n")
self.logger.info("-" * 32)
self.logger.info("Epoch {}: ".format(self.epoch))
# Train and Validate
train_total_loss = self._train_epoch()
valid_total_loss = self._valid_epoch()
self.accelerator.log(
{
"Epoch/Train Total Loss": train_total_loss,
"Epoch/Valid Total Loss": valid_total_loss,
},
step=self.epoch,
)
# Update scheduler
self.accelerator.wait_for_everyone()
self.scheduler.step()
# Check save checkpoint interval
run_eval = False
if self.accelerator.is_main_process:
save_checkpoint = False
for i, num in enumerate(self.save_checkpoint_stride):
if self.epoch % num == 0:
save_checkpoint = True
run_eval |= self.run_eval[i]
# Save checkpoints
self.accelerator.wait_for_everyone()
if self.accelerator.is_main_process and save_checkpoint:
path = os.path.join(
self.checkpoint_dir,
"epoch-{:04d}_step-{:07d}_loss-{:.6f}".format(
self.epoch, self.step, valid_total_loss
),
)
self.accelerator.save_state(path)
json.dump(
self.checkpoints_path,
open(os.path.join(path, "ckpts.json"), "w"),
ensure_ascii=False,
indent=4,
)
# Save eval audios
self.accelerator.wait_for_everyone()
if self.accelerator.is_main_process and run_eval:
for i in range(len(self.valid_dataloader.dataset.eval_audios)):
if self.cfg.preprocess.use_frame_pitch:
eval_audio = self._inference(
self.valid_dataloader.dataset.eval_mels[i],
eval_pitch=self.valid_dataloader.dataset.eval_pitchs[i],
use_pitch=True,
)
else:
eval_audio = self._inference(
self.valid_dataloader.dataset.eval_mels[i]
)
path = os.path.join(
self.checkpoint_dir,
"epoch-{:04d}_step-{:07d}_loss-{:.6f}_eval_audio_{}.wav".format(
self.epoch,
self.step,
valid_total_loss,
self.valid_dataloader.dataset.eval_dataset_names[i],
),
)
path_gt = os.path.join(
self.checkpoint_dir,
"epoch-{:04d}_step-{:07d}_loss-{:.6f}_eval_audio_{}_gt.wav".format(
self.epoch,
self.step,
valid_total_loss,
self.valid_dataloader.dataset.eval_dataset_names[i],
),
)
save_audio(path, eval_audio, self.cfg.preprocess.sample_rate)
save_audio(
path_gt,
self.valid_dataloader.dataset.eval_audios[i],
self.cfg.preprocess.sample_rate,
)
self.accelerator.wait_for_everyone()
self.epoch += 1
# Finish training
self.accelerator.wait_for_everyone()
path = os.path.join(
self.checkpoint_dir,
"epoch-{:04d}_step-{:07d}_loss-{:.6f}".format(
self.epoch, self.step, valid_total_loss
),
)
self.accelerator.save_state(path)
def _train_epoch(self):
"""Training epoch. Should return average loss of a batch (sample) over
one epoch. See ``train_loop`` for usage.
"""
self.model.train()
epoch_total_loss: int = 0
for batch in tqdm(
self.train_dataloader,
desc=f"Training Epoch {self.epoch}",
unit="batch",
colour="GREEN",
leave=False,
dynamic_ncols=True,
smoothing=0.04,
disable=not self.accelerator.is_main_process,
):
# Get losses
total_loss = self._train_step(batch)
self.batch_count += 1
# Log info
if self.batch_count % self.cfg.train.gradient_accumulation_step == 0:
self.accelerator.log(
{
"Step/Learning Rate": self.optimizer.param_groups[0]["lr"],
},
step=self.step,
)
epoch_total_loss += total_loss
self.step += 1
# Get and log total losses
self.accelerator.wait_for_everyone()
epoch_total_loss = (
epoch_total_loss
/ len(self.train_dataloader)
* self.cfg.train.gradient_accumulation_step
)
return epoch_total_loss
def _train_step(self, data):
"""Training forward step. Should return average loss of a sample over
one batch. Provoke ``_forward_step`` is recommended except for special case.
See ``_train_epoch`` for usage.
"""
# Init losses
total_loss = 0
# Use input feature to get predictions
mel_input = data["mel"]
audio_gt = data["audio"]
if self.cfg.preprocess.use_frame_pitch:
pitch_input = data["frame_pitch"]
self.optimizer.zero_grad()
N = audio_gt.shape[0]
t = torch.randint(
0, len(self.cfg.model.diffwave.noise_schedule), [N], device=self.device
)
noise_scale = self.noise_level[t].unsqueeze(1)
noise_scale_sqrt = noise_scale**0.5
noise = torch.randn_like(audio_gt).to(self.device)
noisy_audio = noise_scale_sqrt * audio_gt + (1.0 - noise_scale) ** 0.5 * noise
audio_pred = self.model(noisy_audio, t, mel_input)
total_loss = self.criterion(noise, audio_pred.squeeze(1))
self.accelerator.backward(total_loss)
self.optimizer.step()
return total_loss.item()
def _valid_epoch(self):
"""Testing epoch. Should return average loss of a batch (sample) over
one epoch. See ``train_loop`` for usage.
"""
self.model.eval()
epoch_total_loss: int = 0
for batch in tqdm(
self.valid_dataloader,
desc=f"Validating Epoch {self.epoch}",
unit="batch",
colour="GREEN",
leave=False,
dynamic_ncols=True,
smoothing=0.04,
disable=not self.accelerator.is_main_process,
):
# Get losses
total_loss = self._valid_step(batch)
# Log info
epoch_total_loss += total_loss
# Get and log total losses
self.accelerator.wait_for_everyone()
epoch_total_loss = epoch_total_loss / len(self.valid_dataloader)
return epoch_total_loss
def _valid_step(self, data):
"""Testing forward step. Should return average loss of a sample over
one batch. Provoke ``_forward_step`` is recommended except for special case.
See ``_test_epoch`` for usage.
"""
# Init losses
total_loss = 0
# Use feature inputs to get the predicted audio
mel_input = data["mel"]
audio_gt = data["audio"]
if self.cfg.preprocess.use_frame_pitch:
pitch_input = data["frame_pitch"]
N = audio_gt.shape[0]
t = torch.randint(
0, len(self.cfg.model.diffwave.noise_schedule), [N], device=self.device
)
noise_scale = self.noise_level[t].unsqueeze(1)
noise_scale_sqrt = noise_scale**0.5
noise = torch.randn_like(audio_gt)
noisy_audio = noise_scale_sqrt * audio_gt + (1.0 - noise_scale) ** 0.5 * noise
audio_pred = self.model(noisy_audio, t, mel_input)
total_loss = self.criterion(noise, audio_pred.squeeze(1))
return total_loss.item()
def _inference(self, eval_mel, eval_pitch=None, use_pitch=False):
"""Inference during training for test audios."""
if use_pitch:
eval_pitch = align_length(eval_pitch, eval_mel.shape[1])
eval_audio = vocoder_inference(
self.cfg,
self.model,
torch.from_numpy(eval_mel).unsqueeze(0),
f0s=torch.from_numpy(eval_pitch).unsqueeze(0).float(),
device=next(self.model.parameters()).device,
).squeeze(0)
else:
eval_audio = vocoder_inference(
self.cfg,
self.model,
torch.from_numpy(eval_mel).unsqueeze(0),
device=next(self.model.parameters()).device,
).squeeze(0)
return eval_audio
def _load_model(self, checkpoint_dir, checkpoint_path=None, resume_type="resume"):
"""Load model from checkpoint. If checkpoint_path is None, it will
load the latest checkpoint in checkpoint_dir. If checkpoint_path is not
None, it will load the checkpoint specified by checkpoint_path. **Only use this
method after** ``accelerator.prepare()``.
"""
if checkpoint_path is None:
ls = [str(i) for i in Path(checkpoint_dir).glob("*")]
ls.sort(key=lambda x: int(x.split("_")[-3].split("-")[-1]), reverse=True)
checkpoint_path = ls[0]
if resume_type == "resume":
self.accelerator.load_state(checkpoint_path)
self.epoch = int(checkpoint_path.split("_")[-3].split("-")[-1]) + 1
self.step = int(checkpoint_path.split("_")[-2].split("-")[-1]) + 1
elif resume_type == "finetune":
accelerate.load_checkpoint_and_dispatch(
self.accelerator.unwrap_model(self.model),
os.path.join(checkpoint_path, "pytorch_model.bin"),
)
self.logger.info("Load model weights for finetune SUCCESS!")
else:
raise ValueError("Unsupported resume type: {}".format(resume_type))
return checkpoint_path
def _count_parameters(self):
result = sum(p.numel() for p in self.model.parameters())
return result
|