# 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