329 lines
14 KiB
Python
329 lines
14 KiB
Python
import logging
|
|
import multiprocessing
|
|
import os
|
|
import time
|
|
|
|
import torch
|
|
import torch.distributed as dist
|
|
import torch.multiprocessing as mp
|
|
from torch.cuda.amp import GradScaler, autocast
|
|
from torch.nn import functional as F
|
|
from torch.nn.parallel import DistributedDataParallel as DDP
|
|
from torch.utils.data import DataLoader
|
|
from torch.utils.tensorboard import SummaryWriter
|
|
|
|
import modules.commons as commons
|
|
import utils
|
|
from data_utils import TextAudioCollate, TextAudioSpeakerLoader
|
|
from models import (
|
|
MultiPeriodDiscriminator,
|
|
SynthesizerTrn,
|
|
)
|
|
from modules.losses import discriminator_loss, feature_loss, generator_loss, kl_loss
|
|
from modules.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
|
|
|
|
logging.getLogger('matplotlib').setLevel(logging.WARNING)
|
|
logging.getLogger('numba').setLevel(logging.WARNING)
|
|
|
|
torch.backends.cudnn.benchmark = True
|
|
global_step = 0
|
|
start_time = time.time()
|
|
|
|
# os.environ['TORCH_DISTRIBUTED_DEBUG'] = 'INFO'
|
|
|
|
|
|
def main():
|
|
"""Assume Single Node Multi GPUs Training Only"""
|
|
assert torch.cuda.is_available(), "CPU training is not allowed."
|
|
hps = utils.get_hparams()
|
|
|
|
n_gpus = torch.cuda.device_count()
|
|
os.environ['MASTER_ADDR'] = 'localhost'
|
|
os.environ['MASTER_PORT'] = hps.train.port
|
|
|
|
mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))
|
|
|
|
|
|
def run(rank, n_gpus, hps):
|
|
global global_step
|
|
if rank == 0:
|
|
logger = utils.get_logger(hps.model_dir)
|
|
logger.info(hps)
|
|
utils.check_git_hash(hps.model_dir)
|
|
writer = SummaryWriter(log_dir=hps.model_dir)
|
|
writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
|
|
|
|
# for pytorch on win, backend use gloo
|
|
dist.init_process_group(backend= 'gloo' if os.name == 'nt' else 'nccl', init_method='env://', world_size=n_gpus, rank=rank)
|
|
torch.manual_seed(hps.train.seed)
|
|
torch.cuda.set_device(rank)
|
|
collate_fn = TextAudioCollate()
|
|
all_in_mem = hps.train.all_in_mem # If you have enough memory, turn on this option to avoid disk IO and speed up training.
|
|
train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps, all_in_mem=all_in_mem)
|
|
num_workers = 5 if multiprocessing.cpu_count() > 4 else multiprocessing.cpu_count()
|
|
if all_in_mem:
|
|
num_workers = 0
|
|
train_loader = DataLoader(train_dataset, num_workers=num_workers, shuffle=False, pin_memory=True,
|
|
batch_size=hps.train.batch_size, collate_fn=collate_fn)
|
|
if rank == 0:
|
|
eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps, all_in_mem=all_in_mem,vol_aug = False)
|
|
eval_loader = DataLoader(eval_dataset, num_workers=1, shuffle=False,
|
|
batch_size=1, pin_memory=False,
|
|
drop_last=False, collate_fn=collate_fn)
|
|
|
|
net_g = SynthesizerTrn(
|
|
hps.data.filter_length // 2 + 1,
|
|
hps.train.segment_size // hps.data.hop_length,
|
|
**hps.model).cuda(rank)
|
|
net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
|
|
optim_g = torch.optim.AdamW(
|
|
net_g.parameters(),
|
|
hps.train.learning_rate,
|
|
betas=hps.train.betas,
|
|
eps=hps.train.eps)
|
|
optim_d = torch.optim.AdamW(
|
|
net_d.parameters(),
|
|
hps.train.learning_rate,
|
|
betas=hps.train.betas,
|
|
eps=hps.train.eps)
|
|
net_g = DDP(net_g, device_ids=[rank]) # , find_unused_parameters=True)
|
|
net_d = DDP(net_d, device_ids=[rank])
|
|
|
|
skip_optimizer = False
|
|
try:
|
|
_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g,
|
|
optim_g, skip_optimizer)
|
|
_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d,
|
|
optim_d, skip_optimizer)
|
|
epoch_str = max(epoch_str, 1)
|
|
name=utils.latest_checkpoint_path(hps.model_dir, "D_*.pth")
|
|
global_step=int(name[name.rfind("_")+1:name.rfind(".")])+1
|
|
#global_step = (epoch_str - 1) * len(train_loader)
|
|
except Exception:
|
|
print("load old checkpoint failed...")
|
|
epoch_str = 1
|
|
global_step = 0
|
|
if skip_optimizer:
|
|
epoch_str = 1
|
|
global_step = 0
|
|
|
|
warmup_epoch = hps.train.warmup_epochs
|
|
scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
|
|
scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
|
|
|
|
scaler = GradScaler(enabled=hps.train.fp16_run)
|
|
|
|
for epoch in range(epoch_str, hps.train.epochs + 1):
|
|
# set up warm-up learning rate
|
|
if epoch <= warmup_epoch:
|
|
for param_group in optim_g.param_groups:
|
|
param_group['lr'] = hps.train.learning_rate / warmup_epoch * epoch
|
|
for param_group in optim_d.param_groups:
|
|
param_group['lr'] = hps.train.learning_rate / warmup_epoch * epoch
|
|
# training
|
|
if rank == 0:
|
|
train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler,
|
|
[train_loader, eval_loader], logger, [writer, writer_eval])
|
|
else:
|
|
train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler,
|
|
[train_loader, None], None, None)
|
|
# update learning rate
|
|
scheduler_g.step()
|
|
scheduler_d.step()
|
|
|
|
|
|
def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
|
|
net_g, net_d = nets
|
|
optim_g, optim_d = optims
|
|
scheduler_g, scheduler_d = schedulers
|
|
train_loader, eval_loader = loaders
|
|
if writers is not None:
|
|
writer, writer_eval = writers
|
|
|
|
half_type = torch.bfloat16 if hps.train.half_type=="bf16" else torch.float16
|
|
|
|
# train_loader.batch_sampler.set_epoch(epoch)
|
|
global global_step
|
|
|
|
net_g.train()
|
|
net_d.train()
|
|
for batch_idx, items in enumerate(train_loader):
|
|
c, f0, spec, y, spk, lengths, uv,volume = items
|
|
g = spk.cuda(rank, non_blocking=True)
|
|
spec, y = spec.cuda(rank, non_blocking=True), y.cuda(rank, non_blocking=True)
|
|
c = c.cuda(rank, non_blocking=True)
|
|
f0 = f0.cuda(rank, non_blocking=True)
|
|
uv = uv.cuda(rank, non_blocking=True)
|
|
lengths = lengths.cuda(rank, non_blocking=True)
|
|
mel = spec_to_mel_torch(
|
|
spec,
|
|
hps.data.filter_length,
|
|
hps.data.n_mel_channels,
|
|
hps.data.sampling_rate,
|
|
hps.data.mel_fmin,
|
|
hps.data.mel_fmax)
|
|
|
|
with autocast(enabled=hps.train.fp16_run, dtype=half_type):
|
|
y_hat, ids_slice, z_mask, \
|
|
(z, z_p, m_p, logs_p, m_q, logs_q), pred_lf0, norm_lf0, lf0 = net_g(c, f0, uv, spec, g=g, c_lengths=lengths,
|
|
spec_lengths=lengths,vol = volume)
|
|
|
|
y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
|
|
y_hat_mel = mel_spectrogram_torch(
|
|
y_hat.squeeze(1),
|
|
hps.data.filter_length,
|
|
hps.data.n_mel_channels,
|
|
hps.data.sampling_rate,
|
|
hps.data.hop_length,
|
|
hps.data.win_length,
|
|
hps.data.mel_fmin,
|
|
hps.data.mel_fmax
|
|
)
|
|
y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice
|
|
|
|
# Discriminator
|
|
y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
|
|
|
|
with autocast(enabled=False, dtype=half_type):
|
|
loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
|
|
loss_disc_all = loss_disc
|
|
|
|
optim_d.zero_grad()
|
|
scaler.scale(loss_disc_all).backward()
|
|
scaler.unscale_(optim_d)
|
|
grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
|
|
scaler.step(optim_d)
|
|
|
|
|
|
with autocast(enabled=hps.train.fp16_run, dtype=half_type):
|
|
# Generator
|
|
y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
|
|
with autocast(enabled=False, dtype=half_type):
|
|
loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
|
|
loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
|
|
loss_fm = feature_loss(fmap_r, fmap_g)
|
|
loss_gen, losses_gen = generator_loss(y_d_hat_g)
|
|
loss_lf0 = F.mse_loss(pred_lf0, lf0) if net_g.module.use_automatic_f0_prediction else 0
|
|
loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl + loss_lf0
|
|
optim_g.zero_grad()
|
|
scaler.scale(loss_gen_all).backward()
|
|
scaler.unscale_(optim_g)
|
|
grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
|
|
scaler.step(optim_g)
|
|
scaler.update()
|
|
|
|
if rank == 0:
|
|
if global_step % hps.train.log_interval == 0:
|
|
lr = optim_g.param_groups[0]['lr']
|
|
losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_kl]
|
|
reference_loss=0
|
|
for i in losses:
|
|
reference_loss += i
|
|
logger.info('Train Epoch: {} [{:.0f}%]'.format(
|
|
epoch,
|
|
100. * batch_idx / len(train_loader)))
|
|
logger.info(f"Losses: {[x.item() for x in losses]}, step: {global_step}, lr: {lr}, reference_loss: {reference_loss}")
|
|
|
|
scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr,
|
|
"grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g}
|
|
scalar_dict.update({"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/kl": loss_kl,
|
|
"loss/g/lf0": loss_lf0})
|
|
|
|
# scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
|
|
# scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
|
|
# scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
|
|
image_dict = {
|
|
"slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
|
|
"slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()),
|
|
"all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy())
|
|
}
|
|
|
|
if net_g.module.use_automatic_f0_prediction:
|
|
image_dict.update({
|
|
"all/lf0": utils.plot_data_to_numpy(lf0[0, 0, :].cpu().numpy(),
|
|
pred_lf0[0, 0, :].detach().cpu().numpy()),
|
|
"all/norm_lf0": utils.plot_data_to_numpy(lf0[0, 0, :].cpu().numpy(),
|
|
norm_lf0[0, 0, :].detach().cpu().numpy())
|
|
})
|
|
|
|
utils.summarize(
|
|
writer=writer,
|
|
global_step=global_step,
|
|
images=image_dict,
|
|
scalars=scalar_dict
|
|
)
|
|
|
|
if global_step % hps.train.eval_interval == 0:
|
|
evaluate(hps, net_g, eval_loader, writer_eval)
|
|
utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch,
|
|
os.path.join(hps.model_dir, "G_{}.pth".format(global_step)))
|
|
utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch,
|
|
os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
|
|
keep_ckpts = getattr(hps.train, 'keep_ckpts', 0)
|
|
if keep_ckpts > 0:
|
|
utils.clean_checkpoints(path_to_models=hps.model_dir, n_ckpts_to_keep=keep_ckpts, sort_by_time=True)
|
|
|
|
global_step += 1
|
|
|
|
if rank == 0:
|
|
global start_time
|
|
now = time.time()
|
|
durtaion = format(now - start_time, '.2f')
|
|
logger.info(f'====> Epoch: {epoch}, cost {durtaion} s')
|
|
start_time = now
|
|
|
|
|
|
def evaluate(hps, generator, eval_loader, writer_eval):
|
|
generator.eval()
|
|
image_dict = {}
|
|
audio_dict = {}
|
|
with torch.no_grad():
|
|
for batch_idx, items in enumerate(eval_loader):
|
|
c, f0, spec, y, spk, _, uv,volume = items
|
|
g = spk[:1].cuda(0)
|
|
spec, y = spec[:1].cuda(0), y[:1].cuda(0)
|
|
c = c[:1].cuda(0)
|
|
f0 = f0[:1].cuda(0)
|
|
uv= uv[:1].cuda(0)
|
|
if volume is not None:
|
|
volume = volume[:1].cuda(0)
|
|
mel = spec_to_mel_torch(
|
|
spec,
|
|
hps.data.filter_length,
|
|
hps.data.n_mel_channels,
|
|
hps.data.sampling_rate,
|
|
hps.data.mel_fmin,
|
|
hps.data.mel_fmax)
|
|
y_hat,_ = generator.module.infer(c, f0, uv, g=g,vol = volume)
|
|
|
|
y_hat_mel = mel_spectrogram_torch(
|
|
y_hat.squeeze(1).float(),
|
|
hps.data.filter_length,
|
|
hps.data.n_mel_channels,
|
|
hps.data.sampling_rate,
|
|
hps.data.hop_length,
|
|
hps.data.win_length,
|
|
hps.data.mel_fmin,
|
|
hps.data.mel_fmax
|
|
)
|
|
|
|
audio_dict.update({
|
|
f"gen/audio_{batch_idx}": y_hat[0],
|
|
f"gt/audio_{batch_idx}": y[0]
|
|
})
|
|
image_dict.update({
|
|
"gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy()),
|
|
"gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())
|
|
})
|
|
utils.summarize(
|
|
writer=writer_eval,
|
|
global_step=global_step,
|
|
images=image_dict,
|
|
audios=audio_dict,
|
|
audio_sampling_rate=hps.data.sampling_rate
|
|
)
|
|
generator.train()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main() |