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import logging
import multiprocessing
import time
logging.getLogger('matplotlib').setLevel(logging.WARNING)
logging.getLogger('numba').setLevel(logging.WARNING)
import os
import json
import argparse
import itertools
import math
import torch
from torch import nn, optim
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import torch.multiprocessing as mp
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.cuda.amp import autocast, GradScaler
import modules.commons as commons
import utils
from data_utils import TextAudioSpeakerLoader, TextAudioCollate
from models import (
SynthesizerTrn,
MultiPeriodDiscriminator,
)
from modules.losses import (
kl_loss,
generator_loss, discriminator_loss, feature_loss
)
from modules.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
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:
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
# 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):
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):
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):
# Generator
y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
with autocast(enabled=False):
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)
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()),
"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!=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({
f"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()
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