350 lines
15 KiB
Python
350 lines
15 KiB
Python
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
|
|
from tqdm import tqdm
|
|
import logging
|
|
logging.getLogger('numba').setLevel(logging.WARNING)
|
|
import commons
|
|
import utils
|
|
from data_utils import (
|
|
TextAudioSpeakerLoader,
|
|
TextAudioSpeakerCollate,
|
|
DistributedBucketSampler
|
|
)
|
|
from models import (
|
|
SynthesizerTrn,
|
|
MultiPeriodDiscriminator,
|
|
)
|
|
from losses import (
|
|
generator_loss,
|
|
discriminator_loss,
|
|
feature_loss,
|
|
kl_loss
|
|
)
|
|
from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
|
|
from text.symbols import symbols
|
|
|
|
torch.backends.cudnn.benchmark = True
|
|
torch.backends.cuda.matmul.allow_tf32 = True
|
|
# The flag below controls whether to allow TF32 on cuDNN. This flag defaults to True.
|
|
torch.backends.cudnn.allow_tf32 = True
|
|
torch.set_float32_matmul_precision('medium')
|
|
global_step = 0
|
|
|
|
|
|
def main():
|
|
"""Assume Single Node Multi GPUs Training Only"""
|
|
assert torch.cuda.is_available(), "CPU training is not allowed."
|
|
|
|
n_gpus = torch.cuda.device_count()
|
|
os.environ['MASTER_ADDR'] = 'localhost'
|
|
os.environ['MASTER_PORT'] = '65280'
|
|
|
|
hps = utils.get_hparams()
|
|
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"))
|
|
|
|
dist.init_process_group(backend='nccl', init_method='env://', world_size=n_gpus, rank=rank)
|
|
torch.manual_seed(hps.train.seed)
|
|
torch.cuda.set_device(rank)
|
|
|
|
train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
|
|
train_sampler = DistributedBucketSampler(
|
|
train_dataset,
|
|
hps.train.batch_size,
|
|
[32, 300, 400, 500, 600, 700, 800, 900, 1000],
|
|
num_replicas=n_gpus,
|
|
rank=rank,
|
|
shuffle=True)
|
|
collate_fn = TextAudioSpeakerCollate()
|
|
train_loader = DataLoader(train_dataset, num_workers=24, shuffle=False, pin_memory=True,
|
|
collate_fn=collate_fn, batch_sampler=train_sampler, persistent_workers=True,prefetch_factor=4)
|
|
if rank == 0:
|
|
eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
|
|
eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False,
|
|
batch_size=1, pin_memory=True,
|
|
drop_last=False, collate_fn=collate_fn)
|
|
if "use_noise_scaled_mas" in hps.model.keys() and hps.model.use_noise_scaled_mas == True:
|
|
print("Using noise scaled MAS for VITS2")
|
|
use_noise_scaled_mas = True
|
|
mas_noise_scale_initial = 0.01
|
|
noise_scale_delta = 2e-6
|
|
else:
|
|
print("Using normal MAS for VITS1")
|
|
use_noise_scaled_mas = False
|
|
if "use_spk_conditioned_encoder" in hps.model.keys() and hps.model.use_spk_conditioned_encoder == True:
|
|
if hps.data.n_speakers == 0:
|
|
raise ValueError("n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model")
|
|
use_spk_conditioned_encoder = True
|
|
else:
|
|
print("Using normal encoder for VITS1")
|
|
use_spk_conditioned_encoder = False
|
|
|
|
net_g = SynthesizerTrn(
|
|
len(symbols),
|
|
hps.data.filter_length // 2 + 1,
|
|
hps.train.segment_size // hps.data.hop_length,
|
|
n_speakers=hps.data.n_speakers,
|
|
mas_noise_scale_initial = mas_noise_scale_initial,
|
|
noise_scale_delta = noise_scale_delta,
|
|
**hps.model).cuda(rank)
|
|
|
|
freeze_enc = getattr(hps.model, "freeze_enc", False)
|
|
if freeze_enc:
|
|
print("freeze encoder !!!")
|
|
for param in net_g.enc_p.parameters():
|
|
param.requires_grad = False
|
|
|
|
net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
|
|
optim_g = torch.optim.AdamW(
|
|
filter(lambda p: p.requires_grad, 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],find_unused_parameters=True)
|
|
|
|
pretrain_dir = None
|
|
if pretrain_dir is None:
|
|
try:
|
|
_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g,
|
|
optim_g, skip_optimizer=True)
|
|
_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d,
|
|
optim_d, skip_optimizer=True)
|
|
epoch_str = max(epoch_str, 1)
|
|
global_step = (epoch_str - 1) * len(train_loader)
|
|
except Exception as e:
|
|
print(e)
|
|
epoch_str = 1
|
|
global_step = 0
|
|
else:
|
|
_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(pretrain_dir, "G_*.pth"), net_g,
|
|
optim_g, True)
|
|
_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(pretrain_dir, "D_*.pth"), net_d,
|
|
optim_d, True)
|
|
|
|
|
|
|
|
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):
|
|
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)
|
|
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, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert) in tqdm(enumerate(train_loader)):
|
|
if net_g.module.use_noise_scaled_mas:
|
|
current_mas_noise_scale = net_g.module.mas_noise_scale_initial - net_g.module.noise_scale_delta * global_step
|
|
net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0)
|
|
x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True)
|
|
spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True)
|
|
y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)
|
|
speakers = speakers.cuda(rank, non_blocking=True)
|
|
tone = tone.cuda(rank, non_blocking=True)
|
|
language = language.cuda(rank, non_blocking=True)
|
|
bert = bert.cuda(rank, non_blocking=True)
|
|
|
|
with autocast(enabled=hps.train.fp16_run):
|
|
y_hat, l_length, attn, ids_slice, x_mask, z_mask, \
|
|
(z, z_p, m_p, logs_p, m_q, logs_q) = net_g(x, x_lengths, spec, spec_lengths, speakers, tone, language, bert)
|
|
|
|
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_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_dur = torch.sum(l_length.float())
|
|
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_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl
|
|
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_dur, loss_kl]
|
|
logger.info('Train Epoch: {} [{:.0f}%]'.format(
|
|
epoch,
|
|
100. * batch_idx / len(train_loader)))
|
|
logger.info([x.item() for x in losses] + [global_step, lr])
|
|
|
|
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/dur": loss_dur, "loss/g/kl": loss_kl})
|
|
|
|
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/attn": utils.plot_alignment_to_numpy(attn[0, 0].data.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', 3)
|
|
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:
|
|
logger.info('====> Epoch: {}'.format(epoch))
|
|
|
|
|
|
|
|
def evaluate(hps, generator, eval_loader, writer_eval):
|
|
generator.eval()
|
|
image_dict = {}
|
|
audio_dict = {}
|
|
print("Evaluating ...")
|
|
with torch.no_grad():
|
|
for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert) in enumerate(eval_loader):
|
|
x, x_lengths = x.cuda(), x_lengths.cuda()
|
|
spec, spec_lengths = spec.cuda(), spec_lengths.cuda()
|
|
y, y_lengths = y.cuda(), y_lengths.cuda()
|
|
speakers = speakers.cuda()
|
|
bert = bert.cuda()
|
|
tone = tone.cuda()
|
|
language = language.cuda()
|
|
for use_sdp in [True, False]:
|
|
y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, speakers, tone, language, bert, y=spec, max_len=1000, sdp_ratio=0.0 if not use_sdp else 1.0)
|
|
y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length
|
|
|
|
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_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
|
|
)
|
|
image_dict.update({
|
|
f"gen/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())
|
|
})
|
|
audio_dict.update({
|
|
f"gen/audio_{batch_idx}_{use_sdp}": y_hat[0, :, :y_hat_lengths[0]]
|
|
})
|
|
image_dict.update({f"gt/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())})
|
|
audio_dict.update({f"gt/audio_{batch_idx}": y[0, :, :y_lengths[0]]})
|
|
|
|
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()
|