|
import os |
|
import json |
|
import argparse |
|
import itertools |
|
import math |
|
import torch |
|
import shutil |
|
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, |
|
DurationDiscriminator, |
|
) |
|
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 |
|
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() |
|
if not hps.cont: |
|
shutil.copy('./pretrained_models/D_0.pth','./logs/OUTPUT_MODEL/D_0.pth') |
|
shutil.copy('./pretrained_models/G_0.pth','./logs/OUTPUT_MODEL/G_0.pth') |
|
shutil.copy('./pretrained_models/DUR_0.pth','./logs/OUTPUT_MODEL/DUR_0.pth') |
|
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= '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) |
|
|
|
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=2, shuffle=False, pin_memory=True, |
|
collate_fn=collate_fn, batch_sampler=train_sampler) |
|
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 |
|
mas_noise_scale_initial = 0.0 |
|
noise_scale_delta = 0.0 |
|
if "use_duration_discriminator" in hps.model.keys() and hps.model.use_duration_discriminator == True: |
|
print("Using duration discriminator for VITS2") |
|
use_duration_discriminator = True |
|
net_dur_disc = DurationDiscriminator( |
|
hps.model.hidden_channels, |
|
hps.model.hidden_channels, |
|
3, |
|
0.1, |
|
gin_channels=hps.model.gin_channels if hps.data.n_speakers != 0 else 0, |
|
).cuda(rank) |
|
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) |
|
if net_dur_disc is not None: |
|
optim_dur_disc = torch.optim.AdamW( |
|
net_dur_disc.parameters(), |
|
hps.train.learning_rate, |
|
betas=hps.train.betas, |
|
eps=hps.train.eps) |
|
else: |
|
optim_dur_disc = None |
|
net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True) |
|
net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True) |
|
if net_dur_disc is not None: |
|
net_dur_disc = DDP(net_dur_disc, device_ids=[rank], find_unused_parameters=True) |
|
|
|
pretrain_dir = None |
|
if pretrain_dir is None: |
|
try: |
|
if net_dur_disc is not None: |
|
_, optim_dur_disc, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "DUR_*.pth"), net_dur_disc, optim_dur_disc, skip_optimizer=not hps.cont) |
|
_, optim_g, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, |
|
optim_g, skip_optimizer=not hps.cont) |
|
_, optim_d, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, |
|
optim_d, skip_optimizer=not hps.cont) |
|
|
|
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) |
|
if net_dur_disc is not None: |
|
scheduler_dur_disc = torch.optim.lr_scheduler.ExponentialLR(optim_dur_disc, gamma=hps.train.lr_decay, last_epoch=epoch_str-2) |
|
else: |
|
scheduler_dur_disc = None |
|
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, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], scaler, [train_loader, eval_loader], logger, [writer, writer_eval]) |
|
else: |
|
train_and_evaluate(rank, epoch, hps, [net_g, net_d, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], scaler, [train_loader, None], None, None) |
|
scheduler_g.step() |
|
scheduler_d.step() |
|
if net_dur_disc is not None: |
|
scheduler_dur_disc.step() |
|
|
|
|
|
def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers): |
|
net_g, net_d, net_dur_disc = nets |
|
optim_g, optim_d, optim_dur_disc = optims |
|
scheduler_g, scheduler_d, scheduler_dur_disc = 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() |
|
if net_dur_disc is not None: |
|
net_dur_disc.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), (hidden_x, logw, logw_) = 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) |
|
|
|
|
|
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 |
|
if net_dur_disc is not None: |
|
y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x.detach(), x_mask.detach(), logw.detach(), logw_.detach()) |
|
with autocast(enabled=False): |
|
|
|
loss_dur_disc, losses_dur_disc_r, losses_dur_disc_g = discriminator_loss(y_dur_hat_r, y_dur_hat_g) |
|
loss_dur_disc_all = loss_dur_disc |
|
optim_dur_disc.zero_grad() |
|
scaler.scale(loss_dur_disc_all).backward() |
|
scaler.unscale_(optim_dur_disc) |
|
grad_norm_dur_disc = commons.clip_grad_value_(net_dur_disc.parameters(), None) |
|
scaler.step(optim_dur_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): |
|
|
|
y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat) |
|
if net_dur_disc is not None: |
|
y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw, logw_) |
|
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 |
|
if net_dur_disc is not None: |
|
loss_dur_gen, losses_dur_gen = generator_loss(y_dur_hat_g) |
|
loss_gen_all += loss_dur_gen |
|
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))) |
|
if net_dur_disc is not None: |
|
utils.save_checkpoint(net_dur_disc, optim_dur_disc, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "DUR_{}.pth".format(global_step))) |
|
keep_ckpts = getattr(hps.train, 'keep_ckpts', 5) |
|
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() |
|
|
