"""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'] = '8000'

hps = utils.get_hparams()

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 = UnitAudioLoader(hps.data.training_files, hps.data, True)

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 = UnitAudioCollate()

train_loader = DataLoader(train_dataset, num_workers=8, shuffle=False, pin_memory=True,

collate_fn=collate_fn, batch_sampler=train_sampler)

if rank == 0:

eval_dataset = UnitAudioLoader(hps.data.validation_files, hps.data, False)

eval_loader = DataLoader(eval_dataset, num_workers=8, shuffle=False,

batch_size=hps.train.batch_size, pin_memory=True,

drop_last=False, collate_fn=collate_fn)

# load pretrained F0 model

if hps.model.__contains__('F0_path'):

F0_path = hps.model.F0_path

use_F0_model = True

F0_model = JDCNet(num_class=1, seq_len=192)

params = torch.load(F0_path, map_location='cpu')['net']

F0_model.load_state_dict(params)

F0_model.cuda(rank)

else:

use_F0_model = False

F0_model = None

net_g = SynthesizerTrn(

hps.data.filter_length // 2 + 1,

hps.train.segment_size // hps.data.hop_length,

use_F0_model=use_F0_model,

**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])

net_d = DDP(net_d, device_ids=[rank])

try:

_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g,

optim_g)

_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d,

optim_d)

global_step = (epoch_str - 1) * len(train_loader)

except:

epoch_str = 1

global_step = 0

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, F0_model], use_F0_model, [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, F0_model], use_F0_model, [optim_g, optim_d], [scheduler_g, scheduler_d], scaler,

[train_loader, None], None, None)

scheduler_g.step()

net_g, net_d, F0_model = 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) in enumerate(train_loader):

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)

x_length = x_lengths.float()

with autocast(enabled=hps.train.fp16_run):

if use_F0_model:

y_mels = preprocess(y)

with torch.no_grad():

y_mels = y_mels.half().cuda(rank)

F0 = F0_model.get_feature_GAN(y_mels)

else:

F0 = None

y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)

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, F0=F0)

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, F0_model, use_F0_model, 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)))

global_step += 1

if rank == 0:

generator.eval()

with torch.no_grad():

for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths) in enumerate(eval_loader):

x, x_lengths = x.cuda(0), x_lengths.cuda(0)

spec, spec_lengths = spec.cuda(0), spec_lengths.cuda(0)

x, x_lengths = x.float(), x_lengths.float()

if use_F0_model:

y_mels = preprocess(y)

y_mels = y_mels.half().cuda(0)

F0 = F0_model.get_feature_GAN(y_mels)

else:

F0 = None

y, y_lengths = y.cuda(0), y_lengths.cuda(0)

# remove else

x = x[:1]

x_lengths = x_lengths[:1]

spec = spec[:1]

spec_lengths = spec_lengths[:1]

y = y[:1]

y_lengths = y_lengths[:1]

break

y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, F0=F0, max_len=1000)

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 = {

"gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())

}

audio_dict = {

"gen/audio": y_hat[0, :, :y_hat_lengths[0]]

}

if global_step == 0:

image_dict.update({"gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())})

audio_dict.update({"gt/audio": 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

)