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import os |
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import torch |
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from torch.nn import functional as F |
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from torch.utils.data import DataLoader |
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from torch.utils.tensorboard import SummaryWriter |
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import torch.multiprocessing as mp |
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import torch.distributed as dist |
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from torch.nn.parallel import DistributedDataParallel as DDP |
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from torch.cuda.amp import autocast, GradScaler |
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import commons |
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import utils |
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from data_utils import (TextAudioSpeakerLoader, TextAudioSpeakerCollate, |
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DistributedBucketSampler) |
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from models import ( |
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SynthesizerTrn, |
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MultiPeriodDiscriminator, |
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) |
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from losses import (generator_loss, discriminator_loss, feature_loss, kl_loss) |
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from mel_processing import mel_spectrogram_torch, spec_to_mel_torch |
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torch.backends.cudnn.benchmark = True |
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global_step = 0 |
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def main(): |
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"""Assume Single Node Multi GPUs Training Only""" |
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assert torch.cuda.is_available(), "CPU training is not allowed." |
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n_gpus = torch.cuda.device_count() |
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hps = utils.get_hparams() |
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mp.spawn(run, nprocs=n_gpus, args=( |
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n_gpus, |
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hps, |
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)) |
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def run(rank, n_gpus, hps): |
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global global_step |
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if rank == 0: |
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logger = utils.get_logger(hps.model_dir) |
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logger.info(hps) |
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utils.check_git_hash(hps.model_dir) |
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writer = SummaryWriter(log_dir=hps.model_dir) |
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writer_eval = SummaryWriter( |
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log_dir=os.path.join(hps.model_dir, "eval")) |
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dist.init_process_group(backend='nccl', |
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init_method='env://', |
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world_size=n_gpus, |
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rank=rank) |
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torch.manual_seed(hps.train.seed) |
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torch.cuda.set_device(rank) |
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train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data) |
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train_sampler = DistributedBucketSampler( |
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train_dataset, |
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hps.train.batch_size, [32, 300, 400, 500, 600, 700, 800, 900, 1000], |
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num_replicas=n_gpus, |
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rank=rank, |
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shuffle=True) |
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collate_fn = TextAudioSpeakerCollate() |
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train_loader = DataLoader(train_dataset, |
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num_workers=8, |
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shuffle=False, |
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pin_memory=True, |
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collate_fn=collate_fn, |
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batch_sampler=train_sampler) |
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if rank == 0: |
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eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, |
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hps.data) |
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eval_loader = DataLoader(eval_dataset, |
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num_workers=8, |
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shuffle=False, |
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batch_size=hps.train.batch_size, |
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pin_memory=True, |
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drop_last=False, |
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collate_fn=collate_fn) |
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net_g = SynthesizerTrn(hps.data.num_phones, |
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hps.data.filter_length // 2 + 1, |
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hps.train.segment_size // hps.data.hop_length, |
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n_speakers=hps.data.n_speakers, |
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**hps.model).cuda(rank) |
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net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank) |
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optim_g = torch.optim.AdamW(net_g.parameters(), |
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hps.train.learning_rate, |
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betas=hps.train.betas, |
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eps=hps.train.eps) |
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optim_d = torch.optim.AdamW(net_d.parameters(), |
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hps.train.learning_rate, |
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betas=hps.train.betas, |
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eps=hps.train.eps) |
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net_g = DDP(net_g, device_ids=[rank]) |
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net_d = DDP(net_d, device_ids=[rank]) |
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try: |
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_, _, _, epoch_str = utils.load_checkpoint( |
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utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, |
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optim_g) |
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_, _, _, epoch_str = utils.load_checkpoint( |
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utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, |
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optim_d) |
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global_step = (epoch_str - 1) * len(train_loader) |
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except Exception as e: |
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epoch_str = 1 |
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global_step = 0 |
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scheduler_g = torch.optim.lr_scheduler.ExponentialLR( |
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optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2) |
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scheduler_d = torch.optim.lr_scheduler.ExponentialLR( |
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optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2) |
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scaler = GradScaler(enabled=hps.train.fp16_run) |
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for epoch in range(epoch_str, hps.train.epochs + 1): |
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if rank == 0: |
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train_and_evaluate(rank, epoch, hps, [net_g, net_d], |
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[optim_g, optim_d], [scheduler_g, scheduler_d], |
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scaler, [train_loader, eval_loader], logger, |
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[writer, writer_eval]) |
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else: |
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train_and_evaluate(rank, epoch, hps, [net_g, net_d], |
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[optim_g, optim_d], [scheduler_g, scheduler_d], |
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scaler, [train_loader, None], None, None) |
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scheduler_g.step() |
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scheduler_d.step() |
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def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, |
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loaders, logger, writers): |
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net_g, net_d = nets |
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optim_g, optim_d = optims |
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scheduler_g, scheduler_d = schedulers |
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train_loader, eval_loader = loaders |
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if writers is not None: |
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writer, writer_eval = writers |
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train_loader.batch_sampler.set_epoch(epoch) |
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global global_step |
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net_g.train() |
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net_d.train() |
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for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, |
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speakers) in enumerate(train_loader): |
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x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda( |
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rank, non_blocking=True) |
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spec, spec_lengths = spec.cuda( |
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rank, non_blocking=True), spec_lengths.cuda(rank, |
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non_blocking=True) |
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y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda( |
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rank, non_blocking=True) |
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speakers = speakers.cuda(rank, non_blocking=True) |
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with autocast(enabled=hps.train.fp16_run): |
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y_hat, l_length, attn, ids_slice, x_mask, z_mask, ( |
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z, z_p, m_p, logs_p, m_q, |
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logs_q) = net_g(x, x_lengths, spec, spec_lengths, speakers) |
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mel = spec_to_mel_torch(spec, hps.data.filter_length, |
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hps.data.n_mel_channels, |
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hps.data.sampling_rate, hps.data.mel_fmin, |
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hps.data.mel_fmax) |
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y_mel = commons.slice_segments( |
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mel, ids_slice, hps.train.segment_size // hps.data.hop_length) |
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y_hat_mel = mel_spectrogram_torch( |
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y_hat.squeeze(1), hps.data.filter_length, |
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hps.data.n_mel_channels, hps.data.sampling_rate, |
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hps.data.hop_length, hps.data.win_length, hps.data.mel_fmin, |
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hps.data.mel_fmax) |
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y = commons.slice_segments(y, ids_slice * hps.data.hop_length, |
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hps.train.segment_size) |
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y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach()) |
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with autocast(enabled=False): |
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loss_disc, losses_disc_r, losses_disc_g = discriminator_loss( |
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y_d_hat_r, y_d_hat_g) |
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loss_disc_all = loss_disc |
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optim_d.zero_grad() |
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scaler.scale(loss_disc_all).backward() |
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scaler.unscale_(optim_d) |
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grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None) |
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scaler.step(optim_d) |
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with autocast(enabled=hps.train.fp16_run): |
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y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat) |
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with autocast(enabled=False): |
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loss_dur = torch.sum(l_length.float()) |
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loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel |
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loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, |
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z_mask) * hps.train.c_kl |
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loss_fm = feature_loss(fmap_r, fmap_g) |
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loss_gen, losses_gen = generator_loss(y_d_hat_g) |
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loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl |
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optim_g.zero_grad() |
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scaler.scale(loss_gen_all).backward() |
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scaler.unscale_(optim_g) |
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grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None) |
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scaler.step(optim_g) |
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scaler.update() |
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if rank == 0: |
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if global_step % hps.train.log_interval == 0: |
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lr = optim_g.param_groups[0]['lr'] |
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losses = [ |
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loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl |
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] |
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logger.info('Train Epoch: {} [{:.0f}%]'.format( |
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epoch, 100. * batch_idx / len(train_loader))) |
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logger.info([x.item() for x in losses] + [global_step, lr]) |
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scalar_dict = { |
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"loss/g/total": loss_gen_all, |
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"loss/d/total": loss_disc_all, |
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"learning_rate": lr, |
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"grad_norm_d": grad_norm_d, |
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"grad_norm_g": grad_norm_g |
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} |
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scalar_dict.update({ |
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"loss/g/fm": loss_fm, |
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"loss/g/mel": loss_mel, |
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"loss/g/dur": loss_dur, |
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"loss/g/kl": loss_kl |
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}) |
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scalar_dict.update({ |
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"loss/g/{}".format(i): v |
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for i, v in enumerate(losses_gen) |
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}) |
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scalar_dict.update({ |
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"loss/d_r/{}".format(i): v |
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for i, v in enumerate(losses_disc_r) |
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}) |
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scalar_dict.update({ |
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"loss/d_g/{}".format(i): v |
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for i, v in enumerate(losses_disc_g) |
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}) |
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image_dict = { |
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"slice/mel_org": |
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utils.plot_spectrogram_to_numpy( |
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y_mel[0].data.cpu().numpy()), |
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"slice/mel_gen": |
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utils.plot_spectrogram_to_numpy( |
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y_hat_mel[0].data.cpu().numpy()), |
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"all/mel": |
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utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()), |
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"all/attn": |
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utils.plot_alignment_to_numpy(attn[0, |
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0].data.cpu().numpy()) |
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} |
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utils.summarize(writer=writer, |
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global_step=global_step, |
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images=image_dict, |
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scalars=scalar_dict) |
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if global_step % hps.train.eval_interval == 0: |
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evaluate(hps, net_g, eval_loader, writer_eval) |
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utils.save_checkpoint( |
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net_g, optim_g, hps.train.learning_rate, epoch, |
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os.path.join(hps.model_dir, |
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"G_{}.pth".format(global_step))) |
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utils.save_checkpoint( |
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net_d, optim_d, hps.train.learning_rate, epoch, |
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os.path.join(hps.model_dir, |
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"D_{}.pth".format(global_step))) |
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global_step += 1 |
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if rank == 0: |
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logger.info('====> Epoch: {}'.format(epoch)) |
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def evaluate(hps, generator, eval_loader, writer_eval): |
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generator.eval() |
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with torch.no_grad(): |
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for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, |
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speakers) in enumerate(eval_loader): |
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x, x_lengths = x.cuda(0), x_lengths.cuda(0) |
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spec, spec_lengths = spec.cuda(0), spec_lengths.cuda(0) |
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y, y_lengths = y.cuda(0), y_lengths.cuda(0) |
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speakers = speakers.cuda(0) |
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x = x[:1] |
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x_lengths = x_lengths[:1] |
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spec = spec[:1] |
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spec_lengths = spec_lengths[:1] |
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y = y[:1] |
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y_lengths = y_lengths[:1] |
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speakers = speakers[:1] |
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break |
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y_hat, attn, mask, *_ = generator.module.infer(x, |
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x_lengths, |
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speakers, |
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max_len=1000) |
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y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length |
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mel = spec_to_mel_torch(spec, hps.data.filter_length, |
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hps.data.n_mel_channels, |
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hps.data.sampling_rate, hps.data.mel_fmin, |
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hps.data.mel_fmax) |
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y_hat_mel = mel_spectrogram_torch( |
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y_hat.squeeze(1).float(), hps.data.filter_length, |
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hps.data.n_mel_channels, hps.data.sampling_rate, |
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hps.data.hop_length, hps.data.win_length, hps.data.mel_fmin, |
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hps.data.mel_fmax) |
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image_dict = { |
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"gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy()) |
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} |
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audio_dict = {"gen/audio": y_hat[0, :, :y_hat_lengths[0]]} |
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if global_step == 0: |
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image_dict.update( |
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{"gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())}) |
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audio_dict.update({"gt/audio": y[0, :, :y_lengths[0]]}) |
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utils.summarize(writer=writer_eval, |
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global_step=global_step, |
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images=image_dict, |
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audios=audio_dict, |
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audio_sampling_rate=hps.data.sampling_rate) |
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generator.train() |
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if __name__ == "__main__": |
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main() |
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