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- import logging
- logging.getLogger('matplotlib').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 commons
- import utils
- from data_utils import TextAudioSpeakerLoader, EvalDataLoader
- from models import (
- SynthesizerTrn,
- MultiPeriodDiscriminator,
- )
- from losses import (
- kl_loss,
- generator_loss, discriminator_loss, feature_loss
- )
-
- from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
-
- torch.backends.cudnn.benchmark = True
- global_step = 0
-
-
- # 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"))
-
- 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)
- train_loader = DataLoader(train_dataset, num_workers=8, shuffle=False, pin_memory=True,
- batch_size=hps.train.batch_size)
- if rank == 0:
- eval_dataset = EvalDataLoader(hps.data.validation_files, hps)
- eval_loader = DataLoader(eval_dataset, num_workers=1, shuffle=False,
- batch_size=1, pin_memory=False,
- drop_last=False)
-
- 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])
-
- 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], [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, items in enumerate(train_loader):
- c, f0, spec, y, spk = 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)
- 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) = net_g(c, f0, spec, g=g, mel=mel)
-
- 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_gen_all = loss_gen + loss_fm + loss_mel + 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_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/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()),
- }
-
- 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_g, optim_g, hps.train.learning_rate, epoch,
- os.path.join(hps.model_qizhi_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)))
- utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch,
- os.path.join(hps.model_qizhi_dir, "D_{}.pth".format(global_step)))
- os.system("cd /tmp/script_for_grampus/ &&./uploader_for_gpu " + "/tmp/output/")
- os.remove(os.path.join(hps.model_qizhi_dir, "G_{}.pth".format(global_step)))
- os.remove(os.path.join(hps.model_qizhi_dir, "D_{}.pth".format(global_step)))
- 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 = {}
- with torch.no_grad():
- for batch_idx, items in enumerate(eval_loader):
- c, f0, spec, y, spk = 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)
- 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, g=g, mel=mel)
-
- 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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