import warnings warnings.simplefilter(action='ignore', category=FutureWarning) import itertools import os import time import argparse import json import torch import torch.nn.functional as F from torch.utils.tensorboard import SummaryWriter from torch.utils.data import DistributedSampler, DataLoader import torch.multiprocessing as mp from torch.distributed import init_process_group from torch.nn.parallel import DistributedDataParallel from env import AttrDict, build_env from meldataset import MelDataset, mel_spectrogram, get_dataset_filelist from models import Generator, MultiPeriodDiscriminator, MultiScaleDiscriminator, feature_loss, generator_loss,\ discriminator_loss from utils import plot_spectrogram, scan_checkpoint, load_checkpoint, save_checkpoint torch.backends.cudnn.benchmark = True def train(rank, a, h): if h.num_gpus > 1: init_process_group(backend=h.dist_config['dist_backend'], init_method=h.dist_config['dist_url'], world_size=h.dist_config['world_size'] * h.num_gpus, rank=rank) torch.cuda.manual_seed(h.seed) device = torch.device('cuda:{:d}'.format(rank)) generator = Generator(h).to(device) mpd = MultiPeriodDiscriminator().to(device) msd = MultiScaleDiscriminator().to(device) if rank == 0: print(generator) os.makedirs(a.checkpoint_path, exist_ok=True) print("checkpoints directory : ", a.checkpoint_path) if os.path.isdir(a.checkpoint_path): cp_g = scan_checkpoint(a.checkpoint_path, 'g_') cp_do = scan_checkpoint(a.checkpoint_path, 'do_') steps = 0 if cp_g is None or cp_do is None: state_dict_do = None last_epoch = -1 else: state_dict_g = load_checkpoint(cp_g, device) state_dict_do = load_checkpoint(cp_do, device) generator.load_state_dict(state_dict_g['generator']) mpd.load_state_dict(state_dict_do['mpd']) msd.load_state_dict(state_dict_do['msd']) steps = state_dict_do['steps'] + 1 last_epoch = state_dict_do['epoch'] if h.num_gpus > 1: generator = DistributedDataParallel(generator, device_ids=[rank]).to(device) mpd = DistributedDataParallel(mpd, device_ids=[rank]).to(device) msd = DistributedDataParallel(msd, device_ids=[rank]).to(device) optim_g = torch.optim.AdamW(generator.parameters(), h.learning_rate, betas=[h.adam_b1, h.adam_b2]) optim_d = torch.optim.AdamW(itertools.chain(msd.parameters(), mpd.parameters()), h.learning_rate, betas=[h.adam_b1, h.adam_b2]) if state_dict_do is not None: optim_g.load_state_dict(state_dict_do['optim_g']) optim_d.load_state_dict(state_dict_do['optim_d']) scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=h.lr_decay, last_epoch=last_epoch) scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=h.lr_decay, last_epoch=last_epoch) training_filelist, validation_filelist = get_dataset_filelist(a) trainset = MelDataset(training_filelist, h.segment_size, h.n_fft, h.num_mels, h.hop_size, h.win_size, h.sampling_rate, h.fmin, h.fmax, n_cache_reuse=0, shuffle=False if h.num_gpus > 1 else True, fmax_loss=h.fmax_for_loss, device=device, fine_tuning=a.fine_tuning, base_mels_path=a.input_mels_dir) train_sampler = DistributedSampler(trainset) if h.num_gpus > 1 else None train_loader = DataLoader(trainset, num_workers=h.num_workers, shuffle=False, sampler=train_sampler, batch_size=h.batch_size, pin_memory=True, drop_last=True) if rank == 0: validset = MelDataset(validation_filelist, h.segment_size, h.n_fft, h.num_mels, h.hop_size, h.win_size, h.sampling_rate, h.fmin, h.fmax, False, False, n_cache_reuse=0, fmax_loss=h.fmax_for_loss, device=device, fine_tuning=a.fine_tuning, base_mels_path=a.input_mels_dir) validation_loader = DataLoader(validset, num_workers=1, shuffle=False, sampler=None, batch_size=1, pin_memory=True, drop_last=True) sw = SummaryWriter(os.path.join(a.checkpoint_path, 'logs')) generator.train() mpd.train() msd.train() for epoch in range(max(0, last_epoch), a.training_epochs): if rank == 0: start = time.time() print("Epoch: {}".format(epoch+1)) if h.num_gpus > 1: train_sampler.set_epoch(epoch) for i, batch in enumerate(train_loader): if rank == 0: start_b = time.time() x, y, _, y_mel = batch x = torch.autograd.Variable(x.to(device, non_blocking=True)) y = torch.autograd.Variable(y.to(device, non_blocking=True)) y_mel = torch.autograd.Variable(y_mel.to(device, non_blocking=True)) y = y.unsqueeze(1) y_g_hat = generator(x) y_g_hat_mel = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft, h.num_mels, h.sampling_rate, h.hop_size, h.win_size, h.fmin, h.fmax_for_loss) optim_d.zero_grad() # MPD y_df_hat_r, y_df_hat_g, _, _ = mpd(y, y_g_hat.detach()) loss_disc_f, losses_disc_f_r, losses_disc_f_g = discriminator_loss(y_df_hat_r, y_df_hat_g) # MSD y_ds_hat_r, y_ds_hat_g, _, _ = msd(y, y_g_hat.detach()) loss_disc_s, losses_disc_s_r, losses_disc_s_g = discriminator_loss(y_ds_hat_r, y_ds_hat_g) loss_disc_all = loss_disc_s + loss_disc_f loss_disc_all.backward() optim_d.step() # Generator optim_g.zero_grad() # L1 Mel-Spectrogram Loss loss_mel = F.l1_loss(y_mel, y_g_hat_mel) * 45 y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = mpd(y, y_g_hat) y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = msd(y, y_g_hat) loss_fm_f = feature_loss(fmap_f_r, fmap_f_g) loss_fm_s = feature_loss(fmap_s_r, fmap_s_g) loss_gen_f, losses_gen_f = generator_loss(y_df_hat_g) loss_gen_s, losses_gen_s = generator_loss(y_ds_hat_g) loss_gen_all = loss_gen_s + loss_gen_f + loss_fm_s + loss_fm_f + loss_mel loss_gen_all.backward() optim_g.step() if rank == 0: # STDOUT logging if steps % a.stdout_interval == 0: with torch.no_grad(): mel_error = F.l1_loss(y_mel, y_g_hat_mel).item() print('Steps : {:d}, Gen Loss Total : {:4.3f}, Mel-Spec. Error : {:4.3f}, s/b : {:4.3f}'. format(steps, loss_gen_all, mel_error, time.time() - start_b)) # checkpointing if steps % a.checkpoint_interval == 0 and steps != 0: checkpoint_path = "{}/g_{:08d}".format(a.checkpoint_path, steps) save_checkpoint(checkpoint_path, {'generator': (generator.module if h.num_gpus > 1 else generator).state_dict()}) checkpoint_path = "{}/do_{:08d}".format(a.checkpoint_path, steps) save_checkpoint(checkpoint_path, {'mpd': (mpd.module if h.num_gpus > 1 else mpd).state_dict(), 'msd': (msd.module if h.num_gpus > 1 else msd).state_dict(), 'optim_g': optim_g.state_dict(), 'optim_d': optim_d.state_dict(), 'steps': steps, 'epoch': epoch}) # Tensorboard summary logging if steps % a.summary_interval == 0: sw.add_scalar("training/gen_loss_total", loss_gen_all, steps) sw.add_scalar("training/mel_spec_error", mel_error, steps) # Validation if steps % a.validation_interval == 0: # and steps != 0: generator.eval() torch.cuda.empty_cache() val_err_tot = 0 with torch.no_grad(): for j, batch in enumerate(validation_loader): x, y, _, y_mel = batch y_g_hat = generator(x.to(device)) y_mel = torch.autograd.Variable(y_mel.to(device, non_blocking=True)) y_g_hat_mel = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft, h.num_mels, h.sampling_rate, h.hop_size, h.win_size, h.fmin, h.fmax_for_loss) val_err_tot += F.l1_loss(y_mel, y_g_hat_mel).item() if j <= 4: if steps == 0: sw.add_audio('gt/y_{}'.format(j), y[0], steps, h.sampling_rate) sw.add_figure('gt/y_spec_{}'.format(j), plot_spectrogram(x[0]), steps) sw.add_audio('generated/y_hat_{}'.format(j), y_g_hat[0], steps, h.sampling_rate) y_hat_spec = mel_spectrogram(y_g_hat.squeeze(1), h.n_fft, h.num_mels, h.sampling_rate, h.hop_size, h.win_size, h.fmin, h.fmax) sw.add_figure('generated/y_hat_spec_{}'.format(j), plot_spectrogram(y_hat_spec.squeeze(0).cpu().numpy()), steps) val_err = val_err_tot / (j+1) sw.add_scalar("validation/mel_spec_error", val_err, steps) generator.train() steps += 1 scheduler_g.step() scheduler_d.step() if rank == 0: print('Time taken for epoch {} is {} sec\n'.format(epoch + 1, int(time.time() - start))) def main(): print('Initializing Training Process..') parser = argparse.ArgumentParser() parser.add_argument('--group_name', default=None) parser.add_argument('--input_wavs_dir', default='LJSpeech-1.1/wavs') parser.add_argument('--input_mels_dir', default='ft_dataset') parser.add_argument('--input_training_file', default='LJSpeech-1.1/training.txt') parser.add_argument('--input_validation_file', default='LJSpeech-1.1/validation.txt') parser.add_argument('--checkpoint_path', default='cp_hifigan') parser.add_argument('--config', default='') parser.add_argument('--training_epochs', default=3100, type=int) parser.add_argument('--stdout_interval', default=5, type=int) parser.add_argument('--checkpoint_interval', default=5000, type=int) parser.add_argument('--summary_interval', default=100, type=int) parser.add_argument('--validation_interval', default=1000, type=int) parser.add_argument('--fine_tuning', default=False, type=bool) a = parser.parse_args() with open(a.config) as f: data = f.read() json_config = json.loads(data) h = AttrDict(json_config) build_env(a.config, 'config.json', a.checkpoint_path) torch.manual_seed(h.seed) if torch.cuda.is_available(): torch.cuda.manual_seed(h.seed) h.num_gpus = torch.cuda.device_count() h.batch_size = int(h.batch_size / h.num_gpus) print('Batch size per GPU :', h.batch_size) else: pass if h.num_gpus > 1: mp.spawn(train, nprocs=h.num_gpus, args=(a, h,)) else: train(0, a, h) if __name__ == '__main__': main()