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.logs_path)) 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("--logs_path", default="") 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()