hifigan/train.py

import argparse
import itertools
import json
import os
import time

import torch
import torch.multiprocessing as mp
import torch.nn.functional as F
from fastprogress import master_bar, progress_bar
from torch.cuda.amp.grad_scaler import GradScaler
from torch.distributed import init_process_group
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data import DataLoader, DistributedSampler
from torch.utils.tensorboard import SummaryWriter

from .meldataset import (LogMelSpectrogram, MelDataset, get_dataset_filelist,
                         mel_spectrogram)
from .models import (Generator, MultiPeriodDiscriminator,
                     MultiScaleDiscriminator, discriminator_loss, feature_loss,
                     generator_loss)
from .utils import (AttrDict, build_env, load_checkpoint, plot_spectrogram,
                    save_checkpoint, scan_checkpoint)

torch.backends.cudnn.benchmark = True
USE_ALT_MELCALC = 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']
        print(f"Restored checkpoint from {cp_g} and {cp_do}")

    if h.num_gpus > 1:
        print("Multi-gpu detected")
        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)
    if a.fp16:
        scaler_g = GradScaler()
        scaler_d = GradScaler()

    train_df, valid_df = get_dataset_filelist(a)

    trainset = MelDataset(train_df, 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,
                          audio_root_path=a.audio_root_path, feat_root_path=a.feature_root_path, 
                          use_alt_melcalc=USE_ALT_MELCALC)

    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,
                              persistent_workers=True,
                              drop_last=True)

    alt_melspec = LogMelSpectrogram(h.n_fft, h.num_mels, h.sampling_rate, h.hop_size, h.win_size, h.fmin, h.fmax).to(device)

    if rank == 0:
        validset = MelDataset(valid_df, 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,
                              audio_root_path=a.audio_root_path, feat_root_path=a.feature_root_path, 
                              use_alt_melcalc=USE_ALT_MELCALC)
        validation_loader = DataLoader(validset, num_workers=1, shuffle=False,
                                       sampler=None,
                                       batch_size=1,
                                       pin_memory=True,
                                       persistent_workers=True,
                                       drop_last=True)

        sw = SummaryWriter(os.path.join(a.checkpoint_path, 'logs'))

    generator.train()
    mpd.train()
    msd.train()
    
    if rank == 0: mb = master_bar(range(max(0, last_epoch), a.training_epochs))
    else: mb = range(max(0, last_epoch), a.training_epochs)

    for epoch in mb:
        if rank == 0:
            start = time.time()
            mb.write("Epoch: {}".format(epoch+1))

        if h.num_gpus > 1:
            train_sampler.set_epoch(epoch)

        if rank == 0: pb = progress_bar(enumerate(train_loader), total=len(train_loader), parent=mb)
        else: pb = enumerate(train_loader)
        

        for i, batch in pb:
            if rank == 0:
                start_b = time.time()
            x, y, _, y_mel = batch
            x = x.to(device, non_blocking=True)
            y = y.to(device, non_blocking=True)
            y_mel = y_mel.to(device, non_blocking=True)
            y = y.unsqueeze(1)
            
            with torch.cuda.amp.autocast(enabled=a.fp16):
                y_g_hat = generator(x)
                if USE_ALT_MELCALC:
                    y_g_hat_mel = alt_melspec(y_g_hat.squeeze(1))
                else:
                    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)
            # print(x.shape, y_g_hat.shape, y_g_hat_mel.shape, y_mel.shape, y.shape)
            optim_d.zero_grad()

            with torch.cuda.amp.autocast(enabled=a.fp16):
                # 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

            if a.fp16: 
                scaler_d.scale(loss_disc_all).backward()
                scaler_d.step(optim_d)
                scaler_d.update()
            else: 
                loss_disc_all.backward()
                optim_d.step()

            # Generator
            optim_g.zero_grad()

            with torch.cuda.amp.autocast(enabled=a.fp16):
                # 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

            if a.fp16:
                scaler_g.scale(loss_gen_all).backward()
                scaler_g.step(optim_g)
                scaler_g.update()
            else:
                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()

                    mb.write('Steps : {:,d}, Gen Loss Total : {:4.3f}, Mel-Spec. Error : {:4.3f}, sec/batch : {:4.3f}, peak mem: {:5.2f}GB'. \
                            format(steps, loss_gen_all, mel_error, time.time() - start_b, torch.cuda.max_memory_allocated()/1e9))
                    mb.child.comment = "Steps : {:,d}, Gen Loss Total : {:4.3f}, Mel-Spec. Error : {:4.3f}". \
                            format(steps, loss_gen_all, mel_error)
                    

                # checkpointing
                if steps % a.checkpoint_interval == 0 and steps != 0:
                    checkpoint_path = "{}/g_{:08d}.pt".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}.pt".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)
                    sw.add_scalar("training/disc_loss_total", loss_disc_all, 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 progress_bar(enumerate(validation_loader), total=len(validation_loader), parent=mb):
                            x, y, _, y_mel = batch
                            y_g_hat = generator(x.to(device))
                            y_mel = y_mel.to(device, non_blocking=True)
                            if USE_ALT_MELCALC:
                                y_g_hat_mel = alt_melspec(y_g_hat.squeeze(1))
                                if y_g_hat_mel.shape[-1] != y_mel.shape[-1]:
                                    # pad it 
                                    n_pad = h.hop_size 
                                    y_g_hat = F.pad(y_g_hat, (n_pad//2, n_pad - n_pad//2))
                                    y_g_hat_mel = alt_melspec(y_g_hat.squeeze(1))
                            else:
                                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)
                            #print('valid', x.shape, y_g_hat.shape, y_g_hat_mel.shape, y_mel.shape, y.shape)
                            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)
                                if USE_ALT_MELCALC:
                                    y_hat_spec = alt_melspec(y_g_hat.squeeze(1))
                                else:
                                    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_for_loss)

                                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)
                        mb.write(f"validation run complete at {steps:,d} steps. validation mel spec error: {val_err:5.4f}")

                    generator.train()
                    sw.add_scalar("memory/max_allocated_gb", torch.cuda.max_memory_allocated()/1e9, steps)
                    sw.add_scalar("memory/max_reserved_gb", torch.cuda.max_memory_reserved()/1e9, steps)
                    torch.cuda.reset_peak_memory_stats()
                    torch.cuda.reset_accumulated_memory_stats()

            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('--audio_root_path', required=True)
    parser.add_argument('--feature_root_path', required=True)
    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=1500, 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=25, type=int)
    parser.add_argument('--validation_interval', default=1000, type=int)
    parser.add_argument('--fp16', default=False, type=bool)
    parser.add_argument('--fine_tuning', action='store_true')

    a = parser.parse_args()
    print(a)
    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()