Example #1
0
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)
    torch.cuda.set_device(rank)
    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)))
Example #2
0
def train(rank, a, h, resume_run_id=None):
    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)

    cp_g = None
    cp_do = None
    if resume_run_id:
        restored_g = wandb.restore("g_latest")
        cp_g = restored_g.name
        restored_do = wandb.restore("do_latest")
        cp_do = restored_do.name

    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,
    )
    print(f"train dataset size:{len(trainset)}")
    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,
        )
        print(f"valid dataset size:{len(validset)}")
        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))
                    wandb.log(
                        {
                            "loss/Gen Loss Total": loss_gen_all,
                            "loss/Mel-Spec. Error": mel_error,
                        },
                        step=steps,
                    )

                # checkpointing
                if steps % a.checkpoint_interval == 0 and steps != 0:
                    # generator
                    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_name = "g_{:08d}".format(steps)
                    wandb.save(checkpoint_name)
                    # also save as latest
                    checkpoint_path = "{}/g_latest".format(a.checkpoint_path)
                    save_checkpoint(
                        checkpoint_path,
                        {
                            "generator": (generator.module if h.num_gpus > 1
                                          else generator).state_dict()
                        },
                    )
                    wandb.save("g_latest")
                    # discriminator
                    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,
                        },
                    )
                    checkpoint_name = "do_{:08d}".format(steps)
                    wandb.save(checkpoint_name)
                    # also save as latest
                    checkpoint_path = "{}/do_latest".format(a.checkpoint_path)
                    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,
                        },
                    )
                    wandb.save("do_latest")

                # 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():
                        samples_orig = []
                        samples_pred = []
                        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,
                                    )

                                    # log orig audio to wandb
                                    orig_audio = y.squeeze().cpu()
                                    samples_orig.append(
                                        wandb.Audio(
                                            orig_audio,
                                            caption=f"sample {i}",
                                            sample_rate=h.sampling_rate,
                                        ))

                                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,
                                )

                                # log pred audio to wandb
                                pred_audio = y_g_hat.squeeze().cpu()
                                samples_pred.append(
                                    wandb.Audio(
                                        pred_audio,
                                        caption=f"sample {i}",
                                        sample_rate=h.sampling_rate,
                                    ))

                        val_err = val_err_tot / (j + 1)
                        sw.add_scalar("validation/mel_spec_error", val_err,
                                      steps)

                        # log audios to wandb
                        wandb.log(
                            {
                                "audio/generated": samples_pred,
                            },
                            step=steps,
                        )
                        if steps == 0:
                            wandb.log(
                                {
                                    "audio/original": samples_orig,
                                },
                                step=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)))