src/third_party/BigVGAN/train.py

# Copyright (c) 2024 NVIDIA CORPORATION.
#   Licensed under the MIT license.

# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
#   LICENSE is in incl_licenses directory.


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, MAX_WAV_VALUE

from bigvgan import BigVGAN
from discriminators import (
    MultiPeriodDiscriminator,
    MultiResolutionDiscriminator,
    MultiBandDiscriminator,
    MultiScaleSubbandCQTDiscriminator,
)
from loss import (
    feature_loss,
    generator_loss,
    discriminator_loss,
    MultiScaleMelSpectrogramLoss,
)

from utils import (
    plot_spectrogram,
    plot_spectrogram_clipped,
    scan_checkpoint,
    load_checkpoint,
    save_checkpoint,
    save_audio,
)
import torchaudio as ta
from pesq import pesq
from tqdm import tqdm
import auraloss

torch.backends.cudnn.benchmark = False


def train(rank, a, h):
    if h.num_gpus > 1:
        # initialize distributed
        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,
        )

    # Set seed and device
    torch.cuda.manual_seed(h.seed)
    torch.cuda.set_device(rank)
    device = torch.device(f"cuda:{rank:d}")

    # Define BigVGAN generator
    generator = BigVGAN(h).to(device)

    # Define discriminators. MPD is used by default
    mpd = MultiPeriodDiscriminator(h).to(device)

    # Define additional discriminators. BigVGAN-v1 uses UnivNet's MRD as default
    # New in BigVGAN-v2: option to switch to new discriminators: MultiBandDiscriminator / MultiScaleSubbandCQTDiscriminator
    if h.get("use_mbd_instead_of_mrd", False):  # Switch to MBD
        print(
            "[INFO] using MultiBandDiscriminator of BigVGAN-v2 instead of MultiResolutionDiscriminator"
        )
        # Variable name is kept as "mrd" for backward compatibility & minimal code change
        mrd = MultiBandDiscriminator(h).to(device)
    elif h.get("use_cqtd_instead_of_mrd", False):  # Switch to CQTD
        print(
            "[INFO] using MultiScaleSubbandCQTDiscriminator of BigVGAN-v2 instead of MultiResolutionDiscriminator"
        )
        mrd = MultiScaleSubbandCQTDiscriminator(h).to(device)
    else:  # Fallback to original MRD in BigVGAN-v1
        mrd = MultiResolutionDiscriminator(h).to(device)

    # New in BigVGAN-v2: option to switch to multi-scale L1 mel loss
    if h.get("use_multiscale_melloss", False):
        print(
            "[INFO] using multi-scale Mel l1 loss of BigVGAN-v2 instead of the original single-scale loss"
        )
        fn_mel_loss_multiscale = MultiScaleMelSpectrogramLoss(
            sampling_rate=h.sampling_rate
        )  # NOTE: accepts waveform as input
    else:
        fn_mel_loss_singlescale = F.l1_loss

    # Print the model & number of parameters, and create or scan the latest checkpoint from checkpoints directory
    if rank == 0:
        print(generator)
        print(mpd)
        print(mrd)
        print(f"Generator params: {sum(p.numel() for p in generator.parameters())}")
        print(f"Discriminator mpd params: {sum(p.numel() for p in mpd.parameters())}")
        print(f"Discriminator mrd params: {sum(p.numel() for p in mrd.parameters())}")
        os.makedirs(a.checkpoint_path, exist_ok=True)
        print(f"Checkpoints directory: {a.checkpoint_path}")

    if os.path.isdir(a.checkpoint_path):
        # New in v2.1: If the step prefix pattern-based checkpoints are not found, also check for renamed files in Hugging Face Hub to resume training
        cp_g = scan_checkpoint(
            a.checkpoint_path, prefix="g_", renamed_file="bigvgan_generator.pt"
        )
        cp_do = scan_checkpoint(
            a.checkpoint_path,
            prefix="do_",
            renamed_file="bigvgan_discriminator_optimizer.pt",
        )

    # Load the latest checkpoint if exists
    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"])
        mrd.load_state_dict(state_dict_do["mrd"])
        steps = state_dict_do["steps"] + 1
        last_epoch = state_dict_do["epoch"]

    # Initialize DDP, optimizers, and schedulers
    if h.num_gpus > 1:
        generator = DistributedDataParallel(generator, device_ids=[rank]).to(device)
        mpd = DistributedDataParallel(mpd, device_ids=[rank]).to(device)
        mrd = DistributedDataParallel(mrd, 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(mrd.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
    )

    # Define training and validation datasets

    """
    unseen_validation_filelist will contain sample filepaths outside the seen training & validation dataset
    Example: trained on LibriTTS, validate on VCTK
    """
    training_filelist, validation_filelist, list_unseen_validation_filelist = (
        get_dataset_filelist(a)
    )

    trainset = MelDataset(
        training_filelist,
        h,
        h.segment_size,
        h.n_fft,
        h.num_mels,
        h.hop_size,
        h.win_size,
        h.sampling_rate,
        h.fmin,
        h.fmax,
        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,
        is_seen=True,
    )

    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,
            h.segment_size,
            h.n_fft,
            h.num_mels,
            h.hop_size,
            h.win_size,
            h.sampling_rate,
            h.fmin,
            h.fmax,
            False,
            False,
            fmax_loss=h.fmax_for_loss,
            device=device,
            fine_tuning=a.fine_tuning,
            base_mels_path=a.input_mels_dir,
            is_seen=True,
        )
        validation_loader = DataLoader(
            validset,
            num_workers=1,
            shuffle=False,
            sampler=None,
            batch_size=1,
            pin_memory=True,
            drop_last=True,
        )

        list_unseen_validset = []
        list_unseen_validation_loader = []
        for i in range(len(list_unseen_validation_filelist)):
            unseen_validset = MelDataset(
                list_unseen_validation_filelist[i],
                h,
                h.segment_size,
                h.n_fft,
                h.num_mels,
                h.hop_size,
                h.win_size,
                h.sampling_rate,
                h.fmin,
                h.fmax,
                False,
                False,
                fmax_loss=h.fmax_for_loss,
                device=device,
                fine_tuning=a.fine_tuning,
                base_mels_path=a.input_mels_dir,
                is_seen=False,
            )
            unseen_validation_loader = DataLoader(
                unseen_validset,
                num_workers=1,
                shuffle=False,
                sampler=None,
                batch_size=1,
                pin_memory=True,
                drop_last=True,
            )
            list_unseen_validset.append(unseen_validset)
            list_unseen_validation_loader.append(unseen_validation_loader)

        # Tensorboard logger
        sw = SummaryWriter(os.path.join(a.checkpoint_path, "logs"))
        if a.save_audio:  # Also save audio to disk if --save_audio is set to True
            os.makedirs(os.path.join(a.checkpoint_path, "samples"), exist_ok=True)

    """
    Validation loop, "mode" parameter is automatically defined as (seen or unseen)_(name of the dataset).
    If the name of the dataset contains "nonspeech", it skips PESQ calculation to prevent errors 
    """

    def validate(rank, a, h, loader, mode="seen"):
        assert rank == 0, "validate should only run on rank=0"
        generator.eval()
        torch.cuda.empty_cache()

        val_err_tot = 0
        val_pesq_tot = 0
        val_mrstft_tot = 0

        # Modules for evaluation metrics
        pesq_resampler = ta.transforms.Resample(h.sampling_rate, 16000).cuda()
        loss_mrstft = auraloss.freq.MultiResolutionSTFTLoss(device="cuda")

        if a.save_audio:  # Also save audio to disk if --save_audio is set to True
            os.makedirs(
                os.path.join(a.checkpoint_path, "samples", f"gt_{mode}"),
                exist_ok=True,
            )
            os.makedirs(
                os.path.join(a.checkpoint_path, "samples", f"{mode}_{steps:08d}"),
                exist_ok=True,
            )

        with torch.no_grad():
            print(f"step {steps} {mode} speaker validation...")

            # Loop over validation set and compute metrics
            for j, batch in enumerate(tqdm(loader)):
                x, y, _, y_mel = batch
                y = y.to(device)
                if hasattr(generator, "module"):
                    y_g_hat = generator.module(x.to(device))
                else:
                    y_g_hat = generator(x.to(device))
                y_mel = 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,
                )
                min_t = min(y_mel.size(-1), y_g_hat_mel.size(-1))
                val_err_tot += F.l1_loss(y_mel[...,:min_t], y_g_hat_mel[...,:min_t]).item()

                # PESQ calculation. only evaluate PESQ if it's speech signal (nonspeech PESQ will error out)
                if (
                    not "nonspeech" in mode
                ):  # Skips if the name of dataset (in mode string) contains "nonspeech"

                    # Resample to 16000 for pesq
                    y_16k = pesq_resampler(y)
                    y_g_hat_16k = pesq_resampler(y_g_hat.squeeze(1))
                    y_int_16k = (y_16k[0] * MAX_WAV_VALUE).short().cpu().numpy()
                    y_g_hat_int_16k = (
                        (y_g_hat_16k[0] * MAX_WAV_VALUE).short().cpu().numpy()
                    )
                    val_pesq_tot += pesq(16000, y_int_16k, y_g_hat_int_16k, "wb")

                # MRSTFT calculation
                min_t = min(y.size(-1), y_g_hat.size(-1))
                val_mrstft_tot += loss_mrstft(y_g_hat[...,:min_t], y[...,:min_t]).item()

                # Log audio and figures to Tensorboard
                if j % a.eval_subsample == 0:  # Subsample every nth from validation set
                    if steps >= 0:
                        sw.add_audio(f"gt_{mode}/y_{j}", y[0], steps, h.sampling_rate)
                        if (
                            a.save_audio
                        ):  # Also save audio to disk if --save_audio is set to True
                            save_audio(
                                y[0],
                                os.path.join(
                                    a.checkpoint_path,
                                    "samples",
                                    f"gt_{mode}",
                                    f"{j:04d}.wav",
                                ),
                                h.sampling_rate,
                            )
                        sw.add_figure(
                            f"gt_{mode}/y_spec_{j}",
                            plot_spectrogram(x[0]),
                            steps,
                        )

                    sw.add_audio(
                        f"generated_{mode}/y_hat_{j}",
                        y_g_hat[0],
                        steps,
                        h.sampling_rate,
                    )
                    if (
                        a.save_audio
                    ):  # Also save audio to disk if --save_audio is set to True
                        save_audio(
                            y_g_hat[0, 0],
                            os.path.join(
                                a.checkpoint_path,
                                "samples",
                                f"{mode}_{steps:08d}",
                                f"{j:04d}.wav",
                            ),
                            h.sampling_rate,
                        )
                    # Spectrogram of synthesized audio
                    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(
                        f"generated_{mode}/y_hat_spec_{j}",
                        plot_spectrogram(y_hat_spec.squeeze(0).cpu().numpy()),
                        steps,
                    )

                    """
                    Visualization of spectrogram difference between GT and synthesized audio, difference higher than 1 is clipped for better visualization.
                    """
                    spec_delta = torch.clamp(
                        torch.abs(x[0] - y_hat_spec.squeeze(0).cpu()),
                        min=1e-6,
                        max=1.0,
                    )
                    sw.add_figure(
                        f"delta_dclip1_{mode}/spec_{j}",
                        plot_spectrogram_clipped(spec_delta.numpy(), clip_max=1.0),
                        steps,
                    )

            val_err = val_err_tot / (j + 1)
            val_pesq = val_pesq_tot / (j + 1)
            val_mrstft = val_mrstft_tot / (j + 1)
            # Log evaluation metrics to Tensorboard
            sw.add_scalar(f"validation_{mode}/mel_spec_error", val_err, steps)
            sw.add_scalar(f"validation_{mode}/pesq", val_pesq, steps)
            sw.add_scalar(f"validation_{mode}/mrstft", val_mrstft, steps)

        generator.train()

    # If the checkpoint is loaded, start with validation loop
    if steps != 0 and rank == 0 and not a.debug:
        if not a.skip_seen:
            validate(
                rank,
                a,
                h,
                validation_loader,
                mode=f"seen_{train_loader.dataset.name}",
            )
        for i in range(len(list_unseen_validation_loader)):
            validate(
                rank,
                a,
                h,
                list_unseen_validation_loader[i],
                mode=f"unseen_{list_unseen_validation_loader[i].dataset.name}",
            )
    # Exit the script if --evaluate is set to True
    if a.evaluate:
        exit()

    # Main training loop
    generator.train()
    mpd.train()
    mrd.train()
    for epoch in range(max(0, last_epoch), a.training_epochs):
        if rank == 0:
            start = time.time()
            print(f"Epoch: {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 = 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)

            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
            )

            # MRD
            y_ds_hat_r, y_ds_hat_g, _, _ = mrd(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

            # Set clip_grad_norm value
            clip_grad_norm = h.get("clip_grad_norm", 1000.0)  # Default to 1000

            # Whether to freeze D for initial training steps
            if steps >= a.freeze_step:
                loss_disc_all.backward()
                grad_norm_mpd = torch.nn.utils.clip_grad_norm_(
                    mpd.parameters(), clip_grad_norm
                )
                grad_norm_mrd = torch.nn.utils.clip_grad_norm_(
                    mrd.parameters(), clip_grad_norm
                )
                optim_d.step()
            else:
                print(
                    f"[WARNING] skipping D training for the first {a.freeze_step} steps"
                )
                grad_norm_mpd = 0.0
                grad_norm_mrd = 0.0

            # Generator
            optim_g.zero_grad()

            # L1 Mel-Spectrogram Loss
            lambda_melloss = h.get(
                "lambda_melloss", 45.0
            )  # Defaults to 45 in BigVGAN-v1 if not set
            if h.get("use_multiscale_melloss", False):  # uses wav <y, y_g_hat> for loss
                loss_mel = fn_mel_loss_multiscale(y, y_g_hat) * lambda_melloss
            else:  # Uses mel <y_mel, y_g_hat_mel> for loss
                loss_mel = fn_mel_loss_singlescale(y_mel, y_g_hat_mel) * lambda_melloss

            # MPD loss
            y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = mpd(y, y_g_hat)
            loss_fm_f = feature_loss(fmap_f_r, fmap_f_g)
            loss_gen_f, losses_gen_f = generator_loss(y_df_hat_g)

            # MRD loss
            y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = mrd(y, y_g_hat)
            loss_fm_s = feature_loss(fmap_s_r, fmap_s_g)
            loss_gen_s, losses_gen_s = generator_loss(y_ds_hat_g)

            if steps >= a.freeze_step:
                loss_gen_all = (
                    loss_gen_s + loss_gen_f + loss_fm_s + loss_fm_f + loss_mel
                )
            else:
                print(
                    f"[WARNING] using regression loss only for G for the first {a.freeze_step} steps"
                )
                loss_gen_all = loss_mel

            loss_gen_all.backward()
            grad_norm_g = torch.nn.utils.clip_grad_norm_(
                generator.parameters(), clip_grad_norm
            )
            optim_g.step()

            if rank == 0:
                # STDOUT logging
                if steps % a.stdout_interval == 0:
                    mel_error = (
                        loss_mel.item() / lambda_melloss
                    )  # Log training mel regression loss to stdout
                    print(
                        f"Steps: {steps:d}, "
                        f"Gen Loss Total: {loss_gen_all:4.3f}, "
                        f"Mel Error: {mel_error:4.3f}, "
                        f"s/b: {time.time() - start_b:4.3f} "
                        f"lr: {optim_g.param_groups[0]['lr']:4.7f} "
                        f"grad_norm_g: {grad_norm_g:4.3f}"
                    )

                # Checkpointing
                if steps % a.checkpoint_interval == 0 and steps != 0:
                    checkpoint_path = f"{a.checkpoint_path}/g_{steps:08d}"
                    save_checkpoint(
                        checkpoint_path,
                        {
                            "generator": (
                                generator.module if h.num_gpus > 1 else generator
                            ).state_dict()
                        },
                    )
                    checkpoint_path = f"{a.checkpoint_path}/do_{steps:08d}"
                    save_checkpoint(
                        checkpoint_path,
                        {
                            "mpd": (mpd.module if h.num_gpus > 1 else mpd).state_dict(),
                            "mrd": (mrd.module if h.num_gpus > 1 else mrd).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:
                    mel_error = (
                        loss_mel.item() / lambda_melloss
                    )  # Log training mel regression loss to tensorboard
                    sw.add_scalar("training/gen_loss_total", loss_gen_all.item(), steps)
                    sw.add_scalar("training/mel_spec_error", mel_error, steps)
                    sw.add_scalar("training/fm_loss_mpd", loss_fm_f.item(), steps)
                    sw.add_scalar("training/gen_loss_mpd", loss_gen_f.item(), steps)
                    sw.add_scalar("training/disc_loss_mpd", loss_disc_f.item(), steps)
                    sw.add_scalar("training/grad_norm_mpd", grad_norm_mpd, steps)
                    sw.add_scalar("training/fm_loss_mrd", loss_fm_s.item(), steps)
                    sw.add_scalar("training/gen_loss_mrd", loss_gen_s.item(), steps)
                    sw.add_scalar("training/disc_loss_mrd", loss_disc_s.item(), steps)
                    sw.add_scalar("training/grad_norm_mrd", grad_norm_mrd, steps)
                    sw.add_scalar("training/grad_norm_g", grad_norm_g, steps)
                    sw.add_scalar(
                        "training/learning_rate_d", scheduler_d.get_last_lr()[0], steps
                    )
                    sw.add_scalar(
                        "training/learning_rate_g", scheduler_g.get_last_lr()[0], steps
                    )
                    sw.add_scalar("training/epoch", epoch + 1, steps)

                # Validation
                if steps % a.validation_interval == 0:
                    # Plot training input x so far used
                    for i_x in range(x.shape[0]):
                        sw.add_figure(
                            f"training_input/x_{i_x}",
                            plot_spectrogram(x[i_x].cpu()),
                            steps,
                        )
                        sw.add_audio(
                            f"training_input/y_{i_x}",
                            y[i_x][0],
                            steps,
                            h.sampling_rate,
                        )

                    # Seen and unseen speakers validation loops
                    if not a.debug and steps != 0:
                        validate(
                            rank,
                            a,
                            h,
                            validation_loader,
                            mode=f"seen_{train_loader.dataset.name}",
                        )
                        for i in range(len(list_unseen_validation_loader)):
                            validate(
                                rank,
                                a,
                                h,
                                list_unseen_validation_loader[i],
                                mode=f"unseen_{list_unseen_validation_loader[i].dataset.name}",
                            )
            steps += 1

            # BigVGAN-v2 learning rate scheduler is changed from epoch-level to step-level
            scheduler_g.step()
            scheduler_d.step()

        if rank == 0:
            print(
                f"Time taken for epoch {epoch + 1} is {int(time.time() - start)} sec\n"
            )


def main():
    print("Initializing Training Process..")

    parser = argparse.ArgumentParser()

    parser.add_argument("--group_name", default=None)

    parser.add_argument("--input_wavs_dir", default="LibriTTS")
    parser.add_argument("--input_mels_dir", default="ft_dataset")
    parser.add_argument(
        "--input_training_file", default="tests/LibriTTS/train-full.txt"
    )
    parser.add_argument(
        "--input_validation_file", default="tests/LibriTTS/val-full.txt"
    )

    parser.add_argument(
        "--list_input_unseen_wavs_dir",
        nargs="+",
        default=["tests/LibriTTS", "tests/LibriTTS"],
    )
    parser.add_argument(
        "--list_input_unseen_validation_file",
        nargs="+",
        default=["tests/LibriTTS/dev-clean.txt", "tests/LibriTTS/dev-other.txt"],
    )

    parser.add_argument("--checkpoint_path", default="exp/bigvgan")
    parser.add_argument("--config", default="")

    parser.add_argument("--training_epochs", default=100000, type=int)
    parser.add_argument("--stdout_interval", default=5, type=int)
    parser.add_argument("--checkpoint_interval", default=50000, type=int)
    parser.add_argument("--summary_interval", default=100, type=int)
    parser.add_argument("--validation_interval", default=50000, type=int)

    parser.add_argument(
        "--freeze_step",
        default=0,
        type=int,
        help="freeze D for the first specified steps. G only uses regression loss for these steps.",
    )

    parser.add_argument("--fine_tuning", default=False, type=bool)

    parser.add_argument(
        "--debug",
        default=False,
        type=bool,
        help="debug mode. skips validation loop throughout training",
    )
    parser.add_argument(
        "--evaluate",
        default=False,
        type=bool,
        help="only run evaluation from checkpoint and exit",
    )
    parser.add_argument(
        "--eval_subsample",
        default=5,
        type=int,
        help="subsampling during evaluation loop",
    )
    parser.add_argument(
        "--skip_seen",
        default=False,
        type=bool,
        help="skip seen dataset. useful for test set inference",
    )
    parser.add_argument(
        "--save_audio",
        default=False,
        type=bool,
        help="save audio of test set inference to disk",
    )

    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(f"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()