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train_fix/bert_genV0.py

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+import torch
+from torch.utils.data import DataLoader
+from multiprocessing import Pool
+import commons
+import utils
+from data_utils import TextAudioSpeakerLoader, TextAudioSpeakerCollate
+from tqdm import tqdm
+import warnings
+
+from text import cleaned_text_to_sequence, get_bert
+
+config_path = 'configs/base.json'
+hps = utils.get_hparams_from_file(config_path)
+
+def process_line(line):
+    _id, spk, language_str, text, phones, tone, word2ph = line.strip().split("|")
+    phone = phones.split(" ")
+    tone = [int(i) for i in tone.split(" ")]
+    word2ph = [int(i) for i in word2ph.split(" ")]
+    w2pho = [i for i in word2ph]
+    word2ph = [i for i in word2ph]
+    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
+
+    if hps.data.add_blank:
+        phone = commons.intersperse(phone, 0)
+        tone = commons.intersperse(tone, 0)
+        language = commons.intersperse(language, 0)
+        for i in range(len(word2ph)):
+            word2ph[i] = word2ph[i] * 2
+        word2ph[0] += 1
+    wav_path = f'{_id}'
+
+    bert_path = wav_path.replace(".wav", ".bert.pt")
+    try:
+        bert = torch.load(bert_path)
+        assert bert.shape[-1] == len(phone)
+    except:
+        bert = get_bert(text, word2ph, language_str)
+        assert bert.shape[-1] == len(phone)
+        torch.save(bert, bert_path)
+
+
+if __name__ == '__main__':
+    lines = []
+    with open(hps.data.training_files, encoding='utf-8' ) as f:
+        lines.extend(f.readlines())
+
+   # with open(hps.data.validation_files, encoding='utf-8' ) as f:
+       # lines.extend(f.readlines())
+
+    with Pool(processes=2) as pool: #A100 40GB suitable config,if coom,please decrease the processess number.
+        for _ in tqdm(pool.imap_unordered(process_line, lines)):
+            pass

train_fix/text/__init__.py

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+from text.symbols import *
+
+
+_symbol_to_id = {s: i for i, s in enumerate(symbols)}
+
+
+def cleaned_text_to_sequence(cleaned_text, tones, language):
+    """Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
+    Args:
+      text: string to convert to a sequence
+    Returns:
+      List of integers corresponding to the symbols in the text
+    """
+    phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
+    tone_start = language_tone_start_map[language]
+    tones = [i + tone_start for i in tones]
+    lang_id = language_id_map[language]
+    lang_ids = [lang_id for i in phones]
+    return phones, tones, lang_ids
+
+
+def get_bert(norm_text, word2ph, language, device=None):
+    from .chinese_bert import get_bert_feature as zh_bert
+    from .english_bert_mock import get_bert_feature as en_bert
+    from .japanese_bert import get_bert_feature as jp_bert
+
+    lang_bert_func_map = {"ZH": zh_bert, "EN": en_bert, "JP": jp_bert}
+    bert = lang_bert_func_map[language](norm_text, word2ph, device)
+    return bert

train_fix/train_ms.py

@@ -0,0 +1,607 @@
+# flake8: noqa: E402
+
+import os
+import torch
+from torch.nn import functional as F
+from torch.utils.data import DataLoader
+from torch.utils.tensorboard import SummaryWriter
+import torch.multiprocessing as mp
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.cuda.amp import autocast, GradScaler
+from tqdm import tqdm
+import logging
+
+logging.getLogger("numba").setLevel(logging.WARNING)
+import commons
+import utils
+from data_utils import (
+    TextAudioSpeakerLoader,
+    TextAudioSpeakerCollate,
+    DistributedBucketSampler,
+)
+from models import (
+    SynthesizerTrn,
+    MultiPeriodDiscriminator,
+    DurationDiscriminator,
+)
+from losses import generator_loss, discriminator_loss, feature_loss, kl_loss
+from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
+from text.symbols import symbols
+
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = (
+    True  # If encontered training problem,please try to disable TF32.
+)
+torch.set_float32_matmul_precision("medium")
+torch.backends.cudnn.benchmark = True
+torch.backends.cuda.sdp_kernel("flash")
+torch.backends.cuda.enable_flash_sdp(True)
+torch.backends.cuda.enable_mem_efficient_sdp(
+    True
+)  # Not available if torch version is lower than 2.0
+torch.backends.cuda.enable_math_sdp(True)
+global_step = 0
+
+
+def main():
+    """Assume Single Node Multi GPUs Training Only"""
+    assert torch.cuda.is_available(), "CPU training is not allowed."
+
+    n_gpus = torch.cuda.device_count()
+    os.environ['MASTER_ADDR'] = 'localhost'
+    os.environ['MASTER_PORT'] = '65280'
+
+    hps = utils.get_hparams()
+    mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))
+
+def run(rank, n_gpus, hps):
+    dist.init_process_group(
+        backend="gloo",
+        init_method="env://",  # Due to some training problem,we proposed to use gloo instead of nccl.
+        world_size=n_gpus, 
+        rank=rank
+        )  # Use torchrun instead of mp.spawn
+    # rank = dist.get_rank()
+    # n_gpus = dist.get_world_size()
+    #hps = utils.get_hparams()
+    torch.manual_seed(hps.train.seed)
+    torch.cuda.set_device(rank)
+    global global_step
+    if rank == 0:
+        logger = utils.get_logger(hps.model_dir)
+        logger.info(hps)
+        utils.check_git_hash(hps.model_dir)
+        writer = SummaryWriter(log_dir=hps.model_dir)
+        writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
+    train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
+    train_sampler = DistributedBucketSampler(
+        train_dataset,
+        hps.train.batch_size,
+        [32, 300, 400, 500, 600, 700, 800, 900, 1000],
+        num_replicas=n_gpus,
+        rank=rank,
+        shuffle=True,
+    )
+    collate_fn = TextAudioSpeakerCollate()
+    train_loader = DataLoader(
+        train_dataset,
+        num_workers=16,
+        shuffle=False,
+        pin_memory=True,
+        collate_fn=collate_fn,
+        batch_sampler=train_sampler,
+        persistent_workers=True,
+        prefetch_factor=4,
+    )  # DataLoader config could be adjusted.
+    if rank == 0:
+        eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
+        eval_loader = DataLoader(
+            eval_dataset,
+            num_workers=0,
+            shuffle=False,
+            batch_size=1,
+            pin_memory=True,
+            drop_last=False,
+            collate_fn=collate_fn,
+        )
+    if (
+        "use_noise_scaled_mas" in hps.model.keys()
+        and hps.model.use_noise_scaled_mas is True
+    ):
+        print("Using noise scaled MAS for VITS2")
+        mas_noise_scale_initial = 0.01
+        noise_scale_delta = 2e-6
+    else:
+        print("Using normal MAS for VITS1")
+        mas_noise_scale_initial = 0.0
+        noise_scale_delta = 0.0
+    if (
+        "use_duration_discriminator" in hps.model.keys()
+        and hps.model.use_duration_discriminator is True
+    ):
+        print("Using duration discriminator for VITS2")
+        net_dur_disc = DurationDiscriminator(
+            hps.model.hidden_channels,
+            hps.model.hidden_channels,
+            3,
+            0.1,
+            gin_channels=hps.model.gin_channels if hps.data.n_speakers != 0 else 0,
+        ).cuda(rank)
+    if (
+        "use_spk_conditioned_encoder" in hps.model.keys()
+        and hps.model.use_spk_conditioned_encoder is True
+    ):
+        if hps.data.n_speakers == 0:
+            raise ValueError(
+                "n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model"
+            )
+    else:
+        print("Using normal encoder for VITS1")
+
+    net_g = SynthesizerTrn(
+        len(symbols),
+        hps.data.filter_length // 2 + 1,
+        hps.train.segment_size // hps.data.hop_length,
+        n_speakers=hps.data.n_speakers,
+        mas_noise_scale_initial=mas_noise_scale_initial,
+        noise_scale_delta=noise_scale_delta,
+        **hps.model,
+    ).cuda(rank)
+
+    net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
+    optim_g = torch.optim.AdamW(
+        filter(lambda p: p.requires_grad, net_g.parameters()),
+        hps.train.learning_rate,
+        betas=hps.train.betas,
+        eps=hps.train.eps,
+    )
+    optim_d = torch.optim.AdamW(
+        net_d.parameters(),
+        hps.train.learning_rate,
+        betas=hps.train.betas,
+        eps=hps.train.eps,
+    )
+    if net_dur_disc is not None:
+        optim_dur_disc = torch.optim.AdamW(
+            net_dur_disc.parameters(),
+            hps.train.learning_rate,
+            betas=hps.train.betas,
+            eps=hps.train.eps,
+        )
+    else:
+        optim_dur_disc = None
+    net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True)
+    net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
+    if net_dur_disc is not None:
+        net_dur_disc = DDP(net_dur_disc, device_ids=[rank], find_unused_parameters=True)
+    #dur_resume_lr=0.0003
+    try:
+        if net_dur_disc is not None:
+            _, _, dur_resume_lr, epoch_str = utils.load_checkpoint(
+                utils.latest_checkpoint_path(hps.model_dir, "DUR_*.pth"),
+                net_dur_disc,
+                optim_dur_disc,
+                skip_optimizer=hps.train.skip_optimizer
+                if "skip_optimizer" in hps.train
+                else True,
+            )
+            _, optim_g, g_resume_lr, epoch_str = utils.load_checkpoint(
+                utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"),
+                net_g,
+                optim_g,
+                skip_optimizer=hps.train.skip_optimizer
+                if "skip_optimizer" in hps.train
+                else True,
+            )
+            _, optim_d, d_resume_lr, epoch_str = utils.load_checkpoint(
+                utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"),
+                net_d,
+                optim_d,
+                skip_optimizer=hps.train.skip_optimizer
+                if "skip_optimizer" in hps.train
+                else True,
+            )
+            if not optim_g.param_groups[0].get("initial_lr"):
+                optim_g.param_groups[0]["initial_lr"] = g_resume_lr
+            if not optim_d.param_groups[0].get("initial_lr"):
+                optim_d.param_groups[0]["initial_lr"] = d_resume_lr
+
+        epoch_str = max(epoch_str, 1)
+        global_step = (epoch_str - 1) * len(train_loader)
+    except Exception as e:
+        print(e)
+        epoch_str = 1
+        global_step = 0
+
+    scheduler_g = torch.optim.lr_scheduler.ExponentialLR(
+        optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
+    )
+    scheduler_d = torch.optim.lr_scheduler.ExponentialLR(
+        optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
+    )
+    if net_dur_disc is not None:
+        if not optim_dur_disc.param_groups[0].get("initial_lr"):
+            optim_dur_disc.param_groups[0]["initial_lr"] = dur_resume_lr
+        scheduler_dur_disc = torch.optim.lr_scheduler.ExponentialLR(
+            optim_dur_disc, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
+        )
+    else:
+        scheduler_dur_disc = None
+    scaler = GradScaler(enabled=hps.train.fp16_run)
+
+    for epoch in range(epoch_str, hps.train.epochs + 1):
+        if rank == 0:
+            train_and_evaluate(
+                rank,
+                epoch,
+                hps,
+                [net_g, net_d, net_dur_disc],
+                [optim_g, optim_d, optim_dur_disc],
+                [scheduler_g, scheduler_d, scheduler_dur_disc],
+                scaler,
+                [train_loader, eval_loader],
+                logger,
+                [writer, writer_eval],
+            )
+        else:
+            train_and_evaluate(
+                rank,
+                epoch,
+                hps,
+                [net_g, net_d, net_dur_disc],
+                [optim_g, optim_d, optim_dur_disc],
+                [scheduler_g, scheduler_d, scheduler_dur_disc],
+                scaler,
+                [train_loader, None],
+                None,
+                None,
+            )
+        scheduler_g.step()
+        scheduler_d.step()
+        if net_dur_disc is not None:
+            scheduler_dur_disc.step()
+
+
+def train_and_evaluate(
+    rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers
+):
+    net_g, net_d, net_dur_disc = nets
+    optim_g, optim_d, optim_dur_disc = optims
+    scheduler_g, scheduler_d, scheduler_dur_disc = schedulers
+    train_loader, eval_loader = loaders
+    if writers is not None:
+        writer, writer_eval = writers
+
+    train_loader.batch_sampler.set_epoch(epoch)
+    global global_step
+
+    net_g.train()
+    net_d.train()
+    if net_dur_disc is not None:
+        net_dur_disc.train()
+    for batch_idx, (
+        x,
+        x_lengths,
+        spec,
+        spec_lengths,
+        y,
+        y_lengths,
+        speakers,
+        tone,
+        language,
+        bert,
+        ja_bert,
+    ) in tqdm(enumerate(train_loader)):
+        if net_g.module.use_noise_scaled_mas:
+            current_mas_noise_scale = (
+                net_g.module.mas_noise_scale_initial
+                - net_g.module.noise_scale_delta * global_step
+            )
+            net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0)
+        x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(
+            rank, non_blocking=True
+        )
+        spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(
+            rank, non_blocking=True
+        )
+        y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(
+            rank, non_blocking=True
+        )
+        speakers = speakers.cuda(rank, non_blocking=True)
+        tone = tone.cuda(rank, non_blocking=True)
+        language = language.cuda(rank, non_blocking=True)
+        bert = bert.cuda(rank, non_blocking=True)
+        ja_bert = ja_bert.cuda(rank, non_blocking=True)
+
+        with autocast(enabled=hps.train.fp16_run):
+            (
+                y_hat,
+                l_length,
+                attn,
+                ids_slice,
+                x_mask,
+                z_mask,
+                (z, z_p, m_p, logs_p, m_q, logs_q),
+                (hidden_x, logw, logw_),
+            ) = net_g(
+                x,
+                x_lengths,
+                spec,
+                spec_lengths,
+                speakers,
+                tone,
+                language,
+                bert,
+                ja_bert,
+            )
+            mel = spec_to_mel_torch(
+                spec,
+                hps.data.filter_length,
+                hps.data.n_mel_channels,
+                hps.data.sampling_rate,
+                hps.data.mel_fmin,
+                hps.data.mel_fmax,
+            )
+            y_mel = commons.slice_segments(
+                mel, ids_slice, hps.train.segment_size // hps.data.hop_length
+            )
+            y_hat_mel = mel_spectrogram_torch(
+                y_hat.squeeze(1),
+                hps.data.filter_length,
+                hps.data.n_mel_channels,
+                hps.data.sampling_rate,
+                hps.data.hop_length,
+                hps.data.win_length,
+                hps.data.mel_fmin,
+                hps.data.mel_fmax,
+            )
+
+            y = commons.slice_segments(
+                y, ids_slice * hps.data.hop_length, hps.train.segment_size
+            )  # slice
+
+            # Discriminator
+            y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
+            with autocast(enabled=False):
+                loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(
+                    y_d_hat_r, y_d_hat_g
+                )
+                loss_disc_all = loss_disc
+            if net_dur_disc is not None:
+                y_dur_hat_r, y_dur_hat_g = net_dur_disc(
+                    hidden_x.detach(), x_mask.detach(), logw.detach(), logw_.detach()
+                )
+                with autocast(enabled=False):
+                    # TODO: I think need to mean using the mask, but for now, just mean all
+                    (
+                        loss_dur_disc,
+                        losses_dur_disc_r,
+                        losses_dur_disc_g,
+                    ) = discriminator_loss(y_dur_hat_r, y_dur_hat_g)
+                    loss_dur_disc_all = loss_dur_disc
+                optim_dur_disc.zero_grad()
+                scaler.scale(loss_dur_disc_all).backward()
+                scaler.unscale_(optim_dur_disc)
+                commons.clip_grad_value_(net_dur_disc.parameters(), None)
+                scaler.step(optim_dur_disc)
+
+        optim_d.zero_grad()
+        scaler.scale(loss_disc_all).backward()
+        scaler.unscale_(optim_d)
+        grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
+        scaler.step(optim_d)
+
+        with autocast(enabled=hps.train.fp16_run):
+            # Generator
+            y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
+            if net_dur_disc is not None:
+                y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw, logw_)
+            with autocast(enabled=False):
+                loss_dur = torch.sum(l_length.float())
+                loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
+                loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
+
+                loss_fm = feature_loss(fmap_r, fmap_g)
+                loss_gen, losses_gen = generator_loss(y_d_hat_g)
+                loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl
+                if net_dur_disc is not None:
+                    loss_dur_gen, losses_dur_gen = generator_loss(y_dur_hat_g)
+                    loss_gen_all += loss_dur_gen
+        optim_g.zero_grad()
+        scaler.scale(loss_gen_all).backward()
+        scaler.unscale_(optim_g)
+        grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
+        scaler.step(optim_g)
+        scaler.update()
+
+        if rank == 0:
+            if global_step % hps.train.log_interval == 0:
+                lr = optim_g.param_groups[0]["lr"]
+                losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl]
+                logger.info(
+                    "Train Epoch: {} [{:.0f}%]".format(
+                        epoch, 100.0 * batch_idx / len(train_loader)
+                    )
+                )
+                logger.info([x.item() for x in losses] + [global_step, lr])
+
+                scalar_dict = {
+                    "loss/g/total": loss_gen_all,
+                    "loss/d/total": loss_disc_all,
+                    "learning_rate": lr,
+                    "grad_norm_d": grad_norm_d,
+                    "grad_norm_g": grad_norm_g,
+                }
+                scalar_dict.update(
+                    {
+                        "loss/g/fm": loss_fm,
+                        "loss/g/mel": loss_mel,
+                        "loss/g/dur": loss_dur,
+                        "loss/g/kl": loss_kl,
+                    }
+                )
+                scalar_dict.update(
+                    {"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)}
+                )
+                scalar_dict.update(
+                    {"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)}
+                )
+                scalar_dict.update(
+                    {"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)}
+                )
+
+                image_dict = {
+                    "slice/mel_org": utils.plot_spectrogram_to_numpy(
+                        y_mel[0].data.cpu().numpy()
+                    ),
+                    "slice/mel_gen": utils.plot_spectrogram_to_numpy(
+                        y_hat_mel[0].data.cpu().numpy()
+                    ),
+                    "all/mel": utils.plot_spectrogram_to_numpy(
+                        mel[0].data.cpu().numpy()
+                    ),
+                    "all/attn": utils.plot_alignment_to_numpy(
+                        attn[0, 0].data.cpu().numpy()
+                    ),
+                }
+                utils.summarize(
+                    writer=writer,
+                    global_step=global_step,
+                    images=image_dict,
+                    scalars=scalar_dict,
+                )
+
+            if global_step % hps.train.eval_interval == 0:
+                evaluate(hps, net_g, eval_loader, writer_eval)
+                utils.save_checkpoint(
+                    net_g,
+                    optim_g,
+                    hps.train.learning_rate,
+                    epoch,
+                    os.path.join(hps.model_dir, "G_{}.pth".format(global_step)),
+                )
+                utils.save_checkpoint(
+                    net_d,
+                    optim_d,
+                    hps.train.learning_rate,
+                    epoch,
+                    os.path.join(hps.model_dir, "D_{}.pth".format(global_step)),
+                )
+                if net_dur_disc is not None:
+                    utils.save_checkpoint(
+                        net_dur_disc,
+                        optim_dur_disc,
+                        hps.train.learning_rate,
+                        epoch,
+                        os.path.join(hps.model_dir, "DUR_{}.pth".format(global_step)),
+                    )
+                keep_ckpts = getattr(hps.train, "keep_ckpts", 5)
+                if keep_ckpts > 0:
+                    utils.clean_checkpoints(
+                        path_to_models=hps.model_dir,
+                        n_ckpts_to_keep=keep_ckpts,
+                        sort_by_time=True,
+                    )
+
+        global_step += 1
+
+    if rank == 0:
+        logger.info("====> Epoch: {}".format(epoch))
+
+
+def evaluate(hps, generator, eval_loader, writer_eval):
+    generator.eval()
+    image_dict = {}
+    audio_dict = {}
+    print("Evaluating ...")
+    with torch.no_grad():
+        for batch_idx, (
+            x,
+            x_lengths,
+            spec,
+            spec_lengths,
+            y,
+            y_lengths,
+            speakers,
+            tone,
+            language,
+            bert,
+            ja_bert,
+        ) in enumerate(eval_loader):
+            x, x_lengths = x.cuda(), x_lengths.cuda()
+            spec, spec_lengths = spec.cuda(), spec_lengths.cuda()
+            y, y_lengths = y.cuda(), y_lengths.cuda()
+            speakers = speakers.cuda()
+            bert = bert.cuda()
+            ja_bert = ja_bert.cuda()
+            tone = tone.cuda()
+            language = language.cuda()
+            for use_sdp in [True, False]:
+                y_hat, attn, mask, *_ = generator.module.infer(
+                    x,
+                    x_lengths,
+                    speakers,
+                    tone,
+                    language,
+                    bert,
+                    ja_bert,
+                    y=spec,
+                    max_len=1000,
+                    sdp_ratio=0.0 if not use_sdp else 1.0,
+                )
+                y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length
+
+                mel = spec_to_mel_torch(
+                    spec,
+                    hps.data.filter_length,
+                    hps.data.n_mel_channels,
+                    hps.data.sampling_rate,
+                    hps.data.mel_fmin,
+                    hps.data.mel_fmax,
+                )
+                y_hat_mel = mel_spectrogram_torch(
+                    y_hat.squeeze(1).float(),
+                    hps.data.filter_length,
+                    hps.data.n_mel_channels,
+                    hps.data.sampling_rate,
+                    hps.data.hop_length,
+                    hps.data.win_length,
+                    hps.data.mel_fmin,
+                    hps.data.mel_fmax,
+                )
+                image_dict.update(
+                    {
+                        f"gen/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(
+                            y_hat_mel[0].cpu().numpy()
+                        )
+                    }
+                )
+                audio_dict.update(
+                    {
+                        f"gen/audio_{batch_idx}_{use_sdp}": y_hat[
+                            0, :, : y_hat_lengths[0]
+                        ]
+                    }
+                )
+                image_dict.update(
+                    {
+                        f"gt/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(
+                            mel[0].cpu().numpy()
+                        )
+                    }
+                )
+                audio_dict.update({f"gt/audio_{batch_idx}": y[0, :, : y_lengths[0]]})
+
+    utils.summarize(
+        writer=writer_eval,
+        global_step=global_step,
+        images=image_dict,
+        audios=audio_dict,
+        audio_sampling_rate=hps.data.sampling_rate,
+    )
+    generator.train()
+
+
+if __name__ == "__main__":
+    main()