train_ms.py

# flake8: noqa: E402
import platform
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.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.cuda.amp import autocast, GradScaler
from tqdm import tqdm
import logging
from config import config
import argparse
import datetime
import gc

logging.getLogger("numba").setLevel(logging.WARNING)
logger = logging.getLogger(__name__)
import commons
import utils
from data_utils import (
    TextAudioSpeakerLoader,
    TextAudioSpeakerCollate,
    DistributedBucketSampler,
    AudioVisemesLoader,
)
from models import (
    SynthesizerTrn,
    MultiPeriodDiscriminator,
    DurationDiscriminator,
    WavLMDiscriminator,
    VisemesNet,
)
from losses import (
    generator_loss,
    discriminator_loss,
    feature_loss,
    kl_loss,
    WavLMLoss,
)
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.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
global_step = 0
global_visemes_step = 0

def run_only_visemes(hps):
    # 使用最简单的单机模式，仅训练隐变量z到表情(visemes)的全连接 VisemesFCNet 的参数
    global global_visemes_step
    torch.manual_seed(hps.train.seed)
    torch.cuda.set_device(0)
    train_dataset = AudioVisemesLoader(hps.data.training_visemes_files, hps.data)
    train_loader = DataLoader(train_dataset, num_workers=0, shuffle=False, pin_memory=True,
                              batch_size=1, drop_last=True)
    eval_dataset = AudioVisemesLoader(hps.data.validation_visemes_files, hps.data)
    eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False,
                             batch_size=1, pin_memory=True,
                             drop_last=False)
    net_v = VisemesNet(hps.model.hidden_channels).cuda()
    latest_model_path = utils.latest_checkpoint_path(hps.model_dir, "V_*.pth")
    if latest_model_path is not None:
        _, optim_d, _, epoch_str = utils.load_checkpoint(latest_model_path, net_v, None, skip_optimizer=False)
    else :
        epoch_str = 1
        global_visemes_step = 0
        net_v.init_weights()
    optim_v = torch.optim.AdamW(
        net_v.parameters(),
        hps.train.learning_rate,
        betas=hps.train.betas,
        eps=hps.train.eps)
    optim_v.param_groups[0]['initial_lr'] = hps.train.learning_rate
    scheduler_v = torch.optim.lr_scheduler.ExponentialLR(optim_v, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2, )
    scaler = GradScaler(enabled=hps.train.bf16_run)
    for epoch in range(epoch_str, hps.train.epochs + 1):
        train_visemes_only(epoch, hps, net_v, train_loader, optim_v, scaler)
        scheduler_v.step()
        if epoch % hps.train.eval_interval == 0:
            eval_visemes_only(epoch, hps, net_v, eval_loader)
            utils.save_checkpoint(net_v, optim_v,hps.train.learning_rate , epoch, os.path.join(hps.model_dir, "V_{}.pth".format(epoch)))

def train_visemes_only(epoch, hps, net_v, train_loader, optim_v, scaler):
    for batch_idx, (spec, visemes) in tqdm(enumerate(train_loader)):
        spec, visemes = spec.cuda(), visemes.cuda()
        with autocast(enabled=hps.train.bf16_run):
            # 通过VisemesNet从z生成visemes_hat，和均方差
            visemes_hat = net_v(spec)
            visemes_hat_mse = get_visemes_mse(visemes, visemes_hat)
            optim_v.zero_grad()
            scaler.scale(visemes_hat_mse).backward()
            scaler.unscale_(optim_v)
            grad_norm_v = commons.clip_grad_value_(net_v.parameters(), None)
            scaler.step(optim_v)
            global global_visemes_step
            global_visemes_step += 1
            if batch_idx % hps.train.log_interval == 0:
                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tvisemes_hat_mse: {:.6f}\tgrad_norm_v: {:.6f}'.format(
                    epoch, batch_idx * len(spec), len(train_loader.dataset),
                    100. * batch_idx / len(train_loader), visemes_hat_mse.item(), grad_norm_v))

def get_visemes_mse(visemes, visemes_hat):
    if visemes.shape[-1] != visemes_hat.shape[-1]: # 如果y和x的最低维度不一样
        visemes_hat = F.interpolate(visemes_hat, size=visemes.shape[-1], mode='linear', align_corners=True) # 对x进行线性插值，使其形状与y一致
    visemes_hat_mse = torch.mean(torch.pow(visemes_hat - visemes, 2))
    return visemes_hat_mse


def eval_visemes_only(epoch, hps, net_v, eval_loader):
    net_v.eval()
    with torch.no_grad():
        visemes_hat_mse_sum = 0.0
        for batch_idx, (spec, visemes) in tqdm(enumerate(eval_loader)):
            spec, visemes = spec.cuda(), visemes.cuda()
            # 通过VisemesFCNet从z生成visemes_hat，和均方差
            visemes_hat = net_v(spec)
            visemes_hat_mse = get_visemes_mse(visemes, visemes_hat)
            visemes_hat_mse_sum += visemes_hat_mse
            # print('visemes_hat_mse', visemes_hat_mse)
            break
        visemes_hat_mse_avg = visemes_hat_mse_sum / (batch_idx + 1)
        log_str = '------------------ eval epoch: {} visemes_hat_mse_avg: {:.6f}'.format(epoch, visemes_hat_mse_avg)
        print(log_str)
        logger.warning(log_str)
    net_v.train()



def run():
    # 环境变量解析
    envs = config.train_ms_config.env
    for env_name, env_value in envs.items():
        if env_name not in os.environ.keys():
            print("加载config中的配置{}".format(str(env_value)))
            os.environ[env_name] = str(env_value)
    print(
        "加载环境变量 \nMASTER_ADDR: {},\nMASTER_PORT: {},\nWORLD_SIZE: {},\nRANK: {},\nLOCAL_RANK: {}".format(
            os.environ["MASTER_ADDR"],
            os.environ["MASTER_PORT"],
            os.environ["WORLD_SIZE"],
            os.environ["RANK"],
            os.environ["LOCAL_RANK"],
        )
    )

    backend = "nccl"
    if platform.system() == "Windows":
        backend = "gloo"  # If Windows,switch to gloo backend.
    dist.init_process_group(
        backend=backend,
        init_method="env://",
        timeout=datetime.timedelta(seconds=300),
    )  # Use torchrun instead of mp.spawn
    rank = dist.get_rank()
    local_rank = int(os.environ["LOCAL_RANK"])
    n_gpus = dist.get_world_size()

    # 命令行/config.yml配置解析
    # hps = utils.get_hparams()
    parser = argparse.ArgumentParser()
    # 非必要不建议使用命令行配置，请使用config.yml文件
    parser.add_argument(
        "-c",
        "--config",
        type=str,
        default=config.train_ms_config.config_path,
        help="JSON file for configuration",
    )

    parser.add_argument(
        "-m",
        "--model",
        type=str,
        help="数据集文件夹路径，请注意，数据不再默认放在/logs文件夹下。如果需要用命令行配置，请声明相对于根目录的路径",
        default=config.dataset_path,
    )
    parser.add_argument('--visemes', dest='visemes', action="store_true", default=False, help="train visemes only, lock the encoder and decoder")

    args = parser.parse_args()
    model_dir = os.path.join(args.model, config.train_ms_config.model)
    if not os.path.exists(model_dir):
        os.makedirs(model_dir)
    hps = utils.get_hparams_from_file(args.config)
    hps.model_dir = model_dir
    set_logger(hps)
    if args.visemes:
        run_only_visemes(hps)
    # 比较路径是否相同
    if os.path.realpath(args.config) != os.path.realpath(
        config.train_ms_config.config_path
    ):
        with open(args.config, "r", encoding="utf-8") as f:
            data = f.read()
        with open(config.train_ms_config.config_path, "w", encoding="utf-8") as f:
            f.write(data)

    torch.manual_seed(hps.train.seed)
    torch.cuda.set_device(local_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=min(config.train_ms_config.num_workers, os.cpu_count() - 1),
        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(local_rank)
    else:
        net_dur_disc = None
    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(local_rank)

    if getattr(hps.train, "freeze_ZH_bert", False):
        print("Freezing ZH bert encoder !!!")
        for param in net_g.enc_p.bert_proj.parameters():
            param.requires_grad = False

    if getattr(hps.train, "freeze_EN_bert", False):
        print("Freezing EN bert encoder !!!")
        for param in net_g.enc_p.en_bert_proj.parameters():
            param.requires_grad = False

    if getattr(hps.train, "freeze_JP_bert", False):
        print("Freezing JP bert encoder !!!")
        for param in net_g.enc_p.ja_bert_proj.parameters():
            param.requires_grad = False

    net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(local_rank)
    net_wd = WavLMDiscriminator(
        hps.model.slm.hidden, hps.model.slm.nlayers, hps.model.slm.initial_channel
    ).cuda(local_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,
    )
    optim_wd = torch.optim.AdamW(
        net_wd.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=[local_rank], bucket_cap_mb=512)
    net_d = DDP(net_d, device_ids=[local_rank], bucket_cap_mb=512)
    net_wd = DDP(net_wd, device_ids=[local_rank], bucket_cap_mb=512)
    if net_dur_disc is not None:
        net_dur_disc = DDP(
            net_dur_disc,
            device_ids=[local_rank],
            bucket_cap_mb=512,
        )

    # 下载底模
    if config.train_ms_config.base["use_base_model"]:
        utils.download_checkpoint(
            hps.model_dir,
            config.train_ms_config.base,
            token=config.openi_token,
            mirror=config.mirror,
        )
    dur_resume_lr = hps.train.learning_rate
    wd_resume_lr = hps.train.learning_rate
    if net_dur_disc is not None:
        try:
            _, _, 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,
            )
            if not optim_dur_disc.param_groups[0].get("initial_lr"):
                optim_dur_disc.param_groups[0]["initial_lr"] = dur_resume_lr
        except:
            print("Initialize dur_disc")

    try:
        _, 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)
        global_step = int(
            utils.get_steps(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"))
        )
        print(
            f"******************检测到模型存在，epoch为 {epoch_str}，gloabl step为 {global_step}*********************"
        )
    except Exception as e:
        print(e)
        epoch_str = 1
        global_step = 0

    try:
        _, optim_wd, wd_resume_lr, epoch_str = utils.load_checkpoint(
            utils.latest_checkpoint_path(hps.model_dir, "WD_*.pth"),
            net_wd,
            optim_wd,
            skip_optimizer=hps.train.skip_optimizer
            if "skip_optimizer" in hps.train
            else True,
        )
        if not optim_wd.param_groups[0].get("initial_lr"):
            optim_wd.param_groups[0]["initial_lr"] = wd_resume_lr
    except Exception as e:
        print(e)

    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
    )
    scheduler_wd = torch.optim.lr_scheduler.ExponentialLR(
        optim_wd, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
    )
    if net_dur_disc is not None:
        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.bf16_run)

    wl = WavLMLoss(
        hps.model.slm.model,
        net_wd,
        hps.data.sampling_rate,
        hps.model.slm.sr,
    ).to(local_rank)

    for epoch in range(epoch_str, hps.train.epochs + 1):
        if rank == 0:
            train_and_evaluate(
                rank,
                local_rank,
                epoch,
                hps,
                [net_g, net_d, net_dur_disc, net_wd, wl],
                [optim_g, optim_d, optim_dur_disc, optim_wd],
                [scheduler_g, scheduler_d, scheduler_dur_disc, scheduler_wd],
                scaler,
                [train_loader, eval_loader],
                logger,
                [writer, writer_eval],
            )
        else:
            train_and_evaluate(
                rank,
                local_rank,
                epoch,
                hps,
                [net_g, net_d, net_dur_disc, net_wd, wl],
                [optim_g, optim_d, optim_dur_disc, optim_wd],
                [scheduler_g, scheduler_d, scheduler_dur_disc, scheduler_wd],
                scaler,
                [train_loader, None],
                None,
                None,
            )
        scheduler_g.step()
        scheduler_d.step()
        scheduler_wd.step()
        if net_dur_disc is not None:
            scheduler_dur_disc.step()


def train_and_evaluate(
    rank,
    local_rank,
    epoch,
    hps,
    nets,
    optims,
    schedulers,
    scaler,
    loaders,
    logger,
    writers,
):
    net_g, net_d, net_dur_disc, net_wd, wl = nets
    optim_g, optim_d, optim_dur_disc, optim_wd = optims
    scheduler_g, scheduler_d, scheduler_dur_disc, scheduler_wd = 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()
    net_wd.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,
        en_bert,
    ) in enumerate(tqdm(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(local_rank, non_blocking=True), x_lengths.cuda(
            local_rank, non_blocking=True
        )
        spec, spec_lengths = spec.cuda(
            local_rank, non_blocking=True
        ), spec_lengths.cuda(local_rank, non_blocking=True)
        y, y_lengths = y.cuda(local_rank, non_blocking=True), y_lengths.cuda(
            local_rank, non_blocking=True
        )
        speakers = speakers.cuda(local_rank, non_blocking=True)
        tone = tone.cuda(local_rank, non_blocking=True)
        language = language.cuda(local_rank, non_blocking=True)
        bert = bert.cuda(local_rank, non_blocking=True)
        ja_bert = ja_bert.cuda(local_rank, non_blocking=True)
        en_bert = en_bert.cuda(local_rank, non_blocking=True)

        with autocast(enabled=hps.train.bf16_run, dtype=torch.bfloat16):
            (
                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_, logw_sdp),
                g,
            ) = net_g(
                x,
                x_lengths,
                spec,
                spec_lengths,
                speakers,
                tone,
                language,
                bert,
                ja_bert,
                en_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).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,
            )

            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=hps.train.bf16_run, dtype=torch.bfloat16):
                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(),
                    g.detach(),
                )
                y_dur_hat_r_sdp, y_dur_hat_g_sdp = net_dur_disc(
                    hidden_x.detach(),
                    x_mask.detach(),
                    logw_.detach(),
                    logw_sdp.detach(),
                    g.detach(),
                )
                y_dur_hat_r = y_dur_hat_r + y_dur_hat_r_sdp
                y_dur_hat_g = y_dur_hat_g + y_dur_hat_g_sdp
                with autocast(enabled=hps.train.bf16_run, dtype=torch.bfloat16):
                    # 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)
                # torch.nn.utils.clip_grad_norm_(
                #     parameters=net_dur_disc.parameters(), max_norm=100
                # )
                grad_norm_dur = 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)
        if getattr(hps.train, "bf16_run", False):
            torch.nn.utils.clip_grad_norm_(parameters=net_d.parameters(), max_norm=200)
        grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
        scaler.step(optim_d)

        with autocast(enabled=hps.train.bf16_run, dtype=torch.bfloat16):
            loss_slm = wl.discriminator(
                y.detach().squeeze(), y_hat.detach().squeeze()
            ).mean()

        optim_wd.zero_grad()
        scaler.scale(loss_slm).backward()
        scaler.unscale_(optim_wd)
        # torch.nn.utils.clip_grad_norm_(parameters=net_wd.parameters(), max_norm=200)
        grad_norm_wd = commons.clip_grad_value_(net_wd.parameters(), None)
        scaler.step(optim_wd)

        with autocast(enabled=hps.train.bf16_run, dtype=torch.bfloat16):
            # 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_g = net_dur_disc(hidden_x, x_mask, logw_, logw, g)
                _, y_dur_hat_g_sdp = net_dur_disc(hidden_x, x_mask, logw_, logw_sdp, g)
                y_dur_hat_g = y_dur_hat_g + y_dur_hat_g_sdp
            with autocast(enabled=hps.train.bf16_run, dtype=torch.bfloat16):
                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_lm = wl(y.detach().squeeze(), y_hat.squeeze()).mean()
                loss_lm_gen = wl.generator(y_hat.squeeze())

                loss_gen_all = (
                    loss_gen
                    + loss_fm
                    + loss_mel
                    + loss_dur
                    + loss_kl
                    + loss_lm
                    + loss_lm_gen
                )
                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)
        if getattr(hps.train, "bf16_run", False):
            torch.nn.utils.clip_grad_norm_(parameters=net_g.parameters(), max_norm=500)
        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,
                    "loss/wd/total": loss_slm,
                    "learning_rate": lr,
                    "grad_norm_d": grad_norm_d,
                    "grad_norm_g": grad_norm_g,
                    "grad_norm_dur": grad_norm_dur,
                    "grad_norm_wd": grad_norm_wd,
                }
                scalar_dict.update(
                    {
                        "loss/g/fm": loss_fm,
                        "loss/g/mel": loss_mel,
                        "loss/g/dur": loss_dur,
                        "loss/g/kl": loss_kl,
                        "loss/g/lm": loss_lm,
                        "loss/g/lm_gen": loss_lm_gen,
                    }
                )
                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)}
                )

                if net_dur_disc is not None:
                    scalar_dict.update({"loss/dur_disc/total": loss_dur_disc_all})

                    scalar_dict.update(
                        {
                            "loss/dur_disc_g/{}".format(i): v
                            for i, v in enumerate(losses_dur_disc_g)
                        }
                    )
                    scalar_dict.update(
                        {
                            "loss/dur_disc_r/{}".format(i): v
                            for i, v in enumerate(losses_dur_disc_r)
                        }
                    )

                    scalar_dict.update({"loss/g/dur_gen": loss_dur_gen})
                    scalar_dict.update(
                        {
                            "loss/g/dur_gen_{}".format(i): v
                            for i, v in enumerate(losses_dur_gen)
                        }
                    )

                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)),
                )
                utils.save_checkpoint(
                    net_wd,
                    optim_wd,
                    hps.train.learning_rate,
                    epoch,
                    os.path.join(hps.model_dir, "WD_{}.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 = config.train_ms_config.keep_ckpts
                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

    gc.collect()
    torch.cuda.empty_cache()
    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,
            en_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()
            en_bert = en_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,
                    en_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()


def set_logger(hps):
    # set logger to file and stdout, using hps.model_dir as logging path
    log_format = logging.Formatter(
        "%(asctime)s %(levelname)s %(message)s", datefmt="%Y-%m-%d %H:%M:%S"
    )
    # train.datetime.log
    fh = logging.FileHandler(
        os.path.join(
            hps.model_dir, "train.{}.log".format(datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
        ),
        mode="a",
    )
    fh.setLevel(logging.INFO)
    fh.setFormatter(log_format)
    logger.addHandler(fh)



if __name__ == "__main__":

    run()