Delete train_nsf_sim_cache_sid_load_pretrain.py

train_nsf_sim_cache_sid_load_pretrain.py

@@ -1,765 +0,0 @@
-import sys, os
-import pickle as p
-now_dir = os.getcwd()
-sys.path.append(os.path.join(now_dir))
-sys.path.append(os.path.join(now_dir, "train"))
-import utils
-Loss_Gen_Per_Epoch = []
-Loss_Disc_Per_Epoch = []
-elapsed_time_record = []
-Lowest_lg = 0
-Lowest_ld = 0
-import datetime
-hps = utils.get_hparams()
-overtrain = hps.overtrain
-experiment_name = hps.name
-os.environ["CUDA_VISIBLE_DEVICES"] = hps.gpus.replace("-", ",")
-n_gpus = len(hps.gpus.split("-"))
-from random import shuffle, randint
-import traceback, json, argparse, itertools, math, torch, pdb
-
-torch.backends.cudnn.deterministic = False
-torch.backends.cudnn.benchmark = False
-from torch import nn, optim
-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 lib.infer_pack import commons
-from time import sleep
-from time import time as ttime
-from data_utils import (
-    TextAudioLoaderMultiNSFsid,
-    TextAudioLoader,
-    TextAudioCollateMultiNSFsid,
-    TextAudioCollate,
-    DistributedBucketSampler,
-)
-
-import csv
-
-if hps.version == "v1":
-    from lib.infer_pack.models import (
-        SynthesizerTrnMs256NSFsid as RVC_Model_f0,
-        SynthesizerTrnMs256NSFsid_nono as RVC_Model_nof0,
-        MultiPeriodDiscriminator,
-    )
-else:
-    from lib.infer_pack.models import (
-        SynthesizerTrnMs768NSFsid as RVC_Model_f0,
-        SynthesizerTrnMs768NSFsid_nono as RVC_Model_nof0,
-        MultiPeriodDiscriminatorV2 as MultiPeriodDiscriminator,
-    )
-from losses import generator_loss, discriminator_loss, feature_loss, kl_loss
-from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
-from process_ckpt import savee
-
-global global_step
-global_step = 0
-
-def Calculate_format_elapsed_time(elapsed_time):
-    h = int(elapsed_time/3600)
-    m,s,ms = int(elapsed_time/60 - h*60), int(elapsed_time%60), round((elapsed_time - int(elapsed_time))*10000)
-    return h,m,s,ms
-def right_index(List,Value):
-    index = len(List)-1-List[::-1].index(Value)
-    return index
-def formating_time(time):
-    time = time if time >= 10 else f"0{time}"
-    return time
-class EpochRecorder:
-    def __init__(self):
-        self.last_time = ttime()
-
-    def record(self):
-        now_time = ttime()
-        elapsed_time = now_time - self.last_time
-        self.last_time = now_time
-        elapsed_time_str = str(datetime.timedelta(seconds=elapsed_time))
-        current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        return f"[{current_time}] | ({elapsed_time_str})"
-
-
-def main():
-    n_gpus = torch.cuda.device_count()
-    if torch.cuda.is_available() == False and torch.backends.mps.is_available() == True:
-        n_gpus = 1
-    os.environ["MASTER_ADDR"] = "localhost"
-    os.environ["MASTER_PORT"] = str(randint(20000, 55555))
-    children = []
-    for i in range(n_gpus):
-        subproc = mp.Process(
-            target=run,
-            args=(
-                i,
-                n_gpus,
-                hps,
-            ),
-        )
-        children.append(subproc)
-        subproc.start()
-    for i in range(n_gpus):
-        children[i].join()
-    
-
-
-def run(rank, n_gpus, hps):
-    global global_step, loss_disc, loss_gen_all, Loss_Disc_Per_Epoch, Loss_Gen_Per_Epoch, elapsed_time_record, best_epoch, best_global_step, Min_for_Single_epoch, prev_best_epoch
-    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"))
-
-    dist.init_process_group(
-        backend="gloo", init_method="env://", world_size=n_gpus, rank=rank
-    )
-    torch.manual_seed(hps.train.seed)
-    if torch.cuda.is_available():
-        torch.cuda.set_device(rank)
-
-    if hps.if_f0 == 1:
-        train_dataset = TextAudioLoaderMultiNSFsid(hps.data.training_files, hps.data)
-    else:
-        train_dataset = TextAudioLoader(hps.data.training_files, hps.data)
-    train_sampler = DistributedBucketSampler(
-        train_dataset,
-        hps.train.batch_size * n_gpus,
-        # [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1200,1400],  # 16s
-        [100, 200, 300, 400, 500, 600, 700, 800, 900],  # 16s
-        num_replicas=n_gpus,
-        rank=rank,
-        shuffle=True,
-    )
-    # It is possible that dataloader's workers are out of shared memory. Please try to raise your shared memory limit.
-    # num_workers=8 -> num_workers=4
-    if hps.if_f0 == 1:
-        collate_fn = TextAudioCollateMultiNSFsid()
-    else:
-        collate_fn = TextAudioCollate()
-    train_loader = DataLoader(
-        train_dataset,
-        num_workers=4,
-        shuffle=False,
-        pin_memory=True,
-        collate_fn=collate_fn,
-        batch_sampler=train_sampler,
-        persistent_workers=True,
-        prefetch_factor=8,
-    )
-    if hps.if_f0 == 1:
-        net_g = RVC_Model_f0(
-            hps.data.filter_length // 2 + 1,
-            hps.train.segment_size // hps.data.hop_length,
-            **hps.model,
-            is_half=hps.train.fp16_run,
-            sr=hps.sample_rate,
-        )
-    else:
-        net_g = RVC_Model_nof0(
-            hps.data.filter_length // 2 + 1,
-            hps.train.segment_size // hps.data.hop_length,
-            **hps.model,
-            is_half=hps.train.fp16_run,
-        )
-    if torch.cuda.is_available():
-        net_g = net_g.cuda(rank)
-    net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm)
-    if torch.cuda.is_available():
-        net_d = net_d.cuda(rank)
-    optim_g = torch.optim.AdamW(
-        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,
-    )
-    # 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 torch.cuda.is_available():
-        net_g = DDP(net_g, device_ids=[rank])
-        net_d = DDP(net_d, device_ids=[rank])
-    else:
-        net_g = DDP(net_g)
-        net_d = DDP(net_d)
-
-    try:  # 如果能加载自动resume
-        _, _, _, epoch_str = utils.load_checkpoint(
-            utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d
-        )  # D多半加载没事
-        if rank == 0:
-            logger.info("loaded D")
-        # _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g,load_opt=0)
-        _, _, _, epoch_str = utils.load_checkpoint(
-            utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g
-        )
-        global_step = (epoch_str - 1) * len(train_loader)
-        # epoch_str = 1
-        # global_step = 0
-    except:  # 如果首次不能加载，加载pretrain
-        # traceback.print_exc()
-        epoch_str = 1
-        global_step = 0
-        if hps.pretrainG != "":
-            if rank == 0:
-                logger.info("loaded pretrained %s" % (hps.pretrainG))
-            print(
-                net_g.module.load_state_dict(
-                    torch.load(hps.pretrainG, map_location="cpu")["model"]
-                )
-            )  ##测试不加载优化器
-        if hps.pretrainD != "":
-            if rank == 0:
-                logger.info("loaded pretrained %s" % (hps.pretrainD))
-            print(
-                net_d.module.load_state_dict(
-                    torch.load(hps.pretrainD, map_location="cpu")["model"]
-                )
-            )
-
-    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
-    )
-
-    scaler = GradScaler(enabled=hps.train.fp16_run)
-    #
-    #if hps.total_epoch < 100:
-        #Min_for_Single_epoch = int(hps.total_epoch/2)
-    #else:
-        #Min_for_Single_epoch = 50
-    Min_for_Single_epoch = 1
-    #
-    if os.path.exists(f"Loss_Gen_Per_Epoch_{hps.name}.p") and os.path.exists(f"Loss_Disc_Per_Epoch_{hps.name}.p"):
-        with open(f'Loss_Gen_Per_Epoch_{hps.name}.p', 'rb') as Loss_Gen:
-            Loss_Gen_Per_Epoch = p.load(Loss_Gen)
-            for i in range(len(Loss_Gen_Per_Epoch)-epoch_str+1):
-                Loss_Gen_Per_Epoch.pop()
-        with open(f'Loss_Disc_Per_Epoch_{hps.name}.p', 'rb') as Loss_Disc:
-            Loss_Disc_Per_Epoch = p.load(Loss_Disc)
-            for i in range(len(Loss_Disc_Per_Epoch)-epoch_str+1):
-                Loss_Disc_Per_Epoch.pop()
-    if os.path.exists(f"prev_best_epoch_{hps.name}.p"):
-        with open(f'prev_best_epoch_{hps.name}.p', 'rb') as prev_best_epoch_f:
-            prev_best_epoch = p.load(prev_best_epoch_f)
-    #
-    cache = []
-    for epoch in range(epoch_str, hps.train.epochs+1):
-        start_time = ttime()
-        if rank == 0:
-            train_and_evaluate(
-                rank,
-                epoch,
-                hps,
-                [net_g, net_d],
-                [optim_g, optim_d],
-                [scheduler_g, scheduler_d],
-                scaler,
-                [train_loader, None],
-                logger,
-                [writer, writer_eval],
-                cache,
-            )
-            
-            # Printing and Saving stuff
-            loss_gen_all = loss_gen_all.item()
-            loss_disc = loss_disc.item()
-            # 
-            Loss_Gen_Per_Epoch.append(loss_gen_all)
-            Loss_Disc_Per_Epoch.append(loss_disc)
-            #print(hps.train.epochs, epoch_str)
-            #
-            with open(f'Loss_Gen_Per_Epoch_{hps.name}.p', 'wb') as Loss_Gen:
-                p.dump(Loss_Gen_Per_Epoch, Loss_Gen)
-            Loss_Gen.close()
-            with open(f'Loss_Disc_Per_Epoch_{hps.name}.p', 'wb') as Loss_Disc:
-                p.dump(Loss_Disc_Per_Epoch, Loss_Disc)
-            Loss_Disc.close()
-            #
-            Lowest_lg = f"{min(Loss_Gen_Per_Epoch):.5f}, epoch: {right_index(Loss_Gen_Per_Epoch,min(Loss_Gen_Per_Epoch))+1}"
-            Lowest_ld = f"{min(Loss_Disc_Per_Epoch):.5f}, epoch: {right_index(Loss_Disc_Per_Epoch,min(Loss_Disc_Per_Epoch))+1}"
-            print(f"{hps.name}_e{epoch}_s{global_step} | Loss gen total: {Loss_Gen_Per_Epoch[-1]:.5f} | Lowest loss G: {Lowest_lg}\n Loss disc: {Loss_Disc_Per_Epoch[-1]:.5f} | Lowest loss D: {Lowest_ld}")
-            print(f"Specific Value: loss gen={loss_gen:.3f}, loss fm={loss_fm:.3f},loss mel={loss_mel:.3f}, loss kl={loss_kl:.3f}")
-            #
-            if len(Loss_Gen_Per_Epoch) > Min_for_Single_epoch and epoch % hps.save_every_epoch != 0:
-                if min(Loss_Gen_Per_Epoch[Min_for_Single_epoch::1]) == Loss_Gen_Per_Epoch[-1]:
-                    if hasattr(net_g, "module"):
-                        ckpt = net_g.module.state_dict()
-                    else:
-                        ckpt = net_g.state_dict()
-                    savee(ckpt, hps.sample_rate, hps.if_f0, hps.name + "_e%s_s%s" % (epoch, global_step), epoch, hps.version, hps, experiment_name)
-                    os.rename(f"logs/{hps.name}/weights/{hps.name}_e{epoch}_s{global_step}.pth",f"logs/{hps.name}/weights/{hps.name}_e{epoch}_s{global_step}_Best_Epoch.pth")
-                    print(f"Saved: {hps.name}_e{epoch}_s{global_step}_Best_Epoch.pth")
-                    try:
-                        os.remove(prev_best_epoch)
-                    except:
-                        print("Nothing to remove, if there's is you may need to check again")
-                        pass
-                    else:
-                        print(f"{os.path.split(prev_best_epoch)[-1]} Removed")
-                    best_epoch = epoch
-                    best_global_step = global_step
-                    prev_best_epoch = f"logs/{hps.name}/weights/{hps.name}_e{best_epoch}_s{best_global_step}_Best_Epoch.pth"
-                    with open(f'prev_best_epoch_{hps.name}.p', 'wb') as prev_best_epoch_f:
-                        p.dump(prev_best_epoch, prev_best_epoch_f)
-            #
-            elapsed_time = ttime() - start_time
-            elapsed_time_record.append(elapsed_time)
-            if epoch-1 == epoch_str:
-                elapsed_time_record.pop(0)
-            elapsed_time_avg = sum(elapsed_time_record)/len(elapsed_time_record)
-            time_left = elapsed_time_avg*(hps.total_epoch-epoch)
-            hour, minute, second, millisec = Calculate_format_elapsed_time(elapsed_time)
-            hour_left, minute_left, second_left, millisec_left = Calculate_format_elapsed_time(time_left)
-            print(f"Time Elapsed: {hour}h:{formating_time(minute)}m:{formating_time(second)}s:{millisec}ms  ||  Time left: {hour_left}h:{formating_time(minute_left)}m:{formating_time(second_left)}s:{millisec_left}ms\n")
-            #
-            if ((len(Loss_Gen_Per_Epoch) - right_index(Loss_Gen_Per_Epoch,min(Loss_Gen_Per_Epoch)) + 1) > overtrain and overtrain != -1):
-                logger.info("Over Train threshold reached. Training is done.")
-                print("Over Train threshold reached. Training is done.")
-    
-                if hasattr(net_g, "module"):
-                    ckpt = net_g.module.state_dict()
-                else:
-                    ckpt = net_g.state_dict()
-                logger.info(
-                    "saving final ckpt:%s"
-                    % (
-                        savee(
-                            ckpt, hps.sample_rate, hps.if_f0, hps.name, epoch, hps.version, hps, experiment_name
-                        )
-                    )
-                )
-                sleep(1)
-                with open("csvdb/stop.csv", "w+", newline="") as STOPCSVwrite:
-                    csv_writer = csv.writer(STOPCSVwrite, delimiter=",")
-                    csv_writer.writerow(["False"])
-                os._exit(2333333)
-        
-        else:
-            train_and_evaluate(
-                rank,
-                epoch,
-                hps,
-                [net_g, net_d],
-                [optim_g, optim_d],
-                [scheduler_g, scheduler_d],
-                scaler,
-                [train_loader, None],
-                None,
-                None,
-                cache,
-            )
-        scheduler_g.step()
-        scheduler_d.step()
-        #gathered_tensors_gen = [torch.zeros_like(loss_gen_all) for _ in range(n_gpus)]
-        #gathered_tensors_disc = [torch.zeros_like(loss_disc) for _ in range(n_gpus)]
-        #dist.all_gather(gathered_tensors_gen, loss_gen_all)
-        #dist.all_gather(gathered_tensors_disc, loss_disc)
-
-        
-
-#######
-
-def train_and_evaluate(
-    rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers, cache
-):
-    global loss_gen_all, loss_disc, ckpt, loss_kl, loss_fm, loss_gen, loss_mel
-    net_g, net_d = nets
-    optim_g, optim_d = optims
-    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()
-
-    # Prepare data iterator
-    if hps.if_cache_data_in_gpu == True:
-        # Use Cache
-        data_iterator = cache
-        if cache == []:
-            # Make new cache
-            for batch_idx, info in enumerate(train_loader):
-                # Unpack
-                if hps.if_f0 == 1:
-                    (
-                        phone,
-                        phone_lengths,
-                        pitch,
-                        pitchf,
-                        spec,
-                        spec_lengths,
-                        wave,
-                        wave_lengths,
-                        sid,
-                    ) = info
-                else:
-                    (
-                        phone,
-                        phone_lengths,
-                        spec,
-                        spec_lengths,
-                        wave,
-                        wave_lengths,
-                        sid,
-                    ) = info
-                # Load on CUDA
-                if torch.cuda.is_available():
-                    phone = phone.cuda(rank, non_blocking=True)
-                    phone_lengths = phone_lengths.cuda(rank, non_blocking=True)
-                    if hps.if_f0 == 1:
-                        pitch = pitch.cuda(rank, non_blocking=True)
-                        pitchf = pitchf.cuda(rank, non_blocking=True)
-                    sid = sid.cuda(rank, non_blocking=True)
-                    spec = spec.cuda(rank, non_blocking=True)
-                    spec_lengths = spec_lengths.cuda(rank, non_blocking=True)
-                    wave = wave.cuda(rank, non_blocking=True)
-                    wave_lengths = wave_lengths.cuda(rank, non_blocking=True)
-                # Cache on list
-                if hps.if_f0 == 1:
-                    cache.append(
-                        (
-                            batch_idx,
-                            (
-                                phone,
-                                phone_lengths,
-                                pitch,
-                                pitchf,
-                                spec,
-                                spec_lengths,
-                                wave,
-                                wave_lengths,
-                                sid,
-                            ),
-                        )
-                    )
-                else:
-                    cache.append(
-                        (
-                            batch_idx,
-                            (
-                                phone,
-                                phone_lengths,
-                                spec,
-                                spec_lengths,
-                                wave,
-                                wave_lengths,
-                                sid,
-                            ),
-                        )
-                    )
-        else:
-            # Load shuffled cache
-            shuffle(cache)
-    else:
-        # Loader
-        data_iterator = enumerate(train_loader)
-
-    # Run steps
-    epoch_recorder = EpochRecorder()
-
-    for batch_idx, info in data_iterator:
-        # Data
-        ## Unpack
-        if hps.if_f0 == 1:
-            (
-                phone,
-                phone_lengths,
-                pitch,
-                pitchf,
-                spec,
-                spec_lengths,
-                wave,
-                wave_lengths,
-                sid,
-            ) = info
-        else:
-            phone, phone_lengths, spec, spec_lengths, wave, wave_lengths, sid = info
-        ## Load on CUDA
-        if (hps.if_cache_data_in_gpu == False) and torch.cuda.is_available():
-            phone = phone.cuda(rank, non_blocking=True)
-            phone_lengths = phone_lengths.cuda(rank, non_blocking=True)
-            if hps.if_f0 == 1:
-                pitch = pitch.cuda(rank, non_blocking=True)
-                pitchf = pitchf.cuda(rank, non_blocking=True)
-            sid = sid.cuda(rank, non_blocking=True)
-            spec = spec.cuda(rank, non_blocking=True)
-            spec_lengths = spec_lengths.cuda(rank, non_blocking=True)
-            wave = wave.cuda(rank, non_blocking=True)
-            # wave_lengths = wave_lengths.cuda(rank, non_blocking=True)
-
-        # Calculate
-        with autocast(enabled=hps.train.fp16_run):
-            if hps.if_f0 == 1:
-                (
-                    y_hat,
-                    ids_slice,
-                    x_mask,
-                    z_mask,
-                    (z, z_p, m_p, logs_p, m_q, logs_q),
-                ) = net_g(phone, phone_lengths, pitch, pitchf, spec, spec_lengths, sid)
-            else:
-                (
-                    y_hat,
-                    ids_slice,
-                    x_mask,
-                    z_mask,
-                    (z, z_p, m_p, logs_p, m_q, logs_q),
-                ) = net_g(phone, phone_lengths, spec, spec_lengths, sid)
-            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
-            )
-            with autocast(enabled=False):
-                y_hat_mel = mel_spectrogram_torch(
-                    y_hat.float().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,
-                )
-            if hps.train.fp16_run == True:
-                y_hat_mel = y_hat_mel.half()
-            wave = commons.slice_segments(
-                wave, ids_slice * hps.data.hop_length, hps.train.segment_size
-            )  # slice
-
-            # Discriminator
-            y_d_hat_r, y_d_hat_g, _, _ = net_d(wave, 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
-                )
-        optim_d.zero_grad()
-        scaler.scale(loss_disc).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(wave, y_hat)
-            with autocast(enabled=False):
-                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_kl
-        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"]
-                logger.info( ""
-                    #"Train Epoch: {} [{:.0f}%]".format(
-                        #epoch, 100.0 * batch_idx / len(train_loader)
-                    #)
-                )
-                # Amor For Tensorboard display
-                if loss_mel > 75:
-                    loss_mel = 75
-                if loss_kl > 9:
-                    loss_kl = 9
-
-                logger.info([global_step, lr])
-                logger.info(""
-                    #f"loss_disc={loss_disc:.3f}, loss_gen={loss_gen:.3f}, loss_fm={loss_fm:.3f},loss_mel={loss_mel:.3f}, loss_kl={loss_kl:.3f}"
-                )
-                scalar_dict = {
-                    "loss/g/total": loss_gen_all,
-                    "loss/d/total": loss_disc,
-                    "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/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()
-                    ),
-                }
-                utils.summarize(
-                    writer=writer,
-                    global_step=global_step,
-                    images=image_dict,
-                    scalars=scalar_dict,
-                )
-        global_step += 1
-    # /Run steps
-
-    if epoch % hps.save_every_epoch == 0 and rank == 0:
-        print(f"Saved: {hps.name}_e{epoch}_s{global_step}.pth")
-        if hps.if_latest == 0:
-            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)),
-            )
-        else:
-            utils.save_checkpoint(
-                net_g,
-                optim_g,
-                hps.train.learning_rate,
-                epoch,
-                os.path.join(hps.model_dir, "G_{}.pth".format(2333333)),
-            )
-            utils.save_checkpoint(
-                net_d,
-                optim_d,
-                hps.train.learning_rate,
-                epoch,
-                os.path.join(hps.model_dir, "D_{}.pth".format(2333333)),
-            )
-        if rank == 0 and hps.save_every_weights == "1":
-            if hasattr(net_g, "module"):
-                ckpt = net_g.module.state_dict()
-            else:
-                ckpt = net_g.state_dict()
-            logger.info(
-                "saving ckpt %s_e%s:%s"
-                % (
-                    hps.name,
-                    epoch,
-                    savee(
-                        ckpt,
-                        hps.sample_rate,
-                        hps.if_f0,
-                        hps.name + "_e%s_s%s" % (epoch, global_step),
-                        epoch,
-                        hps.version,
-                        hps,
-                        experiment_name,
-                    ),
-                )
-            )
-
-    try:
-        with open("csvdb/stop.csv") as CSVStop:
-            csv_reader = list(csv.reader(CSVStop))
-            stopbtn = (
-                csv_reader[0][0]
-                if csv_reader is not None
-                else (lambda: exec('raise ValueError("No data")'))()
-            )
-            stopbtn = (
-                lambda stopbtn: True
-                if stopbtn.lower() == "true"
-                else (False if stopbtn.lower() == "false" else stopbtn)
-            )(stopbtn)
-    except (ValueError, TypeError, IndexError):
-        stopbtn = False
-
-    if stopbtn:
-        logger.info("Stop Button was pressed. The program is closed.")
-        if hasattr(net_g, "module"):
-            ckpt = net_g.module.state_dict()
-        else:
-            ckpt = net_g.state_dict()
-        logger.info(
-            "saving final ckpt:%s"
-            % (
-                savee(
-                    ckpt,
-                    hps.sample_rate,
-                    hps.if_f0,
-                    hps.name,
-                    epoch,
-                    hps.version,
-                    hps,
-                    experiment_name,
-                )
-            )
-        )
-        sleep(1)
-        with open("csvdb/stop.csv", "w+", newline="") as STOPCSVwrite:
-            csv_writer = csv.writer(STOPCSVwrite, delimiter=",")
-            csv_writer.writerow(["False"])
-        os._exit(2333333)
-
-    if rank == 0:
-        logger.info('')#"====> Epoch: {} {}".format(epoch, epoch_recorder.record()))
-    if epoch > hps.total_epoch and rank == 0:
-        logger.info("Training is done. The program is closed.")
-
-        if hasattr(net_g, "module"):
-            ckpt = net_g.module.state_dict()
-        else:
-            ckpt = net_g.state_dict()
-        logger.info(
-            "saving final ckpt:%s"
-            % (
-                savee(
-                    ckpt, hps.sample_rate, hps.if_f0, hps.name, epoch, hps.version, hps, experiment_name
-                )
-            )
-        )
-        sleep(1)
-        with open("csvdb/stop.csv", "w+", newline="") as STOPCSVwrite:
-            csv_writer = csv.writer(STOPCSVwrite, delimiter=",")
-            csv_writer.writerow(["False"])
-        os._exit(2333333)
-
-
-if __name__ == "__main__":
-    torch.multiprocessing.set_start_method("spawn")
-    main()