Upload train_nsf_sim_cache_sid_load_pretrain.py

train_nsf_sim_cache_sid_load_pretrain.py

@@ -0,0 +1,765 @@
+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()