diff --git a/train_nsf_sim_cache_sid_load_pretrain.py b/train_nsf_sim_cache_sid_load_pretrain.py index 23f2355..e657023 100644 --- a/train_nsf_sim_cache_sid_load_pretrain.py +++ b/train_nsf_sim_cache_sid_load_pretrain.py @@ -1,584 +1,585 @@ -import sys, os - -now_dir = os.getcwd() -sys.path.append(os.path.join(now_dir)) -sys.path.append(os.path.join(now_dir, "train")) -import utils -import datetime - -hps = utils.get_hparams() -os.environ["CUDA_VISIBLE_DEVICES"] = hps.gpus.replace("-", ",") -n_gpus = len(hps.gpus.split("-")) -from random import shuffle -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 infer_pack import commons -from time import sleep -from time import time as ttime -from data_utils import ( - TextAudioLoaderMultiNSFsid, - TextAudioLoader, - TextAudioCollateMultiNSFsid, - TextAudioCollate, - DistributedBucketSampler, -) - -if hps.version == "v1": - from infer_pack.models import ( - SynthesizerTrnMs256NSFsid as RVC_Model_f0, - SynthesizerTrnMs256NSFsid_nono as RVC_Model_nof0, - MultiPeriodDiscriminator, - ) -else: - from 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_step = 0 - - -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() - os.environ["MASTER_ADDR"] = "localhost" - os.environ["MASTER_PORT"] = "51545" - - 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 - 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 rank == 0: - logger.info("loaded pretrained %s %s" % (hps.pretrainG, hps.pretrainD)) - print( - net_g.module.load_state_dict( - torch.load(hps.pretrainG, map_location="cpu")["model"] - ) - ) ##测试不加载优化器 - 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) - - cache = [] - for epoch in range(epoch_str, hps.train.epochs + 1): - 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, - ) - 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() - - -def train_and_evaluate( - rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers, cache -): - 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: - 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" % epoch, - epoch, - hps.version, - ), - ) - ) - - 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)) - ) - sleep(1) - os._exit(2333333) - - -if __name__ == "__main__": - main() +import sys, os + +now_dir = os.getcwd() +sys.path.append(os.path.join(now_dir)) +sys.path.append(os.path.join(now_dir, "train")) +import utils +import datetime + +hps = utils.get_hparams() +os.environ["CUDA_VISIBLE_DEVICES"] = hps.gpus.replace("-", ",") +n_gpus = len(hps.gpus.split("-")) +from random import shuffle +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 infer_pack import commons +from time import sleep +from time import time as ttime +from data_utils import ( + TextAudioLoaderMultiNSFsid, + TextAudioLoader, + TextAudioCollateMultiNSFsid, + TextAudioCollate, + DistributedBucketSampler, +) + +if hps.version == "v1": + from infer_pack.models import ( + SynthesizerTrnMs256NSFsid as RVC_Model_f0, + SynthesizerTrnMs256NSFsid_nono as RVC_Model_nof0, + MultiPeriodDiscriminator, + ) +else: + from 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_step = 0 + + +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() + os.environ["MASTER_ADDR"] = "localhost" + os.environ["MASTER_PORT"] = "51545" + + 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 + 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 rank == 0: + logger.info("loaded pretrained %s %s" % (hps.pretrainG, hps.pretrainD)) + print( + net_g.module.load_state_dict( + torch.load(hps.pretrainG, map_location="cpu")["model"] + ) + ) ##测试不加载优化器 + 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) + + cache = [] + for epoch in range(epoch_str, hps.train.epochs + 1): + 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, + ) + 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() + + +def train_and_evaluate( + rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers, cache +): + 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: + 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" % epoch, + epoch, + hps.version, + ), + ) + ) + + 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)) + ) + sleep(1) + os._exit(2333333) + + +if __name__ == "__main__": + torch.multiprocessing.set_start_method('spawn') + main()