import sys,os now_dir=os.getcwd() sys.path.append(os.path.join(now_dir,"train")) import utils 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 time as ttime from data_utils import TextAudioLoaderMultiNSFsid,TextAudioLoader, TextAudioCollateMultiNSFsid,TextAudioCollate, DistributedBucketSampler from infer_pack.models import ( SynthesizerTrnMs256NSFsid,SynthesizerTrnMs256NSFsid_nono, MultiPeriodDiscriminator, ) from losses import generator_loss, discriminator_loss, feature_loss, kl_loss from mel_processing import mel_spectrogram_torch, spec_to_mel_torch global_step = 0 def main(): # n_gpus = torch.cuda.device_count() os.environ["MASTER_ADDR"] = "localhost" os.environ["MASTER_PORT"] = "5555" mp.spawn( run, nprocs=n_gpus, args=( n_gpus, hps, ), ) 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 = SynthesizerTrnMs256NSFsid(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 = SynthesizerTrnMs256NSFsid_nono(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() if(cache==[]or hps.if_cache_data_in_gpu==False):#第一个epoch把cache全部填满训练集 # print("caching") for batch_idx, info in enumerate(train_loader): 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 if torch.cuda.is_available(): phone, phone_lengths = phone.cuda(rank, non_blocking=True), phone_lengths.cuda(rank, non_blocking=True ) if (hps.if_f0 == 1):pitch,pitchf = pitch.cuda(rank, non_blocking=True),pitchf.cuda(rank, non_blocking=True) sid = sid.cuda(rank, non_blocking=True) spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True) wave, wave_lengths = wave.cuda(rank, non_blocking=True), wave_lengths.cuda(rank, non_blocking=True) if(hps.if_cache_data_in_gpu==True): 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))) 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 > 50: loss_mel = 50 if loss_kl > 5: loss_kl = 5 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 # if global_step % hps.train.eval_interval == 0: 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)), ) else:#后续的epoch直接使用打乱的cache shuffle(cache) # print("using cache") for batch_idx, info in cache: 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 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 > 50: loss_mel = 50 if loss_kl > 5: loss_kl = 5 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 # if global_step % hps.train.eval_interval == 0: 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: logger.info("====> Epoch: {}".format(epoch)) if(epoch>=hps.total_epoch and rank == 0): logger.info("Training is done. The program is closed.") from process_ckpt import savee#def savee(ckpt,sr,if_f0,name,epoch): 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))) os._exit(2333333) if __name__ == "__main__": main()