import os from lib.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, randint import torch torch.backends.cudnn.deterministic = False torch.backends.cudnn.benchmark = False 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 lib.train.data_utils import ( TextAudioLoaderMultiNSFsid, TextAudioLoader, TextAudioCollateMultiNSFsid, TextAudioCollate, DistributedBucketSampler, ) 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 lib.train.losses import generator_loss, discriminator_loss, feature_loss, kl_loss from lib.train.mel_processing import mel_spectrogram_torch, spec_to_mel_torch from lib.train.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() 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 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) 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_s%s" % (epoch, global_step), epoch, hps.version, hps, ), ) ) 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 ) ) ) sleep(1) os._exit(2333333) if __name__ == "__main__": torch.multiprocessing.set_start_method("spawn") main()