import warnings warnings.filterwarnings("ignore") import os import utils hps = utils.get_hparams(stage=2) os.environ["CUDA_VISIBLE_DEVICES"] = hps.train.gpu_numbers.replace("-", ",") import logging import torch import torch.distributed as dist import torch.multiprocessing as mp from torch.cuda.amp import GradScaler, autocast from torch.nn import functional as F from torch.nn.parallel import DistributedDataParallel as DDP from torch.utils.data import DataLoader from torch.utils.tensorboard import SummaryWriter from tqdm import tqdm logging.getLogger("matplotlib").setLevel(logging.INFO) logging.getLogger("h5py").setLevel(logging.INFO) logging.getLogger("numba").setLevel(logging.INFO) from random import randint from module import commons from module.data_utils import ( DistributedBucketSampler, TextAudioSpeakerCollate, TextAudioSpeakerLoader, ) from module.losses import discriminator_loss, feature_loss, generator_loss, kl_loss from module.mel_processing import mel_spectrogram_torch, spec_to_mel_torch from module.models import ( MultiPeriodDiscriminator, SynthesizerTrn, ) from process_ckpt import savee torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = False ###反正A100fp32更快，那试试tf32吧 torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True torch.set_float32_matmul_precision("medium") # 最低精度但最快（也就快一丁点），对于结果造成不了影响 # from config import pretrained_s2G,pretrained_s2D global_step = 0 device = "cpu" # cuda以外的设备，等mps优化后加入 def main(): if torch.cuda.is_available(): n_gpus = torch.cuda.device_count() else: n_gpus = 1 os.environ["MASTER_ADDR"] = "localhost" os.environ["MASTER_PORT"] = str(randint(20000, 55555)) 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.data.exp_dir) logger.info(hps) # utils.check_git_hash(hps.s2_ckpt_dir) writer = SummaryWriter(log_dir=hps.s2_ckpt_dir) writer_eval = SummaryWriter(log_dir=os.path.join(hps.s2_ckpt_dir, "eval")) dist.init_process_group( backend="gloo" if os.name == "nt" or not torch.cuda.is_available() else "nccl", init_method="env://?use_libuv=False", world_size=n_gpus, rank=rank, ) torch.manual_seed(hps.train.seed) if torch.cuda.is_available(): torch.cuda.set_device(rank) train_dataset = TextAudioSpeakerLoader(hps.data) ######## train_sampler = DistributedBucketSampler( train_dataset, hps.train.batch_size, [ 32, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, ], num_replicas=n_gpus, rank=rank, shuffle=True, ) collate_fn = TextAudioSpeakerCollate() train_loader = DataLoader( train_dataset, num_workers=6, shuffle=False, pin_memory=True, collate_fn=collate_fn, batch_sampler=train_sampler, persistent_workers=True, prefetch_factor=4, ) # if rank == 0: # eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data, val=True) # eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False, # batch_size=1, pin_memory=True, # drop_last=False, collate_fn=collate_fn) net_g = ( SynthesizerTrn( hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, n_speakers=hps.data.n_speakers, **hps.model, ).cuda(rank) if torch.cuda.is_available() else SynthesizerTrn( hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, n_speakers=hps.data.n_speakers, **hps.model, ).to(device) ) net_d = ( MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank) if torch.cuda.is_available() else MultiPeriodDiscriminator(hps.model.use_spectral_norm).to(device) ) for name, param in net_g.named_parameters(): if not param.requires_grad: print(name, "not requires_grad") te_p = list(map(id, net_g.enc_p.text_embedding.parameters())) et_p = list(map(id, net_g.enc_p.encoder_text.parameters())) mrte_p = list(map(id, net_g.enc_p.mrte.parameters())) base_params = filter( lambda p: id(p) not in te_p + et_p + mrte_p and p.requires_grad, net_g.parameters(), ) # te_p=net_g.enc_p.text_embedding.parameters() # et_p=net_g.enc_p.encoder_text.parameters() # mrte_p=net_g.enc_p.mrte.parameters() optim_g = torch.optim.AdamW( # filter(lambda p: p.requires_grad, net_g.parameters()),###默认所有层lr一致 [ {"params": base_params, "lr": hps.train.learning_rate}, { "params": net_g.enc_p.text_embedding.parameters(), "lr": hps.train.learning_rate * hps.train.text_low_lr_rate, }, { "params": net_g.enc_p.encoder_text.parameters(), "lr": hps.train.learning_rate * hps.train.text_low_lr_rate, }, { "params": net_g.enc_p.mrte.parameters(), "lr": hps.train.learning_rate * hps.train.text_low_lr_rate, }, ], 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, ) if torch.cuda.is_available(): net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True) net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True) else: net_g = net_g.to(device) net_d = net_d.to(device) try: # 如果能加载自动resume _, _, _, epoch_str = utils.load_checkpoint( utils.latest_checkpoint_path("%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version), "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("%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version), "G_*.pth"), net_g, optim_g, ) epoch_str += 1 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.train.pretrained_s2G != "" and hps.train.pretrained_s2G != None and os.path.exists(hps.train.pretrained_s2G) ): if rank == 0: logger.info("loaded pretrained %s" % hps.train.pretrained_s2G) print( "loaded pretrained %s" % hps.train.pretrained_s2G, net_g.module.load_state_dict( torch.load(hps.train.pretrained_s2G, map_location="cpu")["weight"], strict=False, ) if torch.cuda.is_available() else net_g.load_state_dict( torch.load(hps.train.pretrained_s2G, map_location="cpu")["weight"], strict=False, ), ) ##测试不加载优化器 if ( hps.train.pretrained_s2D != "" and hps.train.pretrained_s2D != None and os.path.exists(hps.train.pretrained_s2D) ): if rank == 0: logger.info("loaded pretrained %s" % hps.train.pretrained_s2D) print( "loaded pretrained %s" % hps.train.pretrained_s2D, net_d.module.load_state_dict( torch.load(hps.train.pretrained_s2D, map_location="cpu")["weight"], ) if torch.cuda.is_available() else net_d.load_state_dict( torch.load(hps.train.pretrained_s2D, map_location="cpu")["weight"], ), ) # 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) scheduler_g = torch.optim.lr_scheduler.ExponentialLR( optim_g, gamma=hps.train.lr_decay, last_epoch=-1, ) scheduler_d = torch.optim.lr_scheduler.ExponentialLR( optim_d, gamma=hps.train.lr_decay, last_epoch=-1, ) for _ in range(epoch_str): scheduler_g.step() scheduler_d.step() scaler = GradScaler(enabled=hps.train.fp16_run) print("start training from epoch %s" % epoch_str) 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, eval_loader], logger, [writer, writer_eval]) [train_loader, None], logger, [writer, writer_eval], ) 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, ) scheduler_g.step() scheduler_d.step() print("training done") def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers): net_g, net_d = nets optim_g, optim_d = optims # scheduler_g, scheduler_d = schedulers 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() for batch_idx, ( ssl, ssl_lengths, spec, spec_lengths, y, y_lengths, text, text_lengths, ) in enumerate(tqdm(train_loader)): if torch.cuda.is_available(): spec, spec_lengths = ( spec.cuda( rank, non_blocking=True, ), spec_lengths.cuda( rank, non_blocking=True, ), ) y, y_lengths = ( y.cuda( rank, non_blocking=True, ), y_lengths.cuda( rank, non_blocking=True, ), ) ssl = ssl.cuda(rank, non_blocking=True) ssl.requires_grad = False # ssl_lengths = ssl_lengths.cuda(rank, non_blocking=True) text, text_lengths = ( text.cuda( rank, non_blocking=True, ), text_lengths.cuda( rank, non_blocking=True, ), ) else: spec, spec_lengths = spec.to(device), spec_lengths.to(device) y, y_lengths = y.to(device), y_lengths.to(device) ssl = ssl.to(device) ssl.requires_grad = False # ssl_lengths = ssl_lengths.cuda(rank, non_blocking=True) text, text_lengths = text.to(device), text_lengths.to(device) with autocast(enabled=hps.train.fp16_run): ( y_hat, kl_ssl, ids_slice, x_mask, z_mask, (z, z_p, m_p, logs_p, m_q, logs_q), stats_ssl, ) = net_g(ssl, spec, spec_lengths, text, text_lengths) 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) y_hat_mel = mel_spectrogram_torch( y_hat.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, ) y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice # Discriminator y_d_hat_r, y_d_hat_g, _, _ = net_d(y, 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, ) loss_disc_all = loss_disc optim_d.zero_grad() scaler.scale(loss_disc_all).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(y, 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 + kl_ssl * 1 + 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"] losses = [loss_disc, loss_gen, loss_fm, loss_mel, kl_ssl, loss_kl] logger.info( "Train Epoch: {} [{:.0f}%]".format( epoch, 100.0 * batch_idx / len(train_loader), ) ) logger.info([x.item() for x in losses] + [global_step, lr]) scalar_dict = { "loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "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_ssl": kl_ssl, "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 = None try: ###Some people installed the wrong version of matplotlib. 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(), ), "all/stats_ssl": utils.plot_spectrogram_to_numpy( stats_ssl[0].data.cpu().numpy(), ), } except: pass if image_dict: utils.summarize( writer=writer, global_step=global_step, images=image_dict, scalars=scalar_dict, ) else: utils.summarize( writer=writer, global_step=global_step, scalars=scalar_dict, ) global_step += 1 if epoch % hps.train.save_every_epoch == 0 and rank == 0: if hps.train.if_save_latest == 0: utils.save_checkpoint( net_g, optim_g, hps.train.learning_rate, epoch, os.path.join( "%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version), "G_{}.pth".format(global_step), ), ) utils.save_checkpoint( net_d, optim_d, hps.train.learning_rate, epoch, os.path.join( "%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version), "D_{}.pth".format(global_step), ), ) else: utils.save_checkpoint( net_g, optim_g, hps.train.learning_rate, epoch, os.path.join( "%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version), "G_{}.pth".format(233333333333), ), ) utils.save_checkpoint( net_d, optim_d, hps.train.learning_rate, epoch, os.path.join( "%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version), "D_{}.pth".format(233333333333), ), ) if rank == 0 and hps.train.if_save_every_weights == True: 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.name + "_e%s_s%s" % (epoch, global_step), epoch, global_step, hps, ), ) ) if rank == 0: logger.info("====> Epoch: {}".format(epoch)) def evaluate(hps, generator, eval_loader, writer_eval): generator.eval() image_dict = {} audio_dict = {} print("Evaluating ...") with torch.no_grad(): for batch_idx, ( ssl, ssl_lengths, spec, spec_lengths, y, y_lengths, text, text_lengths, ) in enumerate(eval_loader): print(111) if torch.cuda.is_available(): spec, spec_lengths = spec.cuda(), spec_lengths.cuda() y, y_lengths = y.cuda(), y_lengths.cuda() ssl = ssl.cuda() text, text_lengths = text.cuda(), text_lengths.cuda() else: spec, spec_lengths = spec.to(device), spec_lengths.to(device) y, y_lengths = y.to(device), y_lengths.to(device) ssl = ssl.to(device) text, text_lengths = text.to(device), text_lengths.to(device) for test in [0, 1]: y_hat, mask, *_ = ( generator.module.infer( ssl, spec, spec_lengths, text, text_lengths, test=test, ) if torch.cuda.is_available() else generator.infer( ssl, spec, spec_lengths, text, text_lengths, test=test, ) ) y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length 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_hat_mel = mel_spectrogram_torch( y_hat.squeeze(1).float(), 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, ) image_dict.update( { f"gen/mel_{batch_idx}_{test}": utils.plot_spectrogram_to_numpy( y_hat_mel[0].cpu().numpy(), ), } ) audio_dict.update( { f"gen/audio_{batch_idx}_{test}": y_hat[0, :, : y_hat_lengths[0]], }, ) image_dict.update( { f"gt/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy()), }, ) audio_dict.update({f"gt/audio_{batch_idx}": y[0, :, : y_lengths[0]]}) # y_hat, mask, *_ = generator.module.infer(ssl, spec_lengths, speakers, y=None) # audio_dict.update({ # f"gen/audio_{batch_idx}_style_pred": y_hat[0, :, :] # }) utils.summarize( writer=writer_eval, global_step=global_step, images=image_dict, audios=audio_dict, audio_sampling_rate=hps.data.sampling_rate, ) generator.train() if __name__ == "__main__": main()