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2024-01-16 17:38:48 +08:00
parent 143d32f621
commit 41ca6028d6
65 changed files with 139856 additions and 0 deletions
--- a/GPT_SoVITS/s2_train.py
+++ b/GPT_SoVITS/s2_train.py
@@ -0,0 +1,402 @@
+import utils,os
+hps = utils.get_hparams(stage=2)
+os.environ["CUDA_VISIBLE_DEVICES"] = hps.train.gpu_numbers.replace("-", ",")
+import torch
+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,traceback
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.cuda.amp import autocast, GradScaler
+from tqdm import tqdm
+import logging,traceback
+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 (
+    TextAudioSpeakerLoader,
+    TextAudioSpeakerCollate,
+    DistributedBucketSampler
+)
+from module.models import (
+    SynthesizerTrn,
+    MultiPeriodDiscriminator,
+)
+from module.losses import (
+    generator_loss,
+    discriminator_loss,
+    feature_loss,
+    kl_loss
+)
+from module.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
+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
+def main():
+    """Assume Single Node Multi GPUs Training Only"""
+    assert torch.cuda.is_available(), "CPU training is not allowed."
+
+    n_gpus = torch.cuda.device_count()
+    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' else 'nccl', init_method='env://', world_size=n_gpus,rank=rank)
+    torch.manual_seed(hps.train.seed)
+    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=16)
+    # 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)
+
+    net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
+    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)
+    net_g = DDP(net_g, device_ids=[rank],find_unused_parameters=True)
+    net_d = DDP(net_d, device_ids=[rank],find_unused_parameters=True)
+
+    try:  # 如果能加载自动resume
+        _, _, _, epoch_str = utils.load_checkpoint(
+            utils.latest_checkpoint_path("%s/logs_s2"%hps.data.exp_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("%s/logs_s2"%hps.data.exp_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.train.pretrained_s2G != "":
+            if rank == 0:
+                logger.info("loaded pretrained %s" % hps.train.pretrained_s2G)
+            print(
+                net_g.module.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 != "":
+            if rank == 0:
+                logger.info("loaded pretrained %s" % hps.train.pretrained_s2D)
+            print(
+                net_d.module.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)
+
+    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()
+
+
+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 tqdm(enumerate(train_loader)):
+        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)
+
+        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. * 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 = {
+                    "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()),
+                }
+                utils.summarize(
+                    writer=writer,
+                    global_step=global_step,
+                    images=image_dict,
+                    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"%hps.data.exp_dir, "G_{}.pth".format(global_step)),
+            )
+            utils.save_checkpoint(
+                net_d,
+                optim_d,
+                hps.train.learning_rate,
+                epoch,
+                os.path.join("%s/logs_s2"%hps.data.exp_dir, "D_{}.pth".format(global_step)),
+            )
+        else:
+            utils.save_checkpoint(
+                net_g,
+                optim_g,
+                hps.train.learning_rate,
+                epoch,
+                os.path.join("%s/logs_s2"%hps.data.exp_dir, "G_{}.pth".format(233333333333)),
+            )
+            utils.save_checkpoint(
+                net_d,
+                optim_d,
+                hps.train.learning_rate,
+                epoch,
+                os.path.join("%s/logs_s2"%hps.data.exp_dir, "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)
+            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()
+            for test in [0, 1]:
+
+                y_hat, mask, *_ = generator.module.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()