mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2026-07-13 19:41:10 +08:00
628 lines
22 KiB
Python
628 lines
22 KiB
Python
import logging
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import os
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import platform
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import warnings
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from random import randint
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import torch
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import torch.distributed as dist
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import torch.multiprocessing.spawn as mp
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from torch.amp.autocast_mode import autocast
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from torch.amp.grad_scaler import GradScaler
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from torch.nn import functional as F
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.utils.data import DataLoader
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from torch.utils.tensorboard import SummaryWriter
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from tqdm import tqdm
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import GPT_SoVITS.utils as utils
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from GPT_SoVITS.module import commons
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from GPT_SoVITS.module.data_utils import (
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DistributedBucketSampler,
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TextAudioSpeakerCollate,
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TextAudioSpeakerLoader,
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)
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from GPT_SoVITS.module.losses import discriminator_loss, feature_loss, generator_loss, kl_loss
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from GPT_SoVITS.module.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
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from GPT_SoVITS.module.models import (
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MultiPeriodDiscriminator,
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SynthesizerTrn,
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)
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from GPT_SoVITS.process_ckpt import savee
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logging.getLogger("matplotlib").setLevel(logging.INFO)
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logging.getLogger("h5py").setLevel(logging.INFO)
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logging.getLogger("numba").setLevel(logging.INFO)
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hps = utils.get_hparams(stage=2)
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warnings.filterwarnings("ignore")
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic = False
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torch.backends.cuda.matmul.allow_tf32 = True ###反正A100fp32更快，那试试tf32吧
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torch.backends.cudnn.allow_tf32 = True
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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
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global_step = 0
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if torch.cuda.is_available():
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device_str = "cuda"
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else:
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device_str = "cpu"
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multigpu = torch.cuda.device_count() > 1 if torch.cuda.is_available() else False
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def main():
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if torch.cuda.is_available():
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n_gpus = torch.cuda.device_count()
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else:
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n_gpus = 1
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os.environ["MASTER_ADDR"] = "localhost"
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os.environ["MASTER_PORT"] = str(randint(20000, 55555))
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mp.spawn(
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run,
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nprocs=n_gpus,
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args=(
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n_gpus,
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hps,
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),
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)
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def run(rank, n_gpus, hps):
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global global_step
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device = torch.device("{device_str}:{rank}")
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if rank == 0:
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logger = utils.get_logger(hps.data.exp_dir)
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logger.info(hps)
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writer = SummaryWriter(log_dir=hps.s2_ckpt_dir)
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writer_eval = SummaryWriter(log_dir=os.path.join(hps.s2_ckpt_dir, "eval"))
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else:
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logger = writer = writer_eval = None
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if multigpu:
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dist.init_process_group(
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backend="gloo" if os.name == "nt" or not torch.cuda.is_available() else "nccl",
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init_method="env://?use_libuv=False" if platform.system() == "Windows" else "env://",
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world_size=n_gpus,
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rank=rank,
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)
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torch.manual_seed(hps.train.seed)
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if torch.cuda.is_available():
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torch.cuda.set_device(rank)
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train_dataset = TextAudioSpeakerLoader(hps.data, version=hps.model.version)
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train_sampler = DistributedBucketSampler(
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train_dataset,
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hps.train.batch_size,
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[
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32,
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300,
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400,
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500,
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600,
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700,
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800,
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900,
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1000,
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1100,
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1200,
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1300,
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1400,
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1500,
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1600,
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1700,
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1800,
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1900,
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],
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num_replicas=n_gpus,
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rank=rank,
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shuffle=True,
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)
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collate_fn = TextAudioSpeakerCollate(version=hps.model.version)
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train_loader = DataLoader(
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train_dataset,
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num_workers=5,
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shuffle=False,
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pin_memory=True,
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collate_fn=collate_fn,
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batch_sampler=train_sampler,
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persistent_workers=True,
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prefetch_factor=4,
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)
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net_g = SynthesizerTrn(
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hps.data.filter_length // 2 + 1,
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hps.train.segment_size // hps.data.hop_length,
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n_speakers=hps.data.n_speakers,
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**hps.model,
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).to(device)
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net_d = MultiPeriodDiscriminator(
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hps.model.use_spectral_norm,
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version=hps.model.version,
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).to(device)
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for name, param in net_g.named_parameters():
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if not param.requires_grad:
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print(name, "not requires_grad")
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te_p = list(map(id, net_g.enc_p.text_embedding.parameters()))
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et_p = list(map(id, net_g.enc_p.encoder_text.parameters()))
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mrte_p = list(map(id, net_g.enc_p.mrte.parameters()))
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base_params = filter(
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lambda p: id(p) not in te_p + et_p + mrte_p and p.requires_grad,
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net_g.parameters(),
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)
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optim_g = torch.optim.AdamW(
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# filter(lambda p: p.requires_grad, net_g.parameters()),###默认所有层lr一致
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[
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{"params": base_params, "lr": hps.train.learning_rate},
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{
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"params": net_g.enc_p.text_embedding.parameters(),
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"lr": hps.train.learning_rate * hps.train.text_low_lr_rate,
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},
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{
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"params": net_g.enc_p.encoder_text.parameters(),
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"lr": hps.train.learning_rate * hps.train.text_low_lr_rate,
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},
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{
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"params": net_g.enc_p.mrte.parameters(),
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"lr": hps.train.learning_rate * hps.train.text_low_lr_rate,
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},
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],
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hps.train.learning_rate,
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betas=hps.train.betas,
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eps=hps.train.eps,
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)
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optim_d = torch.optim.AdamW(
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net_d.parameters(),
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hps.train.learning_rate,
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betas=hps.train.betas,
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eps=hps.train.eps,
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)
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if multigpu:
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net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True)
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net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
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else:
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net_g = net_g.to(device)
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net_d = net_d.to(device)
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try: # 如果能加载自动resume
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epoch_str = utils.load_checkpoint(
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utils.latest_checkpoint_path(f"{(hps.data.exp_dir,)}/logs_s2_{hps.model.version}", "D_*.pth"),
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net_d,
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optim_d,
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)[-1] # D多半加载没事
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if rank == 0:
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logger.info("loaded D")
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# _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g,load_opt=0)
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epoch_str = utils.load_checkpoint(
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utils.latest_checkpoint_path(f"{hps.data.exp_dir}/logs_s2_{hps.model.version}", "G_*.pth"),
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net_g,
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optim_g,
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)[-1]
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epoch_str += 1
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global_step = (epoch_str - 1) * len(train_loader)
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# epoch_str = 1
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# global_step = 0
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except Exception: # 如果首次不能加载，加载pretrain
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# traceback.print_exc()
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epoch_str = 1
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global_step = 0
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if (
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hps.train.pretrained_s2G != ""
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and hps.train.pretrained_s2G is not None
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and os.path.exists(hps.train.pretrained_s2G)
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):
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if rank == 0:
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logger.info(f"loaded pretrained {hps.train.pretrained_s2G}")
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print(
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"loaded pretrained %s" % hps.train.pretrained_s2G,
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net_g.module.load_state_dict(
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torch.load(hps.train.pretrained_s2G, map_location="cpu")["weight"],
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strict=False,
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)
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if multigpu
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else net_g.load_state_dict(
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torch.load(hps.train.pretrained_s2G, map_location="cpu")["weight"],
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strict=False,
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),
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) ##测试不加载优化器
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if (
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hps.train.pretrained_s2D != ""
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and hps.train.pretrained_s2D is not None
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and os.path.exists(hps.train.pretrained_s2D)
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):
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if rank == 0:
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logger.info("loaded pretrained %s" % hps.train.pretrained_s2D)
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print(
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"loaded pretrained %s" % hps.train.pretrained_s2D,
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net_d.module.load_state_dict(
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torch.load(hps.train.pretrained_s2D, map_location="cpu")["weight"], strict=False
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)
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if multigpu
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else net_d.load_state_dict(
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torch.load(hps.train.pretrained_s2D, map_location="cpu")["weight"],
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),
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)
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# scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
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# scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
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scheduler_g = torch.optim.lr_scheduler.ExponentialLR(
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optim_g,
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gamma=hps.train.lr_decay,
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last_epoch=-1,
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)
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scheduler_d = torch.optim.lr_scheduler.ExponentialLR(
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optim_d,
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gamma=hps.train.lr_decay,
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last_epoch=-1,
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)
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for _ in range(epoch_str):
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scheduler_g.step()
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scheduler_d.step()
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scaler = GradScaler(device=device.type, enabled=hps.train.fp16_run)
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print("start training from epoch %s" % epoch_str)
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for epoch in range(epoch_str, hps.train.epochs + 1):
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if rank == 0:
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train_and_evaluate(
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device,
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epoch,
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hps,
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[net_g, net_d],
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[optim_g, optim_d],
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[scheduler_g, scheduler_d],
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scaler,
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# [train_loader, eval_loader], logger, [writer, writer_eval])
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[train_loader, None],
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logger,
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[writer, writer_eval],
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)
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else:
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train_and_evaluate(
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device,
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epoch,
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hps,
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[net_g, net_d],
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[optim_g, optim_d],
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[scheduler_g, scheduler_d],
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scaler,
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[train_loader, None],
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None,
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None,
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)
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scheduler_g.step()
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scheduler_d.step()
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print("training done")
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def train_and_evaluate(device: torch.device, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
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net_g, net_d = nets
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optim_g, optim_d = optims
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# scheduler_g, scheduler_d = schedulers
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train_loader, eval_loader = loaders
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if writers is not None:
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writer, writer_eval = writers
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train_loader.batch_sampler.set_epoch(epoch)
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global global_step
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net_g.train()
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net_d.train()
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for batch_idx, data in enumerate(tqdm(train_loader)):
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if hps.model.version in {"v2Pro", "v2ProPlus"}:
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ssl, ssl_lengths, spec, spec_lengths, y, y_lengths, text, text_lengths, sv_emb = data
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ssl, ssl_lengths, spec, spec_lengths, y, y_lengths, text, text_lengths, sv_emb = map(
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lambda x: x.to(device, non_blocking=True),
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(ssl, ssl_lengths, spec, spec_lengths, y, y_lengths, text, text_lengths, sv_emb),
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)
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else:
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ssl, ssl_lengths, spec, spec_lengths, y, y_lengths, text, text_lengths = data
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ssl, ssl_lengths, spec, spec_lengths, y, y_lengths, text, text_lengths = map(
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lambda x: x.to(device, non_blocking=True),
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(ssl, ssl_lengths, spec, spec_lengths, y, y_lengths, text, text_lengths),
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)
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ssl.requires_grad = False
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with autocast(device_type=device.type, dtype=torch.float16, enabled=hps.train.fp16_run):
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if hps.model.version in {"v2Pro", "v2ProPlus"}:
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(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(
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ssl, spec, spec_lengths, text, text_lengths, sv_emb
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)
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else:
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(
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y_hat,
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kl_ssl,
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ids_slice,
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x_mask,
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z_mask,
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(z, z_p, m_p, logs_p, m_q, logs_q),
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stats_ssl,
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) = net_g(ssl, spec, spec_lengths, text, text_lengths)
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mel = spec_to_mel_torch(
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spec,
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hps.data.filter_length,
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hps.data.n_mel_channels,
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hps.data.sampling_rate,
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hps.data.mel_fmin,
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hps.data.mel_fmax,
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)
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y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
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y_hat_mel = mel_spectrogram_torch(
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y_hat.squeeze(1),
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hps.data.filter_length,
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hps.data.n_mel_channels,
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hps.data.sampling_rate,
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hps.data.hop_length,
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hps.data.win_length,
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hps.data.mel_fmin,
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hps.data.mel_fmax,
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)
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y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice
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# Discriminator
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y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
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with autocast(device_type=device.type, enabled=False):
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loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(
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y_d_hat_r,
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y_d_hat_g,
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)
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loss_disc_all = loss_disc
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optim_d.zero_grad()
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scaler.scale(loss_disc_all).backward()
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scaler.unscale_(optim_d)
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grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
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scaler.step(optim_d)
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with autocast(device_type=device.type, dtype=torch.float16, enabled=hps.train.fp16_run):
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# Generator
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y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
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with autocast(device_type=device.type, enabled=False):
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loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
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loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
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loss_fm = feature_loss(fmap_r, fmap_g)
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loss_gen, losses_gen = generator_loss(y_d_hat_g)
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loss_gen_all = loss_gen + loss_fm + loss_mel + kl_ssl * 1 + loss_kl
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optim_g.zero_grad()
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scaler.scale(loss_gen_all).backward()
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scaler.unscale_(optim_g)
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grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
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scaler.step(optim_g)
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scaler.update()
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if device.index == 0:
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if global_step % hps.train.log_interval == 0:
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lr = optim_g.param_groups[0]["lr"]
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losses = [loss_disc, loss_gen, loss_fm, loss_mel, kl_ssl, loss_kl]
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logger.info(
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"Train Epoch: {} [{:.0f}%]".format(
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epoch,
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100.0 * batch_idx / len(train_loader),
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)
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)
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logger.info([x.item() for x in losses] + [global_step, lr])
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scalar_dict = {
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"loss/g/total": loss_gen_all,
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"loss/d/total": loss_disc_all,
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"learning_rate": lr,
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"grad_norm_d": grad_norm_d,
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"grad_norm_g": grad_norm_g,
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}
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scalar_dict.update(
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{
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"loss/g/fm": loss_fm,
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"loss/g/mel": loss_mel,
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"loss/g/kl_ssl": kl_ssl,
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"loss/g/kl": loss_kl,
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}
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)
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# scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
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# scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
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# scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
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image_dict = None
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try: # Some people installed the wrong version of matplotlib.
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image_dict = {
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"slice/mel_org": utils.plot_spectrogram_to_numpy(
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y_mel[0].data.cpu().numpy(),
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),
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"slice/mel_gen": utils.plot_spectrogram_to_numpy(
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y_hat_mel[0].data.cpu().numpy(),
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),
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"all/mel": utils.plot_spectrogram_to_numpy(
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mel[0].data.cpu().numpy(),
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),
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"all/stats_ssl": utils.plot_spectrogram_to_numpy(
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stats_ssl[0].data.cpu().numpy(),
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),
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}
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except Exception as _:
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||
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 device.index == 0:
|
||
if hps.train.if_save_latest == 0:
|
||
utils.save_checkpoint(
|
||
net_g,
|
||
optim_g,
|
||
hps.train.learning_rate,
|
||
epoch,
|
||
os.path.join(
|
||
f"{hps.data.exp_dir}/logs_s2_{hps.model.version}",
|
||
f"G_{global_step}.pth",
|
||
),
|
||
)
|
||
utils.save_checkpoint(
|
||
net_d,
|
||
optim_d,
|
||
hps.train.learning_rate,
|
||
epoch,
|
||
os.path.join(
|
||
"{hps.data.exp_dir}/logs_s2_{hps.model.version}",
|
||
"D_{global_step}.pth",
|
||
),
|
||
)
|
||
else:
|
||
utils.save_checkpoint(
|
||
net_g,
|
||
optim_g,
|
||
hps.train.learning_rate,
|
||
epoch,
|
||
os.path.join(
|
||
f"{hps.data.exp_dir}/logs_s2_{hps.model.version}",
|
||
"G_233333333333.pth",
|
||
),
|
||
)
|
||
utils.save_checkpoint(
|
||
net_d,
|
||
optim_d,
|
||
hps.train.learning_rate,
|
||
epoch,
|
||
os.path.join(
|
||
"{hps.data.exp_dir}/logs_s2_{hps.model.version}",
|
||
"D_233333333333.pth",
|
||
),
|
||
)
|
||
if device.index == 0 and hps.train.if_save_every_weights is 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 + f"_e{epoch}_s{global_step}",
|
||
epoch,
|
||
global_step,
|
||
hps,
|
||
model_version=None if hps.model.version not in {"v2Pro", "v2ProPlus"} else hps.model.version,
|
||
),
|
||
)
|
||
)
|
||
|
||
if device.index == 0:
|
||
logger.info(f"====> Epoch: {epoch}")
|
||
|
||
|
||
def evaluate(hps, generator, eval_loader, writer_eval, device):
|
||
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):
|
||
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]]})
|
||
|
||
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()
|