390 lines
16 KiB
Python
390 lines
16 KiB
Python
import os
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from loguru import logger
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import torch
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import glob
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from torch.nn import functional as F
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from torch.utils.data import DataLoader
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from torch.utils.tensorboard import SummaryWriter
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import torch.distributed as dist
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.cuda.amp import autocast, GradScaler
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from utils.audio_utils import mel_spectrogram, spec_to_mel
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from utils.loss import feature_loss, generator_loss, discriminator_loss, kl_loss
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from utils.util import slice_segments, clip_grad_value_
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from models.synthesizer.vits_dataset import (
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VitsDataset,
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VitsDatasetCollate,
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DistributedBucketSampler
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)
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from models.synthesizer.models.vits import (
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Vits,
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MultiPeriodDiscriminator,
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)
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from models.synthesizer.utils.symbols import symbols
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from models.synthesizer.utils.plot import plot_spectrogram_to_numpy, plot_alignment_to_numpy
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from pathlib import Path
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from utils.hparams import HParams
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import torch.multiprocessing as mp
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import argparse
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# torch.backends.cudnn.benchmark = True
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global_step = 0
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def new_train():
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"""Assume Single Node Multi GPUs Training Only"""
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assert torch.cuda.is_available(), "CPU training is not allowed."
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parser = argparse.ArgumentParser()
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parser.add_argument("--syn_dir", type=str, default="../audiodata/SV2TTS/synthesizer", help= \
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"Path to the synthesizer directory that contains the ground truth mel spectrograms, "
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"the wavs, the emos and the embeds.")
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parser.add_argument("-m", "--model_dir", type=str, default="data/ckpt/synthesizer/vits", help=\
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"Path to the output directory that will contain the saved model weights and the logs.")
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parser.add_argument('--ckptG', type=str, required=False,
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help='original VITS G checkpoint path')
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parser.add_argument('--ckptD', type=str, required=False,
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help='original VITS D checkpoint path')
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args, _ = parser.parse_known_args()
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datasets_root = Path(args.syn_dir)
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hparams= HParams(
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model_dir = args.model_dir,
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)
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hparams.loadJson(Path(hparams.model_dir).joinpath("config.json"))
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hparams.data["training_files"] = str(datasets_root.joinpath("train.txt"))
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hparams.data["validation_files"] = str(datasets_root.joinpath("train.txt"))
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hparams.data["datasets_root"] = str(datasets_root)
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hparams.ckptG = args.ckptG
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hparams.ckptD = args.ckptD
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n_gpus = torch.cuda.device_count()
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# for spawn
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os.environ['MASTER_ADDR'] = 'localhost'
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os.environ['MASTER_PORT'] = '8899'
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# mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hparams))
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run(0, 1, hparams)
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def load_checkpoint(checkpoint_path, model, optimizer=None, is_old=False):
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assert os.path.isfile(checkpoint_path)
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checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
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iteration = checkpoint_dict['iteration']
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learning_rate = checkpoint_dict['learning_rate']
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if optimizer is not None:
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if not is_old:
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optimizer.load_state_dict(checkpoint_dict['optimizer'])
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else:
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new_opt_dict = optimizer.state_dict()
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new_opt_dict_params = new_opt_dict['param_groups'][0]['params']
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new_opt_dict['param_groups'] = checkpoint_dict['optimizer']['param_groups']
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new_opt_dict['param_groups'][0]['params'] = new_opt_dict_params
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optimizer.load_state_dict(new_opt_dict)
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saved_state_dict = checkpoint_dict['model']
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if hasattr(model, 'module'):
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state_dict = model.module.state_dict()
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else:
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state_dict = model.state_dict()
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new_state_dict= {}
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for k, v in state_dict.items():
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try:
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new_state_dict[k] = saved_state_dict[k]
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except:
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logger.info("%s is not in the checkpoint" % k)
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new_state_dict[k] = v
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if hasattr(model, 'module'):
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model.module.load_state_dict(new_state_dict, strict=False)
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else:
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model.load_state_dict(new_state_dict, strict=False)
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logger.info("Loaded checkpoint '{}' (iteration {})" .format(
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checkpoint_path, iteration))
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return model, optimizer, learning_rate, iteration
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def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
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logger.info("Saving model and optimizer state at iteration {} to {}".format(
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iteration, checkpoint_path))
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if hasattr(model, 'module'):
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state_dict = model.module.state_dict()
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else:
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state_dict = model.state_dict()
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torch.save({'model': state_dict,
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'iteration': iteration,
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'optimizer': optimizer.state_dict(),
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'learning_rate': learning_rate}, checkpoint_path)
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def latest_checkpoint_path(dir_path, regex="G_*.pth"):
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f_list = glob.glob(os.path.join(dir_path, regex))
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f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
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x = f_list[-1]
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print(x)
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return x
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def run(rank, n_gpus, hps):
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global global_step
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if rank == 0:
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logger.info(hps)
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writer = SummaryWriter(log_dir=hps.model_dir)
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writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
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dist.init_process_group(backend='gloo', init_method='env://', world_size=n_gpus, rank=rank)
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torch.manual_seed(hps.train.seed)
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torch.cuda.set_device(rank)
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train_dataset = VitsDataset(hps.data.training_files, hps.data)
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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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[32, 300, 400, 500, 600, 700, 800, 900, 1000],
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num_replicas=n_gpus,
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rank=rank,
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shuffle=True)
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collate_fn = VitsDatasetCollate()
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train_loader = DataLoader(train_dataset, num_workers=8, shuffle=False, pin_memory=True,
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collate_fn=collate_fn, batch_sampler=train_sampler)
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if rank == 0:
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eval_dataset = VitsDataset(hps.data.validation_files, hps.data)
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eval_loader = DataLoader(eval_dataset, num_workers=8, shuffle=False,
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batch_size=hps.train.batch_size, pin_memory=True,
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drop_last=False, collate_fn=collate_fn)
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net_g = Vits(
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len(symbols),
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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).cuda(rank)
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net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
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optim_g = torch.optim.AdamW(
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net_g.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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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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net_g = DDP(net_g, device_ids=[rank])
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net_d = DDP(net_d, device_ids=[rank])
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ckptG = hps.ckptG
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ckptD = hps.ckptD
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try:
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if ckptG is not None:
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_, _, _, epoch_str = load_checkpoint(ckptG, net_g, optim_g, is_old=True)
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print("加载原版VITS模型G记录点成功")
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else:
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_, _, _, epoch_str = load_checkpoint(latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g,
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optim_g)
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if ckptD is not None:
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_, _, _, epoch_str = load_checkpoint(ckptG, net_g, optim_g, is_old=True)
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print("加载原版VITS模型D记录点成功")
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else:
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_, _, _, epoch_str = load_checkpoint(latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d,
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optim_d)
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global_step = (epoch_str - 1) * len(train_loader)
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except:
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epoch_str = 1
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global_step = 0
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if ckptG is not None or ckptD is not None:
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epoch_str = 1
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global_step = 0
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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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scaler = GradScaler(enabled=hps.train.fp16_run)
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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(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler,
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[train_loader, eval_loader], logger, [writer, writer_eval])
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else:
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train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler,
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[train_loader, None], None, None)
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scheduler_g.step()
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scheduler_d.step()
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def train_and_evaluate(rank, 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, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, emo) in enumerate(train_loader):
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logger.info(f'====> Step: 1 {batch_idx}')
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x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True)
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spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True)
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y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)
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speakers = speakers.cuda(rank, non_blocking=True)
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emo = emo.cuda(rank, non_blocking=True)
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with autocast(enabled=hps.train.fp16_run):
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y_hat, l_length, attn, ids_slice, x_mask, z_mask, \
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(z, z_p, m_p, logs_p, m_q, logs_q) = net_g(x, x_lengths, spec, spec_lengths, speakers, emo)
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mel = spec_to_mel(
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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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y_mel = slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
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y_hat_mel = mel_spectrogram(
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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 = 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(enabled=False):
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loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
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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 = clip_grad_value_(net_d.parameters(), None)
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scaler.step(optim_d)
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logger.info(f'====> Step: 2 {batch_idx}')
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with autocast(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(enabled=False):
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loss_dur = torch.sum(l_length.float())
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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 + loss_dur + loss_kl
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optim_g.zero_grad()
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scaler.scale(loss_gen_all.float()).backward()
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scaler.unscale_(optim_g)
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grad_norm_g = 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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# logger.info(f'====> Step: 3 {batch_idx}')
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if rank == 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, loss_dur, loss_kl]
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logger.info('Train Epoch: {} [{:.0f}%]'.format(
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epoch,
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100. * batch_idx / len(train_loader)))
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logger.info([x.item() for x in losses] + [global_step, lr])
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scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr,
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"grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g}
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scalar_dict.update(
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{"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/dur": loss_dur, "loss/g/kl": loss_kl})
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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 = {
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"slice/mel_org": plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
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"slice/mel_gen": plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()),
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"all/mel": plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
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"all/attn": plot_alignment_to_numpy(attn[0, 0].data.cpu().numpy())
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}
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summarize(
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writer=writer,
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global_step=global_step,
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images=image_dict,
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scalars=scalar_dict)
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if global_step % hps.train.eval_interval == 0:
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evaluate(hps, net_g, eval_loader, writer_eval)
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save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch,
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os.path.join(hps.model_dir, "G_{}.pth".format(global_step)))
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save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch,
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os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
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global_step += 1
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if rank == 0:
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logger.info('====> Epoch: {}'.format(epoch))
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def evaluate(hps, generator, eval_loader, writer_eval):
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generator.eval()
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with torch.no_grad():
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for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, emo) in enumerate(eval_loader):
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x, x_lengths = x.cuda(0), x_lengths.cuda(0)
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spec, spec_lengths = spec.cuda(0), spec_lengths.cuda(0)
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y, y_lengths = y.cuda(0), y_lengths.cuda(0)
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speakers = speakers.cuda(0)
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emo = emo.cuda(0)
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# remove else
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x = x[:1]
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x_lengths = x_lengths[:1]
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spec = spec[:1]
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spec_lengths = spec_lengths[:1]
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y = y[:1]
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y_lengths = y_lengths[:1]
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speakers = speakers[:1]
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emo = emo[:1]
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break
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y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, speakers, emo, max_len=1000)
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y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length
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mel = spec_to_mel(
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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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y_hat_mel = mel_spectrogram(
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y_hat.squeeze(1).float(),
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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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image_dict = {
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"gen/mel": plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())
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}
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audio_dict = {
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"gen/audio": y_hat[0, :, :y_hat_lengths[0]]
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}
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if global_step == 0:
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image_dict.update({"gt/mel": plot_spectrogram_to_numpy(mel[0].cpu().numpy())})
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audio_dict.update({"gt/audio": y[0, :, :y_lengths[0]]})
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summarize(
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writer=writer_eval,
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global_step=global_step,
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images=image_dict,
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audios=audio_dict,
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audio_sampling_rate=hps.data.sampling_rate
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)
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generator.train()
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def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
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for k, v in scalars.items():
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writer.add_scalar(k, v, global_step)
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for k, v in histograms.items():
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writer.add_histogram(k, v, global_step)
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for k, v in images.items():
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writer.add_image(k, v, global_step, dataformats='HWC')
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for k, v in audios.items():
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writer.add_audio(k, v, global_step, audio_sampling_rate)
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