711 lines
25 KiB
Python
711 lines
25 KiB
Python
# flake8: noqa: E402
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import platform
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import os
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import torch
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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 tqdm import tqdm
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import logging
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from config import config
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import argparse
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import datetime
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import gc
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logging.getLogger("numba").setLevel(logging.WARNING)
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import commons
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import utils
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from oldVersion.V210.data_utils import (
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TextAudioSpeakerLoader,
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TextAudioSpeakerCollate,
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DistributedBucketSampler,
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)
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from oldVersion.V210.models import (
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SynthesizerTrn,
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MultiPeriodDiscriminator,
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DurationDiscriminator,
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)
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from losses import generator_loss, discriminator_loss, feature_loss, kl_loss
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from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
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from oldVersion.V210.text.symbols import symbols
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torch.backends.cuda.matmul.allow_tf32 = True
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torch.backends.cudnn.allow_tf32 = (
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True # If encontered training problem,please try to disable TF32.
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)
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torch.set_float32_matmul_precision("medium")
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torch.backends.cuda.sdp_kernel("flash")
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torch.backends.cuda.enable_flash_sdp(True)
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torch.backends.cuda.enable_mem_efficient_sdp(
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True
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) # Not available if torch version is lower than 2.0
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torch.backends.cuda.enable_math_sdp(True)
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global_step = 0
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def run():
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# 环境变量解析
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envs = config.train_ms_config.env
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for env_name, env_value in envs.items():
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if env_name not in os.environ.keys():
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print("加载config中的配置{}".format(str(env_value)))
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os.environ[env_name] = str(env_value)
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print(
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"加载环境变量 \nMASTER_ADDR: {},\nMASTER_PORT: {},\nWORLD_SIZE: {},\nRANK: {},\nLOCAL_RANK: {}".format(
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os.environ["MASTER_ADDR"],
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os.environ["MASTER_PORT"],
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os.environ["WORLD_SIZE"],
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os.environ["RANK"],
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os.environ["LOCAL_RANK"],
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)
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)
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backend = "nccl"
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if platform.system() == "Windows":
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backend = "gloo" # If Windows,switch to gloo backend.
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dist.init_process_group(
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backend=backend,
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init_method="env://",
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timeout=datetime.timedelta(seconds=300),
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) # Use torchrun instead of mp.spawn
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rank = dist.get_rank()
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local_rank = int(os.environ["LOCAL_RANK"])
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n_gpus = dist.get_world_size()
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# 命令行/config.yml配置解析
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# hps = utils.get_hparams()
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parser = argparse.ArgumentParser()
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# 非必要不建议使用命令行配置，请使用config.yml文件
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parser.add_argument(
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"-c",
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"--config",
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type=str,
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default=config.train_ms_config.config_path,
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help="JSON file for configuration",
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)
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parser.add_argument(
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"-m",
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"--model",
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type=str,
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help="数据集文件夹路径，请注意，数据不再默认放在/logs文件夹下。如果需要用命令行配置，请声明相对于根目录的路径",
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default=config.dataset_path,
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)
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args = parser.parse_args()
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model_dir = os.path.join(args.model, config.train_ms_config.model)
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if not os.path.exists(model_dir):
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os.makedirs(model_dir)
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hps = utils.get_hparams_from_file(args.config)
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hps.model_dir = model_dir
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# 比较路径是否相同
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if os.path.realpath(args.config) != os.path.realpath(
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config.train_ms_config.config_path
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):
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with open(args.config, "r", encoding="utf-8") as f:
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data = f.read()
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with open(config.train_ms_config.config_path, "w", encoding="utf-8") as f:
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f.write(data)
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torch.manual_seed(hps.train.seed)
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torch.cuda.set_device(local_rank)
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global global_step
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if rank == 0:
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logger = utils.get_logger(hps.model_dir)
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logger.info(hps)
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utils.check_git_hash(hps.model_dir)
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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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train_dataset = TextAudioSpeakerLoader(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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)
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collate_fn = TextAudioSpeakerCollate()
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train_loader = DataLoader(
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train_dataset,
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num_workers=min(config.train_ms_config.num_workers, os.cpu_count() - 1),
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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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) # DataLoader config could be adjusted.
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if rank == 0:
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eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
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eval_loader = DataLoader(
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eval_dataset,
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num_workers=0,
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shuffle=False,
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batch_size=1,
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pin_memory=True,
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drop_last=False,
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collate_fn=collate_fn,
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)
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if (
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"use_noise_scaled_mas" in hps.model.keys()
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and hps.model.use_noise_scaled_mas is True
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):
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print("Using noise scaled MAS for VITS2")
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mas_noise_scale_initial = 0.01
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noise_scale_delta = 2e-6
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else:
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print("Using normal MAS for VITS1")
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mas_noise_scale_initial = 0.0
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noise_scale_delta = 0.0
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if (
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"use_duration_discriminator" in hps.model.keys()
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and hps.model.use_duration_discriminator is True
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):
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print("Using duration discriminator for VITS2")
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net_dur_disc = DurationDiscriminator(
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hps.model.hidden_channels,
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hps.model.hidden_channels,
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3,
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0.1,
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gin_channels=hps.model.gin_channels if hps.data.n_speakers != 0 else 0,
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).cuda(local_rank)
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if (
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"use_spk_conditioned_encoder" in hps.model.keys()
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and hps.model.use_spk_conditioned_encoder is True
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):
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if hps.data.n_speakers == 0:
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raise ValueError(
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"n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model"
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)
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else:
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print("Using normal encoder for VITS1")
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net_g = SynthesizerTrn(
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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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mas_noise_scale_initial=mas_noise_scale_initial,
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noise_scale_delta=noise_scale_delta,
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**hps.model,
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).cuda(local_rank)
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net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(local_rank)
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optim_g = torch.optim.AdamW(
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filter(lambda p: p.requires_grad, 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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)
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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 net_dur_disc is not None:
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optim_dur_disc = torch.optim.AdamW(
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net_dur_disc.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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else:
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optim_dur_disc = None
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net_g = DDP(net_g, device_ids=[local_rank])
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net_d = DDP(net_d, device_ids=[local_rank])
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dur_resume_lr = None
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if net_dur_disc is not None:
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net_dur_disc = DDP(
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net_dur_disc, device_ids=[local_rank], find_unused_parameters=True
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)
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# 下载底模
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if config.train_ms_config.base["use_base_model"]:
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utils.download_checkpoint(
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hps.model_dir,
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config.train_ms_config.base,
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token=config.openi_token,
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mirror=config.mirror,
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)
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try:
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if net_dur_disc is not None:
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_, _, dur_resume_lr, epoch_str = utils.load_checkpoint(
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utils.latest_checkpoint_path(hps.model_dir, "DUR_*.pth"),
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net_dur_disc,
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optim_dur_disc,
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skip_optimizer=hps.train.skip_optimizer
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if "skip_optimizer" in hps.train
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else True,
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)
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_, optim_g, g_resume_lr, epoch_str = utils.load_checkpoint(
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utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"),
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net_g,
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optim_g,
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skip_optimizer=hps.train.skip_optimizer
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if "skip_optimizer" in hps.train
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else True,
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)
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_, optim_d, d_resume_lr, epoch_str = utils.load_checkpoint(
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utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"),
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net_d,
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optim_d,
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skip_optimizer=hps.train.skip_optimizer
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if "skip_optimizer" in hps.train
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else True,
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)
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if not optim_g.param_groups[0].get("initial_lr"):
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optim_g.param_groups[0]["initial_lr"] = g_resume_lr
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if not optim_d.param_groups[0].get("initial_lr"):
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optim_d.param_groups[0]["initial_lr"] = d_resume_lr
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if not optim_dur_disc.param_groups[0].get("initial_lr"):
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optim_dur_disc.param_groups[0]["initial_lr"] = dur_resume_lr
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epoch_str = max(epoch_str, 1)
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# global_step = (epoch_str - 1) * len(train_loader)
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global_step = int(
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utils.get_steps(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"))
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)
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print(
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f"******************检测到模型存在，epoch为 {epoch_str}，gloabl step为 {global_step}*********************"
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)
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except Exception as e:
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print(e)
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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(
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optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
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)
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scheduler_d = torch.optim.lr_scheduler.ExponentialLR(
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optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
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)
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if net_dur_disc is not None:
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if not optim_dur_disc.param_groups[0].get("initial_lr"):
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optim_dur_disc.param_groups[0]["initial_lr"] = dur_resume_lr
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scheduler_dur_disc = torch.optim.lr_scheduler.ExponentialLR(
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optim_dur_disc, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
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)
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else:
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scheduler_dur_disc = None
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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(
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rank,
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local_rank,
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epoch,
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hps,
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[net_g, net_d, net_dur_disc],
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[optim_g, optim_d, optim_dur_disc],
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[scheduler_g, scheduler_d, scheduler_dur_disc],
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scaler,
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[train_loader, eval_loader],
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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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rank,
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local_rank,
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epoch,
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hps,
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[net_g, net_d, net_dur_disc],
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[optim_g, optim_d, optim_dur_disc],
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[scheduler_g, scheduler_d, scheduler_dur_disc],
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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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if net_dur_disc is not None:
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scheduler_dur_disc.step()
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def train_and_evaluate(
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rank,
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local_rank,
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epoch,
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hps,
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nets,
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optims,
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schedulers,
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scaler,
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loaders,
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logger,
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writers,
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):
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net_g, net_d, net_dur_disc = nets
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optim_g, optim_d, optim_dur_disc = optims
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scheduler_g, scheduler_d, scheduler_dur_disc = 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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if net_dur_disc is not None:
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net_dur_disc.train()
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for batch_idx, (
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x,
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x_lengths,
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spec,
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spec_lengths,
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y,
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y_lengths,
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speakers,
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tone,
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language,
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bert,
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ja_bert,
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en_bert,
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emo,
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) in enumerate(tqdm(train_loader)):
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if net_g.module.use_noise_scaled_mas:
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current_mas_noise_scale = (
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net_g.module.mas_noise_scale_initial
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- net_g.module.noise_scale_delta * global_step
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)
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net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0)
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x, x_lengths = x.cuda(local_rank, non_blocking=True), x_lengths.cuda(
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local_rank, non_blocking=True
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)
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spec, spec_lengths = spec.cuda(
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local_rank, non_blocking=True
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), spec_lengths.cuda(local_rank, non_blocking=True)
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y, y_lengths = y.cuda(local_rank, non_blocking=True), y_lengths.cuda(
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local_rank, non_blocking=True
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)
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speakers = speakers.cuda(local_rank, non_blocking=True)
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tone = tone.cuda(local_rank, non_blocking=True)
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language = language.cuda(local_rank, non_blocking=True)
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bert = bert.cuda(local_rank, non_blocking=True)
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ja_bert = ja_bert.cuda(local_rank, non_blocking=True)
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en_bert = en_bert.cuda(local_rank, non_blocking=True)
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emo = emo.cuda(local_rank, non_blocking=True)
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with autocast(enabled=hps.train.fp16_run):
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(
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y_hat,
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l_length,
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attn,
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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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(hidden_x, logw, logw_),
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loss_commit,
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) = net_g(
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x,
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x_lengths,
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spec,
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spec_lengths,
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speakers,
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tone,
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language,
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bert,
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ja_bert,
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en_bert,
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emo,
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)
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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(
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mel, ids_slice, hps.train.segment_size // hps.data.hop_length
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)
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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(
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y, ids_slice * hps.data.hop_length, hps.train.segment_size
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) # 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(
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y_d_hat_r, y_d_hat_g
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)
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loss_disc_all = loss_disc
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if net_dur_disc is not None:
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y_dur_hat_r, y_dur_hat_g = net_dur_disc(
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hidden_x.detach(), x_mask.detach(), logw.detach(), logw_.detach()
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)
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with autocast(enabled=False):
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# TODO: I think need to mean using the mask, but for now, just mean all
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(
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loss_dur_disc,
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losses_dur_disc_r,
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losses_dur_disc_g,
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) = discriminator_loss(y_dur_hat_r, y_dur_hat_g)
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loss_dur_disc_all = loss_dur_disc
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optim_dur_disc.zero_grad()
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scaler.scale(loss_dur_disc_all).backward()
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scaler.unscale_(optim_dur_disc)
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commons.clip_grad_value_(net_dur_disc.parameters(), None)
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scaler.step(optim_dur_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)
|
||
if net_dur_disc is not None:
|
||
y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw, logw_)
|
||
with autocast(enabled=False):
|
||
loss_dur = torch.sum(l_length.float())
|
||
loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
|
||
loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
|
||
|
||
loss_fm = feature_loss(fmap_r, fmap_g)
|
||
loss_gen, losses_gen = generator_loss(y_d_hat_g)
|
||
loss_gen_all = (
|
||
loss_gen + loss_fm + loss_mel + loss_dur + loss_kl + loss_commit
|
||
)
|
||
if net_dur_disc is not None:
|
||
loss_dur_gen, losses_dur_gen = generator_loss(y_dur_hat_g)
|
||
loss_gen_all += loss_dur_gen
|
||
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, loss_dur, 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/dur": loss_dur,
|
||
"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/attn": utils.plot_alignment_to_numpy(
|
||
attn[0, 0].data.cpu().numpy()
|
||
),
|
||
}
|
||
utils.summarize(
|
||
writer=writer,
|
||
global_step=global_step,
|
||
images=image_dict,
|
||
scalars=scalar_dict,
|
||
)
|
||
|
||
if global_step % hps.train.eval_interval == 0:
|
||
evaluate(hps, net_g, eval_loader, writer_eval)
|
||
utils.save_checkpoint(
|
||
net_g,
|
||
optim_g,
|
||
hps.train.learning_rate,
|
||
epoch,
|
||
os.path.join(hps.model_dir, "G_{}.pth".format(global_step)),
|
||
)
|
||
utils.save_checkpoint(
|
||
net_d,
|
||
optim_d,
|
||
hps.train.learning_rate,
|
||
epoch,
|
||
os.path.join(hps.model_dir, "D_{}.pth".format(global_step)),
|
||
)
|
||
if net_dur_disc is not None:
|
||
utils.save_checkpoint(
|
||
net_dur_disc,
|
||
optim_dur_disc,
|
||
hps.train.learning_rate,
|
||
epoch,
|
||
os.path.join(hps.model_dir, "DUR_{}.pth".format(global_step)),
|
||
)
|
||
keep_ckpts = config.train_ms_config.keep_ckpts
|
||
if keep_ckpts > 0:
|
||
utils.clean_checkpoints(
|
||
path_to_models=hps.model_dir,
|
||
n_ckpts_to_keep=keep_ckpts,
|
||
sort_by_time=True,
|
||
)
|
||
save_compressed_models = hps.train.save_compressed_models
|
||
if save_compressed_models:
|
||
utils.save_compressed_models_checkpoint(
|
||
net_g,
|
||
epoch,
|
||
os.path.join(
|
||
hps.model_dir,
|
||
f"release_{global_step}.pth",
|
||
),
|
||
)
|
||
|
||
global_step += 1
|
||
gc.collect()
|
||
torch.cuda.empty_cache()
|
||
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, (
|
||
x,
|
||
x_lengths,
|
||
spec,
|
||
spec_lengths,
|
||
y,
|
||
y_lengths,
|
||
speakers,
|
||
tone,
|
||
language,
|
||
bert,
|
||
ja_bert,
|
||
en_bert,
|
||
emo,
|
||
) in enumerate(eval_loader):
|
||
x, x_lengths = x.cuda(), x_lengths.cuda()
|
||
spec, spec_lengths = spec.cuda(), spec_lengths.cuda()
|
||
y, y_lengths = y.cuda(), y_lengths.cuda()
|
||
speakers = speakers.cuda()
|
||
bert = bert.cuda()
|
||
ja_bert = ja_bert.cuda()
|
||
en_bert = en_bert.cuda()
|
||
tone = tone.cuda()
|
||
language = language.cuda()
|
||
emo = emo.cuda()
|
||
for use_sdp in [True, False]:
|
||
y_hat, attn, mask, *_ = generator.module.infer(
|
||
x,
|
||
x_lengths,
|
||
speakers,
|
||
tone,
|
||
language,
|
||
bert,
|
||
ja_bert,
|
||
en_bert,
|
||
emo,
|
||
y=spec,
|
||
max_len=1000,
|
||
sdp_ratio=0.0 if not use_sdp else 1.0,
|
||
)
|
||
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}": utils.plot_spectrogram_to_numpy(
|
||
y_hat_mel[0].cpu().numpy()
|
||
)
|
||
}
|
||
)
|
||
audio_dict.update(
|
||
{
|
||
f"gen/audio_{batch_idx}_{use_sdp}": 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__":
|
||
run()
|