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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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|
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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 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 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 text.symbols import symbols |
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|
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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 |
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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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) |
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torch.backends.cuda.enable_math_sdp(True) |
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global_step = 0 |
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|
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def run(): |
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|
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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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|
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backend = "nccl" |
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if platform.system() == "Windows": |
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backend = "gloo" |
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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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) |
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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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|
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|
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parser = argparse.ArgumentParser() |
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|
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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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|
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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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|
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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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|
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torch.manual_seed(hps.train.seed) |
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torch.cuda.set_device(local_rank) |
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|
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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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) |
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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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|
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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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|
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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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|
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|
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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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|
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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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|
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epoch_str = max(epoch_str, 1) |
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|
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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: |
|
print(e) |
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epoch_str = 1 |
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global_step = 0 |
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|
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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"): |
|
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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|
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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], |
|
scaler, |
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[train_loader, eval_loader], |
|
logger, |
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[writer, writer_eval], |
|
) |
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else: |
|
train_and_evaluate( |
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rank, |
|
local_rank, |
|
epoch, |
|
hps, |
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[net_g, net_d, net_dur_disc], |
|
[optim_g, optim_d, optim_dur_disc], |
|
[scheduler_g, scheduler_d, scheduler_dur_disc], |
|
scaler, |
|
[train_loader, None], |
|
None, |
|
None, |
|
) |
|
scheduler_g.step() |
|
scheduler_d.step() |
|
if net_dur_disc is not None: |
|
scheduler_dur_disc.step() |
|
|
|
|
|
def train_and_evaluate( |
|
rank, |
|
local_rank, |
|
epoch, |
|
hps, |
|
nets, |
|
optims, |
|
schedulers, |
|
scaler, |
|
loaders, |
|
logger, |
|
writers, |
|
): |
|
net_g, net_d, net_dur_disc = nets |
|
optim_g, optim_d, optim_dur_disc = optims |
|
scheduler_g, scheduler_d, scheduler_dur_disc = schedulers |
|
train_loader, eval_loader = loaders |
|
if writers is not None: |
|
writer, writer_eval = writers |
|
|
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train_loader.batch_sampler.set_epoch(epoch) |
|
global global_step |
|
|
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net_g.train() |
|
net_d.train() |
|
if net_dur_disc is not None: |
|
net_dur_disc.train() |
|
for batch_idx, ( |
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x, |
|
x_lengths, |
|
spec, |
|
spec_lengths, |
|
y, |
|
y_lengths, |
|
speakers, |
|
tone, |
|
language, |
|
bert, |
|
ja_bert, |
|
en_bert, |
|
emo, |
|
) in tqdm(enumerate(train_loader)): |
|
if net_g.module.use_noise_scaled_mas: |
|
current_mas_noise_scale = ( |
|
net_g.module.mas_noise_scale_initial |
|
- net_g.module.noise_scale_delta * global_step |
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) |
|
net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0) |
|
x, x_lengths = x.cuda(local_rank, non_blocking=True), x_lengths.cuda( |
|
local_rank, non_blocking=True |
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) |
|
spec, spec_lengths = spec.cuda( |
|
local_rank, non_blocking=True |
|
), spec_lengths.cuda(local_rank, non_blocking=True) |
|
y, y_lengths = y.cuda(local_rank, non_blocking=True), y_lengths.cuda( |
|
local_rank, non_blocking=True |
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) |
|
speakers = speakers.cuda(local_rank, non_blocking=True) |
|
tone = tone.cuda(local_rank, non_blocking=True) |
|
language = language.cuda(local_rank, non_blocking=True) |
|
bert = bert.cuda(local_rank, non_blocking=True) |
|
ja_bert = ja_bert.cuda(local_rank, non_blocking=True) |
|
en_bert = en_bert.cuda(local_rank, non_blocking=True) |
|
emo = emo.cuda(local_rank, non_blocking=True) |
|
|
|
with autocast(enabled=hps.train.fp16_run): |
|
( |
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y_hat, |
|
l_length, |
|
attn, |
|
ids_slice, |
|
x_mask, |
|
z_mask, |
|
(z, z_p, m_p, logs_p, m_q, logs_q), |
|
(hidden_x, logw, logw_), |
|
loss_commit, |
|
) = net_g( |
|
x, |
|
x_lengths, |
|
spec, |
|
spec_lengths, |
|
speakers, |
|
tone, |
|
language, |
|
bert, |
|
ja_bert, |
|
en_bert, |
|
emo, |
|
) |
|
mel = spec_to_mel_torch( |
|
spec, |
|
hps.data.filter_length, |
|
hps.data.n_mel_channels, |
|
hps.data.sampling_rate, |
|
hps.data.mel_fmin, |
|
hps.data.mel_fmax, |
|
) |
|
y_mel = commons.slice_segments( |
|
mel, ids_slice, hps.train.segment_size // hps.data.hop_length |
|
) |
|
y_hat_mel = mel_spectrogram_torch( |
|
y_hat.squeeze(1), |
|
hps.data.filter_length, |
|
hps.data.n_mel_channels, |
|
hps.data.sampling_rate, |
|
hps.data.hop_length, |
|
hps.data.win_length, |
|
hps.data.mel_fmin, |
|
hps.data.mel_fmax, |
|
) |
|
|
|
y = commons.slice_segments( |
|
y, ids_slice * hps.data.hop_length, hps.train.segment_size |
|
) |
|
|
|
|
|
y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach()) |
|
with autocast(enabled=False): |
|
loss_disc, losses_disc_r, losses_disc_g = discriminator_loss( |
|
y_d_hat_r, y_d_hat_g |
|
) |
|
loss_disc_all = loss_disc |
|
if net_dur_disc is not None: |
|
y_dur_hat_r, y_dur_hat_g = net_dur_disc( |
|
hidden_x.detach(), x_mask.detach(), logw.detach(), logw_.detach() |
|
) |
|
with autocast(enabled=False): |
|
|
|
( |
|
loss_dur_disc, |
|
losses_dur_disc_r, |
|
losses_dur_disc_g, |
|
) = discriminator_loss(y_dur_hat_r, y_dur_hat_g) |
|
loss_dur_disc_all = loss_dur_disc |
|
optim_dur_disc.zero_grad() |
|
scaler.scale(loss_dur_disc_all).backward() |
|
scaler.unscale_(optim_dur_disc) |
|
commons.clip_grad_value_(net_dur_disc.parameters(), None) |
|
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): |
|
|
|
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, |
|
) |
|
|
|
global_step += 1 |
|
|
|
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() |
|
|