649 lines
23 KiB
Python
649 lines
23 KiB
Python
from collections import OrderedDict
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import json
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import os
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import shutil
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import traceback
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from .lib.train import utils
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import datetime
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from random import shuffle, randint
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import torch
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torch.backends.cudnn.deterministic = False
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torch.backends.cudnn.benchmark = False
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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.multiprocessing as mp
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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 .lib.infer_pack import commons
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from time import sleep
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from time import time as ttime
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from .lib.train.data_utils import (
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TextAudioLoaderMultiNSFsid,
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TextAudioLoader,
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TextAudioCollateMultiNSFsid,
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TextAudioCollate,
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DistributedBucketSampler,
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)
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from .lib.train.losses import generator_loss, discriminator_loss, feature_loss, kl_loss
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from .lib.train.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
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def save_checkpoint(ckpt, name, epoch, hps, model_path=None):
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try:
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opt = OrderedDict()
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opt["weight"] = {}
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for key in ckpt.keys():
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if "enc_q" in key:
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continue
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opt["weight"][key] = ckpt[key].half()
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opt["config"] = [
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hps.data.filter_length // 2 + 1,
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32,
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hps.model.inter_channels,
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hps.model.hidden_channels,
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hps.model.filter_channels,
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hps.model.n_heads,
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hps.model.n_layers,
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hps.model.kernel_size,
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hps.model.p_dropout,
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hps.model.resblock,
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hps.model.resblock_kernel_sizes,
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hps.model.resblock_dilation_sizes,
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hps.model.upsample_rates,
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hps.model.upsample_initial_channel,
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hps.model.upsample_kernel_sizes,
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hps.model.spk_embed_dim,
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hps.model.gin_channels,
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hps.data.sampling_rate,
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]
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opt["info"] = "%sepoch" % epoch
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opt["sr"] = hps.sample_rate
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opt["f0"] = hps.if_f0
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opt["version"] = hps.version
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if model_path is None: model_path=os.path.join(hps.model_dir,name+".pth")
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torch.save(opt, model_path)
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return "Success."
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except:
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return traceback.format_exc()
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class EpochRecorder:
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def __init__(self):
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self.last_time = ttime()
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def record(self):
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now_time = ttime()
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elapsed_time = now_time - self.last_time
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self.last_time = now_time
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elapsed_time_str = str(datetime.timedelta(seconds=elapsed_time))
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current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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return f"[{current_time}] | ({elapsed_time_str})"
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def train_model(hps: "utils.HParams"):
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os.environ["CUDA_VISIBLE_DEVICES"] = hps.gpus.replace("-", ",")
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os.environ["NCCL_P2P_DISABLE"] = "1"
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os.environ["MASTER_ADDR"] = "localhost"
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os.environ["MASTER_PORT"] = str(randint(8189, 8200))
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os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128,garbage_collection_threshold:0.8"
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n_gpus = len(hps.gpus.split("-")) if hps.gpus else torch.cuda.device_count()
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if not torch.cuda.is_available() and torch.backends.mps.is_available():
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n_gpus = 1
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if n_gpus < 1:
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# patch to unblock people without gpus. there is probably a better way.
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print("NO GPU DETECTED: falling back to CPU - this may take a while")
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n_gpus = 1
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gpu_devices = hps.gpus.split("-") if hps.gpus else range(n_gpus)
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children = {}
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for i, device in enumerate(gpu_devices):
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subproc = mp.Process(
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target=run,
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args=(
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i,
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n_gpus,
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hps,
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device
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),
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)
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children[i]=subproc
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subproc.start()
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for i in children:
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children[i].join()
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def run(rank, n_gpus, hps, device):
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print(f"{__name__=}")
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global global_step, least_loss, loss_file
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global_step = 0
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loss_file = os.path.join(hps.model_dir,"losses.json")
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if os.path.isfile(loss_file):
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with open(loss_file,"r") as f:
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data: dict = json.load(f)
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least_loss = data.get("least_loss",40)
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else: least_loss = 40
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if hps.version == "v1":
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from .lib.infer_pack.models import (
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SynthesizerTrnMs256NSFsid as RVC_Model_f0,
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SynthesizerTrnMs256NSFsid_nono as RVC_Model_nof0,
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MultiPeriodDiscriminator,
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)
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else:
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from .lib.infer_pack.models import (
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SynthesizerTrnMs768NSFsid as RVC_Model_f0,
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SynthesizerTrnMs768NSFsid_nono as RVC_Model_nof0,
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MultiPeriodDiscriminatorV2 as MultiPeriodDiscriminator,
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)
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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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dist.init_process_group(
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backend="gloo", init_method="env://", world_size=n_gpus, rank=rank
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)
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torch.manual_seed(hps.train.seed)
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if torch.cuda.is_available():
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torch.cuda.set_device(f"cuda:{device}")
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if hps.if_f0 == 1:
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train_dataset = TextAudioLoaderMultiNSFsid(hps.data.training_files, hps.data)
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else:
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train_dataset = TextAudioLoader(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 * n_gpus,
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# [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1200,1400], # 16s
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[100, 200, 300, 400, 500, 600, 700, 800, 900], # 16s
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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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# It is possible that dataloader's workers are out of shared memory. Please try to raise your shared memory limit.
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# num_workers=8 -> num_workers=4
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if hps.if_f0 == 1:
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collate_fn = TextAudioCollateMultiNSFsid()
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else:
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collate_fn = TextAudioCollate()
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train_loader = DataLoader(
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train_dataset,
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num_workers=4,
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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=8,
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)
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if hps.if_f0 == 1:
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net_g = RVC_Model_f0(
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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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**hps.model,
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is_half=hps.train.fp16_run,
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sr=hps.sample_rate,
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)
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else:
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net_g = RVC_Model_nof0(
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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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**hps.model,
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is_half=hps.train.fp16_run,
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)
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if torch.cuda.is_available():
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net_g = net_g.cuda(rank)
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net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm)
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if torch.cuda.is_available():
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net_d = net_d.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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)
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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 torch.cuda.is_available():
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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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else:
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net_g = DDP(net_g)
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net_d = DDP(net_d)
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try: # resume training
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_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d)
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if rank == 0: logger.info("loaded D")
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_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g)
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if rank == 0: logger.info("loaded G")
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global_step = (epoch_str - 1) * len(train_loader)
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except: # 如果首次不能加载，加载pretrain
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# traceback.print_exc()
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epoch_str = 1
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global_step = 0
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if hps.pretrainG != "":
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if rank == 0:
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logger.info("loaded pretrained %s" % (hps.pretrainG))
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print(
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net_g.module.load_state_dict(
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torch.load(hps.pretrainG, map_location="cpu")["model"]
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)
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) ##测试不加载优化器
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if hps.pretrainD != "":
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if rank == 0:
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logger.info("loaded pretrained %s" % (hps.pretrainD))
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print(
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net_d.module.load_state_dict(
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torch.load(hps.pretrainD, map_location="cpu")["model"]
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)
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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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scaler = GradScaler(enabled=hps.train.fp16_run)
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cache = []
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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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epoch,
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hps,
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[net_g, net_d],
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[optim_g, optim_d],
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[scheduler_g, scheduler_d],
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scaler,
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[train_loader, None],
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logger,
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[writer, writer_eval],
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cache,
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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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epoch,
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hps,
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[net_g, net_d],
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[optim_g, optim_d],
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[scheduler_g, scheduler_d],
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scaler,
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[train_loader, None],
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None,
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None,
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cache,
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)
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scheduler_g.step()
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scheduler_d.step()
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def train_and_evaluate(
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rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers, cache
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):
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net_g, net_d = nets
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optim_g, optim_d = optims
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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, least_loss, loss_file
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net_g.train()
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net_d.train()
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# Prepare data iterator
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if hps.if_cache_data_in_gpu == True:
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# Use Cache
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data_iterator = cache
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if cache == []:
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# Make new cache
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for batch_idx, info in enumerate(train_loader):
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# Unpack
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if hps.if_f0 == 1:
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(
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phone,
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phone_lengths,
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pitch,
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pitchf,
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spec,
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spec_lengths,
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wave,
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wave_lengths,
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sid,
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) = info
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else:
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(
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phone,
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phone_lengths,
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spec,
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spec_lengths,
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wave,
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wave_lengths,
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sid,
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) = info
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# Load on CUDA
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if torch.cuda.is_available():
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phone = phone.cuda(rank, non_blocking=True)
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phone_lengths = phone_lengths.cuda(rank, non_blocking=True)
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if hps.if_f0 == 1:
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pitch = pitch.cuda(rank, non_blocking=True)
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pitchf = pitchf.cuda(rank, non_blocking=True)
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sid = sid.cuda(rank, non_blocking=True)
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spec = spec.cuda(rank, non_blocking=True)
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spec_lengths = spec_lengths.cuda(rank, non_blocking=True)
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wave = wave.cuda(rank, non_blocking=True)
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wave_lengths = wave_lengths.cuda(rank, non_blocking=True)
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# Cache on list
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if hps.if_f0 == 1:
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cache.append(
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(
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batch_idx,
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(
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phone,
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phone_lengths,
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pitch,
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pitchf,
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spec,
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spec_lengths,
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wave,
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wave_lengths,
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sid,
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),
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)
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)
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else:
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cache.append(
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(
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batch_idx,
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(
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phone,
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phone_lengths,
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spec,
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spec_lengths,
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wave,
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wave_lengths,
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sid,
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),
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)
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)
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else:
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# Load shuffled cache
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shuffle(cache)
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else:
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# Loader
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data_iterator = enumerate(train_loader)
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# Run steps
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epoch_recorder = EpochRecorder()
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for batch_idx, info in data_iterator:
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# Data
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## Unpack
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if hps.if_f0 == 1:
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(
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phone,
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phone_lengths,
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pitch,
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pitchf,
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spec,
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spec_lengths,
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wave,
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wave_lengths,
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sid,
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) = info
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else:
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phone, phone_lengths, spec, spec_lengths, wave, wave_lengths, sid = info
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## Load on CUDA
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if (hps.if_cache_data_in_gpu == False) and torch.cuda.is_available():
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phone = phone.cuda(rank, non_blocking=True)
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phone_lengths = phone_lengths.cuda(rank, non_blocking=True)
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if hps.if_f0 == 1:
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pitch = pitch.cuda(rank, non_blocking=True)
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pitchf = pitchf.cuda(rank, non_blocking=True)
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sid = sid.cuda(rank, non_blocking=True)
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spec = spec.cuda(rank, non_blocking=True)
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spec_lengths = spec_lengths.cuda(rank, non_blocking=True)
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wave = wave.cuda(rank, non_blocking=True)
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# wave_lengths = wave_lengths.cuda(rank, non_blocking=True)
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# Calculate
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with autocast(enabled=hps.train.fp16_run):
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if hps.if_f0 == 1:
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(
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y_hat,
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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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) = net_g(phone, phone_lengths, pitch, pitchf, spec, spec_lengths, sid)
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else:
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(
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y_hat,
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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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) = net_g(phone, phone_lengths, spec, spec_lengths, sid)
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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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with autocast(enabled=False):
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y_hat_mel = mel_spectrogram_torch(
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y_hat.float().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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if hps.train.fp16_run == True:
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y_hat_mel = y_hat_mel.half()
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wave = commons.slice_segments(
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wave, 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(wave, 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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optim_d.zero_grad()
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scaler.scale(loss_disc).backward()
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scaler.unscale_(optim_d)
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grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
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scaler.step(optim_d)
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with autocast(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(wave, y_hat)
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with autocast(enabled=False):
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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_kl
|
|
optim_g.zero_grad()
|
|
scaler.scale(loss_gen_all).backward()
|
|
scaler.unscale_(optim_g)
|
|
grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
|
|
scaler.step(optim_g)
|
|
scaler.update()
|
|
|
|
if rank == 0:
|
|
if global_step % hps.train.log_interval == 0:
|
|
lr = optim_g.param_groups[0]["lr"]
|
|
logger.info(
|
|
"Train Epoch: {} [{:.0f}%]".format(epoch, 100.0 * batch_idx / len(train_loader))
|
|
)
|
|
# Amor For Tensorboard display
|
|
if loss_mel > 75:
|
|
loss_mel = 75
|
|
if loss_kl > 9:
|
|
loss_kl = 9
|
|
|
|
logger.info(f"epoch={epoch} steps={global_step} lr={lr} total_loss={loss_gen_all}")
|
|
|
|
logger.info(
|
|
f"loss_disc={loss_disc:.3f}, loss_gen={loss_gen:.3f}, loss_fm={loss_fm:.3f},loss_mel={loss_mel:.3f}, loss_kl={loss_kl:.3f}"
|
|
)
|
|
if loss_gen_all<least_loss:
|
|
least_loss = loss_gen_all
|
|
|
|
if hasattr(net_g, "module"):
|
|
ckpt = net_g.module.state_dict()
|
|
else:
|
|
ckpt = net_g.state_dict()
|
|
|
|
if hps.save_best_model:
|
|
best_model_name = f"{hps.name}_e{epoch}_loss{loss_gen_all:2.2f}"
|
|
status = save_checkpoint(ckpt,best_model_name,epoch,hps)
|
|
with open(loss_file,"w") as f:
|
|
json.dump(dict(least_loss=least_loss.item(),best_model=os.path.join(hps.model_dir,best_model_name+".pth")),f)
|
|
logger.info(f"=== saving best model: epoch {hps.name}_e{epoch}_{loss_gen_all:2.2f}: {status=} ===")
|
|
logger.info(f"[lowest loss] loss_disc={loss_disc:.3f}, loss_gen={loss_gen:.3f}, loss_fm={loss_fm:.3f},loss_mel={loss_mel:.3f}, loss_kl={loss_kl:.3f}")
|
|
|
|
scalar_dict = {
|
|
"loss/g/total": loss_gen_all,
|
|
"loss/d/total": loss_disc,
|
|
"learning_rate": lr,
|
|
"grad_norm_d": grad_norm_d,
|
|
"grad_norm_g": grad_norm_g,
|
|
}
|
|
scalar_dict.update(
|
|
{
|
|
"loss/g/fm": loss_fm,
|
|
"loss/g/mel": loss_mel,
|
|
"loss/g/kl": 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()
|
|
),
|
|
}
|
|
utils.summarize(
|
|
writer=writer,
|
|
global_step=global_step,
|
|
images=image_dict,
|
|
scalars=scalar_dict,
|
|
)
|
|
global_step += 1
|
|
# /Run steps
|
|
|
|
if hps.save_every_epoch>0 and (epoch % hps.save_every_epoch == 0) and rank == 0:
|
|
|
|
if hps.if_latest:
|
|
utils.save_checkpoint(
|
|
net_g,
|
|
optim_g,
|
|
hps.train.learning_rate,
|
|
epoch,
|
|
os.path.join(hps.model_dir, f"G_{hps.total_epoch}00000.pth"),
|
|
)
|
|
utils.save_checkpoint(
|
|
net_d,
|
|
optim_d,
|
|
hps.train.learning_rate,
|
|
epoch,
|
|
os.path.join(hps.model_dir, f"D_{hps.total_epoch}00000.pth"),
|
|
)
|
|
else:
|
|
utils.save_checkpoint(
|
|
net_g,
|
|
optim_g,
|
|
hps.train.learning_rate,
|
|
epoch,
|
|
os.path.join(hps.model_dir, f"G_{global_step}.pth"),
|
|
)
|
|
utils.save_checkpoint(
|
|
net_d,
|
|
optim_d,
|
|
hps.train.learning_rate,
|
|
epoch,
|
|
os.path.join(hps.model_dir, f"D_{global_step}.pth"),
|
|
)
|
|
if rank == 0 and hps.save_every_weights:
|
|
if hasattr(net_g, "module"):
|
|
ckpt = net_g.module.state_dict()
|
|
else:
|
|
ckpt = net_g.state_dict()
|
|
status = save_checkpoint(ckpt,f"{hps.name}_e{epoch}_s{global_step}",epoch,hps)
|
|
logger.info(f"saving ckpt {hps.name}_e{epoch}: {status}")
|
|
|
|
if rank == 0:
|
|
logger.info("====> Epoch: {} {}".format(epoch, epoch_recorder.record()))
|
|
if epoch >= hps.total_epoch and rank == 0:
|
|
logger.info("Training is done. The program is closed.")
|
|
|
|
if hasattr(net_g, "module"):
|
|
ckpt = net_g.module.state_dict()
|
|
else:
|
|
ckpt = net_g.state_dict()
|
|
if hps.save_best_model:
|
|
with open(loss_file,"r") as f:
|
|
data = json.load(f)
|
|
best_model = data["best_model"]
|
|
if os.path.isfile(best_model):
|
|
shutil.copy(best_model,os.path.join(os.path.dirname(hps.model_path),f"{hps.name}-best.pth"))
|
|
|
|
status = save_checkpoint(ckpt,hps.name,epoch,hps,model_path=hps.model_path)
|
|
logger.info(f"saving final ckpt: {status}")
|
|
sleep(1)
|
|
os._exit(0)
|
|
|
|
if __name__ == "__main__":
|
|
torch.multiprocessing.set_start_method("spawn")
|
|
hps = utils.get_hparams()
|
|
train_model(hps) |