592 lines
20 KiB
Python
592 lines
20 KiB
Python
import sys, os
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now_dir = os.getcwd()
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sys.path.append(os.path.join(now_dir))
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sys.path.append(os.path.join(now_dir, "train"))
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import utils
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import datetime
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hps = utils.get_hparams()
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os.environ["CUDA_VISIBLE_DEVICES"] = hps.gpus.replace("-", ",")
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n_gpus = len(hps.gpus.split("-"))
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from random import shuffle, randint
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import traceback, json, argparse, itertools, math, torch, pdb
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torch.backends.cudnn.deterministic = False
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torch.backends.cudnn.benchmark = False
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from torch import nn, optim
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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 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 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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if hps.version == "v1":
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from 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 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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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 process_ckpt import savee
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global_step = 0
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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 main():
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n_gpus = torch.cuda.device_count()
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if torch.cuda.is_available() == False and torch.backends.mps.is_available() == True:
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n_gpus = 1
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os.environ["MASTER_ADDR"] = "localhost"
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os.environ["MASTER_PORT"] = str(randint(20000, 55555))
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children = []
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for i in range(n_gpus):
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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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),
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)
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children.append(subproc)
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subproc.start()
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for i in range(n_gpus):
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children[i].join()
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def run(rank, n_gpus, hps):
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global global_step
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if rank == 0:
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logger = 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(rank)
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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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# net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True)
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# net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
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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: # 如果能加载自动resume
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_, _, _, epoch_str = utils.load_checkpoint(
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utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d
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) # D多半加载没事
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if rank == 0:
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logger.info("loaded D")
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# _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g,load_opt=0)
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_, _, _, epoch_str = utils.load_checkpoint(
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utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g
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)
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global_step = (epoch_str - 1) * len(train_loader)
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# epoch_str = 1
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# global_step = 0
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except: # 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 rank == 0:
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logger.info("loaded pretrained %s %s" % (hps.pretrainG, hps.pretrainD))
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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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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
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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
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loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
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loss_fm = feature_loss(fmap_r, fmap_g)
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loss_gen, losses_gen = generator_loss(y_d_hat_g)
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loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl
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optim_g.zero_grad()
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scaler.scale(loss_gen_all).backward()
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scaler.unscale_(optim_g)
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grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
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scaler.step(optim_g)
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scaler.update()
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if rank == 0:
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if global_step % hps.train.log_interval == 0:
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lr = optim_g.param_groups[0]["lr"]
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logger.info(
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"Train Epoch: {} [{:.0f}%]".format(
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epoch, 100.0 * batch_idx / len(train_loader)
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)
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)
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# Amor For Tensorboard display
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if loss_mel > 75:
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loss_mel = 75
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if loss_kl > 9:
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loss_kl = 9
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logger.info([global_step, lr])
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logger.info(
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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}"
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)
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scalar_dict = {
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"loss/g/total": loss_gen_all,
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"loss/d/total": loss_disc,
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"learning_rate": lr,
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"grad_norm_d": grad_norm_d,
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"grad_norm_g": grad_norm_g,
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}
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scalar_dict.update(
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{
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"loss/g/fm": loss_fm,
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"loss/g/mel": loss_mel,
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"loss/g/kl": loss_kl,
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}
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)
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scalar_dict.update(
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{"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)}
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)
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scalar_dict.update(
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{"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 epoch % hps.save_every_epoch == 0 and rank == 0:
|
||
if hps.if_latest == 0:
|
||
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)),
|
||
)
|
||
else:
|
||
utils.save_checkpoint(
|
||
net_g,
|
||
optim_g,
|
||
hps.train.learning_rate,
|
||
epoch,
|
||
os.path.join(hps.model_dir, "G_{}.pth".format(2333333)),
|
||
)
|
||
utils.save_checkpoint(
|
||
net_d,
|
||
optim_d,
|
||
hps.train.learning_rate,
|
||
epoch,
|
||
os.path.join(hps.model_dir, "D_{}.pth".format(2333333)),
|
||
)
|
||
if rank == 0 and hps.save_every_weights == "1":
|
||
if hasattr(net_g, "module"):
|
||
ckpt = net_g.module.state_dict()
|
||
else:
|
||
ckpt = net_g.state_dict()
|
||
logger.info(
|
||
"saving ckpt %s_e%s:%s"
|
||
% (
|
||
hps.name,
|
||
epoch,
|
||
savee(
|
||
ckpt,
|
||
hps.sample_rate,
|
||
hps.if_f0,
|
||
hps.name + "_e%s" % epoch,
|
||
epoch,
|
||
hps.version,
|
||
hps,
|
||
),
|
||
)
|
||
)
|
||
|
||
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()
|
||
logger.info(
|
||
"saving final ckpt:%s"
|
||
% (
|
||
savee(
|
||
ckpt, hps.sample_rate, hps.if_f0, hps.name, epoch, hps.version, hps
|
||
)
|
||
)
|
||
)
|
||
sleep(1)
|
||
os._exit(2333333)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
torch.multiprocessing.set_start_method("spawn")
|
||
main()
|