708 lines
29 KiB
Python
708 lines
29 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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import numpy as np
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from tqdm import tqdm
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from .lib.audio import SR_MAP
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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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BucketSampler,
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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 LossBalancer, combined_aux_loss, generator_loss, discriminator_loss, feature_loss, gradient_norm_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, 8205+hps.train.num_workers**2))
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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, best_model_name, gradient_clip_value
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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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gradient_clip_value = np.log(hps.train.c_gp/hps.train.learning_rate) if hps.train.get("c_gp",0.)>0 else None
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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",hps.best_model_threshold)
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best_model_name = data.get("best_model_name","")
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else:
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least_loss = hps.best_model_threshold
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best_model_name = ""
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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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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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if n_gpus>1:
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try:
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dist.init_process_group(backend="gloo", init_method="env://", world_size=n_gpus, rank=rank)
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distributed = True
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except Exception as error:
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print(f"Failed to initialize dist: {error=}")
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distributed = False
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else: distributed=False
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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:
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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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if distributed:
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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], # 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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else:
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train_sampler = BucketSampler(
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train_dataset,
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hps.train.batch_size,
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[100, 200, 300, 400, 500, 600, 700, 800, 900], # 16s
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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:
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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=hps.train.num_workers,
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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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hps.sync_log_interval(len(train_loader))
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if hps.if_f0:
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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 distributed:
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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, d_kwargs = 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, g_kwargs = 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 Exception as e:
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logger.error(f"Failed to load saved pretrains: {e}")
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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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if hasattr(net_g,"module"): print(net_g.module.load_state_dict(torch.load(hps.pretrainG, map_location="cpu")["model"]))
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else: print(net_g.load_state_dict(torch.load(hps.pretrainG, map_location="cpu")["model"]))
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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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if hasattr(net_d,"module"): print(net_d.module.load_state_dict(torch.load(hps.pretrainD, map_location="cpu")["model"]))
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else: print(net_d.load_state_dict(torch.load(hps.pretrainD, map_location="cpu")["model"]))
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d_kwargs = g_kwargs = {}
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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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try:
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balancer_state = g_kwargs["balancer"]
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logger.info(f"Using existing balancer: {balancer_state}")
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balancer = LossBalancer(net_g,**balancer_state)
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except Exception as e:
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logger.error(f"Failed to load balancer state: {e}")
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balancer = LossBalancer(
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net_g,
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weights_decay=.5 / (1 + np.exp(-10 * (epoch_str / hps.total_epoch - 0.16)))+.5, #sigmoid scaled ema .8 at 20% epoch
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loss_decay=.8,
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epsilon=hps.train.eps,
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active=hps.train.get("use_balancer",False),
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use_pareto=hps.train.get("use_pareto",False),
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use_norm=not hps.train.get("fast_mode",False)
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)
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cache = []
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for epoch in range(epoch_str, hps.train.epochs + 1):
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train_loader.batch_sampler.set_epoch(epoch)
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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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balancer
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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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balancer
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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, _, scaler, loaders, logger, writers, cache, balancer: "LossBalancer"
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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, _ = loaders
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if writers is not None:
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writer, _ = writers
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global global_step, least_loss, loss_file, best_model_name, gradient_clip_value
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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:
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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:
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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:
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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:
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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 tqdm(data_iterator,desc=f"[Epoch {epoch}]: "):
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# Data
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## Unpack
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if hps.if_f0:
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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 (not hps.if_cache_data_in_gpu) 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:
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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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# Calculate
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with autocast(enabled=hps.train.fp16_run):
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if hps.if_f0:
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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,
|
|
hps.data.mel_fmax,
|
|
)
|
|
y_mel = commons.slice_segments(
|
|
mel, ids_slice, hps.train.segment_size // hps.data.hop_length
|
|
)
|
|
with autocast(enabled=False):
|
|
y_hat_mel = mel_spectrogram_torch(
|
|
y_hat.float().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,
|
|
)
|
|
if hps.train.fp16_run: y_hat_mel = y_hat_mel.half()
|
|
wave_orig = wave.clone()
|
|
wave = commons.slice_segments(wave, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice
|
|
|
|
# Discriminator
|
|
gen_wave = y_hat.detach()
|
|
y_d_hat_r, y_d_hat_g, _, _ = net_d(wave, gen_wave)
|
|
|
|
with autocast(enabled=False):
|
|
gradient_penalty = gradient_norm_loss(wave,gen_wave, net_d, eps=hps.train.eps)*hps.train.c_gp if hps.train.get("c_gp",0.)>0 else 0
|
|
loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
|
|
loss_disc_all = loss_disc+gradient_penalty
|
|
|
|
optim_d.zero_grad()
|
|
scaler.scale(loss_disc_all).backward()
|
|
scaler.unscale_(optim_d)
|
|
grad_norm_d = commons.clip_grad_value_(net_d.parameters(), gradient_clip_value, batch_size=hps.train.batch_size)
|
|
scaler.step(optim_d)
|
|
|
|
with autocast(enabled=hps.train.fp16_run):
|
|
# Generator
|
|
y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(wave, y_hat)
|
|
with autocast(enabled=False):
|
|
loss_mel = F.l1_loss(y_mel, y_hat_mel)*hps.train.get("c_mel",0.)
|
|
loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask)*hps.train.get("c_kl",0.)
|
|
loss_fm = feature_loss(fmap_r, fmap_g)*hps.train.get("c_fm",0.)
|
|
harmonic_loss, tefs_loss, tsi_loss = combined_aux_loss(
|
|
wave, y_hat,n_mels=hps.data.n_mel_channels,sample_rate=hps.data.sampling_rate,
|
|
c_tefs=hps.train.c_tefs if hps.train.get("c_tefs",0.)>0 else 0.,
|
|
c_hd=hps.train.c_hd if hps.train.get("c_hd",0.)>0 else 0.,
|
|
c_tsi=hps.train.c_tsi if hps.train.get("c_tsi",0.)>0 else 0.,
|
|
n_fft=hps.data.filter_length,
|
|
hop_length=hps.data.hop_length,
|
|
win_length=hps.data.win_length,
|
|
eps=hps.train.eps
|
|
)
|
|
loss_gen, losses_gen = generator_loss(y_d_hat_g)
|
|
aux_loss = harmonic_loss + tefs_loss + tsi_loss
|
|
# loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl + aux_loss
|
|
loss_gen_all = balancer.on_train_batch_start(dict(
|
|
loss_gen=loss_gen,
|
|
loss_fm=loss_fm,
|
|
loss_mel=loss_mel,
|
|
loss_kl=loss_kl,
|
|
harmonic_loss=harmonic_loss,
|
|
tsi_loss=tsi_loss,
|
|
tefs_loss=tefs_loss,
|
|
))
|
|
|
|
optim_g.zero_grad()
|
|
scaler.scale(loss_gen_all).backward()
|
|
scaler.unscale_(optim_g)
|
|
grad_norm_g = commons.clip_grad_value_(net_g.parameters(), gradient_clip_value, batch_size=hps.train.batch_size)
|
|
scaler.step(optim_g)
|
|
scaler.update()
|
|
|
|
if rank == 0:
|
|
# if epoch>=max(hps.total_epoch*.2,20): #start saving best model after 20% or 20 epochs
|
|
if loss_gen + loss_fm + loss_mel + loss_kl + aux_loss<least_loss:
|
|
least_loss = loss_gen + loss_fm + loss_mel + loss_kl + aux_loss
|
|
logger.info(f"[lowest loss] {least_loss=:.3f}: {loss_disc=:.3f} {gradient_penalty=:.3f} <==> {loss_gen=:.3f} {loss_fm=:.3f} {loss_mel=:.3f} {loss_kl=:.3f}")
|
|
logger.info(f"[aux] {aux_loss=:.3f}: {harmonic_loss=:.3f}, {tefs_loss=:.3f}, {tsi_loss=:.3f}")
|
|
|
|
if hps.save_best_model:
|
|
if hasattr(net_g, "module"): ckpt = net_g.module.state_dict()
|
|
else: ckpt = net_g.state_dict()
|
|
|
|
best_model_name = f"{hps.name}_e{epoch}_s{global_step}_loss{least_loss:.0f}" if hps.save_every_weights else f"{hps.name}_loss{least_loss:2.0f}"
|
|
status = save_checkpoint(ckpt,best_model_name,epoch,hps)
|
|
logger.info(f"=== saving best model {best_model_name}: {status=} ===")
|
|
|
|
with open(loss_file,"w") as f:
|
|
json.dump(dict(least_loss=least_loss.item(),best_model_name=best_model_name,epoch=epoch,steps=global_step,
|
|
loss_weights = dict(balancer.ema_weights),
|
|
scalar_dict={
|
|
"total/loss/all": commons.serialize_tensor(loss_gen_all+loss_disc_all),
|
|
"total/loss/gen_all": commons.serialize_tensor(loss_gen_all),
|
|
"total/loss/aux": commons.serialize_tensor(aux_loss),
|
|
"total/loss/disc_all": commons.serialize_tensor(loss_disc_all),
|
|
"total/loss/gen": commons.serialize_tensor(loss_gen),
|
|
"total/loss/disc": commons.serialize_tensor(loss_disc),
|
|
"total/loss/fm": commons.serialize_tensor(loss_fm),
|
|
"total/loss/mel": commons.serialize_tensor(loss_mel),
|
|
"total/loss/kl": commons.serialize_tensor(loss_kl),
|
|
"aux/loss/harmonic": commons.serialize_tensor(harmonic_loss),
|
|
"aux/loss/tefs": commons.serialize_tensor(tefs_loss),
|
|
"aux/loss/tsi": commons.serialize_tensor(tsi_loss),
|
|
"gradient/grad_norm_disc": commons.serialize_tensor(grad_norm_d),
|
|
"gradient/grad_norm_gen": commons.serialize_tensor(grad_norm_g),
|
|
"gradient/gradient_penalty": commons.serialize_tensor(gradient_penalty),
|
|
}),f,indent=2)
|
|
|
|
if hps.train.log_interval>0 and global_step % hps.train.log_interval == 0:
|
|
lr = optim_g.param_groups[0]["lr"]
|
|
logger.info(f"Train Epoch: {epoch} [{100.0 * (epoch-1) / hps.total_epoch:.2f}% complete]")
|
|
# Amor For Tensorboard display
|
|
if loss_mel > 75:
|
|
loss_mel = 75
|
|
if loss_kl > 9:
|
|
loss_kl = 9
|
|
|
|
scalar_dict = {
|
|
"total/loss/all": loss_gen_all+loss_disc_all,
|
|
"total/loss/gen_all": loss_gen_all,
|
|
"total/loss/aux": aux_loss,
|
|
"total/loss/disc_all": loss_disc_all,
|
|
"total/loss/gen": loss_gen,
|
|
"total/loss/disc": loss_disc,
|
|
"total/loss/fm": loss_fm,
|
|
"total/loss/mel": loss_mel,
|
|
"total/loss/kl": loss_kl,
|
|
"aux/loss/harmonic": harmonic_loss,
|
|
"aux/loss/tefs": tefs_loss,
|
|
"aux/loss/tsi": tsi_loss,
|
|
"gradient/lr": lr,
|
|
"gradient/grad_norm_disc": grad_norm_d,
|
|
"gradient/grad_norm_gen": grad_norm_g,
|
|
"gradient/gradient_penalty": gradient_penalty,
|
|
**{f"loss/g/{i}": v for i, v in enumerate(losses_gen)},
|
|
**{f"loss/d_r/{i}": v for i, v in enumerate(losses_disc_r)},
|
|
**{f"loss/d_g/{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/diff": utils.plot_spectrogram_to_numpy((y_mel[0]-y_mel[0]).abs().data.cpu().numpy(), cmap="hot")
|
|
}
|
|
|
|
with torch.no_grad():
|
|
if hasattr(net_g, "module"): inference = net_g.module.infer
|
|
else: inference = net_g.infer
|
|
if hps.if_f0: wave_gen = inference(phone, phone_lengths, pitch, pitchf, sid)[0][0, 0].data
|
|
else: wave_gen = inference(phone, phone_lengths, sid)[0][0, 0].data
|
|
|
|
audio_dict = {
|
|
"slice/wave_org": wave_orig[0][0],
|
|
"slice/wave_gen": wave_gen
|
|
}
|
|
utils.summarize(
|
|
writer=writer,
|
|
global_step=global_step,
|
|
images=image_dict,
|
|
scalars=scalar_dict,
|
|
audios=audio_dict,
|
|
audio_sampling_rate=SR_MAP[hps.sample_rate]
|
|
)
|
|
global_step += 1
|
|
# /Run steps
|
|
|
|
if hps.save_every_epoch>0 and (epoch % hps.save_every_epoch == 0) and rank == 0:
|
|
|
|
saved_epoch = 23333 if hps.if_latest else epoch
|
|
utils.save_checkpoint(
|
|
net_g,
|
|
optim_g,
|
|
hps.train.learning_rate,
|
|
epoch,
|
|
os.path.join(hps.model_dir, f"G_{saved_epoch}.pth"),
|
|
balancer=balancer.to_dict()
|
|
)
|
|
utils.save_checkpoint(
|
|
net_d,
|
|
optim_d,
|
|
hps.train.learning_rate,
|
|
epoch,
|
|
os.path.join(hps.model_dir, f"D_{saved_epoch}.pth"),
|
|
)
|
|
if hps.save_every_weights:
|
|
ckpt = net_g.module.state_dict() if hasattr(net_g, "module") else net_g.state_dict()
|
|
save_name = f"{hps.name}_e{epoch}_s{global_step}"
|
|
status = save_checkpoint(ckpt,save_name,epoch,hps)
|
|
logger.info(f"saving ckpt {save_name}: {status}")
|
|
|
|
if rank == 0:
|
|
logger.info(f"====> Epoch {epoch} (Total Loss={(loss_disc_all+loss_gen_all).item():.3f}): {global_step=} {lr=:.2E} {epoch_recorder.record()}")
|
|
logger.info(f"|| {loss_disc_all=:.3f}: {loss_disc=:.3f}, {gradient_penalty=:.3f}")
|
|
logger.info(f"|| {loss_gen_all=:.3f}: {loss_gen=:.3f}, {loss_fm=:.3f}, {loss_mel=:.3f}, {loss_kl=:.3f}")
|
|
logger.info(f"|| {harmonic_loss=:.3f}, {tefs_loss=:.3f}, {tsi_loss=:.3f}")
|
|
balancer.on_epoch_end(.5 / (1 + np.exp(-10 * (epoch / hps.total_epoch - 0.16)))+.5) #sigmoid scaling of ema
|
|
|
|
if epoch >= hps.total_epoch and rank == 0:
|
|
logger.info("Training is done. The program is closed.")
|
|
|
|
ckpt = net_g.module.state_dict() if hasattr(net_g, "module") else net_g.state_dict()
|
|
if hps.save_best_model and os.path.isfile(loss_file):
|
|
with open(loss_file,"r") as f:
|
|
data = json.load(f)
|
|
best_model_name = data.get("best_model_name","")
|
|
best_model_path = os.path.join(hps.model_dir,f"{best_model_name}.pth")
|
|
if os.path.isfile(best_model_path):
|
|
shutil.copy(best_model_path,os.path.join(
|
|
os.path.dirname(hps.model_path),
|
|
f"{os.path.basename(hps.model_path).split('.')[0]}-lowest.pth"))
|
|
|
|
status = save_checkpoint(ckpt,hps.name,epoch,hps,model_path=hps.model_path)
|
|
logger.info(f"saving final ckpt {hps.model_path}: {status}")
|
|
sleep(1)
|
|
os._exit(0)
|
|
|
|
if __name__ == "__main__":
|
|
torch.multiprocessing.set_start_method("spawn")
|
|
hps = utils.get_hparams()
|
|
train_model(hps) |