665 lines
25 KiB
Python
665 lines
25 KiB
Python
from collections import OrderedDict
|
|
import json
|
|
import os
|
|
import shutil
|
|
import traceback
|
|
|
|
from tqdm import tqdm
|
|
from .lib.audio import SR_MAP
|
|
from .lib.train import utils
|
|
import datetime
|
|
from random import shuffle, randint
|
|
import torch
|
|
torch.backends.cudnn.deterministic = False
|
|
torch.backends.cudnn.benchmark = False
|
|
from torch.nn import functional as F
|
|
from torch.utils.data import DataLoader
|
|
from torch.utils.tensorboard import SummaryWriter
|
|
import torch.multiprocessing as mp
|
|
import torch.distributed as dist
|
|
from torch.nn.parallel import DistributedDataParallel as DDP
|
|
from torch.cuda.amp import autocast, GradScaler
|
|
from .lib.infer_pack import commons
|
|
from time import sleep
|
|
from time import time as ttime
|
|
from .lib.train.data_utils import (
|
|
TextAudioLoaderMultiNSFsid,
|
|
TextAudioLoader,
|
|
TextAudioCollateMultiNSFsid,
|
|
TextAudioCollate,
|
|
DistributedBucketSampler,
|
|
)
|
|
from .lib.train.losses import generator_loss, discriminator_loss, feature_loss, kl_loss, mfcc_loss
|
|
from .lib.train.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
|
|
|
|
def save_checkpoint(ckpt, name, epoch, hps, model_path=None):
|
|
try:
|
|
opt = OrderedDict()
|
|
opt["weight"] = {}
|
|
for key in ckpt.keys():
|
|
if "enc_q" in key:
|
|
continue
|
|
opt["weight"][key] = ckpt[key].half()
|
|
opt["config"] = [
|
|
hps.data.filter_length // 2 + 1,
|
|
32,
|
|
hps.model.inter_channels,
|
|
hps.model.hidden_channels,
|
|
hps.model.filter_channels,
|
|
hps.model.n_heads,
|
|
hps.model.n_layers,
|
|
hps.model.kernel_size,
|
|
hps.model.p_dropout,
|
|
hps.model.resblock,
|
|
hps.model.resblock_kernel_sizes,
|
|
hps.model.resblock_dilation_sizes,
|
|
hps.model.upsample_rates,
|
|
hps.model.upsample_initial_channel,
|
|
hps.model.upsample_kernel_sizes,
|
|
hps.model.spk_embed_dim,
|
|
hps.model.gin_channels,
|
|
hps.data.sampling_rate,
|
|
]
|
|
opt["info"] = "%sepoch" % epoch
|
|
opt["sr"] = hps.sample_rate
|
|
opt["f0"] = hps.if_f0
|
|
opt["version"] = hps.version
|
|
if model_path is None: model_path=os.path.join(hps.model_dir,name+".pth")
|
|
torch.save(opt, model_path)
|
|
return "Success."
|
|
except:
|
|
return traceback.format_exc()
|
|
|
|
class EpochRecorder:
|
|
def __init__(self):
|
|
self.last_time = ttime()
|
|
|
|
def record(self):
|
|
now_time = ttime()
|
|
elapsed_time = now_time - self.last_time
|
|
self.last_time = now_time
|
|
elapsed_time_str = str(datetime.timedelta(seconds=elapsed_time))
|
|
current_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
|
return f"[{current_time}] | ({elapsed_time_str})"
|
|
|
|
def train_model(hps: "utils.HParams"):
|
|
os.environ["CUDA_VISIBLE_DEVICES"] = hps.gpus.replace("-", ",")
|
|
os.environ["NCCL_P2P_DISABLE"] = "1"
|
|
os.environ["MASTER_ADDR"] = "localhost"
|
|
os.environ["MASTER_PORT"] = str(randint(8189, 8205+hps.train.num_workers**2))
|
|
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128,garbage_collection_threshold:0.8"
|
|
|
|
n_gpus = len(hps.gpus.split("-")) if hps.gpus else torch.cuda.device_count()
|
|
|
|
if not torch.cuda.is_available() and torch.backends.mps.is_available():
|
|
n_gpus = 1
|
|
if n_gpus < 1:
|
|
# patch to unblock people without gpus. there is probably a better way.
|
|
print("NO GPU DETECTED: falling back to CPU - this may take a while")
|
|
n_gpus = 1
|
|
|
|
gpu_devices = hps.gpus.split("-") if hps.gpus else range(n_gpus)
|
|
|
|
|
|
children = {}
|
|
for i, device in enumerate(gpu_devices):
|
|
subproc = mp.Process(
|
|
target=run,
|
|
args=(
|
|
i,
|
|
n_gpus,
|
|
hps,
|
|
device
|
|
),
|
|
)
|
|
children[i]=subproc
|
|
subproc.start()
|
|
|
|
for i in children:
|
|
children[i].join()
|
|
|
|
def run(rank, n_gpus, hps, device):
|
|
print(f"{__name__=}")
|
|
global global_step, least_loss, loss_file, best_model_name
|
|
global_step = 0
|
|
loss_file = os.path.join(hps.model_dir,"losses.json")
|
|
|
|
if os.path.isfile(loss_file):
|
|
with open(loss_file,"r") as f:
|
|
data: dict = json.load(f)
|
|
least_loss = data.get("least_loss",50)
|
|
best_model_name = data.get("best_model_name","")
|
|
else:
|
|
least_loss = 50
|
|
best_model_name = ""
|
|
|
|
if hps.version == "v1":
|
|
from .lib.infer_pack.models import (
|
|
SynthesizerTrnMs256NSFsid as RVC_Model_f0,
|
|
SynthesizerTrnMs256NSFsid_nono as RVC_Model_nof0,
|
|
MultiPeriodDiscriminator,
|
|
)
|
|
else:
|
|
from .lib.infer_pack.models import (
|
|
SynthesizerTrnMs768NSFsid as RVC_Model_f0,
|
|
SynthesizerTrnMs768NSFsid_nono as RVC_Model_nof0,
|
|
MultiPeriodDiscriminatorV2 as MultiPeriodDiscriminator,
|
|
)
|
|
|
|
if rank == 0:
|
|
logger = utils.get_logger(hps.model_dir)
|
|
logger.info(hps)
|
|
# utils.check_git_hash(hps.model_dir)
|
|
writer = SummaryWriter(log_dir=hps.model_dir)
|
|
writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
|
|
dist.init_process_group(
|
|
backend="gloo", init_method="env://", world_size=n_gpus, rank=rank
|
|
)
|
|
torch.manual_seed(hps.train.seed)
|
|
if torch.cuda.is_available():
|
|
torch.cuda.set_device(f"cuda:{device}")
|
|
|
|
if hps.if_f0:
|
|
train_dataset = TextAudioLoaderMultiNSFsid(hps.data.training_files, hps.data)
|
|
else:
|
|
train_dataset = TextAudioLoader(hps.data.training_files, hps.data)
|
|
|
|
train_sampler = DistributedBucketSampler(
|
|
train_dataset,
|
|
hps.train.batch_size * n_gpus,
|
|
# [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1200,1400], # 16s
|
|
[100, 200, 300, 400, 500, 600, 700, 800, 900], # 16s
|
|
num_replicas=n_gpus,
|
|
rank=rank,
|
|
shuffle=True,
|
|
)
|
|
|
|
# It is possible that dataloader's workers are out of shared memory. Please try to raise your shared memory limit.
|
|
# num_workers=8 -> num_workers=4
|
|
if hps.if_f0:
|
|
collate_fn = TextAudioCollateMultiNSFsid()
|
|
else:
|
|
collate_fn = TextAudioCollate()
|
|
train_loader = DataLoader(
|
|
train_dataset,
|
|
num_workers=hps.train.num_workers,
|
|
shuffle=False,
|
|
pin_memory=True,
|
|
collate_fn=collate_fn,
|
|
batch_sampler=train_sampler,
|
|
persistent_workers=True,
|
|
prefetch_factor=8,
|
|
)
|
|
hps.sync_log_interval(len(train_loader))
|
|
|
|
if hps.if_f0:
|
|
net_g = RVC_Model_f0(
|
|
hps.data.filter_length // 2 + 1,
|
|
hps.train.segment_size // hps.data.hop_length,
|
|
**hps.model,
|
|
is_half=hps.train.fp16_run,
|
|
sr=hps.sample_rate,
|
|
)
|
|
else:
|
|
net_g = RVC_Model_nof0(
|
|
hps.data.filter_length // 2 + 1,
|
|
hps.train.segment_size // hps.data.hop_length,
|
|
**hps.model,
|
|
is_half=hps.train.fp16_run,
|
|
)
|
|
if torch.cuda.is_available():
|
|
net_g = net_g.cuda(rank)
|
|
net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm)
|
|
if torch.cuda.is_available():
|
|
net_d = net_d.cuda(rank)
|
|
optim_g = torch.optim.AdamW(
|
|
net_g.parameters(),
|
|
hps.train.learning_rate,
|
|
betas=hps.train.betas,
|
|
eps=hps.train.eps,
|
|
)
|
|
optim_d = torch.optim.AdamW(
|
|
net_d.parameters(),
|
|
hps.train.learning_rate,
|
|
betas=hps.train.betas,
|
|
eps=hps.train.eps,
|
|
)
|
|
|
|
if torch.cuda.is_available():
|
|
net_g = DDP(net_g, device_ids=[rank])
|
|
net_d = DDP(net_d, device_ids=[rank])
|
|
else:
|
|
net_g = DDP(net_g)
|
|
net_d = DDP(net_d)
|
|
|
|
try: # resume training
|
|
_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d)
|
|
if rank == 0: logger.info("loaded D")
|
|
|
|
_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g)
|
|
if rank == 0: logger.info("loaded G")
|
|
|
|
global_step = (epoch_str - 1) * len(train_loader)
|
|
except: # 如果首次不能加载，加载pretrain
|
|
# traceback.print_exc()
|
|
epoch_str = 1
|
|
global_step = 0
|
|
if hps.pretrainG != "":
|
|
if rank == 0:
|
|
logger.info("loaded pretrained %s" % (hps.pretrainG))
|
|
print(
|
|
net_g.module.load_state_dict(
|
|
torch.load(hps.pretrainG, map_location="cpu")["model"]
|
|
)
|
|
) ##测试不加载优化器
|
|
if hps.pretrainD != "":
|
|
if rank == 0:
|
|
logger.info("loaded pretrained %s" % (hps.pretrainD))
|
|
print(
|
|
net_d.module.load_state_dict(
|
|
torch.load(hps.pretrainD, map_location="cpu")["model"]
|
|
)
|
|
)
|
|
|
|
scheduler_g = torch.optim.lr_scheduler.ExponentialLR(
|
|
optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
|
|
)
|
|
scheduler_d = torch.optim.lr_scheduler.ExponentialLR(
|
|
optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
|
|
)
|
|
|
|
scaler = GradScaler(enabled=hps.train.fp16_run)
|
|
|
|
cache = []
|
|
for epoch in range(epoch_str, hps.train.epochs + 1):
|
|
if rank == 0:
|
|
train_and_evaluate(
|
|
rank,
|
|
epoch,
|
|
hps,
|
|
[net_g, net_d],
|
|
[optim_g, optim_d],
|
|
[scheduler_g, scheduler_d],
|
|
scaler,
|
|
[train_loader, None],
|
|
logger,
|
|
[writer, writer_eval],
|
|
cache,
|
|
)
|
|
else:
|
|
train_and_evaluate(
|
|
rank,
|
|
epoch,
|
|
hps,
|
|
[net_g, net_d],
|
|
[optim_g, optim_d],
|
|
[scheduler_g, scheduler_d],
|
|
scaler,
|
|
[train_loader, None],
|
|
None,
|
|
None,
|
|
cache,
|
|
)
|
|
scheduler_g.step()
|
|
scheduler_d.step()
|
|
|
|
|
|
def train_and_evaluate(
|
|
rank, epoch, hps, nets, optims, _, scaler, loaders, logger, writers, cache
|
|
):
|
|
net_g, net_d = nets
|
|
optim_g, optim_d = optims
|
|
train_loader, _ = loaders
|
|
if writers is not None:
|
|
writer, _ = writers
|
|
|
|
train_loader.batch_sampler.set_epoch(epoch)
|
|
global global_step, least_loss, loss_file, best_model_name
|
|
|
|
net_g.train()
|
|
net_d.train()
|
|
|
|
# Prepare data iterator
|
|
if hps.if_cache_data_in_gpu:
|
|
# Use Cache
|
|
data_iterator = cache
|
|
if cache == []:
|
|
# Make new cache
|
|
for batch_idx, info in enumerate(train_loader):
|
|
# Unpack
|
|
if hps.if_f0 == 1:
|
|
(
|
|
phone,
|
|
phone_lengths,
|
|
pitch,
|
|
pitchf,
|
|
spec,
|
|
spec_lengths,
|
|
wave,
|
|
wave_lengths,
|
|
sid,
|
|
) = info
|
|
else:
|
|
(
|
|
phone,
|
|
phone_lengths,
|
|
spec,
|
|
spec_lengths,
|
|
wave,
|
|
wave_lengths,
|
|
sid,
|
|
) = info
|
|
# Load on CUDA
|
|
if torch.cuda.is_available():
|
|
phone = phone.cuda(rank, non_blocking=True)
|
|
phone_lengths = phone_lengths.cuda(rank, non_blocking=True)
|
|
if hps.if_f0 == 1:
|
|
pitch = pitch.cuda(rank, non_blocking=True)
|
|
pitchf = pitchf.cuda(rank, non_blocking=True)
|
|
sid = sid.cuda(rank, non_blocking=True)
|
|
spec = spec.cuda(rank, non_blocking=True)
|
|
spec_lengths = spec_lengths.cuda(rank, non_blocking=True)
|
|
wave = wave.cuda(rank, non_blocking=True)
|
|
wave_lengths = wave_lengths.cuda(rank, non_blocking=True)
|
|
# Cache on list
|
|
if hps.if_f0 == 1:
|
|
cache.append(
|
|
(
|
|
batch_idx,
|
|
(
|
|
phone,
|
|
phone_lengths,
|
|
pitch,
|
|
pitchf,
|
|
spec,
|
|
spec_lengths,
|
|
wave,
|
|
wave_lengths,
|
|
sid,
|
|
),
|
|
)
|
|
)
|
|
else:
|
|
cache.append(
|
|
(
|
|
batch_idx,
|
|
(
|
|
phone,
|
|
phone_lengths,
|
|
spec,
|
|
spec_lengths,
|
|
wave,
|
|
wave_lengths,
|
|
sid,
|
|
),
|
|
)
|
|
)
|
|
else:
|
|
# Load shuffled cache
|
|
shuffle(cache)
|
|
else:
|
|
# Loader
|
|
data_iterator = enumerate(train_loader)
|
|
|
|
# Run steps
|
|
epoch_recorder = EpochRecorder()
|
|
for batch_idx, info in tqdm(data_iterator,desc=f"[Epoch {epoch}]: "):
|
|
# Data
|
|
## Unpack
|
|
if hps.if_f0 == 1:
|
|
(
|
|
phone,
|
|
phone_lengths,
|
|
pitch,
|
|
pitchf,
|
|
spec,
|
|
spec_lengths,
|
|
wave,
|
|
wave_lengths,
|
|
sid,
|
|
) = info
|
|
else:
|
|
phone, phone_lengths, spec, spec_lengths, wave, wave_lengths, sid = info
|
|
## Load on CUDA
|
|
if (not hps.if_cache_data_in_gpu) and torch.cuda.is_available():
|
|
phone = phone.cuda(rank, non_blocking=True)
|
|
phone_lengths = phone_lengths.cuda(rank, non_blocking=True)
|
|
if hps.if_f0 == 1:
|
|
pitch = pitch.cuda(rank, non_blocking=True)
|
|
pitchf = pitchf.cuda(rank, non_blocking=True)
|
|
sid = sid.cuda(rank, non_blocking=True)
|
|
spec = spec.cuda(rank, non_blocking=True)
|
|
spec_lengths = spec_lengths.cuda(rank, non_blocking=True)
|
|
wave = wave.cuda(rank, non_blocking=True)
|
|
|
|
# Calculate
|
|
with autocast(enabled=hps.train.fp16_run):
|
|
if hps.if_f0 == 1:
|
|
(
|
|
y_hat,
|
|
ids_slice,
|
|
x_mask,
|
|
z_mask,
|
|
(z, z_p, m_p, logs_p, m_q, logs_q),
|
|
) = net_g(phone, phone_lengths, pitch, pitchf, spec, spec_lengths, sid)
|
|
else:
|
|
(
|
|
y_hat,
|
|
ids_slice,
|
|
x_mask,
|
|
z_mask,
|
|
(z, z_p, m_p, logs_p, m_q, logs_q),
|
|
) = net_g(phone, phone_lengths, spec, spec_lengths, sid)
|
|
mel = spec_to_mel_torch(
|
|
spec,
|
|
hps.data.filter_length,
|
|
hps.data.n_mel_channels,
|
|
hps.data.sampling_rate,
|
|
hps.data.mel_fmin,
|
|
hps.data.mel_fmax,
|
|
)
|
|
y_mel = commons.slice_segments(
|
|
mel, ids_slice, hps.train.segment_size // hps.data.hop_length
|
|
)
|
|
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 = 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 = torch.Tensor([0.]).to(wave.device)
|
|
if hps.train.get("gradient_lambda",0.)>0:
|
|
# Compute the gradient penalty
|
|
# Randomly interpolate between real and generated data
|
|
size = [1]*wave.ndim
|
|
size[0] = wave.size(0)
|
|
alpha = torch.rand(*size, device=wave.device)
|
|
interpolated = alpha * wave + (1 - alpha) * gen_wave
|
|
interpolated.requires_grad_(True)
|
|
# Get the discriminator output for the interpolated data
|
|
_, disc_interpolated_output, _, _ = net_d(wave, interpolated)
|
|
# Compute gradients of discriminator output w.r.t. interpolated data
|
|
for output in disc_interpolated_output:
|
|
gradient = torch.autograd.grad(
|
|
outputs=output,
|
|
inputs=interpolated,
|
|
grad_outputs=torch.ones(output.size(), device=wave.device),
|
|
create_graph=True,
|
|
only_inputs=True
|
|
)[0]
|
|
gradient = gradient.view(gradient.size(0), -1)
|
|
gradient_penalty += torch.log(gradient.norm(2,dim=-1)).abs().mean() # force gradnorm=1
|
|
gradient_penalty *= hps.train.gradient_lambda/len(disc_interpolated_output)
|
|
|
|
loss_mfcc = torch.Tensor([0.]).to(wave.device)
|
|
if hps.train.get("timbre_lambda",0)>0:
|
|
loss_mfcc = mfcc_loss(wave, gen_wave, SR_MAP[hps.sample_rate],
|
|
n_fft=hps.data.filter_length,
|
|
hop_length=hps.data.hop_length,
|
|
win_length=hps.data.win_length,
|
|
n_mels=hps.data.n_mel_channels,
|
|
norm="slaney",
|
|
f_min=hps.data.mel_fmin,
|
|
f_max=hps.data.mel_fmax
|
|
) * hps.train.timbre_lambda
|
|
|
|
loss_mfcc = loss_mfcc.squeeze()
|
|
gradient_penalty = gradient_penalty.squeeze()
|
|
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+loss_mfcc
|
|
|
|
optim_d.zero_grad()
|
|
scaler.scale(loss_disc_all).backward()
|
|
scaler.unscale_(optim_d)
|
|
grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
|
|
scaler.step(optim_d)
|
|
|
|
with autocast(enabled=hps.train.fp16_run):
|
|
# Generator
|
|
y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(wave, y_hat)
|
|
with autocast(enabled=False):
|
|
loss_mel = F.smooth_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 epoch>=max(hps.total_epoch*.2,20): #start saving best model after 20% or 20 epochs
|
|
if loss_gen_all+loss_disc_all<least_loss:
|
|
least_loss = loss_gen_all+loss_disc_all
|
|
logger.info(f"[lowest loss] {least_loss=:.3f}: {loss_disc=:.3f} {loss_mfcc=:.3f} {gradient_penalty=:.3f}, {loss_gen=:.3f}, {loss_fm=:.3f}, {loss_mel=:.3f}, {loss_kl=:.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}_loss{least_loss:2.0f}" if hps.if_save_latest else f"{hps.name}_e{epoch}_s{global_step}_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),f)
|
|
|
|
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 / 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/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,
|
|
"total/loss/mfcc": loss_mfcc,
|
|
"learning_rate": lr,
|
|
"gradient/grad_norm_d": grad_norm_d,
|
|
"gradient/grad_norm_g": 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()),
|
|
}
|
|
|
|
audio_dict = {
|
|
"slice/wave_org": wave.flatten(),
|
|
"slice/wave_gen": y_hat.flatten()
|
|
}
|
|
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"),
|
|
)
|
|
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}, {loss_mfcc=:.3f}, {gradient_penalty=:.3f}")
|
|
logger.info(f"|| {loss_gen_all=:.3f}: {loss_gen=:.3f}, {loss_fm=:.3f}, {loss_mel=:.3f}, {loss_kl=:.3f}")
|
|
|
|
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:
|
|
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]}-best.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) |