c8261b2ccc
* Reformat
* rewrite _get_name_params
* Add workflow for automatic formatting
* Revert "Add workflow for automatic formatting"
This reverts commit 9111c5dbc1.
* revert Retrieval_based_Voice_Conversion_WebUI.ipynb
---------
Co-authored-by: 源文雨 <41315874+fumiama@users.noreply.github.com>
684 lines
26 KiB
Python
684 lines
26 KiB
Python
import sys, os
|
||
|
||
now_dir = os.getcwd()
|
||
sys.path.append(os.path.join(now_dir, "train"))
|
||
import utils
|
||
|
||
hps = utils.get_hparams()
|
||
os.environ["CUDA_VISIBLE_DEVICES"] = hps.gpus.replace("-", ",")
|
||
n_gpus = len(hps.gpus.split("-"))
|
||
from random import shuffle
|
||
import traceback, json, argparse, itertools, math, torch, pdb
|
||
|
||
torch.backends.cudnn.deterministic = False
|
||
torch.backends.cudnn.benchmark = False
|
||
from torch import nn, optim
|
||
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 infer_pack import commons
|
||
|
||
from time import time as ttime
|
||
from data_utils import (
|
||
TextAudioLoaderMultiNSFsid,
|
||
TextAudioLoader,
|
||
TextAudioCollateMultiNSFsid,
|
||
TextAudioCollate,
|
||
DistributedBucketSampler,
|
||
)
|
||
from infer_pack.models import (
|
||
SynthesizerTrnMs256NSFsid,
|
||
SynthesizerTrnMs256NSFsid_nono,
|
||
MultiPeriodDiscriminator,
|
||
)
|
||
from losses import generator_loss, discriminator_loss, feature_loss, kl_loss
|
||
from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
|
||
|
||
|
||
global_step = 0
|
||
|
||
|
||
def main():
|
||
# n_gpus = torch.cuda.device_count()
|
||
os.environ["MASTER_ADDR"] = "localhost"
|
||
os.environ["MASTER_PORT"] = "5555"
|
||
|
||
mp.spawn(
|
||
run,
|
||
nprocs=n_gpus,
|
||
args=(
|
||
n_gpus,
|
||
hps,
|
||
),
|
||
)
|
||
|
||
|
||
def run(rank, n_gpus, hps):
|
||
global global_step
|
||
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(rank)
|
||
|
||
if hps.if_f0 == 1:
|
||
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 == 1:
|
||
collate_fn = TextAudioCollateMultiNSFsid()
|
||
else:
|
||
collate_fn = TextAudioCollate()
|
||
train_loader = DataLoader(
|
||
train_dataset,
|
||
num_workers=4,
|
||
shuffle=False,
|
||
pin_memory=True,
|
||
collate_fn=collate_fn,
|
||
batch_sampler=train_sampler,
|
||
persistent_workers=True,
|
||
prefetch_factor=8,
|
||
)
|
||
if hps.if_f0 == 1:
|
||
net_g = SynthesizerTrnMs256NSFsid(
|
||
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 = SynthesizerTrnMs256NSFsid_nono(
|
||
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,
|
||
)
|
||
# net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True)
|
||
# net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True)
|
||
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: # 如果能加载自动resume
|
||
_, _, _, epoch_str = utils.load_checkpoint(
|
||
utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d
|
||
) # D多半加载没事
|
||
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,load_opt=0)
|
||
_, _, _, epoch_str = utils.load_checkpoint(
|
||
utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g
|
||
)
|
||
global_step = (epoch_str - 1) * len(train_loader)
|
||
# epoch_str = 1
|
||
# global_step = 0
|
||
except: # 如果首次不能加载，加载pretrain
|
||
traceback.print_exc()
|
||
epoch_str = 1
|
||
global_step = 0
|
||
if rank == 0:
|
||
logger.info("loaded pretrained %s %s" % (hps.pretrainG, hps.pretrainD))
|
||
print(
|
||
net_g.module.load_state_dict(
|
||
torch.load(hps.pretrainG, map_location="cpu")["model"]
|
||
)
|
||
) ##测试不加载优化器
|
||
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, schedulers, scaler, loaders, logger, writers, cache
|
||
):
|
||
net_g, net_d = nets
|
||
optim_g, optim_d = optims
|
||
train_loader, eval_loader = loaders
|
||
if writers is not None:
|
||
writer, writer_eval = writers
|
||
|
||
train_loader.batch_sampler.set_epoch(epoch)
|
||
global global_step
|
||
|
||
net_g.train()
|
||
net_d.train()
|
||
if cache == [] or hps.if_cache_data_in_gpu == False: # 第一个epoch把cache全部填满训练集
|
||
# print("caching")
|
||
for batch_idx, info in enumerate(train_loader):
|
||
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
|
||
if torch.cuda.is_available():
|
||
phone, phone_lengths = phone.cuda(
|
||
rank, non_blocking=True
|
||
), phone_lengths.cuda(rank, non_blocking=True)
|
||
if hps.if_f0 == 1:
|
||
pitch, pitchf = pitch.cuda(rank, non_blocking=True), pitchf.cuda(
|
||
rank, non_blocking=True
|
||
)
|
||
sid = sid.cuda(rank, non_blocking=True)
|
||
spec, spec_lengths = spec.cuda(
|
||
rank, non_blocking=True
|
||
), spec_lengths.cuda(rank, non_blocking=True)
|
||
wave, wave_lengths = wave.cuda(
|
||
rank, non_blocking=True
|
||
), wave_lengths.cuda(rank, non_blocking=True)
|
||
if hps.if_cache_data_in_gpu == True:
|
||
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,
|
||
),
|
||
)
|
||
)
|
||
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 == True:
|
||
y_hat_mel = y_hat_mel.half()
|
||
wave = commons.slice_segments(
|
||
wave, ids_slice * hps.data.hop_length, hps.train.segment_size
|
||
) # slice
|
||
|
||
# Discriminator
|
||
y_d_hat_r, y_d_hat_g, _, _ = net_d(wave, y_hat.detach())
|
||
with autocast(enabled=False):
|
||
loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(
|
||
y_d_hat_r, y_d_hat_g
|
||
)
|
||
optim_d.zero_grad()
|
||
scaler.scale(loss_disc).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.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
|
||
loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
|
||
loss_fm = feature_loss(fmap_r, fmap_g)
|
||
loss_gen, losses_gen = generator_loss(y_d_hat_g)
|
||
loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl
|
||
optim_g.zero_grad()
|
||
scaler.scale(loss_gen_all).backward()
|
||
scaler.unscale_(optim_g)
|
||
grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
|
||
scaler.step(optim_g)
|
||
scaler.update()
|
||
|
||
if rank == 0:
|
||
if global_step % hps.train.log_interval == 0:
|
||
lr = optim_g.param_groups[0]["lr"]
|
||
logger.info(
|
||
"Train Epoch: {} [{:.0f}%]".format(
|
||
epoch, 100.0 * batch_idx / len(train_loader)
|
||
)
|
||
)
|
||
# Amor For Tensorboard display
|
||
if loss_mel > 50:
|
||
loss_mel = 50
|
||
if loss_kl > 5:
|
||
loss_kl = 5
|
||
|
||
logger.info([global_step, lr])
|
||
logger.info(
|
||
f"loss_disc={loss_disc:.3f}, loss_gen={loss_gen:.3f}, loss_fm={loss_fm:.3f},loss_mel={loss_mel:.3f}, loss_kl={loss_kl:.3f}"
|
||
)
|
||
scalar_dict = {
|
||
"loss/g/total": loss_gen_all,
|
||
"loss/d/total": loss_disc,
|
||
"learning_rate": lr,
|
||
"grad_norm_d": grad_norm_d,
|
||
"grad_norm_g": grad_norm_g,
|
||
}
|
||
scalar_dict.update(
|
||
{
|
||
"loss/g/fm": loss_fm,
|
||
"loss/g/mel": loss_mel,
|
||
"loss/g/kl": loss_kl,
|
||
}
|
||
)
|
||
|
||
scalar_dict.update(
|
||
{"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)}
|
||
)
|
||
scalar_dict.update(
|
||
{
|
||
"loss/d_r/{}".format(i): v
|
||
for i, v in enumerate(losses_disc_r)
|
||
}
|
||
)
|
||
scalar_dict.update(
|
||
{
|
||
"loss/d_g/{}".format(i): v
|
||
for i, v in enumerate(losses_disc_g)
|
||
}
|
||
)
|
||
image_dict = {
|
||
"slice/mel_org": utils.plot_spectrogram_to_numpy(
|
||
y_mel[0].data.cpu().numpy()
|
||
),
|
||
"slice/mel_gen": utils.plot_spectrogram_to_numpy(
|
||
y_hat_mel[0].data.cpu().numpy()
|
||
),
|
||
"all/mel": utils.plot_spectrogram_to_numpy(
|
||
mel[0].data.cpu().numpy()
|
||
),
|
||
}
|
||
utils.summarize(
|
||
writer=writer,
|
||
global_step=global_step,
|
||
images=image_dict,
|
||
scalars=scalar_dict,
|
||
)
|
||
global_step += 1
|
||
# if global_step % hps.train.eval_interval == 0:
|
||
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)),
|
||
)
|
||
|
||
else: # 后续的epoch直接使用打乱的cache
|
||
shuffle(cache)
|
||
# print("using cache")
|
||
for batch_idx, info in cache:
|
||
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
|
||
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 == True:
|
||
y_hat_mel = y_hat_mel.half()
|
||
wave = commons.slice_segments(
|
||
wave, ids_slice * hps.data.hop_length, hps.train.segment_size
|
||
) # slice
|
||
|
||
# Discriminator
|
||
y_d_hat_r, y_d_hat_g, _, _ = net_d(wave, y_hat.detach())
|
||
with autocast(enabled=False):
|
||
loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(
|
||
y_d_hat_r, y_d_hat_g
|
||
)
|
||
optim_d.zero_grad()
|
||
scaler.scale(loss_disc).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.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
|
||
loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
|
||
|
||
loss_fm = feature_loss(fmap_r, fmap_g)
|
||
loss_gen, losses_gen = generator_loss(y_d_hat_g)
|
||
loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl
|
||
optim_g.zero_grad()
|
||
scaler.scale(loss_gen_all).backward()
|
||
scaler.unscale_(optim_g)
|
||
grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
|
||
scaler.step(optim_g)
|
||
scaler.update()
|
||
|
||
if rank == 0:
|
||
if global_step % hps.train.log_interval == 0:
|
||
lr = optim_g.param_groups[0]["lr"]
|
||
logger.info(
|
||
"Train Epoch: {} [{:.0f}%]".format(
|
||
epoch, 100.0 * batch_idx / len(train_loader)
|
||
)
|
||
)
|
||
# Amor For Tensorboard display
|
||
if loss_mel > 50:
|
||
loss_mel = 50
|
||
if loss_kl > 5:
|
||
loss_kl = 5
|
||
|
||
logger.info([global_step, lr])
|
||
logger.info(
|
||
f"loss_disc={loss_disc:.3f}, loss_gen={loss_gen:.3f}, loss_fm={loss_fm:.3f},loss_mel={loss_mel:.3f}, loss_kl={loss_kl:.3f}"
|
||
)
|
||
scalar_dict = {
|
||
"loss/g/total": loss_gen_all,
|
||
"loss/d/total": loss_disc,
|
||
"learning_rate": lr,
|
||
"grad_norm_d": grad_norm_d,
|
||
"grad_norm_g": grad_norm_g,
|
||
}
|
||
scalar_dict.update(
|
||
{
|
||
"loss/g/fm": loss_fm,
|
||
"loss/g/mel": loss_mel,
|
||
"loss/g/kl": loss_kl,
|
||
}
|
||
)
|
||
|
||
scalar_dict.update(
|
||
{"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)}
|
||
)
|
||
scalar_dict.update(
|
||
{
|
||
"loss/d_r/{}".format(i): v
|
||
for i, v in enumerate(losses_disc_r)
|
||
}
|
||
)
|
||
scalar_dict.update(
|
||
{
|
||
"loss/d_g/{}".format(i): v
|
||
for i, v in enumerate(losses_disc_g)
|
||
}
|
||
)
|
||
image_dict = {
|
||
"slice/mel_org": utils.plot_spectrogram_to_numpy(
|
||
y_mel[0].data.cpu().numpy()
|
||
),
|
||
"slice/mel_gen": utils.plot_spectrogram_to_numpy(
|
||
y_hat_mel[0].data.cpu().numpy()
|
||
),
|
||
"all/mel": utils.plot_spectrogram_to_numpy(
|
||
mel[0].data.cpu().numpy()
|
||
),
|
||
}
|
||
utils.summarize(
|
||
writer=writer,
|
||
global_step=global_step,
|
||
images=image_dict,
|
||
scalars=scalar_dict,
|
||
)
|
||
global_step += 1
|
||
# if global_step % hps.train.eval_interval == 0:
|
||
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:
|
||
logger.info("====> Epoch: {}".format(epoch))
|
||
if epoch >= hps.total_epoch and rank == 0:
|
||
logger.info("Training is done. The program is closed.")
|
||
from process_ckpt import savee # def savee(ckpt,sr,if_f0,name,epoch):
|
||
|
||
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))
|
||
)
|
||
os._exit(2333333)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|