856 lines
30 KiB
Python
856 lines
30 KiB
Python
import argparse
|
||
import datetime
|
||
import gc
|
||
import os
|
||
import platform
|
||
|
||
import torch
|
||
import torch.distributed as dist
|
||
from torch.cuda.amp import GradScaler, autocast
|
||
from torch.nn import functional as F
|
||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||
from torch.utils.data import DataLoader
|
||
from torch.utils.tensorboard import SummaryWriter
|
||
from tqdm import tqdm
|
||
|
||
# logging.getLogger("numba").setLevel(logging.WARNING)
|
||
import commons
|
||
import default_style
|
||
import utils
|
||
from common.log import logger
|
||
from common.stdout_wrapper import SAFE_STDOUT
|
||
from config import config
|
||
from data_utils import (
|
||
DistributedBucketSampler,
|
||
TextAudioSpeakerCollate,
|
||
TextAudioSpeakerLoader,
|
||
)
|
||
from losses import discriminator_loss, feature_loss, generator_loss, kl_loss
|
||
from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
|
||
from models import DurationDiscriminator, MultiPeriodDiscriminator, SynthesizerTrn
|
||
from text.symbols import symbols
|
||
|
||
torch.backends.cuda.matmul.allow_tf32 = True
|
||
torch.backends.cudnn.allow_tf32 = (
|
||
True # If encontered training problem,please try to disable TF32.
|
||
)
|
||
torch.set_float32_matmul_precision("medium")
|
||
torch.backends.cuda.sdp_kernel("flash")
|
||
torch.backends.cuda.enable_flash_sdp(True)
|
||
torch.backends.cuda.enable_mem_efficient_sdp(
|
||
True
|
||
) # Not available if torch version is lower than 2.0
|
||
torch.backends.cuda.enable_math_sdp(True)
|
||
|
||
|
||
global_step = 0
|
||
|
||
|
||
def run():
|
||
# Command line configuration is not recommended unless necessary, use config.yml
|
||
parser = argparse.ArgumentParser()
|
||
parser.add_argument(
|
||
"-c",
|
||
"--config",
|
||
type=str,
|
||
default=config.train_ms_config.config_path,
|
||
help="JSON file for configuration",
|
||
)
|
||
parser.add_argument(
|
||
"-m",
|
||
"--model",
|
||
type=str,
|
||
help="数据集文件夹路径，请注意，数据不再默认放在/logs文件夹下。如果需要用命令行配置，请声明相对于根目录的路径",
|
||
default=config.dataset_path,
|
||
)
|
||
parser.add_argument(
|
||
"--assets_root",
|
||
type=str,
|
||
help="Root directory of model assets needed for inference.",
|
||
default=config.assets_root,
|
||
)
|
||
parser.add_argument(
|
||
"--skip_default_style",
|
||
action="store_true",
|
||
help="Skip saving default style config and mean vector.",
|
||
)
|
||
parser.add_argument(
|
||
"--no_progress_bar",
|
||
action="store_true",
|
||
help="Do not show the progress bar while training.",
|
||
)
|
||
args = parser.parse_args()
|
||
|
||
# Set log file
|
||
model_dir = os.path.join(args.model, config.train_ms_config.model_dir)
|
||
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
|
||
logger.add(os.path.join(args.model, f"train_{timestamp}.log"))
|
||
|
||
# Parsing environment variables
|
||
envs = config.train_ms_config.env
|
||
for env_name, env_value in envs.items():
|
||
if env_name not in os.environ.keys():
|
||
logger.info("Loading configuration from config {}".format(str(env_value)))
|
||
os.environ[env_name] = str(env_value)
|
||
logger.info(
|
||
"Loading environment variables \nMASTER_ADDR: {},\nMASTER_PORT: {},\nWORLD_SIZE: {},\nRANK: {},\nLOCAL_RANK: {}".format(
|
||
os.environ["MASTER_ADDR"],
|
||
os.environ["MASTER_PORT"],
|
||
os.environ["WORLD_SIZE"],
|
||
os.environ["RANK"],
|
||
os.environ["LOCAL_RANK"],
|
||
)
|
||
)
|
||
|
||
backend = "nccl"
|
||
if platform.system() == "Windows":
|
||
backend = "gloo" # If Windows,switch to gloo backend.
|
||
dist.init_process_group(
|
||
backend=backend,
|
||
init_method="env://",
|
||
timeout=datetime.timedelta(seconds=300),
|
||
) # Use torchrun instead of mp.spawn
|
||
rank = dist.get_rank()
|
||
local_rank = int(os.environ["LOCAL_RANK"])
|
||
n_gpus = dist.get_world_size()
|
||
|
||
hps = utils.get_hparams_from_file(args.config)
|
||
# This is needed because we have to pass `model_dir` to `train_and_evaluate()`
|
||
hps.model_dir = model_dir
|
||
|
||
# 比较路径是否相同
|
||
if os.path.realpath(args.config) != os.path.realpath(
|
||
config.train_ms_config.config_path
|
||
):
|
||
with open(args.config, "r", encoding="utf-8") as f:
|
||
data = f.read()
|
||
os.makedirs(os.path.dirname(config.train_ms_config.config_path), exist_ok=True)
|
||
with open(config.train_ms_config.config_path, "w", encoding="utf-8") as f:
|
||
f.write(data)
|
||
|
||
"""
|
||
Path constants are a bit complicated...
|
||
TODO: Refactor or rename these?
|
||
(Both `config.yml` and `config.json` are used, which is confusing I think.)
|
||
|
||
args.model: For saving all info needed for training.
|
||
default: `Data/{model_name}`.
|
||
hps.model_dir := model_dir: For saving checkpoints (for resuming training).
|
||
default: `Data/{model_name}/models`.
|
||
(Use `hps` since we have to pass `model_dir` to `train_and_evaluate()`.
|
||
|
||
args.assets_root: The root directory of model assets needed for inference.
|
||
default: config.assets_root == `model_assets`.
|
||
|
||
config.out_dir: The directory for model assets of this model (for inference).
|
||
default: `model_assets/{model_name}`.
|
||
"""
|
||
os.makedirs(config.out_dir, exist_ok=True)
|
||
|
||
if not args.skip_default_style:
|
||
# Save default style to out_dir
|
||
default_style.set_style_config(
|
||
args.config, os.path.join(config.out_dir, "config.json")
|
||
)
|
||
default_style.save_mean_vector(
|
||
os.path.join(args.model, "wavs"),
|
||
os.path.join(config.out_dir, "style_vectors.npy"),
|
||
)
|
||
|
||
torch.manual_seed(hps.train.seed)
|
||
torch.cuda.set_device(local_rank)
|
||
|
||
global global_step
|
||
if rank == 0:
|
||
# logger = utils.get_logger(hps.model_dir)
|
||
# logger.info(hps)
|
||
utils.check_git_hash(model_dir)
|
||
writer = SummaryWriter(log_dir=model_dir)
|
||
writer_eval = SummaryWriter(log_dir=os.path.join(model_dir, "eval"))
|
||
train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
|
||
train_sampler = DistributedBucketSampler(
|
||
train_dataset,
|
||
hps.train.batch_size,
|
||
[32, 300, 400, 500, 600, 700, 800, 900, 1000],
|
||
num_replicas=n_gpus,
|
||
rank=rank,
|
||
shuffle=True,
|
||
)
|
||
collate_fn = TextAudioSpeakerCollate()
|
||
train_loader = DataLoader(
|
||
train_dataset,
|
||
# num_workers=min(config.train_ms_config.num_workers, os.cpu_count() - 1),
|
||
# Slow and often freezes, so use only half of the cores.
|
||
num_workers=min(config.train_ms_config.num_workers, os.cpu_count() // 2),
|
||
shuffle=False,
|
||
pin_memory=True,
|
||
collate_fn=collate_fn,
|
||
batch_sampler=train_sampler,
|
||
persistent_workers=True,
|
||
prefetch_factor=4,
|
||
) # DataLoader config could be adjusted.
|
||
if rank == 0:
|
||
eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
|
||
eval_loader = DataLoader(
|
||
eval_dataset,
|
||
num_workers=0,
|
||
shuffle=False,
|
||
batch_size=1,
|
||
pin_memory=True,
|
||
drop_last=False,
|
||
collate_fn=collate_fn,
|
||
)
|
||
if (
|
||
"use_noise_scaled_mas" in hps.model.keys()
|
||
and hps.model.use_noise_scaled_mas is True
|
||
):
|
||
logger.info("Using noise scaled MAS for VITS2")
|
||
mas_noise_scale_initial = 0.01
|
||
noise_scale_delta = 2e-6
|
||
else:
|
||
logger.info("Using normal MAS for VITS1")
|
||
mas_noise_scale_initial = 0.0
|
||
noise_scale_delta = 0.0
|
||
if (
|
||
"use_duration_discriminator" in hps.model.keys()
|
||
and hps.model.use_duration_discriminator is True
|
||
):
|
||
logger.info("Using duration discriminator for VITS2")
|
||
net_dur_disc = DurationDiscriminator(
|
||
hps.model.hidden_channels,
|
||
hps.model.hidden_channels,
|
||
3,
|
||
0.1,
|
||
gin_channels=hps.model.gin_channels if hps.data.n_speakers != 0 else 0,
|
||
).cuda(local_rank)
|
||
if (
|
||
"use_spk_conditioned_encoder" in hps.model.keys()
|
||
and hps.model.use_spk_conditioned_encoder is True
|
||
):
|
||
if hps.data.n_speakers == 0:
|
||
raise ValueError(
|
||
"n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model"
|
||
)
|
||
else:
|
||
logger.info("Using normal encoder for VITS1")
|
||
|
||
net_g = SynthesizerTrn(
|
||
len(symbols),
|
||
hps.data.filter_length // 2 + 1,
|
||
hps.train.segment_size // hps.data.hop_length,
|
||
n_speakers=hps.data.n_speakers,
|
||
mas_noise_scale_initial=mas_noise_scale_initial,
|
||
noise_scale_delta=noise_scale_delta,
|
||
**hps.model,
|
||
).cuda(local_rank)
|
||
|
||
if getattr(hps.train, "freeze_ZH_bert", False):
|
||
logger.info("Freezing ZH bert encoder !!!")
|
||
for param in net_g.enc_p.bert_proj.parameters():
|
||
param.requires_grad = False
|
||
|
||
if getattr(hps.train, "freeze_EN_bert", False):
|
||
logger.info("Freezing EN bert encoder !!!")
|
||
for param in net_g.enc_p.en_bert_proj.parameters():
|
||
param.requires_grad = False
|
||
|
||
if getattr(hps.train, "freeze_JP_bert", False):
|
||
logger.info("Freezing JP bert encoder !!!")
|
||
for param in net_g.enc_p.ja_bert_proj.parameters():
|
||
param.requires_grad = False
|
||
if getattr(hps.train, "freeze_style", False):
|
||
logger.info("Freezing style encoder !!!")
|
||
for param in net_g.enc_p.style_proj.parameters():
|
||
param.requires_grad = False
|
||
|
||
net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(local_rank)
|
||
optim_g = torch.optim.AdamW(
|
||
filter(lambda p: p.requires_grad, 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 net_dur_disc is not None:
|
||
optim_dur_disc = torch.optim.AdamW(
|
||
net_dur_disc.parameters(),
|
||
hps.train.learning_rate,
|
||
betas=hps.train.betas,
|
||
eps=hps.train.eps,
|
||
)
|
||
else:
|
||
optim_dur_disc = None
|
||
net_g = DDP(net_g, device_ids=[local_rank])
|
||
net_d = DDP(net_d, device_ids=[local_rank])
|
||
dur_resume_lr = None
|
||
if net_dur_disc is not None:
|
||
net_dur_disc = DDP(
|
||
net_dur_disc, device_ids=[local_rank], find_unused_parameters=True
|
||
)
|
||
|
||
if utils.is_resuming(model_dir):
|
||
if net_dur_disc is not None:
|
||
_, _, dur_resume_lr, epoch_str = utils.load_checkpoint(
|
||
utils.latest_checkpoint_path(model_dir, "DUR_*.pth"),
|
||
net_dur_disc,
|
||
optim_dur_disc,
|
||
skip_optimizer=hps.train.skip_optimizer
|
||
if "skip_optimizer" in hps.train
|
||
else True,
|
||
)
|
||
if not optim_dur_disc.param_groups[0].get("initial_lr"):
|
||
optim_dur_disc.param_groups[0]["initial_lr"] = dur_resume_lr
|
||
_, optim_g, g_resume_lr, epoch_str = utils.load_checkpoint(
|
||
utils.latest_checkpoint_path(model_dir, "G_*.pth"),
|
||
net_g,
|
||
optim_g,
|
||
skip_optimizer=hps.train.skip_optimizer
|
||
if "skip_optimizer" in hps.train
|
||
else True,
|
||
)
|
||
_, optim_d, d_resume_lr, epoch_str = utils.load_checkpoint(
|
||
utils.latest_checkpoint_path(model_dir, "D_*.pth"),
|
||
net_d,
|
||
optim_d,
|
||
skip_optimizer=hps.train.skip_optimizer
|
||
if "skip_optimizer" in hps.train
|
||
else True,
|
||
)
|
||
if not optim_g.param_groups[0].get("initial_lr"):
|
||
optim_g.param_groups[0]["initial_lr"] = g_resume_lr
|
||
if not optim_d.param_groups[0].get("initial_lr"):
|
||
optim_d.param_groups[0]["initial_lr"] = d_resume_lr
|
||
|
||
epoch_str = max(epoch_str, 1)
|
||
# global_step = (epoch_str - 1) * len(train_loader)
|
||
global_step = int(
|
||
utils.get_steps(utils.latest_checkpoint_path(model_dir, "G_*.pth"))
|
||
)
|
||
logger.info(
|
||
f"******************Found the model. Current epoch is {epoch_str}, gloabl step is {global_step}*********************"
|
||
)
|
||
else:
|
||
try:
|
||
_ = utils.load_safetensors(
|
||
os.path.join(model_dir, "G_0.safetensors"), net_g
|
||
)
|
||
_ = utils.load_safetensors(
|
||
os.path.join(model_dir, "D_0.safetensors"), net_d
|
||
)
|
||
if net_dur_disc is not None:
|
||
_ = utils.load_safetensors(
|
||
os.path.join(model_dir, "DUR_0.safetensors"), net_dur_disc
|
||
)
|
||
logger.info("Loaded the pretrained models.")
|
||
except Exception as e:
|
||
logger.warning(e)
|
||
logger.warning(
|
||
"It seems that you are not using the pretrained models, so we will train from scratch."
|
||
)
|
||
finally:
|
||
epoch_str = 1
|
||
global_step = 0
|
||
|
||
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
|
||
)
|
||
if net_dur_disc is not None:
|
||
scheduler_dur_disc = torch.optim.lr_scheduler.ExponentialLR(
|
||
optim_dur_disc, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2
|
||
)
|
||
else:
|
||
scheduler_dur_disc = None
|
||
scaler = GradScaler(enabled=hps.train.bf16_run)
|
||
logger.info("Start training.")
|
||
|
||
diff = abs(
|
||
epoch_str * len(train_loader) - (hps.train.epochs + 1) * len(train_loader)
|
||
)
|
||
pbar = None
|
||
if not args.no_progress_bar:
|
||
pbar = tqdm(
|
||
total=global_step + diff,
|
||
initial=global_step,
|
||
smoothing=0.05,
|
||
file=SAFE_STDOUT,
|
||
)
|
||
initial_step = global_step
|
||
|
||
for epoch in range(epoch_str, hps.train.epochs + 1):
|
||
if rank == 0:
|
||
train_and_evaluate(
|
||
rank,
|
||
local_rank,
|
||
epoch,
|
||
hps,
|
||
[net_g, net_d, net_dur_disc],
|
||
[optim_g, optim_d, optim_dur_disc],
|
||
[scheduler_g, scheduler_d, scheduler_dur_disc],
|
||
scaler,
|
||
[train_loader, eval_loader],
|
||
logger,
|
||
[writer, writer_eval],
|
||
pbar,
|
||
initial_step,
|
||
)
|
||
else:
|
||
train_and_evaluate(
|
||
rank,
|
||
local_rank,
|
||
epoch,
|
||
hps,
|
||
[net_g, net_d, net_dur_disc],
|
||
[optim_g, optim_d, optim_dur_disc],
|
||
[scheduler_g, scheduler_d, scheduler_dur_disc],
|
||
scaler,
|
||
[train_loader, None],
|
||
None,
|
||
None,
|
||
pbar,
|
||
initial_step,
|
||
)
|
||
scheduler_g.step()
|
||
scheduler_d.step()
|
||
if net_dur_disc is not None:
|
||
scheduler_dur_disc.step()
|
||
|
||
if epoch == hps.train.epochs:
|
||
# Save the final models
|
||
utils.save_checkpoint(
|
||
net_g,
|
||
optim_g,
|
||
hps.train.learning_rate,
|
||
epoch,
|
||
os.path.join(model_dir, "G_{}.pth".format(global_step)),
|
||
)
|
||
utils.save_checkpoint(
|
||
net_d,
|
||
optim_d,
|
||
hps.train.learning_rate,
|
||
epoch,
|
||
os.path.join(model_dir, "D_{}.pth".format(global_step)),
|
||
)
|
||
if net_dur_disc is not None:
|
||
utils.save_checkpoint(
|
||
net_dur_disc,
|
||
optim_dur_disc,
|
||
hps.train.learning_rate,
|
||
epoch,
|
||
os.path.join(model_dir, "DUR_{}.pth".format(global_step)),
|
||
)
|
||
utils.save_safetensors(
|
||
net_g,
|
||
epoch,
|
||
os.path.join(
|
||
config.out_dir,
|
||
f"{config.model_name}_e{epoch}_s{global_step}.safetensors",
|
||
),
|
||
for_infer=True,
|
||
)
|
||
|
||
if pbar is not None:
|
||
pbar.close()
|
||
|
||
|
||
def train_and_evaluate(
|
||
rank,
|
||
local_rank,
|
||
epoch,
|
||
hps,
|
||
nets,
|
||
optims,
|
||
schedulers,
|
||
scaler,
|
||
loaders,
|
||
logger,
|
||
writers,
|
||
pbar: tqdm,
|
||
initial_step: int,
|
||
):
|
||
net_g, net_d, net_dur_disc = nets
|
||
optim_g, optim_d, optim_dur_disc = optims
|
||
scheduler_g, scheduler_d, scheduler_dur_disc = schedulers
|
||
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 net_dur_disc is not None:
|
||
net_dur_disc.train()
|
||
for batch_idx, (
|
||
x,
|
||
x_lengths,
|
||
spec,
|
||
spec_lengths,
|
||
y,
|
||
y_lengths,
|
||
speakers,
|
||
tone,
|
||
language,
|
||
bert,
|
||
ja_bert,
|
||
en_bert,
|
||
style_vec,
|
||
) in enumerate(train_loader):
|
||
if net_g.module.use_noise_scaled_mas:
|
||
current_mas_noise_scale = (
|
||
net_g.module.mas_noise_scale_initial
|
||
- net_g.module.noise_scale_delta * global_step
|
||
)
|
||
net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0)
|
||
x, x_lengths = x.cuda(local_rank, non_blocking=True), x_lengths.cuda(
|
||
local_rank, non_blocking=True
|
||
)
|
||
spec, spec_lengths = spec.cuda(
|
||
local_rank, non_blocking=True
|
||
), spec_lengths.cuda(local_rank, non_blocking=True)
|
||
y, y_lengths = y.cuda(local_rank, non_blocking=True), y_lengths.cuda(
|
||
local_rank, non_blocking=True
|
||
)
|
||
speakers = speakers.cuda(local_rank, non_blocking=True)
|
||
tone = tone.cuda(local_rank, non_blocking=True)
|
||
language = language.cuda(local_rank, non_blocking=True)
|
||
bert = bert.cuda(local_rank, non_blocking=True)
|
||
ja_bert = ja_bert.cuda(local_rank, non_blocking=True)
|
||
en_bert = en_bert.cuda(local_rank, non_blocking=True)
|
||
style_vec = style_vec.cuda(local_rank, non_blocking=True)
|
||
|
||
with autocast(enabled=hps.train.bf16_run, dtype=torch.bfloat16):
|
||
(
|
||
y_hat,
|
||
l_length,
|
||
attn,
|
||
ids_slice,
|
||
x_mask,
|
||
z_mask,
|
||
(z, z_p, m_p, logs_p, m_q, logs_q),
|
||
(hidden_x, logw, logw_),
|
||
) = net_g(
|
||
x,
|
||
x_lengths,
|
||
spec,
|
||
spec_lengths,
|
||
speakers,
|
||
tone,
|
||
language,
|
||
bert,
|
||
ja_bert,
|
||
en_bert,
|
||
style_vec,
|
||
)
|
||
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
|
||
)
|
||
y_hat_mel = mel_spectrogram_torch(
|
||
y_hat.squeeze(1).float(),
|
||
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,
|
||
)
|
||
|
||
y = commons.slice_segments(
|
||
y, ids_slice * hps.data.hop_length, hps.train.segment_size
|
||
) # slice
|
||
|
||
# Discriminator
|
||
y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
|
||
with autocast(enabled=hps.train.bf16_run, dtype=torch.bfloat16):
|
||
loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(
|
||
y_d_hat_r, y_d_hat_g
|
||
)
|
||
loss_disc_all = loss_disc
|
||
if net_dur_disc is not None:
|
||
y_dur_hat_r, y_dur_hat_g = net_dur_disc(
|
||
hidden_x.detach(), x_mask.detach(), logw.detach(), logw_.detach()
|
||
)
|
||
with autocast(enabled=hps.train.bf16_run, dtype=torch.bfloat16):
|
||
# TODO: I think need to mean using the mask, but for now, just mean all
|
||
(
|
||
loss_dur_disc,
|
||
losses_dur_disc_r,
|
||
losses_dur_disc_g,
|
||
) = discriminator_loss(y_dur_hat_r, y_dur_hat_g)
|
||
loss_dur_disc_all = loss_dur_disc
|
||
optim_dur_disc.zero_grad()
|
||
scaler.scale(loss_dur_disc_all).backward()
|
||
scaler.unscale_(optim_dur_disc)
|
||
commons.clip_grad_value_(net_dur_disc.parameters(), None)
|
||
scaler.step(optim_dur_disc)
|
||
|
||
optim_d.zero_grad()
|
||
scaler.scale(loss_disc_all).backward()
|
||
scaler.unscale_(optim_d)
|
||
if getattr(hps.train, "bf16_run", False):
|
||
torch.nn.utils.clip_grad_norm_(parameters=net_d.parameters(), max_norm=200)
|
||
grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
|
||
scaler.step(optim_d)
|
||
|
||
with autocast(enabled=hps.train.bf16_run, dtype=torch.bfloat16):
|
||
# Generator
|
||
y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
|
||
if net_dur_disc is not None:
|
||
y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw, logw_)
|
||
with autocast(enabled=hps.train.bf16_run, dtype=torch.bfloat16):
|
||
loss_dur = torch.sum(l_length.float())
|
||
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_dur + loss_kl
|
||
if net_dur_disc is not None:
|
||
loss_dur_gen, losses_dur_gen = generator_loss(y_dur_hat_g)
|
||
loss_gen_all += loss_dur_gen
|
||
optim_g.zero_grad()
|
||
scaler.scale(loss_gen_all).backward()
|
||
scaler.unscale_(optim_g)
|
||
if getattr(hps.train, "bf16_run", False):
|
||
torch.nn.utils.clip_grad_norm_(parameters=net_g.parameters(), max_norm=500)
|
||
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"]
|
||
losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl]
|
||
# logger.info(
|
||
# "Train Epoch: {} [{:.0f}%]".format(
|
||
# epoch, 100.0 * batch_idx / len(train_loader)
|
||
# )
|
||
# )
|
||
# logger.info([x.item() for x in losses] + [global_step, lr])
|
||
|
||
scalar_dict = {
|
||
"loss/g/total": loss_gen_all,
|
||
"loss/d/total": loss_disc_all,
|
||
"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/dur": loss_dur,
|
||
"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()
|
||
),
|
||
"all/attn": utils.plot_alignment_to_numpy(
|
||
attn[0, 0].data.cpu().numpy()
|
||
),
|
||
}
|
||
utils.summarize(
|
||
writer=writer,
|
||
global_step=global_step,
|
||
images=image_dict,
|
||
scalars=scalar_dict,
|
||
)
|
||
|
||
if (
|
||
global_step % hps.train.eval_interval == 0
|
||
and global_step != 0
|
||
and initial_step != global_step
|
||
):
|
||
evaluate(hps, net_g, eval_loader, writer_eval)
|
||
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)),
|
||
)
|
||
if net_dur_disc is not None:
|
||
utils.save_checkpoint(
|
||
net_dur_disc,
|
||
optim_dur_disc,
|
||
hps.train.learning_rate,
|
||
epoch,
|
||
os.path.join(hps.model_dir, "DUR_{}.pth".format(global_step)),
|
||
)
|
||
keep_ckpts = config.train_ms_config.keep_ckpts
|
||
if keep_ckpts > 0:
|
||
utils.clean_checkpoints(
|
||
path_to_models=hps.model_dir,
|
||
n_ckpts_to_keep=keep_ckpts,
|
||
sort_by_time=True,
|
||
)
|
||
# Save safetensors (for inference) to `model_assets/{model_name}`
|
||
utils.save_safetensors(
|
||
net_g,
|
||
epoch,
|
||
os.path.join(
|
||
config.out_dir,
|
||
f"{config.model_name}_e{epoch}_s{global_step}.safetensors",
|
||
),
|
||
for_infer=True,
|
||
)
|
||
|
||
global_step += 1
|
||
if pbar is not None:
|
||
pbar.set_description(
|
||
"Epoch {}({:.0f}%)/{}".format(
|
||
epoch, 100.0 * batch_idx / len(train_loader), hps.train.epochs
|
||
)
|
||
)
|
||
pbar.update()
|
||
# 本家ではこれをスピードアップのために消すと書かれていたので、一応消してみる
|
||
# gc.collect()
|
||
# torch.cuda.empty_cache()
|
||
if pbar is None and rank == 0:
|
||
logger.info(f"====> Epoch: {epoch}, step: {global_step}")
|
||
|
||
|
||
def evaluate(hps, generator, eval_loader, writer_eval):
|
||
generator.eval()
|
||
image_dict = {}
|
||
audio_dict = {}
|
||
logger.info("Evaluating ...")
|
||
with torch.no_grad():
|
||
for batch_idx, (
|
||
x,
|
||
x_lengths,
|
||
spec,
|
||
spec_lengths,
|
||
y,
|
||
y_lengths,
|
||
speakers,
|
||
tone,
|
||
language,
|
||
bert,
|
||
ja_bert,
|
||
en_bert,
|
||
style_vec,
|
||
) in enumerate(eval_loader):
|
||
x, x_lengths = x.cuda(), x_lengths.cuda()
|
||
spec, spec_lengths = spec.cuda(), spec_lengths.cuda()
|
||
y, y_lengths = y.cuda(), y_lengths.cuda()
|
||
speakers = speakers.cuda()
|
||
bert = bert.cuda()
|
||
ja_bert = ja_bert.cuda()
|
||
en_bert = en_bert.cuda()
|
||
tone = tone.cuda()
|
||
language = language.cuda()
|
||
style_vec = style_vec.cuda()
|
||
for use_sdp in [True, False]:
|
||
y_hat, attn, mask, *_ = generator.module.infer(
|
||
x,
|
||
x_lengths,
|
||
speakers,
|
||
tone,
|
||
language,
|
||
bert,
|
||
ja_bert,
|
||
en_bert,
|
||
style_vec,
|
||
y=spec,
|
||
max_len=1000,
|
||
sdp_ratio=0.0 if not use_sdp else 1.0,
|
||
)
|
||
y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length
|
||
|
||
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_hat_mel = mel_spectrogram_torch(
|
||
y_hat.squeeze(1).float(),
|
||
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,
|
||
)
|
||
image_dict.update(
|
||
{
|
||
f"gen/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(
|
||
y_hat_mel[0].cpu().numpy()
|
||
)
|
||
}
|
||
)
|
||
audio_dict.update(
|
||
{
|
||
f"gen/audio_{batch_idx}_{use_sdp}": y_hat[
|
||
0, :, : y_hat_lengths[0]
|
||
]
|
||
}
|
||
)
|
||
image_dict.update(
|
||
{
|
||
f"gt/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(
|
||
mel[0].cpu().numpy()
|
||
)
|
||
}
|
||
)
|
||
audio_dict.update({f"gt/audio_{batch_idx}": y[0, :, : y_lengths[0]]})
|
||
|
||
utils.summarize(
|
||
writer=writer_eval,
|
||
global_step=global_step,
|
||
images=image_dict,
|
||
audios=audio_dict,
|
||
audio_sampling_rate=hps.data.sampling_rate,
|
||
)
|
||
generator.train()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
run()
|