Spaces:
Paused
Paused
import os | |
import sys | |
import logging | |
logger = logging.getLogger(__name__) | |
now_dir = os.getcwd() | |
sys.path.append(os.path.join(now_dir)) | |
import datetime | |
from infer.lib.train import utils | |
# hps = utils.get_hparams() | |
# os.environ["CUDA_VISIBLE_DEVICES"] = hps.gpus.replace("-", ",") | |
# n_gpus = len(hps.gpus.split("-")) | |
from random import randint, shuffle | |
import torch | |
try: | |
import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import | |
if torch.xpu.is_available(): | |
from infer.modules.ipex import ipex_init | |
from infer.modules.ipex.gradscaler import gradscaler_init | |
from torch.xpu.amp import autocast | |
GradScaler = gradscaler_init() | |
ipex_init() | |
else: | |
from torch.cuda.amp import GradScaler, autocast | |
except Exception: | |
from torch.cuda.amp import GradScaler, autocast | |
torch.backends.cudnn.deterministic = False | |
torch.backends.cudnn.benchmark = False | |
from time import time as ttime | |
from torch.nn import functional as F | |
from torch.utils.data import DataLoader | |
from torch.utils.tensorboard import SummaryWriter | |
from infer.lib.infer_pack import commons | |
from infer.lib.train.data_utils import ( | |
DistributedBucketSampler, | |
TextAudioCollate, | |
TextAudioCollateMultiNSFsid, | |
TextAudioLoader, | |
TextAudioLoaderMultiNSFsid, | |
) | |
# if hps.version == "v1": | |
# from infer.lib.infer_pack.models import MultiPeriodDiscriminator | |
# from infer.lib.infer_pack.models import SynthesizerTrnMs256NSFsid as RVC_Model_f0 | |
# from infer.lib.infer_pack.models import ( | |
# SynthesizerTrnMs256NSFsid_nono as RVC_Model_nof0, | |
# ) | |
# else: | |
from infer.lib.infer_pack.models import ( | |
SynthesizerTrnMs768NSFsid as RVC_Model_f0, | |
SynthesizerTrnMs768NSFsid_nono as RVC_Model_nof0, | |
MultiPeriodDiscriminatorV2 as MultiPeriodDiscriminator, | |
) | |
from infer.lib.train.losses import ( | |
discriminator_loss, | |
feature_loss, | |
generator_loss, | |
kl_loss, | |
) | |
from infer.lib.train.mel_processing import mel_spectrogram_torch, spec_to_mel_torch | |
from infer.lib.train.process_ckpt import savee | |
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(exp_dir: str): | |
state = {"global_step": 0} | |
hps = utils.get_hparams_from_dir(exp_dir) | |
hps.experiment_dir = exp_dir | |
hps.save_every_epoch = 5 | |
hps.name = os.path.basename(exp_dir) | |
hps.total_epoch = 100 | |
hps.pretrainG = "assets/pretrained_v2/f0G40k.pth" | |
hps.pretrainD = "assets/pretrained_v2/f0D40k.pth" | |
hps.version = "v2" | |
hps.gpus = "0" | |
hps.sample_rate = "40k" | |
hps.if_f0 = 1 | |
hps.if_latest = 0 | |
hps.save_every_weights = "0" | |
hps.if_cache_data_in_gpu = True | |
hps.data.training_files = "%s/filelist.txt" % exp_dir | |
logger = utils.get_logger(hps.model_dir) | |
run(0, 1, hps, logger, state) | |
def run(rank, n_gpus, hps, logger: logging.Logger, state): | |
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")) | |
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 = 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, | |
) | |
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 | |
) | |
state["global_step"] = (epoch_str - 1) * len(train_loader) | |
print("loaded", epoch_str) | |
epoch_str += 1 | |
except: # 如果首次不能加载,加载pretrain | |
# traceback.print_exc() | |
epoch_str = 1 | |
state["global_step"] = 0 | |
if hps.pretrainG != "": | |
if rank == 0: | |
logger.info("loaded pretrained %s" % (hps.pretrainG)) | |
if hasattr(net_g, "module"): | |
logger.info( | |
net_g.module.load_state_dict( | |
torch.load(hps.pretrainG, map_location="cpu")["model"] | |
) | |
) ##测试不加载优化器 | |
else: | |
logger.info( | |
net_g.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)) | |
if hasattr(net_d, "module"): | |
logger.info( | |
net_d.module.load_state_dict( | |
torch.load(hps.pretrainD, map_location="cpu")["model"] | |
) | |
) | |
else: | |
logger.info( | |
net_d.load_state_dict( | |
torch.load(hps.pretrainD, map_location="cpu")["model"] | |
) | |
) | |
print("new") | |
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 = [] | |
saved = 0 | |
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, | |
state, | |
) | |
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, | |
state, | |
) | |
scheduler_g.step() | |
scheduler_d.step() | |
if epoch % hps.save_every_epoch == 0 and rank == 0: | |
saved += 1 | |
if saved >= 2: | |
break | |
def train_and_evaluate( | |
rank, | |
epoch, | |
hps, | |
nets, | |
optims, | |
schedulers, | |
scaler, | |
loaders, | |
logger, | |
writers, | |
cache, | |
state, | |
): | |
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) | |
net_g.train() | |
net_d.train() | |
# Prepare data iterator | |
if hps.if_cache_data_in_gpu == True: | |
# 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 data_iterator: | |
# 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 (hps.if_cache_data_in_gpu == False) 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) | |
# wave_lengths = wave_lengths.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 == 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 state["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 > 75: | |
loss_mel = 75 | |
if loss_kl > 9: | |
loss_kl = 9 | |
logger.info([state["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=state["global_step"], | |
images=image_dict, | |
scalars=scalar_dict, | |
) | |
state["global_step"] += 1 | |
# /Run steps | |
if epoch % hps.save_every_epoch == 0 and rank == 0: | |
if hps.if_latest == 0: | |
utils.save_checkpoint( | |
net_g, | |
optim_g, | |
hps.train.learning_rate, | |
epoch, | |
os.path.join(hps.model_dir, "G_{}.pth".format(state["global_step"])), | |
) | |
utils.save_checkpoint( | |
net_d, | |
optim_d, | |
hps.train.learning_rate, | |
epoch, | |
os.path.join(hps.model_dir, "D_{}.pth".format(state["global_step"])), | |
) | |
else: | |
utils.save_checkpoint( | |
net_g, | |
optim_g, | |
hps.train.learning_rate, | |
epoch, | |
os.path.join(hps.model_dir, "G_{}.pth".format(2333333)), | |
) | |
utils.save_checkpoint( | |
net_d, | |
optim_d, | |
hps.train.learning_rate, | |
epoch, | |
os.path.join(hps.model_dir, "D_{}.pth".format(2333333)), | |
) | |
if rank == 0 and hps.save_every_weights == "1": | |
if hasattr(net_g, "module"): | |
ckpt = net_g.module.state_dict() | |
else: | |
ckpt = net_g.state_dict() | |
logger.info( | |
"saving ckpt %s_e%s:%s" | |
% ( | |
hps.name, | |
epoch, | |
savee( | |
ckpt, | |
hps.sample_rate, | |
hps.if_f0, | |
hps.name + "_e%s_s%s" % (epoch, state["global_step"]), | |
epoch, | |
hps.version, | |
hps, | |
), | |
) | |
) | |
if rank == 0: | |
logger.info("====> Epoch: {} {}".format(epoch, epoch_recorder.record())) | |
if epoch >= hps.total_epoch and rank == 0: | |
logger.info("Training is done. The program is closed.") | |
if hasattr(net_g, "module"): | |
ckpt = net_g.module.state_dict() | |
else: | |
ckpt = net_g.state_dict() | |
logger.info( | |
"saving final ckpt:%s" | |
% ( | |
savee( | |
ckpt, hps.sample_rate, hps.if_f0, hps.name, epoch, hps.version, hps | |
) | |
) | |
) | |