import torch
import sys
import os
import datetime
from utils import (
get_hparams,
plot_spectrogram_to_numpy,
summarize,
load_checkpoint,
save_checkpoint,
latest_checkpoint_path,
)
from random import randint, shuffle
from time import sleep
from time import time as ttime
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
import torch.distributed as dist
import torch.multiprocessing as mp
now_dir = os.getcwd()
sys.path.append(os.path.join(now_dir))
from data_utils import (
DistributedBucketSampler,
TextAudioCollate,
TextAudioCollateMultiNSFsid,
TextAudioLoader,
TextAudioLoaderMultiNSFsid,
)
from losses import (
discriminator_loss,
feature_loss,
generator_loss,
kl_loss,
)
from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
from rvc.train.process.extract_model import extract_model
from rvc.lib.infer_pack import commons
hps = get_hparams()
if hps.version == "v1":
from rvc.lib.infer_pack.models import MultiPeriodDiscriminator
from rvc.lib.infer_pack.models import SynthesizerTrnMs256NSFsid as RVC_Model_f0
from rvc.lib.infer_pack.models import (
SynthesizerTrnMs256NSFsid_nono as RVC_Model_nof0,
)
elif hps.version == "v2":
from rvc.lib.infer_pack.models import (
SynthesizerTrnMs768NSFsid as RVC_Model_f0,
SynthesizerTrnMs768NSFsid_nono as RVC_Model_nof0,
MultiPeriodDiscriminatorV2 as MultiPeriodDiscriminator,
)
os.environ["CUDA_VISIBLE_DEVICES"] = hps.gpus.replace("-", ",")
n_gpus = len(hps.gpus.split("-"))
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.benchmark = False
global_step = 0
lowest_value = {"step": 0, "value": float("inf"), "epoch": 0}
last_loss_gen_all = 0
epochs_since_last_lowest = 0
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 = round(elapsed_time, 1)
elapsed_time_str = str(datetime.timedelta(seconds=int(elapsed_time)))
current_time = datetime.datetime.now().strftime("%H:%M:%S")
return f"time={current_time} | training_speed={elapsed_time_str}"
def main():
n_gpus = torch.cuda.device_count()
if torch.cuda.is_available() == False and torch.backends.mps.is_available() == True:
n_gpus = 1
if n_gpus < 1:
print("GPU not detected, reverting to CPU (not recommended)")
n_gpus = 1
children = []
pid_file_path = os.path.join(now_dir, "rvc", "train", "train_pid.txt")
with open(pid_file_path, "w") as pid_file:
for i in range(n_gpus):
subproc = mp.Process(
target=run,
args=(i, n_gpus, hps),
)
children.append(subproc)
subproc.start()
pid_file.write(str(subproc.pid) + "\n")
for i in range(n_gpus):
children[i].join()
def run(
rank,
n_gpus,
hps,
):
global global_step
if rank == 0:
writer = SummaryWriter(log_dir=hps.model_dir)
writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = str(randint(20000, 55555))
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)
else:
train_dataset = TextAudioLoader(hps.data)
train_sampler = DistributedBucketSampler(
train_dataset,
hps.train.batch_size * n_gpus,
[100, 200, 300, 400, 500, 600, 700, 800, 900],
num_replicas=n_gpus,
rank=rank,
shuffle=True,
)
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,
)
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:
print("Starting training...")
_, _, _, epoch_str = load_checkpoint(
latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d
)
_, _, _, epoch_str = load_checkpoint(
latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g
)
global_step = (epoch_str - 1) * len(train_loader)
except:
epoch_str = 1
global_step = 0
if hps.pretrainG != "":
if rank == 0:
print(f"Loaded pretrained_G {hps.pretrainG}")
if hasattr(net_g, "module"):
print(
net_g.module.load_state_dict(
torch.load(hps.pretrainG, map_location="cpu")["model"]
)
)
else:
print(
net_g.load_state_dict(
torch.load(hps.pretrainG, map_location="cpu")["model"]
)
)
if hps.pretrainD != "":
if rank == 0:
print(f"Loaded pretrained_D {hps.pretrainD}")
if hasattr(net_d, "module"):
print(
net_d.module.load_state_dict(
torch.load(hps.pretrainD, map_location="cpu")["model"]
)
)
else:
print(
net_d.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],
scaler,
[train_loader, None],
[writer, writer_eval],
cache,
)
else:
train_and_evaluate(
rank,
epoch,
hps,
[net_g, net_d],
[optim_g, optim_d],
scaler,
[train_loader, None],
None,
cache,
)
scheduler_g.step()
scheduler_d.step()
def train_and_evaluate(rank, epoch, hps, nets, optims, scaler, loaders, writers, cache):
global global_step, last_loss_gen_all, lowest_value, epochs_since_last_lowest
if epoch == 1:
lowest_value = {"step": 0, "value": float("inf"), "epoch": 0}
last_loss_gen_all = 0.0
epochs_since_last_lowest = 0
net_g, net_d = nets
optim_g, optim_d = optims
train_loader = loaders[0] if loaders is not None else None
if writers is not None:
writer = writers[0]
train_loader.batch_sampler.set_epoch(epoch)
net_g.train()
net_d.train()
if hps.if_cache_data_in_gpu == True:
data_iterator = cache
if cache == []:
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.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)
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:
shuffle(cache)
else:
data_iterator = enumerate(train_loader)
epoch_recorder = EpochRecorder()
for batch_idx, info in data_iterator:
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 (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)
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
)
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):
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
if loss_gen_all < lowest_value["value"]:
lowest_value["value"] = loss_gen_all
lowest_value["step"] = global_step
lowest_value["epoch"] = epoch
# print(f'Lowest generator loss updated: {lowest_value["value"]} at epoch {epoch}, step {global_step}')
if epoch > lowest_value["epoch"]:
print(
"Alert: The lower generating loss has been exceeded by a lower loss in a subsequent epoch."
)
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"]
# print("Epoch: {} [{:.0f}%]".format(epoch, 100.0 * batch_idx / len(train_loader)))
if loss_mel > 75:
loss_mel = 75
if loss_kl > 9:
loss_kl = 9
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": plot_spectrogram_to_numpy(
y_mel[0].data.cpu().numpy()
),
"slice/mel_gen": plot_spectrogram_to_numpy(
y_hat_mel[0].data.cpu().numpy()
),
"all/mel": plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
}
summarize(
writer=writer,
global_step=global_step,
images=image_dict,
scalars=scalar_dict,
)
# optim_g.step()
# optim_d.step()
global_step += 1
if epoch % hps.save_every_epoch == 0 and rank == 0:
checkpoint_suffix = "{}.pth".format(
global_step if hps.if_latest == 0 else 2333333
)
save_checkpoint(
net_g,
optim_g,
hps.train.learning_rate,
epoch,
os.path.join(hps.model_dir, "G_" + checkpoint_suffix),
)
save_checkpoint(
net_d,
optim_d,
hps.train.learning_rate,
epoch,
os.path.join(hps.model_dir, "D_" + checkpoint_suffix),
)
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()
extract_model(
ckpt,
hps.sample_rate,
hps.if_f0,
hps.name,
os.path.join(
hps.model_dir, "{}_{}e_{}s.pth".format(hps.name, epoch, global_step)
),
epoch,
global_step,
hps.version,
hps,
)
if hps.overtraining_detector == 1:
if lowest_value["value"] < last_loss_gen_all:
epochs_since_last_lowest += 1
else:
epochs_since_last_lowest = 0
if epochs_since_last_lowest >= hps.overtraining_threshold:
print(
"Stopping training due to possible overtraining. Lowest generator loss: {} at epoch {}, step {}".format(
lowest_value["value"], lowest_value["epoch"], lowest_value["step"]
)
)
os._exit(2333333)
if rank == 0:
if epoch > 1:
print(hps.overtraining_threshold)
print(
f"{hps.name} | epoch={epoch} | step={global_step} | {epoch_recorder.record()} | lowest_value={lowest_value['value']} (epoch {lowest_value['epoch']} and step {lowest_value['step']})"
)
else:
print(
f"{hps.name} | epoch={epoch} | step={global_step} | {epoch_recorder.record()}"
)
last_loss_gen_all = loss_gen_all
if epoch >= hps.total_epoch and rank == 0:
print(
f"Training has been successfully completed with {epoch} epoch, {global_step} steps and {round(loss_gen_all.item(), 3)} loss gen."
)
print(
f"Lowest generator loss: {lowest_value['value']} at epoch {lowest_value['epoch']}, step {lowest_value['step']}"
)
pid_file_path = os.path.join(now_dir, "rvc", "train", "train_pid.txt")
os.remove(pid_file_path)
if hasattr(net_g, "module"):
ckpt = net_g.module.state_dict()
else:
ckpt = net_g.state_dict()
extract_model(
ckpt,
hps.sample_rate,
hps.if_f0,
hps.name,
os.path.join(
hps.model_dir, "{}_{}e_{}s.pth".format(hps.name, epoch, global_step)
),
epoch,
global_step,
hps.version,
hps,
)
sleep(1)
os._exit(2333333)
if __name__ == "__main__":
torch.multiprocessing.set_start_method("spawn")
main()