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import os, json, argparse, yaml
import numpy as np
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch.cuda.amp import autocast, GradScaler
from diffusers import DDIMScheduler
from dataset import VCDecLPCDataset, VCDecLPCBatchCollate, VCDecLPCTest
from models.unet import UNetVC
from modules.BigVGAN.inference import load_model
from utils import save_plot, save_audio
from utils import minmax_norm_diff, reverse_minmax_norm_diff
parser = argparse.ArgumentParser()
parser.add_argument('-config', type=str, default='config/DiffWorld_24k_log.yaml')
parser.add_argument('-seed', type=int, default=98)
parser.add_argument('-amp', type=bool, default=True)
parser.add_argument('-compile', type=bool, default=False)
parser.add_argument('-data_dir', type=str, default='../24k_center/')
parser.add_argument('-lpc_dir', type=str, default='world')
parser.add_argument('-vocoder_dir', type=str, default='modules/BigVGAN/ckpt/bigvgan_base_24khz_100band/g_05000000')
parser.add_argument('-train_frames', type=int, default=128)
parser.add_argument('-batch_size', type=int, default=32)
parser.add_argument('-test_size', type=int, default=1)
parser.add_argument('-num_workers', type=int, default=4)
parser.add_argument('-lr', type=float, default=5e-5)
parser.add_argument('-weight_decay', type=int, default=1e-6)
parser.add_argument('-epochs', type=int, default=80)
parser.add_argument('-save_every', type=int, default=2)
parser.add_argument('-log_step', type=int, default=200)
parser.add_argument('-log_dir', type=str, default='logs_dec_world_24k')
parser.add_argument('-ckpt_dir', type=str, default='ckpt_world_24k')
args = parser.parse_args()
args.save_ori = True
config = yaml.load(open(args.config), Loader=yaml.FullLoader)
mel_cfg = config['logmel']
ddpm_cfg = config['ddpm']
unet_cfg = config['unet']
f0_type = unet_cfg['pitch_type']
if __name__ == "__main__":
torch.manual_seed(args.seed)
np.random.seed(args.seed)
if torch.cuda.is_available():
args.device = 'cuda'
torch.cuda.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
torch.backends.cuda.matmul.allow_tf32 = True
if torch.backends.cudnn.is_available():
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
else:
args.device = 'cpu'
if os.path.exists(args.log_dir) is False:
os.makedirs(args.log_dir)
if os.path.exists(args.ckpt_dir) is False:
os.makedirs(args.ckpt_dir)
print('Initializing vocoder...')
hifigan, cfg = load_model(args.vocoder_dir, device=args.device)
print('Initializing data loaders...')
train_set = VCDecLPCDataset(args.data_dir, subset='train', content_dir=args.lpc_dir, f0_type=f0_type)
collate_fn = VCDecLPCBatchCollate(args.train_frames)
train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True,
collate_fn=collate_fn, num_workers=args.num_workers, drop_last=True)
val_set = VCDecLPCTest(args.data_dir, content_dir=args.lpc_dir, f0_type=f0_type)
val_loader = DataLoader(val_set, batch_size=1, shuffle=False)
print('Initializing and loading models...')
model = UNetVC(**unet_cfg).to(args.device)
print('Number of parameters = %.2fm\n' % (model.nparams / 1e6))
# prepare DPM scheduler
noise_scheduler = DDIMScheduler(num_train_timesteps=ddpm_cfg['num_train_steps'])
print('Initializing optimizers...')
optimizer = torch.optim.AdamW(params=model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
scaler = GradScaler()
if args.compile:
model = torch.compile(model)
print('Start training.')
global_step = 0
for epoch in range(1, args.epochs + 1):
print(f'Epoch: {epoch} [iteration: {global_step}]')
model.train()
losses = []
for step, batch in enumerate(tqdm(train_loader)):
optimizer.zero_grad()
# make spectrogram range from -1 to 1
mel = batch['mel1'].to(args.device)
mel = minmax_norm_diff(mel, vmax=mel_cfg['max'], vmin=mel_cfg['min'])
if unet_cfg["use_ref_t"]:
mel_ref = batch['mel2'].to(args.device)
mel_ref = minmax_norm_diff(mel_ref, vmax=mel_cfg['max'], vmin=mel_cfg['min'])
else:
mel_ref = None
f0 = batch['f0_1'].to(args.device)
mean = batch['content1'].to(args.device)
mean = minmax_norm_diff(mean, vmax=mel_cfg['max'], vmin=mel_cfg['min'])
noise = torch.randn(mel.shape).to(args.device)
timesteps = torch.randint(0, noise_scheduler.num_train_timesteps,
(args.batch_size,),
device=args.device, ).long()
noisy_mel = noise_scheduler.add_noise(mel, noise, timesteps)
if args.amp:
with autocast():
noise_pred = model(x=noisy_mel, mean=mean, f0=f0, t=timesteps, ref=mel_ref, embed=None)
loss = F.mse_loss(noise_pred, noise)
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
else:
noise_pred = model(x=noisy_mel, mean=mean, f0=f0, t=timesteps, ref=mel_ref, embed=None)
loss = F.mse_loss(noise_pred, noise)
# Backward propagation
loss.backward()
optimizer.step()
losses.append(loss.item())
global_step += 1
if global_step % args.log_step == 0:
losses = np.asarray(losses)
# msg = 'Epoch %d: loss = %.4f\n' % (epoch, np.mean(losses))
msg = '\nEpoch: [{}][{}]\t' \
'Batch: [{}][{}]\tLoss: {:.6f}\n'.format(epoch,
args.epochs,
step+1,
len(train_loader),
np.mean(losses))
with open(f'{args.log_dir}/train_dec.log', 'a') as f:
f.write(msg)
losses = []
if epoch % args.save_every > 0:
continue
print('Saving model...\n')
ckpt = model.state_dict()
torch.save(ckpt, f=f"{args.ckpt_dir}/lpc_vc_{epoch}.pt")
print('Inference...\n')
noise = None
noise_scheduler.set_timesteps(ddpm_cfg['inference_steps'])
model.eval()
with torch.no_grad():
for i, batch in enumerate(val_loader):
# optimizer.zero_grad()
generator = torch.Generator(device=args.device).manual_seed(args.seed)
mel = batch['mel1'].to(args.device)
mel = minmax_norm_diff(mel, vmax=mel_cfg['max'], vmin=mel_cfg['min'])
if unet_cfg["use_ref_t"]:
mel_ref = batch['mel2'].to(args.device)
mel_ref = minmax_norm_diff(mel_ref, vmax=mel_cfg['max'], vmin=mel_cfg['min'])
else:
mel_ref = None
f0 = batch['f0_1'].to(args.device)
embed = batch['embed'].to(args.device)
mean = batch['content1'].to(args.device)
mean = minmax_norm_diff(mean, vmax=mel_cfg['max'], vmin=mel_cfg['min'])
# make spectrogram range from -1 to 1
if noise is None:
noise = torch.randn(mel.shape,
generator=generator,
device=args.device,
)
pred = noise
for t in noise_scheduler.timesteps:
pred = noise_scheduler.scale_model_input(pred, t)
model_output = model(x=pred, mean=mean, f0=f0, t=t, ref=mel_ref, embed=None)
pred = noise_scheduler.step(model_output=model_output,
timestep=t,
sample=pred,
eta=ddpm_cfg['eta'], generator=generator).prev_sample
if os.path.exists(f'{args.log_dir}/audio/{i}/') is False:
os.makedirs(f'{args.log_dir}/audio/{i}/')
os.makedirs(f'{args.log_dir}/pic/{i}/')
# save pred
pred = reverse_minmax_norm_diff(pred, vmax=mel_cfg['max'], vmin=mel_cfg['min'])
save_plot(pred.squeeze().cpu(), f'{args.log_dir}/pic/{i}/{epoch}_pred.png')
audio = hifigan(pred)
save_audio(f'{args.log_dir}/audio/{i}/{epoch}_pred.wav', mel_cfg['sampling_rate'], audio)
if args.save_ori is True:
# save ref
# mel_ref = reverse_minmax_norm_diff(mel_ref, vmax=mel_cfg['max'], vmin=mel_cfg['min'])
# save_plot(mel_ref.squeeze().cpu(), f'{args.log_dir}/pic/{i}/{epoch}_ref.png')
# audio = hifigan(mel_ref)
# save_audio(f'{args.log_dir}/audio/{i}/{epoch}_ref.wav', mel_cfg['sampling_rate'], audio)
# save source
mel = reverse_minmax_norm_diff(mel, vmax=mel_cfg['max'], vmin=mel_cfg['min'])
save_plot(mel.squeeze().cpu(), f'{args.log_dir}/pic/{i}/{epoch}_source.png')
audio = hifigan(mel)
save_audio(f'{args.log_dir}/audio/{i}/{epoch}_source.wav', mel_cfg['sampling_rate'], audio)
# save content
mean = reverse_minmax_norm_diff(mean, vmax=mel_cfg['max'], vmin=mel_cfg['min'])
save_plot(mean.squeeze().cpu(), f'{args.log_dir}/pic/{i}/{epoch}_avg.png')
audio = hifigan(mean)
save_audio(f'{args.log_dir}/audio/{i}/{epoch}_avg.wav', mel_cfg['sampling_rate'], audio)
args.save_ori = False
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