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import os, json, argparse, yaml
import numpy as np
from tqdm import tqdm
import librosa
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 dataset.diffpitch import DiffPitch
from models.transformer import PitchFormer
from utils import minmax_norm_diff, reverse_minmax_norm_diff, save_curve_plot
parser = argparse.ArgumentParser()
parser.add_argument('-config', type=str, default='config/DiffPitch.yaml')
parser.add_argument('-seed', type=int, default=9811)
parser.add_argument('-amp', type=bool, default=False)
parser.add_argument('-compile', type=bool, default=False)
parser.add_argument('-data_dir', type=str, default='data/')
parser.add_argument('-content_dir', type=str, default='world')
parser.add_argument('-train_frames', type=int, default=256)
parser.add_argument('-test_frames', type=int, default=256)
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=1)
parser.add_argument('-save_every', type=int, default=20)
parser.add_argument('-log_step', type=int, default=100)
parser.add_argument('-log_dir', type=str, default='logs_transformer_pitch')
parser.add_argument('-ckpt_dir', type=str, default='ckpt_transformer_pitch')
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']
def RMSE(gen_f0, gt_f0):
# Get voiced part
gt_f0 = gt_f0[0]
gen_f0 = gen_f0[0]
nonzero_idxs = np.where((gen_f0 != 0) & (gt_f0 != 0))[0]
gen_f0_voiced = np.log2(gen_f0[nonzero_idxs])
gt_f0_voiced = np.log2(gt_f0[nonzero_idxs])
# log F0 RMSE
if len(gen_f0_voiced) != 0:
f0_rmse = np.sqrt(np.mean((gen_f0_voiced - gt_f0_voiced) ** 2))
else:
f0_rmse = 0
return f0_rmse
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 data loaders...')
trainset = DiffPitch('data/', 'train', args.train_frames, shift=True)
train_loader = DataLoader(trainset, batch_size=args.batch_size, num_workers=args.num_workers,
drop_last=True, shuffle=True)
val_set = DiffPitch('data/', 'val', args.test_frames, shift=True)
val_loader = DataLoader(val_set, batch_size=1, shuffle=False)
test_set = DiffPitch('data/', 'test', args.test_frames, shift=True)
test_loader = DataLoader(test_set, batch_size=1, shuffle=False)
real_set = DiffPitch('data/', 'real', args.test_frames, shift=False)
read_loader = DataLoader(real_set, batch_size=1, shuffle=False)
print('Initializing and loading models...')
model = PitchFormer(mel_cfg['n_mels'], 512).to(args.device)
ckpt = torch.load('ckpt_transformer_pitch/transformer_pitch_460.pt')
model.load_state_dict(ckpt)
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()
mel, midi, f0 = batch
mel = mel.to(args.device)
midi = midi.to(args.device)
f0 = f0.to(args.device)
if args.amp:
with autocast():
f0_pred = model(sp=mel, midi=midi)
loss = F.mse_loss(f0_pred, f0)
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
else:
f0_pred = model(sp=mel, midi=midi)
loss = F.l1_loss(f0_pred, f0)
# 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}/transformer_pitch_{epoch}.pt")
print('Inference...\n')
model.eval()
with torch.no_grad():
val_loss = []
val_rmse = []
for i, batch in enumerate(val_loader):
# optimizer.zero_grad()
mel, midi, f0 = batch
mel = mel.to(args.device)
midi = midi.to(args.device)
f0 = f0.to(args.device)
f0_pred = model(sp=mel, midi=midi)
# save pred
f0_pred[f0_pred < librosa.note_to_hz('C2')] = 0
f0_pred[f0_pred > librosa.note_to_hz('C6')] = librosa.note_to_hz('C6')
val_loss.append(F.l1_loss(f0_pred, f0).item())
val_rmse.append(RMSE(f0_pred.cpu().numpy(), f0.cpu().numpy()))
if i <= 4:
save_path = f'{args.log_dir}/pic/{i}/{epoch}_val.png'
if os.path.exists(os.path.dirname(save_path)) is False:
os.makedirs(os.path.dirname(save_path))
save_curve_plot(f0_pred.cpu().squeeze(), midi.cpu().squeeze(), f0.cpu().squeeze(), save_path)
# else:
# break
msg = '\nEpoch: [{}][{}]\tLoss: {:.6f}\tRMSE:{:.6f}\n'.\
format(epoch, args.epochs, np.mean(val_loss), np.mean(val_rmse))
with open(f'{args.log_dir}/eval_dec.log', 'a') as f:
f.write(msg)
test_loss = []
test_rmse = []
for i, batch in enumerate(test_loader):
# optimizer.zero_grad()
mel, midi, f0 = batch
mel = mel.to(args.device)
midi = midi.to(args.device)
f0 = f0.to(args.device)
f0_pred = model(sp=mel, midi=midi)
# save pred
f0_pred[f0_pred < librosa.note_to_hz('C2')] = 0
f0_pred[f0_pred > librosa.note_to_hz('C6')] = librosa.note_to_hz('C6')
test_loss.append(F.l1_loss(f0_pred, f0).item())
test_rmse.append(RMSE(f0_pred.cpu().numpy(), f0.cpu().numpy()))
if i <= 4:
save_path = f'{args.log_dir}/pic/{i}/{epoch}_test.png'
if os.path.exists(os.path.dirname(save_path)) is False:
os.makedirs(os.path.dirname(save_path))
save_curve_plot(f0_pred.cpu().squeeze(), midi.cpu().squeeze(), f0.cpu().squeeze(), save_path)
msg = '\nEpoch: [{}][{}]\tLoss: {:.6f}\tRMSE:{:.6f}\n'. \
format(epoch, args.epochs, np.mean(test_loss), np.mean(test_rmse))
with open(f'{args.log_dir}/test_dec.log', 'a') as f:
f.write(msg)
for i, batch in enumerate(read_loader):
# optimizer.zero_grad()
mel, midi, f0 = batch
mel = mel.to(args.device)
midi = midi.to(args.device)
f0 = f0.to(args.device)
f0_pred = model(sp=mel, midi=midi)
f0_pred[f0 == 0] = 0
# save pred
f0_pred[f0_pred < librosa.note_to_hz('C2')] = 0
f0_pred[f0_pred > librosa.note_to_hz('C6')] = librosa.note_to_hz('C6')
save_path = f'{args.log_dir}/pic/{i}/{epoch}_real.png'
if os.path.exists(os.path.dirname(save_path)) is False:
os.makedirs(os.path.dirname(save_path))
save_curve_plot(f0_pred.cpu().squeeze(), midi.cpu().squeeze(), f0.cpu().squeeze(), save_path)
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