1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
|
import os
import argparse
import random
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
import tqdm
import plc
from plc_dataset import PLCDataset
parser = argparse.ArgumentParser()
parser.add_argument('features', type=str, help='path to feature file in .f32 format')
parser.add_argument('loss', type=str, help='path to signal file in .s8 format')
parser.add_argument('output', type=str, help='path to output folder')
parser.add_argument('--suffix', type=str, help="model name suffix", default="")
parser.add_argument('--cuda-visible-devices', type=str, help="comma separates list of cuda visible device indices, default: CUDA_VISIBLE_DEVICES", default=None)
model_group = parser.add_argument_group(title="model parameters")
model_group.add_argument('--cond-size', type=int, help="first conditioning size, default: 128", default=128)
model_group.add_argument('--gru-size', type=int, help="GRU size, default: 128", default=128)
training_group = parser.add_argument_group(title="training parameters")
training_group.add_argument('--batch-size', type=int, help="batch size, default: 512", default=512)
training_group.add_argument('--lr', type=float, help='learning rate, default: 1e-3', default=1e-3)
training_group.add_argument('--epochs', type=int, help='number of training epochs, default: 20', default=20)
training_group.add_argument('--sequence-length', type=int, help='sequence length, default: 15', default=15)
training_group.add_argument('--lr-decay', type=float, help='learning rate decay factor, default: 1e-4', default=1e-4)
training_group.add_argument('--initial-checkpoint', type=str, help='initial checkpoint to start training from, default: None', default=None)
args = parser.parse_args()
if args.cuda_visible_devices != None:
os.environ['CUDA_VISIBLE_DEVICES'] = args.cuda_visible_devices
# checkpoints
checkpoint_dir = os.path.join(args.output, 'checkpoints')
checkpoint = dict()
os.makedirs(checkpoint_dir, exist_ok=True)
# training parameters
batch_size = args.batch_size
lr = args.lr
epochs = args.epochs
sequence_length = args.sequence_length
lr_decay = args.lr_decay
adam_betas = [0.8, 0.95]
adam_eps = 1e-8
features_file = args.features
loss_file = args.loss
# model parameters
cond_size = args.cond_size
checkpoint['batch_size'] = batch_size
checkpoint['lr'] = lr
checkpoint['lr_decay'] = lr_decay
checkpoint['epochs'] = epochs
checkpoint['sequence_length'] = sequence_length
checkpoint['adam_betas'] = adam_betas
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
checkpoint['model_args'] = ()
checkpoint['model_kwargs'] = {'cond_size': cond_size, 'gru_size': args.gru_size}
print(checkpoint['model_kwargs'])
model = plc.PLC(*checkpoint['model_args'], **checkpoint['model_kwargs'])
if type(args.initial_checkpoint) != type(None):
checkpoint = torch.load(args.initial_checkpoint, map_location='cpu')
model.load_state_dict(checkpoint['state_dict'], strict=False)
checkpoint['state_dict'] = model.state_dict()
dataset = PLCDataset(features_file, loss_file, sequence_length=sequence_length)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=4)
optimizer = torch.optim.AdamW(model.parameters(), lr=lr, betas=adam_betas, eps=adam_eps)
# learning rate scheduler
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer=optimizer, lr_lambda=lambda x : 1 / (1 + lr_decay * x))
states = None
plc_loss = plc.plc_loss(18, device=device)
if __name__ == '__main__':
model.to(device)
for epoch in range(1, epochs + 1):
running_loss = 0
running_l1_loss = 0
running_ceps_loss = 0
running_band_loss = 0
running_pitch_loss = 0
print(f"training epoch {epoch}...")
with tqdm.tqdm(dataloader, unit='batch') as tepoch:
for i, (features, lost, target) in enumerate(tepoch):
optimizer.zero_grad()
features = features.to(device)
lost = lost.to(device)
target = target.to(device)
out, states = model(features, lost)
loss, l1_loss, ceps_loss, band_loss, pitch_loss = plc_loss(target, out)
loss.backward()
optimizer.step()
#model.clip_weights()
scheduler.step()
running_loss += loss.detach().cpu().item()
running_l1_loss += l1_loss.detach().cpu().item()
running_ceps_loss += ceps_loss.detach().cpu().item()
running_band_loss += band_loss.detach().cpu().item()
running_pitch_loss += pitch_loss.detach().cpu().item()
tepoch.set_postfix(loss=f"{running_loss/(i+1):8.5f}",
l1_loss=f"{running_l1_loss/(i+1):8.5f}",
ceps_loss=f"{running_ceps_loss/(i+1):8.5f}",
band_loss=f"{running_band_loss/(i+1):8.5f}",
pitch_loss=f"{running_pitch_loss/(i+1):8.5f}",
)
# save checkpoint
checkpoint_path = os.path.join(checkpoint_dir, f'plc{args.suffix}_{epoch}.pth')
checkpoint['state_dict'] = model.state_dict()
checkpoint['loss'] = running_loss / len(dataloader)
checkpoint['epoch'] = epoch
torch.save(checkpoint, checkpoint_path)
|
