/
train.py
193 lines (159 loc) · 7.92 KB
/
train.py
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
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
import torch
import torch.nn as nn
import torch.nn.functional as F
from multiprocessing import cpu_count
import numpy as np
import argparse
import os
import time
import math
from model import FastSpeech
from loss import DNNLoss
from dataset import BufferDataset, DataLoader
from dataset import get_data_to_buffer, collate_fn_tensor
from optimizer import ScheduledOptim
import hparams as hp
import utils
def main(args):
# Get device
device = torch.device('cuda'if torch.cuda.is_available()else 'cpu')
# Define model
print("Use FastSpeech")
model = nn.DataParallel(FastSpeech()).to(device)
print("Model Has Been Defined")
num_param = utils.get_param_num(model)
print('Number of TTS Parameters:', num_param)
# Get buffer
print("Load data to buffer")
buffer = get_data_to_buffer()
# Optimizer and loss
optimizer = torch.optim.Adam(model.parameters(),
betas=(0.9, 0.98),
eps=1e-9)
scheduled_optim = ScheduledOptim(optimizer,
hp.decoder_dim,
hp.n_warm_up_step,
args.restore_step)
fastspeech_loss = DNNLoss().to(device)
print("Defined Optimizer and Loss Function.")
# Load checkpoint if exists
try:
checkpoint = torch.load(os.path.join(
hp.checkpoint_path, 'checkpoint_%d.pth.tar' % args.restore_step))
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("\n---Model Restored at Step %d---\n" % args.restore_step)
except:
print("\n---Start New Training---\n")
if not os.path.exists(hp.checkpoint_path):
os.mkdir(hp.checkpoint_path)
# Init logger
if not os.path.exists(hp.logger_path):
os.mkdir(hp.logger_path)
# Get dataset
dataset = BufferDataset(buffer)
# Get Training Loader
training_loader = DataLoader(dataset,
batch_size=hp.batch_expand_size * hp.batch_size,
shuffle=True,
collate_fn=collate_fn_tensor,
drop_last=True,
num_workers=0)
total_step = hp.epochs * len(training_loader) * hp.batch_expand_size
# Define Some Information
Time = np.array([])
Start = time.perf_counter()
# Training
model = model.train()
for epoch in range(hp.epochs):
for i, batchs in enumerate(training_loader):
# real batch start here
for j, db in enumerate(batchs):
start_time = time.perf_counter()
current_step = i * hp.batch_expand_size + j + args.restore_step + \
epoch * len(training_loader) * hp.batch_expand_size + 1
# Init
scheduled_optim.zero_grad()
# Get Data
character = db["text"].long().to(device)
mel_target = db["mel_target"].float().to(device)
duration = db["duration"].int().to(device)
mel_pos = db["mel_pos"].long().to(device)
src_pos = db["src_pos"].long().to(device)
max_mel_len = db["mel_max_len"]
# Forward
mel_output, mel_postnet_output, duration_predictor_output = model(character,
src_pos,
mel_pos=mel_pos,
mel_max_length=max_mel_len,
length_target=duration)
# Cal Loss
mel_loss, mel_postnet_loss, duration_loss = fastspeech_loss(mel_output,
mel_postnet_output,
duration_predictor_output,
mel_target,
duration)
total_loss = mel_loss + mel_postnet_loss + duration_loss
# Logger
t_l = total_loss.item()
m_l = mel_loss.item()
m_p_l = mel_postnet_loss.item()
d_l = duration_loss.item()
with open(os.path.join("logger", "total_loss.txt"), "a") as f_total_loss:
f_total_loss.write(str(t_l)+"\n")
with open(os.path.join("logger", "mel_loss.txt"), "a") as f_mel_loss:
f_mel_loss.write(str(m_l)+"\n")
with open(os.path.join("logger", "mel_postnet_loss.txt"), "a") as f_mel_postnet_loss:
f_mel_postnet_loss.write(str(m_p_l)+"\n")
with open(os.path.join("logger", "duration_loss.txt"), "a") as f_d_loss:
f_d_loss.write(str(d_l)+"\n")
# Backward
total_loss.backward()
# Clipping gradients to avoid gradient explosion
nn.utils.clip_grad_norm_(
model.parameters(), hp.grad_clip_thresh)
# Update weights
if args.frozen_learning_rate:
scheduled_optim.step_and_update_lr_frozen(
args.learning_rate_frozen)
else:
scheduled_optim.step_and_update_lr()
# Print
if current_step % hp.log_step == 0:
Now = time.perf_counter()
str1 = "Epoch [{}/{}], Step [{}/{}]:".format(
epoch+1, hp.epochs, current_step, total_step)
str2 = "Mel Loss: {:.4f}, Mel PostNet Loss: {:.4f}, Duration Loss: {:.4f};".format(
m_l, m_p_l, d_l)
str3 = "Current Learning Rate is {:.6f}.".format(
scheduled_optim.get_learning_rate())
str4 = "Time Used: {:.3f}s, Estimated Time Remaining: {:.3f}s.".format(
(Now-Start), (total_step-current_step)*np.mean(Time))
print("\n" + str1)
print(str2)
print(str3)
print(str4)
with open(os.path.join("logger", "logger.txt"), "a") as f_logger:
f_logger.write(str1 + "\n")
f_logger.write(str2 + "\n")
f_logger.write(str3 + "\n")
f_logger.write(str4 + "\n")
f_logger.write("\n")
if current_step % hp.save_step == 0:
torch.save({'model': model.state_dict(), 'optimizer': optimizer.state_dict(
)}, os.path.join(hp.checkpoint_path, 'checkpoint_%d.pth.tar' % current_step))
print("save model at step %d ..." % current_step)
end_time = time.perf_counter()
Time = np.append(Time, end_time - start_time)
if len(Time) == hp.clear_Time:
temp_value = np.mean(Time)
Time = np.delete(
Time, [i for i in range(len(Time))], axis=None)
Time = np.append(Time, temp_value)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--restore_step', type=int, default=0)
parser.add_argument('--frozen_learning_rate', type=bool, default=False)
parser.add_argument("--learning_rate_frozen", type=float, default=1e-3)
args = parser.parse_args()
main(args)