-
Notifications
You must be signed in to change notification settings - Fork 3
/
Copy pathtrain.py
144 lines (122 loc) · 5.68 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
import argparse
from datetime import datetime
import math
import numpy as np
import os
import subprocess
import time
import tensorflow as tf
import traceback
from datasets.datafeeder import DataFeeder
from hparams import hparams, hparams_debug_string
from models import create_model
from text import sequence_to_text
from util import audio, plot, ValueWindow
def add_stats(model):
with tf.variable_scope('stats') as scope:
tf.summary.histogram('linear_outputs', model.linear_outputs)
tf.summary.histogram('linear_targets', model.linear_targets)
tf.summary.histogram('mel_outputs', model.mel_outputs)
tf.summary.histogram('mel_targets', model.mel_targets)
tf.summary.scalar('loss_mel', model.mel_loss)
tf.summary.scalar('loss_linear', model.linear_loss)
tf.summary.scalar('learning_rate', model.learning_rate)
tf.summary.scalar('loss', model.loss)
gradient_norms = [tf.norm(grad) for grad in model.gradients]
tf.summary.histogram('gradient_norm', gradient_norms)
tf.summary.scalar('max_gradient_norm', tf.reduce_max(gradient_norms))
return tf.summary.merge_all()
def time_string():
return datetime.now().strftime('%Y-%m-%d %H:%M')
def train(log_dir, args):
checkpoint_path = os.path.join(log_dir, 'model.ckpt')
input_path = os.path.join(args.base_dir, args.input)
tf.logging.info('Checkpoint path: %s' % checkpoint_path)
tf.logging.info('Loading training data from: %s' % input_path)
tf.logging.info('Using model: %s' % args.model)
tf.logging.info(hparams_debug_string())
# Set up DataFeeder:
coord = tf.train.Coordinator()
with tf.variable_scope('datafeeder') as scope:
feeder = DataFeeder(coord, input_path, hparams)
# Set up model:
global_step = tf.Variable(0, name='global_step', trainable=False)
with tf.variable_scope('model') as scope:
model = create_model(args.model, hparams)
model.initialize(feeder.inputs, feeder.input_lengths, feeder.mel_targets, feeder.linear_targets)
model.add_loss()
model.add_optimizer(global_step)
stats = add_stats(model)
# Bookkeeping:
step = 0
time_window = ValueWindow(100)
loss_window = ValueWindow(100)
saver = tf.train.Saver(max_to_keep=5, keep_checkpoint_every_n_hours=2)
# Train!
with tf.Session() as sess:
try:
summary_writer = tf.summary.FileWriter(log_dir, sess.graph)
sess.run(tf.global_variables_initializer())
if args.restore_step:
# Restore from a checkpoint if the user requested it.
restore_path = '%s-%d' % (checkpoint_path, args.restore_step)
saver.restore(sess, restore_path)
tf.logging.info('Resuming from checkpoint: %s' % restore_path)
else:
tf.logging.info('Starting new training run')
feeder.start_in_session(sess)
while not coord.should_stop():
start_time = time.time()
step, loss, opt = sess.run([global_step, model.loss, model.optimize])
time_window.append(time.time() - start_time)
loss_window.append(loss)
message = 'Step %-7d [%.03f sec/step, loss=%.05f, avg_loss=%.05f]' % (
step, time_window.average, loss, loss_window.average)
tf.logging.info(message)
if loss > 100 or math.isnan(loss):
tf.logging.info('Loss exploded to %.05f at step %d!' % (loss, step))
raise Exception('Loss Exploded')
if step % args.summary_interval == 0:
tf.logging.info('Writing summary at step: %d' % step)
summary_writer.add_summary(sess.run(stats), step)
if step % args.checkpoint_interval == 0:
tf.logging.info('Saving checkpoint to: %s-%d' % (checkpoint_path, step))
saver.save(sess, checkpoint_path, global_step=step)
tf.logging.info('Saving audio and alignment...')
input_seq, spectrogram, alignment = sess.run([
model.inputs[0], model.linear_outputs[0], model.alignments[0]])
waveform = audio.inv_spectrogram(spectrogram.T)
# Teacher forcing results.
audio.save_wav(waveform, os.path.join(log_dir, 'step-%d-audio.wav' % step))
plot.plot_alignment(alignment, os.path.join(log_dir, 'step-%d-align.png' % step),
info='%s, %s, step=%d, loss=%.5f' % (args.model, time_string(), step, loss))
tf.logging.info('Input: %s' % sequence_to_text(input_seq))
except Exception as e:
tf.logging.info('Exiting due to exception: %s' % e)
traceback.print_exc()
coord.request_stop(e)
def main():
tf.logging.set_verbosity(tf.logging.INFO)
parser = argparse.ArgumentParser()
parser.add_argument('--base_dir', default='.')
parser.add_argument('--input', default='training/train.txt')
parser.add_argument('--model', default='tacotron')
parser.add_argument('--name', help='Name of the run. Used for logging. Defaults to model name.')
parser.add_argument('--hparams', default='',
help='Hyperparameter overrides as a comma-separated list of name=value pairs')
parser.add_argument('--restore_step', type=int, help='Global step to restore from checkpoint.')
parser.add_argument('--summary_interval', type=int, default=100,
help='Steps between running summary ops.')
parser.add_argument('--checkpoint_interval', type=int, default=100,
help='Steps between writing checkpoints.')
parser.add_argument('--tf_log_level', type=int, default=1, help='Tensorflow C++ log level.')
parser.add_argument('--git', action='store_true', help='If set, verify that the client is clean.')
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_log_level)
run_name = args.name or args.model
log_dir = os.path.join(args.base_dir, 'logs-%s' % run_name)
os.makedirs(log_dir, exist_ok=True)
hparams.parse(args.hparams)
train(log_dir, args)
if __name__ == '__main__':
main()