forked from Kyubyong/tacotron
-
Notifications
You must be signed in to change notification settings - Fork 3
/
data_load.py
165 lines (139 loc) · 5.88 KB
/
data_load.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
# -*- coding: utf-8 -*-
#/usr/bin/python2
'''
By kyubyong park. kbpark.linguist@gmail.com.
https://www.github.com/kyubyong/tacotron
'''
from functools import wraps
import threading
import tensorflow as tf
import numpy as np
from tensorflow.python.platform import tf_logging as logging
from hyperparams import Hyperparams as hp
from utils import *
from prepro import *
# Adapted from the `sugartensor` code.
# https://github.com/buriburisuri/sugartensor/blob/master/sugartensor/sg_queue.py
def producer_func(func):
r"""Decorates a function `func` as producer_func.
Args:
func: A function to decorate.
"""
@wraps(func)
def wrapper(inputs, dtypes, capacity, num_threads):
r"""
Args:
inputs: A inputs queue list to enqueue
dtypes: Data types of each tensor
capacity: Queue capacity. Default is 32.
num_threads: Number of threads. Default is 1.
"""
# enqueue function
def enqueue_func(sess, op):
# read data from source queue
data = func(sess.run(inputs))
# create feeder dict
feed_dict = {}
for ph, col in zip(placeholders, data):
feed_dict[ph] = col
# run session
sess.run(op, feed_dict=feed_dict)
# create place holder list
placeholders = []
for dtype in dtypes:
placeholders.append(tf.placeholder(dtype=dtype))
# create FIFO queue
queue = tf.FIFOQueue(capacity, dtypes=dtypes)
# enqueue operation
enqueue_op = queue.enqueue(placeholders)
# create queue runner
runner = _FuncQueueRunner(enqueue_func, queue, [enqueue_op] * num_threads)
# register to global collection
tf.train.add_queue_runner(runner)
# return de-queue operation
return queue.dequeue()
return wrapper
class _FuncQueueRunner(tf.train.QueueRunner):
def __init__(self, func, queue=None, enqueue_ops=None, close_op=None,
cancel_op=None, queue_closed_exception_types=None,
queue_runner_def=None):
# save ad-hoc function
self.func = func
# call super()
super(_FuncQueueRunner, self).__init__(queue, enqueue_ops, close_op, cancel_op,
queue_closed_exception_types, queue_runner_def)
# pylint: disable=broad-except
def _run(self, sess, enqueue_op, coord=None):
if coord:
coord.register_thread(threading.current_thread())
decremented = False
try:
while True:
if coord and coord.should_stop():
break
try:
self.func(sess, enqueue_op) # call enqueue function
except self._queue_closed_exception_types: # pylint: disable=catching-non-exception
# This exception indicates that a queue was closed.
with self._lock:
self._runs_per_session[sess] -= 1
decremented = True
if self._runs_per_session[sess] == 0:
try:
sess.run(self._close_op)
except Exception as e:
# Intentionally ignore errors from close_op.
logging.vlog(1, "Ignored exception: %s", str(e))
return
except Exception as e:
# This catches all other exceptions.
if coord:
coord.request_stop(e)
else:
logging.error("Exception in QueueRunner: %s", str(e))
with self._lock:
self._exceptions_raised.append(e)
raise
finally:
# Make sure we account for all terminations: normal or errors.
if not decremented:
with self._lock:
self._runs_per_session[sess] -= 1
def get_batch():
"""Loads training data and put them in queues"""
with tf.device('/cpu:0'):
# Load data
texts, sound_files = load_train_data() # byte, string
# calc total batch count
num_batch = len(texts) // hp.batch_size
# Convert to tensor
texts = tf.convert_to_tensor(texts)
sound_files = tf.convert_to_tensor(sound_files)
# Create Queues
text, sound_file = tf.train.slice_input_producer([texts, sound_files], shuffle=True)
@producer_func
def get_text_and_spectrograms(_inputs):
'''From `_inputs`, which has been fetched from slice queues,
makes text, spectrogram, and magnitude,
then enqueue them again.
'''
_text, _sound_file = _inputs
# Processing
_text = np.fromstring(_text, np.int32) # byte to int
_spectrogram, _magnitude = get_spectrograms(_sound_file)
_spectrogram = reduce_frames(_spectrogram, hp.r)
_magnitude = reduce_frames(_magnitude, hp.r)
return _text, _spectrogram, _magnitude
# Decode sound file
x, y, z = get_text_and_spectrograms(inputs=[text, sound_file],
dtypes=[tf.int32, tf.float32, tf.float32],
capacity=128,
num_threads=32)
# create batch queues
x, y, z = tf.train.batch([x, y, z],
shapes=[(None,), (None, hp.n_mels*hp.r), (None, (1+hp.n_fft//2)*hp.r)],
num_threads=32,
batch_size=hp.batch_size,
capacity=hp.batch_size*32,
dynamic_pad=True)
return x, y, z, num_batch