-
-
Notifications
You must be signed in to change notification settings - Fork 639
/
lstm_mfcc_ctc_to_words.py
executable file
·160 lines (140 loc) · 6.07 KB
/
lstm_mfcc_ctc_to_words.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
#!/usr/bin/env python
#!/usr/local/bin/python
from __future__ import print_function
import numpy as np
import tensorflow as tf
import speech_data
from speech_data import Source,Target
from tensorflow.python.ops import ctc_ops as ctc
# import layer
# from layer import net
import time
start=int(time.time())
display_step = 1
test_step = 10
save_step = 100
learning_rate = 0.0001
# 0.0001 Step 300 Loss= 1.976625 Accuracy= 0.250 Time= 303s
# Step 24261 Loss= 0.011786 Accuracy= 1.000 Time= 33762s takes time but works
training_iters = 300000 #steps
batch_size = 64
width=features=20 # mfcc input features
height=max_input_length=80 # (max) length of input utterance (mfcc slices)
classes = num_characters = 32
max_word_length = 20 # max length of output (characters per word)
# classes=10 # digits
keep_prob=dropout=0.7
# batch = speech_data.mfcc_batch_generator(batch_size, target=Target.word)
batch = speech_data.mfcc_batch_generator(batch_size, source=Source.WORD_WAVES, target=Target.hotword)
X,Y=next(batch)
print("lable shape",np.array(Y).shape)
# inputs=tf.placeholder(tf.float32, shape=(batch_size,max_length,features))
x= inputX=inputs=tf.placeholder(tf.float32, shape=(batch_size, features, max_input_length))
# inputs = tf.transpose(inputs, [0, 2, 1]) # inputs must be a `Tensor` of shape: `[batch_size, max_time, ...]`
inputs = tf.transpose(inputs, [2, 0, 1]) # [max_time, batch_size, features] to split:
# Split data because rnn cell needs a list of inputs for the RNN inner loop
inputs = tf.split(axis=0, num_or_size_splits=max_input_length, value=inputs) # n_steps * (batch_size, features)
num_hidden = 100 #features
cell = tf.nn.rnn_cell.LSTMCell(num_hidden, state_is_tuple=True)
# rnn=tf.nn.rnn(cell,inputs)
# rnn=tf.nn.dynamic_rnn(cell,inputs)
# manual:
state = cell.zero_state(batch_size, dtype=tf.float32)
if "manual" == 0:
outputs = []
for input_ in inputs:
input_= tf.reshape(input_, [batch_size,features])
output, state = cell(input_, state)
outputs.append(output)
y_=output
else:
# inputs = tf.reshape(inputs, [-1, features])
inputs=[tf.reshape(input_, [batch_size,features]) for input_ in inputs]
outputs, states = tf.nn.rnn(cell, inputs, initial_state=state)
# only last output as target for now
# y_=outputs[-1]
# optimize
y = target = tf.placeholder(tf.float32, shape=(batch_size, max_word_length, classes)) # -> seq2seq!
# dense
logits=[]
costs=[]
i=0
accuracy=0
# for output in outputs:
for i in range(0, max_word_length):
output=outputs[-i-1]
weights = tf.Variable(tf.random_uniform([num_hidden, classes], minval=-1. / width, maxval=1./width), name="weights_%d"%i)
bias = tf.Variable(tf.random_uniform([classes], minval=-1. / width, maxval=1. / width), name="bias_dense_%d"%i)
y_ = outputY = tf.matmul(output, weights, name="dense_%d" % i) + bias
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y_, labels=y[:,i,:]), name="cost") # prediction, target
costs.append(cost)
logits.append(y_)
correct_pred = tf.equal(tf.argmax(outputY, 1), tf.argmax(y[:,i], 1))
accuraci = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
accuracy+= accuraci
# costs=tf.reduce_sum(costs)*10
# y_ = outputY = tf.pack(logits)
# targetIxs = tf.placeholder(tf.int64, shape=(batch_size, None),name="indices")
# targetVals = tf.placeholder(tf.int32,name="values")
# targetShape = tf.placeholder(tf.int64,name="targetShape")
# targetY = tf.SparseTensor(targetIxs, targetVals, targetShape)
targetY = tf.SparseTensor()
####Optimizing
logits=y_
logits3d = tf.stack(logits)
seqLengths=[20]*batch_size
cost = tf.reduce_mean(ctc.ctc_loss(logits3d, targetY, seqLengths))
# CTCLoss op expects the reserved blank label to be the largest value! REALLY?
# if 1D:
tf.summary.scalar('cost', cost)
tf.summary.scalar('costs', costs)
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(costs)
# prediction = y_
# Evaluate model
# accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# tf.scalar_summary('accuracy', accuracy)
# predictions = tf.to_int32(ctc.ctc_beam_search_decoder(logits3d, seqLengths)[0][0])
# accuracy = tf.reduce_mean(tf.reduce_mean(logits))
# reduced_sum = tf.reduce_sum(tf.edit_distance(predictions, targetY, normalize=False))
# errorRate = reduced_sum / tf.to_float(tf.size(targetY.values))
steps = 9999999
session=tf.Session()
try:saver = tf.train.Saver(tf.global_variables())
except:saver = tf.train.Saver(tf.global_variables())
snapshot = "lstm_mfcc"
checkpoint = tf.train.latest_checkpoint(checkpoint_dir="checkpoints")
if checkpoint:
print("LOADING " + checkpoint + " !!!")
try:saver.restore(session, checkpoint)
except: print("incompatible checkpoint")
try: session.run([tf.global_variables_initializer()])
except: session.run([tf.global_variables_initializer()])# tf <12
#train
step = 0 # show first
try:summaries = tf.summary.merge_all()
except:summaries = tf.summary.merge_all() # tf<12
try:summary_writer = tf.summary.FileWriter("logs", session.graph) #
except:summary_writer = tf.summary.FileWriter("logs", session.graph) # tf<12
while step < steps:
batch_xs, batch_ys = next(batch)
# tf.train.shuffle_batch_join(example_list, batch_size, capacity=min_queue_size + batch_size * 16, min_queue_size)
# Fit training using batch data
feed_dict = {x: batch_xs, y: batch_ys}
# feed_dict = {inputX: batch_xs, targetIxs: batch_ys.indices, targetVals: batch_ys.values,targetShape: 20}
#, seqLengths: batchSeqLengths
loss, _ = session.run([costs, optimizer], feed_dict=feed_dict)
if step % display_step == 0:
seconds = int(time.time()) - start
# Calculate batch accuracy, loss
feed = {x: batch_xs, y: batch_ys} #, keep_prob: 1., train_phase: False}
acc, summary = session.run([accuracy, summaries], feed_dict=feed)
# summary_writer.add_summary(summary, step) # only test summaries for smoother curve
print("\rStep {:d} Loss={:.4f} Fit={:.1f}% Time={:d}s".format(step, loss, acc, seconds), end=' ')
if str(loss) == "nan":
print("\nLoss gradiant explosion, quitting!!!") # restore!
quit(0)
# if step % test_step == 0: test(step)
if step % save_step == 0 and step > 0:
print("SAVING snapshot %s" % snapshot)
saver.save(session, "checkpoints/" + snapshot + ".ckpt", step)
step = step +1