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lstm_ctc_to_chars.py
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lstm_ctc_to_chars.py
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#!/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)
# cell = tf.nn.rnn_cell.EmbeddingWrapper(num_hidden, state_is_tuple=True)
# in many cases it may be more efficient to not use this wrapper,
# but instead concatenate the whole sequence of your outputs in time,
# do the projection on this batch-concatenated sequence, then split it
# if needed or directly feed into a softmax.
# cell = tf.nn.rnn_cell.OutputProjectionWrapper(cell,)
cell = tf.nn.rnn_cell.MultiRNNCell(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
target_shape = (batch_size, max_word_length, classes)
y = target = tf.placeholder(tf.float32, shape=target_shape) # -> seq2seq!
# dense
logits = []
costs = []
i = 0
accuracy = 0
# for output in outputs:
for i in range(0, max_word_length):
output = outputs[-i - 1]
uniform = tf.random_uniform([num_hidden, classes], minval=-1. / width, maxval=1. / width)
weights = tf.Variable(uniform, name="weights_%d" % i)
uniform_bias = tf.random_uniform([classes], minval=-1. / width, maxval=1. / width)
bias = tf.Variable(uniform_bias, 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 " + 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