tensorflow练习11：语音识别程序
找工作的事情暂时告一段落，感觉还需要不断提升自己，不说多少了，直接步入正题。
语音识别是人机交互、人工智能等领域必不可少的一个研究领域，下面就以该例为标准。
使用的数据集：THCHS30（Dong Wang, Xuewei Zhang, Zhiyong Zhang发布的开放语音数据集）；
地址：
语音文件：http://data.cslt.org/thchs30/zip/wav.tgz
对应文本：http://data.cslt.org/thchs30/zip/doc.tgz
下载后，解压放到data文件夹下。
tensorflow环境：0.12.1
1）加载文件、分词等预处理操作：
#coding=utf-8
import tensorflow as tf
import numpy as np
import os
from collections import Counter
import librosa
import time
#训练样本路径
wav_path = 'data/wav/train'
label_file = 'data/doc/trans/train.word.txt'
# 获得训练用的wav文件路径列表
def get_wave_files(wav_path=wav_path):
wav_files = []
for (dirpath,dirnames,filenames) in os.walk(wav_path):#访问文件夹下的所有文件
#os.walk() 方法用于通过在目录树种游走输出在目录中的文件名，向上或者向下
for filename in filenames:
if filename.endswith('.wav') or filename.endswith('.WAV'):
#endswith() 方法用于判断字符串是否以指定后缀结尾，如果以指定后缀结尾返回True，否则返回False
filename_path = os.sep.join([dirpath,filename])#定义文件路径(连)
if os.stat(filename_path).st_size < 240000:#st_size文件的大小，以位为单位
continue
wav_files.append(filename_path)#加载文件
return wav_files
wav_files = get_wave_files()#获取文件名列表
#读取wav文件对应的label
def get_wav_label(wav_files=wav_files,label_file=label_file):
labels_dict = {}
with open(label_file,encoding='utf-8') as f:
for label in f :
label =label.strip('\n')
label_id = label.split(' ',1)[0]
label_text = label.split(' ',1)[1]
labels_dict[label_id]=label_text#以字典格式保存相应内容
labels=[]
new_wav_files = []
for wav_file in wav_files:
wav_id = os.path.basename(wav_file).split('.')[0]
#得到相应的文件名后进行'.'分割
if wav_id in labels_dict:
labels.append(labels_dict[wav_id])#存在该标签则放入
new_wav_files.append(wav_file)
return new_wav_files,labels#返回标签和对应的文件
wav_files,labels = get_wav_label()#得到标签和对应的语音文件
print("加载训练样本:",time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
print("样本数:",len(wav_files))
#词汇表（参考对话、诗词生成）
all_words = []
for label in labels:
all_words += [word for word in label]
counter = Counter(all_words)
count_pairs =sorted(counter.items(),key=lambda x: -x[1])
words,_=zip(*count_pairs)
words_size =len(words)#词汇表尺寸
print('词汇表大小:',words_size)
#词汇映射成id表示
word_num_map = dict(zip(words,range(len(words))))
to_num = lambda word: word_num_map.get(word,len(words))#词汇映射函数
labels_vector =[list(map(to_num,label)) for label in labels]
label_max_len= np.max([len(label) for label in labels_vector])#获取最长字数
print('最长句子的字数:',label_max_len)
wav_max_len=0
for wav in wav_files:
wav,sr = librosa.load(wav,mono=True)#处理语音信号的库librosa
#加载音频文件作为a floating point time series.（可以是wav,mp3等格式）mono=True：signal->mono
mfcc=np.transpose(librosa.feature.mfcc(wav,sr),[1,0])#转置特征参数
#librosa.feature.mfcc特征提取函数
if len(mfcc)>wav_max_len:
wav_max_len = len(mfcc)
print("最长的语音:",wav_max_len)以上程序加载训练文件并进行分词等操作。
2）定义初始训练细节步骤：
batch_size=16#每次取16个文件
n_batch = len(wav_files)//batch_size#大约560个batch
pointer =0#全局变量初值为0，定义该变量用以逐步确定batch
def get_next_batches(batch_size):
global pointer
batches_wavs = []
batches_labels = []
for i in range(batch_size):
wav,sr=librosa.load(wav_files[pointer],mono=True)
mfcc =np.transpose(librosa.feature.mfcc(wav,sr),[1,0])
batches_wavs.append(mfcc.tolist())#转换成列表表存入
batches_labels.append(labels_vector[pointer])
pointer+=1
#补0对齐
for mfcc in batches_wavs:
while len(mfcc)0]*20)#补一个全0列表
for label in batches_labels:
while len(label)0)
return batches_wavs,batches_labels
X=tf.placeholder(dtype=tf.float32,shape=[batch_size,None,20])#定义输入格式
sequence_len = tf.reduce_sum(tf.cast(tf.not_equal(tf.reduce_sum(X,reduction_indices=2), 0.), tf.int32), reduction_indices=1)
Y= tf.placeholder(dtype=tf.int32,shape=[batch_size,None])#输出格式 以上代码确定一些训练的细节。
3）定义网络结构：
# 定义神经网络
def speech_to_text_network(n_dim=128, n_blocks=3):
#卷积层输出
out = conv1d_layer(input_tensor=X, size=1, dim=n_dim, activation='tanh', scale=0.14, bias=False)
# skip connections
def residual_block(input_sensor, size, rate):
conv_filter = aconv1d_layer(input_sensor, size=size, rate=rate, activation='tanh', scale=0.03, bias=False)
conv_gate = aconv1d_layer(input_sensor, size=size, rate=rate, activation='sigmoid', scale=0.03, bias=False)
out = conv_filter * conv_gate
out = conv1d_layer(out, size=1, dim=n_dim, activation='tanh', scale=0.08, bias=False)
return out + input_sensor, out
skip = 0
for _ in range(n_blocks):
for r in [1, 2, 4, 8, 16]:
out, s = residual_block(out, size=7, rate=r)#根据采样频率发生变化
skip += s
#两层卷积
logit = conv1d_layer(skip, size=1, dim=skip.get_shape().as_list()[-1], activation='tanh', scale=0.08, bias=False)
logit = conv1d_layer(logit, size=1, dim=words_size, activation=None, scale=0.04, bias=True)
return logit
其中，上述代码中skip connection是CNN中的一种训练技巧，具体可以参照博客：极深网络（ResNet/DenseNet）: Skip Connection为何有效及其它
上述代码中：conv1d_layer与aconv1d_layer代码如下：
#第一层卷积
conv1d_index = 0
def conv1d_layer(input_tensor,size,dim,activation,scale,bias):
global conv1d_index
with tf.variable_scope('conv1d_'+str(conv1d_index)):
W= tf.get_variable('W', (size, input_tensor.get_shape().as_list()[-1], dim), dtype=tf.float32, initializer=tf.random_uniform_initializer(minval=-scale, maxval=scale))
if bias:
b= tf.get_variable('b',[dim],dtype=tf.float32,initializer=tf.constant_initializer(0))
out = tf.nn.conv1d(input_tensor, W, stride=1, padding='SAME')#输出与输入同纬度
if not bias:
beta = tf.get_variable('beta', dim, dtype=tf.float32, initializer=tf.constant_initializer(0))
gamma = tf.get_variable('gamma', dim, dtype=tf.float32, initializer=tf.constant_initializer(1))
#均值
mean_running = tf.get_variable('mean', dim, dtype=tf.float32, initializer=tf.constant_initializer(0))
#方差
variance_running = tf.get_variable('variance', dim, dtype=tf.float32,
initializer=tf.constant_initializer(1))
mean, variance = tf.nn.moments(out, axes=range(len(out.get_shape()) - 1))
#可以根据矩（均值和方差）来做normalize，见tf.nn.moments
def update_running_stat():
decay =0.99
#mean_running、variance_running更新操作
update_op = [mean_running.assign(mean_running * decay + mean * (1 - decay)),
variance_running.assign(variance_running * decay + variance * (1 - decay))]
with tf.control_dependencies(update_op):
return tf.identity(mean), tf.identity(variance)
#返回mean,variance
m, v = tf.cond(tf.Variable(False, trainable=False, collections=[tf.GraphKeys.LOCAL_VARIABLES]),
update_running_stat, lambda: (mean_running, variance_running))
out = tf.nn.batch_normalization(out, m, v, beta, gamma, 1e-8)#batch_normalization
if activation == 'tanh':
out = tf.nn.tanh(out)
if activation == 'sigmoid':
out = tf.nn.sigmoid(out)
conv1d_index += 1
return out
# aconv1d_layer
aconv1d_index = 0
def aconv1d_layer(input_tensor, size, rate, activation, scale, bias):
global aconv1d_index
with tf.variable_scope('aconv1d_' + str(aconv1d_index)):
shape = input_tensor.get_shape().as_list()#以list的形式返回tensor的shape
W = tf.get_variable('W', (1, size, shape[-1], shape[-1]), dtype=tf.float32,
initializer=tf.random_uniform_initializer(minval=-scale, maxval=scale))
if bias:
b = tf.get_variable('b', [shape[-1]], dtype=tf.float32, initializer=tf.constant_initializer(0))
out = tf.nn.atrous_conv2d(tf.expand_dims(input_tensor, dim=1), W, rate=rate, padding='SAME')
#tf.expand_dims(input_tensor,dim=1)==>在第二维添加了一维，rate：采样率
out = tf.squeeze(out, [1])#去掉第二维
#同上
if not bias:
beta = tf.get_variable('beta', shape[-1], dtype=tf.float32, initializer=tf.constant_initializer(0))
gamma = tf.get_variable('gamma', shape[-1], dtype=tf.float32, initializer=tf.constant_initializer(1))
mean_running = tf.get_variable('mean', shape[-1], dtype=tf.float32, initializer=tf.constant_initializer(0))
variance_running = tf.get_variable('variance', shape[-1], dtype=tf.float32,
initializer=tf.constant_initializer(1))
mean, variance = tf.nn.moments(out, axes=range(len(out.get_shape()) - 1))
def update_running_stat():
decay = 0.99
update_op = [mean_running.assign(mean_running * decay + mean * (1 - decay)),
variance_running.assign(variance_running * decay + variance * (1 - decay))]
with tf.control_dependencies(update_op):
return tf.identity(mean), tf.identity(variance)
m, v = tf.cond(tf.Variable(False, trainable=False, collections=[tf.GraphKeys.LOCAL_VARIABLES]),
update_running_stat, lambda: (mean_running, variance_running))
out = tf.nn.batch_normalization(out, m, v, beta, gamma, 1e-8)
if activation == 'tanh':
out = tf.nn.tanh(out)
if activation == 'sigmoid':
out = tf.nn.sigmoid(out)
aconv1d_index += 1
return out
4）训练代码：
#对优化类进行一些自定义操作。
class MaxPropOptimizer(tf.train.Optimizer):
def __init__(self, learning_rate=0.001, beta2=0.999, use_locking=False, name="MaxProp"):
super(MaxPropOptimizer, self).__init__(use_locking, name)
self._lr = learning_rate
self._beta2 = beta2
self._lr_t = None
self._beta2_t = None
def _prepare(self):
self._lr_t = tf.convert_to_tensor(self._lr, name="learning_rate")
self._beta2_t = tf.convert_to_tensor(self._beta2, name="beta2")
def _create_slots(self, var_list):
for v in var_list:
self._zeros_slot(v, "m", self._name)
def _apply_dense(self, grad, var):
lr_t = tf.cast(self._lr_t, var.dtype.base_dtype)
beta2_t = tf.cast(self._beta2_t, var.dtype.base_dtype)
if var.dtype.base_dtype == tf.float16:
eps = 1e-7
else:
eps = 1e-8
m = self.get_slot(var, "m")
m_t = m.assign(tf.maximum(beta2_t * m + eps, tf.abs(grad)))
g_t = grad / m_t
var_update = tf.assign_sub(var, lr_t * g_t)
return tf.group(*[var_update, m_t])
def _apply_sparse(self, grad, var):
return self._apply_dense(grad, var)
def train_speech_to_text_network():
print("开始训练:",time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
logit = speech_to_text_network()
# CTC loss
indices = tf.where(tf.not_equal(tf.cast(Y, tf.float32), 0.))
target = tf.SparseTensor(indices=indices, values=tf.gather_nd(Y, indices) - 1, shape=tf.cast(tf.shape(Y), tf.int64))
loss = tf.nn.ctc_loss(logit, target, sequence_len, time_major=False)
# optimizer
lr = tf.Variable(0.001, dtype=tf.float32, trainable=False)
optimizer = MaxPropOptimizer(learning_rate=lr, beta2=0.99)
var_list = [t for t in tf.trainable_variables()]
gradient = optimizer.compute_gradients(loss, var_list=var_list)
optimizer_op = optimizer.apply_gradients(gradient)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())#初始化变量
saver = tf.train.Saver(tf.global_variables())
for epoch in range(16):
print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
print("第%d次循环迭代:"%(epoch))
sess.run(tf.assign(lr, 0.001 * (0.97 ** epoch)))
global pointer
pointer = 0#根据pointer来确定
for batch in range(n_batch):
batches_wavs, batches_labels = get_next_batches(batch_size)
train_loss, _ = sess.run([loss, optimizer_op], feed_dict={X: batches_wavs, Y: batches_labels})
print(epoch, batch, train_loss)
if epoch % 5 == 0:
print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
print("第%d次模型保存结果:"%(epoch//5))
saver.save(sess, './speech.module', global_step=epoch)
print("结束训练时刻:",time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
# 训练
train_speech_to_text_network()
以上设置16个epoch;非gpu训练时间大概2到3天。
5)测试使用代码：
def speech_to_text(wav_file):
wav, sr = librosa.load(wav_file, mono=True)
mfcc = np.transpose(np.expand_dims(librosa.feature.mfcc(wav, sr), axis=0), [0, 2, 1])
logit = speech_to_text_network()
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('.'))
decoded = tf.transpose(logit, perm=[1, 0, 2])
decoded, _ = tf.nn.ctc_beam_search_decoder(decoded, sequence_len, merge_repeated=False)
predict = tf.sparse_to_dense(decoded[0].indices, decoded[0].shape, decoded[0].values) + 1
output = sess.run(decoded, feed_dict={X: mfcc})
print(output)以上仅为个人见解，欢迎各位批评指正。