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yuyin.py
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#!/usr/bin/python3
# -*- coding: utf-8 -*-
import os
import sys
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf
import scipy.io.wavfile as wav
from python_speech_features import *
import numpy as np
import sklearn.preprocessing
import speech_lib
print (sys.version_info.major)
path_film = os.path.abspath('.')
path = path_film + "/data/xunlian/"
test_path = path_film + "/data/test_data/"
isnot_test_path = path_film + "/data/isnot_test_path/"
#使用one-hot编码，将离散特征的取值扩展到了欧式空间
#全局one-hot编码空间
label_binarizer = ""
def def_one_hot(x):
if label_binarizer == "":
binarizer = sklearn.preprocessing.LabelBinarizer()
else:
binarizer = label_binarizer
binarizer.fit(range(max(x)+1))
y= binarizer.transform(x)
return y
def read_wav_path(path):
map_path, map_relative = [str(path) + str(x) for x in os.listdir(path) if os.path.isfile(str(path) + str(x))], [y for y in os.listdir(path)]
return map_path, map_relative
def def_wav_read_mfcc(file_name):
fs, audio = wav.read(file_name)
audio = audio[...,0] #取左声道
#audio = speech_lib.adjust_vol(audio)
#audio = speech_lib.filt(audio)
wav_feature = mfcc(audio, samplerate=fs, nfft=2000)
#d_mfcc_feat = delta(wav_feature, 1)
#d_mfcc_feat2 = delta(wav_feature, 2)
#feature = np.hstack((wav_feature, d_mfcc_feat, d_mfcc_feat2))
return wav_feature
def find_matrix_max_shape(audio):
h, l = 0, 0
for a in audio:
a, b = np.array(a).shape
if a > h:
h=a
if b > l:
l=b
return 200, 13
def matrix_make_up(audio):
h, l = find_matrix_max_shape(audio)
print(h,l)
print(type(h))
new_audio = []
for aa in audio:
zeros_matrix = np.zeros([h, l],np.int16)
a, b = np.array(aa).shape
#print("shape: "+str(a)+", "+str(b))
for i in range(a):
for j in range(b):
zeros_matrix[i, j]=zeros_matrix[i,j]+aa[i,j]
new_audio.append(zeros_matrix)
return new_audio,h,l
def read_wav_matrix(path):
map_path, map_relative = read_wav_path(path)
audio=[]
labels=[]
for idx, folder in enumerate(map_path):
processed_audio_delta = def_wav_read_mfcc(folder)
audio.append(processed_audio_delta)
labels.append(int(map_relative[idx].split(".")[0].split("_")[0]))
x_data,h,l = matrix_make_up(audio)
x_data = np.array(x_data)
x_label = np.array(def_one_hot(labels))
return x_data, x_label, h, l
#初始化权值
def weight_variable(shape,name):
initial = tf.truncated_normal(shape,stddev=0.01)#生成一个截断的正态分布
return tf.Variable(initial,name=name)
#初始化偏置
def bias_variable(shape,name):
initial = tf.constant(0.01,shape=shape)
return tf.Variable(initial,name=name)
#卷积层
def conv2d(x,W):
#x input tensor of shape `[batch, in_height, in_width, in_channels]`
#W filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels]
#`strides[0] = strides[3] = 1`. strides[1]代表x方向的步长，strides[2]代表y方向的步长
#padding: A `string` from: `"SAME", "VALID"`
return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')
#池化层
def max_pool_2x2(x):
#ksize [1,x,y,1]
return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
def xunlianlo(path,test_path):
x_train, y_train, h, l = read_wav_matrix(path)
x_test, y_test, h, l = read_wav_matrix(test_path)
test_file, test_file_relative = read_wav_path(test_path)
#print (test_file)
print(x_train.shape)
m,n = y_train.shape
mtest,ntest = y_test.shape
# 命名空间
# 定义两个placeholder
x = tf.placeholder(tf.float32, [None, h, l], name='x-input')
y = tf.placeholder(tf.float32, [None, n], name='y-input')
# 改变x的格式转为4D的向量[batch, in_height, in_width, in_channels]`
x_image = tf.reshape(x, [-1, h, l, 1], name='x_image')# 200*13*1
# 初始化第一个卷积层的权值和偏置
W_conv1 = weight_variable([5, 5, 1, 32], name='W_conv1') # 5*5的采样窗口，32个卷积核从3个平面抽取特征 # 200*13*32
b_conv1 = bias_variable([32], name='b_conv1') # 每一个卷积核一个偏置值
# 把x_image和权值向量进行卷积，再加上偏置值，然后应用于relu激活函数
conv2d_1 = conv2d(x_image, W_conv1) + b_conv1
h_conv1 = tf.nn.leaky_relu(conv2d_1)
h_pool1 = max_pool_2x2(h_conv1) # 进行max-pooling # 100*7*32
# 初始化第二个卷积层的权值和偏置
W_conv2 = weight_variable([5, 5, 32, 64], name='W_conv2') # 5*5的采样窗口，64个卷积核从32个平面抽取特征 # 100*7*64
b_conv2 = bias_variable([64], name='b_conv2') # 每一个卷积核一个偏置值
# 把h_pool1和权值向量进行卷积，再加上偏置值，然后应用于relu激活函数
conv2d_2 = conv2d(h_pool1, W_conv2) + b_conv2
h_conv2 = tf.nn.leaky_relu(conv2d_2)
h_pool2 = max_pool_2x2(h_conv2) # 进行max-pooling # 50*4*64
# 300*300的图片第一次卷积后还是300*300，第一次池化后变为150*150
# 第二次卷积后为150*150，第二次池化后变为了75*75
# 进过上面操作后得到64张7*7的平面
# 初始化第一个全连接层的权值
W_fc1 = weight_variable([50*4*64, 1024], name='W_fc1') # 上一场有75*75*64个神经元，全连接层有1024个神经元
b_fc1 = bias_variable([1024], name='b_fc1') # 1024个节点
# 把池化层2的输出扁平化为1维
h_pool2_flat = tf.reshape(h_pool2, [-1, 50*4*64], name='h_pool2_flat')
# 求第一个全连接层的输出
wx_plus_b1 = tf.matmul(h_pool2_flat, W_fc1) + b_fc1
h_fc1 = tf.nn.leaky_relu(wx_plus_b1)
# keep_prob用来表示神经元的输出概率
keep_prob = tf.placeholder(tf.float32, name='keep_prob')
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob, name='h_fc1_drop')
# 初始化第二个全连接层
W_fc2 = weight_variable([1024, n], name='W_fc2')
b_fc2 = bias_variable([n], name='b_fc2')
wx_plus_b2 = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
# 计算输出
prediction = tf.nn.leaky_relu(wx_plus_b2)
tf.add_to_collection('predictions', prediction)
p = tf.nn.softmax(wx_plus_b2)
tf.add_to_collection('p', p)
# 交叉熵代价函数
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction),
name='cross_entropy')
# 使用AdamOptimizer进行优化
train_step = tf.train.AdamOptimizer(1e-5).minimize(cross_entropy)
# train_step = tf.train.GradientDescentOptimizer(5).minimize(cross_entropy)
tf.add_to_collection('train_step', train_step)
# 求准确率
# 结果存放在一个布尔列表中
correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1)) # argmax返回一维张量中最大的值所在的位置
# 求准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.add_to_collection('accuracy', accuracy)
#保存模型使用环境
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# 创建一个协调器，管理线程
coord = tf.train.Coordinator()
# 启动QueueRunner, 此时文件名队列已经进队
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for i in range(500):
# 训练模型
sess.run(train_step, feed_dict={x: x_train, y: y_train, keep_prob: 0.7})
print ("------------------------------------------------------------------------")
print ("训练第 " + str(i) + " 次")
#############
kekao = 0
for j in range(mtest):
file_path = test_file[j].split('/')
#print ("")
#print ("文件 :"+str(file_path[-1]))
result = sess.run(prediction, feed_dict={x: np.array([x_test[j]]),keep_prob:1.0})
haha = sess.run(p, feed_dict={x: np.array([x_test[j]]), keep_prob: 1.0})
#print("取值置信度"+str(haha))
#print("实际 :"+str(np.argmax(y_test[j]))+" ,预测: "+str(np.argmax(result))+" ,预测可靠度: "+str(np.max(haha)))
kekao += haha[0, np.argmax(y_test[j])]
kekao = kekao/mtest
print ("平均预测可靠度: "+str(kekao))
############
test_acc = sess.run(accuracy, feed_dict={x: x_test, y: y_test, keep_prob: 1.0})
train_acc = sess.run(accuracy, feed_dict={x: x_train, y: y_train, keep_prob: 1.0})
print ("")
print("训练集准确率= " + str(train_acc) + " , 测试集准确率= " + str(test_acc))
if test_acc == 1 and train_acc >= 0.95 and kekao >= 0.7:
print("准确率完爆了")
# 保存模型
break
saver.save(sess, 'nn1/my_net.ckpt')
# 通知其他线程关闭
coord.request_stop()
# 其他所有线程关闭之后，这一函数才能返回
coord.join(threads)
def xunlian_continue(path,test_path):
x_train, y_train, h, l = read_wav_matrix(path)
x_test, y_test, h, l = read_wav_matrix(test_path)
test_file, test_file_relative = read_wav_path(test_path)
#print (test_file)
print(x_train.shape)
m,n = y_train.shape
mtest,ntest = y_test.shape
#saver2 = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# 保存模型使用环境
saver = tf.train.import_meta_graph("nn1/my_net.ckpt.meta")
saver.restore(sess, 'nn1/my_net.ckpt')
prediction = tf.get_collection('predictions')[0]
p = tf.get_collection('p')[0]
train_step = tf.get_collection('train_step')[0]
accuracy = tf.get_collection('accuracy')[0]
graph = tf.get_default_graph()
x = graph.get_operation_by_name('x-input').outputs[0]
y = graph.get_operation_by_name('y-input').outputs[0]
keep_prob = graph.get_operation_by_name('keep_prob').outputs[0]
##########################################################################################
# 创建一个协调器，管理线程
coord = tf.train.Coordinator()
# 启动QueueRunner, 此时文件名队列已经进队
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for i in range(20):
# 训练模型
sess.run(train_step, feed_dict={x: x_train, y: y_train, keep_prob: 1.0})
print ("------------------------------------------------------------------------")
print ("训练第 " + str(i) + " 次")
#############
kekao = 0
for j in range(mtest):
file_path = test_file[j].split('/')
#print ("")
#print ("文件 :"+str(file_path[-1]))
result = sess.run(prediction, feed_dict={x: np.array([x_test[j]]),keep_prob:1.0})
haha = sess.run(p, feed_dict={x: np.array([x_test[j]]), keep_prob: 1.0})
#print("取值置信度"+str(haha))
#print("实际 :"+str(np.argmax(y_test[j]))+" ,预测: "+str(np.argmax(result))+" ,预测可靠度: "+str(np.max(haha)))
kekao += haha[0, np.argmax(y_test[j])]
kekao = kekao/mtest
print ("平均预测可靠度: "+str(kekao))
############
test_acc = sess.run(accuracy, feed_dict={x: x_test, y: y_test, keep_prob: 1.0})
train_acc = sess.run(accuracy, feed_dict={x: x_train, y: y_train, keep_prob: 1.0})
print ("")
print("训练集准确率= " + str(train_acc) + " , 测试集准确率= " + str(test_acc))
if test_acc >= 0.95 and train_acc >= 0.95 and kekao >= 0.9:
print("准确率完爆了")
# 保存模型
break
#saver2.save(sess, 'nn/my_net2.ckpt')
# 通知其他线程关闭
saver.save(sess, 'nn2/my_net.ckpt')
coord.request_stop()
# 其他所有线程关闭之后，这一函数才能返回
coord.join(threads)
def test_main(isnot_test_path):
# 本地情况下生成数据
x_test, y_test, h, l = read_wav_matrix(isnot_test_path)
map_path, map_relative = read_wav_path(isnot_test_path)
m,n = y_test.shape
# 迭代网络
with tf.Session() as sess:
# 保存模型使用环境
saver = tf.train.import_meta_graph("nn1/my_net.ckpt.meta")
saver.restore(sess, 'nn1/my_net.ckpt')
predictions = tf.get_collection('predictions')[0]
p = tf.get_collection('p')[0]
graph = tf.get_default_graph()
input_x = graph.get_operation_by_name('x-input').outputs[0]
keep_prob = graph.get_operation_by_name('keep_prob').outputs[0]
for i in range(m):
result = sess.run(predictions, feed_dict={input_x: np.array([x_test[i]]),keep_prob:1.0})
haha = sess.run(p, feed_dict={input_x: np.array([x_test[i]]), keep_prob: 1.0})
file_n = (map_path[i]).split('/')
file_n = file_n[-1]
print("")
print('Wave_'+str(file_n))
print("取值置信度"+str(haha))
print("实际 :"+str(np.argmax(y_test[i]))+" ,预测: "+str(np.argmax(result))+" ,预测可靠度: "+str(np.max(haha)))
if __name__ == '__main__':
xunlianlo(path,test_path)
#xunlian_continue(path, test_path)
# test_main(isnot_test_path)