#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: nl8590687
"""
import platform as plat
import os
import time
from general_function.file_wav import *
from general_function.file_dict import *
from general_function.gen_func import *
# LSTM_CNN
import keras as kr
import numpy as np
import random
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Input, Reshape,GRU # , Flatten,LSTM,Convolution1D,MaxPooling1D,Merge
from keras.layers import Conv1D,LSTM,MaxPooling1D, Lambda, TimeDistributed, Activation,Conv2D, MaxPooling2D #, Merge,Conv1D
from keras.layers.merge import add, concatenate
from keras import backend as K
from keras.optimizers import SGD, Adadelta
from readdata24 import DataSpeech
class ModelSpeech(): # 语音模型类
def __init__(self, datapath):
'''
初始化
默认输出的拼音的表示大小是1424，即1423个拼音+1个空白块
'''
MS_OUTPUT_SIZE = 1424
self.MS_OUTPUT_SIZE = MS_OUTPUT_SIZE # 神经网络最终输出的每一个字符向量维度的大小
#self.BATCH_SIZE = BATCH_SIZE # 一次训练的batch
self.label_max_string_length = 64
self.AUDIO_LENGTH = 1600
self.AUDIO_FEATURE_LENGTH = 200
self._model, self.base_model = self.CreateModel()
self.datapath = datapath
self.slash = ''
system_type = plat.system() # 由于不同的系统的文件路径表示不一样，需要进行判断
if(system_type == 'Windows'):
self.slash='\\' # 反斜杠
elif(system_type == 'Linux'):
self.slash='/' # 正斜杠
else:
print('*[Message] Unknown System\n')
self.slash='/' # 正斜杠
if(self.slash != self.datapath[-1]): # 在目录路径末尾增加斜杠
self.datapath = self.datapath + self.slash
def CreateModel(self):
'''
定义CNN/LSTM/CTC模型，使用函数式模型
输入层：200维的特征值序列，一条语音数据的最大长度设为1600（大约16s）
隐藏层一：3*3卷积层
隐藏层二：池化层，池化窗口大小为2
隐藏层三：Dropout层，需要断开的神经元的比例为0.2，防止过拟合
隐藏层四：循环层、LSTM/GRU层
隐藏层五：Dropout层，需要断开的神经元的比例为0.2，防止过拟合
隐藏层六：全连接层，神经元数量为self.MS_OUTPUT_SIZE，使用softmax作为激活函数，
输出层：自定义层，即CTC层，使用CTC的loss作为损失函数，实现连接性时序多输出
'''
# 每一帧使用13维mfcc特征及其13维一阶差分和13维二阶差分表示，最大信号序列长度为1500
input_data = Input(name='the_input', shape=(self.AUDIO_LENGTH, self.AUDIO_FEATURE_LENGTH, 1))
layer_h1 = Conv2D(32, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(input_data) # 卷积层
layer_h1 = Dropout(0.1)(layer_h1)
layer_h2 = Conv2D(32, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h1) # 卷积层
layer_h3 = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h2) # 池化层
#layer_h3 = Dropout(0.2)(layer_h2) # 随机中断部分神经网络连接，防止过拟合
layer_h3 = Dropout(0.2)(layer_h3)
layer_h4 = Conv2D(64, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h3) # 卷积层
layer_h4 = Dropout(0.2)(layer_h4)
layer_h5 = Conv2D(64, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h4) # 卷积层
layer_h6 = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h5) # 池化层
layer_h6 = Dropout(0.3)(layer_h6)
layer_h7 = Conv2D(128, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h6) # 卷积层
layer_h7 = Dropout(0.3)(layer_h7)
layer_h8 = Conv2D(128, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h7) # 卷积层
layer_h9 = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h8) # 池化层
layer_h9 = Dropout(0.3)(layer_h9)
layer_h10 = Conv2D(128, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h9) # 卷积层
layer_h10 = Dropout(0.4)(layer_h10)
layer_h11 = Conv2D(128, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h10) # 卷积层
layer_h12 = MaxPooling2D(pool_size=1, strides=None, padding="valid")(layer_h11) # 池化层
#test=Model(inputs = input_data, outputs = layer_h6)
#test.summary()
layer_h13 = Reshape((200, 3200))(layer_h12) #Reshape层
layer_h13 = Dropout(0.4)(layer_h13)
layer_h14 = Dense(128, activation="relu", use_bias=True, kernel_initializer='he_normal')(layer_h13) # 全连接层
layer_h14 = Dropout(0.4)(layer_h14)
inner = layer_h14
#layer_h5 = LSTM(256, activation='relu', use_bias=True, return_sequences=True)(layer_h4) # LSTM层
rnn_size=128
gru_1 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru1')(inner)
gru_1b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru1_b')(inner)
gru1_merged = add([gru_1, gru_1b])
gru_2 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru2')(gru1_merged)
gru_2b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru2_b')(gru1_merged)
gru2 = concatenate([gru_2, gru_2b])
#layer_h12 = GRU(128,activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='he_normal', recurrent_initializer='orthogonal', bias_initializer='zeros', return_sequences=True)(layer_h11)
layer_h15 = Dropout(0.4)(gru2)
layer_h16 = Dense(128, activation="relu", use_bias=True, kernel_initializer='he_normal')(layer_h15) # 全连接层
layer_h16 = Dropout(0.5)(layer_h16) # 随机中断部分神经网络连接，防止过拟合
layer_h17 = Dense(self.MS_OUTPUT_SIZE, use_bias=True, kernel_initializer='he_normal')(layer_h16) # 全连接层
y_pred = Activation('softmax', name='Activation0')(layer_h17)
model_data = Model(inputs = input_data, outputs = y_pred)
#model_data.summary()
labels = Input(name='the_labels', shape=[self.label_max_string_length], dtype='float32')
input_length = Input(name='input_length', shape=[1], dtype='int64')
label_length = Input(name='label_length', shape=[1], dtype='int64')
# Keras doesn't currently support loss funcs with extra parameters
# so CTC loss is implemented in a lambda layer
#layer_out = Lambda(ctc_lambda_func,output_shape=(self.MS_OUTPUT_SIZE, ), name='ctc')([y_pred, labels, input_length, label_length])#(layer_h6) # CTC
loss_out = Lambda(self.ctc_lambda_func, output_shape=(1,), name='ctc')([y_pred, labels, input_length, label_length])
model = Model(inputs=[input_data, labels, input_length, label_length], outputs=loss_out)
model.summary()
# clipnorm seems to speeds up convergence
#sgd = SGD(lr=0.0001, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5)
ada_d = Adadelta(lr = 0.01, rho = 0.95, epsilon = 1e-06)
#model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=sgd)
model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer = ada_d)
# captures output of softmax so we can decode the output during visualization
test_func = K.function([input_data], [y_pred])
print('[*提示] 创建模型成功，模型编译成功')
return model, model_data
def ctc_lambda_func(self, args):
y_pred, labels, input_length, label_length = args
y_pred = y_pred[:, :, :]
#y_pred = y_pred[:, 2:, :]
return K.ctc_batch_cost(labels, y_pred, input_length, label_length)
def TrainModel(self, datapath, epoch = 2, save_step = 1000, batch_size = 32, filename = 'model_speech/m26/speech_model26'):
'''
训练模型
参数：
datapath: 数据保存的路径
epoch: 迭代轮数
save_step: 每多少步保存一次模型
filename: 默认保存文件名，不含文件后缀名
'''
data=DataSpeech(datapath, 'train')
num_data = data.GetDataNum() # 获取数据的数量
yielddatas = data.data_genetator(batch_size, self.AUDIO_LENGTH)
for epoch in range(epoch): # 迭代轮数
print('[running] train epoch %d .' % epoch)
n_step = 0 # 迭代数据数
while True:
try:
print('[message] epoch %d . Have train datas %d+'%(epoch, n_step*save_step))
# data_genetator是一个生成器函数
#self._model.fit_generator(yielddatas, save_step, nb_worker=2)
self._model.fit_generator(yielddatas, save_step)
n_step += 1
except StopIteration:
print('[error] generator error. please check data format.')
break
self.SaveModel(comment='_e_'+str(epoch)+'_step_'+str(n_step * save_step))
self.TestModel(self.datapath, str_dataset='train', data_count = 4)
self.TestModel(self.datapath, str_dataset='dev', data_count = 4)
def LoadModel(self,filename='model_speech/m26/speech_model26.model'):
'''
加载模型参数
'''
self._model.load_weights(filename)
self.base_model.load_weights(filename + '.base')
def SaveModel(self,filename='model_speech/m26/speech_model26',comment=''):
'''
保存模型参数
'''
self._model.save_weights(filename+comment+'.model')
self.base_model.save_weights(filename + comment + '.model.base')
f = open('step26.txt','w')
f.write(filename+comment)
f.close()
def TestModel(self, datapath='', str_dataset='dev', data_count = 32, out_report = False, show_ratio = True):
'''
测试检验模型效果
'''
data=DataSpeech(self.datapath, str_dataset)
#data.LoadDataList(str_dataset)
num_data = data.GetDataNum() # 获取数据的数量
if(data_count <= 0 or data_count > num_data): # 当data_count为小于等于0或者大于测试数据量的值时，则使用全部数据来测试
data_count = num_data
try:
ran_num = random.randint(0,num_data - 1) # 获取一个随机数
words_num = 0
word_error_num = 0
nowtime = time.strftime('%Y%m%d_%H%M%S',time.localtime(time.time()))
if(out_report == True):
txt_obj = open('Test_Report_' + str_dataset + '_' + nowtime + '.txt', 'w', encoding='UTF-8') # 打开文件并读入
txt = ''
for i in range(data_count):
data_input, data_labels = data.GetData((ran_num + i) % num_data) # 从随机数开始连续向后取一定数量数据
# 数据格式出错处理 开始
# 当输入的wav文件长度过长时自动跳过该文件，转而使用下一个wav文件来运行
num_bias = 0
while(data_input.shape[0] > self.AUDIO_LENGTH):
print('*[Error]','wave data lenghth of num',(ran_num + i) % num_data, 'is too long.','\n A Exception raise when test Speech Model.')
num_bias += 1
data_input, data_labels = data.GetData((ran_num + i + num_bias) % num_data) # 从随机数开始连续向后取一定数量数据
# 数据格式出错处理 结束
pre = self.Predict(data_input, data_input.shape[0] // 8)
words_n = data_labels.shape[0] # 获取每个句子的字数
words_num += words_n # 把句子的总字数加上
edit_distance = GetEditDistance(data_labels, pre) # 获取编辑距离
if(edit_distance <= words_n): # 当编辑距离小于等于句子字数时
word_error_num += edit_distance # 使用编辑距离作为错误字数
else: # 否则肯定是增加了一堆乱七八糟的奇奇怪怪的字
word_error_num += words_n # 就直接加句子本来的总字数就好了
if(i % 10 == 0 and show_ratio == True):
print('测试进度：',i,'/',data_count)
txt = ''
if(out_report == True):
txt += str(i) + '\n'
txt += 'True:\t' + str(data_labels) + '\n'
txt += 'Pred:\t' + str(pre) + '\n'
txt += '\n'
txt_obj.write(txt)
print('*[测试结果] 语音识别 ' + str_dataset + ' 集语音单字错误率：', word_error_num / words_num * 100, '%')
if(out_report == True):
txt = '*[测试结果] 语音识别 ' + str_dataset + ' 集语音单字错误率： ' + str(word_error_num / words_num * 100) + ' %'
txt_obj.write(txt)
txt_obj.close()
except StopIteration:
print('[Error] Model Test Error. please check data format.')
def Predict(self, data_input, input_len):
'''
预测结果
返回语音识别后的拼音符号列表
'''
batch_size = 1
in_len = np.zeros((batch_size),dtype = np.int32)
in_len[0] = input_len
x_in = np.zeros((batch_size, 1600, self.AUDIO_FEATURE_LENGTH, 1), dtype=np.float)
for i in range(batch_size):
x_in[i,0:len(data_input)] = data_input
base_pred = self.base_model.predict(x = x_in)
#print('base_pred:\n', base_pred)
#y_p = base_pred
#for j in range(200):
# mean = np.sum(y_p[0][j]) / y_p[0][j].shape[0]
# print('max y_p:',np.max(y_p[0][j]),'min y_p:',np.min(y_p[0][j]),'mean y_p:',mean,'mid y_p:',y_p[0][j][100])
# print('argmin:',np.argmin(y_p[0][j]),'argmax:',np.argmax(y_p[0][j]))
# count=0
# for i in range(y_p[0][j].shape[0]):
# if(y_p[0][j][i] < mean):
# count += 1
# print('count:',count)
base_pred =base_pred[:, :, :]
#base_pred =base_pred[:, 2:, :]
r = K.ctc_decode(base_pred, in_len, greedy = True, beam_width=100, top_paths=1)
#print('r', r)
r1 = K.get_value(r[0][0])
#print('r1', r1)
#r2 = K.get_value(r[1])
#print(r2)
r1=r1[0]
return r1
pass
def RecognizeSpeech(self, wavsignal, fs):
'''
最终做语音识别用的函数，识别一个wav序列的语音
'''
#data = self.data
#data = DataSpeech('E:\\语音数据集')
#data.LoadDataList('dev')
# 获取输入特征
#data_input = GetMfccFeature(wavsignal, fs)
#t0=time.time()
data_input = GetFrequencyFeature2(wavsignal, fs)
#t1=time.time()
#print('time cost:',t1-t0)
input_length = len(data_input)
input_length = input_length // 8
data_input = np.array(data_input, dtype = np.float)
#print(data_input,data_input.shape)
data_input = data_input.reshape(data_input.shape[0],data_input.shape[1],1)
#t2=time.time()
r1 = self.Predict(data_input, input_length)
#t3=time.time()
#print('time cost:',t3-t2)
list_symbol_dic = GetSymbolList(self.datapath) # 获取拼音列表
r_str=[]
for i in r1:
r_str.append(list_symbol_dic[i])
return r_str
pass
def RecognizeSpeech_FromFile(self, filename):
'''
最终做语音识别用的函数，识别指定文件名的语音
'''
wavsignal,fs = read_wav_data(filename)
r = self.RecognizeSpeech(wavsignal, fs)
return r
pass
@property
def model(self):
'''
返回keras model
'''
return self._model
if(__name__=='__main__'):
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
#进行配置，使用70%的GPU
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.93
#config.gpu_options.allow_growth=True #不全部占满显存, 按需分配
set_session(tf.Session(config=config))
datapath = ''
modelpath = 'model_speech'
if(not os.path.exists(modelpath)): # 判断保存模型的目录是否存在
os.makedirs(modelpath) # 如果不存在，就新建一个，避免之后保存模型的时候炸掉
system_type = plat.system() # 由于不同的系统的文件路径表示不一样，需要进行判断
if(system_type == 'Windows'):
datapath = 'E:\\语音数据集'
modelpath = modelpath + '\\'
elif(system_type == 'Linux'):
datapath = 'dataset'
modelpath = modelpath + '/'
else:
print('*[Message] Unknown System\n')
datapath = 'dataset'
modelpath = modelpath + '/'
ms = ModelSpeech(datapath)
#ms.LoadModel(modelpath + 'm26/speech_model26_e_0_step_397000.model')
ms.TrainModel(datapath, epoch = 50, batch_size = 16, save_step = 500)
#ms.TestModel(datapath, str_dataset='test', data_count = 128, out_report = True)
#r = ms.RecognizeSpeech_FromFile('E:\\语音数据集\\ST-CMDS-20170001_1-OS\\20170001P00241I0053.wav')
#r = ms.RecognizeSpeech_FromFile('E:\\语音数据集\\ST-CMDS-20170001_1-OS\\20170001P00020I0087.wav')
#r = ms.RecognizeSpeech_FromFile('E:\\语音数据集\\wav\\train\\A11\\A11_167.WAV')
#r = ms.RecognizeSpeech_FromFile('E:\\语音数据集\\wav\\test\\D4\\D4_750.wav')
#print('*[提示] 语音识别结果：\n',r)
