445 lines
16 KiB
Python
445 lines
16 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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@author: nl8590687
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一个对于单一音频时间长度不作限制的版本，正在测试
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"""
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import platform as plat
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import os
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import time
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from general_function.file_wav import *
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from general_function.file_dict import *
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from general_function.gen_func import *
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import keras as kr
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import numpy as np
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import random
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from keras.models import Sequential, Model
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from keras.layers import Dense, Dropout, Input, Reshape, BatchNormalization # , Flatten
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from keras.layers import Lambda, TimeDistributed, Activation,Conv2D, MaxPooling2D #, Merge
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from keras import backend as K
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from keras.optimizers import SGD, Adadelta, Adam
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from readdata24 import DataSpeech
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abspath = ''
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ModelName='251'
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#NUM_GPU = 2
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class ModelSpeech(): # 语音模型类
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def __init__(self, datapath):
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'''
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初始化
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默认输出的拼音的表示大小是1424，即1423个拼音+1个空白块
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'''
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MS_OUTPUT_SIZE = 1424
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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
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#self.BATCH_SIZE = BATCH_SIZE # 一次训练的batch
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self.label_max_string_length = 64
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self.AUDIO_LENGTH = 1600
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self.AUDIO_FEATURE_LENGTH = 200
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self._model, self.base_model = self.CreateModel()
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self.datapath = datapath
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self.slash = ''
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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
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if(system_type == 'Windows'):
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self.slash='\\' # 反斜杠
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elif(system_type == 'Linux'):
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self.slash='/' # 正斜杠
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else:
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print('*[Message] Unknown System\n')
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self.slash='/' # 正斜杠
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if(self.slash != self.datapath[-1]): # 在目录路径末尾增加斜杠
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self.datapath = self.datapath + self.slash
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def CreateModel(self):
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'''
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定义CNN/LSTM/CTC模型，使用函数式模型
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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）
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隐藏层：卷积池化层，卷积核大小为3x3，池化窗口大小为2
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隐藏层：全连接层
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输出层：全连接层，神经元数量为self.MS_OUTPUT_SIZE，使用softmax作为激活函数，
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CTC层：使用CTC的loss作为损失函数，实现连接性时序多输出
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'''
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input_data = Input(name='the_input', shape=(None, self.AUDIO_FEATURE_LENGTH, 1))
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layer_h1 = Conv2D(32, (3,3), use_bias=False, activation='relu', padding='same', kernel_initializer='he_normal')(input_data) # 卷积层
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layer_h1 = Dropout(0.05)(layer_h1)
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layer_h2 = Conv2D(32, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h1) # 卷积层
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layer_h3 = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h2) # 池化层
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#layer_h3 = Dropout(0.2)(layer_h2) # 随机中断部分神经网络连接，防止过拟合
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layer_h3 = Dropout(0.05)(layer_h3)
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layer_h4 = Conv2D(64, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h3) # 卷积层
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layer_h4 = Dropout(0.1)(layer_h4)
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layer_h5 = Conv2D(64, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h4) # 卷积层
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layer_h6 = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h5) # 池化层
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layer_h6 = Dropout(0.1)(layer_h6)
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layer_h7 = Conv2D(128, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h6) # 卷积层
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layer_h7 = Dropout(0.15)(layer_h7)
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layer_h8 = Conv2D(128, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h7) # 卷积层
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layer_h9 = MaxPooling2D(pool_size=2, strides=None, padding="valid")(layer_h8) # 池化层
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layer_h9 = Dropout(0.15)(layer_h9)
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layer_h10 = Conv2D(128, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h9) # 卷积层
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layer_h10 = Dropout(0.2)(layer_h10)
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layer_h11 = Conv2D(128, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h10) # 卷积层
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layer_h12 = MaxPooling2D(pool_size=1, strides=None, padding="valid")(layer_h11) # 池化层
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layer_h12 = Dropout(0.2)(layer_h12)
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layer_h13 = Conv2D(128, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h12) # 卷积层
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layer_h13 = Dropout(0.2)(layer_h13)
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layer_h14 = Conv2D(128, (3,3), use_bias=True, activation='relu', padding='same', kernel_initializer='he_normal')(layer_h13) # 卷积层
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layer_h15 = MaxPooling2D(pool_size=1, strides=None, padding="valid")(layer_h14) # 池化层
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#test=Model(inputs = input_data, outputs = layer_h12)
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#test.summary()
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layer_h16 = Reshape((-1, 3200))(layer_h15) #Reshape层
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#layer_h5 = LSTM(256, activation='relu', use_bias=True, return_sequences=True)(layer_h4) # LSTM层
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#layer_h6 = Dropout(0.2)(layer_h5) # 随机中断部分神经网络连接，防止过拟合
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layer_h16 = Dropout(0.3)(layer_h16)
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layer_h17 = Dense(128, activation="relu", use_bias=True, kernel_initializer='he_normal')(layer_h16) # 全连接层
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layer_h17 = Dropout(0.3)(layer_h17)
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layer_h18 = Dense(self.MS_OUTPUT_SIZE, use_bias=True, kernel_initializer='he_normal')(layer_h17) # 全连接层
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y_pred = Activation('softmax', name='Activation0')(layer_h18)
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model_data = Model(inputs = input_data, outputs = y_pred)
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#model_data.summary()
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labels = Input(name='the_labels', shape=[self.label_max_string_length], dtype='float32')
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input_length = Input(name='input_length', shape=[1], dtype='int64')
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label_length = Input(name='label_length', shape=[1], dtype='int64')
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# Keras doesn't currently support loss funcs with extra parameters
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# so CTC loss is implemented in a lambda layer
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#layer_out = Lambda(ctc_lambda_func,output_shape=(self.MS_OUTPUT_SIZE, ), name='ctc')([y_pred, labels, input_length, label_length])#(layer_h6) # CTC
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loss_out = Lambda(self.ctc_lambda_func, output_shape=(1,), name='ctc')([y_pred, labels, input_length, label_length])
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model = Model(inputs=[input_data, labels, input_length, label_length], outputs=loss_out)
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model.summary()
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# clipnorm seems to speeds up convergence
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#sgd = SGD(lr=0.0001, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5)
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#opt = Adadelta(lr = 0.01, rho = 0.95, epsilon = 1e-06)
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opt = Adam(lr = 0.001, beta_1 = 0.9, beta_2 = 0.999, decay = 0.0, epsilon = 10e-8)
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#model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=sgd)
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model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer = opt)
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# captures output of softmax so we can decode the output during visualization
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test_func = K.function([input_data], [y_pred])
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#print('[*提示] 创建模型成功，模型编译成功')
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print('[*Info] Create Model Successful, Compiles Model Successful. ')
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return model, model_data
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def ctc_lambda_func(self, args):
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y_pred, labels, input_length, label_length = args
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y_pred = y_pred[:, :, :]
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#y_pred = y_pred[:, 2:, :]
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return K.ctc_batch_cost(labels, y_pred, input_length, label_length)
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def TrainModel(self, datapath, epoch = 2, save_step = 1000, batch_size = 32, filename = abspath + 'model_speech/m' + ModelName + '/speech_model'+ModelName):
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'''
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训练模型
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参数：
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datapath: 数据保存的路径
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epoch: 迭代轮数
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save_step: 每多少步保存一次模型
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filename: 默认保存文件名，不含文件后缀名
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'''
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data=DataSpeech(datapath, 'train')
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num_data = data.GetDataNum() # 获取数据的数量
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yielddatas = data.data_genetator(batch_size, self.AUDIO_LENGTH)
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for epoch in range(epoch): # 迭代轮数
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print('[running] train epoch %d .' % epoch)
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n_step = 0 # 迭代数据数
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while True:
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try:
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print('[message] epoch %d . Have train datas %d+'%(epoch, n_step*save_step))
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# data_genetator是一个生成器函数
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#self._model.fit_generator(yielddatas, save_step, nb_worker=2)
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self._model.fit_generator(yielddatas, save_step)
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n_step += 1
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except StopIteration:
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print('[error] generator error. please check data format.')
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break
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self.SaveModel(comment='_e_'+str(epoch)+'_step_'+str(n_step * save_step))
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self.TestModel(self.datapath, str_dataset='train', data_count = 4)
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self.TestModel(self.datapath, str_dataset='dev', data_count = 4)
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def LoadModel(self,filename = abspath + 'model_speech/m'+ModelName+'/speech_model'+ModelName+'.model'):
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'''
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加载模型参数
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'''
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self._model.load_weights(filename)
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self.base_model.load_weights(filename + '.base')
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def SaveModel(self,filename = abspath + 'model_speech/m'+ModelName+'/speech_model'+ModelName,comment=''):
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'''
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保存模型参数
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'''
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self._model.save_weights(filename + comment + '.model')
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self.base_model.save_weights(filename + comment + '.model.base')
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# 需要安装 hdf5 模块
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self._model.save(filename + comment + '.h5')
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self.base_model.save(filename + comment + '.base.h5')
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f = open('step'+ModelName+'.txt','w')
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f.write(filename+comment)
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f.close()
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def TestModel(self, datapath='', str_dataset='dev', data_count = 32, out_report = False, show_ratio = True, io_step_print = 10, io_step_file = 10):
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'''
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测试检验模型效果
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io_step_print
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为了减少测试时标准输出的io开销，可以通过调整这个参数来实现
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io_step_file
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为了减少测试时文件读写的io开销，可以通过调整这个参数来实现
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'''
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data=DataSpeech(self.datapath, str_dataset)
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#data.LoadDataList(str_dataset)
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num_data = data.GetDataNum() # 获取数据的数量
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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
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data_count = num_data
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try:
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ran_num = random.randint(0,num_data - 1) # 获取一个随机数
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words_num = 0
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word_error_num = 0
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nowtime = time.strftime('%Y%m%d_%H%M%S',time.localtime(time.time()))
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if(out_report == True):
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txt_obj = open('Test_Report_' + str_dataset + '_' + nowtime + '.txt', 'w', encoding='UTF-8') # 打开文件并读入
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txt = '测试报告\n模型编号 ' + ModelName + '\n\n'
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for i in range(data_count):
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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
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# 数据格式出错处理 开始
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# 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
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num_bias = 0
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while(data_input.shape[0] > self.AUDIO_LENGTH):
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print('*[Error]','wave data lenghth of num',(ran_num + i) % num_data, 'is too long.','\n A Exception raise when test Speech Model.')
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num_bias += 1
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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
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# 数据格式出错处理 结束
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pre = self.Predict(data_input, data_input.shape[0] // 8)
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words_n = data_labels.shape[0] # 获取每个句子的字数
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words_num += words_n # 把句子的总字数加上
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edit_distance = GetEditDistance(data_labels, pre) # 获取编辑距离
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if(edit_distance <= words_n): # 当编辑距离小于等于句子字数时
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word_error_num += edit_distance # 使用编辑距离作为错误字数
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else: # 否则肯定是增加了一堆乱七八糟的奇奇怪怪的字
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word_error_num += words_n # 就直接加句子本来的总字数就好了
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if((i % io_step_print == 0 or i == data_count - 1) and show_ratio == True):
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#print('测试进度：',i,'/',data_count)
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print('Test Count: ',i,'/',data_count)
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if(out_report == True):
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if(i % io_step_file == 0 or i == data_count - 1):
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txt_obj.write(txt)
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txt = ''
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txt += str(i) + '\n'
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txt += 'True:\t' + str(data_labels) + '\n'
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txt += 'Pred:\t' + str(pre) + '\n'
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txt += '\n'
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#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, '%')
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print('*[Test Result] Speech Recognition ' + str_dataset + ' set word error ratio: ', word_error_num / words_num * 100, '%')
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if(out_report == True):
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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) + ' %'
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txt_obj.write(txt)
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txt = ''
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txt_obj.close()
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except StopIteration:
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print('[Error] Model Test Error. please check data format.')
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def Predict(self, data_input, input_len):
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'''
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预测结果
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返回语音识别后的拼音符号列表
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'''
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batch_size = 1
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in_len = np.zeros((batch_size),dtype = np.int32)
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in_len[0] = input_len
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x_in = np.zeros((batch_size, 1600, self.AUDIO_FEATURE_LENGTH, 1), dtype=np.float)
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for i in range(batch_size):
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x_in[i,0:len(data_input)] = data_input
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base_pred = self.base_model.predict(x = x_in)
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#print('base_pred:\n', base_pred)
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#y_p = base_pred
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#for j in range(200):
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# mean = np.sum(y_p[0][j]) / y_p[0][j].shape[0]
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# 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])
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# print('argmin:',np.argmin(y_p[0][j]),'argmax:',np.argmax(y_p[0][j]))
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# count=0
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# for i in range(y_p[0][j].shape[0]):
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# if(y_p[0][j][i] < mean):
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# count += 1
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# print('count:',count)
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base_pred =base_pred[:, :, :]
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#base_pred =base_pred[:, 2:, :]
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r = K.ctc_decode(base_pred, in_len, greedy = True, beam_width=100, top_paths=1)
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#print('r', r)
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r1 = K.get_value(r[0][0])
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#print('r1', r1)
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#r2 = K.get_value(r[1])
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#print(r2)
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r1=r1[0]
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return r1
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pass
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def RecognizeSpeech(self, wavsignal, fs):
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'''
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最终做语音识别用的函数，识别一个wav序列的语音
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'''
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#data = self.data
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#data = DataSpeech('E:\\语音数据集')
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#data.LoadDataList('dev')
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# 获取输入特征
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#data_input = GetMfccFeature(wavsignal, fs)
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#t0=time.time()
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data_input = GetFrequencyFeature3(wavsignal, fs)
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#t1=time.time()
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#print('time cost:',t1-t0)
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input_length = len(data_input)
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input_length = input_length // 8
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data_input = np.array(data_input, dtype = np.float)
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#print(data_input,data_input.shape)
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data_input = data_input.reshape(data_input.shape[0],data_input.shape[1],1)
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#t2=time.time()
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r1 = self.Predict(data_input, input_length)
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#t3=time.time()
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#print('time cost:',t3-t2)
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list_symbol_dic = GetSymbolList(self.datapath) # 获取拼音列表
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r_str=[]
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for i in r1:
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r_str.append(list_symbol_dic[i])
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return r_str
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pass
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def RecognizeSpeech_FromFile(self, filename):
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'''
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最终做语音识别用的函数，识别指定文件名的语音
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'''
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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95%的GPU
|
||
#config = tf.ConfigProto()
|
||
#config.gpu_options.per_process_gpu_memory_fraction = 0.95
|
||
#config.gpu_options.allow_growth=True #不全部占满显存, 按需分配
|
||
#set_session(tf.Session(config=config))
|
||
|
||
|
||
datapath = abspath + ''
|
||
modelpath = abspath + '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 = abspath + 'dataset'
|
||
modelpath = modelpath + '/'
|
||
else:
|
||
print('*[Message] Unknown System\n')
|
||
datapath = 'dataset'
|
||
modelpath = modelpath + '/'
|
||
|
||
ms = ModelSpeech(datapath)
|
||
|
||
|
||
#ms.LoadModel(modelpath + 'm251/speech_model251_e_0_step_100000.model')
|
||
#ms.TrainModel(datapath, epoch = 50, batch_size = 16, save_step = 500)
|
||
|
||
#t1=time.time()
|
||
#ms.TestModel(datapath, str_dataset='train', data_count = 128, out_report = True)
|
||
#ms.TestModel(datapath, str_dataset='dev', data_count = 128, out_report = True)
|
||
#ms.TestModel(datapath, str_dataset='test', data_count = 128, out_report = True)
|
||
#t2=time.time()
|
||
#print('Test Model Time Cost:',t2-t1,'s')
|
||
#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)
|