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speech_model_05.py
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speech_model_05.py
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#-*- coding:utf-8 -*-
#author:zhangwei
'''
此脚本是用来测试8层dcnn模型，模型的结构架构为32-32-64-64-128-128-128-128-pool-512-1422
'''
from general_function.file_wav import *
from general_function.file_wav import *
from general_function.file_dict import *
from general_function.feature_extract import *
from general_function.edit_distance import *
from SENET.se_block import squeeze_excite_block , squeeze_excite_block_up
import keras as kr
import numpy as np
import random
from keras.utils import plot_model
from keras.models import Model
from keras.layers import Dense , Dropout , Input , Reshape , multiply
from keras.layers import Conv2D , MaxPooling2D , Lambda , Activation , regularizers
from keras.layers.normalization import BatchNormalization
from keras import backend as K
from keras.optimizers import SGD , Adadelta , Adam
from SENET.se_block import squeeze_excite_block , squeeze_excite_block_up
from readdata_05 import DataSpeech
class ModelSpeech():
def __init__(self , datapath):
MS_OUTPUT_SIZE = 1422
self.MS_OUTPUT_SIZE = MS_OUTPUT_SIZE
self.label_max_string_length = 64
self.AUDIO_LENGTH = 1600
self.AUDIO_FEATURE_LENGTH = 200
self.datapath = datapath
self._model , self.base_model = self.creat_model()
self.slash = '/'
if self.datapath[-1] != self.slash:
self.datapath = self.datapath + self.slash
pass
def creat_model(self):
input_data = Input(shape=[self.AUDIO_LENGTH , self.AUDIO_FEATURE_LENGTH , 1] , name='Input')
conv1 = Conv2D(filters=32 , kernel_size=[3,3] , padding='same' , activation='relu' , use_bias=True , kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(10e-7))(input_data)
conv1 = BatchNormalization(epsilon=0.0002)(conv1)
conv2 = Conv2D(filters=32 , kernel_size=[3,3] , padding='same' , activation='relu' , use_bias=True , kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(10e-7))(conv1)
conv2 = BatchNormalization(epsilon=0.0002)(conv2)
# conv2_se = squeeze_excite_block_up(input=conv2, ratio=4)
maxpool1 = MaxPooling2D(pool_size=[2,2] , strides=None , padding='valid')(conv2)
maxpool1 = Dropout(0.5)(maxpool1)
conv3 = Conv2D(filters=64 , kernel_size=[3,3] , padding='same' , activation='relu' , use_bias=True , kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(10e-7))(maxpool1)
conv3 = BatchNormalization(epsilon=0.0002)(conv3)
conv4 = Conv2D(filters=64 , kernel_size=[3,3] , padding='same' , activation='relu' , use_bias=True , kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(10e-7))(conv3)
conv4 = BatchNormalization(epsilon=0.0002)(conv4)
# conv4_se = squeeze_excite_block_up(input=conv4, ratio=4)
maxpool2 = MaxPooling2D(pool_size=[2,2] , strides=None , padding='valid')(conv4)
maxpool2 = Dropout(0.5)(maxpool2)
conv5 = Conv2D(filters=128 , kernel_size=[3,3] , padding='same' , activation='relu' , use_bias=True , kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(10e-7))(maxpool2)
conv5 = BatchNormalization(epsilon=0.0002)(conv5)
conv6 = Conv2D(filters=128 , kernel_size=[3,3] , padding='same' , activation='relu' , use_bias=True , kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(10e-7))(conv5)
conv6 = BatchNormalization(epsilon=0.0002)(conv6)
# conv6_se = squeeze_excite_block_up(input=conv6, ratio=4)
maxpool3 = MaxPooling2D(pool_size=[2,2] , strides=None , padding='valid')(conv6)
maxpool3 = Dropout(0.5)(maxpool3)
conv7 = Conv2D(filters=128 , kernel_size=[3, 3], padding='same', activation='relu', use_bias=True , kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(10e-7))(maxpool3)
conv7 = BatchNormalization(epsilon=0.0002)(conv7)
conv8 = Conv2D(filters=128 , kernel_size=[3, 3], padding='same', activation='relu', use_bias=True , kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(10e-7))(conv7)
conv8 = BatchNormalization(epsilon=0.0002)(conv8)
# conv8_se = squeeze_excite_block_up(input=conv8 , ratio=4)
maxpool4 = MaxPooling2D(pool_size=[2, 2], strides=None, padding='valid')(conv8)
maxpool4 = Dropout(0.5)(maxpool4)
reshape = Reshape([100 , 1536])(maxpool4)
dense1 = Dense(units=512 , activation='relu' , use_bias=True , kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(10e-7))(reshape)
dense1 = BatchNormalization(epsilon=0.0002)(dense1)
dense1 = Dropout(0.5)(dense1)
# dense1 = Dense(units=512, activation='relu', use_bias=True, kernel_initializer='he_normal')(reshape)
# attention_prob = Dense(units=512, activation='softmax', name='attention_vec')(dense1)
# attention_mul = multiply([dense1, attention_prob])
#
# dense1 = BatchNormalization(epsilon=0.0002)(attention_mul)
# dense1 = Dropout(0.3)(dense1)
dense2 = Dense(units=self.MS_OUTPUT_SIZE , use_bias=True , kernel_initializer='he_uniform', kernel_regularizer=regularizers.l2(1e-7))(dense1)
y_pred = Activation(activation='softmax' , name='activation')(dense2)
model_data = Model(inputs=input_data , outputs=y_pred)
model_data.summary()
labels = Input(shape=[self.label_max_string_length], name='labels', dtype='float32')
input_length = Input(shape=[1], name='input_length', dtype='int64')
label_length = Input(shape=[1], name='label_length', dtype='int64')
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()
sgd = SGD(lr=0.0001, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5)
ada_d = Adadelta(lr=0.001, rho=0.95, epsilon=1e-6)
adam = Adam(lr=0.00005 , epsilon=1e-6, decay=10e-3)
model.compile(optimizer=adam , loss={'ctc': lambda y_true, y_pred: y_pred})
print('==========================模型创建成功=================================')
return model, model_data
def ctc_lambda_func(self , args):
y_pred , labels , input_length , label_length = args
y_pred = y_pred[: , : , :]
return K.ctc_batch_cost(y_true=labels , y_pred=y_pred , input_length=input_length , label_length=label_length)
def train_model(self , datapath , epoch=4 , save_step=2000 , batch_size=8):
data = DataSpeech(datapath , 'train')
num_data = data.get_datanum()
yielddatas = data.data_generator(batch_size , self.AUDIO_LENGTH)
for epoch in range(epoch):
print('[*running] train epoch %d .' % epoch)
n_step = 0
while True:
try:
print('[*message] epoch %d , Having training data %d+' % (epoch , n_step * save_step))
self._model.fit_generator(yielddatas , save_step)
n_step += 1
except StopIteration:
print('======================Error StopIteration==============================')
break
self.save_model(comments='_e_' + str(epoch) + '_step_' + str(n_step * save_step))
self.test_model(datapath=self.datapath , str_dataset='train' , data_count=4)
self.test_model(datapath=self.datapath , str_dataset='dev' , data_count=16)
def load_model(self , filename='model_speech_e_0_step_16000.model'):
self._model.load_weights(filename)
self.base_model.load_weights(filename + '.base')
def test_model(self , datapath='' , str_dataset='dev' , data_count=1):
data = DataSpeech(self.datapath , str_dataset)
num_data = data.get_datanum()
# print num_data
if data_count <=0 and data_count > num_data:
data_count = num_data
try:
ran_num = random.randint(0 , num_data - 1)
words_num = 0.
word_error_num = 0.
for i in range(data_count):
data_input , data_labels = data.get_data((ran_num + i) % num_data)
# print data_input
num_bias = 0
while data_input.shape[0] > self.AUDIO_LENGTH:
print('[*Error] data input is too long %d' % ((ran_num + i) % num_data))
num_bias += 1
data_input , data_labels = data.get_data((ran_num + i + num_bias) % num_data)
pre = self.predict(data_input=data_input , input_len=data_input.shape[0] // 16)
words_n = data_labels.shape[0]
words_num += words_n
edit_distance = get_edit_distance(data_labels , pre)
if edit_distance <= words_n:
word_error_num += edit_distance
else:
word_error_num += words_n
# print type(words_num)
print('[*Test Result] Speech Recognition ' + str_dataset + ' set word error ratio : ' + str(word_error_num / words_num * 100) , '%')
except StopIteration:
print('=======================Error StopIteration 01======================')
def save_model(self , filename='/home/zhangwei/speech_model/speech_model' , comments=''):
self._model.save_weights(filename + comments + '.model')
self.base_model.save_weights(filename + comments + '.model.base')
f = open('steps24.txt' , 'w')
f.write(filename + comments)
f.close()
def predict(self , data_input , input_len):
batch_size = 1
in_len = np.zeros((batch_size) , dtype=np.int32)
in_len[0] = input_len
x_in = np.zeros(shape=[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)
base_pred = base_pred[: , : , :]
r = K.ctc_decode(base_pred , in_len , greedy=True , beam_width=100 , top_paths=1)
r1 = K.get_value(r[0][0])
r1 = r1[0]
return r1
def redognize_speech(self , wavsignal , fs):
data_input = get_frequency_feature(wavsignal , fs)
input_length = len(data_input)
input_length = input_length // 16
data_input = np.array(data_input , dtype=np.float)
data_input = data_input.reshape(data_input.shape[0] , data_input.shape[1] , 1)
r1 = self.predict(data_input , input_length)
# print r1
list_symbol_dic = get_list_symbol(self.datapath)
r_str = []
for i in r1:
r_str.append(list_symbol_dic[i])
return r_str
def recognize_speech_fromfile(self , filename):
wavsignal , fs = read_wav_data(filename)
r = self.redognize_speech(wavsignal , fs)
return r
if __name__ == '__main__':
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.90
set_session(tf.Session(config=config))
datapath = '/home/zhangwei/PycharmProjects/ASR_Thchs30/data_list/'
speech = ModelSpeech(datapath=datapath)
# speech.creat_model()
speech.train_model(datapath=datapath)