diff --git a/SpeechModel.py b/SpeechModel.py
index 992f3bf..a8649cd 100644
--- a/SpeechModel.py
+++ b/SpeechModel.py
@@ -8,10 +8,12 @@ import os
 
 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 # , Flatten,LSTM,Convolution1D,MaxPooling1D,Merge
@@ -174,6 +176,7 @@ class ModelSpeech(): # 语音模型类
 					break
 				
 				self.SaveModel(comment='_e_'+str(epoch)+'_step_'+str(n_step * save_step))
+				ms.TestModel(self.datapath, str_dataset='dev', data_count = 16)
 				
 				
 	def LoadModel(self, filename = 'model_speech/speech_model_e_0_step_1.model'):
@@ -203,16 +206,22 @@ class ModelSpeech(): # 语音模型类
 			data_count = num_data
 		
 		try:
-			gen = data.data_genetator(data_count)
-			#for i in range(1):
-			#	[X, y, input_length, label_length ], labels = gen
-			#r = self._model.test_on_batch([X, y, input_length, label_length ], labels)
-			r = self._model.evaluate_generator(generator = gen, steps = 1, max_queue_size = data_count, workers = 1, use_multiprocessing = False)
-			print(r)
+			ran_num = random.randint(0,num_data - 1) # 获取一个随机数
+			
+			words_num = 0
+			word_error_num = 0
+			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
+				pre = self.Predict(data_input, data_input.shape[0] // 4)
+				
+				words_num += max(data_labels.shape[0], pre.shape[0])
+				word_error_num += GetEditDistance(data_labels, pre)
+			
+			print('*[测试结果] 语音识别语音单字错误率：', word_error_num / words_num * 100, '%')
 		except StopIteration:
 			print('[Error] Model Test Error. please check data format.')
 
-	def Predict(self, batch_size, data_input, in_len):
+	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
@@ -220,8 +229,8 @@ class ModelSpeech(): # 语音模型类
 		batch_size = 1 
 		
 		in_len = np.zeros((batch_size),dtype = np.int32)
-		print(in_len.shape)
-		in_len[0] = in_len[0] - 2
+		#print(in_len.shape)
+		in_len[0] = input_len - 2
 		
 		
 		x_in = np.zeros((batch_size, 1600, 200), dtype=np.float)
@@ -230,10 +239,10 @@ class ModelSpeech(): # 语音模型类
 			x_in[i,0:len(data_input)] = data_input
 		
 		base_pred = self.base_model.predict(x = x_in)
-		print('base_pred:\n', base_pred)
+		#print('base_pred:\n', base_pred)
 		
 		y_p = base_pred
-		print('base_pred0:\n',base_pred[0][0].shape)
+		#print('base_pred0:\n',base_pred[0][0].shape)
 		
 		#for j in range(200):
 		#	mean = np.sum(y_p[0][j]) / y_p[0][j].shape[0]
@@ -247,48 +256,27 @@ class ModelSpeech(): # 语音模型类
 		
 		base_pred =base_pred[:, 2:, :]
 		r = K.ctc_decode(base_pred, in_len, greedy = True, beam_width=100, top_paths=1)
-		print('r', r)
+		#print('r', r)
 		#r = K.cast(r[0][0], dtype='float32')
 		#print('r1', r)
 		#print('解码完成')
 		
 		r1 = K.get_value(r[0][0])
-		print('r1', r1)
+		#print('r1', r1)
 		
-		print('r0', r[1])
+		#print('r0', r[1])
 		r2 = K.get_value(r[1])
-		print(r2)
-		print('解码完成')
+		#print('r2', r2)
+		#print('解码完成')
 		list_symbol_dic = GetSymbolList(self.datapath) # 获取拼音列表
 		
 		r1=r1[0]
 		
-		r_str=[]
-		for i in r1:
-			r_str.append(list_symbol_dic[i])
-		
-		#print(r_str)
-		
-		return r_str
+		return r1
 		pass
 	
-	def show_edit_distance(self, num):
-		num_left = num
-		mean_norm_ed = 0.0
-		mean_ed = 0.0
-		while num_left > 0:
-			word_batch = next(self.text_img_gen)[0]
-			num_proc = min(word_batch['the_input'].shape[0], num_left)
-			decoded_res = decode_batch(self.test_func, word_batch['the_input'][0:num_proc])
-			for j in range(num_proc):
-				edit_dist = editdistance.eval(decoded_res[j], word_batch['source_str'][j])
-				mean_ed += float(edit_dist)
-				mean_norm_ed += float(edit_dist) / len(word_batch['source_str'][j])
-			num_left -= num_proc
-		mean_norm_ed = mean_norm_ed / num
-		mean_ed = mean_ed / num
-		print('\nOut of %d samples:  Mean edit distance: %.3f Mean normalized edit distance: %0.3f'
-				% (num, mean_ed, mean_norm_ed))
+	
+   
 	
 	def RecognizeSpeech(self, wavsignal, fs):
 		'''
@@ -305,9 +293,15 @@ class ModelSpeech(): # 语音模型类
 		data_input = np.array(data_input, dtype = np.float)
 		
 		
-		r = self.Predict(1, data_input, input_length)
+		r1 = self.Predict(data_input, input_length)
 		
-		return r
+		r_str=[]
+		for i in r1:
+			r_str.append(list_symbol_dic[i])
+		
+		#print(r_str)
+		
+		return r_str
 		pass
 		
 	def RecognizeSpeech_FromFile(self, filename):
@@ -350,8 +344,8 @@ if(__name__=='__main__'):
 	
 	ms = ModelSpeech(datapath)
 	
-	#ms.LoadModel(modelpath + 'speech_model_e_0_step_1.model')
-	ms.TrainModel(datapath, epoch = 2, batch_size = 8, save_step = 1)
-	#ms.TestModel(datapath, str_dataset='dev', data_count = 32)
+	ms.LoadModel(modelpath + 'm1\\speech_model_e_1_step_100.model')
+	#ms.TrainModel(datapath, epoch = 2, batch_size = 8, save_step = 1)
+	ms.TestModel(datapath, str_dataset='dev', data_count = 8)
 	#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)
diff --git a/general_function/gen_func.py b/general_function/gen_func.py
new file mode 100644
index 0000000..ffd8e13
--- /dev/null
+++ b/general_function/gen_func.py
@@ -0,0 +1,22 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+'''
+一些通用函数
+'''
+
+import difflib
+
+def GetEditDistance(str1, str2):
+		leven_cost = 0
+		s = difflib.SequenceMatcher(None, str1, str2)
+		for tag, i1, i2, j1, j2 in s.get_opcodes():
+			#print('{:7} a[{}: {}] --> b[{}: {}] {} --> {}'.format(tag, i1, i2, j1, j2, str1[i1: i2], str2[j1: j2]))
+			
+			if tag == 'replace':
+				leven_cost += max(i2-i1, j2-j1)
+			elif tag == 'insert':
+				leven_cost += (j2-j1)
+			elif tag == 'delete':
+				leven_cost += (i2-i1)
+		return leven_cost
\ No newline at end of file