diff --git a/evaluate_speech_model.py b/evaluate_speech_model.py
index 5d07ebc..2093ef4 100644
--- a/evaluate_speech_model.py
+++ b/evaluate_speech_model.py
@@ -32,18 +32,19 @@ from speech_features import Spectrogram
 
 os.environ["CUDA_VISIBLE_DEVICES"] = "0"
 
-audio_length = 1600
-audio_feature_length = 200
-channels = 1
+AUDIO_LENGTH = 1600
+AUDIO_FEATURE_LENGTH = 200
+CHANNELS = 1
 # 默认输出的拼音的表示大小是1428，即1427个拼音+1个空白块
-output_size = 1428
+OUTPUT_SIZE = 1428
 sm251 = SpeechModel251(
-    input_shape=(audio_length, audio_feature_length, channels),
-    output_size=output_size
+    input_shape=(AUDIO_LENGTH, AUDIO_FEATURE_LENGTH, CHANNELS),
+    output_size=OUTPUT_SIZE
     )
 feat = Spectrogram()
 evalue_data = DataLoader('dev')
 ms = ModelSpeech(sm251, feat, max_label_length=64)
 
 ms.load_model('save_models/' + sm251.get_model_name() + '.h5')
-ms.evaluate_model(data_loader=evalue_data, data_count=-1, out_report=True, show_ratio=True, show_per_step=100)
+ms.evaluate_model(data_loader=evalue_data, data_count=-1,
+    out_report=True, show_ratio=True, show_per_step=100)
diff --git a/train_speech_model.py b/train_speech_model.py
index 8efd99e..0ac541b 100644
--- a/train_speech_model.py
+++ b/train_speech_model.py
@@ -34,14 +34,14 @@ from speech_features import Spectrogram
 
 os.environ["CUDA_VISIBLE_DEVICES"] = "0"
 
-audio_length = 1600
-audio_feature_length = 200
-channels = 1
+AUDIO_LENGTH = 1600
+AUDIO_FEATURE_LENGTH = 200
+CHANNELS = 1
 # 默认输出的拼音的表示大小是1428，即1427个拼音+1个空白块
-output_size = 1428
+OUTPUT_SIZE = 1428
 sm251 = SpeechModel251(
-    input_shape=(audio_length, audio_feature_length, channels),
-    output_size=output_size
+    input_shape=(AUDIO_LENGTH, AUDIO_FEATURE_LENGTH, CHANNELS),
+    output_size=OUTPUT_SIZE
     )
 feat = Spectrogram()
 train_data = DataLoader('train')
@@ -49,5 +49,6 @@ opt = Adam(lr = 0.0001, beta_1 = 0.9, beta_2 = 0.999, decay = 0.0, epsilon = 10e
 ms = ModelSpeech(sm251, feat, max_label_length=64)
 
 #ms.load_model('save_models/' + sm251.get_model_name() + '.h5')
-ms.train_model(optimizer=opt, data_loader=train_data, epochs=1, save_step=1, batch_size=16, last_epoch=0)
+ms.train_model(optimizer=opt, data_loader=train_data,
+    epochs=1, save_step=1, batch_size=16, last_epoch=0)
 ms.save_model('save_models/' + sm251.get_model_name())