diff --git a/speech_test.py b/speech_test.py
index 95cc5da..32b330c 100644
--- a/speech_test.py
+++ b/speech_test.py
@@ -103,6 +103,38 @@ def evaluate_siamese(records_file,audio_group='audio',weights = 'siamese_speech_
     "expected","predicted","success"])
     result_data.to_csv('./outputs/' + audio_group + '.results.csv')
 
+def inspect_tfrecord(records_file,audio_group='audio'):
+    record_iterator,records_count = record_generator_count(records_file)
+    all_results = []
+    for (i,string_record) in tqdm(enumerate(record_iterator),total=records_count):
+        # string_record = next(record_iterator)
+        example = tf.train.Example()
+        example.ParseFromString(string_record)
+        spec_n1 = example.features.feature['spec_n1'].int64_list.value[0]
+        spec_n2 = example.features.feature['spec_n2'].int64_list.value[0]
+        word = example.features.feature['word'].bytes_list.value[0].decode()
+        phoneme1 = example.features.feature['phoneme1'].bytes_list.value[0].decode()
+        phoneme2 = example.features.feature['phoneme2'].bytes_list.value[0].decode()
+        voice1 = example.features.feature['voice1'].bytes_list.value[0].decode()
+        voice2 = example.features.feature['voice2'].bytes_list.value[0].decode()
+        language = example.features.feature['language'].bytes_list.value[0].decode()
+        rate1 = example.features.feature['rate1'].int64_list.value[0]
+        rate2 = example.features.feature['rate2'].int64_list.value[0]
+        variant1 = example.features.feature['variant1'].bytes_list.value[0].decode()
+        variant2 = example.features.feature['variant2'].bytes_list.value[0].decode()
+        file1 = example.features.feature['file1'].bytes_list.value[0].decode()
+        file2 = example.features.feature['file2'].bytes_list.value[0].decode()
+        output_arr = np.asarray([example.features.feature['output'].int64_list.value])
+        expected = output_arr[0]
+        result = {"phoneme1":phoneme1,"phoneme2":phoneme2,"voice1":voice1
+                 ,"voice2":voice2,"rate1":rate1,"rate2":rate2,"spec_n1":spec_n1
+                 ,"spec_n2":spec_n2,"variant1":variant1,"variant2":variant2
+                 ,"file1":file1,"file2":file2,"expected":expected[0]}
+        all_results.append(result)
+    result_data = pd.DataFrame(all_results,columns=["phoneme1","phoneme2"
+    ,"voice1","voice2","rate1","rate2","spec_n1","spec_n2","variant1","variant2","file1","file2",
+    "expected"])
+    result_data.to_csv('./outputs/' + audio_group + '.pairs.csv')
 
 def play_results(audio_group='audio'):
     result_data = pd.read_csv('./outputs/' + audio_group + '.results.csv')
@@ -133,8 +165,10 @@ def play_results(audio_group='audio'):
 def visualize_results(audio_group='audio'):
     # %matplotlib inline
     audio_group = 'story_phrases'
+    source = pd.read_csv('./outputs/'+audio_group+'.pairs.csv',index_col=0)
+    source.groupby(['voice1','voice2']).size()
     result = pd.read_csv('./outputs/' + audio_group + '.results.csv',index_col=0)
-    result.groupby('success').size().plot(kind='bar')
+    # result.groupby('success').size().plot(kind='bar')
     result.describe(include=['object'])
     failed = result[result['success'] == False]
     same_failed = failed[failed['variant1'] == failed['variant2']]
@@ -145,9 +179,10 @@ def visualize_results(audio_group='audio'):
 if __name__ == '__main__':
     # evaluate_siamese('./outputs/story_words_test.train.tfrecords',audio_group='story_words.gpu',weights ='siamese_speech_model-58-epoch-0.00-acc.h5')
     # evaluate_siamese('./outputs/story_words.test.tfrecords',audio_group='story_words',weights ='siamese_speech_model-675-epoch-0.00-acc.h5')
-    evaluate_siamese('./outputs/story_phrases.test.tfrecords',audio_group='story_phrases',weights ='siamese_speech_model-329-epoch-0.00-acc.h5')
+    # evaluate_siamese('./outputs/story_phrases.test.tfrecords',audio_group='story_phrases',weights ='siamese_speech_model-329-epoch-0.00-acc.h5')
     # play_results('story_words')
-    visualize_results('story_words.gpu')
+    inspect_tfrecord('./outputs/story_phrases.test.tfrecords',audio_group='story_phrases')
+    # visualize_results('story_words.gpu')
 # test_with('rand_edu')
 # sunflower_data,sunflower_result = get_word_pairs_data('sweater',15)
 # print(np.argmax(model.predict([sunflower_data[:, 0], sunflower_data[:, 1]]),axis=1))