diff --git a/speech_data.py b/speech_data.py
index 9dadb67..83b5d6d 100644
--- a/speech_data.py
+++ b/speech_data.py
@@ -26,7 +26,7 @@ def siamese_pairs(rightGroup, wrongGroup):
     random.shuffle(rightRightPairs)
     # return (random.sample(same,10), random.sample(diff,10))
     # return rightRightPairs[:10],rightWrongPairs[:10]
-    return rightRightPairs[:32],rightWrongPairs[:32]
+    return rightRightPairs[:16],rightWrongPairs[:16]
     # return rightRightPairs,rightWrongPairs
 
 def create_spectrogram_tfrecords(audio_group='audio'):
@@ -42,9 +42,9 @@ def create_spectrogram_tfrecords(audio_group='audio'):
     audio_samples['file_path'] = audio_samples.loc[:, 'file'].apply(lambda x: 'outputs/' + audio_group + '/' + x)
     audio_samples['file_exists'] = apply_by_multiprocessing(audio_samples['file_path'], os.path.exists)
     audio_samples = audio_samples[audio_samples['file_exists'] == True].reset_index()
-    audio_samples['rate_int'] = apply_by_multiprocessing(audio_samples['rate'], str.isdigit)
-    audio_samples = audio_samples[audio_samples['rate_int'] == True].reset_index().drop(['level_0'],axis=1)
-    audio_samples['rate'] = audio_samples['rate'].astype(int)
+    # audio_samples['rate_int'] = apply_by_multiprocessing(audio_samples['rate'], str.isdigit)
+    # audio_samples = audio_samples[audio_samples['rate_int'] == True].reset_index().drop(['level_0'],axis=1)
+    # audio_samples['rate'] = audio_samples['rate'].astype(int)
     def _float_feature(value):
       return tf.train.Feature(float_list=tf.train.FloatList(value=value))
 
@@ -131,22 +131,26 @@ def audio_samples_word_count(audio_group='audio'):
     return len(audio_samples.groupby(audio_samples['word']))
 
 def fix_csv(audio_group='audio'):
-    audio_group = 'story_all'
-    audio_samples = pd.read_csv( './outputs/story_words.csv'
-        , names=['word','phonemes', 'voice', 'language', 'rate', 'variant', 'file']
-        , quoting=csv.QUOTE_NONE)
-    voice_set = set(audio_samples['voice'].unique().tolist())
     audio_csv_lines = open('./outputs/' + audio_group + '.csv','r').readlines()
     audio_csv_data = [i.strip().split(',') for i in audio_csv_lines]
-    to_be_fixed = [i for i in audio_csv_data if len(i) > 7]
-    def unite_words(entries):
-        entries = to_be_fixed[0]
-        word_entries = next(((entries[:i],entries[i:])  for (i,e) in enumerate(entries) if e in voice_set),'')
-        word_entries[1]
-        return
-    to_be_fixed[0]
-    entries = [unite_words for e in to_be_fixed]
-    [i for i in entries if len(i) % 2 != 0]
+    # audio_samples = pd.read_csv( './outputs/story_words.csv'
+    #     , names=['word','phonemes', 'voice', 'language', 'rate', 'variant', 'file']
+    #     , quoting=csv.QUOTE_NONE)
+    # voice_set = set(audio_samples['voice'].unique().tolist())
+    # to_be_fixed = [i for i in audio_csv_data if len(i) > 7]
+    # def unite_words(entries):
+    #     entries = to_be_fixed[0]
+    #     word_entries = next(((entries[:i],entries[i:])  for (i,e) in enumerate(entries) if e in voice_set),'')
+    #     word_entries[1]
+    #     return
+    # to_be_fixed[0]
+    # entries = [unite_words for e in to_be_fixed]
+    # [i for i in entries if len(i) % 2 != 0]
+    proper_rows = [i for i in audio_csv_data if len(i) == 7]
+    with open('./outputs/' + audio_group + '-new.csv','w') as fixed_csv:
+        fixed_csv_w = csv.writer(fixed_csv, quoting=csv.QUOTE_MINIMAL)
+        fixed_csv_w.writerows(proper_rows)
+
 
 if __name__ == '__main__':
     # sunflower_pairs_data()