diff --git a/speech_data.py b/speech_data.py
index 48d2ad8..8b3cd84 100644
--- a/speech_data.py
+++ b/speech_data.py
@@ -143,15 +143,16 @@ def read_siamese_tfrecords_generator(audio_group='audio',batch_size=32,test_size
     output_class = []
     const_file = os.path.join('./outputs',audio_group+'.constants')
     (n_spec,n_features,n_records) = pickle.load(open(const_file,'rb'))
-    print('reading tfrecords({}-train)...'.format(audio_group))
 
     # @threadsafe_iter
     def record_generator():
+        print('reading tfrecords({}-train)...'.format(audio_group))
         input_data = []
         output_data = []
         while True:
-            record_iterator = tf.python_io.tf_record_iterator(path=records_file)
-            #tqdm(enumerate(record_iterator),total=n_records)
+            record_iterator,records_count = record_generator_count(records_file)
+            #tqdm(enumerate(record_iterator),total=records_count)
+            #enumerate(record_iterator)
             for (i,string_record) in enumerate(record_iterator):
                 example = tf.train.Example()
                 example.ParseFromString(string_record)
@@ -173,11 +174,9 @@ def read_siamese_tfrecords_generator(audio_group='audio',batch_size=32,test_size
                     output_data = []
 
     # Read test in one-shot
-    te_records_file  = os.path.join('./outputs',audio_group+'.test.tfrecords')
-    te_re_iterator = tf.python_io.tf_record_iterator(path=records_file)
-    te_n_records = len([i for i in te_re_iterator])
-    te_re_iterator = tf.python_io.tf_record_iterator(path=records_file)
     print('reading tfrecords({}-test)...'.format(audio_group))
+    te_records_file  = os.path.join('./outputs',audio_group+'.test.tfrecords')
+    te_re_iterator,te_n_records = record_generator_count(records_file)
     test_size = min([test_size,te_n_records]) if test_size > 0 else te_n_records
     input_data = np.zeros((test_size,2,n_spec,n_features))
     output_data = np.zeros((test_size,2))
@@ -204,7 +203,9 @@ def audio_samples_word_count(audio_group='audio'):
 
 def record_generator_count(records_file):
     record_iterator = tf.python_io.tf_record_iterator(path=records_file)
-    count = len([i for i in record_iterator])
+    count = 0
+    for i in record_iterator:
+        count+=1
     record_iterator = tf.python_io.tf_record_iterator(path=records_file)
     return record_iterator,count
 
@@ -248,7 +249,7 @@ if __name__ == '__main__':
     # create_spectrogram_tfrecords('story_all',sample_count=25)
     # fix_csv('story_words_test')
     #fix_csv('story_phrases')
-    create_spectrogram_tfrecords('story_phrases',sample_count=10,train_test_ratio=0.1)
+    create_spectrogram_tfrecords('story_phrases',sample_count=0,train_test_ratio=0.1)
     # create_spectrogram_tfrecords('audio',sample_count=50)
     # read_siamese_tfrecords_generator('audio')
     # padd_zeros_siamese_tfrecords('audio')
diff --git a/speech_test.py b/speech_test.py
index 32b330c..d2ab72b 100644
--- a/speech_test.py
+++ b/speech_test.py
@@ -29,14 +29,13 @@ def test_with(audio_group):
 def evaluate_siamese(records_file,audio_group='audio',weights = 'siamese_speech_model-final.h5'):
     # audio_group='audio';model_file = 'siamese_speech_model-305-epoch-0.20-acc.h5'
     # records_file  = os.path.join('./outputs',eval_group+'.train.tfrecords')
-    const_file = os.path.join('./outputs',audio_group+'.constants')
+    const_file = os.path.join('./models/'+audio_group+'/',audio_group+'.constants')
     arch_file='./models/'+audio_group+'/siamese_speech_model_arch.yaml'
     weight_file='./models/'+audio_group+'/'+weights
     (n_spec,n_features,n_records) = pickle.load(open(const_file,'rb'))
     print('evaluating {}...'.format(records_file))
     model = load_model_arch(arch_file)
     # model = siamese_model((n_spec, n_features))
-    n_spec = 422
     model.load_weights(weight_file)
     record_iterator,records_count = record_generator_count(records_file)
     total,same_success,diff_success,skipped,same_failed,diff_failed = 0,0,0,0,0,0
@@ -179,9 +178,9 @@ 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_words_test.train.tfrecords',audio_group='story_phrases',weights ='siamese_speech_model-231-epoch-0.00-acc.h5')
     # play_results('story_words')
-    inspect_tfrecord('./outputs/story_phrases.test.tfrecords',audio_group='story_phrases')
+    #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)