diff --git a/.gitignore b/.gitignore
index d2d0b11..3ce1b01 100644
--- a/.gitignore
+++ b/.gitignore
@@ -143,3 +143,5 @@ inputs/audio*
 logs/*
 models/*
 *.pkl
+temp/*
+trained/*
diff --git a/speech_data.py b/speech_data.py
index 4df9aa2..7eff4fb 100644
--- a/speech_data.py
+++ b/speech_data.py
@@ -147,7 +147,7 @@ def read_siamese_tfrecords_generator(audio_group='audio',batch_size=32,test_size
     (n_spec,n_features,n_records) = pickle.load(open(const_file,'rb'))
 
     def copy_read_consts(dest_dir):
-        shutil.copy2(const_file,dest_dir)
+        shutil.copy2(const_file,dest_dir+'/constants.pkl')
         return (n_spec,n_features,n_records)
     # @threadsafe_iter
     def record_generator():
@@ -210,15 +210,14 @@ def record_generator_count(records_file):
     record_iterator = tf.python_io.tf_record_iterator(path=records_file)
     count,spec_n = 0,0
     for i in record_iterator:
-        example = tf.train.Example()
-        example.ParseFromString(i)
-        spec_n1 = example.features.feature['spec_n1'].int64_list.value[0]
-        spec_n2 = example.features.feature['spec_n2'].int64_list.value[0]
-        spec_n = max([spec_n,spec_n1,spec_n2])
-        import pdb; pdb.set_trace()
+    #    example = tf.train.Example()
+    #    example.ParseFromString(i)
+    #    spec_n1 = example.features.feature['spec_n1'].int64_list.value[0]
+    #    spec_n2 = example.features.feature['spec_n2'].int64_list.value[0]
+    #    spec_n = max([spec_n,spec_n1,spec_n2])
         count+=1
     record_iterator = tf.python_io.tf_record_iterator(path=records_file)
-    return record_iterator,count,spec_n
+    return record_iterator,count #,spec_n
 
 def fix_csv(audio_group='audio'):
     audio_csv_lines = open('./outputs/' + audio_group + '.csv','r').readlines()
@@ -260,8 +259,8 @@ if __name__ == '__main__':
     # create_spectrogram_tfrecords('story_all',sample_count=25)
     # fix_csv('story_words_test')
     #fix_csv('audio')
-    # create_spectrogram_tfrecords('story_words_test',sample_count=100,train_test_ratio=0.1)
-    record_generator_count()
+    create_spectrogram_tfrecords('story_words_test',sample_count=10,train_test_ratio=0.1)
+    #record_generator_count()
     # create_spectrogram_tfrecords('audio',sample_count=50)
     # read_siamese_tfrecords_generator('audio')
     # padd_zeros_siamese_tfrecords('audio')
diff --git a/speech_model.py b/speech_model.py
index ab9145c..820bf20 100644
--- a/speech_model.py
+++ b/speech_model.py
@@ -114,7 +114,7 @@ def train_siamese(audio_group = 'audio'):
                         , epochs=1000
                         , steps_per_epoch=epoch_n_steps
                         , validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y)
-                        , max_queue_size=32
+                        , max_queue_size=8
                         , callbacks=[tb_cb, cp_cb])
     model.save(model_dir+'/siamese_speech_model-final.h5')
 
@@ -125,4 +125,4 @@ def train_siamese(audio_group = 'audio'):
 
 
 if __name__ == '__main__':
-    train_siamese('story_phrases')
+    train_siamese('story_words_test')
diff --git a/speech_test.py b/speech_test.py
index ddf2d7b..4fcd2fc 100644
--- a/speech_test.py
+++ b/speech_test.py
@@ -29,7 +29,7 @@ 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('./models/'+audio_group+'/',audio_group+'.constants')
+    const_file = os.path.join('./models/'+audio_group+'/','constants.pkl')
     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'))
@@ -41,7 +41,6 @@ def evaluate_siamese(records_file,audio_group='audio',weights = 'siamese_speech_
     total,same_success,diff_success,skipped,same_failed,diff_failed = 0,0,0,0,0,0
     all_results = []
     for (i,string_record) in tqdm(enumerate(record_iterator),total=records_count):
-        # string_record = next(record_iterator)
         total+=1
         example = tf.train.Example()
         example.ParseFromString(string_record)
@@ -178,7 +177,7 @@ 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_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.train.tfrecords',audio_group='story_words_test.10',weights ='siamese_speech_model-891-epoch-0.02-acc.h5')
     # play_results('story_words')
     #inspect_tfrecord('./outputs/story_phrases.test.tfrecords',audio_group='story_phrases')
     # visualize_results('story_words.gpu')