diff --git a/project/generate.py b/project/generate.py
index 333712a..9a5023b 100644
--- a/project/generate.py
+++ b/project/generate.py
@@ -3,6 +3,7 @@
 import numpy as np
 import midi
 import tensorflow as tf
+import pypianoroll as roll
 from keras.layers import Input, Dense, Conv2D
 from keras.models import Model
 from tensorflow.keras import layers
@@ -17,20 +18,22 @@ trained_model_path = sys.argv[1]
 output_path = sys.argv[2]
 treshold = float(sys.argv[3])
 
-#random seed
-generate_seed = np.random.rand(12288).reshape(1,96,128)
-
 # load and predict
 model = pickle.load(open(trained_model_path, 'rb'))
-generated_sample = model.predict(generate_seed)
-generated_sample = generated_sample.reshape(96,128)
+
+music = np.empty((4,96,128))
+for x in range(4):
+    generate_seed = np.random.randint(0, 127, 12288).reshape(1,96,128)
+    music[x] = model.predict(generate_seed).reshape(96,128)
+
+generated_sample = music.reshape(4*96,128)
 
 # binarize generated music
 generated_sample = generated_sample > treshold * generated_sample.max()
+# generated_sample = np.clip(generated_sample,0,1) * 128
 
-#save to midi
-midi.to_midi(generated_sample, output_path='{}.mid'.format(output_path) )
+# save to midi
+generated_midi = midi.to_midi(generated_sample, output_path='{}.mid'.format(output_path), is_drum=True, program=0,  )
 
-#save piano roll to png
-plt.imshow(generated_sample, cmap = plt.get_cmap('gray'))
-plt.savefig('{}.png'.format(output_path))
+#save plot for preview
+roll.plot(generated_midi, filename='{}.png'.format(output_path))