diff --git a/.gitignore b/.gitignore
new file mode 100644
index 00000000..02036bd3
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,4 @@
+data/*
+config.txt
+history
+results/*
\ No newline at end of file
diff --git a/README.md b/README.md
index 338023d1..c027352c 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,58 @@
 # Composer
-Generates video game music using neural networks.
-https://youtu.be/UWxfnNXlVy8
+Generate tunable music using neural networks.
+
+Repository to ["Generating Songs With Neural Networks (Neural Composer)"](https://youtu.be/UWxfnNXlVy8).
+
+## How to install
+
+* Install dependencies in python3 by running `pip install -r requirements.txt`.
+
+## How to run
+
+* Find some dataset to train on. More info on where to find datasets can be found in data/raw/README.md. A default bach dataset is included in the repository.
+* Run preprocess_songs.py. This will load all midi files from your midi files folder data/raw/ into data/interim/samples.npy & lengths.npy.
+  You can point the script to a location using the --data_folder flag one or multiple times to make it include more paths to  index midi files.
+* Run train.py. This will train your network and store the progress from time to time (EPOCHS_TO_SAVE to change storing frequency).
+  Only stops if you interrupt it or after 2000 epochs.
+* Run composer.py --model_path "e300" where "e300" indicates the folder the stored model is in. Look into the results/history folder to find your trained model folders.
+
+## Composer
+
+The Composer script will load your model trained on the midi files you provided
+into the music generator.
+Internally, the model tries to compress a song into only 120 numbers called the latent space.
+Seen from the other perspective, the latent space numbers encode each song that the model
+is trained on. Changing the latent numbers allows us to sample new, unknown songs.
+To make the sampling more efficient, we use PCA, a way to extract the dimensions in which there is 
+most variations across all songs, to make the influence on the new songs strong. 
+The sliders in the composer adjust those numbers and are ordered from most
+important/influential (left) to least important/influential (right).  Just the
+top 40 are shown on screen.  For more details about how
+this works and other fun projects, please check out my
+YouTube channel 'CodeParade'.  Have fun making music!
+
+=========================================================
+```
+Composer Controls:
+ Right Click - Reset all sliders
+         'R' - Random Song
+         'T' - Randomize off-screen sliders
+         'M' - Save song as .mid file
+         'W' - Save song as .wav file
+         'S' - Save slider values to text file
+     'Space' - Play/Pause
+       'Tab' - Seek to start of song
+         '1' - Square wave instrument
+         '2' - Sawtooth wave instrument
+         '3' - Triangle wave instrument
+         '4' - Sine wave instrument
+```
+
+## Pretrained models
+
+Below are some pretrained models for different datasets. To use the model, download the zip from the
+link below and extract it into the results/history folder of your project. The zip should contain a folder 
+named e and some number, indicating the number of epochs the model was trained and some model.h5.
+Pay attention when extracting the model not to override one of your own trained, most beloved models!
+
+* Bach dataset: https://drive.google.com/open?id=1P_hOF0v55m1Snzvri5OBVP5yy099Lzgx
diff --git a/composer.py b/composer.py
new file mode 100644
index 00000000..4cd9f685
--- /dev/null
+++ b/composer.py
@@ -0,0 +1,542 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""
+Neural composer: Play and edit music generated by the trained model.
+"""
+
+import argparse
+import math
+import wave
+
+import numpy as np
+import pyaudio
+import pygame
+
+import midi_utils
+
+import keras
+from keras.models import Model, load_model
+from keras import backend as K
+
+# User constants
+dir_name = 'results/history/'
+sub_dir_name = 'e1/'
+sample_rate = 48000
+note_dt = 2000  # num samples
+note_duration = 20000  # num samples
+note_decay = 5.0 / sample_rate
+num_params = 120
+num_measures = 16
+num_sigmas = 5.0
+note_threshold = 32
+use_pca = True
+is_ae = True
+
+# colors
+background_color = (210, 210, 210)
+edge_color = (60, 60, 60)
+slider_colors = [(90, 20, 20), (90, 90, 20), (20, 90, 20), (20, 90, 90), (20, 20, 90), (90, 20, 90)]
+
+note_w = 96
+note_h = 96
+note_pad = 2
+
+notes_rows = int(num_measures / 8)
+notes_cols = 8
+
+slider_num = min(40, num_params)
+slider_h = 200
+slider_pad = 5
+tick_pad = 4
+
+control_w = 210
+control_h = 30
+control_pad = 5
+control_num = 3
+control_colors = [(255, 0, 0), (0, 255, 0), (0, 0, 255)]
+control_inits = [0.75, 0.5, 0.5]
+
+# derived constants
+notes_w = notes_cols * (note_w + note_pad * 2)
+notes_h = notes_rows * (note_h + note_pad * 2)
+sliders_w = notes_w
+sliders_h = slider_h + slider_pad * 2
+controls_w = control_w * control_num
+controls_h = control_h
+window_w = notes_w
+window_h = notes_h + sliders_h + controls_h
+slider_w = int((window_w - slider_pad * 2) / slider_num)
+notes_x = 0
+notes_y = sliders_h
+sliders_x = slider_pad
+sliders_y = slider_pad
+controls_x = int((window_w - controls_w) / 2)
+controls_y = notes_h + sliders_h
+
+# global variables
+prev_mouse_pos = None
+mouse_pressed = 0
+cur_slider_ix = 0
+cur_control_ix = 0
+volume = 3000
+instrument = 0
+needs_update = True
+current_params = np.zeros((num_params,), dtype=np.float32)
+current_notes = np.zeros((num_measures, note_h, note_w), dtype=np.uint8)
+cur_controls = np.array(control_inits, dtype=np.float32)
+songs_loaded = False
+
+# setup audio stream
+audio = pyaudio.PyAudio()
+audio_notes = []
+audio_time = 0
+note_time = 0
+note_time_dt = 0
+audio_reset = False
+audio_pause = False
+
+
+def audio_callback(in_data, frame_count, time_info, status):
+    """
+    Audio call-back to influence playback of music with input.
+    :param in_data:
+    :param frame_count:
+    :param time_info:
+    :param status:
+    :return:
+    """
+    global audio_time
+    global audio_notes
+    global audio_reset
+    global note_time
+    global note_time_dt
+
+    # check if needs restart
+    if audio_reset:
+        audio_notes = []
+        audio_time = 0
+        note_time = 0
+        note_time_dt = 0
+        audio_reset = False
+
+    # check if paused
+    if audio_pause and status is not None:
+        data = np.zeros((frame_count,), dtype=np.float32)
+        return data.tobytes(), pyaudio.paContinue
+
+    # find and add any notes in this time window
+    cur_dt = note_dt
+    while note_time_dt < audio_time + frame_count:
+        measure_ix = int(note_time / note_h)
+        if measure_ix >= num_measures:
+            break
+        note_ix = note_time % note_h
+        notes = np.where(current_notes[measure_ix, note_ix] >= note_threshold)[0]
+        for note in notes:
+            freq = 2 * 38.89 * pow(2.0, note / 12.0) / sample_rate
+            audio_notes.append((note_time_dt, freq))
+        note_time += 1
+        note_time_dt += cur_dt
+
+    # generate the tones
+    data = np.zeros((frame_count,), dtype=np.float32)
+    for t, f in audio_notes:
+        x = np.arange(audio_time - t, audio_time + frame_count - t)
+        x = np.maximum(x, 0)
+
+        if instrument == 0:
+            w = np.sign(1 - np.mod(x * f, 2))  # Square
+        elif instrument == 1:
+            w = np.mod(x * f - 1, 2) - 1  # Sawtooth
+        elif instrument == 2:
+            w = 2 * np.abs(np.mod(x * f - 0.5, 2) - 1) - 1  # Triangle
+        elif instrument == 3:
+            w = np.sin(x * f * math.pi)  # Sine
+
+        # w = np.floor(w*8)/8
+        w[x == 0] = 0
+        w *= volume * np.exp(-x * note_decay)
+        data += w
+    data = np.clip(data, -32000, 32000).astype(np.int16)
+
+    # remove notes that are too old
+    audio_time += frame_count
+    audio_notes = [(t, f) for t, f in audio_notes if audio_time < t + note_duration]
+
+    # reset if loop occurs
+    if note_time / note_h >= num_measures:
+        audio_time = 0
+        note_time = 0
+        note_time_dt = 0
+        audio_notes = []
+
+    # return the sound clip
+    return data.tobytes(), pyaudio.paContinue
+
+
+def update_mouse_click(mouse_pos):
+    """
+    Update control stated based on where the mouse clicked.
+    :param mouse_pos:
+    :return:
+    """
+    global cur_slider_ix
+    global cur_control_ix
+    global mouse_pressed
+    x = (mouse_pos[0] - sliders_x)
+    y = (mouse_pos[1] - sliders_y)
+
+    if 0 <= x < sliders_w and 0 <= y < sliders_h:
+        cur_slider_ix = int(x / slider_w)
+        mouse_pressed = 1
+
+    x = (mouse_pos[0] - controls_x)
+    y = (mouse_pos[1] - controls_y)
+    if 0 <= x < controls_w and 0 <= y < controls_h:
+        cur_control_ix = int(x / control_w)
+        mouse_pressed = 2
+
+
+def apply_controls():
+    """
+    Change parameters based on controls.
+    :return:
+    """
+    global note_threshold
+    global note_dt
+    global volume
+
+    note_threshold = (1.0 - cur_controls[0]) * 200 + 10
+    note_dt = (1.0 - cur_controls[1]) * 1800 + 200
+    volume = cur_controls[2] * 6000
+
+
+def update_mouse_move(mouse_pos):
+    """
+    Update sliders/controls based on mouse input.
+    :param mouse_pos:
+    :return:
+    """
+    global needs_update
+
+    if mouse_pressed == 1:
+        # change sliders
+        y = (mouse_pos[1] - sliders_y)
+        if 0 <= y <= slider_h:
+            val = (float(y) / slider_h - 0.5) * (num_sigmas * 2)
+            current_params[int(cur_slider_ix)] = val
+            needs_update = True
+    elif mouse_pressed == 2:
+        # change controls
+        x = (mouse_pos[0] - (controls_x + cur_control_ix * control_w))
+        if control_pad <= x <= control_w - control_pad:
+            val = float(x - control_pad) / (control_w - control_pad * 2)
+            cur_controls[int(cur_control_ix)] = val
+            apply_controls()
+
+
+def draw_controls(screen):
+    """
+    Draw volume and threshold controls to screen.
+    :param screen:
+    :return:
+    """
+    for i in range(control_num):
+        x = controls_x + i * control_w + control_pad
+        y = controls_y + control_pad
+        w = control_w - control_pad * 2
+        h = control_h - control_pad * 2
+        col = control_colors[i]
+
+        pygame.draw.rect(screen, col, (x, y, int(w * cur_controls[i]), h))
+        pygame.draw.rect(screen, (0, 0, 0), (x, y, w, h), 1)
+
+
+def draw_sliders(screen):
+    """
+    Draw sliders to screen.
+    :param screen:
+    :return:
+    """
+    for i in range(slider_num):
+        slider_color = slider_colors[i % len(slider_colors)]
+        x = sliders_x + i * slider_w
+        y = sliders_y
+
+        cx = x + slider_w / 2
+        cy_1 = y
+        cy_2 = y + slider_h
+        pygame.draw.line(screen, slider_color, (cx, cy_1), (cx, cy_2))
+
+        cx_1 = x + tick_pad
+        cx_2 = x + slider_w - tick_pad
+        for j in range(int(num_sigmas * 2 + 1)):
+            ly = y + slider_h / 2.0 + (j - num_sigmas) * slider_h / (num_sigmas * 2.0)
+            ly = int(ly)
+            col = (0, 0, 0) if j - num_sigmas == 0 else slider_color
+            pygame.draw.line(screen, col, (cx_1, ly), (cx_2, ly))
+
+        py = y + int((current_params[i] / (num_sigmas * 2) + 0.5) * slider_h)
+        pygame.draw.circle(screen, slider_color, (int(cx), int(py)), int((slider_w - tick_pad) / 2))
+
+
+def get_pianoroll_from_notes(notes):
+    """
+    Draw piano roll of notes.
+    :param notes:
+    :return:
+    """
+
+    output = np.full((3, int(notes_h), int(notes_w)), 64, dtype=np.uint8)
+
+    for i in range(notes_rows):
+        for j in range(notes_cols):
+            x = note_pad + j * (note_w + note_pad * 2)
+            y = note_pad + i * (note_h + note_pad * 2)
+            ix = i * notes_cols + j
+
+            measure = np.rot90(notes[ix])
+            played_only = np.where(measure >= note_threshold, 255, 0)
+            output[0, y:y + note_h, x:x + note_w] = np.minimum(measure * (255.0 / note_threshold), 255.0)
+            output[1, y:y + note_h, x:x + note_w] = played_only
+            output[2, y:y + note_h, x:x + note_w] = played_only
+
+    return np.transpose(output, (2, 1, 0))
+
+
+def draw_notes(screen, notes_surface):
+    """
+    Draw pianoroll notes to screen.
+    :param screen:
+    :param notes_surface:
+    :return:
+    """
+
+    pygame.surfarray.blit_array(notes_surface, get_pianoroll_from_notes(current_notes))
+
+    measure_ix = int(note_time / note_h)
+    note_ix = note_time % note_h
+    x = notes_x + note_pad + (measure_ix % notes_cols) * (note_w + note_pad * 2) + note_ix
+    y = notes_y + note_pad + int(measure_ix / notes_cols) * (note_h + note_pad * 2)
+
+    pygame.draw.rect(screen, (255, 255, 0), (x, y, 4, note_h), 0)
+
+
+def play():
+    global mouse_pressed
+    global current_notes
+    global audio_pause
+    global needs_update
+    global current_params
+    global prev_mouse_pos
+    global audio_reset
+    global instrument
+    global songs_loaded
+
+    print("Keras version: " + keras.__version__)
+
+    K.set_image_data_format('channels_first')
+
+    print("Loading encoder...")
+    model = load_model(dir_name + 'model.h5')
+    encoder = Model(inputs=model.input, outputs=model.get_layer('encoder').output)
+    decoder = K.function([model.get_layer('decoder').input, K.learning_phase()],
+                         [model.layers[-1].output])
+
+    print("Loading gaussian/pca statistics...")
+    latent_means = np.load(dir_name + sub_dir_name + '/latent_means.npy')
+    latent_stds = np.load(dir_name + sub_dir_name + '/latent_stds.npy')
+    latent_pca_values = np.load(dir_name + sub_dir_name + '/latent_pca_values.npy')
+    latent_pca_vectors = np.load(dir_name + sub_dir_name + '/latent_pca_vectors.npy')
+
+    # open a window
+    pygame.init()
+    pygame.font.init()
+    screen = pygame.display.set_mode((int(window_w), int(window_h)))
+    notes_surface = screen.subsurface((notes_x, notes_y, notes_w, notes_h))
+    pygame.display.set_caption('Neural Composer')
+
+    # start the audio stream
+    audio_stream = audio.open(
+        format=audio.get_format_from_width(2),
+        channels=1,
+        rate=sample_rate,
+        output=True,
+        stream_callback=audio_callback)
+    audio_stream.start_stream()
+
+    # main loop
+    running = True
+    random_song_ix = 0
+    cur_len = 0
+    apply_controls()
+    while running:
+        # process events
+        for event in pygame.event.get():
+            if event.type == pygame.QUIT:  # QUIT BUTTON HIT
+                running = False
+                break
+
+            elif event.type == pygame.MOUSEBUTTONDOWN:  # MOUSE BUTTON DOWN
+                if pygame.mouse.get_pressed()[0]:
+                    prev_mouse_pos = pygame.mouse.get_pos()
+                    update_mouse_click(prev_mouse_pos)
+                    update_mouse_move(prev_mouse_pos)
+                elif pygame.mouse.get_pressed()[2]:
+                    current_params = np.zeros((num_params,), dtype=np.float32)
+                    needs_update = True
+
+            elif event.type == pygame.MOUSEBUTTONUP:   # MOUSE BUTTON UP
+                mouse_pressed = 0
+                prev_mouse_pos = None
+
+            elif event.type == pygame.MOUSEMOTION and mouse_pressed > 0:  # MOUSE MOTION WHILE PRESSED
+                update_mouse_move(pygame.mouse.get_pos())
+
+            elif event.type == pygame.KEYDOWN:
+                if event.key == pygame.K_r:  # KEYDOWN R
+                    # generate random song
+                    current_params = np.clip(np.random.normal(0.0, 1.0, (num_params,)), -num_sigmas, num_sigmas)
+                    needs_update = True
+                    audio_reset = True
+
+                if event.key == pygame.K_e:  # KEYDOWN E
+                    # generate random song with larger variance
+                    current_params = np.clip(np.random.normal(0.0, 2.0, (num_params,)), -num_sigmas, num_sigmas)
+                    needs_update = True
+                    audio_reset = True
+
+                if event.key == pygame.K_o:  # KEYDOWN O
+
+                    if not songs_loaded:
+                        print("Loading songs...")
+                        try:
+                            y_samples = np.load('data/interim/samples.npy')
+                            y_lengths = np.load('data/interim/lengths.npy')
+                            songs_loaded = True
+                        except Exception as e:
+                            print("This functionality is to check if the model training went well by reproducing an original song. "
+                                  "The composer could not load samples and lengths from model training. "
+                                  "If you have the midi files, the model was trained with, process them by using"
+                                  " the preprocess_songs.py to find the requested files in data/interim "
+                                  "(Load exception: {0}".format(e))
+
+                    if songs_loaded:
+                        # check how well the autoencoder can reconstruct a random song
+                        print("Random Song Index: " + str(random_song_ix))
+                        if is_ae:
+                            example_song = y_samples[cur_len:cur_len + num_measures]
+                            current_notes = example_song * 255
+                            latent_x = encoder.predict(np.expand_dims(example_song, 0), batch_size=1)[0]
+                            cur_len += y_lengths[random_song_ix]
+                            random_song_ix += 1
+                        else:
+                            random_song_ix = np.array([random_song_ix], dtype=np.int64)
+                            latent_x = encoder.predict(random_song_ix, batch_size=1)[0]
+                            random_song_ix = (random_song_ix + 1) % model.layers[0].input_dim
+
+                        if use_pca:
+                            current_params = np.dot(latent_x - latent_means, latent_pca_vectors.T) / latent_pca_values
+                        else:
+                            current_params = (latent_x - latent_means) / latent_stds
+
+                        needs_update = True
+                        audio_reset = True
+
+                if event.key == pygame.K_m:  # KEYDOWN M
+                    # save song as midi
+                    audio_pause = True
+                    audio_reset = True
+                    midi_utils.samples_to_midi(current_notes, 'results/live.mid', note_threshold)
+                    audio_pause = False
+
+                if event.key == pygame.K_w:  # KEYDOWN W
+                    # save song as wave
+                    audio_pause = True
+                    audio_reset = True
+                    save_audio = b''
+                    while True:
+                        save_audio += audio_callback(None, 1024, None, None)[0]
+                        if audio_time == 0:
+                            break
+                    wave_output = wave.open('results/live.wav', 'w')
+                    wave_output.setparams((1, 2, sample_rate, 0, 'NONE', 'not compressed'))
+                    wave_output.writeframes(save_audio)
+                    wave_output.close()
+                    audio_pause = False
+
+                if event.key == pygame.K_ESCAPE:  # KEYDOWN ESCAPE
+                    # exit application
+                    running = False
+                    break
+
+                if event.key == pygame.K_SPACE:  # KEYDOWN SPACE
+                    # toggle pause/play audio
+                    audio_pause = not audio_pause
+
+                if event.key == pygame.K_TAB:  # KEYDOWN TAB
+                    # reset audio playing
+                    audio_reset = True
+
+                if event.key == pygame.K_1:  # KEYDOWN 1
+                    # play instrument 0
+                    instrument = 0
+
+                if event.key == pygame.K_2:  # KEYDOWN 2
+                    # play instrument 1
+                    instrument = 1
+
+                if event.key == pygame.K_3:  # KEYDOWN 3
+                    # play instrument 2
+                    instrument = 2
+
+                if event.key == pygame.K_4:  # KEYDOWN 4
+                    # play instrument 3
+                    instrument = 3
+
+                if event.key == pygame.K_c:  # KEYDOWN C
+                    #
+                    y = np.expand_dims(np.where(current_notes > note_threshold, 1, 0), 0)
+                    latent_x = encoder.predict(y)[0]
+                    if use_pca:
+                        current_params = np.dot(latent_x - latent_means, latent_pca_vectors.T) / latent_pca_values
+                    else:
+                        current_params = (latent_x - latent_means) / latent_stds
+                    needs_update = True
+
+        # check if params were changed so that a new song should be generated
+        if needs_update:
+            if use_pca:
+                latent_x = latent_means + np.dot(current_params * latent_pca_values, latent_pca_vectors)
+            else:
+                latent_x = latent_means + latent_stds * current_params
+            latent_x = np.expand_dims(latent_x, axis=0)
+            y = decoder([latent_x, 0])[0][0]
+            current_notes = (y * 255.0).astype(np.uint8)
+            needs_update = False
+
+        # draw GUI to the screen
+        screen.fill(background_color)
+        draw_notes(screen, notes_surface)
+        draw_sliders(screen)
+        draw_controls(screen)
+
+        # flip the screen buffer
+        pygame.display.flip()
+        pygame.time.wait(10)
+
+    # if app is exited, close the audio stream
+    audio_stream.stop_stream()
+    audio_stream.close()
+    audio.terminate()
+
+
+if __name__ == "__main__":
+    # configure parser and parse arguments
+    parser = argparse.ArgumentParser(description='Neural Composer: Play and edit music of a trained model.')
+    parser.add_argument('--model_path', type=str, help='The folder the model is stored in (e.g. a folder named e and a number located in results/history/).', required=True)
+
+    args = parser.parse_args()
+    sub_dir_name = args.model_path
+    play()
diff --git a/cpu.theanorc b/cpu.theanorc
deleted file mode 100644
index 57841b4c..00000000
--- a/cpu.theanorc
+++ /dev/null
@@ -1,3 +0,0 @@
-[global]
-floatX=float32
-device=cpu
diff --git a/data/interim/.gitkeep b/data/interim/.gitkeep
new file mode 100644
index 00000000..e69de29b
diff --git a/data/raw/README.md b/data/raw/README.md
new file mode 100644
index 00000000..15d5f7f1
--- /dev/null
+++ b/data/raw/README.md
@@ -0,0 +1,11 @@
+How to get some midi data
+-------------------------
+
+Get some midi files of music you are interested in to work with.
+
+Examples are:
+* https://github.com/FlorianColombo/BachProp/tree/master/data (Bach midi files)
+* https://mega.nz/#!Elg1TA7T!MXEZPzq9s9YObiUcMCoNQJmCbawZqzAkHzY4Ym6Gs_Q (Large pack of all genres of midi files)
+* https://www.vgmusic.com/ (Scrape midi files from here)
+
+Place the midi files in this folder or any subfolder to use them as the training set.
\ No newline at end of file
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diff --git a/gpu.theanorc b/gpu.theanorc
deleted file mode 100644
index 53921a38..00000000
--- a/gpu.theanorc
+++ /dev/null
@@ -1,11 +0,0 @@
-[global]
-floatX=float32
-device=cuda
-
-[nvcc]
-compiler_bindir=C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\bin
-
-[dnn]
-enabled=True
-include_path=C:\CUDA\v8.0\include
-library_path=C:\CUDA\v8.0\lib\x64
diff --git a/live_edit.py b/live_edit.py
deleted file mode 100644
index 05055fef..00000000
--- a/live_edit.py
+++ /dev/null
@@ -1,427 +0,0 @@
-import pygame
-import random, math
-import numpy as np
-import cv2
-import pyaudio
-import midi
-import wave
-
-#User constants
-device = "cpu"
-dir_name = 'History/'
-sub_dir_name = 'e2000/'
-sample_rate = 48000
-note_dt = 2000        #Num Samples
-note_duration = 20000 #Num Samples
-note_decay = 5.0 / sample_rate
-num_params = 120
-num_measures = 16
-num_sigmas = 5.0
-note_thresh = 32
-use_pca = True
-is_ae = True
-
-background_color = (210, 210, 210)
-edge_color = (60, 60, 60)
-slider_colors = [(90, 20, 20), (90, 90, 20), (20, 90, 20), (20, 90, 90), (20, 20, 90), (90, 20, 90)]
-
-note_w = 96
-note_h = 96
-note_pad = 2
-
-notes_rows = num_measures / 8
-notes_cols = 8
-
-slider_num = min(40, num_params)
-slider_h = 200
-slider_pad = 5
-tick_pad = 4
-
-control_w = 210
-control_h = 30
-control_pad = 5
-control_num = 3
-control_colors = [(255,0,0), (0,255,0), (0,0,255)]
-control_inits = [0.75, 0.5, 0.5]
-
-#Derived constants
-notes_w = notes_cols * (note_w + note_pad*2)
-notes_h = notes_rows * (note_h + note_pad*2)
-sliders_w = notes_w
-sliders_h = slider_h + slider_pad*2
-controls_w = control_w * control_num
-controls_h = control_h
-window_w = notes_w
-window_h = notes_h + sliders_h + controls_h
-slider_w = (window_w - slider_pad*2) / slider_num
-notes_x = 0
-notes_y = sliders_h
-sliders_x = slider_pad
-sliders_y = slider_pad
-controls_x = (window_w - controls_w) / 2
-controls_y = notes_h + sliders_h
-
-#Global variables
-prev_mouse_pos = None
-mouse_pressed = 0
-cur_slider_ix = 0
-cur_control_ix = 0
-volume = 3000
-instrument = 0
-needs_update = True
-cur_params = np.zeros((num_params,), dtype=np.float32)
-cur_notes = np.zeros((num_measures, note_h, note_w), dtype=np.uint8)
-cur_controls = np.array(control_inits, dtype=np.float32)
-
-#Setup audio stream
-audio = pyaudio.PyAudio()
-audio_notes = []
-audio_time = 0
-note_time = 0
-note_time_dt = 0
-audio_reset = False
-audio_pause = False
-def audio_callback(in_data, frame_count, time_info, status):
-	global audio_time
-	global audio_notes
-	global audio_reset
-	global note_time
-	global note_time_dt
-
-	#Check if needs restart
-	if audio_reset:
-		audio_notes = []
-		audio_time = 0
-		note_time = 0
-		note_time_dt = 0
-		audio_reset = False
-	
-	#Check if paused
-	if audio_pause and status is not None:
-		data = np.zeros((frame_count,), dtype=np.float32)
-		return (data.tobytes(), pyaudio.paContinue)
-	
-	#Find and add any notes in this time window
-	cur_dt = note_dt
-	while note_time_dt < audio_time + frame_count:
-		measure_ix = note_time / note_h
-		if measure_ix >= num_measures:
-			break
-		note_ix = note_time % note_h
-		notes = np.where(cur_notes[measure_ix, note_ix] >= note_thresh)[0]
-		for note in notes:
-			freq = 2 * 38.89 * pow(2.0, note / 12.0) / sample_rate
-			audio_notes.append((note_time_dt, freq))
-		note_time += 1
-		note_time_dt += cur_dt
-			
-	#Generate the tones
-	data = np.zeros((frame_count,), dtype=np.float32)
-	for t,f in audio_notes:
-		x = np.arange(audio_time - t, audio_time + frame_count - t)
-		x = np.maximum(x, 0)
-
-		if instrument == 0:
-			w = np.sign(1 - np.mod(x * f, 2))            #Square
-		elif instrument == 1:
-			w = np.mod(x * f - 1, 2) - 1                 #Sawtooth
-		elif instrument == 2:
-			w = 2*np.abs(np.mod(x * f - 0.5, 2) - 1) - 1 #Triangle
-		elif instrument == 3:
-			w = np.sin(x * f * math.pi)                  #Sine
-		
-		#w = np.floor(w*8)/8
-		w[x == 0] = 0
-		w *= volume * np.exp(-x*note_decay)
-		data += w
-	data = np.clip(data, -32000, 32000).astype(np.int16)
-
-	#Remove notes that are too old
-	audio_time += frame_count
-	audio_notes = [(t,f) for t,f in audio_notes if audio_time < t + note_duration]
-	
-	#Reset if loop occurs
-	if note_time / note_h >= num_measures:
-		audio_time = 0
-		note_time = 0
-		note_time_dt = 0
-		audio_notes = []
-	
-	#Return the sound clip
-	return (data.tobytes(), pyaudio.paContinue)
-	
-#Keras
-print "Loading Keras..."
-import os
-os.environ['THEANORC'] = "./" + device + ".theanorc"
-os.environ['KERAS_BACKEND'] = "theano"
-import theano
-print "Theano Version: " + theano.__version__
-import keras
-print "Keras Version: " + keras.__version__
-from keras.models import Model, Sequential, load_model
-from keras.layers import Dense, Activation, Dropout, Flatten, Reshape
-from keras.layers.convolutional import Conv2D, Conv2DTranspose, ZeroPadding2D
-from keras.layers.pooling import MaxPooling2D
-from keras.layers.noise import GaussianNoise
-from keras.layers.local import LocallyConnected2D
-from keras.optimizers import Adam, RMSprop, SGD
-from keras.regularizers import l2
-from keras.losses import binary_crossentropy
-from keras.layers.advanced_activations import ELU
-from keras.preprocessing.image import ImageDataGenerator
-from keras.utils import plot_model
-from keras import backend as K
-K.set_image_data_format('channels_first')
-
-print "Loading Encoder..."
-model = load_model(dir_name + 'model.h5')
-enc = K.function([model.get_layer('encoder').input, K.learning_phase()],
-				 [model.layers[-1].output])
-enc_model = Model(inputs=model.input, outputs=model.get_layer('pre_encoder').output)
-
-print "Loading Statistics..."
-means = np.load(dir_name + sub_dir_name + 'means.npy')
-evals = np.load(dir_name + sub_dir_name + 'evals.npy')
-evecs = np.load(dir_name + sub_dir_name + 'evecs.npy')
-stds = np.load(dir_name + sub_dir_name + 'stds.npy')
-
-print "Loading Songs..."
-y_samples = np.load('samples.npy')
-y_lengths = np.load('lengths.npy')
-
-#Open a window
-pygame.init()
-pygame.font.init()
-screen = pygame.display.set_mode((window_w, window_h))
-notes_surface = screen.subsurface((notes_x, notes_y, notes_w, notes_h))
-pygame.display.set_caption('MusicEdit')
-font = pygame.font.SysFont("monospace", 15)
-
-#Start the audio stream
-audio_stream = audio.open(
-	format=audio.get_format_from_width(2),
-	channels=1,
-	rate=sample_rate,
-	output=True,
-	stream_callback=audio_callback)
-audio_stream.start_stream()
-
-def update_mouse_click(mouse_pos):
-	global cur_slider_ix
-	global cur_control_ix
-	global mouse_pressed
-	x = (mouse_pos[0] - sliders_x)
-	y = (mouse_pos[1] - sliders_y)
-
-	if x >= 0 and y >= 0 and x < sliders_w and y < sliders_h:
-		cur_slider_ix = x / slider_w
-		mouse_pressed = 1
-		
-	x = (mouse_pos[0] - controls_x)
-	y = (mouse_pos[1] - controls_y)
-	if x >= 0 and y >= 0 and x < controls_w and y < controls_h:
-		cur_control_ix = x / control_w
-		mouse_pressed = 2
-
-def apply_controls():
-	global note_thresh
-	global note_dt
-	global volume
-
-	note_thresh = (1.0 - cur_controls[0]) * 200 + 10
-	note_dt = (1.0 - cur_controls[1]) * 1800 + 200
-	volume = cur_controls[2] * 6000
-		
-def update_mouse_move(mouse_pos):
-	global needs_update
-
-	if mouse_pressed == 1:
-		y = (mouse_pos[1] - sliders_y)
-		if y >= 0 and y <= slider_h:
-			val = (float(y) / slider_h - 0.5) * (num_sigmas * 2)
-			cur_params[cur_slider_ix] = val
-			needs_update = True
-	elif mouse_pressed == 2:
-		x = (mouse_pos[0] - (controls_x + cur_control_ix*control_w))
-		if x >= control_pad and x <= control_w - control_pad:
-			val = float(x - control_pad) / (control_w - control_pad*2)
-			cur_controls[cur_control_ix] = val
-			apply_controls()
-
-def draw_controls():
-	for i in xrange(control_num):
-		x = controls_x + i * control_w + control_pad
-		y = controls_y + control_pad
-		w = control_w - control_pad*2
-		h = control_h - control_pad*2
-		col = control_colors[i]
-
-		pygame.draw.rect(screen, col, (x, y, int(w*cur_controls[i]), h))
-		pygame.draw.rect(screen, (0,0,0), (x, y, w, h), 1)
-		
-def draw_sliders():
-	for i in xrange(slider_num):
-		slider_color = slider_colors[i % len(slider_colors)]
-		x = sliders_x + i * slider_w
-		y = sliders_y
-
-		cx = x + slider_w / 2
-		cy_1 = y
-		cy_2 = y + slider_h
-		pygame.draw.line(screen, slider_color, (cx, cy_1), (cx, cy_2))
-		
-		cx_1 = x + tick_pad
-		cx_2 = x + slider_w - tick_pad
-		for j in xrange(int(num_sigmas * 2 + 1)):
-			ly = y + slider_h/2.0 + (j-num_sigmas)*slider_h/(num_sigmas*2.0)
-			ly = int(ly)
-			col = (0,0,0) if j - num_sigmas == 0 else slider_color
-			pygame.draw.line(screen, col, (cx_1, ly), (cx_2, ly))
-			
-		py = y + int((cur_params[i] / (num_sigmas * 2) + 0.5) * slider_h)
-		pygame.draw.circle(screen, slider_color, (cx, py), (slider_w - tick_pad)/2)
-
-def notes_to_img(notes):
-	output = np.full((3, notes_h, notes_w), 64, dtype=np.uint8)
-
-	for i in xrange(notes_rows):
-		for j in xrange(notes_cols):
-			x = note_pad + j*(note_w + note_pad*2)
-			y = note_pad + i*(note_h + note_pad*2)
-			ix = i*notes_cols + j
-
-			measure = np.rot90(notes[ix])
-			played_only = np.where(measure >= note_thresh, 255, 0)
-			output[0,y:y+note_h,x:x+note_w] = np.minimum(measure * (255.0 / note_thresh), 255.0)
-			output[1,y:y+note_h,x:x+note_w] = played_only
-			output[2,y:y+note_h,x:x+note_w] = played_only
-
-	return np.transpose(output, (2, 1, 0))
-			
-def draw_notes():
-	pygame.surfarray.blit_array(notes_surface, notes_to_img(cur_notes))
-
-	measure_ix = note_time / note_h
-	note_ix = note_time % note_h
-	x = notes_x + note_pad + (measure_ix % notes_cols) * (note_w + note_pad*2) + note_ix
-	y = notes_y +note_pad + (measure_ix / notes_cols) * (note_h + note_pad*2)
-	pygame.draw.rect(screen, (255,255,0), (x, y, 4, note_h), 0)
-	
-#Main loop
-running = True
-rand_ix = 0
-cur_len = 0
-apply_controls()
-while running:
-	#Process events
-	for event in pygame.event.get():
-		if event.type == pygame.QUIT:
-			running = False
-			break
-		elif event.type == pygame.MOUSEBUTTONDOWN:
-			if pygame.mouse.get_pressed()[0]:
-				prev_mouse_pos = pygame.mouse.get_pos()
-				update_mouse_click(prev_mouse_pos)
-				update_mouse_move(prev_mouse_pos)
-			elif pygame.mouse.get_pressed()[2]:
-				cur_params = np.zeros((num_params,), dtype=np.float32)
-				needs_update = True
-		elif event.type == pygame.MOUSEBUTTONUP:
-			mouse_pressed = 0
-			prev_mouse_pos = None
-		elif event.type == pygame.MOUSEMOTION and mouse_pressed > 0:
-			update_mouse_move(pygame.mouse.get_pos())
-		elif event.type == pygame.KEYDOWN:
-			if event.key == pygame.K_r:
-				cur_params = np.clip(np.random.normal(0.0, 1.0, (num_params,)), -num_sigmas, num_sigmas)
-				needs_update = True
-				audio_reset = True
-			if event.key == pygame.K_e:
-				cur_params = np.clip(np.random.normal(0.0, 2.0, (num_params,)), -num_sigmas, num_sigmas)
-				needs_update = True
-				audio_reset = True
-			if event.key == pygame.K_o:
-				print "RandIx: " + str(rand_ix)
-				if is_ae:
-					example_song = y_samples[cur_len:cur_len + num_measures]
-					cur_notes = example_song * 255
-					x = enc_model.predict(np.expand_dims(example_song, 0), batch_size=1)[0]
-					cur_len += y_lengths[rand_ix]
-					rand_ix += 1
-				else:
-					rand_ix = np.array([rand_ix], dtype=np.int64)
-					x = enc_model.predict(rand_ix, batch_size=1)[0]
-					rand_ix = (rand_ix + 1) % model.layers[0].input_dim
-				
-				if use_pca:
-					cur_params = np.dot(x - means, evecs.T) / evals
-				else:
-					cur_params = (x - means) / stds
-
-				needs_update = True
-				audio_reset = True
-			if event.key == pygame.K_g:
-				audio_pause = True
-				audio_reset = True
-				midi.samples_to_midi(cur_notes, 'live.mid', 16, note_thresh)
-				save_audio = ''
-				while True:
-					save_audio += audio_callback(None, 1024, None, None)[0]
-					if audio_time == 0:
-						break
-				wave_output = wave.open('live.wav', 'w')
-				wave_output.setparams((1, 2, sample_rate, 0, 'NONE', 'not compressed'))
-				wave_output.writeframes(save_audio)
-				wave_output.close()
-				audio_pause = False
-			if event.key == pygame.K_ESCAPE:
-				running = False
-				break
-			if event.key == pygame.K_SPACE:
-				audio_pause = not audio_pause
-			if event.key == pygame.K_TAB:
-				audio_reset = True
-			if event.key == pygame.K_1:
-				instrument = 0
-			if event.key == pygame.K_2:
-				instrument = 1
-			if event.key == pygame.K_3:
-				instrument = 2
-			if event.key == pygame.K_4:
-				instrument = 3
-			if event.key == pygame.K_c:
-				y = np.expand_dims(np.where(cur_notes > note_thresh, 1, 0), 0)
-				x = enc_model.predict(y)[0]
-				if use_pca:
-					cur_params = np.dot(x - means, evecs.T) / evals
-				else:
-					cur_params = (x - means) / stds
-				needs_update = True
-
-	#Check if we need an update
-	if needs_update:
-		if use_pca:
-			x = means + np.dot(cur_params * evals, evecs)
-		else:
-			x = means + stds * cur_params
-		x = np.expand_dims(x, axis=0)
-		y = enc([x, 0])[0][0]
-		cur_notes = (y * 255.0).astype(np.uint8)
-		needs_update = False
-	
-	#Draw to the screen
-	screen.fill(background_color)
-	draw_notes()
-	draw_sliders()
-	draw_controls()
-	
-	#Flip the screen buffer
-	pygame.display.flip()
-	pygame.time.wait(10)
-
-#Close the audio stream
-audio_stream.stop_stream()
-audio_stream.close()
-audio.terminate()
-
diff --git a/load_songs.py b/load_songs.py
deleted file mode 100644
index 48bde9f1..00000000
--- a/load_songs.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import midi
-import os
-import util
-import numpy as np
-
-patterns = {}
-dirs = ["Music", "download", "rag", "pop", "misc"]
-all_samples = []
-all_lens = []
-print "Loading Songs..."
-for dir in dirs:
-	for root, subdirs, files in os.walk(dir):
-		for file in files:
-			path = root + "\\" + file
-			if not (path.endswith('.mid') or path.endswith('.midi')):
-				continue
-			try:
-				samples = midi.midi_to_samples(path)
-			except:
-				print "ERROR ", path
-				continue
-			if len(samples) < 8:
-				continue
-				
-			samples, lens = util.generate_add_centered_transpose(samples)
-			all_samples += samples
-			all_lens += lens
-	
-assert(sum(all_lens) == len(all_samples))
-print "Saving " + str(len(all_samples)) + " samples..."
-all_samples = np.array(all_samples, dtype=np.uint8)
-all_lens = np.array(all_lens, dtype=np.uint32)
-np.save('samples.npy', all_samples)
-np.save('lengths.npy', all_lens)
-print "Done"
diff --git a/midi.py b/midi.py
deleted file mode 100644
index 404e6731..00000000
--- a/midi.py
+++ /dev/null
@@ -1,93 +0,0 @@
-from mido import MidiFile, MidiTrack, Message
-import numpy as np
-
-num_notes = 96
-samples_per_measure = 96
-
-def midi_to_samples(fname):
-	has_time_sig = False
-	flag_warning = False
-	mid = MidiFile(fname)
-	ticks_per_beat = mid.ticks_per_beat
-	ticks_per_measure = 4 * ticks_per_beat
-
-	for i, track in enumerate(mid.tracks):
-		for msg in track:
-			if msg.type == 'time_signature':
-				new_tpm = msg.numerator * ticks_per_beat * 4 / msg.denominator
-				if has_time_sig and new_tpm != ticks_per_measure:
-					flag_warning = True
-				ticks_per_measure = new_tpm
-				has_time_sig = True
-	if flag_warning:
-		print "  ^^^^^^ WARNING ^^^^^^"
-		print "    " + fname
-		print "    Detected multiple distinct time signatures."
-		print "  ^^^^^^ WARNING ^^^^^^"
-		return []
-	
-	all_notes = {}
-	for i, track in enumerate(mid.tracks):
-		abs_time = 0
-		for msg in track:
-			abs_time += msg.time
-			if msg.type == 'note_on':
-				if msg.velocity == 0:
-					continue
-				note = msg.note - (128 - num_notes)/2
-				assert(note >= 0 and note < num_notes)
-				if note not in all_notes:
-					all_notes[note] = []
-				else:
-					single_note = all_notes[note][-1]
-					if len(single_note) == 1:
-						single_note.append(single_note[0] + 1)
-				all_notes[note].append([abs_time * samples_per_measure / ticks_per_measure])
-			elif msg.type == 'note_off':
-				if len(all_notes[note][-1]) != 1:
-					continue
-				all_notes[note][-1].append(abs_time * samples_per_measure / ticks_per_measure)
-	for note in all_notes:
-		for start_end in all_notes[note]:
-			if len(start_end) == 1:
-				start_end.append(start_end[0] + 1)
-	samples = []
-	for note in all_notes:
-		for start, end in all_notes[note]:
-			sample_ix = start / samples_per_measure
-			while len(samples) <= sample_ix:
-				samples.append(np.zeros((samples_per_measure, num_notes), dtype=np.uint8))
-			sample = samples[sample_ix]
-			start_ix = start - sample_ix * samples_per_measure
-			if False:
-				end_ix = min(end - sample_ix * samples_per_measure, samples_per_measure)
-				while start_ix < end_ix:
-					sample[start_ix, note] = 1
-					start_ix += 1
-			else:
-				sample[start_ix, note] = 1
-	return samples
-
-def samples_to_midi(samples, fname, ticks_per_sample, thresh=0.5):
-	mid = MidiFile()
-	track = MidiTrack()
-	mid.tracks.append(track)
-	ticks_per_beat = mid.ticks_per_beat
-	ticks_per_measure = 4 * ticks_per_beat
-	ticks_per_sample = ticks_per_measure / samples_per_measure
-	abs_time = 0
-	last_time = 0
-	for sample in samples:
-		for y in xrange(sample.shape[0]):
-			abs_time += ticks_per_sample
-			for x in xrange(sample.shape[1]):
-				note = x + (128 - num_notes)/2
-				if sample[y,x] >= thresh and (y == 0 or sample[y-1,x] < thresh):
-					delta_time = abs_time - last_time
-					track.append(Message('note_on', note=note, velocity=127, time=delta_time))
-					last_time = abs_time
-				if sample[y,x] >= thresh and (y == sample.shape[0]-1 or sample[y+1,x] < thresh):
-					delta_time = abs_time - last_time
-					track.append(Message('note_off', note=note, velocity=127, time=delta_time))
-					last_time = abs_time
-	mid.save(fname)
diff --git a/midi_utils.py b/midi_utils.py
new file mode 100644
index 00000000..ab44d2f8
--- /dev/null
+++ b/midi_utils.py
@@ -0,0 +1,162 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""
+Utils to read and write midi.
+"""
+
+from mido import MidiFile, MidiTrack, Message
+import numpy as np
+
+
+def midi_to_samples(file_name, encode_length=False, num_notes=96, samples_per_measure=96):
+    """
+    Turn a midi file into a sample.
+    :param file_name:
+    :param encode_length:
+    :param num_notes:
+    :param samples_per_measure:
+    :return:
+    """
+    has_time_sig = False
+    mid = MidiFile(file_name)
+
+    ticks_per_beat = mid.ticks_per_beat  # get ticks per beat
+    ticks_per_measure = 4 * ticks_per_beat  # get ticks per measure
+
+    # detect the time signature of the midi
+    for track in mid.tracks:
+        for msg in track:
+            if msg.type == 'time_signature':
+                new_tpm = ticks_per_measure * msg.numerator / msg.denominator  # adapt ticks per measure of this specific song
+
+                # skip if we find multiple time signatures in the song
+                if has_time_sig and new_tpm != ticks_per_measure:
+                    raise NotImplementedError('Multiple time signatures not supported')
+
+                ticks_per_measure = new_tpm
+                has_time_sig = True
+
+    # turn tracks into pianoroll representation
+    all_notes = {}
+    for track in mid.tracks:
+
+        abs_time = 0
+        for msg in track:
+            abs_time += msg.time  # step time forward
+
+            # we skip programs 0x70-0x7F which are percussion and sound effects
+            if msg.type == 'program_change' and msg.program >= 0x70:
+                break
+
+            # if a note starts
+            if msg.type == 'note_on':
+
+                # we skip notes without a velocity (basically how strong a note is played to make it sound human)
+                if msg.velocity == 0:
+                    continue
+
+                # transform the notes into the 96 heights
+                note = msg.note - (128 - num_notes) / 2
+                if note < 0 or note >= num_notes:  # ignore a note that is outside of that range
+                    print('Ignoring', file_name, 'note is outside 0-%d range' % (num_notes - 1))
+                    return []
+
+                # count the number of played notes per pitch
+                if note not in all_notes:
+                    all_notes[note] = []
+                else:
+                    single_note = all_notes[note][-1]
+                    if len(single_note) == 1:
+                        single_note.append(single_note[0] + 1)
+
+                # store the time a note has been played
+                all_notes[note].append([abs_time * samples_per_measure / ticks_per_measure])
+
+            # if a note ends
+            elif msg.type == 'note_off':
+
+                # if the note has already ended before (note_on, note_off, note_off), we skip the event
+                if len(all_notes[note][-1]) != 1:
+                    continue
+                # store the time a note stops playing
+                all_notes[note][-1].append(abs_time * samples_per_measure / ticks_per_measure)
+
+    # any note did not end playing, we end it one time tick later
+    for note in all_notes:
+        for start_end in all_notes[note]:
+            if len(start_end) == 1:
+                start_end.append(start_end[0] + 1)
+
+    # put the notes into their respective sample/measure panel (96 x 96)
+    samples = []
+    for note in all_notes:
+        for start, end in all_notes[note]:
+            sample_ix = int(start / samples_per_measure)  # find the sample/measure this belongs into
+            assert (sample_ix < 1024 * 1024)
+
+            # fill in silence until the appropriate sample/measure is reached
+            while len(samples) <= sample_ix:
+                samples.append(np.zeros((samples_per_measure, num_notes), dtype=np.uint8))
+
+            # get sample and find its start to encode the start of the note
+            sample = samples[sample_ix]
+            start_ix = int(start - sample_ix * samples_per_measure)
+            sample[start_ix, int(note)] = 1
+
+            # play note until it ends if we encode length
+            if encode_length:
+                end_ix = min(end - sample_ix * samples_per_measure, samples_per_measure)
+                while start_ix < end_ix:
+                    sample[start_ix, int(note)] = 1
+                    start_ix += 1
+
+    return samples
+
+
+def samples_to_midi(samples, file_name, threshold=0.5, num_notes=96, samples_per_measure=96):
+    """
+    Turn the samples/measures back into midi.
+    :param samples:
+    :param file_name:
+    :param threshold:
+    :param num_notes:
+    :param samples_per_measure:
+    :return:
+    """
+    # TODO: Encode the certainties of the notes into the volume of the midi for the notes that are above threshold
+
+    mid = MidiFile()
+    track = MidiTrack()
+    mid.tracks.append(track)
+
+    ticks_per_beat = mid.ticks_per_beat
+    ticks_per_measure = 4 * ticks_per_beat
+    ticks_per_sample = ticks_per_measure / samples_per_measure
+
+    # add instrument for track
+    # https://en.wikipedia.org/wiki/General_MIDI#Program_change_events
+    piano = 1
+    honky_tonk_piano = 4
+    xylophone = 14
+    program_message = Message('program_change', program=piano, time=0, channel=0)
+    track.append(program_message)
+
+    abs_time = 0
+    last_time = 0
+    for sample in samples:
+        for y in range(sample.shape[0]):
+            abs_time += ticks_per_sample
+            for x in range(sample.shape[1]):
+                note = x + (128 - num_notes) / 2
+
+                if sample[y, x] >= threshold and (y == 0 or sample[y - 1, x] < threshold):
+                    delta_time = abs_time - last_time
+                    track.append(Message('note_on', note=int(note), velocity=127, time=int(delta_time)))
+                    last_time = abs_time
+
+                if sample[y, x] >= threshold and (y == sample.shape[0] - 1 or sample[y + 1, x] < threshold):
+                    delta_time = abs_time - last_time
+                    track.append(Message('note_off', note=int(note), velocity=127, time=int(delta_time)))
+                    last_time = abs_time
+    mid.save(file_name)
diff --git a/models.py b/models.py
new file mode 100644
index 00000000..2e9a94f5
--- /dev/null
+++ b/models.py
@@ -0,0 +1,102 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""
+The models used for music generation.
+"""
+
+from keras import backend as K
+from keras.layers import Input, Dense, Activation, Dropout, Flatten, Reshape, TimeDistributed, Lambda
+from keras.layers.embeddings import Embedding
+from keras.layers.normalization import BatchNormalization
+from keras.models import Model
+
+
+def vae_sampling(args):
+    z_mean, z_log_sigma_sq, vae_b1 = args
+    epsilon = K.random_normal(shape=K.shape(z_mean), mean=0.0, stddev=vae_b1)
+    return z_mean + K.exp(z_log_sigma_sq * 0.5) * epsilon
+
+
+def create_autoencoder_model(input_shape, latent_space_size, dropout_rate, max_windows, batchnorm_momentum, use_vae=False, vae_b1=0.02, use_embedding=False, embedding_input_shape=None, embedding_shape=None):
+    """
+    Create larger autoencoder with the options of making it variational and embedding.
+    :param input_shape:
+    :param latent_space_size:
+    :param dropout_rate:
+    :param max_windows:
+    :param batchnorm_momentum:
+    :param use_vae:
+    :param vae_b1:
+    :param use_embedding:
+    :param embedding_input_shape:
+    :param embedding_shape:
+    :return:
+    """
+    if use_embedding:
+        x_in = Input(shape=embedding_input_shape)
+        print((None,) + embedding_input_shape)
+
+        x = Embedding(embedding_shape, latent_space_size, input_length=1)(x_in)
+        x = Flatten(name='encoder')(x)
+    else:
+        x_in = Input(shape=input_shape)
+        print((None,) + input_shape)
+
+        x = Reshape((input_shape[0], -1))(x_in)
+        print(K.int_shape(x))
+
+        x = TimeDistributed(Dense(2000, activation='relu'))(x)
+        print(K.int_shape(x))
+
+        x = TimeDistributed(Dense(200, activation='relu'))(x)
+        print(K.int_shape(x))
+
+        x = Flatten()(x)
+        print(K.int_shape(x))
+
+        x = Dense(1600, activation='relu')(x)
+        print(K.int_shape(x))
+
+        if use_vae:
+            z_mean = Dense(latent_space_size)(x)
+            z_log_sigma_sq = Dense(latent_space_size)(x)
+            x = Lambda(vae_sampling, output_shape=(latent_space_size,), name='encoder')([z_mean, z_log_sigma_sq, vae_b1])
+        else:
+            x = Dense(latent_space_size)(x)
+            x = BatchNormalization(momentum=batchnorm_momentum, name='encoder')(x)
+    print(K.int_shape(x))
+
+    # LATENT SPACE
+
+    x = Dense(1600, name='decoder')(x)
+    x = BatchNormalization(momentum=batchnorm_momentum)(x)
+    x = Activation('relu')(x)
+    if dropout_rate > 0:
+        x = Dropout(dropout_rate)(x)
+    print(K.int_shape(x))
+
+    x = Dense(max_windows * 200)(x)
+    print(K.int_shape(x))
+    x = Reshape((max_windows, 200))(x)
+    x = TimeDistributed(BatchNormalization(momentum=batchnorm_momentum))(x)
+    x = Activation('relu')(x)
+    if dropout_rate > 0:
+        x = Dropout(dropout_rate)(x)
+    print(K.int_shape(x))
+
+    x = TimeDistributed(Dense(2000))(x)
+    x = TimeDistributed(BatchNormalization(momentum=batchnorm_momentum))(x)
+    x = Activation('relu')(x)
+    if dropout_rate > 0:
+        x = Dropout(dropout_rate)(x)
+    print(K.int_shape(x))
+
+    x = TimeDistributed(Dense(input_shape[1] * input_shape[2], activation='sigmoid'))(x)
+    print(K.int_shape(x))
+    x = Reshape((input_shape[0], input_shape[1], input_shape[2]))(x)
+    print(K.int_shape(x))
+
+    model = Model(x_in, x)
+
+    return model
diff --git a/music_utils.py b/music_utils.py
new file mode 100644
index 00000000..ab6ced68
--- /dev/null
+++ b/music_utils.py
@@ -0,0 +1,51 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""
+Utils to edit music.
+"""
+
+import numpy as np
+
+
+def find_sample_range(samples):
+    """
+    Find sample range.
+    :param samples:
+    :return:
+    """
+
+    # merge all samples
+    merged_sample = np.zeros_like(samples[0])
+    for sample in samples:
+        merged_sample = np.maximum(merged_sample, sample)
+
+    # get all pitches being played
+    merged_sample = np.amax(merged_sample, axis=0)
+
+    # get min and max note
+    min_note = np.argmax(merged_sample)
+    max_note = merged_sample.shape[0] - np.argmax(merged_sample[::-1])
+    return min_note, max_note
+
+
+def generate_centered_transpose(samples):
+    """
+    Center samples towards the middle of the pitch range.
+    :param samples:
+    :return:
+    """
+    num_notes = samples[0].shape[1]
+    min_note, max_note = find_sample_range(samples)
+
+    # find deviation from pitch center
+    center_deviation = num_notes / 2 - (max_note + min_note) / 2
+    out_samples = samples
+    out_lengths = [len(samples), len(samples)]
+
+    # center every sample by moving it by center_deviation
+    for i in range(len(samples)):
+        out_sample = np.zeros_like(samples[i])
+        out_sample[:, min_note + int(center_deviation):max_note + int(center_deviation)] = samples[i][:, min_note:max_note]
+        out_samples.append(out_sample)
+    return out_samples, out_lengths
diff --git a/plot_utils.py b/plot_utils.py
new file mode 100644
index 00000000..7f9520f8
--- /dev/null
+++ b/plot_utils.py
@@ -0,0 +1,26 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""
+Plot utility functions.
+"""
+
+import os
+import cv2
+import numpy as np
+
+
+def plot_sample(file_name, sample, threshold=None):
+    if threshold is not None:
+        inverted = np.where(sample > threshold, 0, 1)
+    else:
+        inverted = 1.0 - sample
+    cv2.imwrite(file_name, inverted * 255)
+
+
+def plot_samples(folder, samples, threshold=None):
+    if not os.path.exists(folder):
+        os.makedirs(folder)
+
+    for i in range(samples.shape[0]):
+        plot_sample(folder + '/s' + str(i) + '.png', samples[i], threshold)
diff --git a/preprocess_songs.py b/preprocess_songs.py
new file mode 100644
index 00000000..7000bdbd
--- /dev/null
+++ b/preprocess_songs.py
@@ -0,0 +1,79 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""
+Load songs from midi, preprocess and save them in numpy format.
+"""
+
+import midi_utils
+import os
+import music_utils
+import numpy as np
+import argparse
+
+
+def preprocess_songs(data_folders):
+    """
+    Load and preprocess the songs from the data folders and turn them into a dataset of samples/pitches and lengths of the tones.
+    :param data_folders:
+    :return:
+    """
+
+    all_samples = []
+    all_lengths = []
+
+    # keep some statistics
+    succeeded = 0
+    failed = 0
+    ignored = 0
+
+    # load songs
+    print("Loading songs...")
+    # walk folders and look for midi files
+    for folder in data_folders:
+        for root, _, files in os.walk(folder):
+            for file in files:
+                path = os.path.join(root, file)
+                if not (path.endswith('.mid') or path.endswith('.midi')):
+                    continue
+
+                # turn midi into samples
+                try:
+                    samples = midi_utils.midi_to_samples(path)
+                except Exception as e:
+                    print("ERROR ", path)
+                    print(e)
+                    failed += 1
+                    continue
+
+                # if the midi does not produce the minimal number of sample/measures, we skip it
+                if len(samples) < 16:
+                    print('WARN', path, 'Sample too short, unused')
+                    ignored += 1
+                    continue
+
+                # transpose samples (center them in full range to get more training samples for the same tones)
+                samples, lengths = music_utils.generate_centered_transpose(samples)
+                all_samples += samples
+                all_lengths += lengths
+                print('SUCCESS', path, len(samples), 'samples')
+                succeeded += 1
+
+    assert (sum(all_lengths) == len(all_samples))  # assert equal number of samples and lengths
+
+    # save all to disk
+    print("Saving " + str(len(all_samples)) + " samples...")
+    all_samples = np.array(all_samples, dtype=np.uint8)  # reduce size when saving
+    all_lengths = np.array(all_lengths, dtype=np.uint32)
+    np.save('data/interim/samples.npy', all_samples)
+    np.save('data/interim/lengths.npy', all_lengths)
+    print('Done: ', succeeded, 'succeded,', ignored, 'ignored,', failed, 'failed of', succeeded + ignored + failed, 'in total')
+
+
+if __name__ == "__main__":
+    # configure parser and parse arguments
+    parser = argparse.ArgumentParser(description='Load songs, preprocess them and put them into a dataset.')
+    parser.add_argument('--data_folder', default=["data/raw"], type=str, help='The path to the midi data', action='append')
+
+    args = parser.parse_args()
+    preprocess_songs(args.data_folder)
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 00000000..730f2daf
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,13 @@
+matplotlib==3.0.2
+pygame==1.9.6
+pyaudio==0.2.11
+
+pydot==1.4.1
+keras==2.2.4
+tensorflow==1.14.0
+pyaudio==0.2.11
+numpy==1.16.4
+
+mido==1.2.9
+
+argparse==1.4.0
\ No newline at end of file
diff --git a/results/.gitkeep b/results/.gitkeep
new file mode 100644
index 00000000..e69de29b
diff --git a/run_pipeline.ipynb b/run_pipeline.ipynb
new file mode 100644
index 00000000..33dbc8bf
--- /dev/null
+++ b/run_pipeline.ipynb
@@ -0,0 +1,79 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# Run the full pipeline to train a composer model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Uncomment the line below and run this cell to get your data from github into colab (only runnable in colab, not ordinary jupyter notebook):\n",
+    "! git clone https://github.com/benelot/Composer.git && mv Composer/* . && rm Composer -r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from load_songs import load_songs\n",
+    "\n",
+    "data_folder = 'data/raw'\n",
+    "load_songs(data_folder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from train import train\n",
+    "\n",
+    "train()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from live_edit import play\n",
+    "\n",
+    "sub_dir_name = 'e1/'\n",
+    "play()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/train.py b/train.py
index 232d63ed..49971fcd 100644
--- a/train.py
+++ b/train.py
@@ -1,346 +1,374 @@
-import sys, random, os
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""
+Train an autoencoder model to learn to encode songs.
+"""
+
+import random
+
 import numpy as np
 from matplotlib import pyplot as plt
-import pydot
-import cv2
-import util
-import midi
 
-NUM_EPOCHS = 2000
-LR = 0.001
+import midi_utils
+import plot_utils
+import models
+
+import argparse
+
+#  Load Keras
+print("Loading keras...")
+import os
+import keras
+
+print("Keras version: " + keras.__version__)
+
+from keras.models import Model, load_model
+from keras.utils import plot_model
+from keras import backend as K
+from keras.losses import binary_crossentropy
+from keras.optimizers import Adam, RMSprop
+
+EPOCHS_QTY = 2000
+EPOCHS_TO_SAVE = [1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180, 200, 250, 300, 350, 400, 450]
+LEARNING_RATE = 0.001  # learning rate
 CONTINUE_TRAIN = False
-PLAY_ONLY = False
-USE_EMBEDDING = False
-USE_VAE = False
+GENERATE_ONLY = False
+
 WRITE_HISTORY = True
 NUM_RAND_SONGS = 10
-DO_RATE = 0.1
-BN_M = 0.9
+
+# network params
+DROPOUT_RATE = 0.1
+BATCHNORM_MOMENTUM = 0.9  # weighted normalization with the past
+USE_EMBEDDING = False
+USE_VAE = False
 VAE_B1 = 0.02
 VAE_B2 = 0.1
 
 BATCH_SIZE = 350
-MAX_LENGTH = 16
-PARAM_SIZE = 120
+MAX_WINDOWS = 16  # the maximal number of measures a song can have
+LATENT_SPACE_SIZE = 120
 NUM_OFFSETS = 16 if USE_EMBEDDING else 1
 
-def plotScores(scores, fname, on_top=True):
-	plt.clf()
-	ax = plt.gca()
-	ax.yaxis.tick_right()
-	ax.yaxis.set_ticks_position('both')
-	ax.yaxis.grid(True)
-	plt.plot(scores)
-	plt.ylim([0.0, 0.009])
-	plt.xlabel('Epoch')
-	loc = ('upper right' if on_top else 'lower right')
-	plt.draw()
-	plt.savefig(fname)
-
-def save_config():
-	with open('config.txt', 'w') as fout:
-		fout.write('LR:          ' + str(LR) + '\n')
-		fout.write('BN_M:        ' + str(BN_M) + '\n')
-		fout.write('BATCH_SIZE:  ' + str(BATCH_SIZE) + '\n')
-		fout.write('NUM_OFFSETS: ' + str(NUM_OFFSETS) + '\n')
-		fout.write('DO_RATE:     ' + str(DO_RATE) + '\n')
-		fout.write('num_songs:   ' + str(num_songs) + '\n')
-		fout.write('optimizer:   ' + type(model.optimizer).__name__ + '\n')
-
-###################################
-#  Load Keras
-###################################
-print "Loading Keras..."
-import os, math
-os.environ['THEANORC'] = "./gpu.theanorc"
-os.environ['KERAS_BACKEND'] = "theano"
-import theano
-print "Theano Version: " + theano.__version__
-
-import keras
-print "Keras Version: " + keras.__version__
-from keras.layers import Input, Dense, Activation, Dropout, Flatten, Reshape, Permute, RepeatVector, ActivityRegularization, TimeDistributed, Lambda, SpatialDropout1D
-from keras.layers.convolutional import Conv1D, Conv2D, Conv2DTranspose, UpSampling2D, ZeroPadding2D
-from keras.layers.embeddings import Embedding
-from keras.layers.local import LocallyConnected2D
-from keras.layers.pooling import MaxPooling2D, AveragePooling2D
-from keras.layers.noise import GaussianNoise
-from keras.layers.normalization import BatchNormalization
-from keras.layers.recurrent import LSTM, SimpleRNN
-from keras.initializers import RandomNormal
-from keras.losses import binary_crossentropy
-from keras.models import Model, Sequential, load_model
-from keras.optimizers import Adam, RMSprop, SGD
-from keras.preprocessing.image import ImageDataGenerator
-from keras.regularizers import l2
-from keras.utils import plot_model
-from keras import backend as K
-from keras import regularizers
-from keras.engine.topology import Layer
 K.set_image_data_format('channels_first')
 
-#Fix the random seed so that training comparisons are easier to make
+# Fix the random seed so that training comparisons are easier to make
 np.random.seed(0)
 random.seed(0)
 
-if WRITE_HISTORY:
-	#Create folder to save models into
-	if not os.path.exists('History'):
-		os.makedirs('History')
-
-###################################
-#  Load Dataset
-###################################
-print "Loading Data..."
-y_samples = np.load('samples.npy')
-y_lengths = np.load('lengths.npy')
-num_samples = y_samples.shape[0]
-num_songs = y_lengths.shape[0]
-print "Loaded " + str(num_samples) + " samples from " + str(num_songs) + " songs."
-print np.sum(y_lengths)
-assert(np.sum(y_lengths) == num_samples)
-
-print "Padding Songs..."
-x_shape = (num_songs * NUM_OFFSETS, 1)
-y_shape = (num_songs * NUM_OFFSETS, MAX_LENGTH) + y_samples.shape[1:]
-x_orig = np.expand_dims(np.arange(x_shape[0]), axis=-1)
-y_orig = np.zeros(y_shape, dtype=y_samples.dtype)
-cur_ix = 0
-for i in xrange(num_songs):
-	for ofs in xrange(NUM_OFFSETS):
-		ix = i*NUM_OFFSETS + ofs
-		end_ix = cur_ix + y_lengths[i]
-		for j in xrange(MAX_LENGTH):
-			k = (j + ofs) % (end_ix - cur_ix)
-			y_orig[ix,j] = y_samples[cur_ix + k]
-	cur_ix = end_ix
-assert(end_ix == num_samples)
-x_train = np.copy(x_orig)
-y_train = np.copy(y_orig)
-
-def to_song(encoded_output):
-	return np.squeeze(decoder([np.round(encoded_output), 0])[0])
-
-def reg_mean_std(x):
-	s = K.log(K.sum(x * x))
-	return s*s
-
-def vae_sampling(args):
-	z_mean, z_log_sigma_sq = args
-	epsilon = K.random_normal(shape=K.shape(z_mean), mean=0.0, stddev=VAE_B1)
-	return z_mean + K.exp(z_log_sigma_sq * 0.5) * epsilon
-
-def vae_loss(x, x_decoded_mean):
-	xent_loss = binary_crossentropy(x, x_decoded_mean)
-	kl_loss = VAE_B2 * K.mean(1 + z_log_sigma_sq - K.square(z_mean) - K.exp(z_log_sigma_sq), axis=None)
-	return xent_loss - kl_loss
-	
-test_ix = 0
-y_test_song = np.copy(y_train[test_ix:test_ix+1])
-x_test_song = np.copy(x_train[test_ix:test_ix+1])
-midi.samples_to_midi(y_test_song[0], 'gt.mid', 16)
-
-###################################
-#  Create Model
-###################################
-if CONTINUE_TRAIN or PLAY_ONLY:
-	print "Loading Model..."
-	model = load_model('model.h5', custom_objects=custom_objects)
-else:
-	print "Building Model..."
-
-	if USE_EMBEDDING:
-		x_in = Input(shape=x_shape[1:])
-		print (None,) + x_shape[1:]
-		x = Embedding(x_train.shape[0], PARAM_SIZE, input_length=1)(x_in)
-		x = Flatten(name='pre_encoder')(x)
-	else:
-		x_in = Input(shape=y_shape[1:])
-		print (None,) + y_shape[1:]
-		x = Reshape((y_shape[1], -1))(x_in)
-		print K.int_shape(x)
-		
-		x = TimeDistributed(Dense(2000, activation='relu'))(x)
-		print K.int_shape(x)
-		
-		x = TimeDistributed(Dense(200, activation='relu'))(x)
-		print K.int_shape(x)
-
-		x = Flatten()(x)
-		print K.int_shape(x)
-
-		x = Dense(1600, activation='relu')(x)
-		print K.int_shape(x)
-		
-		if USE_VAE:
-			z_mean = Dense(PARAM_SIZE)(x)
-			z_log_sigma_sq = Dense(PARAM_SIZE)(x)
-			x = Lambda(vae_sampling, output_shape=(PARAM_SIZE,), name='pre_encoder')([z_mean, z_log_sigma_sq])
-		else:
-			x = Dense(PARAM_SIZE)(x)
-			x = BatchNormalization(momentum=BN_M, name='pre_encoder')(x)
-	print K.int_shape(x)
-	
-	x = Dense(1600, name='encoder')(x)
-	x = BatchNormalization(momentum=BN_M)(x)
-	x = Activation('relu')(x)
-	if DO_RATE > 0:
-		x = Dropout(DO_RATE)(x)
-	print K.int_shape(x)
-
-	x = Dense(MAX_LENGTH * 200)(x)
-	print K.int_shape(x)
-	x = Reshape((MAX_LENGTH, 200))(x)
-	x = TimeDistributed(BatchNormalization(momentum=BN_M))(x)
-	x = Activation('relu')(x)
-	if DO_RATE > 0:
-		x = Dropout(DO_RATE)(x)
-	print K.int_shape(x)
-
-	x = TimeDistributed(Dense(2000))(x)
-	x = TimeDistributed(BatchNormalization(momentum=BN_M))(x)
-	x = Activation('relu')(x)
-	if DO_RATE > 0:
-		x = Dropout(DO_RATE)(x)
-	print K.int_shape(x)
-
-	x = TimeDistributed(Dense(y_shape[2] * y_shape[3], activation='sigmoid'))(x)
-	print K.int_shape(x)
-	x = Reshape((y_shape[1], y_shape[2], y_shape[3]))(x)
-	print K.int_shape(x)
-	
-	if USE_VAE:
-		model = Model(x_in, x)
-		model.compile(optimizer=Adam(lr=LR), loss=vae_loss)
-	else:
-		model = Model(x_in, x)
-		model.compile(optimizer=RMSprop(lr=LR), loss='binary_crossentropy')
-
-	plot_model(model, to_file='model.png', show_shapes=True)
-
-###################################
-#  Train
-###################################
-print "Compiling SubModels..."
-func = K.function([model.get_layer('encoder').input, K.learning_phase()],
-				  [model.layers[-1].output])
-enc = Model(inputs=model.input, outputs=model.get_layer('pre_encoder').output)
-
-rand_vecs = np.random.normal(0.0, 1.0, (NUM_RAND_SONGS, PARAM_SIZE))
-np.save('rand.npy', rand_vecs)
-
-def make_rand_songs(write_dir, rand_vecs):
-	for i in xrange(rand_vecs.shape[0]):
-		x_rand = rand_vecs[i:i+1]
-		y_song = func([x_rand, 0])[0]
-		midi.samples_to_midi(y_song[0], write_dir + 'rand' + str(i) + '.mid', 16, 0.25)
-
-def make_rand_songs_normalized(write_dir, rand_vecs):
-	if USE_EMBEDDING:
-		x_enc = np.squeeze(enc.predict(x_orig))
-	else:
-		x_enc = np.squeeze(enc.predict(y_orig))
-	
-	x_mean = np.mean(x_enc, axis=0)
-	x_stds = np.std(x_enc, axis=0)
-	x_cov = np.cov((x_enc - x_mean).T)
-	u, s, v = np.linalg.svd(x_cov)
-	e = np.sqrt(s)
-
-	print "Means: ", x_mean[:6]
-	print "Evals: ", e[:6]
-	
-	np.save(write_dir + 'means.npy', x_mean)
-	np.save(write_dir + 'stds.npy', x_stds)
-	np.save(write_dir + 'evals.npy', e)
-	np.save(write_dir + 'evecs.npy', v)
-
-	x_vecs = x_mean + np.dot(rand_vecs * e, v)
-	make_rand_songs(write_dir, x_vecs)
-	
-	title = ''
-	if '/' in write_dir:
-		title = 'Epoch: ' + write_dir.split('/')[-2][1:]
-	
-	plt.clf()
-	e[::-1].sort()
-	plt.title(title)
-	plt.bar(np.arange(e.shape[0]), e, align='center')
-	plt.draw()
-	plt.savefig(write_dir + 'evals.png')
-
-	plt.clf()
-	plt.title(title)
-	plt.bar(np.arange(e.shape[0]), x_mean, align='center')
-	plt.draw()
-	plt.savefig(write_dir + 'means.png')
-	
-	plt.clf()
-	plt.title(title)
-	plt.bar(np.arange(e.shape[0]), x_stds, align='center')
-	plt.draw()
-	plt.savefig(write_dir + 'stds.png')
-
-if PLAY_ONLY:
-	print "Generating Songs..."
-	make_rand_songs_normalized('', rand_vecs)
-	for i in xrange(20):
-		x_test_song = x_train[i:i+1]
-		y_song = model.predict(x_test_song, batch_size=BATCH_SIZE)[0]
-		midi.samples_to_midi(y_song, 'gt' + str(i) + '.mid', 16)
-	exit(0)
-		  
-print "Training..."
-save_config()
-train_loss = []
-ofs = 0
-
-for iter in xrange(NUM_EPOCHS):
-	if USE_EMBEDDING:
-		history = model.fit(x_train, y_train, batch_size=BATCH_SIZE, epochs=1)
-	else:
-		cur_ix = 0
-		for i in xrange(num_songs):
-			end_ix = cur_ix + y_lengths[i]
-			for j in xrange(MAX_LENGTH):
-				k = (j + ofs) % (end_ix - cur_ix)
-				y_train[i,j] = y_samples[cur_ix + k]
-			cur_ix = end_ix
-		assert(end_ix == num_samples)
-		ofs += 1
-
-		history = model.fit(y_train, y_train, batch_size=BATCH_SIZE, epochs=1)
-
-	loss = history.history["loss"][-1]
-	train_loss.append(loss)
-	print "Train Loss: " + str(train_loss[-1])
-	
-	if WRITE_HISTORY:
-		plotScores(train_loss, 'History/Scores.png', True)
-	else:
-		plotScores(train_loss, 'Scores.png', True)
-	
-	i = iter + 1
-	if i in [1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180, 200, 250, 300, 350, 400, 450] or (i % 100 == 0):
-		write_dir = ''
-		if WRITE_HISTORY:
-			#Create folder to save models into
-			write_dir = 'History/e' + str(i)
-			if not os.path.exists(write_dir):
-				os.makedirs(write_dir)
-			write_dir += '/'
-			model.save('History/model.h5')
-		else:
-			model.save('model.h5')
-		print "Saved"
-
-		if USE_EMBEDDING:
-			y_song = model.predict(x_test_song, batch_size=BATCH_SIZE)[0]
-		else:
-			y_song = model.predict(y_test_song, batch_size=BATCH_SIZE)[0]
-		util.samples_to_pics(write_dir + 'test', y_song)
-		midi.samples_to_midi(y_song, write_dir + 'test.mid', 16)
-
-		make_rand_songs_normalized(write_dir, rand_vecs)
-
-print "Done"
+
+def vae_loss(x, x_decoded_mean, z_log_sigma_sq, z_mean):
+    """
+    Variational autoencoder loss function.
+    :param x:
+    :param x_decoded_mean:
+    :param z_log_sigma_sq:
+    :param z_mean:
+    :return:
+    """
+    xent_loss = binary_crossentropy(x, x_decoded_mean)
+    kl_loss = VAE_B2 * K.mean(1 + z_log_sigma_sq - K.square(z_mean) - K.exp(z_log_sigma_sq), axis=None)
+    return xent_loss - kl_loss
+
+
+def plot_losses(scores, f_name, on_top=True):
+    """
+    Plot loss.
+    :param scores:
+    :param f_name:
+    :param on_top:
+    :return:
+    """
+    plt.clf()
+    ax = plt.gca()
+    ax.yaxis.tick_right()
+    ax.yaxis.set_ticks_position('both')
+    ax.yaxis.grid(True)
+    plt.plot(scores)
+    plt.ylim([0.0, 0.009])
+    plt.xlabel('Epoch')
+    loc = ('upper right' if on_top else 'lower right')
+    plt.draw()
+    plt.savefig(f_name)
+
+
+def save_training_config(num_songs, model, learning_rate):
+    """
+    Save configuration of training.
+    :param num_songs:
+    :param model:
+    :return:
+    """
+    with open('results/config.txt', 'w') as file_out:
+        file_out.write('LEARNING_RATE:       ' + str(learning_rate) + '\n')
+        file_out.write('BATCHNORM_MOMENTUM:  ' + str(BATCHNORM_MOMENTUM) + '\n')
+        file_out.write('BATCH_SIZE:          ' + str(BATCH_SIZE) + '\n')
+        file_out.write('NUM_OFFSETS:         ' + str(NUM_OFFSETS) + '\n')
+        file_out.write('DROPOUT_RATE:        ' + str(DROPOUT_RATE) + '\n')
+        file_out.write('num_songs:           ' + str(num_songs) + '\n')
+        file_out.write('optimizer:           ' + type(model.optimizer).__name__ + '\n')
+
+
+def generate_random_songs(decoder, write_dir, random_vectors):
+    """
+    Generate random songs using random latent vectors.
+    :param decoder:
+    :param write_dir:
+    :param random_vectors:
+    :return:
+    """
+    for i in range(random_vectors.shape[0]):
+        random_latent_x = random_vectors[i:i + 1]
+        y_song = decoder([random_latent_x, 0])[0]
+        midi_utils.samples_to_midi(y_song[0], write_dir + 'random_vectors' + str(i) + '.mid', 32)
+
+
+def calculate_and_store_pca_statistics(encoder, x_orig, y_orig, write_dir):
+    """
+    Calculate means, stddevs, covariance singular values (pca values), covariance singular vectors (pca vectors)
+    to more efficiently navigate/find configurations in the latent space.
+    :param encoder:
+    :param x_orig:
+    :param y_orig:
+    :param write_dir:
+    :return:
+    """
+    if USE_EMBEDDING:
+        latent_x = np.squeeze(encoder.predict(x_orig))
+    else:
+        latent_x = np.squeeze(encoder.predict(y_orig))
+
+    latent_mean = np.mean(latent_x, axis=0)
+    latent_stds = np.std(latent_x, axis=0)
+    latent_cov = np.cov((latent_x - latent_mean).T)
+    _, latent_pca_values, latent_pca_vectors = np.linalg.svd(latent_cov)
+    latent_pca_values = np.sqrt(latent_pca_values)
+
+    print("Latent Mean values: ", latent_mean[:6])
+    print("Latent PCA values: ", latent_pca_values[:6])
+
+    np.save(write_dir + 'latent_means.npy', latent_mean)
+    np.save(write_dir + 'latent_stds.npy', latent_stds)
+    np.save(write_dir + 'latent_pca_values.npy', latent_pca_values)
+    np.save(write_dir + 'latent_pca_vectors.npy', latent_pca_vectors)
+    return latent_mean, latent_stds, latent_pca_values, latent_pca_vectors
+
+
+def generate_normalized_random_songs(x_orig, y_orig, encoder, decoder, random_vectors, write_dir):
+    """
+    Generate a number of random songs from some normal latent vector samples.
+    :param encoder:
+    :param x_orig:
+    :param y_orig:
+    :param decoder:
+    :param write_dir:
+    :param random_vectors:
+    :return:
+    """
+    latent_mean, latent_stds, pca_values, pca_vectors = calculate_and_store_pca_statistics(encoder, x_orig, y_orig, write_dir)
+
+    latent_vectors = latent_mean + np.dot(random_vectors * pca_values, pca_vectors)
+    generate_random_songs(decoder, write_dir, latent_vectors)
+
+    title = ''
+    if '/' in write_dir:
+        title = 'Epoch: ' + write_dir.split('/')[-2][1:]
+
+    plt.clf()
+    pca_values[::-1].sort()
+    plt.title(title)
+    plt.bar(np.arange(pca_values.shape[0]), pca_values, align='center')
+    plt.draw()
+    plt.savefig(write_dir + 'latent_pca_values.png')
+
+    plt.clf()
+    plt.title(title)
+    plt.bar(np.arange(pca_values.shape[0]), latent_mean, align='center')
+    plt.draw()
+    plt.savefig(write_dir + 'latent_means.png')
+
+    plt.clf()
+    plt.title(title)
+    plt.bar(np.arange(pca_values.shape[0]), latent_stds, align='center')
+    plt.draw()
+    plt.savefig(write_dir + 'latent_stds.png')
+
+
+def train(samples_path='data/interim/samples.npy', lengths_path='data/interim/lengths.npy', epochs_qty=EPOCHS_QTY, learning_rate=LEARNING_RATE):
+    """
+    Train model.
+    :return:
+    """
+
+    # Create folders to save models into
+    if not os.path.exists('results'):
+        os.makedirs('results')
+    if WRITE_HISTORY and not os.path.exists('results/history'):
+        os.makedirs('results/history')
+
+    # Load dataset into memory
+    print("Loading Data...")
+    if not os.path.exists(samples_path) or not os.path.exists(lengths_path):
+        print('No input data found, run preprocess_songs.py first.')
+        exit(1)
+
+    y_samples = np.load(samples_path)
+    y_lengths = np.load(lengths_path)
+
+    samples_qty = y_samples.shape[0]
+    songs_qty = y_lengths.shape[0]
+    print("Loaded " + str(samples_qty) + " samples from " + str(songs_qty) + " songs.")
+    print(np.sum(y_lengths))
+    assert (np.sum(y_lengths) == samples_qty)
+
+    print("Preparing song samples, padding songs...")
+    x_shape = (songs_qty * NUM_OFFSETS, 1)  # for embedding
+    x_orig = np.expand_dims(np.arange(x_shape[0]), axis=-1)
+
+    y_shape = (songs_qty * NUM_OFFSETS, MAX_WINDOWS) + y_samples.shape[1:]  # (songs_qty, max number of windows, window pitch qty, window beats per measure)
+    y_orig = np.zeros(y_shape, dtype=y_samples.dtype)  # prepare dataset array
+
+    # fill in measure of songs into input windows for network
+    song_start_ix = 0
+    song_end_ix = y_lengths[0]
+    for song_ix in range(songs_qty):
+        for offset in range(NUM_OFFSETS):
+            ix = song_ix * NUM_OFFSETS + offset  # calculate the index of the song with its offset
+            song_end_ix = song_start_ix + y_lengths[song_ix]  # get song end ix
+            for window_ix in range(MAX_WINDOWS):  # get a maximum number of measures from a song
+                song_measure_ix = (window_ix + offset) % y_lengths[song_ix]  # chosen measure of song to be placed in window (modulo song length)
+                y_orig[ix, window_ix] = y_samples[song_start_ix + song_measure_ix]  # move measure into window
+        song_start_ix = song_end_ix  # new song start index is previous song end index
+    assert (song_end_ix == samples_qty)
+    x_train = np.copy(x_orig)
+    y_train = np.copy(y_orig)
+
+    # copy some song from the samples and write it to midi again
+    test_ix = 0
+    y_test_song = np.copy(y_train[test_ix: test_ix + 1])
+    x_test_song = np.copy(x_train[test_ix: test_ix + 1])
+    midi_utils.samples_to_midi(y_test_song[0], 'data/interim/gt.mid')
+
+    #  create model
+    if CONTINUE_TRAIN or GENERATE_ONLY:
+        print("Loading model...")
+        model = load_model('results/model.h5')
+    else:
+        print("Building model...")
+
+        model = models.create_autoencoder_model(input_shape=y_shape[1:],
+                                                latent_space_size=LATENT_SPACE_SIZE,
+                                                dropout_rate=DROPOUT_RATE,
+                                                max_windows=MAX_WINDOWS,
+                                                batchnorm_momentum=BATCHNORM_MOMENTUM,
+                                                use_vae=USE_VAE,
+                                                vae_b1=VAE_B1,
+                                                use_embedding=USE_EMBEDDING,
+                                                embedding_input_shape=x_shape[1:],
+                                                embedding_shape=x_train.shape[0])
+
+        if USE_VAE:
+            model.compile(optimizer=Adam(lr=learning_rate), loss=vae_loss)
+        else:
+            model.compile(optimizer=RMSprop(lr=learning_rate), loss='binary_crossentropy')
+
+        # plot model with graphvis if installed
+        try:
+            plot_model(model, to_file='results/model.png', show_shapes=True)
+        except OSError as e:
+            print(e)
+
+    #  train
+    print("Referencing sub-models...")
+    decoder = K.function([model.get_layer('decoder').input, K.learning_phase()], [model.layers[-1].output])
+    encoder = Model(inputs=model.input, outputs=model.get_layer('encoder').output)
+
+    random_vectors = np.random.normal(0.0, 1.0, (NUM_RAND_SONGS, LATENT_SPACE_SIZE))
+    np.save('data/interim/random_vectors.npy', random_vectors)
+
+    if GENERATE_ONLY:
+        print("Generating songs...")
+        generate_normalized_random_songs(x_orig, y_orig, encoder, decoder, random_vectors, 'results/')
+        for save_epoch in range(20):
+            x_test_song = x_train[save_epoch:save_epoch + 1]
+            y_song = model.predict(x_test_song, batch_size=BATCH_SIZE)[0]
+            midi_utils.samples_to_midi(y_song, 'results/gt' + str(save_epoch) + '.mid')
+        exit(0)
+
+    save_training_config(songs_qty, model, learning_rate)
+    print("Training model...")
+    train_loss = []
+    offset = 0
+
+    for epoch in range(epochs_qty):
+        print("Training epoch: ", epoch, "of", epochs_qty)
+        if USE_EMBEDDING:
+            history = model.fit(x_train, y_train, batch_size=BATCH_SIZE, epochs=1)
+        else:
+            # produce songs from its samples with a different starting point of the song each time
+            song_start_ix = 0
+            for song_ix in range(songs_qty):
+                song_end_ix = song_start_ix + y_lengths[song_ix]
+                for window_ix in range(MAX_WINDOWS):
+                    song_measure_ix = (window_ix + offset) % y_lengths[song_ix]
+                    y_train[song_ix, window_ix] = y_samples[song_start_ix + song_measure_ix]
+                song_start_ix = song_end_ix
+            assert (song_end_ix == samples_qty)
+            offset += 1
+
+            history = model.fit(y_train, y_train, batch_size=BATCH_SIZE, epochs=1)  # train model on reconstruction loss
+
+        # store last loss
+        loss = history.history["loss"][-1]
+        train_loss.append(loss)
+        print("Train loss: " + str(train_loss[-1]))
+
+        if WRITE_HISTORY:
+            plot_losses(train_loss, 'results/history/losses.png', True)
+        else:
+            plot_losses(train_loss, 'results/losses.png', True)
+
+        # save model periodically
+        save_epoch = epoch + 1
+        if save_epoch in EPOCHS_TO_SAVE or (save_epoch % 100 == 0) or save_epoch == epochs_qty:
+            write_dir = ''
+            if WRITE_HISTORY:
+                # Create folder to save models into
+                write_dir += 'results/history/e' + str(save_epoch)
+                if not os.path.exists(write_dir):
+                    os.makedirs(write_dir)
+                write_dir += '/'
+                model.save('results/history/model.h5')
+            else:
+                model.save('results/model.h5')
+
+            print("...Saved.")
+
+            if USE_EMBEDDING:
+                y_song = model.predict(x_test_song, batch_size=BATCH_SIZE)[0]
+            else:
+                y_song = model.predict(y_test_song, batch_size=BATCH_SIZE)[0]
+
+            plot_utils.plot_samples(write_dir + 'test', y_song)
+            midi_utils.samples_to_midi(y_song, write_dir + 'test.mid')
+
+            generate_normalized_random_songs(x_orig, y_orig, encoder, decoder, random_vectors, write_dir)
+
+    print("...Done.")
+
+
+if __name__ == "__main__":
+    # configure parser and parse arguments
+    parser = argparse.ArgumentParser(description='Train to reconstruct midi in autoencoder.')
+    parser.add_argument('--samples_path', default='data/interim/samples.npy', type=str, help='Path to samples numpy array.')
+    parser.add_argument('--lengths_path', default='data/interim/lengths.npy', type=str, help='Path to sample lengths numpy array.')
+    parser.add_argument('--epochs_qty', default=EPOCHS_QTY, type=int, help='The number of epochs to be trained.')
+    parser.add_argument('--learning_rate', default=LEARNING_RATE, type=float, help='The learning rate to train the model.')
+
+    args = parser.parse_args()
+    epochs_qty = args.epochs_qty
+    learning_rate = args.learning_rate
+    samples_path = args.samples_path
+    lengths_path = args.lengths_path
+    train(samples_path, lengths_path, epochs_qty, learning_rate)
diff --git a/util.py b/util.py
deleted file mode 100644
index 39d65bb4..00000000
--- a/util.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import numpy as np
-import cv2
-import os
-
-def transpose_range(samples):
-	merged_sample = np.zeros_like(samples[0])
-	for sample in samples:
-		merged_sample = np.maximum(merged_sample, sample)
-	merged_sample = np.amax(merged_sample, axis=0)
-	min_note = np.argmax(merged_sample)
-	max_note = merged_sample.shape[0] - np.argmax(merged_sample[::-1])
-	return min_note, max_note
-
-def generate_add_centered_transpose(samples):
-	num_notes = samples[0].shape[1]
-	min_note, max_note = transpose_range(samples)
-	s = num_notes/2 - (max_note + min_note)/2
-	out_samples = samples
-	out_lens = [len(samples), len(samples)]
-	for i in xrange(len(samples)):
-		out_sample = np.zeros_like(samples[i])
-		out_sample[:,min_note+s:max_note+s] = samples[i][:,min_note:max_note]
-		out_samples.append(out_sample)
-	return out_samples, out_lens
-	
-def generate_all_transpose(samples, radius=6):
-	num_notes = samples[0].shape[1]
-	min_note, max_note = transpose_range(samples)
-	min_shift = -min(radius, min_note)
-	max_shift = min(radius, num_notes - max_note)
-	out_samples = []
-	out_lens = []
-	for s in xrange(min_shift, max_shift):
-		for i in xrange(len(samples)):
-			out_sample = np.zeros_like(samples[i])
-			out_sample[:,min_note+s:max_note+s] = samples[i][:,min_note:max_note]
-			out_samples.append(out_sample)
-		out_lens.append(len(samples))
-	return out_samples, out_lens
-
-def sample_to_pic(fname, sample, thresh=None):
-	if thresh is not None:
-		inverted = np.where(sample > thresh, 0, 1)
-	else:
-		inverted = 1.0 - sample
-	cv2.imwrite(fname, inverted * 255)
-
-def samples_to_pics(dir, samples, thresh=None):
-	if not os.path.exists(dir): os.makedirs(dir)
-	for i in xrange(samples.shape[0]):
-		sample_to_pic(dir + '/s' + str(i) + '.png', samples[i], thresh)
-
-def pad_songs(y, y_lens, max_len):
-	y_shape = (y_lens.shape[0], max_len) + y.shape[1:]
-	y_train = np.zeros(y_shape, dtype=np.float32)
-	cur_ix = 0
-	for i in xrange(y_lens.shape[0]):
-		end_ix = cur_ix + y_lens[i]
-		for j in xrange(max_len):
-			k = j % (end_ix - cur_ix)
-			y_train[i,j] = y[cur_ix + k]
-		cur_ix = end_ix
-	assert(end_ix == y.shape[0])
-	return y_train
-
-def sample_to_pattern(sample, ix, size):
-	num_pats = 0
-	pat_types = {}
-	pat_list = []
-	num_samples = len(sample) if type(sample) is list else sample.shape[0]
-	for i in xrange(size):
-		j = (ix + i) % num_samples
-		measure = sample[j].tobytes()
-		if measure not in pat_types:
-			pat_types[measure] = num_pats
-			num_pats += 1
-		pat_list.append(pat_types[measure])
-	return str(pat_list), pat_types
-
-def embed_samples(samples):
-	note_dict = {}
-	n, m, p = samples.shape
-	samples.flags.writeable = False
-	e_samples = np.empty(samples.shape[:2], dtype=np.int32)
-	for i in xrange(n):
-		for j in xrange(m):
-			note = samples[i,j].data
-			if note not in note_dict:
-				note_dict[note] = len(note_dict)
-			e_samples[i,j] = note_dict[note]
-	samples.flags.writeable = True
-	lookup = np.empty((len(note_dict), p), dtype=np.float32)
-	for k in note_dict:
-		lookup[note_dict[k]] = k
-	return e_samples, note_dict, lookup
-
-def e_to_samples(e_samples, lookup):
-	samples = np.empty(e_samples.shape + lookup.shape[-1:], dtype=np.float32)
-	n, m = e_samples.shape
-	for i in xrange(n):
-		for j in xrange(m):
-			samples[i,j] = lookup[e_samples[i,j]]
-	return samples
-	
\ No newline at end of file