Migration to python3 and increase chance to make it run on your own machine

.gitignore

@@ -0,0 +1,4 @@
+data/*
+config.txt
+history
+results/*

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