Clara: A Neural Net Music Generator

Train an AWD-LSTM to generate piano or violin/piano music
Project overview is here.
Sample generations are here.
Detailed paper is here.

Requirements:

• PyTorch version 0.3.0.post4
• FastAI
• GeneralUser: install in data folder (this is needed to translate midi files to mp3)
• MIT's music21

Note: From inside the musical-neural-net home directory, run:

ln -s ./replace/this/with/your/path/to/fastai/library fastai 

to create a symbolic link to the fastai library.

Basic:

Run the Jupyter Notebook BasicIntro.ipynb or follow the individual instructions here. To create generations with a pretrained notewise model, using only the default settings, run:

python make_test_train.py --example
python generator.py -model notewise_generator -output notewise_generation_samples

The output samples will be in data/output/notewise_generation_samples, or open Playlist.ipynb to listen to the output samples. I recommend the free program MuseScore to translate the midi files into sheet music.

Note, you must first make sure the requirements (above) are installed.

Data:

If you use your own midi files, they should go in data/composers/midi/piano_solo or data/composers/midi/chamber (the project expects to see a folder of midi files for each composer, ie: data/composers/midi/piano_solo/bach/example_piece.mid).

Run:

python midi-to-encoding.py

to translate midi files to text files in the various notewise and chordwise options.

My dataset is available here (you can download any or all):
Put these in data/composers/notewise:

• Notewise piano solo text files
• Notewise piano/violin text files

Put these in data/composers/notewise:

• Chordwise piano solo text files
• Chordwise piano/violin text files

(Run tar -zxvf .tar.gz to expand each one.)

Training and Generation:

• make_test_train.py - create the training and testing datasets (adjust notewise/chordwise, optionally create only a small sample size)
• train.py - train an AWD-LSTM (adjust model parameters, dropout, and training regime)
• generate.py - generate new samples (adjust generation size)

Each script has default settings which should be reasonable, but use --help to see the different options and parameters which can be modified.
Playlist.ipynb is a simple Jupyter Notebook which creates a nicely formatted playlist for listening to all the generations.

Music Critic:

• make_critic_data.py - create the training and test datasests (requires a trained generation model to create the fake data)
• critic.py - trains a classifier to predict if a sample is human-composed or LSTM-composed

Composer Classifier:

• make_composer_data.py - create the training and test datasests (all from human composed pieces)
• composer_classifier.py - trains a classifier to predict which human composed the piece

Pretrained Models:

Sample pretrained models are included in this repository. They were trained using the default settings (all composers, notewise using a sample frequency 12, chordwise using a sample frequency 4).

• notewise_generator
• chordwise_generator
• chamber_generator (uses notewise encoding)
• notewise_critic
• notewise_composer_classifier

For example, use:

python generator.py -model notewise_generator -output notewise_generation_samples --random_freq 0.8 --trunc 3

to generate musical samples.