deepbach-pytorch 1.0.0

DeepBach implementation for harmonization

DeepBach

This repository contains implementations of the DeepBach model described in

DeepBach: a Steerable Model for Bach chorales generation
Gaëtan Hadjeres, François Pachet, Frank Nielsen
ICML 2017 arXiv:1612.01010

The code uses python 3.6 together with PyTorch v1.0 and music21 libraries.

For the original Keras version, please checkout the original_keras branch.

Examples of music generated by DeepBach are available on this website

Installation

You can clone this repository, install dependencies using Anaconda and download a pretrained model together with a dataset
with the following commands:

git clone [e-mail]:Ghadjeres/DeepBach.git
cd DeepBach
conda env create --name deepbach_pytorch -f environment.yml
bash dl_dataset_and_models.sh

This will create a conda env named deepbach_pytorch.

music21 editor

You might need to Open a four-part chorale. Press enter on the server address, a list of computed models should appear. Select and (re)load a model. Configure properly the music editor called by music21. On Ubuntu you can eg. use MuseScore:

sudo apt install musescore
python -c 'import music21; music21.environment.set("musicxmlPath", "/usr/bin/musescore")'

For usage on a headless server (no X server), just set it to a dummy command:

python -c 'import music21; music21.environment.set("musicxmlPath", "/bin/true")'

Usage

Usage: deepBach.py [OPTIONS]

Options:
  --note_embedding_dim INTEGER    size of the note embeddings
  --meta_embedding_dim INTEGER    size of the metadata embeddings
  --num_layers INTEGER            number of layers of the LSTMs
  --lstm_hidden_size INTEGER      hidden size of the LSTMs
  --dropout_lstm FLOAT            amount of dropout between LSTM layers
  --linear_hidden_size INTEGER    hidden size of the Linear layers
  --batch_size INTEGER            training batch size
  --num_epochs INTEGER            number of training epochs
  --train                         train or retrain the specified model
  --num_iterations INTEGER        number of parallel pseudo-Gibbs sampling
                                  iterations
  --sequence_length_ticks INTEGER
                                  length of the generated chorale (in ticks)
  --help                          Show this message and exit.

Examples

You can generate a four-bar chorale with the pretrained model and display it in MuseScore by simply running

python deepBach.py

You can train a new model from scratch by adding the --train flag.

Usage with NONOTO

The command

python flask_server.py

starts a Flask server listening on port 5000. You can then use NONOTO to compose with DeepBach in an interactive way.

This server can also been started using Docker with:

docker run -p 5000:5000 -it --rm ghadjeres/deepbach

(CPU version), with or

docker run --runtime=nvidia -p 5000:5000 -it --rm ghadjeres/deepbach

(GPU version, requires nvidia-docker.

Usage within MuseScore

Deprecated

Put deepBachMuseScore.qml file in your MuseScore plugins directory, and run

python musescore_flask_server.py

Open MuseScore and activate deepBachMuseScore plugin using the Plugin manager. You can then click on the Compose button without any selection to create a new chorale from scratch. You can then select a region in the chorale score and click on the Compose button to regenerated this region using DeepBach.

Issues

Music21 editor not set

music21.converter.subConverters.SubConverterException: Cannot find a valid application path for format musicxml. Specify this in your Environment by calling environment.set(None, '/path/to/application')

Either set it to MuseScore or similar (on a machine with GUI) to to a dummy command (on a server). See the installation section.

Citing

Please consider citing this work or emailing me if you use DeepBach in musical projects.

@InProceedings{pmlr-v70-hadjeres17a,
  title = 	 {{D}eep{B}ach: a Steerable Model for {B}ach Chorales Generation},
  author = 	 {Ga{\"e}tan Hadjeres and Fran{\c{c}}ois Pachet and Frank Nielsen},
  booktitle = 	 {Proceedings of the 34th International Conference on Machine Learning},
  pages = 	 {1362--1371},
  year = 	 {2017},
  editor = 	 {Doina Precup and Yee Whye Teh},
  volume = 	 {70},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {International Convention Centre, Sydney, Australia},
  month = 	 {06--11 Aug},
  publisher = 	 {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v70/hadjeres17a/hadjeres17a.pdf},
  url = 	 {http://proceedings.mlr.press/v70/hadjeres17a.html},
}