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
You can clone this repository, install dependencies using Anaconda and download a pretrained
model together with a dataset
with the following commands:
git clone git@github.com: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.
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: 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.
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.
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.
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.
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.
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},
}