Version 1.0 of this repository contains the implementation of the DeepBach model described in
DeepBach: a Steerable Model for Bach chorales generation
Gaëtan Hadjeres, François Pachet
arXiv preprint arXiv:1612.01010
The code uses python 3.5 together with Keras and music21 libraries.
Version 2.0 (this one) elaborates on this approach. Results will be presented in an upcoming paper. This version contains a Python Flask server and a MuseScore plugin providing an interactive use of DeepBach.
You can download and install DeepBach's dependencies with the following commands:
git clone git@github.com:SonyCSL-Paris/DeepBach.git
cd DeepBach
sudo pip3 install -r requirements.txt
Make sure either Theano or Tensorflow is installed. You also need to configure properly the music editor called by music21.
usage: deepBach.py [-h] [--timesteps TIMESTEPS] [-b BATCH_SIZE_TRAIN]
[-s SAMPLES_PER_EPOCH] [--num_val_samples NUM_VAL_SAMPLES]
[-u NUM_UNITS_LSTM [NUM_UNITS_LSTM ...]] [-d NUM_DENSE]
[-n {deepbach,skip}] [-i NUM_ITERATIONS] [-t [TRAIN]]
[-p [PARALLEL]] [--overwrite] [-m [MIDI_FILE]] [-l LENGTH]
[--ext EXT] [-o [OUTPUT_FILE]] [--dataset [DATASET]]
[-r [REHARMONIZATION]]
optional arguments:
-h, --help show this help message and exit
--timesteps TIMESTEPS
model's range (default: 16)
-b BATCH_SIZE_TRAIN, --batch_size_train BATCH_SIZE_TRAIN
batch size used during training phase (default: 128)
-s SAMPLES_PER_EPOCH, --samples_per_epoch SAMPLES_PER_EPOCH
number of samples per epoch (default: 89600)
--num_val_samples NUM_VAL_SAMPLES
number of validation samples (default: 1280)
-u NUM_UNITS_LSTM [NUM_UNITS_LSTM ...], --num_units_lstm NUM_UNITS_LSTM [NUM_UNITS_LSTM ...]
number of lstm units (default: [200, 200])
-d NUM_DENSE, --num_dense NUM_DENSE
size of non recurrent hidden layers (default: 200)
-n {deepbach,skip}, --name {deepbach,skip}
model name (default: deepbach)
-i NUM_ITERATIONS, --num_iterations NUM_ITERATIONS
number of gibbs iterations (default: 20000)
-t [TRAIN], --train [TRAIN]
train models for N epochs (default: 15)
-p [PARALLEL], --parallel [PARALLEL]
number of parallel updates (default: 16)
--overwrite overwrite previously computed models
-m [MIDI_FILE], --midi_file [MIDI_FILE]
relative path to midi file
-l LENGTH, --length LENGTH
length of unconstrained generation
--ext EXT extension of model name
-o [OUTPUT_FILE], --output_file [OUTPUT_FILE]
path to output file
--dataset [DATASET] path to dataset folder
-r [REHARMONIZATION], --reharmonization [REHARMONIZATION]
reharmonization of a melody from the corpus identified
by its id
Generate a chorale of length 100:
python3 deepBach.py -l 100
Create a DeepBach model with three stacked lstm layers of size 200, hidden layers of size 500 and train it for 10 epochs before sampling:
python3 deepBach.py --ext big -u 200 200 200 -d 500 -t 10
Generate chorale harmonization with soprano extracted from midi/file/path.mid using parallel Gibbs sampling with 10000 updates (total number of updates)
python3 deepBach.py -m midi/file/path.mid -p -i 20000
Use another model with custom parameters:
python3 deepBach.py --ext big -t 30 --timesteps 32 -u 512 256 -d 256 -b 16
Use another database, your dataset folder must contain .xml or .mid files with the same number of voices:
python3 deepBach.py --dataset /path/to/dataset/folder/ --ext dowland -t 30 --timesteps 32 -u 256 256 -d 256 -b 32
Reharmonization of a melody from the training or testing set:
python3 deepBach.py -p -i 40000 -r 25
Default values load pre-trained DeepBach model and generate a chorale using sequential Gibbs sampling with 20000 iterations
Put deepBachMuseScore.qml file in your MuseScore2/Plugins directory.
Run local Flask server:
export FLASK_APP=plugin_flask_server.py
flask run
or a public server (only one connection is supported for the moment).
export FLASK_APP=plugin_flask_server.py
flask run --host 0.0.0.0
Open MuseScore and activate deepBachMuseScore plugin using the Plugin manager. Open a four-part chorale. Press enter on the server address, a list of computed models should appear. Select and (re)load a model. Select a zone in the chorale and click on the compose button.
This plugin only generates C major/A minor chorales with cadences every to bars. This is a limitation of the plugin, not the model itself.
Please consider citing this work or email me if you use DeepBach in musical projects.
A pretrained model is available here. Extract the archive contents in the DeepBach project root folder.
ImportError issues: Make sure DeepBach project is in your PYTHONPATH
export PYTHONPATH=/Path/to/DeepBach/Project