DeepBach
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.
Installation
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
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,mlp,maxent}] [-i NUM_ITERATIONS] [-t [TRAIN]]
[-p [PARALLEL]] [--overwrite] [-m [MIDI_FILE]] [-l LENGTH]
[--ext EXT]
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,mlp,maxent}, --name {deepbach,mlp,maxent}
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
Examples
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 10000
Default values load pre-trained DeepBach model and generate a chorale using sequential Gibbs sampling with 20000 iterations