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