This re-implementation is meant for the COMP6248 reproducibility challenge.

Re-implementation of music transformer paper published for ICLR 2019.
Link to paper: https://openreview.net/forum?id=rJe4ShAcF7

We focus on re-implementing a subset of the experiments described in the published paper, only using the JSB Chorales dataset for training three model variations listed below:

• Baseline Transformer (TF) (5L, 256hs, 256att, 1024ff, 8h)
• Baseline TF + concatenating positional sinusoids
• TF with efficient relative attention (5L, 512hs, 512att, 512ff, 256r, 8h)

Unfortunately, we were unable to successfully reproduce the results shown in the published paper. There are a number of reasons that contribute to this, but mainly due to insufficient details provided for the JSB dataset and how the data was processed for the transformer model.

Nonetheless, these are the results obtained after training for 300 epochs:

Please read the report for more detailed information regarding the re-implementation.

Note: Please create the following folders in this directory before running the scripts:

• ./weights/ - for storing the trained weights.
• ./outputs/ - for storing training/validation loss values and generated outputs.
• ./plots/ - for storing the plots.

To train the models, simply run train.py and add the arguments accordingly:

python train.py -src_data datasets/JSB-Chorales-dataset/Jsb16thSeparated.npz -epochs 300 -weights_name baselineTF_300epoch -device cuda:2 -checkpoint 10

To generate music using a trained model, run generate.py and add the arguments accordingly:

python generate.py -src_data datasets/JSB-Chorales-dataset/Jsb16thSeparated.npz -load_weights weights -weights_name baselineTF_300epoch -device cuda:1 -k 3

To generate the plots for training/validation loss, please use gen_training_plots.py:

python gen_training_plots.py -t_loss_file <t_loss_npy_file> -v_loss_file <v_loss_npy_file>

<t_loss_npy_file> and <t_loss_npy_file> are generated during training and are saved in the outputs directory.

The generated sequences can be plotted and listened using this IPython Notebook.
Please note that this requires Magenta to be installed.

1. JSB Chorales dataset

• https://github.com/czhuang/JSB-Chorales-dataset

2. MAESTRO dataset (Not used)

• https://magenta.tensorflow.org/datasets/maestro

1. PyTorch with CUDA-enabled GPUs.

• Install CUDA 9.0 and CUDNN 7.4.1.5
• Then follow these steps:

conda create -n torch python=3.6
conda activate torch
conda install pytorch torchvision cuda90 -c pytorch

2. Magenta for plotting and playing the generated notes.
   Steps for installing on Ubuntu:

conda create -n magenta python=3.6
conda activate magenta
sudo apt-get update
sudo apt-get install build-essential libasound2-dev libjack-dev libfluidsynth1 fluid-soundfont-gm
pip install --pre python-rtmidi
pip install jupyter magenta pyfluidsynth pretty_midi