Variable-Length-Piano-Infilling

The official implementation of Variable-Length Piano Infilling (VLI). (paper: Variable-Length Music Score Infilling via XLNet and Musically Specialized Positional Encoding)

VLI is a new Transformer-based model for music score infilling, i.e., to generate a polyphonic music sequence that fills in the gap between given past and future contexts. Our model can infill a variable number of notes for different time spans.

Installation

1. Clone and install the modified Huggingface Transformer package.
2. Clone this repo and install the required packages.

git clone https://github.com/reichang182/Variable-Length-Piano-Infilling.git
cd  Variable-Length-Piano-Infilling
pip install -r requirement.txt

3. Download and unzip the AIlabs.tw Pop1K7 dataset. (Download link: here).

Training & Testing

# Prepare data
python prepare_data.py \
	--midi-folder datasets/midi/midi_synchronized/ \
	--save-folder ./

# Train the model
python train.py --train

# Test the trained model
python train.py

Baselines

The codes to run baselines in our paper are in the baselines folder. We implement ILM and FELIX according to their paper (ILM and FELIX) and based on the implementation of Transformer-XL and BERT in Huggingface Transformer. They can also be trained and tested through the same command as our model does above.

# cd baselines/ILM or cd baselines/FELIX

# Train the model
python train.py --train \
	--dict-file ../../dictionary.pickle \
	--data-file ../../worded_data.pickle

# Test the trained model
python train.py \
	--dict-file ../../dictionary.pickle \
	--data-file ../../worded_data.pickle

Architecture

Results