Multitrack Music Transformer

Prerequisites

Set up development environment

We recommend using Conda. You can create the environment with the following command.

conda env create -f environment.yml

Preprocessing

Download the datasets

Please download the Symbolic orchestral database (SOD). You may also download in command line directly by wget https://qsdfo.github.io/LOP/database/SOD.zip.

We also support the following two datasets:

Lakh MIDI Dataset (LMD)

Download in command line directly via wget http://hog.ee.columbia.edu/craffel/lmd/lmd_full.tar.gz

SymphonyNet Dataset

Download in command line directly via gdown https://drive.google.com/u/0/uc?id=1j9Pvtzaq8k_QIPs8e2ikvCR-BusPluTb&export=download

Prepare the name list

Get a list of filenames for each dataset.

find data/sod/SOD -type f -name *.mid -o -name *.xml | cut -c 14- > data/sod/original-names.txt

Note: Change the number in the cut command for different datasets.

Convert the data

Convert the MIDI and MusicXML files into MusPy files for processing.

python convert_sod.py

Note: You may enable multiprocessing via the -j {JOBS} option. For example, python convert_sod.py -j 10 will run the script with 10 jobs.

Extract the note list

Extract a list of notes from the MusPy JSON files.

python extract.py -d sod

Split training/validation/test sets

Split the processed data into training, validation and test sets.

python split.py -d sod

Training

Train a Multitrack Music Transformer model.

Absolute positional embedding (APE):

python mtmt/train.py -d sod -o exp/sod/ape -g 0
Relative positional embedding (RPE):

python mtmt/train.py -d sod -o exp/sod/rpe --no-abs_pos_emb --rel_pos_emb -g 0
No positional embedding (NPE):

python mtmt/train.py -d sod -o exp/sod/npe --no-abs_pos_emb --no-rel_pos_emb -g 0

Please run python mtmt/train.py -h to see additional options.

Evaluation

Evaluate the trained model.

python mtmt/evaluate.py -d sod -o exp/sod/ape -ns 100 -g 0

Please run python mtmt/evaluate.py -h to see additional options.

Generation (inference)

Generate new samples using a trained model.

python mtmt/generate.py -d sod -o exp/sod/ape -g 0

Please run python mtmt/generate.py -h to see additional options.

placiana/mmt