PyTorch Implementation of HPPNet Piano Transcription Model

This is a PyTorch implementation of HPPNet model, using the Maestro dataset v3 for training and the Disklavier portion of the MAPS database for testing.

Instructions

This project is quite resource-intensive; 32 GB or larger system memory and 8 GB or larger GPU memory is recommended.

Downloading Dataset

To download the Maestro dataset, first make sure that you have ffmpeg executable and run prepare_maestro.sh script:

ffmpeg -version
cd data
./prepare_maestro.sh

This will download the full Maestro dataset from Google's server and automatically unzip and encode them as FLAC files in order to save storage. However, you'll still need about 200 GB of space for intermediate storage.

Training

All package requirements are contained in requirements.txt. To train the model, run:

pip install -r requirements.txt
python train.py

train.py is written using sacred, and accepts configuration options such as:

python train.py with logdir=runs/model iterations=1000000

Trained models will be saved in the specified logdir, otherwise at a timestamped directory under runs/.

Testing

To evaluate the trained model using the MAPS database, run the following command to calculate the note and frame metrics:

python evaluate.py runs/transcriber/model-600000.pt MAPS test

Specifying --save-path will output the transcribed MIDI file along with the piano roll images:

python evaluate.py runs/model/model-100000.pt --save-path output/

In order to test on the Maestro dataset's test split instead of the MAPS database, run:

python evaluate.py runs/transcriber/model-600000.pt MAESTRO test

Acknowledgements

This project is based on the PyTorch implementation of Onsets and Frames model => https://github.com/jongwook/onsets-and-frames

Citation

@inproceedings{Wei2022HPPNet,
  author       = {Weixing Wei and
                  Peilin Li and
                  Yi Yu and
                  Wei Li},
  title        = {HPPNet: Modeling the Harmonic Structure and Pitch Invariance in Piano
                  Transcription},
  booktitle    = {Proceedings of the 23rd International Society for Music Information
                  Retrieval Conference, {ISMIR} 2022, Bengaluru, India, December 4-8,
                  2022},
  pages        = {709--716},
  year         = {2022},
  url          = {https://archives.ismir.net/ismir2022/paper/000085.pdf},
}