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PyTorch Implementation of Onsets and Frames

This is a PyTorch implementation of Google's Onsets and Frames model, using the Maestro dataset 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

The data subdirectory already contains the MAPS database. 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/model/model-100000.pt

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/model/model-100000.pt Maestro test

Implementation Details

This implementation contains a few of the additional improvements on the model that were reported in the Maestro paper, including:

  • Offset head
  • Increased model capacity, making it 26M parameters by default
  • Gradient stopping of inter-stack connections
  • L2 Gradient clipping of each parameter at 3
  • Using the HTK mel frequencies

Meanwhile, this implementation does not include the following features:

  • Variable-length input sequences that slices at silence or zero crossings
  • Harmonically decaying weights on the frame loss

Despite these, this implementation is able to achieve a comparable performance to what is reported on the Maestro paper as the performance without data augmentation.