KO | EN

GitLite

Minimalist Open Source Repository Search Engine

gitlite — search

#dsh #dsh-plugin #typescript #ai-agent #developer-tools #claude-code #codex #deepseek-harness #ai-agents #cli #react #mcp

MIDI2ScoreTransformer

★ 100 Open GitHub ↗

Code for the ISMIR 2024 paper "End-to-end Piano Performance-MIDI to Score Conversion with Transformers"

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# MIDI2ScoreTransformer
Code for the ISMIR 2024 paper ["End-to-end Piano Performance-MIDI to Score Conversion with Transformers"](https://arxiv.org/abs/2410.00210)

## Installation
The code is written in Python 3.11 and relies on the following packages
- everything mentioned in `requirements.txt`
- `MuseScore`: https://github.com/musescore/MuseScore (depending on your platform, please adjust the path in `constants.py`)

Due to delays and difficulties merging changes with upstream versions, we currently require installing custom versions of the following packages (will be done automatically with `requirements.txt`):

- `music21`: https://github.com/TimFelixBeyer/music21 (fixes tie-stripping and contains various other tweaks)
- `score_transformer`: https://github.com/TimFelixBeyer/ScoreTransformer (for score_similarity metrics and tokenization comparisons)

In addition, you must manually clone and install this package:
- `muster`: https://github.com/TimFelixBeyer/amtevaluation.github.io (fixes various memory leak issues of the original version)

### Datasets
Please use this version of the ASAP-Dataset as it contains some fixes.

- `ASAPDataset`: [https://github.com/TimFelixBeyer/ASAPDataset](https://github.com/TimFelixBeyer/asap-dataset/tree/8cba199e15931975542010a7ea2ff94a6fc9cbee) (contains a few fixes for the ASAP dataset, make sure you select the correct commit for reproducibility and place the `asap-dataset` folder into the `data` folder).
- `ACPAS`: [https://cheriell.github.io/research/ACPAS_dataset/](https://cheriell.github.io/research/ACPAS_dataset/) (only download `metadata_R.csv` and `metadata_S.csv`, place them into a folder called `ACPAS-dataset` and put it into the `data` folder)

Afterwards, your `data` folder should look like this:
```
data
├── ACPAS-dataset
│   ├── metadata_R.csv
│   └── metadata_S.csv
└── asap-dataset
```

### Setup (Inference)
1. Download the `MIDI2ScoreTF.ckpt` from GitHub ('Releases' section) and place it where you like.

### Setup (Training)
1. Run `dataset.py` to preprocess the datasets and populate the cache.
2. Run `chunker.py` to create the beat-aligned chunks.
3. More instructions to follow

MIDI2ScoreTransformer

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README