---
library_name: symupe
license: cc-by-nc-sa-4.0
base_model:
- SyMuPe/Aria-MIDI-MLM
datasets:
- SyMuPe/PianoCoRe
tags:
- music
- piano
- midi
- classification
- quality-assessment
- transformer
---

# SyMuPe: MIDI Quality Classifier

**MIDI-Quality-Classifier** is a model trained to automatically assess the quality of symbolic piano performances. It classifies MIDI files into four distinct categories: `score` (inexpressive/rendered), `high quality`, `low quality`, and `corrupted`.

Introduced in the paper: [**PianoCoRe: Combined and Refined Piano MIDI Dataset**](https://doi.org/10.5334/tismir.333).

- **TISMIR:** https://doi.org/10.5334/tismir.333
- **arXiv:** https://arxiv.org/abs/2605.06627
- **SyMuPe:** https://github.com/ilya16/SyMuPe
- **PianoCoRe:** https://github.com/ilya16/PianoCoRe
- **Dataset:** https://huggingface.co/datasets/SyMuPe/PianoCoRe

## Architecture

- **Type:** Transformer Encoder
- **Backbone:** [12-layer Transformer (80M parameters)](https://huggingface.co/SyMuPe/Aria-MIDI-MLM) pre-trained on the deduped subset of the [Aria-MIDI](https://huggingface.co/datasets/loubb/aria-midi) dataset using a Multi-Mask Language Modeling (mMLM) objective.
- **Classification Module:** Single layer transformer and a classification head.
- **Objective:** Sequence Classification (4 classes).
- **Inputs (score-agnostic):** `Pitch`, `Velocity`, `TimeShift`, `Duration`, absolute `TimePosition`
- **Classes:** 
  - **Score (S):** Rendered or synthesized scores with constant tempo/dynamics.
  - **High Quality (HQ):** Clean expressive performances (recorded or high-fidelity transcriptions).
  - **Low Quality (LQ):** Transcriptions with noticeable noise or minor errors.
  - **Corrupted (C):** Broken files or severely failed transcriptions.
- **Training:** Trained for 20,000 iterations on created subset of the [PianoCoRe](https://huggingface.co/datasets/SyMuPe/PianoCoRe) dataset as described in the paper.

## Quick Start

Before using this model, ensure you have the `symupe` library installed:
```shell
pip install -U symupe
```

Use the following code to classify MIDI files:
```python
import torch
from symupe import AutoClassifier

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Build Classifier by loading the model and tokenizer directly from the Hub
classifier = AutoClassifier.from_pretrained(
    "SyMuPe/MIDI-Quality-Classifier", device=device
)
# model, tokenizer, labels = classifier.model, classifier.tokenizer, classifier.labels

# Classify a MIDI file
result = classifier("performance.mid")
# result is MusicClassificationResult(...) containing:
# - midi, seq, probabilities, prediction, label, all_logits, all_probabilities, all_predictions,
#   sequences and window_indices

print(f"Predicted Label: {result.label}")
print(f"Probabilities: {result.probabilities}")
```

## License

The model weights are distributed under the [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en) license.

## Citation

If you use this model or the associated dataset in your research, please cite:

```bibtex
@inproceedings{borovik2025symupe,
  title = {{SyMuPe: Affective and Controllable Symbolic Music Performance}},
  author = {Borovik, Ilya and Gavrilev, Dmitrii and Viro, Vladimir},
  year = {2025},
  booktitle = {Proceedings of the 33rd ACM International Conference on Multimedia},
  pages = {10699--10708},
  doi = {10.1145/3746027.3755871}
}
```

```bibtex
@article{borovik2026pianocore,
  title = {{PianoCoRe: Combined and Refined Piano MIDI Dataset}},
  author = {Borovik, Ilya},
  year = {2026},
  journal = {Transactions of the International Society for Music Information Retrieval},
  volume = {9},
  number = {1},
  pages = {144--163},
  doi = {10.5334/tismir.333}
}
```