---
license: apache-2.0
language:
- en
tags:
- music
- MIDI
- piano
- representation-learning
- jepa
base_model: loubb/aria-medium
---

# MAJEPPA

`MAJEPPA` is a unified self-supervised model for piano performance that both **generates** and **understands** solo-piano MIDI in a single framework. Built on a pre-trained MIDI autoregressive backbone, it combines score-conditioned next-token prediction with a Joint-Embedding Predictive Architecture (JEPA) objective — learning to produce expressive continuations while also yielding transferable embeddings for downstream analysis.

Applications include score-conditioned generation across expertise levels, performance quality assessment, competition ranking, technique classification, mistake detection, and expressive-style analysis.

📖 Paper: *MAJEPPA: Morphing and Assessing in a Unified Piano Performance Space* (ISMIR 2026)  
💻 Code (loading, inference, generation): [github.com/anusfoil/majeppa](https://github.com/anusfoil/majeppa)  
📊 Benchmark: [github.com/anusfoil/evpmr](https://github.com/anusfoil/evpmr)

## Model Details

- **Base model**: [`loubb/aria-medium`](https://huggingface.co/loubb/aria-medium) — 660M-parameter LLaMA-style autoregressive MIDI transformer.
- **Adaptation**: Low-Rank Adaptation (LoRA, rank 512) on attention projections; a linear projection head 1536 → 512 for global embedding output; and additional learned tokens for conditioning (`[COND_perf]`, `[COND_rec]`) and prediction (`[PRED]`).
- **Context length**: 4096 tokens (linear RoPE scaling).

`model.safetensors` contains the full set of weights (base model, LoRA adapters, projection head, and added token embeddings), ready to be loaded together.

## Quickstart

```bash
pip install torch safetensors transformers
pip install git+https://github.com/EleutherAI/aria-utils.git
```

Load via the reference implementation in the [MAJEPPA repository](https://github.com/anusfoil/majeppa):

### Understanding — global embedding

```python
from majeppa import load_model, load_tokenizer

model = load_model("anusfoil/majeppa", device="cuda")
tokenizer = load_tokenizer()

tokens = tokenizer.encode_from_file("performance.mid", return_tensors="pt")
embedding = model.encode(tokens.to("cuda"))       # (1, 1536)
token_emb, ts = model.encode_tokens(tokens.to("cuda"))  # (T, 1536), (T,)
```

### Generation — score-conditioned performance

```python
score_tokens = tokenizer.encode_from_file("score.mid", return_tensors="pt")

# Condition on performer type and recording context
performance = model.generate(
    score_tokens.to("cuda"),
    cond_performer="virtuoso",      # or "child_beginner", "adult_intermediate", ...
    cond_recording="concert",       # or "practice", "sight_reading", "demo", ...
    max_new_tokens=2048,
    temperature=0.8,
    top_k=50,
)
tokenizer.decode_to_file(performance, "generated.mid")
```

See the [repository](https://github.com/anusfoil/majeppa) for the full list of condition tags and advanced usage.

## Intended Use

For non-commercial research on symbolic piano performance modelling. Refer to the paper for evaluation protocols and limitations.

## Citation

```bibtex
@inproceedings{zhou2026majeppa,
  title  = {MAJEPPA: Morphing and Assessing in a Unified Piano Performance Space},
  author = {Zhou, Jinwen and Zhang, Huan and Zhai, Weixi and Liang, Jinhua and Hogg, Aidan O. T. and Dixon, Simon},
  booktitle = {Proc. International Society for Music Information Retrieval Conference (ISMIR)},
  year   = {2026}
}
```