README.md

---
license: apache-2.0
datasets:
- amaai-lab/melodySim
---

# MelodySim: Measuring Melody-aware Music Similarity for Plagiarism Detection

[Github](https://github.com/AMAAI-Lab/MelodySim) | [Paper](https://arxiv.org/abs/2505.20979) | [Dataset](https://huggingface.co/datasets/amaai-lab/melodySim)

This is a checkpoint for [MelodySim](https://github.com/AMAAI-Lab/MelodySim), a MERT-based music audio similarity model which can be used for melody similarity detection. This checkpoint contains pre-trained weights of ```m-a-p/MERT-v1-95M```.

## Usage

1. Clone the MelodySim github repo
```bash
git clone https://github.com/AMAAI-Lab/MelodySim.git
cd MelodySim
pip install -r requirements.txt
```

2. Download model checkpoint
```python
from huggingface_hub import hf_hub_download

repo_id = "amaai-lab/MelodySim"
model_path = hf_hub_download(repo_id=repo_id, filename="siamese_net_20250328.ckpt")
```
or using wget in linux ```wget https://huggingface.co/amaai-lab/MelodySim/resolve/main/siamese_net_20250328.ckpt```

3. Run inference
Try out ```inference.py``` to run the model on two audio files, analyzing their similarity and reaching a decesion on whether or not they are the same song. We provide a positive pair and a negative pair as examples. Try out
```
python inference.py -audio-path1 ./data/example_wavs/Track01968_original.mp3 -audio-path2 ./data/example_wavs/Track01976_original.mp3 -ckpt-path path/to/checkpoint.ckpt
python inference.py -audio-path1 ./data/example_wavs/Track01976_original.mp3 -audio-path2 ./data/example_wavs/Track01976_version1.mp3 -ckpt-path path/to/checkpoint.ckpt
```
Feel free to play around the hyperparameters 
- ```-window-len-sec```, ```-hop-len-sec``` (the way segmenting the input audios);
- ```--proportion-thres``` (how many similar segments should we consider the two pieces to be the same);
- ```--decision-thres``` (between 0 and 1, the smallest similarity value that we consider to be the same);
- ```--min-hits``` (for each window in piece1, the minimum number of similar windows in piece2 to assign that window to be plagiarized).

4. Training and testing details are summarized in [MelodySim Github](https://github.com/AMAAI-Lab/MelodySim). You may need the [MelodySim](https://huggingface.co/datasets/amaai-lab/melodySim) dataset, containing 1,710 valid synthesized pieces originated from Slakh2100 dataset, each having 4 different versions (through various augmentation settings), with a total duration of 419 hours.

The testing results for the checkpoint on MelodySim Dataset testing split are as follows:

|           |**Precision**| **Recall** |   **F1**   |
|-----------|-------------|------------|------------|
| Different | 1.00        | 0.94       | 0.97       |
| Similar   | 0.94        | 1.00       | 0.97       |
| Average   | 0.97        | 0.97       | 0.97       |
| Accuracy  |             |            | 0.97       |


## Citation

If you find this work useful in your research, please cite:

```bibtex
@article{lu2025melodysim,
  title={Text2midi-InferAlign: Improving Symbolic Music Generation with Inference-Time Alignment},
  author={Tongyu Lu and Charlotta-Marlena Geist and Jan Melechovsky and Abhinaba Roy and Dorien Herremans},
  year={2025},
  journal={arXiv:2505.20979}
}
```