README.md

---
license: cc-by-nc-4.0
library_name: pytorch
tags:
  - audio
  - deepfake-detection
  - icml-2026
---

# SONAR weights

Pretrained checkpoints for *SONAR: Spectral-Contrastive Audio Residuals
for Generalizable Deepfake Detection* (ICML 2026).

| File | ITW EER | Architecture | License |
|---|---:|---|---|
| `xlsr2_300m.pt` | — | XLSR-300M backbone (fairseq, derivative of [facebookresearch/fairseq](https://github.com/facebookresearch/fairseq/tree/main/examples/wav2vec/xlsr)). | CC-BY-NC-4.0 (upstream) |
| `baseline_xlsr_aasist.pth` | ~10.5% | Single XLSR + AASIST baseline (paper Table 1 row "XLSR+AASIST"). | CC-BY-NC-4.0 |
| `sonar_full_xlsr_aasist_eer6.pth` | **6.0%** | SONAR-Full: dual XLSR + RFE + cross-attention + AASIST + JS-alignment loss. Matches `guided_model.GuidedModel`. | CC-BY-NC-4.0 |
| `sonar_finetune_xlsr_mamba_eer5p5.pth` | **5.5%** | SONAR-Finetune: frozen XLSR-Mamba content branch + RFE/NFE + cross-attention + Conformer head + JS-alignment loss. | CC-BY-NC-4.0 |

Code: <https://github.com/idonithid/SONAR-Audio-DF-Detection>
Project page: <https://idonithid.github.io/SONAR-Audio-DF-Detection/>

## Loading

```python
from huggingface_hub import hf_hub_download
import torch
from argparse import Namespace
from sonar.guided_model import GuidedModel

ckpt = hf_hub_download(repo_id="idonithid/SONAR-weights",
                       filename="sonar_full_xlsr_aasist_eer6.pth")
xlsr = hf_hub_download(repo_id="idonithid/SONAR-weights",
                       filename="xlsr2_300m.pt")
import os; os.environ["SONAR_XLSR_CKPT"] = xlsr

model = GuidedModel(Namespace(algo=4, batch_size=1, device="cuda"), "cuda").cuda()
model.load_state_dict(torch.load(ckpt, map_location="cuda"), strict=False)
model.eval()
```

## Citation

```bibtex
@inproceedings{hidekel2026sonar,
  title     = {{SONAR}: Spectral-Contrastive Audio Residuals for Generalizable Deepfake Detection},
  author    = {Hidekel, Ido Nitzan and Lifshitz, Gal and Cohen, Khen and Raviv, Dan},
  booktitle = {Proceedings of the 43rd International Conference on Machine Learning (ICML)},
  year      = {2026}
}
```