---
license: apache-2.0
tags:
- audio-deepfake-detection
- anti-spoofing
- self-supervised-learning
- speech
- benchmark
library_name: pytorch
pretty_name: Spoof-SUPERB Detector Checkpoints
---

# Spoof-SUPERB: Detector Checkpoints

Trained weights for every detector in **Spoof-SUPERB**, a benchmark of
self-supervised speech representations for audio deepfake detection.

- Code: https://github.com/issflab/spoof_SUPERB
- Score files: https://huggingface.co/datasets/issf/spoof-superb-scores
- Leaderboard: https://huggingface.co/spaces/issf/Spoof-SUPERB

## These are downstream weights only

Spoof-SUPERB evaluates SSL front-ends **frozen**. Only three things are trained:
the learnable layer-weighting over the upstream's hidden states, a projector, and
a linear classifier. Each checkpoint here holds exactly those:

```
ssl_model.featurizer.weights   (n_layers + 1,)   learnable layer weighting
projector.weight               (256, upstream_dim)
projector.bias                 (256,)
post_net.linear.weight         (2, 256)
post_net.linear.bias           (2,)
```

The frozen upstream is **not** included. It is loaded from s3prl at run time
under its own name, so nothing here redistributes wav2vec 2.0, HuBERT, WavLM,
XLS-R or any other published encoder.

This is why the whole set is 19 MB rather than 12.2 GB. The original training
checkpoints embedded a full copy of the frozen encoder, 766x larger in total and
byte-identical to what s3prl already serves.

**Verified, not assumed.** The encoder tensors in the original checkpoints were
compared against freshly instantiated s3prl upstreams: identical for every
comparable tensor across `apc` (14/14), `wav2vec2_base_960` (218/218) and `tera`
(57/58). The single TERA difference is `_pseudo_wavs`, a random scratch buffer
s3prl regenerates on load, not a learned parameter.

## Contents

19 SSL detectors, one per row of the paper's main results table:

`apc`, `vq_apc`, `npc`, `mockingjay_960hr`, `tera`, `decoar2`, `wav2vec`,
`wav2vec2_base_960`, `wav2vec2_large_ll60k`, `hubert_base`, `hubert_large_ll60k`,
`multires_hubert_multilingual_large600k`, `xls_r_300m`, `unispeech_sat_large`,
`data2vec_large_ll60k`, `wavlablm_ek_40k`, `wavlm_large`, `ssast_frame_base`,
`mae_ast_frame`

Plus the two non-SSL reference systems under `non_ssl/`:

- `aasist_raw_best_epoch44.pth` — AASIST trained end-to-end on raw waveform.
  Full weights, since nothing about it is frozen. This is the best-dev-EER epoch
  (1.178%), which is what the paper scores from.
- `lfcc_gmm/gmm_bonafide.pkl`, `lfcc_gmm/gmm_spoof.pkl` — the two 512-component
  diagonal-covariance Gaussian mixtures. Scored as the difference of mean
  per-frame log-likelihoods.

## Training setup

All SSL detectors share one recipe: ASVspoof 2019 LA train, frozen upstream,
weighted layer aggregation, a lightweight fully connected classifier, binary
cross-entropy with class weights [0.1, 0.9], 50 epochs, batch size 64, SWA. The
checkpoint is the SWA average.

## Usage

Load through the benchmark's own scoring driver, which builds the model, fetches
the matching s3prl upstream and applies these weights.

**Requires the repository at commit `02bf38e` or later.** Earlier revisions
loaded checkpoints with `strict=True` and will reject these files for missing the
upstream tensors. The current loader accepts both shapes and still rejects a
checkpoint that is genuinely incomplete.

```bash
git clone https://github.com/issflab/spoof_SUPERB
python -m spoof_superb.scoring.driver --model linear_head \
    --ssl_model xls_r_300m --model_path xls_r_300m.pth \
    --source protocol_csv --output_file out.txt
```

## Integrity

`SHA256SUMS` covers every file in this repository:

```bash
sha256sum -c SHA256SUMS
```

## Licence

Apache-2.0, matching the benchmark code. The upstream encoders these detectors
sit on top of remain under their own licences and are not redistributed here.

## Citation

```bibtex
@article{ali2026spoofsuperb,
  title  = {Spoof-SUPERB: A Comprehensive Benchmark of Self-Supervised Speech
            Representations for Audio Deepfake Detection},
  author = {Ali, Hashim and Adupa, Nithin Sai and Malik, Hafiz},
  year   = {2026}
}
```