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issf/
spoof-superb-models

Name: spoof-superb-models
Availability: InStock

pytorch

audio-deepfake-detection

anti-spoofing

self-supervised-learning

speech

benchmark

apache-2.0

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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.

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:

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}
}

issf/spoof-superb-models