Add SpoofSUPERB-XLSR submission for LRLspoof (1-SRR=26.6708)

submissions/spoofsuperb-xlsr.yaml

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+schema_version: 4
+system:
+  name: SpoofSUPERB-XLSR
+  slug: spoofsuperb-xlsr
+  description: 'Spoof-SUPERB linear-head detector (Ali et al., 2026) over a frozen XLS-R 300M (wav2vec
+    2.0, 24 layers, 1024-d; s3prl `xls_r_300m`): the s3prl Featurizer softmax-weights all 25 hidden states
+    (24 transformer-layer inputs + the encoder output; no per-layer norm), then Linear(1024,256) -> mean
+    pooling over frames -> Linear(256,2). Only the layer weights, projector and classifier are trained
+    (ASVspoof 2019 LA train, 50 epochs, SWA); the authors'' released head with the s3prl converted upstream.
+    Input as the authors'' scoring code: 16 kHz, first 64600 samples (shorter clips tiled), the upstream''s
+    own per-utterance waveform layer-norm; FP32. score = logits[:,1] (bona fide).'
+  code: https://github.com/issflab/spoof_SUPERB/tree/8b457584e4120ba525f720e714d0a4b2ed400d68
+  checkpoint: https://huggingface.co/SpeechAntiSpoofingBenchmarks/SpoofSUPERB-XLSR/blob/1994736c380f9ef1477e4f7ee03bce9835420706/xls_r_300m.pth
+  params_millions: 317.653531
+  paper:
+    arxiv_id: '2603.01482'
+    url: https://arxiv.org/abs/2603.01482
+    bibtex: "@article{ali2026superbstyle,\n  title={A SUPERB-Style Benchmark of Self-Supervised Speech\
+      \ Models for Audio Deepfake Detection},\n  author={Ali, Hashim and Adupa, Nithin Sai and Subramani,\
+      \ Surya and Malik, Hafiz},\n  journal={arXiv preprint arXiv:2603.01482},\n  year={2026}\n}\n"
+dataset:
+  id: lab260/LRLspoof
+  revision: ac76f0e5472fdb68a6726e1fa94a148316e76916
+  split: test
+scores:
+  srr_complement: 26.67077656346685
+  n_trials: 1304455
+  n_skipped: 286
+calibration:
+  source_dataset: SpeechAntiSpoofingBenchmarks/DeepVoice
+  threshold: -1.73866981740113
+artifact:
+  scores_url: https://huggingface.co/SpeechAntiSpoofingBenchmarks/SpoofSUPERB-XLSR/resolve/b9ff74c67b54653b769420e332b94e40fecc300a/.eval_results/lab260/LRLspoof/scores.txt
+  scores_sha256: 3412e6a1876bca59b3cfb49fcbd4458b692a0e9b5c6918a7c2cee4b3910b972e
+  bench_version: speech-spoof-bench==0.4.1
+reproduction:
+  reproduced_by: SpeechAntiSpoofingBenchmarks
+  reproduced_at: '2026-11-09'
+  reproduced_bench_version: speech-spoof-bench==0.4.1
+  match: inference
+submitter:
+  hf_username: korallll
+  contact: k.n.borodin@mtuci.ru
+submitted_at: '2026-11-09'
+notes: 'SpoofSUPERB-XLSR = HF issf/spoof-superb-models @ 5245a543c8e5fa44eb87e5f353a3cb9f91f703b6 xls_r_300m.pth
+  (downstream-only head: ssl_model.featurizer.weights[25], projector, post_net.linear; sha256 0b9c4fb48fcd9ad49d8dc99ba24241bad14c9c7f826791c5cbadd86434741703,
+  matches the repo''s SHA256SUMS; Apache-2.0) on the frozen upstream HF s3prl/converted_ckpts @ 8cad0b370e7e35f8d56951d95d2be036ea85510c
+  xlsr2_300m.pt (sha256 9e998d92419b8b7d6c6768853e14a37ca1b159352801c8cd92afb02e2cef87ea), i.e. exactly
+  what s3prl 0.4.18''s hub entry `xls_r_300m` downloads. Model = github.com/issflab/spoof_SUPERB @ 8b457584
+  spoof_superb/models/linear_head.py (UtteranceLevel, MeanPooling, FrameLevel) + scoring/backends.py (pad,
+  logits[:,1]); the upstream is built with s3prl''s S3PRLUpstream/Featurizer from the local checkpoint
+  (no download); head loaded per backends.py (all 5 trained tensors present, nothing unexpected; upstream
+  loaded strict by s3prl). Verified bit-identical (max |d| = 0) to the authors'' code on In-the-Wild clips,
+  and within <0.01 logit of the authors'' published per-utterance In-the-Wild scores (issf/spoof-superb-scores)
+  when fed the original float wavs; the Arena''s PCM_16 FLAC copy of In-the-Wild moves logits slightly
+  more. Trained on ASVspoof 2019 LA train only (the Arena''s ASVspoof2019_LA is the eval split) -> no
+  trained_on entries. Self-reported EER% (paper main table): 19LA 1.81 / 21LA 14.10 / 21DF 4.33 / ASVspoof5
+  14.39 / In-the-Wild 17.46.'