Add Vocoded-VoxCeleb2-SSL-P2 submission for LRLspoof (1-SRR=24.1617)

submissions/vocoded-voxceleb2-ssl-p2.yaml

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+schema_version: 4
+system:
+  name: Vocoded-VoxCeleb2-SSL-P2
+  slug: vocoded-voxceleb2-ssl-p2
+  description: 'NII vocoded-VoxCeleb2 SSL countermeasure, model ID P2 of Wang & Yamagishi (ICASSP 2024):
+    two wav2vec 2.0 front ends whose last-layer outputs are subtracted (v.vox minus XLSR-53), where v.vox
+    is XLSR-53 continually pre-trained (self-supervised, fairseq) on >9,000 h of VoxCeleb2 copy-synthesised
+    by multiple neural vocoders. Back end: Linear 1024->128, three Linear+LeakyReLU+MC-dropout (p=0.5)
+    blocks, mean pooling over frames and Linear 128->2. Whole model fine-tuned on voc.LA (vocoded ASVspoof
+    2019 LA train bona fide, ~10 h) with RawBoost. Score = logit of the mean bona-fide softmax over 144
+    Monte-Carlo-dropout samples (the authors'' scoring), with fixed per-clip dropout masks. Authors''
+    released seed-01 checkpoint (BSD-3). FP32, deterministic first-64600-sample window (shorter clips
+    repeat-tiled) -- NOT the paper''s full-utterance protocol.'
+  code: https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts/tree/b529705b65588d62db17b56f98636ee734ff725e/project/10-asvspoof-vocoded-trn-ssl
+  checkpoint: https://huggingface.co/SpeechAntiSpoofingBenchmarks/Vocoded-VoxCeleb2-SSL-P2/blob/eb3f38231935a53d37b5cfeaca3e12cb8bc73c64/model.safetensors
+  params_millions: 634.9622
+  paper:
+    arxiv_id: '2309.06014'
+    url: https://arxiv.org/abs/2309.06014
+    bibtex: "@inproceedings{wang2024vocoded,\n  title     = {Can Large-Scale Vocoded Spoofed Data Improve\
+      \ Speech Spoofing Countermeasure with a Self-Supervised Front End?},\n  author    = {Wang, Xin and\
+      \ Yamagishi, Junichi},\n  booktitle = {ICASSP 2024 - 2024 IEEE International Conference on Acoustics,\
+      \ Speech and Signal Processing (ICASSP)},\n  year      = {2024},\n  eprint    = {2309.06014},\n\
+      \  archivePrefix = {arXiv}\n}\n"
+dataset:
+  id: lab260/LRLspoof
+  revision: ac76f0e5472fdb68a6726e1fa94a148316e76916
+  split: test
+scores:
+  srr_complement: 24.161669231518314
+  n_trials: 1304455
+  n_skipped: 286
+calibration:
+  source_dataset: SpeechAntiSpoofingBenchmarks/DeepVoice
+  threshold: -4.724761562711864
+artifact:
+  scores_url: https://huggingface.co/SpeechAntiSpoofingBenchmarks/Vocoded-VoxCeleb2-SSL-P2/resolve/f0e4d55fa232d2e393ab1e98523683297557968a/.eval_results/lab260/LRLspoof/scores.txt
+  scores_sha256: 823a0c4a084921e704483b719cc2a29beaad8a0b4f15033d357a7e863b5688a8
+  bench_version: speech-spoof-bench==0.4.1
+reproduction: {}
+submitter:
+  hf_username: korallll
+  contact: k.n.borodin@mtuci.ru
+submitted_at: '2026-11-04'
+notes: 'Code nii-yamagishilab/project-NN-Pytorch-scripts @ b529705 (BSD-3-Clause), project/10-asvspoof-vocoded-trn-ssl/model-ID-P2/model.py;
+  weights Zenodo record 8337778 project10-cm-ssl-model-ID-P2.tar, trained-for-paper/01/trained_network.pt
+  (a plain state_dict), re-saved unchanged as model.safetensors; strict load. P2: two SSL front ends,
+  back-end input = v.vox(x) - xlsr(x) (source model-ID-P2 FrontEnd: ssl_model = wav2vec2_custom v.vox,
+  ssl_model2 = xlsr_53); both SSLs fine-tuned. The s3prl front end is rebuilt as a fairseq Wav2Vec2Model
+  from the XLSR-53 architecture config (no hub download), with s3prl''s per-utterance waveform layer_norm
+  and last-layer (after final encoder LayerNorm) features. Score = logit(mean over 144 MC-dropout samples
+  of softmax[:, 1]) (bona fide = class 1; source compute_llr_eps_ale, incl. its 1e-7 floors); dropout
+  only in the back end, as in the source; the masks are drawn per clip from a CUDA generator seeded by
+  blake2b(seed 1, clip window), so scores are deterministic and batch-independent (batched == single).
+  Deviation (maintainer decision): fixed first 64600 samples, repeat-tiled, instead of the authors'' full-utterance
+  evaluation; consequently the source''s "< 3000 samples -> score 0" rule never applies. Full-utterance
+  debug run of this port reproduces the authors'' released In-the-Wild scores (see implementation-notes.md).
+  Released-log EERs (full utterance, seed 01): 19LA 1.43 / 21LA 15.03 / 21DF 7.37 / ITW 4.22. Training:
+  SSL continual pre-training on vocoded VoxCeleb2 (unlabelled); CM fine-tuning on voc.LA (vocoded ASVspoof
+  2019 LA train bona fide), ASVspoof 2019 LA dev for early stopping; no Arena test set used.'