Upload submissions/xlsr-sls.yaml with huggingface_hub (#5)

- Upload submissions/xlsr-sls.yaml with huggingface_hub (f1e6b9e8264f6987cda4981051f863afcb16d4e0)
- Fill reproduction block (scoring) for merge (30f8c18447fb85451dae92b772abcc9736fd287d)

submissions/xlsr-sls.yaml

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+schema_version: 4
+system:
+  name: XLSR-SLS
+  slug: xlsr-sls
+  description: wav2vec 2.0 (XLS-R 300M) self-supervised front-end with the SLS (Sensitive Layer Selection)
+    classifier for audio deepfake detection. SLS gates and fuses the hidden states of all XLS-R transformer
+    layers — each layer contributing distinct discriminative cues — via a per-layer sigmoid attention,
+    sums the weighted multi-layer feature, then a BN + max-pool + two-layer MLP head emits a 2-way log-softmax.
+    Official QiShanZhang/SLSforASVspoof-2021-DF checkpoint (model_15, dev-EER 1.45%), trained on ASVspoof2019
+    LA, FP32, deterministic first-64600-sample window (no random crop).
+  code: https://github.com/QiShanZhang/SLSforASVspoof-2021-DF
+  checkpoint: https://huggingface.co/SpeechAntiSpoofingBenchmarks/XLSR-SLS
+  params_millions: 340.79
+  paper:
+    arxiv_id: 10.1145/3664647.3681345
+    url: https://doi.org/10.1145/3664647.3681345
+    bibtex: "@inproceedings{zhang2024audio,\n  title={Audio Deepfake Detection with Self-Supervised XLS-R\
+      \ and SLS Classifier},\n  author={Zhang, Qishan and Wen, Shuangbing and Hu, Tao},\n  booktitle={Proceedings\
+      \ of the 32nd ACM International Conference on Multimedia},\n  pages={6765--6773},\n  year={2024},\n\
+      \  doi={10.1145/3664647.3681345}\n}\n"
+dataset:
+  id: SpeechAntiSpoofingBenchmarks/CFAD
+  revision: 53d7855c1c378524f7b7b1030bcb6b2caa327fe6
+  split: test
+scores:
+  eer_percent: 12.807142857142855
+  n_trials: 62999
+  n_skipped: 0
+artifact:
+  scores_url: https://huggingface.co/SpeechAntiSpoofingBenchmarks/XLSR-SLS/resolve/2a94902e5eb55d9ec2111932eded6686610e183b/.eval_results/SpeechAntiSpoofingBenchmarks/CFAD/scores.txt
+  scores_sha256: 4ffc8f63c68cf05370c8fe677b32c07885442b203264b0c3f9ec4956dcca775e
+  bench_version: speech-spoof-bench==0.3.4
+reproduction:
+  reproduced_by: SpeechAntiSpoofingBenchmarks
+  reproduced_at: '2026-06-08'
+  reproduced_bench_version: speech-spoof-bench==0.3.4
+  match: scoring
+submitter:
+  hf_username: korallll
+  contact: k.n.borodin@mtuci.ru
+submitted_at: '2026-06-05'
+notes: 'XLS-R 300M (wav2vec 2.0) front-end + SLS (Sensitive Layer Selection) classifier, from QiShanZhang/SLSforASVspoof-2021-DF
+  (ACM MM 2024). Architecture is built from the base xlsr2_300m.pt model config (shared with the W2V2-AASIST
+  submission), then every weight is overwritten by the fine-tuned checkpoint. SLS pools every transformer
+  layer''s hidden state, gates each by a learned sigmoid attention, and fuses them before a small MLP
+  head. Deterministic first-64600-sample window (no random crop); the head''s fc1 expects this fixed length.
+  score = log-softmax output for class 1 (bona fide); higher = more bona fide (source main.py: batch_score
+  = batch_out[:, 1]).'