Upload submissions/w2v2-aasist.yaml with huggingface_hub (#7)

- Upload submissions/w2v2-aasist.yaml with huggingface_hub (b9b95f799c77f6f0ea3bd02377b030d9d31888c5)
- Add reproduction block (match: scoring) (f5895013fed6fd1fca36335909b6f87ea43a2225)

submissions/w2v2-aasist.yaml

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+schema_version: 4
+system:
+  name: W2V2-AASIST
+  slug: w2v2-aasist
+  description: wav2vec 2.0 (XLS-R 300M) self-supervised front-end fine-tuned end-to-end with an AASIST
+    spectro-temporal graph-attention back-end for speech anti-spoofing. The XLS-R features are projected
+    to 128-d, max-pooled, and fed through a RawNet2-style residual encoder and heterogeneous stacking
+    graph-attention layers. Official TakHemlata/SSL_Anti-spoofing LA checkpoint (LA_model.pth), trained
+    on ASVspoof2019 LA with RawBoost augmentation, FP32, deterministic first-64600-sample window (no random
+    crop).
+  code: https://github.com/TakHemlata/SSL_Anti-spoofing
+  checkpoint: https://huggingface.co/SpeechAntiSpoofingBenchmarks/W2V2-AASIST
+  params_millions: 317.8378
+  paper:
+    arxiv_id: '2202.12233'
+    url: https://arxiv.org/abs/2202.12233
+    bibtex: "@inproceedings{tak2022automatic,\n  title={Automatic speaker verification spoofing and deepfake\
+      \ detection using wav2vec 2.0 and data augmentation},\n  author={Tak, Hemlata and Todisco, Massimiliano\
+      \ and Wang, Xin and Jung, Jee-weon and Yamagishi, Junichi and Evans, Nicholas},\n  booktitle={The\
+      \ Speaker and Language Recognition Workshop (Odyssey 2022)},\n  pages={112--119},\n  year={2022}\n\
+      }\n"
+dataset:
+  id: SpeechAntiSpoofingBenchmarks/CFAD
+  revision: 53d7855c1c378524f7b7b1030bcb6b2caa327fe6
+  split: test
+scores:
+  eer_percent: 17.28177532263441
+  n_trials: 62999
+  n_skipped: 0
+artifact:
+  scores_url: https://huggingface.co/SpeechAntiSpoofingBenchmarks/W2V2-AASIST/resolve/faa422a70ca9dfb45ed211557afceab318db9319/.eval_results/SpeechAntiSpoofingBenchmarks/CFAD/scores.txt
+  scores_sha256: 611aec73cb8b2e0415523336b02bc3da19cfc04d546efc2e78e9774201e0466a
+  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: '2026-06-08'
+notes: XLS-R 300M (wav2vec 2.0) front-end + AASIST back-end ("W2V2-AASIST"), the LA variant (LA_model.pth)
+  from TakHemlata/SSL_Anti-spoofing. Architecture is built from the base xlsr2_300m.pt model config, then
+  every weight is overwritten by the fine-tuned checkpoint. Deterministic first-64600-sample window (no
+  random crop), matching the source data_utils_SSL.py::pad used at eval. score = output logit for class
+  1 (bona fide); higher = more bona fide.