submissions: add aasist-l (#14)

- submissions: add aasist-l (ff743343794c25ffd349474c5ea7bdcda67f80a9)
- re-dispatch verify-pr (dac9217c16de2b1d6e5de17971f422fb02770fa6)
- fill reproduction (match: scoring) for aasist-l (f186f822129c7401a810994f13032f16d6e7a062)

submissions/aasist-l.yaml

@@ -0,0 +1,54 @@
+schema_version: 4
+system:
+  name: AASIST-L
+  slug: aasist-l
+  description: 'AASIST-L: the lightweight variant of AASIST (audio anti-spoofing using
+    integrated spectro-temporal graph attention networks). Same architecture as AASIST
+    — sinc-convolution front-end, RawNet2-style residual encoder, and heterogeneous
+    stacking graph attention over spectral and temporal sub-graphs with a learnable
+    readout — but with a narrower residual stack and graph dimensions (~85k params
+    vs AASIST''s ~298k). Official clovaai/aasist ASVspoof2019 LA pretrained checkpoint,
+    FP32, deterministic first-64600-sample window (no random crop).
+
+    '
+  code: https://github.com/clovaai/aasist
+  checkpoint: https://huggingface.co/SpeechAntiSpoofingBenchmarks/AASIST-L/blob/e4185b270ec20077c918e06a45093717a1bd5e30/AASIST-L.pth
+  paper:
+    arxiv_id: '2110.01200'
+    url: https://arxiv.org/abs/2110.01200
+    bibtex: "@inproceedings{jung2022aasist,\n  title={{AASIST}: Audio Anti-Spoofing\
+      \ Using Integrated Spectro-Temporal Graph Attention Networks},\n  author={Jung,\
+      \ Jee-weon and Heo, Hee-Soo and Tak, Hemlata and Shim, Hye-jin and Chung, Joon\
+      \ Son and Lee, Bong-Jin and Yu, Ha-Jin and Evans, Nicholas},\n  booktitle={ICASSP\
+      \ 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal\
+      \ Processing (ICASSP)},\n  pages={6367--6371},\n  year={2022},\n  organization={IEEE}\n\
+      }\n"
+  params_millions: 0.085306
+dataset:
+  id: SpeechAntiSpoofingBenchmarks/CFAD
+  revision: e9476bfb54b6e6c1bbc1da59fc962bdaf5070131
+  split: test
+scores:
+  eer_percent: 47.3952380952381
+  n_trials: 62999
+  n_skipped: 0
+artifact:
+  scores_url: https://huggingface.co/SpeechAntiSpoofingBenchmarks/AASIST-L/resolve/bdcda54aa8913f9288cbe48f5a5c1f75e3d8b22d/.eval_results/SpeechAntiSpoofingBenchmarks/CFAD/scores.txt
+  scores_sha256: 3ba9cb5a470c95379ec3b87b0f6719bb80c24f711443dfefed9bd80370e09386
+  bench_version: speech-spoof-bench==0.3.4
+reproduction:
+  reproduced_by: SpeechAntiSpoofingBenchmarks
+  reproduced_at: '2026-06-09'
+  reproduced_bench_version: speech-spoof-bench==0.3.4
+  match: scoring
+submitter:
+  hf_username: korallll
+  contact: k.n.borodin@mtuci.ru
+submitted_at: '2026-06-09'
+notes: 'Lightweight AASIST-L variant (not the full AASIST). Same _net.py Model class
+  as AASIST; differs only in model_config (filts [..,[32,24],[24,24]], gat_dims [24,32],
+  pool_ratios [0.4,0.5,0.7,0.5]) and checkpoint. Deterministic first-64600-sample
+  window (no random crop), matching clovaai/aasist data_utils.pad() used at eval.
+  Checkpoint mirrored to SpeechAntiSpoofingBenchmarks/AASIST-L (pinned at publish).
+
+  '