Add spectra-aasist submission for EmoFake_test (EER 0.40%, reproduced: scoring)

submissions/spectra-aasist.yaml

@@ -0,0 +1,47 @@
+schema_version: 4
+system:
+  name: Spectra-AASIST
+  slug: spectra-aasist
+  description: 'Speech deepfake detector: a wav2vec 2.0 XLS-R-300m self-supervised
+    front-end feeding a single-layer MLP bridge (1024->128) into an AASIST 2-class
+    classifier with four-branch spectro-temporal graph attention. Pre-release lab260/Spectra-AASIST
+    checkpoint. FP32, preemphasis (0.97), deterministic first-64600-sample window
+    (no random crop). score = output logit for class 1 (bona fide). Unpublished /
+    pre-release model (no paper).
+
+    '
+  code: https://huggingface.co/lab260/Spectra-AASIST
+  checkpoint: https://huggingface.co/lab260/Spectra-AASIST
+  params_millions: 316.0122
+dataset:
+  id: SpeechAntiSpoofingBenchmarks/EmoFake_test
+  revision: d09e30505b3002b774903e4fad47a94ceeaa172c
+  split: test
+scores:
+  eer_percent: 0.4
+  n_trials: 17500
+  n_skipped: 0
+artifact:
+  scores_url: https://huggingface.co/lab260/Spectra-AASIST/resolve/dbc652cfd76ca1845c4761eb475cb2908748c6f3/.eval_results/SpeechAntiSpoofingBenchmarks/EmoFake_test/scores.txt
+  scores_sha256: f1b14a0b864fae75756622d865151aa1752d2abc3bce380ffce5c93c7f3a4257
+  bench_version: speech-spoof-bench==0.4.1
+reproduction:
+  reproduced_by: SpeechAntiSpoofingBenchmarks
+  reproduced_at: '2026-06-24'
+  reproduced_bench_version: speech-spoof-bench==0.4.1
+  match: scoring
+submitter:
+  hf_username: korallll
+  contact: k.n.borodin@mtuci.ru
+submitted_at: '2026-06-24'
+notes: 'Spectra-AASIST (wav2vec2 XLS-R-300m SSL front-end -> MLP bridge -> AASIST)
+  from lab260/Spectra-AASIST (model.safetensors, self-contained incl. the SSL encoder).
+  Loaded via PyTorchModelHubMixin.from_pretrained; the XLS-R-300m base is built from
+  facebook/wav2vec2-xls-r-300m then every weight is overwritten by the checkpoint.
+  Preemphasis (0.97) applied to the full waveform before a deterministic first-64600-sample
+  window, matching the source README eval pipeline. score = logit for class 1 (bona
+  fide); higher = more bona fide (README: index 0 = spoof, index 1 = bonafide; classify()
+  thresholds logits[:, 1]). Unpublished / pre-release -> no paper -> Unpublished/Proprietary
+  tier (unranked).
+
+  '