Add Forensics-WavLM-Base submission for DECRO (EER=1.7187)

submissions/forensics-wavlm-base.yaml

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+schema_version: 4
+system:
+  name: Forensics-WavLM-Base
+  slug: forensics-wavlm-base
+  description: 'Forensics 0.3B Base speech deepfake detector (Eliya Segev, 2026): a fully fine-tuned WavLM-Large
+    front-end whose 24 transformer hidden states are pooled by an AASIST-style spectro-temporal graph-attention
+    pool (layer x time graphs, GAT dims 128/64), followed by a 3-block residual MLP classifier (320-d)
+    and a single logit (label 1 = real); trained with label-smoothed CE + OC-Softmax + real-anchor supervised-contrastive
+    losses. Authors'' checkpoint_epoch_5 as released. Input as the authors'' inference.py: peak-normalised
+    waveform, 5 s (80000 samples) centre crop, shorter clips repeat-padded. score = the logit (sigmoid
+    = P(real)). Unpublished model (HF model card only, no paper).'
+  code: https://huggingface.co/eliya/forensics_0.3B_base_deepfake_classifier/tree/cfd11365b02167214dc3b392378809fef6ca3cfa
+  checkpoint: https://huggingface.co/SpeechAntiSpoofingBenchmarks/Forensics-WavLM-Base/blob/4202d397214764a88e91d6b69364861306c53f2d/checkpoint_epoch_5.safetensors
+  params_millions: 317.257863
+dataset:
+  id: SpeechAntiSpoofingBenchmarks/DECRO
+  revision: e54c29df5b81bac75e5cc294d1ab0d4db72a5c71
+  split: test
+scores:
+  eer_percent: 1.7186749879980798
+  n_trials: 37314
+  n_skipped: 0
+artifact:
+  scores_url: https://huggingface.co/SpeechAntiSpoofingBenchmarks/Forensics-WavLM-Base/resolve/c050448ac4187f7fb652e991baf6c456cf6f8143/.eval_results/SpeechAntiSpoofingBenchmarks/DECRO/scores.txt
+  scores_sha256: 896cc15fa761af552f62121563fc279dbe79edd8fb97abb8614c6c70a638d382
+  bench_version: speech-spoof-bench==0.4.1
+reproduction:
+  reproduced_by: SpeechAntiSpoofingBenchmarks
+  reproduced_at: '2026-11-07'
+  reproduced_bench_version: speech-spoof-bench==0.4.1
+  match: inference
+submitter:
+  hf_username: korallll
+  contact: k.n.borodin@mtuci.ru
+submitted_at: '2026-11-07'
+notes: 'Forensics-WavLM-Base = HF eliya/forensics_0.3B_base_deepfake_classifier @ cfd11365b02167214dc3b392378809fef6ca3cfa,
+  checkpoint_epoch_5.safetensors (sha256 803d2c57...90fb), CC-BY-NC-4.0, re-hosted unchanged. Architecture
+  = the repo''s model.py (byte-identical across the WavLM Forensics models); the WavLM-Large backbone
+  is built from the microsoft/wavlm-large config and every weight is overwritten by the checkpoint (strict
+  load, incl. the weight-normed pos_conv; the 4 training-only projection.* tensors, unused by model.py,
+  are dropped via an asserted allow-list). Preprocessing = inference.py: peak normalisation wav/(max|wav|+1e-8)
+  over the whole clip, then a centred 80000-sample (5 s) crop or repeat-padding; no resampling; FP32.
+  Score = the single output logit (higher = bona fide); verified bit-identical to the authors'' model.py
+  + inference.py on 20 In-the-Wild clips. Training data: the current model card says 5M+ samples from
+  a mix of proprietary and public data; earlier card revisions listed SpeechFake, MD-CommonVoice, DFADD,
+  CodecFake, ASVspoof2019-LA, EnvSDD (later ones add ASVspoof2021, CommonVoice, Aishell); splits are not
+  stated, so InTheWild, DFADD, ASVspoof2021_DF and ASVspoof2019_LA are marked trained_on (n/a*), as for
+  Forensics-XLSR-Wild. Self-reported EER: In-the-Wild 1.38%, ASVspoof2019-LA 0.26%, ASVspoof2021-LA 1.56%.
+  No paper -> Unpublished tier.'