Add RawNetLite submission for DECRO (EER=35.7177)

submissions/rawnetlite.yaml

@@ -0,0 +1,61 @@
+schema_version: 4
+system:
+  name: RawNetLite
+  slug: rawnetlite
+  description: 'RawNetLite (Di Pierno et al., IJCNN VERIMEDIA 2025): a lightweight end-to-end raw-waveform
+    detector -- Conv1D(1->64) + BatchNorm + ReLU, three 1-D residual blocks (64 ch), adaptive average
+    pooling to 64 steps, a bidirectional GRU (128 per direction, last step), Linear(256,64) -> Linear(64,1).
+    Released checkpoint triple_cross_domain_focal_rawnet_lite.pt: trained with focal loss on a balanced
+    mix of FakeOrReal, "AVSpoof2021" (the authors'' name for ASVspoof 2021) and CodecFake, no augmentation.
+    Input as the authors'' audio_preprocessor.py: 16 kHz mono, peak-normalised (divide by max |x| of the
+    whole clip), first 48000 samples (3 s, within the Arena 4 s cap), right zero-pad. score = -logit(fake)
+    (the pre-sigmoid fc2 output negated; the source''s sigmoid(logit) is P(fake)). FP32 (TF32 off).'
+  code: https://github.com/adipiz99/RawNetLite/tree/fcb0fe8d0f40146d110e322c00a0723ba27d5520
+  checkpoint: https://huggingface.co/SpeechAntiSpoofingBenchmarks/RawNetLite/blob/7209f7f94f1c69bc0d2320e854914e0966b12bd4/triple_cross_domain_focal_rawnet_lite.pt
+  params_millions: 0.240769
+  paper:
+    arxiv_id: '2504.20923'
+    url: https://arxiv.org/abs/2504.20923
+    bibtex: "@inproceedings{dipierno2025rawnetlite,\n  title     = {End-to-end Audio Deepfake Detection\
+      \ from RAW Waveforms: a RawNet-Based Approach with Cross-Dataset Evaluation},\n  author    = {Andrea\
+      \ Di Pierno and Luca Guarnera and Dario Allegra and Sebastiano Battiato},\n  booktitle = {International\
+      \ Joint Conference on Neural Networks (IJCNN) - VERIMEDIA Workshop},\n  year      = {2025}\n}\n"
+dataset:
+  id: SpeechAntiSpoofingBenchmarks/DECRO
+  revision: e54c29df5b81bac75e5cc294d1ab0d4db72a5c71
+  split: test
+scores:
+  eer_percent: 35.717714834373496
+  n_trials: 37314
+  n_skipped: 0
+artifact:
+  scores_url: https://huggingface.co/SpeechAntiSpoofingBenchmarks/RawNetLite/resolve/c756857cbc017c729f3f66acc4e5065f8ef7f950/.eval_results/SpeechAntiSpoofingBenchmarks/DECRO/scores.txt
+  scores_sha256: ed4b5cc48d140ca0d266395e923aa46324d3ecb7bdf9ddd0050f58292519d1df
+  bench_version: speech-spoof-bench==0.4.1
+reproduction:
+  reproduced_by: SpeechAntiSpoofingBenchmarks
+  reproduced_at: '2026-11-10'
+  reproduced_bench_version: speech-spoof-bench==0.4.1
+  match: inference
+submitter:
+  hf_username: korallll
+  contact: k.n.borodin@mtuci.ru
+submitted_at: '2026-11-10'
+notes: 'RawNetLite = github.com/adipiz99/RawNetLite @ fcb0fe8d0f40146d110e322c00a0723ba27d5520 (MIT; RawNetLite.py
+  and audio_preprocessor.py vendored), checkpoint models/triple_cross_domain_focal_rawnet_lite.pt (sha256
+  6154d91f34f0a49d58e30bac602915f6b6853419419804be63b5274603891a9b; plain state_dict, 61 tensors, weights_only,
+  strict load, all used; re-hosted under MIT). Wrapper vs the authors'' RawNetLite + preprocess_audio
+  + tester.py scoring on J-SPAW clips: max |d P(fake)| 8.5e-8. Deviations: score is -logit instead of
+  the sigmoid output (same ranking, avoids float saturation ties); a silent clip (source: division by
+  zero -> NaN) is scored as zeros; no resampling (authors'' target is 16 kHz too). The authors'' 3 s window
+  is kept (shorter than the 4 s cap). Training data (paper Tables I-III + README): FakeOrReal (25,600+25,600),
+  "AVSpoof2021" 6,400+6,400 and CodecFake 6,400+6,400 training clips. The paper never names the ASVspoof
+  2021 track and 2021 has no train split (the training clips come from the released evaluation data),
+  so both ASVspoof2021_LA and ASVspoof2021_DF are marked trained_on (n/a*). Its citation for "AVSpoof2021"
+  is the ASVspoof 2019 overview paper and it mentions replay attacks, so ASVspoof 2019 LA/PA or 2021 PA
+  data cannot be excluded either (not marked). CodecFake is linked to roger-tseng/CodecFake (Wu et al.,
+  VCTK-based) in the README and cited as Xie et al.''s Codecfake in the paper; neither is the Arena''s
+  CD-ADD (Li et al., LibriTTS-based), and FakeOrReal is not an Arena set -> no further entries. Self-reported
+  EER% (paper Table XII, this checkpoint): FoR 2.66 / AVSpoof2021 19.96 / CodecFake 21.13 / triple-domain
+  test 16.44. 2000-clip eval subsets with this wrapper: ASVspoof2019_LA 13.82, ASVspoof2021_LA 20.24,
+  ASVspoof2021_DF 21.25. J-SPAW_LA scored with this wrapper (EER 34.19%).'