Add Whisper-MFCC-MesoNet submission for PyAra

submissions/whisper-mfcc-mesonet.yaml

@@ -0,0 +1,42 @@
+schema_version: 4
+system:
+  name: Whisper-MFCC-MesoNet
+  slug: whisper-mfcc-mesonet
+  description: '(Whisper + MFCC) MesoNet anti-spoofing countermeasure: a Whisper tiny.en audio encoder
+    front-end concatenated (2 channels) with an MFCC+Δ+ΔΔ front-end, feeding a MesoInception4 classifier.
+    Whisper encoder fine-tuned end-to-end. FP32. Upstream eval pipeline reproduced: sox silence-trim (silence
+    1 0.2 1% -1 0.2 1%) then a 30 s (480000-sample) repeat-pad window. This is the best MesoNet configuration
+    from the paper.'
+  code: https://github.com/piotrkawa/deepfake-whisper-features
+  checkpoint: https://huggingface.co/SpeechAntiSpoofingBenchmarks/WhisperMFCCMesoNet
+  params_millions: 7.660881
+  paper:
+    arxiv_id: '2306.01428'
+    url: https://arxiv.org/abs/2306.01428
+    bibtex: "@inproceedings{kawa23b_interspeech,\n  title     = {Improved DeepFake Detection Using Whisper\
+      \ Features},\n  author    = {Piotr Kawa and Marcin Plata and Micha{\\l} Czuba and Piotr Szyma{\\\
+      'n}ski and Piotr Syga},\n  year      = {2023},\n  booktitle = {Proc. INTERSPEECH 2023},\n  pages\
+      \     = {4009--4013},\n  doi       = {10.21437/Interspeech.2023-1537},\n}\n"
+dataset:
+  id: SpeechAntiSpoofingBenchmarks/PyAra
+  revision: 43f03384ee9ad701a64e0baaa531c8aedd724cd8
+  split: test
+scores:
+  eer_percent: 15.173341668591931
+  n_trials: 201778
+  n_skipped: 0
+artifact:
+  scores_url: https://huggingface.co/SpeechAntiSpoofingBenchmarks/WhisperMFCCMesoNet/resolve/64c5419db2446414f43699c297f9f9bac6c96ffc/.eval_results/SpeechAntiSpoofingBenchmarks/PyAra/scores.txt
+  scores_sha256: 406703c0e5e0eb6187a0aa686e8367eb85837de28bda401c7639f698b47c061f
+  bench_version: speech-spoof-bench==0.3.4
+reproduction:
+  reproduced_by: SpeechAntiSpoofingBenchmarks
+  reproduced_at: '2026-06-10'
+  reproduced_bench_version: speech-spoof-bench==0.3.4
+  match: scoring
+submitter:
+  hf_username: korallll
+  contact: k.n.borodin@mtuci.ru
+submitted_at: '2026-06-10'
+notes: Fine-tuned Whisper+MFCC MesoNet (best MesoNet config from the paper). Reproduces the paper's In-the-Wild
+  EER (26.72%) to within 0.01 pp with the upstream sox silence-trim + 30 s repeat-pad preprocessing.