Add RawBMamba submission for HABLA (EER=36.71206896551724)

submissions/rawbmamba.yaml

@@ -0,0 +1,42 @@
+schema_version: 4
+system:
+  name: RawBMamba
+  slug: rawbmamba
+  description: 'RawBMamba: end-to-end bidirectional state-space (Mamba) model for audio deepfake detection. Sinc/Res front-end
+    + bidirectional Mamba blocks with attention pooling and an angular-margin (A-softmax) head. Raw-waveform, FP32, deterministic
+    first-64600-sample (4 s) window. Higher score = more bona fide.
+
+    '
+  code: https://github.com/cyjie429/RawBMamba
+  checkpoint: https://huggingface.co/SpeechAntiSpoofingBenchmarks/RawBMamba/blob/main/rawbmamba_best.pt
+  params_millions: 0.7202
+  paper:
+    arxiv_id: '2406.06086'
+    url: https://arxiv.org/abs/2406.06086
+    bibtex: "@inproceedings{chen2024rawbmamba,\n  title     = {RawBMamba: End-to-End Bidirectional State Space Model for Audio\
+      \ Deepfake Detection},\n  author    = {Chen, Yujie and Yi, Jiangyan and Xue, Jianhua and Wang, Chenglong and Zhang,\
+      \ Xiaohui and Dong, Shunbo and Zeng, Siding and Tao, Jianhua and Zhao, Lv and Wang, Cunhang},\n  booktitle = {Interspeech\
+      \ 2024},\n  year      = {2024}\n}\n"
+dataset:
+  id: SpeechAntiSpoofingBenchmarks/HABLA
+  revision: efb50f35a2c63928e06aacb4d97d64ccb193f7c0
+  split: test
+scores:
+  eer_percent: 36.71206896551724
+  n_trials: 80816
+  n_skipped: 0
+artifact:
+  scores_url: https://huggingface.co/SpeechAntiSpoofingBenchmarks/RawBMamba/resolve/dc412ed10b3834723ee2aece18595de658c1d72c/.eval_results/SpeechAntiSpoofingBenchmarks/HABLA/scores.txt
+  scores_sha256: 57f590bcdff23be443c6ea3753ac982aeb4b850c88a5d86ffedfca59a4403b4f
+  bench_version: speech-spoof-bench==0.4.1
+reproduction:
+  reproduced_by: SpeechAntiSpoofingBenchmarks
+  reproduced_at: '2026-06-26'
+  reproduced_bench_version: speech-spoof-bench==0.4.1
+  match: scoring
+submitter:
+  hf_username: korallll
+  contact: k.n.borodin@mtuci.ru
+submitted_at: '2026-06-26'
+notes: Pretrained rawbmamba_best.pt. Mamba selective-scan run on the pure-PyTorch reference path (CUDA kernels optional).
+  Deterministic first-64600-sample window.