Add RawBMamba submission for LRLspoof (1-SRR=34.1313)

submissions/rawbmamba.yaml

@@ -0,0 +1,44 @@
+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: lab260/LRLspoof
+  revision: ac76f0e5472fdb68a6726e1fa94a148316e76916
+  split: test
+scores:
+  srr_complement: 34.13131273630948
+  n_trials: 1304455
+  n_skipped: 286
+calibration:
+  source_dataset: SpeechAntiSpoofingBenchmarks/DeepVoice
+  threshold: -9.563568593949045
+artifact:
+  scores_url: https://huggingface.co/SpeechAntiSpoofingBenchmarks/RawBMamba/resolve/fd7e83515eb809a263722ffef5e0c3ba40af46db/.eval_results/lab260/LRLspoof/scores.txt
+  scores_sha256: e12e3f3a27db0f1a19a8faa0f42dae58d46f8761e6bb7a7ff68708b851430f34
+  bench_version: speech-spoof-bench==0.4.1
+reproduction:
+  reproduced_by: SpeechAntiSpoofingBenchmarks
+  reproduced_at: '2026-10-29'
+  reproduced_bench_version: speech-spoof-bench==0.4.1
+  match: scoring
+submitter:
+  hf_username: korallll
+  contact: k.n.borodin@mtuci.ru
+submitted_at: '2026-10-29'
+notes: Pretrained rawbmamba_best.pt. Mamba selective-scan run on the pure-PyTorch reference path (CUDA
+  kernels optional). Deterministic first-64600-sample window.