Add mms-300m-antideepfake-nda LRLspoof submission (srr_complement 1.26%)

submissions/mms-300m-antideepfake-nda.yaml

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+schema_version: 5
+system:
+  name: MMS-300M-AntiDeepfake-NDA
+  slug: mms-300m-antideepfake-nda
+  description: AntiDeepfake detector (MMS-300M self-supervised backbone) post-trained
+    for deepfake speech detection without data augmentation (NDA variant); head =
+    AdaptiveAvgPool1d + Linear(D,2), score = real logit. Released by nii-yamagishilab
+    (arXiv:2506.21090). Evaluated with a deterministic first-4 s window + batching
+    (NOT the paper's whole-utterance protocol).
+  code: https://github.com/nii-yamagishilab/AntiDeepfake
+  checkpoint: https://huggingface.co/SpeechAntiSpoofingBenchmarks/MMS-300M-AntiDeepfake-NDA/blob/2dbb7bbfc823781af6594425cad8cce0c065e280/model.safetensors
+  params_millions: 317.3926
+  paper:
+    arxiv_id: '2506.21090'
+    url: https://arxiv.org/abs/2506.21090
+    bibtex: "@article{ge2025posttraining,\n  title={Post-training for Deepfake Speech\
+      \ Detection},\n  author={Ge, Wanying and Wang, Xin and Yamagishi, Junichi and\
+      \ others},\n  journal={arXiv preprint arXiv:2506.21090},\n  year={2025}\n}\n"
+dataset:
+  id: lab260/LRLspoof
+  revision: 5caf11349cf96aa198527cfb040cdba585cdd49e
+  split: test
+scores:
+  srr_complement: 1.2624897540119417
+  n_trials: 1304455
+  n_skipped: 286
+calibration:
+  source_dataset: SpeechAntiSpoofingBenchmarks/DeepVoice
+  threshold: 1.7011841082802548
+artifact:
+  scores_url: https://huggingface.co/SpeechAntiSpoofingBenchmarks/MMS-300M-AntiDeepfake-NDA/resolve/aabfed2a697dbcc538beecc5292cd4b425d08254/.eval_results/lab260/LRLspoof/scores.txt
+  scores_sha256: 487d16ea43cfc018ae494e3e11f54bb7d200c88b7b5caee46ba8946ecfc9920a
+  bench_version: speech-spoof-bench==0.4.1
+reproduction:
+  reproduced_by: SpeechAntiSpoofingBenchmarks
+  reproduced_at: '2026-06-23'
+  reproduced_bench_version: speech-spoof-bench==0.4.1
+  match: scoring
+submitter:
+  hf_username: korallll
+  contact: k.n.borodin@mtuci.ru
+submitted_at: '2026-06-23'
+notes: MMS-300M-AntiDeepfake-NDA on LRLspoof (spoof-only, srr_complement = 1-SRR,
+  lower better). TensorRT engine (parity-verified vs PyTorch). Scored 1,304,455 of
+  1,304,455 TTS utterances; 286 source files are empty/zero-length (skipped), 172
+  corrupt-header WAVs recovered via ffmpeg. Audio resampled per-file to 16 kHz; score
+  = net(x)[:,1] (higher = bona fide). t* = this model's own DeepVoice EER operating-point
+  threshold (DeepVoice EER 5.73%), transferred via the calibration block. Verified
+  with reproduce --scoring.