Add MMS-300M-AntiDeepfake submission for PyAra

submissions/mms-300m-antideepfake.yaml

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+schema_version: 4
+system:
+  name: MMS-300M-AntiDeepfake
+  slug: mms-300m-antideepfake
+  description: AntiDeepfake detector (MMS-300M self-supervised backbone) post-trained
+    for deepfake speech detection with RawBoost data augmentation (default 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/blob/349df41552922caeb0290078c614356f6b232f9c/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: SpeechAntiSpoofingBenchmarks/PyAra
+  revision: 40167badd6d8a289f02054761ee5411f49227b87
+  split: test
+scores:
+  eer_percent: 0.5653479743962601
+  n_trials: 201778
+  n_skipped: 0
+artifact:
+  scores_url: https://huggingface.co/SpeechAntiSpoofingBenchmarks/MMS-300M-AntiDeepfake/resolve/766465cc182a7d83d079b916708d29f72d10ad49/.eval_results/SpeechAntiSpoofingBenchmarks/PyAra/scores.txt
+  scores_sha256: 1673de4e825a06eb389e64f16294eae78e6f68aa3f4325f750cdfe5980f9c2ec
+  bench_version: speech-spoof-bench==0.3.4
+submitter:
+  hf_username: korallll
+  contact: k.n.borodin@mtuci.ru
+submitted_at: '2026-06-11'
+notes: Fixed first-4 s (64000-sample) window + batching; NOT the paper's whole-utterance
+  protocol. Score = real logit. CC BY-NC-SA 4.0.
+reproduction:
+  reproduced_by: SpeechAntiSpoofingBenchmarks
+  reproduced_at: '2026-06-11'
+  reproduced_bench_version: speech-spoof-bench==0.3.4
+  match: scoring