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#6
by korallll - opened
- submissions/nes2net.yaml +54 -0
submissions/nes2net.yaml
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schema_version: 4
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system:
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name: Nes2Net
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slug: nes2net
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description: 'wav2vec 2.0 (XLS-R 300M) self-supervised front-end fine-tuned end-to-end
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with a Nes2Net-X (Nested Res2Net TDNN) back-end for speech anti-spoofing. The
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nested Res2Net structure couples multi-scale residual groups with squeeze- excitation,
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replacing dimensionality-reducing necks; mean temporal pooling + linear classifier.
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Only ~0.51M back-end params. Official Nes2Net-X single checkpoint (ASVspoof2021
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LA 1.73% / DF 1.65% EER as reported), trained on ASVspoof2019 LA with RawBoost,
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FP32, deterministic first-64600-sample window (no random crop).
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'
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code: https://github.com/Liu-Tianchi/Nes2Net_ASVspoof_ITW
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checkpoint: https://huggingface.co/SpeechAntiSpoofingBenchmarks/Nes2Net
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params_millions: 317.9026
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paper:
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arxiv_id: '2504.05657'
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url: https://arxiv.org/abs/2504.05657
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bibtex: "@article{Nes2Net,\n author={Liu, Tianchi and Truong, Duc-Tuan and Das,\
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\ Rohan Kumar and Lee, Kong Aik and Li, Haizhou},\n journal={IEEE Transactions\
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\ on Information Forensics and Security},\n title={Nes2Net: A Lightweight Nested\
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\ Architecture for Foundation Model Driven Speech Anti-Spoofing},\n year={2025},\n\
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\ volume={20},\n pages={12005--12018},\n doi={10.1109/TIFS.2025.3626963}\n\
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}\n"
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dataset:
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id: SpeechAntiSpoofingBenchmarks/CFAD
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revision: 53d7855c1c378524f7b7b1030bcb6b2caa327fe6
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split: test
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scores:
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eer_percent: 18.06276489356636
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n_trials: 62999
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n_skipped: 0
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artifact:
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scores_url: https://huggingface.co/SpeechAntiSpoofingBenchmarks/Nes2Net/resolve/eb476b9849317d6fb594ea61e3f3b03faacfdc97/.eval_results/SpeechAntiSpoofingBenchmarks/CFAD/scores.txt
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scores_sha256: 3d44886047eff5db5fdea04feee865b89057d8a368fdce2a5ba4abd59311a807
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bench_version: speech-spoof-bench==0.3.4
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submitter:
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hf_username: korallll
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contact: k.n.borodin@mtuci.ru
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submitted_at: '2026-06-08'
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notes: XLS-R 300M (wav2vec 2.0) front-end + Nes2Net-X back-end, the single (non-averaged)
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checkpoint from Liu-Tianchi/Nes2Net_ASVspoof_ITW (Nes_ratio [8,8], SE_ratio [1],
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pool_func 'mean', dilation 2). Architecture is built from the base xlsr2_300m.pt
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model config, then every weight is overwritten by the fine-tuned checkpoint. Deterministic
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first-64600-sample window (no random crop), matching the source data_utils_SSL.py::pad
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used at eval (default --test_protocol 4sec). score = output logit for class 1 (bona
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fide); higher = more bona fide. Back-end params ~0.51M; params_millions reports
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the full deployed model incl. the XLS-R front-end.
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reproduction:
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reproduced_by: SpeechAntiSpoofingBenchmarks
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reproduced_at: '2026-06-08'
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reproduced_bench_version: speech-spoof-bench==0.3.4
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match: scoring
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