Add meta.yaml (HF download-stats query file) to enable download tracking

meta.yaml

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+system:
+  name: "Nes2Net"
+  slug: "nes2net"
+  description: >
+    wav2vec 2.0 (XLS-R 300M) self-supervised front-end fine-tuned end-to-end with
+    a Nes2Net-X (Nested Res2Net TDNN) back-end for speech anti-spoofing. The
+    nested Res2Net structure couples multi-scale residual groups with squeeze-
+    excitation, replacing dimensionality-reducing necks; mean temporal pooling +
+    linear classifier. Only ~0.51M back-end params. Official Nes2Net-X single
+    checkpoint (ASVspoof2021 LA 1.73% / DF 1.65% EER as reported), trained on
+    ASVspoof2019 LA with RawBoost, FP32, deterministic first-64600-sample window
+    (no random crop).
+  code: "https://github.com/Liu-Tianchi/Nes2Net_ASVspoof_ITW"
+  checkpoint: "https://huggingface.co/SpeechAntiSpoofingBenchmarks/Nes2Net"
+  params_millions: 317.9026
+  paper:
+    arxiv_id: "2504.05657"
+    url: "https://arxiv.org/abs/2504.05657"
+    bibtex: |
+      @article{Nes2Net,
+        author={Liu, Tianchi and Truong, Duc-Tuan and Das, Rohan Kumar and Lee, Kong Aik and Li, Haizhou},
+        journal={IEEE Transactions on Information Forensics and Security},
+        title={Nes2Net: A Lightweight Nested Architecture for Foundation Model Driven Speech Anti-Spoofing},
+        year={2025},
+        volume={20},
+        pages={12005--12018},
+        doi={10.1109/TIFS.2025.3626963}
+      }
+notes: >
+  XLS-R 300M (wav2vec 2.0) front-end + Nes2Net-X back-end, the single (non-averaged)
+  checkpoint from Liu-Tianchi/Nes2Net_ASVspoof_ITW (Nes_ratio [8,8], SE_ratio [1],
+  pool_func 'mean', dilation 2). Architecture is built from the base xlsr2_300m.pt
+  model config, then every weight is overwritten by the fine-tuned checkpoint.
+  Deterministic first-64600-sample window (no random crop), matching the source
+  data_utils_SSL.py::pad used at eval (default --test_protocol 4sec). score = output
+  logit for class 1 (bona fide); higher = more bona fide. Back-end params ~0.51M;
+  params_millions reports the full deployed model incl. the XLS-R front-end.