Add model card with Arena badges (all 5 datasets)

README.md

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+---
+license: mit
+tags:
+  - audio
+  - anti-spoofing
+  - audio-deepfake-detection
+  - speech
+  - asvspoof
+  - wav2vec2
+  - nes2net
+---
+
+# Nes2Net
+
+[![EER% 0.13 on ASVspoof2019_LA](https://img.shields.io/badge/EER%25%20on%20ASVspoof2019__LA-0.13%25-brightgreen)](https://huggingface.co/spaces/SpeechAntiSpoofingBenchmarks/SpeechAntiSpoofingArena?system=nes2net)
+[![EER% 6.14 on ASVspoof2021_LA](https://img.shields.io/badge/EER%25%20on%20ASVspoof2021__LA-6.14%25-green)](https://huggingface.co/spaces/SpeechAntiSpoofingBenchmarks/SpeechAntiSpoofingArena?system=nes2net)
+[![EER% 3.61 on ASVspoof2021_DF](https://img.shields.io/badge/EER%25%20on%20ASVspoof2021__DF-3.61%25-brightgreen)](https://huggingface.co/spaces/SpeechAntiSpoofingBenchmarks/SpeechAntiSpoofingArena?system=nes2net)
+[![EER% 8.48 on InTheWild](https://img.shields.io/badge/EER%25%20on%20InTheWild-8.48%25-green)](https://huggingface.co/spaces/SpeechAntiSpoofingBenchmarks/SpeechAntiSpoofingArena?system=nes2net)
+[![EER% 20.55 on CD-ADD](https://img.shields.io/badge/EER%25%20on%20CD--ADD-20.55%25-yellow)](https://huggingface.co/spaces/SpeechAntiSpoofingBenchmarks/SpeechAntiSpoofingArena?system=nes2net)
+[![arena tier](https://img.shields.io/endpoint?url=https://speechantispoofingbenchmarks-speechantispoofingarena.hf.space/badge/nes2net/tier.json)](https://huggingface.co/spaces/SpeechAntiSpoofingBenchmarks/SpeechAntiSpoofingArena?system=nes2net)
+[![arena rank](https://img.shields.io/endpoint?url=https://speechantispoofingbenchmarks-speechantispoofingarena.hf.space/badge/nes2net/rank.json)](https://huggingface.co/spaces/SpeechAntiSpoofingBenchmarks/SpeechAntiSpoofingArena?system=nes2net)
+
+A **wav2vec 2.0 (XLS-R 300M) + Nes2Net-X** anti-spoofing model, from
+*"Nes2Net: A Lightweight Nested Architecture for Foundation Model Driven Speech
+Anti-Spoofing"* (Liu, Truong, Das, Lee & Li, IEEE T-IFS 2025). A self-supervised
+XLS-R front-end is fine-tuned end-to-end with a **nested Res2Net** back-end that
+operates directly on the foundation-model features — no dimensionality-reducing
+neck — using only ~0.51 M back-end parameters. The model takes a raw speech
+waveform and returns a score where **higher = more bona fide**.
+
+- **Code:** https://github.com/Liu-Tianchi/Nes2Net_ASVspoof_ITW
+- **Paper:** https://arxiv.org/abs/2504.05657 (DOI 10.1109/TIFS.2025.3626963)
+- **Parameters:** 317,902,600 (317.90 M total; Nes2Net-X back-end only 0.51 M)
+- **Checkpoint:** [`nes2net_x_DF1.65.pth`](./nes2net_x_DF1.65.pth) (single Nes2Net-X)
+
+The exact wrapper used to produce the Arena scores is in
+[`nes2net.py`](./nes2net.py); the network definition is in [`_net.py`](./_net.py).
+
+## Architecture
+
+1. **wav2vec 2.0 XLS-R (300M) front-end** — a self-supervised transformer
+   (`fairseq` `Wav2Vec2Model`) producing 1024-d frame features, fine-tuned
+   end-to-end with the rest of the network.
+2. **Nes2Net-X back-end** — a *nested* Res2Net TDNN: outer Res2Net groups, each an
+   inner Res2Net (`Bottle2neck`) with squeeze-and-excitation and a learnable
+   weighted multi-scale sum, applied directly to the 1024-d XLS-R features
+   (`Nes_ratio=[8,8]`, `SE_ratio=[1]`), then mean temporal pooling and a linear
+   classifier.
+3. The 2-logit output is read at **index 1 = bona fide**.
+
+## How it was trained
+
+- **Data:** ASVspoof 2019 **Logical Access (LA)**, with RawBoost data augmentation.
+- **Input length:** raw audio at 16 kHz cropped/padded to 64,600 samples (~4 s).
+- **Output:** 2-class logits; the bona-fide logit (index 1) is the score.
+
+See the [source repository](https://github.com/Liu-Tianchi/Nes2Net_ASVspoof_ITW) for
+the full training and evaluation code.
+
+## Benchmark result (Speech Anti-Spoofing Arena)
+
+Evaluated through the reproducible [Speech Anti-Spoofing Arena](https://huggingface.co/spaces/SpeechAntiSpoofingBenchmarks/SpeechAntiSpoofingArena?system=nes2net).
+Scores were computed with a **deterministic first-64,600-sample window** (no random
+crop), so the numbers are exactly reproducible from the pinned score file.
+
+| Dataset | Split | EER % | Trials | Skipped | Notes |
+|---|---|---|---|---|---|
+| ASVspoof2019_LA | test | **0.13** | 71,237 | 0 | in-domain (training data) |
+| ASVspoof2021_DF | test | **3.61** | 611,829 | 0 | cross-dataset generalization |
+| ASVspoof2021_LA | test | **6.14** | 181,566 | 0 | cross-dataset generalization |
+| InTheWild | test | **8.48** | 31,779 | 0 | out-of-domain (real-world deepfakes) |
+| CD-ADD | test | **20.55** | 20,786 | 0 | out-of-domain (modern neural-TTS) |
+
+Despite a back-end ~30× smaller than typical SSL countermeasures, Nes2Net-X
+generalizes strongly to unseen attacks — beating a wav2vec 2.0 + AASIST baseline on
+every dataset on this benchmark, most strikingly out-of-domain (CD-ADD and
+ASVspoof2021 DF).
+
+## Usage
+
+The checkpoint is a `state_dict` for the `Model` network defined in
+[`_net.py`](./_net.py). Constructing the network requires the base XLS-R 300M
+checkpoint **`xlsr2_300m.pt`** next to the wrapper (only used to build the
+wav2vec 2.0 architecture; every weight is then overwritten by the fine-tuned
+checkpoint):
+
+```bash
+wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/xlsr2_300m.pt
+```
+
+The input is windowed to exactly 64,600 samples at 16 kHz mono with `pad_fixed`
+(first 64,600 samples, tile-repeat if shorter).
+
+```python
+import numpy as np
+from nes2net import Nes2Net               # _net.py + nes2net.py are in this repo
+
+m = Nes2Net()
+m.load()                                          # loads nes2net_x_DF1.65.pth (+ xlsr2_300m.pt)
+audio = np.random.randn(48000).astype(np.float32) # float32 mono 16 kHz
+print(m.score_batch([audio], [16000])[0])         # higher = more bona fide
+m.unload()
+```
+
+Internally the wrapper windows the input, runs the network, and returns
+`logits[:, 1]` (class 1 = bona fide). [`nes2net.py`](./nes2net.py) is the exact
+`speech_spoof_bench` model that produced the Arena `scores.txt`.
+
+## Citation
+
+```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}
+}
+```
+
+## License
+
+MIT — see the [source repository](https://github.com/Liu-Tianchi/Nes2Net_ASVspoof_ITW).