Add model card with arena badges

README.md

@@ -0,0 +1,69 @@
+---
+license: mit
+tags:
+  - audio
+  - anti-spoofing
+  - audio-deepfake-detection
+  - speech
+  - asvspoof
+---
+
+# AASIST
+
+[![EER% 0.83 on ASVspoof2019_LA](https://img.shields.io/badge/EER%25%20on%20ASVspoof2019__LA-0.83%25-brightgreen)](https://huggingface.co/spaces/SpeechAntiSpoofingBenchmarks/SpeechAntiSpoofingArena?system=aasist)
+[![EER% 12.35 on ASVspoof2021_LA](https://img.shields.io/badge/EER%25%20on%20ASVspoof2021__LA-12.35%25-yellow)](https://huggingface.co/spaces/SpeechAntiSpoofingBenchmarks/SpeechAntiSpoofingArena?system=aasist)
+[![EER% 17.04 on ASVspoof2021_DF](https://img.shields.io/badge/EER%25%20on%20ASVspoof2021__DF-17.04%25-yellow)](https://huggingface.co/spaces/SpeechAntiSpoofingBenchmarks/SpeechAntiSpoofingArena?system=aasist)
+[![EER% 43.01 on InTheWild](https://img.shields.io/badge/EER%25%20on%20InTheWild-43.01%25-red)](https://huggingface.co/spaces/SpeechAntiSpoofingBenchmarks/SpeechAntiSpoofingArena?system=aasist)
+[![EER% 51.05 on CD-ADD](https://img.shields.io/badge/EER%25%20on%20CD--ADD-51.05%25-red)](https://huggingface.co/spaces/SpeechAntiSpoofingBenchmarks/SpeechAntiSpoofingArena?system=aasist)
+[![arena tier](https://img.shields.io/endpoint?url=https://speechantispoofingbenchmarks-speechantispoofingarena.hf.space/badge/aasist/tier.json)](https://huggingface.co/spaces/SpeechAntiSpoofingBenchmarks/SpeechAntiSpoofingArena?system=aasist)
+[![arena rank](https://img.shields.io/endpoint?url=https://speechantispoofingbenchmarks-speechantispoofingarena.hf.space/badge/aasist/rank.json)](https://huggingface.co/spaces/SpeechAntiSpoofingBenchmarks/SpeechAntiSpoofingArena?system=aasist)
+
+AASIST audio anti-spoofing (voice-deepfake detection) countermeasure from
+*"AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention
+Networks"* (Jung et al., ICASSP 2022). This is the **official `AASIST` variant**
+(not AASIST-L), using the upstream [clovaai/aasist](https://github.com/clovaai/aasist)
+ASVspoof2019 LA pretrained checkpoint. The model takes a raw speech waveform and
+returns a score where **higher = more bona fide**.
+
+- **Code:** https://github.com/clovaai/aasist
+- **Paper:** https://arxiv.org/abs/2110.01200
+- **Parameters:** 297,866 (0.298 M)
+- **Checkpoint:** [`AASIST.pth`](./AASIST.pth)
+
+This repo is self-contained for inference: the network definition is in
+[`_net.py`](./_net.py) and the exact wrapper used to produce the Arena scores in
+[`aasist.py`](./aasist.py).
+
+## Architecture
+
+AASIST operates directly on the raw waveform: a sinc-convolution front-end and a
+RawNet2-style residual encoder produce a spectro-temporal feature map, which is
+modelled by heterogeneous stacking graph attention layers over spectral and
+temporal sub-graphs with a learnable max/average readout, followed by a 2-class
+output (bona fide vs. spoof). The Arena score is the bona-fide logit.
+
+## Reproducing the Arena scores
+
+Inference uses a deterministic first-64600-sample window (no random crop),
+matching the upstream `data_utils.pad()` used at eval. Audio is provided as
+float32 mono at 16 kHz (no resampling in the wrapper).
+
+```python
+from aasist import AASIST
+m = AASIST(); m.load()
+scores = m.score_batch([wav], [16000])   # higher = more bona fide
+```
+
+| Dataset | EER % | n_trials |
+|---------|------:|---------:|
+| ASVspoof2019_LA (in-domain) | 0.83 | 71,237 |
+| ASVspoof2021_LA | 12.35 | 181,566 |
+| ASVspoof2021_DF | 17.04 | 611,829 |
+| InTheWild | 43.01 | 31,779 |
+| CD-ADD | 51.05 | 20,786 |
+
+The in-domain ASVspoof2019 LA result reproduces the paper's reported EER (~0.83%).
+
+## License
+
+MIT (inherited from clovaai/aasist; see [`LICENSE`](./LICENSE)).