SpeechAntiSpoofingBenchmarks / DF_Arena_500M_V_1

DF Arena 500M - Antispoofing Model

We are excited to release DF Arena 500M Universal Antispoofing model 🔥trained on traditional speech antispoofing datasets in addition to singing and environmental deepfake data. Check out the release on DF Arena leaderboard

Training Data

• ASVspoof 2019, 2024
• Codecfake
• LibriSeVoc
• DFADD
• CTRSVDD
• SpoofCeleb
• MLAAD
• EnvSDD

Usage

from transformers import pipeline
import librosa

 #load model
pipe = pipeline("antispoofing", model="Speech-Arena-2025/DF_Arena_500M_V_1", trust_remote_code=True, device='cuda')
audio, sr = librosa.load("sample.wav", sr=16000)
result = pipe(audio)
print(result)
# Output: 
{'label': 'spoof', 'logits': [[1.5515458583831787, -1.2254822254180908]], 'score': 0.9414217472076416, 'all_scores': {'spoof': 0.9414217472076416, 'bonafide': 0.05857823044061661}}

Evaluation

Evaluation

License

We use a non-commercial license which can be found here

Contact

For questions or issues, please open an issue on the model repository or contact us at [email protected] .

Stay tuned for upcoming versions of our models!

Citation

If you use this model in your work, it can be cited as :

@misc{kulkarni2026compactsslbackbonesmatter,
      title={Do Compact SSL Backbones Matter for Audio Deepfake Detection? A Controlled Study with RAPTOR}, 
      author={Ajinkya Kulkarni and Sandipana Dowerah and Atharva Kulkarni and Tanel Alumäe and Mathew Magimai Doss},
      year={2026},
      eprint={2603.06164},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2603.06164}, 
}