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We are excited to release DF Arena 1B Universal Antispoofing model 🔥trained on traditional speech antispoofing datasets in addition to singing and environmental deepfake data. Check out the release on DF Arena...
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We are excited to release DF Arena 1B 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
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from transformers import pipeline
import librosa
#load model
pipe = pipeline("antispoofing", model="Speech-Arena-2025/DF_Arena_1B_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}}
| Dataset | EER (%) | F1-score | Accuracy (%) |
|---|---|---|---|
| dfadd | 0.00 | 0.9993 | 99.97 |
| add_2023_round_2 | 11.54 | 0.9188 | 88.46 |
| codecfake | 8.37 | 0.8695 | 91.63 |
| asvspoof_2021_la | 4.66 | 0.8037 | 95.34 |
| in_the_wild | 0.91 | 0.9928 | 99.10 |
| asvspoof_2019 | 1.14 | 0.9473 | 98.86 |
| add_2022_track_1 | 22.21 | 0.6678 | 77.79 |
| fake_or_real |
We use a non-commercial license which can be found here
For questions or issues, please open an issue on the model repository or contact us at ajinkya.kulkarni@idiap.ch.
Stay tuned for upcoming versions of our models!
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},
}
--- language: - en tags: - audio - audio-classification - antispoofing - deepfake-detection - speech license: other pipeline_tag: audio-classification --- # DF Arena 1B - Antispoofing Model We are excited to release DF Arena 1B 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](https://huggingface.co/spaces/Speech-Arena-2025/Speech-DF-Arena) # Training Data - **ASVspoof 2019, 2024** - **Codecfake** - **LibriSeVoc** - **DFADD** - **CTRSVDD** - **SpoofCeleb** - **MLAAD** - **EnvSDD** ## Usage ```python from transformers import pipeline import librosa #load model pipe = pipeline("antispoofing", model="Speech-Arena-2025/DF_Arena_1B_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 | Dataset | EER (%) | F1-score | Accuracy (%) | |-------------------------|----------|-----------|---------------| | dfadd | 0.00 | 0.9993 | 99.97 | | add_2023_round_2 | 11.54 | 0.9188 | 88.46 | | codecfake | 8.37 | 0.8695 | 91.63 | | asvspoof_2021_la | 4.66 | 0.8037 | 95.34 | | in_the_wild | 0.91 | 0.9928 | 99.10 | | asvspoof_2019 | 1.14 | 0.9473 | 98.86 | | add_2022_track_1 | 22.21 | 0.6678 | 77.79 | | fake_or_real | 2.92 | 0.9711 | 97.11 | | asvspoof_2024 | 17.25 | 0.6615 | 82.75 | | add_2022_track_3 | 2.20 | 0.9357 | 97.80 | | add_2023_round_1 | 5.08 | 0.9639 | 94.92 | | librisevoc | 0.15 | 0.9958 | 99.84 | | asvspoof_2021_df | 1.75 | 0.7577 | 98.25 | | sonar | 1.09 | 0.9903 | 98.89 | | Average | 5.919 | 0.8863 | 94.079 | | Pooled | 9.52 | 0.81 | 90.47 | ## License We use a non-commercial license which can be found [here](./LICENSE.txt) ## Contact For questions or issues, please open an issue on the model repository or contact us at ajinkya.kulkarni@idiap.ch. Stay tuned for upcoming versions of our models! ## Citation If you use this model in your work, it can be cited as : ```bibtex @misc{kulkarni2026compactsslbackbonesmatter, title={Do Compact SSL Backbones Matter for Audio Deepfake Detection? A Controlled Study with RAPTOR}, author={Ajinkya K...
Source context: 6 downloads · 0 likes · Pipeline audio-classification · Repo sparkaiur/dfd-1b-v1
| 2.92 |
| 0.9711 |
| 97.11 |
| asvspoof_2024 | 17.25 | 0.6615 | 82.75 |
| add_2022_track_3 | 2.20 | 0.9357 | 97.80 |
| add_2023_round_1 | 5.08 | 0.9639 | 94.92 |
| librisevoc | 0.15 | 0.9958 | 99.84 |
| asvspoof_2021_df | 1.75 | 0.7577 | 98.25 |
| sonar | 1.09 | 0.9903 | 98.89 |
| Average | 5.919 | 0.8863 | 94.079 |
| Pooled | 9.52 | 0.81 | 90.47 |