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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...
Fuente del modelo
Descripción de la fuente
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
Fuentes
1 fuenteVerificado 6 sept
Artefactos del modelo
1 artefactoExtractos de fuentes
2 extractos
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 |