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DF Arena 1B V 1

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...

Fontehuggingface.co/Speech-Arena-2025/DF_Arena_1B_V_1/tree/fb6ce85de12c2c5a509d89114adaf827dd75f49f
TipoOtherVersãoRevision fb6ce85Bnota

Fonte do modelo

Tipo
Other
Tamanho do arquivo
4,28 GB
Versão
vRevision fb6ce85
Atualizado
22 de set. de 2026
Downloads
2,2 mil
Fonte
Hugging Face

Descrição da fonte

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

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_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

DatasetEER (%)F1-scoreAccuracy (%)
dfadd0.000.999399.97
add_2023_round_211.540.918888.46
codecfake8.370.869591.63
asvspoof_2021_la4.660.803795.34
in_the_wild0.910.992899.10
asvspoof_20191.140.947398.86
add_2022_track_122.210.667877.79
fake_or_real2.920.971197.11
asvspoof_202417.250.661582.75
add_2022_track_32.200.935797.80
add_2023_round_15.080.963994.92
librisevoc0.150.995899.84
asvspoof_2021_df1.750.757798.25
sonar1.090.990398.89
Average5.9190.886394.079
Pooled9.520.8190.47

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}, 
}

Fontes

1 fonte
Hugging FacePrimárioVersão fb6ce85de12c2c5a509d89114adaf827dd75f49f
huggingface.co/Speech-Arena-2025/DF_Arena_1B_V_1/tree/fb6ce85de12c2c5a509d89114adaf827dd75f49f

Verificado 5 de set.

Artefatos de modelo

1 artefato

pytorch_model.bin

bin · 4,28 GB · SHA-256 780bc14fd4c1…7988 · Hugging Face

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Trechos de fonte

2 trechos
Readme5 de set.huggingface.co/Speech-Arena-2025/DF_Arena_1B_V_1

--- 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 [email protected]. 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...

Note5 de set.huggingface.co/Speech-Arena-2025/DF_Arena_1B_V_1

Source context: 2197 downloads · 9 likes · Pipeline audio-classification · Repo Speech-Arena-2025/DF_Arena_1B_V_1