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Dfd 1b v1

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...

Sourcehuggingface.co/sparkaiur/dfd-1b-v1/tree/63dd064bc7519a7e919485edc0505921e2d1cc2a
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Other
File size
4.28 GB
Version
vRevision 63dd064
Updated
Sep 22, 2026
Downloads
6
Source
Hugging Face

Source description

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 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 :

@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}, 
}

Sources

1 source
Hugging FacePrimaryVersion 63dd064bc7519a7e919485edc0505921e2d1cc2a
huggingface.co/sparkaiur/dfd-1b-v1/tree/63dd064bc7519a7e919485edc0505921e2d1cc2a

Verified Sep 6

Model artifacts

1 artifact

pytorch_model.bin

bin · 4.28 GB · SHA-256 780bc14fd4c1…7988 · Hugging Face

Download file

Source excerpts

2 excerpts
ReadmeSep 6huggingface.co/sparkaiur/dfd-1b-v1

--- 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...

NoteSep 6huggingface.co/sparkaiur/dfd-1b-v1

Source context: 6 downloads · 0 likes · Pipeline audio-classification · Repo sparkaiur/dfd-1b-v1