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Dfd 500m v1

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...

Quellehuggingface.co/sparkaiur/dfd-500m-v1/tree/115bee442118b1c837e2f5c650a0e5e86c7dccd4

ArtOtherVersionRevision 115bee4CBewertung

Modellquelle

Art: Other
Dateigröße: 1,63 GB
Version: vRevision 115bee4
Aktualisiert: 22. Sept. 2026
Downloads: 2
Quelle: Hugging Face

Quellenbeschreibung

DF Arena 500M - Antispoofing Model

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

Dataset	EER (%)	F1-score	Accuracy (%)
dfadd	0.00	0.9993	99.97
add_2023_round_2	12.30	0.9133	87.70
codecfake	6.36	0.8997	93.65
asvspoof_2021_la	4.23	0.8191	95.77
in_the_wild	1.76	0.9860	98.24
asvspoof_2019	1.09	0.9494	98.91
add_2022_track_1	23.98	0.6453	76.02
fake_or_real	2.30	0.9773	97.73
asvspoof_2024	12.39	0.7423	87.61
add_2022_track_3	2.77	0.9200	97.23
add_2023_round_1	7.47	0.9465	92.53
librisevoc	0.12	0.9955	99.87
asvspoof_2021_df	3.30	0.6200	96.70
sonar	1.90	0.9837	98.13
Average	5.78	0.884	94.19
Pooled	10.88	0.78	89.11

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}, 
}

Quellen

1 Quelle

Hugging FacePrimärVersion 115bee442118b1c837e2f5c650a0e5e86c7dccd4

huggingface.co/sparkaiur/dfd-500m-v1/tree/115bee442118b1c837e2f5c650a0e5e86c7dccd4

Verifiziert 6. Sept.

Modellartefakte

1 Artefakt

pytorch_model.bin

bin · 1,63 GB · SHA-256 ebd7662047f3…66a2 · Hugging Face

Download file

Quellenauszüge

2 Auszüge

Readme6. Sept.huggingface.co/sparkaiur/dfd-500m-v1

--- language: - en tags: - audio - audio-classification - antispoofing - deepfake-detection - speech license: other pipeline_tag: audio-classification --- # 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](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_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 | Dataset | EER (%) | F1-score | Accuracy (%) | |----------------------|----------|-----------|---------------| | dfadd | 0.00 | 0.9993 | 99.97 | | add_2023_round_2 | 12.30 | 0.9133 | 87.70 | | codecfake | 6.36 | 0.8997 | 93.65 | | asvspoof_2021_la | 4.23 | 0.8191 | 95.77 | | in_the_wild | 1.76 | 0.9860 | 98.24 | | asvspoof_2019 | 1.09 | 0.9494 | 98.91 | | add_2022_track_1 | 23.98 | 0.6453 | 76.02 | | fake_or_real | 2.30 | 0.9773 | 97.73 | | asvspoof_2024 | 12.39 | 0.7423 | 87.61 | | add_2022_track_3 | 2.77 | 0.9200 | 97.23 | | add_2023_round_1 | 7.47 | 0.9465 | 92.53 | | librisevoc | 0.12 | 0.9955 | 99.87 | | asvspoof_2021_df | 3.30 | 0.6200 | 96.70 | | sonar | 1.90 | 0.9837 | 98.13 | | **Average** | **5.78** | **0.884**| **94.19** | | **Pooled** | **10.88** | **0.78** | **89.11** | ## 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 C...

Note6. Sept.huggingface.co/sparkaiur/dfd-500m-v1

Source context: 2 downloads · 0 likes · Pipeline audio-classification · Repo sparkaiur/dfd-500m-v1