|
|
--- |
|
|
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, 2021, 2023, 2024** |
|
|
- **Codecfake** |
|
|
- **LibriSeVoc** |
|
|
- **DFADD** |
|
|
- **CTRSVDD** |
|
|
- **SpoofCeleb** |
|
|
- **MLAAD** |
|
|
- **SONAR** |
|
|
- **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 speech.arena.eval@gmail.com. |
|
|
|
|
|
Stay tuned for upcoming versions of our models! |
|
|
|
|
|
## Citation |
|
|
|
|
|
If you use this model in your work, it can be cited as : |
|
|
|
|
|
```bibtex |
|
|
@misc{kulkarni_2024_df_arena_1b, |
|
|
author = {Ajinkya Kulkarni and Atharva Kulkarni and Sandipana Dowerah and Matthew Magimai Doss and Tanel Alumäe}, |
|
|
title = {DF_Arena_1B_V_1 - Universal Audio Deepfake Detection}, |
|
|
year = {2025}, |
|
|
publisher = {Hugging Face}, |
|
|
url = {https://huggingface.co/Speech-Arena-2025/DF_Arena_1B_V_1/} |
|
|
} |
|
|
``` |
