README.md

---
license: apache-2.0
language: [en]
pretty_name: In-the-Wild Audio Deepfake Dataset
task_categories: [audio-classification]
size_categories: [10K<n<100K]
configs:
  - config_name: default
    data_files:
      - {split: test, path: "data/test-*.parquet"}
tags:
  - anti-spoofing
  - audio-deepfake-detection
  - speech
  - benchmark
  - arena-ready
paperswithcode_id:
arxiv:
  - "2203.16263"
---

# In-the-Wild Audio Deepfake Dataset

Benchmark-ready packaging of the **In-the-Wild** audio deepfake dataset for speech
anti-spoofing / synthetic-voice detection.

## Overview

In-the-Wild (Müller et al., *Does Audio Deepfake Detection Generalize?*, arXiv
2203.16263) pairs genuine speech with audio deepfakes of politicians and public
figures, collected from publicly available sources. It is a **cross-domain
generalization** benchmark: models trained on lab datasets (e.g. ASVspoof) are
evaluated here against real-world conditions. The task is binary classification:
**bonafide** (genuine human speech) vs. **spoof** (deepfake). 31,779 clips
(19,963 bonafide / 11,816 spoof), 16 kHz mono.

## License & redistribution

Redistributed under the **Apache License 2.0**; the full text is in `LICENSE.txt`.
Audio is the original 16 kHz mono signal encoded to FLAC (16-bit PCM). We thank
'VocalSynthesis' for the audio deepfakes included in the source dataset.

## Schema

Canonical 4-column parquet: `path` (string), `audio` (`Audio(16000)`), `label`
(`ClassLabel[bonafide, spoof]`), `notes` (JSON string with a unique
`utterance_id`, the `speaker` name, and the source `label` string).

## Citation

```bibtex
@inproceedings{muller2022does,
  title={Does Audio Deepfake Detection Generalize?},
  author={M{\"u}ller, Nicolas M and Czempin, Pavel and Dieckmann, Franziska and Froghyar, Adam and B{\"o}ttinger, Konstantin},
  booktitle={Interspeech},
  year={2022}
}
```