Datasets:
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README.md
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---
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datasets: null
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license: cc-by-sa-4.0
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task_categories:
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- audio-classification
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language:
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- en
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modalities:
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- audio
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tags:
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- audio
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- deepfake
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- detection
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- in-the-wild
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- deepfake-detection
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- audio-deepfake-detection
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- antispoofing
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pretty_name: In The Wild
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size_categories:
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- 10K<n<100K
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---
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# In-the-Wild: A Deepfake Detection Dataset
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Welcome to **In-the-Wild**, a dataset for *real-world audio deepfake detection*.
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It accompanies the paper:
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> **Does Audio Deepfake Detection Generalize?**
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> Nicolas M. Müller, Pavel Czempin, Franziska Dieckmann, Adam Froghyar, and Konstantin Böttinger
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> Fraunhofer AISEC, Technical University of Munich, why do birds GmbH
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> [arXiv:2203.16263](https://arxiv.org/abs/2203.16263)
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---
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## Dataset Summary
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The **In-the-Wild** dataset contains real and synthetic speech recordings of **58 celebrities and politicians**, collected from publicly available media such as interviews, podcasts, and online videos.
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It provides a realistic benchmark for testing how well *audio deepfake detection models generalize* beyond laboratory data such as ASVspoof.
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- **Task:** Audio Classification (Deepfake / Genuine)
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- **Languages:** English
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- **Modality:** Audio
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- **Size:** 37.9 hours total
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- 17.2 hours fake
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- 20.7 hours real
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---
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## Download
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You can download the full dataset as a single ZIP file directly from this repository or via the Hugging Face `datasets` library.
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### Option 1: With the `datasets` library
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```python
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from datasets import load_dataset
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ds = load_dataset("mueller91/In-The-Wild")
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```
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### Option 2: wget
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```
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wget https://huggingface.co/datasets/mueller91/In-The-Wild/resolve/main/release_in_the_wild.zip
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unzip release_in_the_wild.zip
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```
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## Citation
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```
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@article{muller2022does,
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title={Does audio deepfake detection generalize?},
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author={M{\"u}ller, Nicolas M and Czempin, Pavel and Dieckmann, Franziska and Froghyar, Adam and B{\"o}ttinger, Konstantin},
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journal={arXiv preprint arXiv:2203.16263},
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year={2022}
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}
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```
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