Duplicate from mueller91/In-The-Wild

Co-authored-by: Nicolas Müller <mueller91@users.noreply.huggingface.co>

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+# Audio files - uncompressed
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+# Audio files - compressed
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README.md

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+---
+datasets: null
+license: cc-by-sa-4.0
+task_categories:
+- audio-classification
+language:
+- en
+modalities:
+- audio
+tags:
+- audio
+- deepfake
+- detection
+- in-the-wild
+- deepfake-detection
+- audio-deepfake-detection
+- antispoofing
+pretty_name: In The Wild
+size_categories:
+- 10K<n<100K
+---
+
+# In-the-Wild: A Deepfake Detection Dataset
+
+Welcome to **In-the-Wild**, a dataset for evaluationg *audio deepfake detection*.  
+It accompanies the paper: Does Audio Deepfake Detection Generalize?  [arXiv:2203.16263](https://arxiv.org/abs/2203.16263)
+
+---
+
+## Dataset Summary
+
+The **In-the-Wild** dataset contains real and synthetic speech recordings of **58 celebrities and politicians**, collected from online videos.
+
+It provides a realistic benchmark for testing how well *audio deepfake detection models generalize* beyond laboratory data such as ASVspoof.
+
+- **Task:** Audio Classification (Deepfake / Genuine)  
+- **Languages:** English  
+- **Modality:** Audio  
+- **Size:** 37.9 hours total  
+  - 17.2 hours fake  
+  - 20.7 hours real  
+
+---
+
+## Download
+
+You can download the full dataset as a single ZIP file directly from this repository or via the Hugging Face `datasets` library.
+
+### Option 1: With the `datasets` library
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("mueller91/In-The-Wild")
+```
+### Option 2: wget
+```
+wget https://huggingface.co/datasets/mueller91/In-The-Wild/resolve/main/release_in_the_wild.zip
+unzip release_in_the_wild.zip
+```
+
+## Citation
+```
+@article{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},
+  journal={arXiv preprint arXiv:2203.16263},
+  year={2022}
+}
+```
+

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