README.md

---
license: apache-2.0
language:
- en
- zh
- ja
tags:
- audio
- synthetic-speech-detection
- deepfake
- deepfake-audio
- security
- voice-spoofing
- anti-spoofing
configs:
- config_name: tts
  data_files:
  - split: test
    path: TTS/*.tar.gz
- config_name: real
  data_files:
  - split: test
    path: real_data_flac/*.tar.gz
- config_name: vocoders
  data_files:
  - split: test
    path: Vocoders/**/*.tar.gz
---
This repository introduces:  🌀 *ShiftySpeech*: A Large-Scale Synthetic Speech Dataset with Distribution Shifts

## 🔥 Key Features
- 3000+ hours of synthetic speech
- **Diverse Distribution Shifts**: The dataset spans **7 key distribution shifts**, including:  
  - 📖 **Reading Style**  
  - 🎙️ **Podcast**  
  - 🎥 **YouTube**  
  - 🗣️ **Languages (Three different languages)**  
  - 🌎 **Demographics (including variations in age, accent, and gender)**  
- **Multiple Speech Generation Systems**: Includes data synthesized from various **TTS models** and **vocoders**.
  
## 💡 Why We Built This Dataset
> Driven by advances in self-supervised learning for speech, state-of-the-art synthetic speech detectors have achieved low error rates on popular benchmarks such as ASVspoof. However, prior benchmarks do not address the wide range of real-world variability in speech. Are reported error rates realistic in real-world conditions? To assess detector failure modes and robustness under controlled distribution shifts, we introduce **ShiftySpeech**, a benchmark with more than 3000 hours of synthetic speech from 7 domains, 6 TTS systems, 12 vocoders, and 3 languages.

## ⚙️ Usage 

Ensure that you have soundfile or librosa installed for proper audio decoding: 

```bash 
pip install soundfile librosa
```
##### 📌 Example: Loading the AISHELL Dataset Vocoded with APNet2

```bash
from datasets import load_dataset

dataset = load_dataset("ash56/ShiftySpeech", data_files={"data": f"Vocoders/apnet2/apnet2_aishell_flac.tar.gz"})["data"]
```
**⚠️ Note:** It is recommended to load data from a specific folder to avoid unnecessary memory usage.

The source datasets covered by different TTS and Vocoder systems are listed in [tts.yaml](https://huggingface.co/datasets/ash56/ShiftySpeech/blob/main/tts.yaml) and [vocoders.yaml](https://huggingface.co/datasets/ash56/ShiftySpeech/blob/main/vocoders.yaml)

## 📄 More Information  

For detailed information on dataset sources and analysis, see our paper: *[ShiftySpeech: A Large-Scale Synthetic Speech Dataset with Distribution Shifts](https://arxiv.org/pdf/2502.05674)* 

You can also find the full implementation on [GitHub](https://github.com/Ashigarg123/ShiftySpeech/tree/main)

### **Citation**

If you find this dataset useful, please cite our work:
```bibtex
@article{garg2025shiftyspeech,
  title={ShiftySpeech: A Large-Scale Synthetic Speech Dataset with Distribution Shifts},
  author={Garg, Ashi and Cai, Zexin and Zhang, Lin and Xinyuan, Henry Li and Garc{\'\i}a-Perera, Leibny Paola and Duh, Kevin and Khudanpur, Sanjeev and Wiesner, Matthew and Andrews, Nicholas},
  journal={arXiv preprint arXiv:2502.05674},
  year={2025}
}
```

### ✉️ **Contact** 

If you have any questions or comments about the resource, please feel free to reach out to us at: [[email protected]](mailto:[email protected]) or [[email protected]](mailto:[email protected])