HKUSTAudio/AudioX-IFcaps

---
license: cc-by-nc-nd-4.0
task_categories:
- text-to-audio
size_categories:
- 1M<n<10M
pretty_name: AudioX-IFcaps
---

# [ICLR 2026] AudioX-IFcaps: Instruction-Following Audio Caption Dataset

<a href="https://zeyuet.github.io/AudioX/" target="_blank"><img src="https://img.shields.io/badge/🌐%20Project%20Page-blue" alt="Project Page"></a>
<a href="https://github.com/ZeyueT/AudioX" target="_blank"><img src="https://img.shields.io/badge/💻%20GitHub-ffffff?logo=github&logoColor=181717" alt="GitHub"></a>
<a href="https://arxiv.org/pdf/2503.10522" target="_blank"><img src="https://img.shields.io/badge/📄%20Paper-ICLR%202026-red" alt="Paper"></a>

**AudioX-IFcaps** (Instruction-Following) is a large-scale, high-quality multimodal dataset designed for training unified audio and music generation models. The dataset contains over **7 million samples** with fine-grained, structured annotations that enable precise control over audio generation, including sound event categories, counts, temporal ordering, and timestamps.

## 📊 Dataset Statistics

- **General Audio**: ~1.3m 10-second video-audio clips
- **Music**: ~5.7m 10-second video-music clips
- **Total Duration**: ~16k hours of audio content

## 📝 Citation

If you use this dataset in your research, please cite:

```bibtex
@article{tian2025audiox,
title={Audiox: Diffusion transformer for anything-to-audio generation},
author={Tian, Zeyue and Jin, Yizhu and Liu, Zhaoyang and Yuan, Ruibin and Tan, Xu and Chen, Qifeng and Xue, Wei and Guo, Yike},
journal={arXiv preprint arXiv:2503.10522},
year={2025}
}

@inproceedings{tian2025vidmuse,
title={Vidmuse: A simple video-to-music generation framework with long-short-term modeling},
author={Tian, Zeyue and Liu, Zhaoyang and Yuan, Ruibin and Pan, Jiahao and Liu, Qifeng and Tan, Xu and Chen, Qifeng and Xue, Wei and Guo, Yike},
booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
pages={18782--18793},
year={2025}
}
```

## 🔗 Related Resources

- **Paper**: <a href="https://arxiv.org/pdf/2503.10522" target="_blank">AudioX: Diffusion Transformer for Anything-to-Audio Generation</a> (Accepted to ICLR 2026)
- **Project Page**: <a href="https://zeyuet.github.io/AudioX/" target="_blank">https://zeyuet.github.io/AudioX/</a>
- **Code**: <a href="https://github.com/ZeyueT/AudioX" target="_blank">GitHub Repository</a>

---

**Note**: This dataset is part of the AudioX project. For more information, please refer to the paper and project page.