--- license: cc-by-nc-nd-4.0 task_categories: - text-to-audio size_categories: - 1M

**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**: AudioX: Diffusion Transformer for Anything-to-Audio Generation (Accepted to ICLR 2026) - **Project Page**: https://zeyuet.github.io/AudioX/ - **Code**: GitHub Repository --- **Note**: This dataset is part of the AudioX project. For more information, please refer to the paper and project page.