Step-Audio-EditX Technical Report
Abstract
We present Step-Audio-EditX, the first open-source LLM-based audio model excelling at expressive and iterative audio editing encompassing emotion, speaking style, and paralinguistics alongside robust zero-shot text-to-speech (TTS) capabilities. Our core innovation lies in leveraging only large-margin synthetic data, which circumvents the need for embedding-based priors or auxiliary modules. This large-margin learning approach enables both iterative control and high expressivity across voices, and represents a fundamental pivot from the conventional focus on representation-level disentanglement. Evaluation results demonstrate that Step-Audio-EditX surpasses both MiniMax-2.6-hd and Doubao-Seed-TTS-2.0 in emotion editing and other fine-grained control tasks.
Cite
@article{arxiv.2511.03601,
title = {Step-Audio-EditX Technical Report},
author = {Chao Yan and Boyong Wu and Peng Yang and Pengfei Tan and Guoqiang Hu and Li Xie and Yuxin Zhang and Xiangyu and Zhang and Fei Tian and Xuerui Yang and Xiangyu Zhang and Daxin Jiang and Shuchang Zhou and Gang Yu},
journal= {arXiv preprint arXiv:2511.03601},
year = {2025}
}