Step-Audio-EditX Technical Report
Authors: name: Chao Yan, name: Boyong Wu, name: Peng Yang, name: Pengfei Tan, name: Guoqiang Hu, name: Yuxin Zhang, name: Xiangyu, name: Zhang, name: Fei Tian, name: Xuerui Yang, name: Xiangyu Zhang, name: Daxin Jiang, name: Gang Yu
Organization: StepFun

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.