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
Published: 2025-11-05 16:22:19

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.