VIBEVOICE-ASR Technical Report
Authors: name: Zhiliang Peng, name: Jianwei Yu, name: Yaoyao Chang, name: Zilong Wang, name: Li Dong, name: Yingbo Hao, name: Yujie Tu, name: Chenyu Yang, name: Wenhui Wang, name: Songchen Xu, name: Yutao Sun, name: Hangbo Bao, name: Weijiang Xu, name: Yi Zhu, name: Zehua Wang, name: Ting Song, name: Yan Xia, name: Zewen Chi, name: Shaohan Huang, name: Liang Wang, name: Chuang Ding, name: Shuai Wang, name: Xie Chen, name: Furu Wei
Organization: Microsoft

This report presents VibeVoice-ASR, a general-purpose speech understanding framework built upon VibeVoice, designed to address the persistent challenges of context fragmentation and multi-speaker complexity in long-form audio (e.g., meetings, podcasts) that remain despite recent advancements in short-form speech recognition. Unlike traditional pipelined approaches that rely on audio chunking, VibeVoice-ASRsupports single-pass processing for up to 60 minutes of audio. It unifies Automatic Speech Recognition, Speaker Diarization, and Timestamping into a single end-to-end generation task. In addition, VibeVoice-ASR supports over 50 languages, requires no explicit language setting, and natively handles code-switching within and across utterances. Furthermore, we introduce a prompt-based context injection mechanism that allows users to supply customized conetxt, significantly improving accuracy on domain-specific terminology and polyphonic character disambiguation.