# VibeVoice-ASR-Streaming Technical Report

> Source: <http://arxiv.org/abs/2609.02812v1>
> Published: 2026-09-03 13:47:48+00:00

# Electrical Engineering and Systems Science > Audio and Speech Processing

[Submitted on 2 Sep 2026]

# Title:VibeVoice-ASR-Streaming Technical Report

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Abstract:Traditional speaker-attributed ASR systems treated ASR and speaker diarization as two separate tasks. Recently, end-to-end models such as VibeVoice-ASR have unified the two tasks within a single model. However, existing unified models still mainly support offline recognition, making it difficult to meet the low-latency requirements of real-time voice assistants and agents. To tackle this issue, we present VibeVoice-ASR-Streaming, one of the first LLM-based end-to-end approaches to streaming speaker-attributed ASR. It interleaves fixed-size audio chunks, a small amount of lookahead audio and previous text. This allows the model to produce ''who said what'' as speech arrives, without a separate diarization stage. For transcription accuracy, our 7B model achieves the lowest average WER/CER across five evaluation sets. For speaker attribution, it achieves the best or tied-best on 12 of 13 evaluation settings. We release the 1.5B and 7B model weights together with inference code.

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