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arXiv · 2607.21075

VibeVoice-ASR-BitNet Technical Report

Songchen Xu·Ting Song·Shaohan Huang·Zhiliang Peng·Yan Xia·Yujie Tu·Xin Huang·Xun Wu·Wenhui Wang·Yaoyao Chang·Jianwei Yu·Li Dong·Furu Wei
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Abstract

We present VibeVoice-ASR-BitNet, a compressed variant of VibeVoice-ASR optimized for real-time inference on edge CPUs. We apply heterogeneous quantization tailored to the computational characteristics of each stage: the VAE acoustic tokenizer uses full-pipeline INT8 quantization (I8_S) with kernel fusion and SIMD optimization, while the autoregressive language model adopts BitNet-style ternary weights (I2_S). To preserve accuracy under aggressive compression, we employ a progressive quantization-aware training strategy. For inference, we implement custom SIMD kernels and fused operators within the ggml framework targeting both ARM and x86 platforms, achieving real-time recognition (RTF < 1) on low-thread-count CPUs. VibeVoice-ASR-BitNet is 1.6--2.3x faster than Whisper.cpp at comparable model sizes (~1.6 GB), with only modest accuracy degradation compared to the FP16 baseline.

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Songchen Xu, Ting Song, Shaohan Huang, Zhiliang Peng, Yan Xia, Yujie Tu, Xin Huang, Xun Wu, Wenhui Wang, Yaoyao Chang, Jianwei Yu, Li Dong, Furu Wei. 2026-07-23. VibeVoice-ASR-BitNet Technical Report. https://arxiv.org/abs/2607.21075

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