VibeVoice-ASR-BitNet Technical Report

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 with RTF < 1 using as few as 3 CPU threads. VibeVoice-ASR-BitNet is 1.6-2.3x faster than this http URL at comparable model sizes (~1.6 GB), with only modest accuracy degradation compared to the FP16 baseline.

Sound (cs.SD); Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)

arXiv:2607.21075 [cs.SD]

(or arXiv:2607.21075v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2607.21075 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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