VibeVoice-ASR-BitNet Technical Report
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 < ...
| Authors | Songchen Xu, Ting Song, Shaohan Huang et al. |
|---|---|
| Primary category | cs.SD |
| Published | 23 July 2026 |
| Last updated at source | 25 July 2026 |
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