VibeVoice-ASR-BitNet achieves real-time speech recognition on edge CPUs

Researchers have developed VibeVoice-ASR-BitNet, a highly compressed version of the VibeVoice-ASR model designed for real-time performance on edge CPUs. This new model utilizes aggressive quantization techniques, including INT8 for the VAE acoustic tokenizer and ternary weights for the language model, to achieve significant speedups. Optimized with custom SIMD kernels and fused operators within the ggml framework, VibeVoice-ASR-BitNet can perform real-time speech recognition using minimal CPU threads and is notably faster than previous iterations at comparable model sizes with only a slight drop in accuracy. AI

IMPACT Enables real-time speech recognition on resource-constrained edge devices, potentially expanding AI applications in mobile and IoT.

RANK_REASON The cluster contains a technical report detailing a new model variant and its performance optimizations. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• Arm Holdings
• arXiv
• BITNET
• ggml
• Hugging Face
• variational auto-encoder
• VibeVoice-ASR
• VibeVoice-ASR-BitNet
• x86

• model release
• infra

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