README.md

metadata

language:
  - en
  - ja
license: mit
pipeline_tag: automatic-speech-recognition
library_name: transformers
base_model: microsoft/VibeVoice-ASR
tags:
  - automatic-speech-recognition
  - audio-to-text
  - speech-recognition
  - vibevoice
  - qwen2
  - awq
  - int4

VibeVoice-ASR AWQ INT4

This repository contains a 4-bit AWQ quantized export of microsoft/VibeVoice-ASR.

Quantization

• Method: AWQ
• Bits: 4
• Group size: 128
• Logical parameter count: 8,674,021,857

Repository layout

This model is stored in a split VibeVoice layout:

• root directory: VibeVoice audio and non-decoder weights
• decoder-awq/: quantized Qwen2 decoder weights

Keep this layout intact when downloading or mirroring the repository.

Metadata

The root config.json includes:

• vibevoice_metadata
• vibevoice_decoder_model_path
• vibevoice_decoder_quantization

These fields identify the split decoder path and preserve the logical source-model metadata.

Validation

This AWQ export was validated against the full upstream VibeVoice-ASR model on short audio samples.

• outputs remained valid JSON transcript arrays
• output similarity to the full model remained high on tested samples

Serving note for vLLM 0.17.x

On current vLLM 0.17.x CUDA builds, this checkpoint is compatible with the faster Marlin-backed AWQ path.

• prefer letting vLLM infer the backend from config.json
• if you must set it explicitly, use awq_marlin rather than plain awq

In local testing on an RTX A6000, forcing plain awq was substantially slower than letting vLLM auto-select the Marlin kernel.

Upstream references

• Code: https://github.com/microsoft/VibeVoice
• Base model: https://huggingface.co/microsoft/VibeVoice-ASR
• Report: https://arxiv.org/pdf/2601.18184

Notes

• This is a quantized derivative export, not the original upstream checkpoint.
• Base model licensing and usage terms follow the upstream VibeVoice-ASR release.
• Pure-VibeVoice compatibility patches for vLLM 0.17.x are included under patches/vllm_0_17/.