Model reference · open weights

VibeVoice-ASR

Audio microsoft Speech→text 1 build Open weights 699k dl/mo

VibeVoice-ASR is an open-weight audio or speech model from microsoft. VibeVoice-ASR (BF16) weighs 17.3 GB; the smallest configuration that runs it is RTX 4090 24 GB.

  • VibeVoice-ASR is an 8.7B parameter automatic speech recognition model developed by Microsoft that processes up to 60 minutes of audio in a single pass.
  • It generates structured transcriptions including speaker identification, timestamps, and content while supporting over 50 languages and code-switching.
  • The model is available under the MIT license.

Summary of the microsoft/VibeVoice-ASR model card, 2026-10-01

What it is

Released bymicrosoft
Released2026-01-21
Parameters8.7B
VRAM17.3 GB for the weights

What it runs on

Memory and cards for VibeVoice-ASR (BF16)

17.3 GBweights, file size
1.6 GBruntime overhead
CardOne streamMemory
RTX 3060 12 GB … RTX 4060 Ti 16 GBdoes not fit
RTX 3090 24 GBfits23.4 GB
RTX 4090 24 GBfits23.4 GB
RTX 5090 32 GBfits31.0 GB
L40S 48 GBfits44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 GB

From the model card

What microsoft says about VibeVoice-ASR

Read the model card

VibeVoice-ASR

VibeVoice-ASR is a unified speech-to-text model designed to handle 60-minute long-form audio in a single pass, generating structured transcriptions containing Who (Speaker), When (Timestamps), and What (Content), with support for Customized Hotwords and over 50 languages.

➡️ Code: microsoft/VibeVoice ➡️ Demo: VibeVoice-ASR-Demo ➡️ Report: VibeVoice-ASR Technical Report ➡️ Finetuning: Finetuning ➡️ vLLM: vLLM-VibeVoice-ASR

🔥 Key Features

  • 🕒 60-minute Single-Pass Processing: Unlike conventional ASR models that slice audio into short chunks (often losing global context), VibeVoice ASR accepts up to 60 minutes of continuous audio input within 64K token length. This ensures consistent speaker tracking and semantic coherence across the entire hour.

  • 👤 Customized Hotwords: Users can provide customized hotwords (e.g., specific names, technical terms, or background info) to guide the recognition process, significantly improving accuracy on domain-specific content.

  • 📝 Rich Transcription (Who, When, What): The model jointly performs ASR, diarization, and timestamping, producing a structured output that indicates who said what and when.

  • 🌍 Multilingual & Code-Switching Support: It supports over 50 languages, requires no explicit language setting, and natively handles code-switching within and across utterances. Language distribution can be found here.

Evaluation

Installation and Usage

Please refer to GitHub README.

Language Distribution

License

This project is licensed under the MIT License.

Contact

This project was conducted by members of Microsoft Research. We welcome feedback and collaboration from our audience. If you have suggestions, questions, or observe unexpected/offensive behavior in our technology, please contact us at VibeVoice@microsoft.com. If the team receives reports of undesired behavior or identifies issues independently, we will update this repository with appropriate mitigations.

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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