This is a simplified guide to an AI model called VibeVoice-ASR maintained by microsoft. If you like these kinds of analysis, join AIModels.fyi or follow us on Twitter.
Model overview
VibeVoice-ASR is a unified speech-to-text model created by Microsoft that handles long-form audio transcription in a single pass. Unlike conventional ASR models that process audio in short chunks, this model accepts up to 60 minutes of continuous audio and produces structured transcriptions containing speaker identification, precise timestamps, and content. The model distinguishes itself through joint processing of automatic speech recognition, speaker diarization, and time-stamping in one unified framework.
Model inputs and outputs
The model takes continuous audio input up to 60 minutes in length, along with optional customized hotwords to improve recognition accuracy on domain-specific terminology. It outputs rich, structured transcriptions that identify who spoke, when they spoke, and what they said, maintaining consistency across the entire audio duration.
Inputs
- Audio: Up to 60 minutes of continuous speech in a single pass
- Customized Hotwords: Optional domain-specific terms, names, or background information to guide recognition
Outputs
- Speaker Identification: Which speaker produced each segment
- Timestamps: Precise temporal markers for when each speaker contributed
- Transcribed Content: The recognized text from each speaker segment
Capabilities
The model processes long-form audio without breaking it into short chunks, which preserves global context and ensures consistent speaker tracking throughout the entire recording. It performs speaker diarization simultaneously with transcription, eliminating the need for separate post-processing steps. The customized hotwords feature allows users to inject domain knowledge, significantly improving accuracy on specialized vocabulary and proper nouns that the base model might struggle with.
What can I use it for?
This model suits applications requiring comprehensive meeting transcription, podcast documentation, interview analysis, and recorded conversation processing. Businesses can use it for automated meeting notes generation, compliance recording transcription, and content archive creation. The structured output with speaker attribution makes it ideal for creating searchable transcription databases and generating speaker-attributed highlights from long recordings. Organizations handling sensitive conversations can benefit from the consistent speaker identification across hour-long sessions.
Things to try
Experiment with customized hotwords for your specific domain—provide technical terms, product names, or participant names to the model and observe how accuracy improves on those elements. Test the model against recordings with multiple speakers and varying audio quality to understand how well it maintains speaker consistency throughout the full duration. Compare the structured output against manually created transcripts to verify the accuracy of timestamp placement and speaker attribution, particularly during overlapping speech or quick speaker transitions.