A meeting summarizer records or receives a meeting's audio, transcribes it, and produces a short summary of what was decided and who needs to do what.
In plain words
It is a pipeline of three steps. First, speech recognition turns the recording into text. Second, speaker diarization marks which person said each part, so the notes can say who agreed to what. Third, a language model condenses the long transcript into a short version that keeps the important points. Diarization in particular is far from perfect, so people should check the notes before relying on them.
A quick example
Imagine a 45-minute project call. The tool produces a transcript labelled by speaker, then a summary with three decisions and a list of tasks, each with the name of the person who said they would do it.
Where you'll meet it
- Notes for team calls and interviews.
- Catching up on meetings you missed.
Related
Sources
- DocswhisperX, Max Bain (GitHub) · read 29 Sept 2026
- DocsSummarization, Hugging Face Tasks · read 29 Sept 2026
- DocsAutomatic Speech Recognition, Hugging Face Tasks · read 29 Sept 2026
Checked by WhatIsAI against the sources above · 29 Sept 2026. Spotted an error? Report it.