RAG for audio and meeting recordings: transcription first
Audio has to be transcribed before it can be retrieved at all, and how the transcript is segmented - by timestamp and speaker turn - matters more for retrieval quality than the last few points of word accuracy.
How do you use audio and meetings in RAG?
Audio has to be transcribed before it can be retrieved at all, and how the transcript is segmented - by timestamp and speaker turn - matters more for retrieval quality than the last few points of word accuracy.
Transcription is the conversion
There is no way to embed sound directly for text retrieval. A speech recognition model turns the audio into text, and everything after that is ordinary RAG. Which means the transcript's quality is a hard ceiling on how good retrieval over that recording can be.
Timestamps turn an answer into a citation
A transcript chunked without timestamps can tell you a topic was discussed. One chunked per segment, with its start time carried in the metadata, can tell you it was discussed 41 minutes in - so a person can go and listen. That difference is most of the practical value of converting a meeting at all.
Speaker turns are natural chunk boundaries
A speaker change is usually a topic or stance change, which makes it a far better place to cut than a fixed word count. Chunking across turns produces passages that mix two people's positions and retrieve as though one person said both.
Watch for a fixture transcript
Some tools ship a placeholder speech engine so the pipeline can be tested without model weights. If your transcripts look suspiciously generic or identical across files, check whether a real model is actually loaded - a fixture transcript looks like a successful conversion right up until you search it. Media and speech steps on that path come from Remade with Rust — OCR and audio without binding to a C runtime.