How to apply LLMs to multi-speaker audio recordings

Applying LLMs to multi-speaker recordings requires speaker diarization before prompting so transcripts retain attribution rather than blending participants’ views, commitments, questions, and disagreements into a single voice. Enabling speaker labels produces timestamped utterances assigned to generic speakers, while optional Speaker Identification can map labels to names or roles when supported by the conversation context. Accuracy depends heavily on adequate speech from each participant, limited overlap, distinguishable voices, and carefully chosen speaker-count ranges, since overly restrictive caps can merge speakers and overly broad limits can split one speaker into several labels. For individual recordings and simple questions, a diarized transcript can be sent directly through an LLM endpoint, whereas long recordings or collections of audio benefit from a Haystack retrieval-augmented generation pipeline that chunks, embeds, retrieves, and explicitly includes speaker metadata in prompts. The discussion also notes 2026 API and tooling changes, including the replacement of LeMUR with LLM Gateway and deprecation of built-in transcript summarization parameters, while emphasizing that reliable transcript and diarization quality is essential because incorrect attribution can lead models to produce confident but misleading conclusions.

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