The hardest part of voice-to-data isn't the transcription. It's making sense of someone thinking out loud. I've been building Voice Tables — a tool that lets you speak naturally and get structured spreadsheet rows back. No forms, no typing, just talk. Under the hood, it's a two-stage pipeline: Whisper handles the transcription, then a custom prompt chain extracts structured fields from the raw text. Here's how it actually works, and where things get tricky. The Pipeline The architecture is deceptively simple: Voice recording → Whisper transcription → Prompt-based extraction → Structured row Enter fullscreen mode Exit fullscreen mode Stage one (Whisper) is mostly a solved problem. Stage two — turning a messy human monologue into clean, column-mapped data — is where the real engineering lives. Whisper Configuration For Voice Tables, I run Whisper with these considerations: // Whisper config essentials const whisperConfig = { model : " whisper-1 " , language : null , // auto-detect — users speak CZ, EN, DE...…