Lion Cipher Lab
Transcribe audio privately on your own machine with Whisper
- AI Security
- Beginner
- ~30 min
- Free · no account required
Why it matters
Speech-to-text is one of the AI tools people adopt fastest and question least. The easy version is a website: you upload a recording and a transcript comes back. What that convenience hides is that your audio left your machine and landed on someone else's servers, subject to their retention, their staff, and their policies. For a lot of real audio — a private meeting, a source protected by a journalist, a recording with health or legal detail, anything said in confidence — that upload is exactly the thing you shouldn't do casually. Whisper, OpenAI's open-source speech recognition model, can…
What you'll learn
- local speech-to-text
- running Whisper offline
- privacy-preserving AI
- choosing model sizes
- judging transcription accuracy
- cloud vs local data trade-offs
Steps in this lab
- What Whisper is, and why running it locally is the whole point
- Get your machine ready: Python, ffmpeg, and one audio file you own
- Install Whisper
- Transcribe your file — entirely on your machine
- Model sizes: the trade-off between speed and accuracy
- A lighter alternative: whisper.cpp
- Where this is genuinely useful — and the cloud contrast that makes local matter
- What you built, and the instinct to keep