Audio and podcasting decision

MacWhisper

A capable developer can recreate the core local speech-to-text workflow in about a week using existing open-source Whisper/STT projects; only product polish and commercial packaging are left as paid conveniences.

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You pay

Not priced

No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.

You’d pay instead

$50one-off30 h to build

$0/mo6 h/mo upkeep

No published price to break even against.

Open-source builds that already do this

Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need - the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship. All MacWhisper alternatives, with the arithmetic →

What a replacement has to do

  • Record audio from the macOS microphone, run speech-to-text inference with a local/hosted Whisper-compatible model, display live and final transcripts in a simple UI, export transcripts (TXT/SRT) and basic timestamps.

What it still won’t have

  • Polished macOS native UX and system integration (installer, auto-updates)
  • Commercial support and warranty
  • Any proprietary convenience features or packaged optimizations the vendor may provide

What remains hard

  • Product polish and ongoing maintenance
Read the build prompt

First-year cost

No published price

MacWhisper does not publish a price we could read, so there is nothing to compare against. What building costs is below.

Money you would actually spend

Keep paying
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Subscription price × seats × 12

Build it
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AI build —APIs + hosting —

Time you would spend

—

—

What you would spend

What we assumed

The verdict above measures whether you could build it. This one is only about money.

Runnable build prompt

Not run yet
Build a minimal macOS local speech-to-text app using Swift (AppKit) or Electron + a Rust/Python backend. Include: 1) a microphone capture service that captures audio frames and saves recordings; 2) a model inference adapter that can call a local Whisper-compatible binary or a hosted API (configurable); 3) a simple UI that shows streaming partial transcripts and final transcripts, allows pausing/starting recording, and exports TXT and SRT; 4) persistent settings for model choice and export path. Out of scope: code-signing, App Store packaging, commercial auto-update server. Require: error handling for microphone access and model failures, unit tests for the inference adapter, and an end-to-end smoke test that records 5s and produces a transcript.
How we checked3 sources · 2/3 runs agreed · evidence score 86

How the score was reached

  • Build verdict base78
  • An open-source build was found+5
  • 3 cited sources+3
  • Evidence score86

The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.

How scoring works →

Cited sources · 3

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

! 2 of 3 runs agreed; the verdict is the majority✓ Citations limited to fetched pages! 1 moat recorded