RUNLOCALAIv38
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RUNLOCALAI · v38
← Home·/apps·Voice / transcription

MacWhisper

Fully offline

Native macOS app for Whisper transcription. Drag a file in, get a transcript out.

Editorial verdict: “Best Whisper desktop app on macOS. Pay once, transcribe locally forever.”

Voice / transcription
Paid
Proprietary
★ 4.6 / 5
↗ Homepage

Compatibility at a glance

Which runtime + OS combos this app works against. Source of truth for "will it run on my setup?"

§ Runtimes supported
whisper-cpp
§ OS / platform
macos
§ Hardware + model hint
Minimum VRAM
2 GB
Recommended starter model
Whisper Large v3 Q5_0
→ Build the rest of the stack with /stack-builder→ Pick a GPU for this app

What it is

MacWhisper is for macOS users who want Whisper transcription without touching a terminal or managing Python environments. It wraps whisper-cpp in a native SwiftUI app that ships with quantized models from tiny to large-v3 Q5_0, so you can drag in audio files and get SRT, VTT, or plain text output entirely offline. Real-time mic transcription works well on Apple Silicon, and batch processing handles multiple files. The pay-once model means no subscription, but it’s closed-source — you’re trusting the developer’s privacy claims rather than auditing the code yourself. If you need cross-platform support or open-source transparency, look elsewhere.

✓ Strengths

  • +Native macOS app with great UX
  • +Real-time mic transcription on Apple Silicon
  • +Pay-once model — no subscription

△ Caveats

  • −macOS only
  • −Closed-source — you trust the developer's privacy claims

About the Voice / transcription category

Transcription, speech-to-text, or text-to-speech.

§ Other voice / transcription apps
OpenedAI-Speech

Best 'drop-in local TTS for OpenAI clients'. Bridge solution for existing pipelines.

Buzz

Best open-source Whisper desktop app. Cross-platform, free, less polish than MacWhisper.

Where to go from here

Stack Builder →

Pre-filled with this app's recommended use case + budget tier. Get the full rig + runtime + model picks.

Back to /apps →

The full directory — filter by category, runtime, OS, privacy posture, or VRAM.

Runtimes (/tools) →

What this app talks to: Ollama, vLLM, llama.cpp, MLX, LM Studio. The upstream layer.

Community benchmarks →

Did this app work for you on a specific rig? Submit the benchmark — it powers the model + hardware pages.