How TypeWhisper Keeps Speech-to-Text Private

Updated

TypeWhisper keeps speech-to-text private by running local engines for on-device transcription, so core dictation does not depend on cloud services, and by publishing open-source code that users can inspect to verify these claims. This approach suits people who dictate sensitive material—client notes, internal drafts, personal journaling—and want the audio processed on their own Mac, Windows PC, or iOS device rather than sent to a third-party server. The trade-off is that local processing depends on your hardware and the engine you choose, so performance and accuracy can vary by device.

Where the privacy comes from

TypeWhisper is described as private speech-to-text for macOS, Windows, and iOS, offering system-wide dictation, file transcription, AI workflows, and local engines. The privacy model rests on three practical pillars:

  • On-device engines. Transcription runs locally through the app's local engines. The audio you speak or import is processed by software on your machine.
  • No cloud dependency for core dictation. Basic dictation is designed to work without routing audio through a remote service. This is the part that matters most for everyday use.
  • Open-source code. Because the source is public, you or a technical colleague can review how audio is handled instead of trusting a marketing page.

What stays on your device

Based on how local-engine dictation works, the following remain on your machine during core use:

Data Where it goes with local engines
Microphone audio during dictation Processed on-device by the local engine
Imported audio/video files for transcription Read and transcribed locally
Resulting text Produced locally; where it is saved depends on your settings and apps

The important caveat: "local engines" describes the transcription path, not every feature. The site also lists AI workflows, which by nature may involve sending text to a model. If your priority is strict privacy, use local engines for dictation and check each AI workflow's configuration before enabling it.

How to verify it yourself

  1. Start with a controlled test. Disconnect from the network, then dictate a short sentence using a local engine. If transcription still works, that confirms the audio path is on-device for that engine.
  2. Inspect the source. Since the project is open source, review the repository for how microphone input and file transcription are handled, and look for any network calls tied to the features you plan to use.
  3. Separate features by risk. Keep core dictation on local engines. Treat AI workflows as a separate decision and confirm what each one sends and where.
  4. Check platform behavior. On iOS especially, confirm the app's permissions (microphone, files) match what you expect, since mobile OS sandboxing affects what an app can access.

When this approach fits—and when it doesn't

Choose TypeWhisper's local-engine setup if you dictate frequently, handle confidential content, and want to avoid sending audio to a cloud transcription service. It is a reasonable fit for writers, developers, and professionals on macOS, Windows, or iOS who value inspectable code.

Be more cautious if you need the highest transcription accuracy on weak hardware, or if you plan to lean heavily on AI workflows—those may reintroduce network transmission, so evaluate them feature by feature rather than assuming the whole app is offline.