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Use a two-stage workflow: run speech recognition on your microphone audio, then optionally send the resulting transcript to a local large language model (LLM) to clean up punctuation, remove obvious filler words, or apply formatting. The recognizer turns speech into text; the LLM edits that text. Review the result before inserting it, because an LLM can change names, numbers, code, or meaning.
What a local LLM adds to speech-to-text dictation
Speech recognition and text refinement are separate jobs. A speech recognition system, also called automatic speech recognition (ASR), processes audio and produces a transcript. A text LLM can then revise that transcript according to instructions. It does not have to receive the original audio.
A typical dictation flow is: start recording with a hotkey or control, speak, stop recording, transcribe locally, optionally refine the transcript, review it, and copy or insert the result in the application where you are working. The refinement step is optional; you can compare the raw transcript with the edited version and skip the LLM when its changes are not useful.
Choose an integration approach
| Approach | What it handles | Trade-offs |
|---|---|---|
| Desktop dictation wrapper | A wrapper can combine recording controls, hotkeys, transcription settings, optional LLM cleanup, and insertion at the cursor. A community macOS project documents a workflow using whisper.cpp and Ollama, including punctuation cleanup, filler-word removal, and numbered-list formatting. | Fewer components to connect, but features and behavior depend on that particular app. Check its supported operating system, configuration, text-processing behavior, and data flow rather than treating one project’s claims as universal. |
| Assemble the components | Use a speech-recognition runtime such as whisper.cpp and connect its transcript output to an LLM runtime or a small custom workflow. | Offers more control over model choice and processing, but you must configure recording, handoff between stages, refinement instructions, and text insertion yourself. |
whisper.cpp is a cross-platform inference implementation of OpenAI’s Whisper speech-recognition model. Its project documentation describes command-line transcription, microphone streaming, and CPU/GPU and other acceleration options. Actual performance depends on the computer, model, and configuration; the available documentation does not establish a universal fastest or most accurate setup for dictation.
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LocalAI also documents an audio-to-text route. The available material does not establish a universal ranking among transcription options, so compare candidates using your own language, vocabulary, hardware, and acceptable delay.
Set up and test the two-stage workflow
- Choose how to capture speech. Use a recording control or push-to-talk flow. A wrapper may provide the hotkey and recording interface; with a lower-level runtime, you may need to arrange capture separately.
- Configure speech recognition. Select a transcription model and runtime, then test the system with representative speech. Pay attention to names, technical vocabulary, language, and the delay on your own computer. Do not assume a custom vocabulary or prompt will produce a particular accuracy improvement; the cited project materials do not quantify one.
- Send the transcript—not necessarily the audio—to the refiner. If using a local LLM, configure the application to pass the recognized text to the local model. Keep the raw transcript available for comparison or recovery.
- Use conservative editing instructions. For example, an illustrative prompt could say:
Correct punctuation and remove obvious verbal fillers. Preserve the speaker's wording and intended meaning. Do not change names, numbers, technical terms, or code. Return only the revised text.This is an instruction example, not a tested prompt or guarantee against errors. - Review before insertion. Compare the output with what you said, particularly for proper nouns, quantities, code, and consequential wording. Edit or discard changes that alter meaning.
- Insert or copy the approved text. A desktop wrapper may insert the result at the cursor; a component-based setup can copy it to the clipboard or use another insertion method you configure.
Decide whether refinement is helping
Run the same kind of short, non-sensitive dictation with refinement off and on. Check whether punctuation and formatting reduce editing time, and whether the LLM introduces changes you must undo. Test examples that reflect your actual writing, such as a paragraph with spoken fillers, a short numbered list, or a passage containing technical terms. The available sources describe features, not an independent end-to-end accuracy evaluation, so a local comparison is more useful than assuming the extra stage improves every transcript.
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Some app-specific workflows can skip or adjust processing for terminals and code editors. That is a feature documented by the community macOS project, not a behavior to expect from every dictation tool. For code or commands, keeping refinement off is often the safer choice unless you have verified the output carefully.
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A local-first setup requires checking both the transcription stage and the LLM stage, as well as the surrounding app’s configuration. Local model execution is different from using a cloud-hosted model, even when both are offered through the same product.
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Ollama’s Privacy Policy, dated March 2026, says: “We do not collect, store, transmit, or have access to your prompts, responses, model interactions, or other content you process locally.” That statement applies to content processed locally under the policy; it should not be extended to cloud inference. Ollama’s cloud-model documentation says cloud models use inference compute on ollama.com and require sign-in. Check the selected model, endpoint settings, and network behavior rather than assuming every Ollama request stays on your computer.
- Confirm which speech-recognition runtime and LLM model the app is actually using.
- Inspect whether the configured model endpoint is local or cloud-hosted, and whether sign-in or network access is involved.
- Review the application’s privacy statements and settings. A project README is a project claim, not an independent security audit; it does not by itself verify telemetry, logs, update behavior, model downloads, or every configured endpoint.
- If local handling is essential, test with non-sensitive content first and verify the configuration before dictating confidential material.
Hardware and model choices
whisper.cpp documents multiple platforms and acceleration routes, but that does not establish a minimum computer specification or a single best model for this workflow. Model size, available storage, latency, language, and recognition of your vocabulary are practical comparison points. Test on the hardware you intend to use instead of relying on a universal performance claim.
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