Openwhisp

Voice to text, entirely on your machine. Hold a hotkey, speak, release — your words are transcribed, polished, and pasted right where you need them. No cloud, no account, no latency.

Built in a weekend because I kept getting ads for Wispr Flow and thought — why not build it myself?

How it works

1. Hold the hotkey — OpenWhisp starts listening (Fn on macOS, Ctrl+Shift+Space on Windows)
2. Speak — your voice is captured locally
3. Release — Whisper transcribes your speech, a local LLM polishes the text, and the result is pasted into whatever app you were using

The entire pipeline runs locally via Whisper (speech-to-text) and Ollama (text enhancement).

Features

• Fully local — no data leaves your machine
• Cross-platform — works on macOS and Windows
• Styles — switch between Conversation and Vibe Coding modes depending on context
• Enhancement levels — from raw transcription (No Filter) to professional polish (High)
• Intent resolution — if you change your mind mid-sentence ("make it white... actually, black"), OpenWhisp resolves to your final intent
• Auto-paste — refined text is pasted directly into the active app
• Auto-launch Ollama — if Ollama is installed, OpenWhisp starts it automatically
• Setup wizard — guided first-launch experience for permissions, models, and configuration
• Minimal overlay — a small audio-reactive grid appears at the bottom of your screen during dictation

Styles

Style	Use case
Conversation	Messages, emails, notes, everyday writing
Vibe Coding	Developer communication — translates casual speech into proper engineering language

Each style has four enhancement levels: No Filter, Soft, Medium, and High.

Requirements

macOS

• macOS (Apple Silicon recommended)
• Ollama — OpenWhisp auto-launches it if installed
• ~10 GB disk space for models (downloaded on first launch)

Windows

• Windows 10 or later
• Ollama — OpenWhisp auto-launches it if installed
• ~10 GB disk space for models (downloaded on first launch)

Note: On Windows, a C++ compiler is not required. If the native helper binary is not found, OpenWhisp automatically falls back to Electron's built-in globalShortcut API for the hotkey and PowerShell for paste simulation. If you want to build the optional native helper for slightly better focus detection, you can install Visual Studio Build Tools (select "Desktop development with C++") and run npm run build:native:win from a Developer Command Prompt.

Getting started

# Clone the repo
git clone https://github.com/user/openwhisp.git
cd openwhisp

# Install dependencies
npm install

# Compile the native helper
npm run build:native        # auto-detects platform
# npm run build:native:mac  # macOS only (Swift)
# npm run build:native:win  # Windows only (C++)

# Start the app
npm run dev

On first launch, the setup wizard will walk you through:

1. Ollama — if not installed, the wizard links you to the download. If installed, OpenWhisp launches it automatically.
2. Speech model — downloads Whisper Base Multilingual (~150 MB) for local speech recognition.
3. Text model — downloads Gemma 4 E4B (~9.6 GB) for local text enhancement. This takes a few minutes on the first run.
4. Permissions (macOS only) — microphone access for recording, plus Accessibility and Input Monitoring for Fn key listening and auto-paste.

After setup, click into the text field where you want the text to go (an email, chat, code editor, etc.), then hold the hotkey and speak. When you release, the transcribed and enhanced text is automatically pasted into that field. If you move away or no text field is selected, the text is still copied to your clipboard — just paste it wherever you need.

Hotkeys

Platform	Hotkey
macOS	Fn (hold to dictate, release to paste)
Windows	Ctrl+Shift+Space (toggle — press to start, press again to stop and paste)

Default models

Purpose	Model	Size
Speech-to-text	onnx-community/whisper-base	~150 MB
Text enhancement	gemma4:e4b	~9.6 GB

You can switch to any Ollama-compatible model from the Models page.

Tech stack

• Electron + React + TypeScript — desktop shell and UI
• @huggingface/transformers — local Whisper inference
• Ollama — local LLM inference via API
• Swift — native macOS helper for Fn key listening, focus detection, and paste simulation
• C++ — native Windows helper for global hotkey, focus detection, and paste simulation (uses Win32 API and UI Automation)
• electron-vite — build tooling
• Hugeicons — UI icons

Building for distribution

# macOS — produces .dmg and .zip
npm run package

# Windows — produces NSIS installer and portable exe
npm run package

Builds the Electron app, compiles the native helper for the current platform, and packages everything into the release/ directory.

Project structure

src/
  main/           # Electron main process
    dictation.ts    # Transcription + rewrite pipeline
    native-helper.ts # Cross-platform native helper bridge
    ollama.ts       # Ollama API client + auto-launch
    prompts.ts      # Global rules + style + level prompt matrix
    permissions.ts  # Platform-aware permission handling
    settings.ts     # Settings persistence
    windows.ts      # Window creation and positioning
  renderer/       # React UI
    App.tsx         # Sidebar layout, pages, setup wizard, overlay
    styles.css      # Complete styling
    audio-recorder.ts # Web Audio recorder with level metering
  preload/        # Electron preload bridge
  shared/         # Shared types and constants
swift/
  OpenWhispHelper.swift  # Native macOS helper
native/
  windows/
    openwhisp-helper.cpp  # Native Windows helper

License

MIT

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Made by Raelume