79 lines
2.3 KiB
Markdown
79 lines
2.3 KiB
Markdown
# Whisper Dictation
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Local GPU speech-to-text dictation tool. Hold a hotkey to record, release to transcribe and type the result into the active window. Runs fully offline — no cloud, no API key.
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## Features
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- System tray icon with settings GUI (tkinter)
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- Configurable hotkey, model, language, audio device
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- Shared config via git (`config.json`, `vocabulary.json`)
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- Machine-specific settings stored locally (audio device, GPU settings)
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- Windows: GPU acceleration via CUDA; Linux: CPU
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## Requirements
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### Windows
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- Python 3.13
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- NVIDIA GPU with CUDA 12 drivers
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- [PortAudio](http://www.portaudio.com/) (bundled with most Python sounddevice wheels)
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### Linux
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- Python 3.10+
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- PortAudio: `sudo apt install portaudio19-dev`
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## Installation
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### Windows
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```bat
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install.bat
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```
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This creates a `.venv-windows` virtual environment, installs all dependencies and the CUDA 12 DLLs required by faster-whisper.
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### Linux
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```bash
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chmod +x install.sh start.sh
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./install.sh
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```
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Creates a `.venv-linux` virtual environment. GPU support on Linux requires a manually installed CUDA environment; by default runs on CPU.
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## Usage
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### Windows
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```bat
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start.bat
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```
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### Linux
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```bash
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./start.sh
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```
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The app starts in the system tray. Hold the hotkey (default: `Ctrl+Shift+Space`) to record, release to transcribe and type into the active window.
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## Configuration
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`config.json` (shared, stored in the repo):
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| Key | Default | Description |
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|-----|---------|-------------|
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| `hotkey` | `ctrl+shift+space` | Recording trigger |
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| `model` | `medium` | Whisper model size (`tiny`, `base`, `small`, `medium`, `large-v2`, `large-v3`) |
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| `language` | `de` | Transcription language (`de`, `en`, `fr`, `es`, `it`, `null` = auto) |
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| `sample_rate` | `16000` | Audio sample rate in Hz |
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Machine-specific settings (GPU device, compute type, audio device) are stored separately and not tracked by git:
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- **Windows:** `%LOCALAPPDATA%\WhisperDictation\config_local.json`
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- **Linux:** `~/.local/share/WhisperDictation/config_local.json`
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## Vocabulary
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Custom vocabulary/replacements can be added to `vocabulary.json`. These are passed as initial prompts to improve recognition of domain-specific terms.
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## Model Download
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On first start the selected Whisper model is downloaded automatically from HuggingFace (~500 MB for `medium`). Subsequent starts use the cached model.
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