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LocalWisp 🎙️

LocalWisp is an offline voice dictation utility for Windows. It allows you to dictate text directly into any active application by holding a shortcut, with all audio processing occurring entirely on your local CPU.

Features

  • Privacy First: 100% offline; no audio or text data is ever transmitted over the internet.
  • CPU Inference: Optimized for multi-core processors using faster-whisper and int8 quantization.
  • High Accuracy: Powered by the large-v3-turbo model and silero-vad for robust speech detection.
  • Minimalist HUD: A semi-transparent overlay provides real-time status feedback (Recording/Processing).
  • Direct Injection: Automatically pastes the transcribed text at your current cursor position.

Hardware & Current Optimization

The utility is currently tuned for a specific performance profile based on the developer's hardware:

  • Reference CPU: AMD Ryzen 7 5700G (8 Cores / 16 Threads).
  • Thread Management: Hardcoded to 8 CPU threads to ensure fast processing without system lag.
  • Memory: 32 GB RAM recommended for the large-v3-turbo model footprint.

Future Development & Collaboration

LocalWisp is an open project. While built for a specific setup, future iterations aim to:

  • Implement automatic hardware detection to dynamically set thread counts.
  • Improve flexibility for different model sizes based on available system resources.
  • We welcome collaborative efforts to improve hardware abstraction and performance.

Installation & Usage

  1. Install dependencies: pip install -r requirements.txt.
  2. Setup models: python setup_models.py.
  3. Run: Use Launch_LocalWisp.bat to start the tool in the background.
  4. Operation: Hold Win + Ctrl to record. Release the keys to transcribe and paste.

License

Copyright (c) 2026 ATTrindade and LocalWisp Contributors. Distributed under the MIT License.

About

LocalWisp is an offline voice dictation utility for Windows. It captures audio while a hotkey is held and types the transcription directly into the active text field upon release. The tool is built for CPU-based inference and is currently configured for the performance profile of an 8-core processor, specifically tested on a Ryzen 7 5700G.

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