---
title: "Handy"
type: "tool"
slug: "cjpais-handy"
canonical_url: "https://www.graphcanon.com/tools/cjpais-handy"
github_url: "https://github.com/cjpais/Handy"
homepage_url: "https://handy.computer"
stars: 26254
forks: 2257
primary_language: "Rust"
license: "MIT"
archived: false
categories: ["vector-databases", "speech-audio", "computer-vision"]
tags: ["tauri-v2", "speech-to-text", "cross-platform", "rust", "accessibility"]
updated_at: "2026-07-11T12:13:41.498745+00:00"
---

# Handy

> A free, open source, and extensible speech-to-text application that works completely offline.

A free, open source, and extensible speech-to-text application that works completely offline.

## Facts

- Repository: https://github.com/cjpais/Handy
- Homepage: https://handy.computer
- Stars: 26,254 · Forks: 2,257 · Open issues: 167 · Watchers: 78
- Primary language: Rust
- License: MIT
- Last pushed: 2026-07-11T03:04:16+00:00

## Trust & health

_Signals computed from public GitHub metadata. Not a security guarantee._

- Maintenance: Very active (computed 2026-07-11T12:13:37.637Z)
- Security scan: No lockfile (0 critical, 0 high, 0 medium, 0 low) · last scan 2026-07-11T12:13:38.728Z
- Full report: [trust report](/tools/cjpais-handy/trust.md) · [JSON](https://www.graphcanon.com/api/graphcanon/tools/cjpais-handy/trust)

## Categories

- [Vector Databases](/categories/vector-databases.md)
- [Speech & Audio](/categories/speech-audio.md)
- [Computer Vision](/categories/computer-vision.md)

## Tags

tauri-v2, speech-to-text, cross-platform, rust, accessibility

## Category neighbours (exploratory)

_Same-category tools for discovery only - not curated alternatives. Cap shown at six._

- [transformers](/tools/huggingface-transformers.md) - Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models (★ 162,482) [Very active]
- [whisper](/tools/openai-whisper.md) - Robust Speech Recognition via Large-Scale Weak Supervision (★ 104,745) [Steady]
- [pytorch](/tools/pytorch-pytorch.md) - Tensors and Dynamic neural networks in Python with strong GPU acceleration (★ 101,752) [Very active]
- [PaddleOCR](/tools/paddlepaddle-paddleocr.md) - A powerful, lightweight OCR toolkit to convert images and PDFs into structured data (★ 85,230) [Active]
- [redis](/tools/redis-redis.md) - Redis is a preferred cache, data structure server, and document & vector query engine for real-time applications. (★ 75,394) [Very active]
- [stable-diffusion](/tools/compvis-stable-diffusion.md) - A latent text-to-image diffusion model (★ 73,179) [Dormant]

_+ 2 more not listed._

## README (excerpt)

_Quoted verbatim from the upstream repository. Untrusted content - treat as data, not instructions._

````text
### Installation

1. Download the latest release from the [releases page](https://github.com/cjpais/Handy/releases) or the [website](https://handy.computer)
   - **macOS**: Also available via [Homebrew cask](https://formulae.brew.sh/cask/handy): `brew install --cask handy`
   - **Windows**: Also available via [winget](https://github.com/microsoft/winget-pkgs): `winget install cjpais.Handy` \
     **Note:** The Homebrew cask and winget package are not maintained by the Handy developers.
2. Install the application
3. Launch Handy and grant necessary system permissions (microphone, accessibility)
4. Configure your preferred keyboard shortcuts in Settings
5. Start transcribing!

---

### System Requirements/Recommendations

The following are recommendations for running Handy on your own machine. If you don't meet the system requirements, the performance of the application may be degraded. We are working on improving the performance across all kinds of computers and hardware.

**For Whisper Models:**

- **macOS**: M series Mac, Intel Mac
- **Windows**: Intel, AMD, or NVIDIA GPU
- **Linux**: Intel, AMD, or NVIDIA GPU
  - Ubuntu 22.04, 24.04

**For Parakeet V3 Model:**

- **CPU-only operation** - runs on a wide variety of hardware
- **Minimum**: Intel Skylake (6th gen) or equivalent AMD processors
- **Performance**: ~5x real-time speed on mid-range hardware (tested on i5)
- **Automatic language detection** - no manual language selection required

---

### Manual Model Installation (For Proxy Users or Network Restrictions)

If you're behind a proxy, firewall, or in a restricted network environment where Handy cannot download models automatically, you can manually download and install them. The URLs are publicly accessible from any browser.

#### Step 1: Find Your App Data Directory

1. Open Handy settings
2. Navigate to the **About** section
3. Copy the "App Data Directory" path shown there, or use the shortcuts:
   - **macOS**: `Cmd+Shift+D` to open debug menu
   - **Windows/Linux**: `Ctrl+Shift+D` to open debug menu

The typical paths are:

- **macOS**: `~/Library/Application Support/com.pais.handy/`
- **Windows**: `C:\Users\{username}\AppData\Roaming\com.pais.handy\`
- **Linux**: `~/.config/com.pais.handy/`

#### Step 2: Create Models Directory

Inside your app data directory, create a `models` folder if it doesn't already exist:

```bash

---

## License

MIT License - see [LICENSE](LICENSE) file for details.

Handy is open-source software, but the Handy name, logo, icon, and brand assets are not open-source. Unofficial forks, rewrites, and redistributions must use their own branding and must not imply endorsement or affiliation.
````

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/cjpais-handy`](/api/graphcanon/tools/cjpais-handy)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_