whisper.cpp vs handy
Two of the top projects in Voice, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
whisper.cpp
C/C++ port of OpenAI Whisper for local speech-to-text transcription
handy
Offline push-to-talk dictation app that pastes transcripts into any text field
Verdict
Both score 89; whisper.cpp ranks higher on stars, 54,839 to 36,146. Of the 4 score parts we measure for both, whisper.cpp leads on 2, handy on 1 and 1 is even.
- Adoptionwhisper.cpp leads by 8 points, 90 to 82.
- FreshnessEven at 100.
- Maintenancewhisper.cpp leads by 7 points, 97 to 90.
- Agent-readyhandy leads by 40 points, 70 to 30: a wide gap.
Pick whisper.cpp if you are
Developers embedding offline speech-to-text in apps, servers or devices
- No dependencies in core; zero memory allocations at runtime
- Wide backend coverage: Metal, Core ML, CUDA, Vulkan, ROCm, OpenVINO, CANN, MUSA, NPU
Pick handy if you are
Desktop users who want private, local dictation into any app
- Runs fully offline on Windows, macOS and Linux with Whisper or Parakeet models
- Hold-to-talk or toggle shortcuts, plus CLI flags and signals for remote control
| What we compare | whisper.cpp | handy |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 90, widely used | 82, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 97, healthy | 90, healthy |
| Agent-ready | 30, minimal | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 54.8k | 36.1k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | C++ | Rust |
| Docker | Yes | No |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Mentioned | Mentioned |
whisper.cpp
whisper.cpp runs OpenAI's Whisper speech recognition models in plain C/C++ on top of the ggml library, with a C-style API in whisper.h and a whisper-cli example for transcribing 16-bit WAV files. It runs on CPU or can offload to Metal, Core ML, CUDA, Vulkan, ROCm, OpenVINO, Ascend CANN, Moore Threads MUSA and AMD Ryzen AI NPUs. It supports integer quantization and voice activity detection, and builds for desktop, mobile, WebAssembly and Docker.
Who it is for: Developers embedding offline speech-to-text in apps, servers or devices
Strengths
- No dependencies in core; zero memory allocations at runtime
- Wide backend coverage: Metal, Core ML, CUDA, Vulkan, ROCm, OpenVINO, CANN, MUSA, NPU
- Runs on iOS, Android, WebAssembly, Raspberry Pi, and Java bindings exist
- Q5_0-style integer quantization shrinks models; base model needs about 388 MB RAM
Weaknesses
- whisper-cli accepts only 16-bit WAV unless built with optional FFmpeg support
- Core ML, OpenVINO and VitisAI encoders need a separate Python conversion step
- Models must be in ggml format; downloaded via script, not bundled
- Library and CLI only; no built-in web UI or multi-user features
- GPU optional
- Docker
- Needs CMake, ffmpeg (optional), CUDA / ROCm / Vulkan / OpenVINO (optional backends)
- Models: Whisper ggml (tiny, base, small, medium, large-v1/v2/v3, large-v3-turbo), Quantized ggml (e.g. q5_0)
handy
Handy is a cross-platform desktop app (Rust, Tauri) for local speech-to-text. Hold or toggle a global shortcut, speak, and the transcript is pasted into the focused app. Silero VAD filters silence, and transcription runs locally on Whisper models (Small, Medium, Turbo, Large) or Parakeet, with no audio sent to the cloud.
Who it is for: Desktop users who want private, local dictation into any app
Strengths
- Runs fully offline on Windows, macOS and Linux with Whisper or Parakeet models
- Hold-to-talk or toggle shortcuts, plus CLI flags and signals for remote control
- Accepts custom Whisper GGML models and manually downloaded GGUF files
- Release artifacts are signed, with documented minisign verification steps
Weaknesses
- Linux Wayland support is limited and needs wtype, dotool or ydotool for text input
- Clipboard paste can insert stale clipboard content; Reliable Paste is still experimental
- fn/Globe key shortcuts work only on Apple keyboards on macOS
- Model downloads are large, from 487 MB to 1.6 GB for Whisper variants
- GPU optional
- Needs xdotool (X11), wtype or dotool (Wayland), libgtk-layer-shell (Linux)
- Models: Whisper GGML (.bin), Parakeet (GGUF), Silero VAD
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