whisper.cpp is a lightweight, high-performance C/C++ port of OpenAI's Whisper automatic speech recognition model. It runs on CPU and GPU across all major platforms with zero dependencies and zero runtime memory allocations.
Scanned 6/8/2026
Install via CLI
openskills install agentskillexchange/skills---
name: "whisper.cpp High-Performance C/C++ Speech Recognition Engine"
slug: "whisper-cpp-speech-recognition-engine"
description: "whisper.cpp is a lightweight, high-performance C/C++ port of OpenAI's Whisper automatic speech recognition model. It runs on CPU and GPU across all major platforms with zero dependencies and zero runtime memory allocations."
github_stars: 48315
verification: "security_reviewed"
source: "https://github.com/ggml-org/whisper.cpp"
category: "Media & Transcription"
framework: "Multi-Framework"
tool_ecosystem:
github_repo: "ggml-org/whisper.cpp"
github_stars: 48315
---
# whisper.cpp High-Performance C/C++ Speech Recognition Engine
whisper.cpp is a lightweight, high-performance C/C++ port of OpenAI's Whisper automatic speech recognition model. It runs on CPU and GPU across all major platforms with zero dependencies and zero runtime memory allocations.
## Installation
Use the upstream install or setup path that matches your environment:
- git clone https://github.com/ggml-org/whisper.cpp.git
- cmake -B build
- cmake --build build -j --config Release
Requirements and caveats from upstream:
- [x] [Docker](https://github.com/ggml-org/whisper.cpp/pkgs/container/whisper.cpp)
Basic usage or getting-started notes:
- As an example, here is a video of running the model on an iPhone 13 device - fully offline, on-device: [whisper.objc](examples/whisper.objc)
- First clone the repository:
- bash
- Source: https://github.com/ggml-org/whisper.cpp
- Extracted from upstream docs: https://raw.githubusercontent.com/ggml-org/whisper.cpp/HEAD/README.md
## Source
- [Agent Skill Exchange](https://agentskillexchange.com/skills/whisper-cpp-speech-recognition-engine/)
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Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.