whisper.cpp

whisper.cpp is a C/C++ implementation of OpenAI's Whisper model that performs automatic speech recognition with high efficiency across diverse hardware architectures.

This software provides a lightweight, dependency-free implementation of the Whisper automatic speech recognition model. It utilizes C/C++ for efficient inference, allowing the model to run fully offline on diverse hardware including CPUs, GPUs, and NPUs. The implementation supports mixed F16/F32 precision and integer quantization to optimize memory usage and processing speed. Developers integrate this library to enable on-device voice recognition and voice assistants on mobile, desktop, and embedded systems. It includes specific optimizations for Apple Silicon, NVIDIA GPUs, AMD ROCm, and Intel OpenVINO.

whisper.cpp at a glance

Best for: Software engineer, Researcher
Runs on: Mac OS, iOS, Android, Linux, FreeBSD, WebAssembly, Windows, Raspberry Pi, Docker
Pricing: Free
Released: September 2022
Category: AI audio tool
Website: github.com/ggml-org/whisper.cpp

Key features

Automatic speech recognition via OpenAI Whisper model
Zero memory allocations at runtime
Integer quantization for reduced memory usage
Hardware acceleration via Apple Silicon, NVIDIA, AMD, and Intel GPUs
Voice Activity Detection capability
CPU-only inference mode for broad compatibility
Native C/C++ implementation without external dependencies

Use it for

Developing offline voice assistant applications
Transcribing audio files on mobile devices
Running speech recognition on embedded systems like Raspberry Pi
Integrating speech-to-text into desktop software

Alternatives to whisper.cpp

faster-whisper — Transcribes audio faster with lower memory usage than standard implementations.
WhisperX — It enables 70x faster, batched transcription with high-accuracy alignment.
Whisper ASR Webservice — Provides a REST API for multiple ASR engines including WhisperX and Faster Whisper.
WhisperLive — It provides real-time speech-to-text using multiple backends like Faster Whisper and TensorRT.
Whisper Diarization — Combines OpenAI Whisper with voice activity detection and speaker embedding.
browser-whisper — Transcribes audio and video locally with zero servers involved.
AssemblyAI — Industry-leading speech models deliver high accuracy and low latency for voice applications.
Whisper Timestamped — Generates accurate word-level timestamps without extra neural networks.
audio.cpp — Runs modern audio models locally with high-performance, native C++ execution.
Whisper by Remskill — Runs fully offline in local mode or connects to cloud models.
WeNet — Achieves state-of-the-art results on public speech datasets.
Speech-to-Speech — Fully modular, swappable components with local or hosted execution options.

All whisper.cpp alternatives