Free Speech to Text

What Is Whisper and How Does This Speech to Text Tool Work?

This free speech-to-text tool is powered by OpenAI Whisper, the most widely used open-source automatic speech recognition model in the world. Whisper was trained on 680,000 hours of multilingual audio data, making it capable of transcribing speech in 99 languages with near-professional accuracy. The model behind this tool runs entirely in your browser — your audio is never uploaded to any server.

The browser-based implementation uses whisper.cpp, a lightweight C/C++ port of Whisper with over 48,000 GitHub stars and zero external dependencies. Originally built to run on everything from Raspberry Pi to iPhone, whisper.cpp has been compiled to WebAssembly so it can run directly in your web browser with hardware acceleration. This gives you the same transcription quality as cloud-based services like Otter.ai or Rev, but with complete privacy.

You can record directly from your microphone or upload audio files in MP3, WAV, M4A, WebM, or OGG format. Multiple model sizes are available — from "tiny" for fast results on any device, to "small" for the highest accuracy. The model files are downloaded once and cached in your browser, so repeat visits load almost instantly.

For Developers: Deploy Whisper in Your Own Applications

whisper.cpp is part of the ggml ecosystem — the same organization behind llama.cpp and the GGML tensor library that powers much of the local AI movement. The project provides a plain C/C++ implementation with first-class support for Apple Silicon (ARM NEON, Accelerate, Metal, Core ML), x86 AVX intrinsics, and GPU acceleration via CUDA, Vulkan, Metal, and OpenVINO. It achieves zero memory allocations at runtime and supports mixed F16/F32 precision with integer quantization.

For web deployment, whisper.cpp compiles to WebAssembly and can be integrated via the @aspect-build/aspect-wasm package or through Hugging Face Transformers.js. The C-style API makes it straightforward to build custom voice interfaces, transcription pipelines, real-time captioning systems, or voice-controlled applications. It runs on macOS, iOS, Android, Linux, FreeBSD, Windows, Docker, and any modern browser — making it one of the most portable speech recognition solutions available.