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 available. 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 runs entirely in your browser — your audio is never uploaded to any server.

The browser-based implementation uses Hugging Face Transformers.js, a JavaScript library that brings state-of-the-art machine learning models to the web. Transformers.js converts Whisper model weights to ONNX format and executes them via ONNX Runtime compiled to WebAssembly, allowing the full Whisper pipeline to run in a browser tab without any server, plugin, or extension. The models are quantized to 8-bit integers for smaller downloads and faster inference while maintaining high transcription accuracy.

You can record directly from your microphone or upload audio files in MP3, WAV, M4A, WebM, OGG, or FLAC format. Choose from three model sizes — Tiny for fast results on any device, Base for balanced accuracy and speed, or Small for the highest quality. Enable segment or word-level timestamps for subtitle creation, select a specific language to improve accuracy, or translate foreign-language audio to English. The model files are downloaded once and cached in your browser, so repeat visits load almost instantly.

How Whisper Speech Recognition Works in the Browser

This tool is built on Hugging Face Transformers.js, the JavaScript counterpart to the widely-used Python transformers library. Transformers.js provides a pipeline API that mirrors the Python version — creating an automatic speech recognition pipeline is a single function call. Under the hood, it uses ONNX Runtime for inference with WebAssembly (WASM) as the default execution provider and optional WebGPU support for GPU acceleration in supported browsers. Models are loaded as quantized ONNX files from Hugging Face Hub and cached in the browser using the Cache API.

The automatic-speech-recognition pipeline supports several Whisper-specific parameters: return_timestamps (boolean or "word" for word-level precision), language (ISO code to hint the spoken language), task ("transcribe" or "translate" for English translation), chunk_length_s and stride_length_s for processing long audio in overlapping windows, and standard generation config options. Transformers.js supports Whisper Tiny, Base, Small, Medium, and Large model variants, as well as distilled and quantized checkpoints from the ONNX Community on Hugging Face Hub. The library runs in any modern browser, Node.js, Deno, and Bun — making it one of the most versatile options for deploying speech recognition in JavaScript applications.