Transcribe audio to text using voice input
Transcribe audio to text
fast-whisper
Transcribe audio recordings to text
Transcribe audio to text
ML-powered speech recognition directly in your browser
Transcribe spoken words into text
This is for now working on telugu s2t transcriptions.
Transcribe audio to text
Transcribe voice recordings into text
Transcribe audio to text
preparing for fine tuning with Khmer dataset
Transcribe audio in realtime - Gradio UI version
Whisper.cpp WASM is a high-performance, WebAssembly-based implementation of the Whisper.cpp transcription tool. It is designed to transcribe audio into text using the Whisper model, which is a pre-trained model developed by OpenAI. This tool is optimized for real-time audio transcription and supports multiple audio formats and languages. Whisper.cpp WASM offers a lightweight and efficient way to perform transcription tasks directly in web browsers or other environments that support WebAssembly.
• Real-time transcription: Transcribes audio input as it is being captured. • Multiple audio formats: Supports popular audio formats like WAV, MP3, and AAC. • Multilingual support: Can transcribe speech in various languages. • WebAssembly optimization: Runs efficiently in web browsers or other WASM-compatible environments. • Low latency: Provides quick responses with minimal delay. • Offline functionality: Can operate without an internet connection. • Customizable: Allows users to tweak settings for better accuracy or performance.
What is Whisper.cpp WASM used for?
Whisper.cpp WASM is used for transcribing audio into text in real-time, making it ideal for applications like voice memos, live captions, or podcast transcription.
Do I need to install anything to use Whisper.cpp WASM?
No, Whisper.cpp WASM is a WebAssembly module that runs directly in your browser or compatible environment. No installation is required.
Can I customize Whisper.cpp WASM for my specific needs?
Yes, Whisper.cpp WASM is highly customizable. You can adjust parameters like model size, sampling rate, and threading to optimize performance for your use case.