r-shafi / bangla-speech-to-text

Automatic speech recognition for the Bangla language, one of the world's most spoken languages. This repository provides a comprehensive solution for converting spoken words into accurate and meaningful text.

Home Page:https://bangla-stt.netlify.app/

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Bangla Speech-to-Text

Overview

Welcome to the Bangla Speech-to-Text repository! This project aims to provide a simple and accessible solution for converting spoken Bangla words into text using minimal HTML, CSS, and JavaScript. Built with native browser APIs, this open project encourages collaboration and welcomes contributions from the community.

Features

  • Lightweight: Minimalist design with a focus on simplicity.

  • Browser APIs: Utilizes built-in browser capabilities for speech recognition, making it easy to integrate and use.

  • Bangla Language Support: Tailored for the Bangla language, capturing various accents and dialects.

Getting Started

  1. Clone the repository:

    git clone https://github.com/r-shafi/bangla-speech-to-text.git
  2. Open the index.html file in a web browser.

  3. Start speaking in Bangla, and the system will transcribe your speech into text.

Contributing

I welcome contributions from the community to enhance and improve this project. Whether it's bug fixes, feature additions, or documentation improvements, your input is valuable.

Issues and Support

If you encounter any issues or have questions, please create an issue. I appreciate your feedback!

License

This project is open-source and available under the MIT License. Feel free to use, modify, and distribute it according to the terms of the license.

Let's collaborate to make speech-to-text technology accessible for Bangla speakers. Happy coding!

About

Automatic speech recognition for the Bangla language, one of the world's most spoken languages. This repository provides a comprehensive solution for converting spoken words into accurate and meaningful text.

https://bangla-stt.netlify.app/


Languages

Language:JavaScript 39.2%Language:HTML 30.9%Language:CSS 29.9%