This repository contains the code for an Audio-to-Sign Language Converter, a project that aims to bridge the communication gap between the hearing and the hearing-impaired by translating spoken language into visual sign language.
Introduction Features How It Works Installation Usage Contributing License Contact
Introduction Communication is a fundamental human need. For individuals with hearing impairments, spoken language can be a significant barrier. This project provides a solution by converting audio input into a sequence of signs, which can then be displayed visually. This tool can be invaluable in various scenarios, including educational settings, public announcements, and personal communication.
Features Real-time Audio Processing: Converts spoken words to signs as they are uttered. Sign Language Generation: Generates visual representations of signs (e.g., images, animations, or textual descriptions of movements). Modular Design: Allows for easy integration of new sign language datasets and translation models. User-Friendly Interface (Planned/Conceptual): Aims to provide an intuitive experience for users to input audio and view the signs. How It Works The core functionality of the Audio-to-Sign Language Converter involves several key steps:
Speech Recognition: The audio input is first processed by a speech-to-text engine to convert spoken words into written text. Natural Language Processing (NLP): The transcribed text undergoes NLP to understand its meaning and context. This step is crucial for accurately mapping words to signs, especially considering variations in sentence structure and idiomatic expressions. Sign Language Mapping: The processed text is then mapped to a corresponding sign language lexicon. This involves a database or model that links words/phrases to their respective sign representations. Sign Generation: Finally, the system generates the visual output of the signs. This could be in the form of displaying static images of signs, animating sign movements, or providing detailed textual descriptions for a human interpreter. Installation To set up the Audio-to-Sign Language Converter locally, follow these steps:
Clone the repository:
git clone https://ommishra-2012/audio-to-sign-converter.git cd audio-to-sign-converter
Create and activate a virtual environment (recommended): python -m venv venv
.\venv\Scripts\activate
source venv/bin/activate
Install the required dependencies: pip install -r requirements.txt Note: The requirements.txt file will list all necessary libraries such as SpeechRecognition, PyDub, OpenCV (if visual generation is implemented), and any NLP libraries used.
Usage Once installed, you can run the application. Specific usage instructions will depend on the implemented interface and features. A basic example might involve:
Run the main script: python main.py Provide audio input: This could be via a microphone or by specifying an audio file. View the generated signs: The output will be displayed on the screen or in a designated output area. (Further detailed instructions and examples will be added here once the project development progresses and specific input/output methods are finalized.)
Contributing We welcome contributions to this project! If you'd like to contribute, please follow these steps:
Fork the repository. Create a new branch for your feature or bug fix: git checkout -b feature/your-feature-name or bugfix/fix-bug-name. Make your changes and ensure they are well-documented and tested. Commit your changes: git commit -m "Add: brief description of your changes" Push to your fork: git push origin feature/your-feature-name Open a Pull Request to the main branch of this repository, describing your changes in detail. Please read our CONTRIBUTING.md (if available) for more details on our contribution guidelines.
Contact If you have any questions, suggestions, or feedback, feel free to reach out:
Project Maintainer: Om Mishra GitHub Issues: https://github.com/ommishra-2012/audio-to-sign-converter/issues