• Project Description
• Getting Started
  • Prerequisites
  • Installation
• Usage
• Roadmap
• Contributing
• License
• Contact
• Acknowledgements

Recent years have witnessed a remarkable growth in the use of Machine/Deep learning to enhance the business and end-user experience using virtual assistants varying from simple question answering agents to fully-edged AI agents like Amazon Alexa. The use of virtual assistants helps businesses to reduce the costs spent on human customer support operators while significantly enhancing the end-user experience by providing quick and convenient solutions across multiple services. State of the virtual art assistants are powered with appropriate deep learning-based natural language techniques such as recurrent neural networks and transformers.

However, most of the existing virtual assistants are based on English language and are not catered for the sentiments of the Sri Lankan service sector. Moreover, they fail to capture and respond to the unique characteristics in Sinhala/Singlish languages. In order to address these issues, in this project, we aim to develop a deep learning-based virtual assistant in Sinhala language scrutinizing the unique requirements and characteristics of Sri Lankan services.

• i) Integrate voice to text and text to voice translation
• ii) Improve the framework with hierarchical transformer model and rule based pre and post processing expert system
• iii) Connect the virtual assistant to IoT smart home network and operate IoT devices using voice commands
• iv) Develop the system call functionalities that help the user to execute day to day operations such as retrieve time, weather information, make calls and appointment, etc. via the virtual assistant

• Python
• Deep Learning Frameworks (PyTorch, TensorFlow)
• Natural Language Processing Tools (spaCy, NLTK)
• Rule-Based Systems
• IoT Technologies

• Project Manager
• Data Scientist
• NLP Engineer
• Software Engineer
• IoT Engineer

• Deep Learning-based Virtual Assistant in Sinhala Language
• Documentations (API documentation, Technical documentation, User manuals)
• Source code
• Dataset

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• npm

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Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Distributed under the MIT License. See LICENSE for more information.

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