Learn-Vocabulary-in-Context

This small web app uses GPT-4o-mini to convert whatever you input into a mixed-language content and marks those words worth learning. A free demo is here: https://fakeforeigner-jiayangguizi.streamlit.app/

• Chinese - English

• English - Spanish

• Japanese - French

Overview

The Learn-Vocabulary-in-Context App is an interactive tool designed to help language learners practice and improve their skills. It uses AI-powered translation and language mixing to create custom learning content across multiple languages. Welcome to use, star, and discuss!

Key features:

• Support for multiple languages including English, Chinese, Spanish, French, German, Japanese, Korean, Arabic
• AI-powered translation and language mixing
• Custom text input or pre-defined example sentences
• Interactive web interface built with Streamlit

The original motivation for this project is to develop a tool for creating datasets to improve the Whisper's ability to transcribe mixed-language speeches through fine-tuning. Based on fine-tuned Whisper, We are also working on a meeting assisant product. It is still an on-going project.

Prerequisites

Before you begin, ensure you have met the following requirements:

• You have a Python 3.7+ environment
• You have an OpenAI API key

Installation

1. Clone this repository:

   git clone https://github.com/yourusername/learn-vocabulary-in-context.git
   cd learn-vocabulary-in-context
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

Usage

1. Run the Streamlit app:

   streamlit run app.py
   

2. Open your web browser and go to the URL displayed in the terminal (usually http://localhost:8501).

3. Use the app:

   • Select your primary language and target language
   • Choose to enter custom text or use an example sentence
   • Click "Convert" to generate mixed-language content

Configuration

Fill your openai api key in the config.json file. You can also customize the supported languages and example sentences by editing the config.json file.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

• OpenAI for providing the AI models used in this project
• Streamlit for the web app framework

Contact

If you have any questions or feedback, please open an issue on this GitHub repository.