Multilingual Audio–Text Summarizer
The Multilingual Audio–Text Summarizer is an intelligent system designed to automatically convert audio or text content in multiple languages into a concise and meaningful summary. The project integrates speech recognition, natural language processing (NLP), and machine learning techniques to help users quickly understand long audio recordings or textual documents.
In this system, audio inputs are first converted into text using speech-to-text technology. The language of the input is then detected automatically, followed by preprocessing steps such as noise removal, tokenization, and sentence segmentation. After preprocessing, the summarization module extracts or generates the most important information from the content. The final summary can be provided in the same language or translated into another preferred language.
This project aims to reduce the time and effort required to analyze lengthy multilingual content, especially in domains such as education, news, business meetings, podcasts, and customer support calls. By supporting multiple languages, the system improves accessibility and usability for users from different linguistic backgrounds.
Overall, the Multilingual Audio–Text Summarizer enhances information accessibility, improves productivity, and demonstrates the practical application of AI and NLP technologies in real-world multilingual communication systems.
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