Extracting Audio Summaries using ML Techniques
Journal Title: International Journal for Modern Trends in Science and Technology - Year 2024, Vol 10, Issue 9
Abstract
In a world with an ever-expanding array of audio content, ranging from podcasts and lectures to conference calls and interviews, the ability to efficiently extract key information from these recordings has become paramount. With the vast amount of audio data available, it becomes essential to develop efficient methods for summarization to aid users in quickly understanding the content. Our project aims to use machine learning to automatically extract concise summaries from audio files. The process begins to take input in the form of video or audio format then with speech recognition capabilities, it can transcribe the audio into text. Subsequently, it uses also machine learning techniques are employed to analyze. Through this comprehensive approach, our audio summarization system endeavors to revolutionize the way individuals interact with and extract value from audio content and empowering users with efficient access to knowledge. This system is capable of handling diverse types of audio content, includinglectures, interviews, podcasts, and speeches. With the use of vosk speech recognition model and the transformers library, the system transcribes the audio content into text and subsequently generates concise summaries. The system is built using the Streamlit framework, ensuring a user-friendly interface for seamless interaction. Overall, our projectaims to simplify the process of extracting key insights from audio content, enhancing accessibility and usability for various applications such as education, media and entertainment. A user-friendly solution developed for extracting key insights from spoken-word content. This web application provides a workflow for users to upload spoken-wordcontent, receive a summarized text output, and listen to the corresponding audio summary
Authors and Affiliations
CH Venkatesh, J Priyanka, G Aamani, B B S N Swetha
Keywords
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- EP ID EP747886
- DOI https://doi.org/10.46501/IJMTST1009009
- Views 2
- Downloads 0
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