- Developed a web application for video summarization using Flask and which would transcript and summarize the videos.
- Used OpenAi Whisper for transcription and fine-Tuned BART for Video summarization
- Evaluated the performance of algorithm using metric BLEU and ROUGE score to campare the performance with state of the art Video summarization Technology
- Made it easier for users to understand the content of the video with minimal effort
- Develped a Audio based upon the video summary to understnad the evasily our complete summary
Video Transcription: users can upload video files or provide video linkes and our application will transcribe the video into text.
Translations: Transcribed Text can be Translated into different languages.
Summarization: :application Summarize the Transcribed text to provide a concise overview of the video content,making it easier for users to understand the main content of the video.
Audio Play: :users can listen to the summary of Video through text-to-speech conversion ,enhancing user experience
1.Keyword Extraction: A keyword extraction algorithm that can identify important keywords in video.analyze the transcript and extract keyword and convert video into short clips.
2.Video Segmentation: we can Develop a mechanism to segment the original video into shorter clips based on keywords. we can split the video where keyword is prominently used.
3.Clip Summarization: Summarize the clip that will give user quick overview of clips.
4.User Interface: Give user to give keyword and generate clips.
5.Playlist generation: Generate a playlist for all the keyword with summarization.
6.Content selection and RankingK: Select content and rank them which have more contextually relevent.
7.User Feedback Loop: Take user feedback as accuracy and relevent of suggested clips that can improve algorithm.
8.Advanced Keyword Matching: Explore advanced technique like NER to extract specific entities and entities related to keywords.
9.Fine Tuning and optimization: Continuous optimization And Fine Tuning.