JordanSearch is my final project for CS 499 that focuses on parsing and indexing audio and visual content within videos so users can locate content within a large corpus of videos.

JordanSearch uses ffmpeg, Vosk, ElasticSearch, and ImageAI.

• Download your desired Vosk Model and your ImageAI model and place it in the models directory. I used vosk-model-en-us-0.42-gigaspeech and YOLOv3 in my development. If you use different models, you will need to change vosk_model_path, imageai_model_path, and the ImageAI model type in audio_parser.py and image_parser.py respectively.

• Place all video files in the input folder. They must be .mp4 files.

• Run ElasticSearch with docker-compose up -d.

• Run main.py with the -p flag to parse all video files.

• Now, you can search for queries and if will return the top results including the file name and timestamp.

• As long as you are using the same ElasticSearch instance, re-running main.py without the -p flag will skip parsing and begin query entry.

• If you use the -f flag, you will search for full videos rather than finding specific timestamps within those videos.

• Implement opening up the source files at the chosen timestamps

• Implement a GUI

• Use an LLM to generate keywords summarizing what the audio is about, so users don't have to search by exact match of content

• Use an audio model that can also parse sound effects, rather than just dialogue

• Use an image model that can identify more than just 80 items