Interview Practise - Whisper and RAG

This code was written as practise for an interview with a company (skipping the name for privacy reasons)

Features

Identify changes in audio file directory: see if there are new files, delete old files
Cache results in a dataframe to avoid reprocessing files. Only run whisper on new files. Delete old files from the dataframe
Use Whisper to transcribe multiple audio files
Use RAG to analyse the transcriptions and find relevant audio giles given a search term
Basic spellcheck
Text statistics (word count, unique word count, average word length, etc)
Basic UI to display RAG results

main.py: main file to run. Calls all other functions
whisper folder: contains all the whisper code (directly copied from the whisper repo)
base.py: contains all the ML code
utils.py: contains all the utility functions that are used throughout the code
ui.py: contains the UI code (uses gradio. But this is not tested yet)

This was not meant to be a production ready application, but rather a proof of concept. I spent around a day on it, so there is a lot of room for improvement.

Clone the repo
Install the requirements
Get audio files and save them to the audio folder
run main.py
If there are random files as output, delete data/transcription.csv and run again