How To Remove Filler Words With Python [tutorial]

Filler-words (umm, uhh, like etc.)🤬 are super annoying to listen to and also a pain to edit out in post production. Usually, it is a manual process where I have to find the exact timestamps 🕐 for each occurrence and cut it out 🎬 in software. Here's how you can use Python and a AI model to completely remove filler words from a video. We use a speech-to-text model to transcribe (aka. convert speech to text) an input video. The model gives timestamps for each word uttered. Based on this information we do some Python post-processing to cut the video into chunks and merge the chunks into one final video. The results are ... weird We use an implementation of the OpenAI whisper model called whisper-timestamped to achieve this: https://github.com/linto-ai/whisper-timestamped Source code: https://github.com/mhkamal1/yt-content/tree/main/removefillers 00:00 Intro 00:42 The process 01:34 Exploring speech-to-text options in Python 02:28 Whisper-timestamped 02:50 Code: Extract audio from video 03:22 Code: Load whisper model and transcribe 04:45 SUPER COOL Transcription results !! 05:41 Code: Get filler words with timestamps 07:13 Code: Split video into chunks 07:53 Code: Merge chunks into one 08:15 DEMO of Final Results!! 09:04 Closing thoughts In short we cover: - Finding a speech-to-text model in Python - Extract audio from video - Transcribe audio - Find filler words from transcription - Split video according to timestamps - Merge chunks into one final video - How to have fun with Python Please consider liking if you found the video useful and subscribe for more! Subscribe here: https://www.youtube.com/channel/UCKId3YaJ-uQ0Z4PMEGtg88A?sub_confirmation=1