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In this post, I’ll explore one use case and implementation for AI-assisted post-processing that can make video presenters’ lives a little easier. We’ll go through a small demo which lets you remove disfluencies, also known as filler words, from any MP4 file. These can include words like “um”, “uh”, and similar. I will cover:
whisper-timestamped, which is a layer on top of the Whisper set of models enabling us to get accurate word timestamps and include filler words in transcription output. This transcriber downloads the selected Whisper model to the machine running the demo and no third-party API keys are required.
To run the demo locally, be sure to have Python 3.11 and FFmpeg installed.
To run the demo locally, be sure to have Python 3.11 and FFmpeg installed.