I think it would be more useful to more people to utilize the existing solution because it runs in a very small footprint for the end user. Most people wouldn't be able to run an entire TTS/LLM workflow without processing videos in the background or waiting some multiple of real-time in order to watch a video.
You could maybe do it with just LLM tagging the transcript, but that's still a ton of processing just to watch a video.
God that'd be so much wasted energy if every person wanting to skip in-video ads was running an entire LLM to do so.
Circle back to original topic, eventually having local LLMs intercepting EVERYTHING in background to reproject a curated personal bubble filter feels like wasteful but sensible end game. Not just to hide ads / skip sponsors, run every image through anime filter, live editing xyz, if there'es some centralized DB to grab timestamps do it, but otherwise we could be burning tokens none stop to modify reality, and some add manipulation is just rounding error that comes along for the ride. I click a video, background processes play opens 10 instances of the video at 4x speed, processes, gives me version of video without ads, sponsorblocs, xyz content filtered out... or just generate video to conform to my biases because it makes me feel good.
No idea if it's good enough for all podcasts that are out there. It works for me and the podcasts I listen to, and I have tried testing it against a larger corpus, but I'm one developer and I just wanted to solve a problem for me.