That app was exactly what I was looking for, something like SponsorBlock but for podcasts but I suppose using AI for finding the ads works too. Any chance it'll release on Android?
Yep it's something I wanted for a while too; there were existing apps that did this, but had two issues: they were paid, and the UI was subpar. So for mine, I made sure it's fully free and that the UI is on par with Apple Podcasts, Spotify, etc.
Making the ad-finding cheap enough such that I could make it free turned out to be harder than expected. The main issue you run into is dynamic, location-targeted ads. So I came up with a novel technique that uses Shazam-style audio fingerprints for accurate matching, instead of their normal use case, which is identification. This technique is what allows the ad finding to be very cheap, allowing me to make it free.
The SponsorBlock model would actually not work for podcasts, due to dynamic ads. I.e. the location and content of the ads in episodes these days varies by download location. You need the media to be static, like YouTube, for SponsorBlock model to work. Therefore, using an LLM to find the ads + the fingerprints matching in combination is an efficient technique.
Android has def been the most requested thing thus far haha. It'll be a decent undertaking due to me having written the app fully in Swift, i.e. it'll be a complete rewrite. I'll also need to replace FluidAudio with some good, fast Android equivalent.
The goal of making this app was to create something impressive so that I could get a job. Haven't gotten a job yet, but if and when I do, then I'll have time & resources to think about doing an Android version. Currently a bit stressed and occupied from the job search lol.
Could you speak more on the Shazam style part? Don't you need a database of all ads to figure out if some snippet is an ad or not? That's how Shazam works for songs at least.
A native implementation I had thought of was running the audio through a STT LLM that then detects the ad timestamps then returns that to the UI to block.
Yeah that's what I do (STT -> LLM -> timestamps to block), but if you did that for every episode download, due to location-targeted dynamic ads, your LLM API bill would run up very fast. So that's where the fingerprints matching comes in, for further versions of an episode (version = same episode but with different dynamic ads inside).
Making the ad-finding cheap enough such that I could make it free turned out to be harder than expected. The main issue you run into is dynamic, location-targeted ads. So I came up with a novel technique that uses Shazam-style audio fingerprints for accurate matching, instead of their normal use case, which is identification. This technique is what allows the ad finding to be very cheap, allowing me to make it free.
The SponsorBlock model would actually not work for podcasts, due to dynamic ads. I.e. the location and content of the ads in episodes these days varies by download location. You need the media to be static, like YouTube, for SponsorBlock model to work. Therefore, using an LLM to find the ads + the fingerprints matching in combination is an efficient technique.
Android has def been the most requested thing thus far haha. It'll be a decent undertaking due to me having written the app fully in Swift, i.e. it'll be a complete rewrite. I'll also need to replace FluidAudio with some good, fast Android equivalent.
The goal of making this app was to create something impressive so that I could get a job. Haven't gotten a job yet, but if and when I do, then I'll have time & resources to think about doing an Android version. Currently a bit stressed and occupied from the job search lol.