He trained a small model to cut his breaths out of recordings and saved hundreds of editing hours
Editing his own audio meant cutting out every breath by hand. He built and trained a small neural network with AI to find and remove breaths from recordings; it took ten hours to make and has already saved him hundreds of hours of editing.
How it works
- Gather his recordings
- Build and train a small model on breath sounds
- Test on real sessions
- Run it on his editing work
- Hundreds of hours saved
Why it’s worth knowing
Ten hours to build, hundreds saved: the maths only works because the task is one narrow thing he does forever.
Where it’s from
Summarized from a public post on reddit.com. See the original ↗