Remove Um, Uh and Filler Words
"Um", "uh" and long pauses make a podcast drag. This tool tightens the recording by reducing the dead-air around fillers, for a more confident, fast-moving edit.
How it works
We detect low-energy gaps and hesitations and attenuate them, trimming the draggy spaces between thoughts. It's the quick first pass before fine manual editing.
What it's good for
- Podcast and interview editing
- Course and webinar audio
- Tightening rambling takes
- Pre-edit cleanup
Details
- Engine
- DSP
- Formats
- MP3, WAV, M4A, FLAC, OGG, AAC
- Price
- Free to try
Frequently asked questions
The fast pass targets the silences and hesitations around fillers by energy. Word-accurate, transcript-based filler cutting is on our roadmap; today it tightens the pacing automatically.
It focuses on low-energy gaps, not spoken words, so content is preserved while the dead-air shrinks. Review the result before publishing.
Yes — run silence removal for the long gaps and this for the hesitation spaces; together they make a noticeably tighter episode.
It varies with how hesitant the speaker is, but tightening the pauses around fillers usually shaves a meaningful slice off a rambling take and gives the episode a brisker feel. Gap-heavy interviews benefit most.
No. It works purely on the audio energy to find low-energy hesitation gaps, so nothing is sent to a speech-to-text engine. Word-accurate, transcript-based cutting is a separate feature still on our roadmap.
Not necessarily. Because the pass is energy-based rather than word-aware, a filler spoken at full volume mid-sentence can survive, while the dead-air around it is tightened. Plan on a quick manual review for anything you publish.
Yes. Any spoken-word recording with frequent hesitations, including lectures, webinars and meeting captures, tightens up the same way a podcast does.
Common audio formats are supported for upload, and you get a cleaned file back to drop straight into your editor before the final cut.