Delete filler words from video

Find every "um," "uh," and stammered repeat, and cut them with one click.

The first run downloads the speech model to your browser (cached after that). Best on desktop — mobile devices may be slower.

Drop a video or audio file to edit by transcript

or click to browse

Processed on your device — never on a server.

How it works

1

Drop a video or audio file in. WavyVid transcribes it with AI, right in your browser.

2

Click any sentence in the transcript to mark it for removal — it's struck through, not deleted, so you can undo anytime.

3

Export: the marked sentences are cut and the rest is stitched back together, with an updated caption file to match.

When you'd use this

  • You notice you say "um" and "uh" constantly on camera and want them gone without editing every instance by hand.
  • You stammered through a sentence and restarted it, and want the false start cut automatically.
  • You're prepping a voiceover for a course or presentation and want it to sound polished, not conversational.

Frequently asked questions

Will it catch every filler word?

It catches standalone filler utterances ("um," "uh," "like" said on their own) and exact repeated phrases — it won't catch a filler word buried in the middle of an otherwise-clean sentence, since cuts happen at whole-segment boundaries.

Does it delete things automatically without asking?

No — detected filler words are highlighted for review, and nothing is removed until you click "Remove all" (or approve each one individually) and then export.

Can I also manually cut other sentences at the same time?

Yes — filler-word detection and manual sentence-by-sentence editing use the same transcript view, so you can do both in one pass.

Is this only for video, or does it work on audio files too?

Both — drop in a video or an audio-only file (podcast episode, voice memo) and the same filler-removal workflow applies.

Will removing filler words make the video sound choppy?

No — cuts land at segment boundaries with proper padding, the same underlying approach used by the transcript editor's manual cuts, so the audio and video stay in sync without abrupt jumps.

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