Add AI Video Subtitles to Any Clip in Two Clicks
Your raw clip just finished uploading, and the preview sits silent while viewers scroll past. You need accurate AI video subtitles burned onto the video now, not…
Cut out 'um', 'uh' and 'like' automatically — cleaner audio, free to try.
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Removing filler words from a video means finding every "um", "uh" or verbal tic in the transcript and cutting it out along with a brief silence around it, so the sentence stays fluid. The result: a clean delivery, without listening back to the whole video to hunt down every hesitation by hand.
The tool transcribes the video first, then compares every word against a list of common hesitations: um, uh, like, ah… in English, and euh, hum, bah… in French. Detection works on single words — a full phrase like "you know" or "I mean" isn't matched as a phrase, only the individual filler words inside it are.
Each filler word is removed with a small margin of silence around it, and cuts that land too close together are merged into one to avoid a choppy edit. The sentence keeps its natural rhythm: you hear continuous speech, not a string of jump cuts.
Detection matches words, not meaning — it works very well on steady, hesitation-heavy speech. It can, rarely, cut a word like "like" when it's used as a real word ("I like this") rather than as a filler, since it's the exact word being targeted. It's worth watching the result before publishing, especially with fast talkers or overlapping voices.
Drop the video file you want to remove filler words from (MP4, MOV, AVI, MKV, WebM, up to 2 GB).
AI transcription spots every filler word — um, uh, like… — and cuts it with the silence around it.
Get a video with a clean delivery, ready to publish or to move on to the next editing step.
The AI transcribes the video, then spots every filler word in English and French, so you don't have to relisten to every sentence to catch them.
Every cut keeps a margin of silence and nearby cuts get merged, so the sentence keeps its natural rhythm to the ear.
Only the passages containing a filler word are cut: the rest of the video's picture and sound stays untouched.
No editing software to open: upload the video in your browser — a free account is all it takes to start.
A guest's hesitations make listening painful: removing them gives a smooth exchange without re-recording anything.
A trainer who piles up "ums" loses the room: removing filler words tightens the explanation without changing its substance.
Speech without filler words yields punchier short clips once you cut them from the cleaned-up version.
A live screen recording narrated on the fly tends to collect hesitations; removing them makes the demo sound more professional without a full re-record.
A webinar or talk recorded in one take often keeps its verbal tics; removing them before it goes out makes the delivery sound more confident.
Upload the video: AI transcription spots every filler word (um, uh, like…) and cuts it along with a small margin of silence, so the edit sounds natural. The rest of the picture and sound is never touched. You then download the clean video, ready to publish.
The tool matches common hesitations word by word: in English (um, uh, like, ah…) and in French (euh, hum, bah…). Detection works on single words, not on full phrases like "you know" or "I mean".
No. Each filler word is removed along with a small margin of silence around it, and cuts that land too close together are merged into one to avoid a choppy edit. You hear a continuous sentence, not a string of jump cuts.
Not in this tool: only filler words are cut here. To also remove dead air and long pauses between sentences, use the dedicated silence remover; the two chain together in Klipa Studio on the same video.
Rarely, yes: detection matches a word list, not the meaning of the sentence. "Like" used as a real word ("I like this") is the main risk, since it's the exact word being targeted as a filler. It's worth checking the result before publishing.
No logo is added: the cleaned file is delivered clean. The tool needs a free account and uses your credits based on the video's length; the credits granted on sign-up cover your first clean-ups.
MP4, MOV, AVI, MKV and WebM, up to 2 GB per file — plenty for a full episode or a whole talk. The clean video is delivered as MP4, with picture and sound unchanged outside the cut passages.
It's returned to you unchanged, with no cuts at all. Processing doesn't fail either way: you simply get your original file back, ready to download, with no needless cutting and no time lost retrying.
No. Everything happens online in the browser, without opening any editing software or starting a project: upload the video, the AI transcribes it and cuts the filler words, and you get the clean file back directly.
The filler word remover targets verbal hesitations (um, uh, like…); the silence remover targets dead air between sentences, whatever caused it. If a recording is mostly full of verbal tics, start with filler words; if it has long silent gaps, start with the silence remover.

Your raw clip just finished uploading, and the preview sits silent while viewers scroll past. You need accurate AI video subtitles burned onto the video now, not…

You’ve just finished recording your video, and it’s almost perfect — except for those awkward pauses, dead air, and hesitations that stretch your 2‑minute message…
Removing filler words from a video by hand means listening to every sentence and cutting at the right spot. Klipa transcribes the video, spots verbal tics in English and French, and cuts them automatically — you just check the result, never re-record the whole thing.
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