How do I remove filler words from a video?

Upload the video to Valmera and type "remove all the ums and uhs". The AI agent locates every filler in the word-level transcript built during analysis, cuts each one at its exact word boundaries, and shows you a preview. Refine in chat, then export a full-quality MP4 rendered from your original file.

Which filler words does Valmera remove automatically?

The built-in list covers um, uh, er, and hmm. On top of that you can name any word or phrase you want treated as a filler — "also cut every 'like' and 'you know'" — and the agent adds it to the pass.

Can I remove 'like' and 'you know' from a video?

Yes. Custom fillers are a plain-English request: "cut the filler words, including 'like', 'you know', and 'sort of'". Because every cut comes from the transcript, you can review exactly what was removed and restore any instance you wanted to keep.

Will cutting filler words make the video sound choppy?

Each cut snaps to the word's own boundaries in the transcript, so neighboring words stay whole and the sentence keeps its natural spacing. If a particular cut feels abrupt in the preview, say so — "that edit at 2:14 feels harsh, restore it" — and it comes back. You control how aggressive the pass is.

Is there a free filler word remover without a watermark?

Free, yes — fully watermark-free, only on Plus and Pro. Valmera's free plan includes 20 credits every day (they reset daily and don't accumulate) plus a one-time 150-credit welcome bonus, no credit card required. On Free, a small Valmera mark sits in the corner of your export; on Plus and Pro there is no watermark at all. Every export closes with a brief (~2.5-second) Valmera end card after your video.

How accurate is filler word removal?

Cuts are timed against a word-level transcript, so each removed filler is cut at its own start and end rather than guessed from the waveform. You can verify the result two ways: read the editable transcript to see what was said, and trust the honesty layer — every agent reply is checked against the edits actually made, so it can't claim a cut that didn't happen.

Can I remove filler words and silences at the same time?

Yes — one request covers both: "remove the filler words and the dead air". The agent runs it as a single cleanup pass and returns one preview. Uploads go up to 2GB or 3 hours; the one-time analysis shows visible progress on longer files.

Remove Filler Words from Video

Every "um" you cut makes you sound sharper. Valmera finds them all in the transcript and cuts each one exactly — no waveform hunting.

To remove filler words from a video, upload it to Valmera and type "remove all the ums and uhs". The AI agent reads a word-level transcript of your footage, finds every um, uh, er, and hmm, and cuts each one at its exact word boundary. You can add your own fillers too — "like", "you know", "sort of".

How to Remove Filler Words from a Video

1
Upload your video
Drag in your video — MP4, MOV, MKV, or WebM up to 2GB or 3 hours. A one-time analysis with visible progress builds the word-level transcript that every cut is timed against.
2
Type "remove all the ums and uhs"
That covers the built-in fillers: um, uh, er, and hmm. To go further, try "also cut every 'like' and 'you know'" — the agent treats any word you name as a filler.
3
Preview & export
Play the preview, spot-check a few cuts, and adjust in plain English — "put back the 'you know' at 2:14". Then export a full-quality H.264 MP4 rendered from your original upload.

Analysis runs once per upload with visible progress; after that, every request is just a message.

EXAMPLE PROMPTS

"remove all the ums and uhs"
"cut the filler words, including 'like' and 'you know'"
"remove the ums but leave my thinking pauses"
"clean up the speech in the first section only"
"put back the 'you know' at 2:14"

Cuts Every Um, Uh, Er, and Hmm by Default

One request — "remove all the ums and uhs" — runs the built-in filler pass: um, uh, er, and hmm. Each instance is located in the word-level transcript with its own start and end time, and the cut lands exactly on those boundaries. You don't mark anything, and it doesn't matter whether there are three fillers or three hundred: it's the same single sentence to you.

This is the edit most people dread doing by hand, because it's dozens of tiny, fiddly cuts where a few frames too many clips a real word. Snapping to transcript boundaries is what makes the automated version safe.

Add Your Own Filler Words — "Like", "You Know", "Kind Of"

Everyone has crutch words the standard lists miss. Name yours and the agent treats them as fillers: "also cut every 'like' and 'basically'", "remove 'you know' wherever I say it". Because the pass is transcript-driven, you can see exactly which instances were removed — and if a cut took a use you actually meant, one message restores it.

Transcript-Timed Precision, Not Waveform Guesswork

Filler words are short, quiet, and glued to real words — the worst case for tools that cut by audio level. Valmera cuts from language: the analysis produces a word-level transcript where every word carries its own timing, so a removed "um" takes nothing with it. You can open the editable transcript to review what was said and fix any misheard word.

There's a trust guarantee on top: Valmera's honesty layer checks every agent reply against the edits actually made. If the reply says the fillers are gone, the server verified it — the agent cannot claim a cut it didn't make.

Keep It Sounding Human

Not every filler should die. A "hmm" before a genuine answer reads as thought; stripping absolutely everything can make you sound like a text-to-speech voice. Valmera takes direction: "only remove the ums, keep everything else", "clean up the intro but leave the interview answers natural". And since nothing is destructive, any cut range can be restored after you watch the preview.

Who reaches for this? Course creators cleaning up lesson recordings, podcasters tightening clips, founders polishing demo and pitch videos, and anyone whose first take is fluent except for the ums. If you also repeat lines until they land, pair this with the repeated-take remover.

More Than a Filler-Word Remover

The same chat handles the rest of the cleanup and the finish: remove the silences, make any cut you can describe, add word-accurate captions, music with automatic ducking, reframing, and color — all by typing. One request can combine jobs: "remove the fillers and the dead air, then caption it".

Start on the free plan — 20 credits every day plus a one-time 150-credit welcome bonus, no credit card. You're charged for the work actually done, so simple cleanup passes cost the least; see plans and pricing or browse the full AI video editing tools hub and the cuts & cleanup docs.