How to Remove Silences and Filler Words from Video (2026)
A 30-minute raw recording usually contains 4-7 minutes of pure dead space (silences, “um”s, “uh”s, false starts, breath pauses) once you start counting honestly. Manual cleanup of that dead space is the work that makes editing podcasts and talking-head video tedious. In 2026, removing it is a one-prompt operation in any modern editor that supports text-based editing.
This guide is the 2026 workflow for cleaning up filler and silence without losing the rhythm that makes the clip feel human. I’ll show how to do it in ChatCut because that’s where I work, and I’ll cover Descript and Gling fairly because they invented this category and remain strong choices.
Why remove silences and filler words at all?
The math is simple: cleaner audio means higher completion rate, and completion rate is the dominant signal in every modern social and YouTube algorithm.
Concrete impact, from the audio-cleanup work I’ve watched across creator workflows:
- A 60-minute podcast tightened to 50 minutes by removing silences usually gets 12-18% higher completion rate
- A 5-minute YouTube tutorial with filler words removed (typically 30-90 seconds of “um”s and “uh”s in a 5-minute talk) consistently outperforms its uncleaned version on session watch time
- A vertical short clip cut from a podcast lands far better when the source has been tightened first; the 25-second window has no room for dead air
The audience effect is equally real. Silences and filler words make speakers sound less authoritative; the same content delivered tightly reads as more confident. Even when the audience can’t articulate why, they respond to the cleanup.
How do you remove filler words automatically?
The 2026 workflow uses text-based editing as the substrate. Instead of scrubbing the timeline looking for “um”s, you let the AI find them and you decide which to remove.
The ChatCut Agent approach:
Remove all filler words (um, uh, like, you know, I mean) from this video
The Agent scans the transcript, identifies every filler instance, and removes the corresponding video frames. You see the result as a tightened video plus an audit log of what got removed. Total time on a 30-minute talking-head clip: under 2 minutes including the Agent run.
The same workflow in Descript looks similar; Descript pioneered the filler-word removal feature and it’s still excellent. Descript’s Remove Filler Words tool catches the standard set (“um”, “uh”, “like”, “you know”, “so”, “actually”) with one click. Gling AI does the same thing with a slightly different UI.
The differentiation between these tools in 2026 is mostly:
- What counts as a filler word. Some creators want “actually” and “so” preserved (they’re not really fillers in some contexts); others want everything cleaned. Most modern tools let you customize the filler list.
- How aggressive the trim is. Tight trims remove the filler plus the surrounding pause; loose trims remove only the filler word itself. ChatCut and Descript both let you tune this.
- Whether the AI removes only words or also non-word fillers. Breath pauses, lip smacks, mouth clicks are technically not filler words but listeners hear them as audio noise. Tools differ on whether they catch these by default.
For most workflows, the default settings work fine. The customization matters for high-stakes content where the speaker’s natural cadence is the brand.
How do you remove silences from a video?
Silence removal is mechanically similar but conceptually different from filler removal. A filler is a recognizable word the AI can match against a list. A silence is the absence of audio, defined by a threshold (anything quieter than X dB for longer than Y milliseconds).
The ChatCut workflow:
Remove all silences longer than 0.7 seconds from this video
The Agent scans the audio, finds every gap that exceeds the threshold, and removes the corresponding frames. The 0.7-second threshold is a reasonable default for talking-head content; for podcasts you might go to 1.0 second to preserve more natural rhythm; for fast-paced explainers you might tighten to 0.3 seconds.
The threshold setting is the most important variable. Too loose and you don’t remove enough; too tight and you remove the natural pauses that make speech feel human. The right setting depends on the content:
- Conversational podcasts: 0.8-1.2 seconds. Preserve breathing room.
- Solo talking-head: 0.5-0.7 seconds. Tighter pacing without sounding rushed.
- Tutorial / explainer: 0.3-0.5 seconds. Maximum pacing for retention.
- Interview cuts for social: 0.3 seconds. Tight, social-first delivery.
Descript 3.7’s Remove Silence feature added one-step silence detection and batch export in 2026, narrowing the workflow to a single click. ChatCut handles it through the Agent prompt above; the underlying logic is similar.
For talking-head and interview workflows specifically, silence removal is usually the first cleanup step. Filler word removal comes second. Manual review (for any cut that took out something you wanted to keep) comes third.
Should you remove ALL pauses?
No. This is where automated cleanup gets a bad reputation, and where most beginners over-correct.
Pauses serve rhetorical purposes:
- The dramatic pause before a punch line. If the speaker pauses for 2 seconds before “and that’s why we lost the deal”, that pause is the joke.
- The thinking pause when the speaker is genuinely processing. Removing this makes the speaker sound robotic.
- The breath pause at the end of a sentence. Without it, sentences run into each other and the listener can’t follow the structure.
- The transition pause between topics. Helps the audience reset before the next idea.
The honest workflow: run automated cleanup at a moderate threshold (0.7 seconds), then watch the result with a finger on the pause button. Wherever the cleanup made the audio feel rushed or unnatural, restore the original pause manually. ChatCut’s text-based editor lets you do this without losing the rest of the cleanup.
A useful rule: aim for a cleaned version that’s 80-90% of the original duration, not 60-70%. The deepest cuts read as over-edited; the moderate ones read as professional.
Descript vs ChatCut vs Gling: how do they differ?
All three handle the same core job (text-based editing with automated silence and filler removal). The differentiation is in everything else.
Descript. Invented this category. The most mature single-purpose tool for podcast editing and talking-head cleanup. Strongest standalone feature set: filler removal, Studio Sound (audio enhancement), Overdub for voice replacement (with constraints), and Underlord (their conversational editing assistant) for higher-level edits. Tradeoffs: long-form videos can hit performance issues on the desktop app; pricing in 2026 has moved to a credit-based model that some users find expensive at high volume.
ChatCut. Combines text-based editing with a fuller editing surface (motion graphics generation, AI video generation via Seedance 2.0, multi-track timeline). The wedge: one prompt drives multi-step edits (“remove silences AND add captions AND export 9:16”). Browser-based, no install. The ChatCut vs Descript comparison covers the feature-by-feature differences.
Gling AI. Lightweight, focused tool. Specifically built for the “long-form recording → tightened video” workflow. Less full-featured than the other two but quick to learn and reliable on the narrow job. Good fit if you don’t need motion graphics, AI video, or a full editor.
The honest meta-recommendation: pick Descript if you’re a podcast-only producer who values the most mature filler removal and Studio Sound; pick ChatCut if you’re producing video that mixes talking-head with other formats (motion graphics, AI B-roll, social repurposing); pick Gling if you want the simplest possible tool focused only on this one job.
For audio that needs more than silence and filler cleanup (background noise, mic bleed, room reverb), AI noise removal handles a different layer of the problem and works in combination with silence/filler removal.
A practical sequence I’ve seen work for talking-head workflows: noise removal first (clean the audio so silence detection works on a quiet baseline), then silence removal at a moderate threshold (0.7s), then filler word removal, then a manual review pass to restore intentional pauses. Each step is one prompt or one click. The whole stack runs in under 5 minutes on a 30-minute clip and produces audio that consistently rates as more professional than uncleaned source.
FAQ
Can these tools remove “um” but keep “you know” if I want?
Yes, in all three of the major tools (ChatCut, Descript, Gling). Each has a customizable filler word list. The defaults catch the universal fillers; specific phrases you want to preserve can be excluded.
What happens if the AI removes a word I wanted to keep?
ChatCut and Descript both let you undo any specific cut. The text-based editor shows the removed words struck through; you can restore them individually. Most workflows go: run the cleanup, watch the result, restore the 5-10 words you actually wanted.
Does silence removal affect the audio quality of the surrounding speech?
Done right, no. The cuts happen at zero-crossings in the audio waveform, so there’s no click or pop at the cut point. Done badly (older tools, or tools that cut on visual frames rather than audio waveforms), you can hear the cuts. Modern tools do this well by default.
How long does it take to clean up a 60-minute podcast?
Auto-cleanup itself runs in under 5 minutes. Manual review (restoring pauses you wanted, fixing edge cases) typically takes 15-25 minutes for an hour of content. The total is under 30 minutes, vs the 2-3 hours manual cleanup used to take.
Will this work on languages other than English?
For silence removal, yes; silence is silence in any language. For filler word removal, the tool needs a filler-word list for the target language. ChatCut and Descript both support filler removal in major languages including Chinese, Spanish, French, and German. Less common languages are hit-or-miss.
Try the cleanup workflow
Open ChatCut, upload a recording you’ve been meaning to clean up, and try:
Remove all filler words and silences longer than 0.7 seconds from this video
You’ll get a tightened version in your timeline in under three minutes. Watch it once and restore any pauses that felt necessary. You describe the edit. ChatCut executes it.