How to remove filler words from audio (and keep it sounding human)

What counts as a filler word, and how many should I expect?

Fillers are the sounds and phrases that fill a pause while you think: um, uh, er, hmm, and sometimes “like” or “you know”. Research on spontaneous speech puts filled pauses at roughly 2 to 6 per 100 words, so a 40 minute interview can hold a few hundred of them. Most are harmless. A handful are distracting, and those are the ones worth cutting.

This page is for someone holding an audio file with no picture: a podcast episode, a voiceover, course narration. If your source is video, the advice about hiding jump cuts is different, and it lives in our video guide to silence and filler words. For the full episode workflow, loudness and exports, see video podcasts.

Before you pick a tool, listen to the file once and mark where the fillers really bother you. A speaker who says um twice a minute needs a lighter touch than one who stacks four in a sentence. That mark-up tells you whether you want a full automatic pass, a reviewed pass or three manual cuts, and it saves you paying for a plan you do not need.

Which tools take an audio file and let me review each cut?

Most pages that rank for this are a vendor's upload button with a short pitch. None we read listed what it cannot do, so the last column of the table is ours: what we could not verify. Everything was read on the vendor's page on October 20, 2026. We did not test these tools on audio.

We could not confirm whether Adobe Podcast or Audition offers one-click filler removal today. We found only a beginner guide that advises removing fillers by hand and an old feature request. We also did not verify any third-party filler plugin for Audacity, so none is named here.

What is actually free?

Three routes cost nothing, and each has a catch. Audacity is free, but it has no filler detector in its manual, so you find and cut every um yourself. Its Truncate Silence effect shortens quiet passages by a threshold, either to a number of seconds or by a compression percentage, which helps with pauses but does not touch the ums. Descript lists a Free plan and Cleanvoice offers a free trial without a card, but both cap what you can process, and the caps change. Read the current limits before you commit an episode.

Honest rule of thumb: free is fine for a single short file. For a weekly show, manual editing costs hours and capped plans run out, so the real price is time or a subscription.

Cloud or local: where does my audio go?

Cleanvoice and Podsqueeze work by uploading your file to their servers. That is quick to start and needs no setup. It also means client calls, interviews and unreleased episodes leave your computer, so read each vendor's data policy before you upload anything sensitive. A local tool keeps the file on your machine, at the cost of slower transcription on a laptop. For more on that trade-off, see local vs cloud AI editing and offline Whisper transcripts.

How do I keep breaths and room tone natural?

This is the part most tool pages skip. Removing a word leaves a hole, and a hole of dead silence in a recording with room noise sounds like an edit. Two vendors address it in different ways. Descript's help page describes an “Avoid harsh cuts” option that skips fillers that cannot be removed without clipping nearby words. Cleanvoice says it fills the gap with room noise and silences rather than deletes, so the length stays the same.

Here is a method that works with any tool above:

1. 1Record 10 to 20 seconds of room tone at the start of every session, with nobody speaking. Keep it as a spare clip.
2. 2Cut only fillers with clear space on both sides. Skip one that rolls into the next word: a podcaster in the same thread calls that a soft rule, because it tends to create an unnatural chop.
3. 3Leave the breath before a sentence. If a cut removes a breath, put it back or leave the filler in.
4. 4Shorten pauses instead of deleting them. Trim a long gap to a beat, not to zero.
5. 5Where a cut leaves a dead hole, lay room tone under it, or add a short fade or crossfade across the join, a few milliseconds up to a tenth of a second.
6. 6Listen to every join once at normal speed with headphones on. A cut you can hear on the first pass will be heard by listeners.

Some people go further and remove the sound inside a spectral editor rather than deleting time. That keeps the rhythm, but it is slow, and we did not test it.

Can I remove filler words from audio on an iPhone?

We found no Apple documentation of filler removal in Voice Memos. The fetch of Apple's support pages returned only the generic guide, so we say “we found none”, not “none exists”. Two paths remain: a web uploader in Safari, or an app such as Detail, which shows a transcript, lets you tap Fillers and delete them, but applies the edit to the video itself, so it is video first. We did not test any iPhone workflow. Try a one minute clip first. Rushilo has no iPhone app: it edits on Mac and Windows.

How does Rushilo handle an audio-only file?

Per the source, Rushilo imports MP3, M4A, WAV, OGG and FLAC and places audio on an audio track. The timeline transcript builds from any clip that is not muted, whatever its track type, and your AI can transcribe an untranscribed clip first. Transcription runs on your machine with a local model, and your own OpenAI, Groq or Gemini key is optional. The workflow:

1. 1Import the audio file and let the transcript build.
2. 2Run filler removal and read the preview, which shows how many fillers and how many seconds it would take out.
3. 3Apply it. Cuts ripple every track and close the gap, as a single undo step.
4. 4Tighten pauses with a limit of 0.5, 1, 1.5 or 2 seconds. Each side keeps half the limit, so a pause is shortened, not deleted.
5. 5Listen to the joins, then export with the Audio only option, which writes an M4A file.

The limits matter as much as the features:

• The filler list is short: um, uh, er, hmm and similar. “Like” and “you know” are matched only when set off by a comma, a sentence end or a 0.3 second pause. It does not catch “so”, “basically” or “actually”.
• Speech models often leave ums out of the transcript, so the count can be low.
• Pauses come from word timing. There is no breath removal and no loudness-based silence detection.
• No noise reduction, EQ, de-esser or voice isolation. Clean the file first if it needs it.
• Export is M4A only: no WAV or MP3.

We did not exercise whether the transcript panel shows an audio clip with no video present. The code path says it should. Rushilo costs one payment starting at $19 for one computer. If that fits, Buy Rushilo.

When should I leave the ums in?

More often than the tool pages suggest. Podcasters in r/podcasting are blunt about it, and one commenter says AI edits that remove every filler do not sound good. Fillers carry rhythm: a short um before a hard answer tells the listener a thought is forming. Leave them when the speaker is not overdoing it, when removal would clip a neighbouring word, or when the take is emotional. A light pass that removes the obvious ones and trims long pauses is usually the right size.

If you want more of the transcript-led approach for spoken audio, read text-based editing. Whatever you choose, review the result: no tool in this table claims to be right every time.

If the recording also has hiss or room noise, removing background noise from video is a separate step worth doing first.

Patel, O. (2026, October 22). How to remove filler words from audio (and keep it sounding human). Rushilo. https://rushilo.com/blog/remove-filler-words-from-audio

About the author

Om Patel

Founder, Rushilo

Om Patel is the founder of Rushilo, a desktop video editor driven by the user's own AI, and of BigIdeasDB, a research platform that reads real complaints and reviews to help founders validate products. He writes Rushilo's research articles with the same method: primary sources, dated numbers and the words real users write.

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