Cleanvoice AI

Editing a podcast is two completely different jobs wearing one name. There is the creative edit — what stays, what goes, where the story tightens — and there is the janitorial edit: scrubbing out three hundred instances of "um", the lip smack before every sentence, the neighbour's dog, and the four-second silence where someone was thinking.

The second job is mechanical, tedious, and takes longer than the first. Cleanvoice automates it, and then — crucially — gives the result back as an editable timeline rather than a finished file.

This review covers why that export decision is the whole product, which of the removers you should actually turn on, why breath removal deserves a warning, and how it sits against Descript.

Cleanvoice In Five Lines

• Cleanvoice AI automatically removes filler words, background noise, mouth sounds, breaths, stutters and dead air from podcast and video audio.
• Timeline export is the differentiator — the edit comes back into Adobe Audition, Reaper, DaVinci Resolve, Ultraschall or Audacity instead of trapping you in a web editor.
• Multitrack support matters for interviews, where each speaker needs cleaning without affecting the others.
• It also does the paperwork: transcription, summaries and show notes generated from the same upload.
• EU data storage, GDPR and ISO 27001, with a free tier of 30 minutes to test on your own audio.

Why Timeline Export Changes The Purchase

Most audio AI tools — Descript most obviously — want to be your editor. You upload, you edit in their interface, you export a finished file. That is a reasonable product and it is a bad fit for anyone who already has a workflow.

Cleanvoice exports an editable timeline into Adobe Audition, Reaper, DaVinci Resolve, Ultraschall or Audacity. That means the automated cleanup becomes a step in your existing process rather than a replacement for it — and every decision it made stays reversible in the software you already know.

That reversibility is the part professionals care about. Automated cleanup is very good and occasionally wrong: it will cut a pause that was doing dramatic work, or clip the front of a word. If those decisions are baked into a rendered file, your options are accept it or redo it. If they arrive as timeline edits, you nudge the one that is wrong and move on.

There is an API too — Python, JavaScript and REST — which points at the same philosophy. This is designed as a stage in a pipeline, not a destination.

Automated cleanup is right 98% of the time. The entire question is what happens to the other 2%, and a rendered file gives you no answer.

Which Removers To Actually Turn On

The list is long and they are not equally safe. Turning everything to maximum is the fastest way to make a podcast sound like a robot reading a transcript.

• Mouth sounds and background noise — turn these on and forget about them. Nobody has ever wanted a lip smack, and removing them is pure gain.
• Dead air — safe and high-value. The four-second gap where someone unmuted is not a dramatic pause, it is a mistake.
• Stutters — generally good, and the one to spot-check on a speaker with a natural speech pattern you do not want smoothed away.
• Filler words — powerful and worth using with judgement. Removing every "um" makes speech unnaturally clean; a conversation with zero hesitation stops sounding like a conversation.
• Breaths — the one to be careful with, discussed below.
• Studio Sound — the enhancement pass. Excellent on a bad recording, and capable of making an already-good one sound processed.

The Breath Warning Nobody Gives You

Breath removal is the feature that most often gets over-applied, and the result is uncanny in a way people cannot name.

Speech contains breathing because speaking requires it. A voice with every breath stripped out does not sound cleaner, it sounds like it was assembled — the listener's ear expects the small intake before a long sentence, and its absence registers as wrong even when they cannot say why.

The same is true, more mildly, of filler words. "Um" carries information: it signals thinking, hesitation, a change of direction. A transcript reads better without them; a conversation frequently does not.

The practical rule is to reduce rather than eliminate. Cut the distracting breaths — the gasps, the ones directly on the microphone — and leave the ordinary ones. Cut the fillers that cluster and leave the ones doing work.

This is the difference between audio that sounds professionally edited and audio that sounds synthetic, and it is entirely a settings decision rather than a limitation of the tool.

Multitrack, And Why Interviews Are Different

Multitrack editing sounds like a feature-list item and is actually the thing that decides whether this works for interview podcasts.

A two-person conversation recorded on separate tracks has a specific problem: each microphone picks up the other person, quietly. Process the tracks as one mixed file and cleaning one speaker's breaths means touching the other's audio too.

Handling tracks separately means each speaker gets cleaned against their own noise floor, their own mouth sounds, their own room. It also means dead air can be removed from one track without leaving a hole in the other.

For a solo show none of this matters and a single track is simpler. For anything with a guest, it is the difference between a clean edit and a subtly damaged one — and it is worth confirming your recording setup actually produces separate tracks before you rely on it.

For a transcript you intend to publish or certify, a dedicated service such as Rev is still the more accountable route. The transcription, summarisation and show-notes features run off the same upload, which is a sensible bundling — you were going to need show notes anyway, and the audio is already there.

Fitting It Into An Existing Workflow

The order matters, and most people get it backwards by cleaning last.

1. Record multitrack if you have guests. Everything downstream is better and it costs nothing at record time.
2. Run Cleanvoice before your creative edit, not after — cleaning first means you make content decisions on audio that already sounds finished.
3. Start conservative on filler words and breaths. You can always run it again harder; you cannot un-strip a performance.
4. Export the timeline into your own DAW rather than taking the rendered file.
5. Listen to a two-minute stretch on headphones before accepting. Over-processing is audible immediately and invisible in a waveform.
6. Spot-check any speaker with an accent or an unusual cadence, where automated cleanup is most likely to misfire.
7. Use the generated transcript and show notes rather than writing them separately — that is time back for free.

Cleanup Tools, Exports And Compliance

Does This Belong In Your Edit?

Genuinely Saves Time If

• You publish regularly and cleanup eats more time than the creative edit
• You record interviews on separate tracks
• You already work in Audition, Reaper, Resolve or Audacity and want to stay there
• Your recording conditions are imperfect — home rooms, varying microphones
• You need transcripts and show notes anyway

Not Needed When

• You record in a treated room with good microphones and clean delivery
• You want a full editor rather than a cleanup stage
• Your episodes are short enough that manual cleanup is quicker than uploading
• You would strip every breath and filler and call it finished
• Your workflow is entirely inside a tool that already does this