Cleanvoice
AI that removes um, uh, mouth sounds, and silence from your podcast automatically
Cleanvoice uses AI to detect and remove filler words (um, uh, ah, you know, like), mouth sounds (lip smacks, tongue clicks), stutters, and dead silence from podcast recordings and voice-overs. Upload your audio, review the proposed cuts in an editable timeline, and export a polished recording in minutes — without manually scrubbing through hours of audio. Supports 40+ languages and $0.015 per minute of audio.
Visit CleanvoiceWhat is Cleanvoice?
Cleanvoice is an AI-powered audio editing tool designed specifically for podcasters and voice-over artists who want to remove verbal tics and dead air without spending hours on manual editing. The core workflow is simple: upload an audio or video file, select the types of audio artifacts to remove (filler words, mouth sounds, silence, stutters), and let the AI analyze the recording. The result is an editable timeline that highlights every proposed cut with the exact word or sound that triggered it.
Unlike audio tools that apply destructive edits automatically, Cleanvoice puts you in review mode first. You see every single cut before it is applied — you can approve the AI's suggestions, restore any word that was incorrectly flagged, or adjust the silence threshold to match your preferences. This review step is what separates Cleanvoice from cheaper tools that produce over-cut audio with choppy transitions and unnatural pacing.
The pay-per-minute pricing model is a deliberate choice that keeps Cleanvoice accessible to independent podcasters without a monthly commitment. At $0.015 per minute, a 45-minute podcast costs about $0.68 to process — far less than the cost of 30 minutes of manual editing time. For high-volume podcasters, prepaid credit packs lower the per-minute rate further.
Cleanvoice handles filler word detection in 40+ languages, making it one of the most multilingual audio editing tools available. The AI is trained on speech patterns specific to each language, rather than relying on a translation layer, which improves accuracy for non-English podcasters. Silence removal and mouth sound detection work on any audio regardless of language since they analyze acoustic patterns rather than speech content.
Key Features
Filler Word Detection
Detects and marks um, uh, ah, you know, like, so, right, and other language-specific filler words in 40+ languages. Sensitivity is adjustable — set a confidence threshold to only flag high-confidence filler words and reduce false positives.
Mouth Sound Removal
Identifies and removes lip smacks, tongue clicks, breathing sounds, and other mouth noises that are distracting in close-mic recordings. Particularly useful for recordings made with condenser microphones that pick up subtle sounds.
Silence Trimming
Removes dead air from between sentences and between speakers in interviews. Configure minimum silence duration and the gap length to leave — keeps natural breathing room while eliminating the long pauses that slow down pacing.
Editable Cut Timeline
Every proposed edit appears in a visual timeline with the word or sound that triggered each cut. Review individually, approve in bulk, or restore any falsely flagged moment before exporting. Non-destructive until you confirm the edits.
40+ Language Support
Native filler word detection for English, Spanish, French, German, Portuguese, Italian, Dutch, Polish, Japanese, Korean, Chinese, and dozens more. Language-specific training means better accuracy than generic speech recognition tools.
API Access
Cleanvoice offers an API for automated processing pipelines. Production podcast workflows can upload recordings directly from your recording software or storage bucket and receive cleaned audio without the web interface.
Pricing
Cleanvoice uses pay-per-minute pricing with no subscription. You prepay for audio minutes and use them as needed. Credit packs offer better per-minute rates for high-volume users.
| Plan | Price | Minutes | Per Minute |
|---|---|---|---|
| Pay-as-you-go | $0.015/min | No minimum | $0.015 |
| Starter Pack | ~$10 | ~700 min | ~$0.014 |
| Pro Pack | ~$25 | ~1,800 min | ~$0.014 |
| API Access | Same rates | Any volume | Volume discounts available |
Check cleanvoice.ai for current pricing. Packs do not expire.
Pros & Cons
Pros
- Pay-per-minute pricing — no subscription waste if you publish infrequently
- Editable timeline lets you review every cut before applying
- 40+ language support with language-specific filler word models
- API available for automated podcast production workflows
- Fast processing — a 60-min episode typically processes in 5-10 minutes
Cons
- No background noise reduction — need a separate tool for hiss/hum
- Occasional false positives require manual review of every edit
- No direct DAW integration — download and re-import edited audio
- Multi-track recordings require uploading tracks separately
Alternatives to Cleanvoice
If you need background noise reduction in addition to filler removal, or prefer a subscription model, these tools take complementary approaches to podcast audio quality.
Auphonic
Automated audio post-production with loudness normalization, noise reduction, and multi-track support. Better for overall audio quality rather than filler-specific editing.
Adobe Podcast
AI microphone enhancement and transcription-based editing. Better for improving recording quality; Cleanvoice is more focused on filler word removal.
Descript
Full podcast editing suite with word-level transcript editing and filler word removal. More expensive but includes full production workflow.
Podcastle
AI-powered recording and editing platform with noise removal and filler word tools built into a browser-based DAW.
Frequently Asked Questions
What is Cleanvoice?
Cleanvoice is an AI audio editing tool that automatically removes filler words (um, uh, ah, you know), mouth sounds (lip smacks, tongue clicks), stutters, and dead silence from podcast recordings. You upload an audio or video file, review all proposed cuts in an editable timeline, and export a cleaned recording. It supports 40+ languages for filler word detection and charges $0.015 per minute of audio with no subscription required.
How much does Cleanvoice cost?
Cleanvoice charges $0.015 per minute of audio processed — there is no subscription. A 60-minute podcast episode costs about $0.90 to process. Prepaid credit packs offer slight volume discounts. Credits do not expire, so occasional podcasters can buy a small pack and use it over months without wasting money on unused subscription time.
Does Cleanvoice remove background noise?
No — Cleanvoice focuses on word-level editing: filler words, mouth sounds, and silence. It does not reduce broadband background noise like room hiss, HVAC hum, or keyboard clicks. For background noise reduction, use Auphonic, Adobe Podcast Enhance, or a dedicated noise reduction plugin in your DAW. Many podcasters run audio through Adobe Podcast Enhance first for noise removal, then Cleanvoice for filler word cleanup.
What file formats does Cleanvoice support?
Cleanvoice accepts MP3, WAV, M4A, FLAC, and video files (MP4, MOV). Output is available as MP3 or WAV. For multi-speaker recordings, upload each speaker's track separately to get independent cleaning, then merge the tracks in your editing software.
Is Cleanvoice accurate — does it cut real words?
Cleanvoice is accurate for clear filler words but occasionally flags real words that sound like fillers. This is why the editable timeline review is the default mode — you see every proposed cut and can restore any incorrectly flagged word before applying changes. In practice, most users find 85-95% of proposed cuts are correct, with a short review pass to restore the occasional false positive.
What languages does Cleanvoice support?
Cleanvoice supports filler word detection in 40+ languages including English, Spanish, French, German, Portuguese, Italian, Dutch, Polish, Japanese, Korean, and Chinese. Silence removal and mouth sound detection work for any language since they analyze audio patterns rather than speech content. Language-specific filler word models are trained on native speech data, giving better results than tools that use translation or language-agnostic detection.
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