Audio Profanity Remover — Clean Version Maker

Tool for cleaning profanity out of an audio track — a music single, a podcast episode, a voice message or an audiobook. Speech recognition reads the recording, lays every word out on a waveform, and stamps each swear word with its exact start and end so the censor lands on the word and nothing else. The cleaned audio is rebuilt and saved as MP3, WAV, M4A, OGG or FLAC at the bitrate you choose. Recognition covers nine languages — Russian, English, Spanish, Portuguese, German, French, Indonesian, Korean and Arabic; you pick the audio language, independently of the interface language. Processing runs in your browser via WebAssembly.

How to remove profanity from audio

1

Drop in an audio file — a track, podcast, voice message or audiobook chapter (MP3, WAV, M4A, OGG and more)

2

Let speech recognition read the recording; progress, time elapsed and time remaining appear as it works

3

Walk the waveform: click a word in the transcript to spare it, or drag over the wave to silence a missed swear

4

Set the censor sound — beep pitch and level, silence, a music duck, or your own clip — and listen back in the player

5

Press "Download clean file" and choose the audio format and bitrate for your release

Bleep the swearing out of a song or podcast straight on the waveform

🤬
Drop an audio file here or click to browse
Drag & drop or browse
Loading file...
0%
Click the wave to seek · drag to mute a section manually · section edges are draggable
0:00 / 0:00 Found 0 Fuzzy 0 Muted 0

Transcript

Click any word to mute it or remove the mute.

Will be muted

No profanity found. Click a word in the transcript or drag on the wave to add one.
Audio file up to 20 MB; the first 30 seconds are used and looped over long sections.
How much of the word is muted. 100% = the whole word; lower keeps the word edges audible (more recognizable).
Custom word lists (blacklist and whitelist)
Audio is rebuilt with smooth fades; in videos the picture is copied without re-encoding.

Features

Made for a music or podcast mix
The recognizer picks out swear words even when they sit under a beat or a music bed, so a song or episode comes back clean.
Edit on the waveform
Every detected word is marked on the audio wave; scrub, click a word in the transcript, or drag across the wave to mute a spot the model missed.
Choose how it sounds
Replace each word with a 200–2000 Hz beep, clean silence, a volume duck under the music, or a sound clip of your own — with smooth fades so there is no click.
Audio export the way you need it
Render the clean version to MP3, WAV, M4A, OGG or FLAC and pick the bitrate — ready to upload to a streaming or podcast platform.
Tune what counts as profanity
Keep obscene roots on, switch rude words or euphemisms on or off, and feed your own blacklist and whitelist for an artist or show name.
Cue sheet of every edit
Export a timecoded list of every muted word as TXT, CSV, SRT or JSON for your records or a label submission.

Why use this audio cleaner

Hunting for swear words by ear in an audio editor means scrubbing the same minute over and over and still missing one before the song or episode goes live. This tool transcribes the recording, pins each word to its timecode on the waveform, and lets you beep, silence or duck it in a click — then hands you a clean MP3, WAV, M4A, OGG or FLAC at the bitrate your distributor wants. Whether it is a radio edit of a single, a brand-safe podcast cut, or an audiobook for a younger audience, the clean version is ready in minutes instead of an afternoon of manual editing.

Frequently Asked Questions

Will it catch swearing in a song with a backing track?

Yes. The models are trained on real recordings and read vocals over music, though a very dense mix can blur a word. Anything it misses you can mute by hand by dragging the spot on the waveform.

What audio formats can I save?

The cleaned track exports to MP3, WAV, M4A, OGG or FLAC, and you set the bitrate — so you can match a streaming master, a podcast feed, or a lossless archive copy.

Does the song stay in sync after cleaning?

Yes. Words are muted in place rather than cut, so the running time is identical and nothing drifts; short fades at each edge keep the audio smooth.

Can I drop in my own bleep sound?

Yes. Switch to "My own sound" and upload a short clip; its first 30 seconds replace each muted word, looping over longer ones, with the level under your control.

Which language does the recogniser understand?

Nine languages: Russian, English, Spanish, Portuguese, German, French, Indonesian, Korean and Arabic. You pick the audio language in the list above the upload zone (or let it be detected), independently of the interface language — so you can open the site in Arabic and clean an English podcast. Russian runs on GigaAM (trained on 700,000 hours of Russian audio); the other languages run on Whisper. Recognition accuracy varies by language, so review the words that were found — anything missed can be muted by hand on the waveform.

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Who is this tool for?
🎙️ Podcasters 📡 Streamers & Broadcasters ✨ Content Creators 👨‍👩‍👧 Parents & Families
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