Users upload single or multi-track audio files (MP3, WAV, M4A, FLAC) to the Cleanvoice dashboard or submit files via the REST API. Acoustic machine learning models scan the speech waveforms to distinguish authentic phonemes from verbal artifacts, such as hesitant fillers ('um', 'er'), stuttered consonants, tongue clicks, and breathing pauses.
The engine applies non-destructive edits to mute or excise the artifacts while smoothing boundaries to prevent jarring audio pops or artificial cadence. Users can review the processed file in the online visual waveform player, toggle specific cleaning categories on or off, and either download the processed audio or export timeline cut markers for their native DAW.