AudioEditor

Automate audio editing by transcribing audio with word-level timestamps, detecting dead air and filler words, and removing unnecessary segments automatically. This streamlines post-production workflows for podcasters and content creators without manual timeline scrubbing.

Intelligent cut detection uses Claude to analyze transcribed text and timestamps, identifying filler words, dead air, and unnecessary segments. It then applies ffmpeg editing commands to remove them and inserts crossfades with room tone extraction for seamless transitions.

Yes, Whisper handles transcription with word-level timestamps while ffmpeg executes the actual audio and video editing operations. Claude analyzes the transcription data to detect cuts, and ffmpeg applies crossfades, room tone extraction, and segment removal.

Cleanvoice is an optional dependency for cloud-based audio polish. The core transcription, intelligent cut detection, and automated editing with ffmpeg work independently without requiring cloud processing or a Cleanvoice subscription.

Apply crossfades and room tone extraction automatically through Claude's analysis of transcription timestamps paired with ffmpeg processing. The intelligent cut detection identifies segment boundaries and generates ffmpeg commands for seamless transitions between edited audio regions.

Automated cut detection relies on Whisper transcription accuracy and Claude's semantic analysis of filler words and dead air. Complex audio with overlapping speech, heavy accents, or poor recording quality may reduce transcription precision and affect edit point detection accuracy.