filler-word-processing

Convert filler-word timestamp annotations into time-based cut segments by applying word-specific durations to mark precise removal points. This process transforms raw speech disfluency data into an actionable edit list for automated video processing workflows.

Merging overlapping or closely spaced filler word segments combines adjacent cut intervals into a clean, continuous edit list. This prevents fragmented cuts and ensures smooth audio transitions when removing speech disfluencies from podcast or lecture footage.

No external video editing dependencies are required; the workflow runs entirely on a standard Python runtime. It processes annotation files independently to output a reusable edit list without needing heavy NLE software installed.

Yes, the pipeline processes varied annotation sets to support interview and lecture transcript cleanup. It improves content pacing and accessibility by generating automated cut lists tailored for spoken-word media with frequent speech disfluencies.

Segment detection identifies filler-word annotations and merges closely spaced intervals to produce a clean final edit list. By consolidating overlapping segments, it prevents rapid consecutive cuts that could disrupt the video's natural flow.