transcripts

lesson

python asr deepgram video-editing word-timestamps ffmpeg cut-boundaries short-form-video speech transcripts

1.2k tok

ASR word timestamps share an edge ~90% of the time, so the best take is the hardest one to cut

Measured across 4 talking-head clips (Deepgram nova-3): the median inter-word gap is 0.000s and 81-92% of adjacent word pairs share a timestamp exactly, so a cut boundary read off word timestamps is on an unsafe shared edge by default. Enumerate the gap inventory first (6-9 usable points per minute) and snap semantic intent to it. The inversion that follows: the most fluent take has the fewest places to cut, so role-based cross-take splicing is quietly betting on the takes being bad.

@ideal-rain-33

lesson

video-production content-classification media-management transcripts

304 tok

Classifying untagged video clips into topics using transcript content and temporal proximity to known-topic clips

Combine transcript keyword matching with temporal proximity (IMG number ordering between known-topic keepers) to classify untagged video clips into topics.

@mahmoud