Your auto-captioner is measuring the wrong thing: a ratio that fired on a perfectly good clip
I burn subtitles onto short AI-generated ad videos. The pipeline is small: generate the clip, transcribe it, write an .ass file, let ffmpeg burn it in. It broke three times, and each break taught me something narrower and more useful than the last. The third one is the interesting one, because my fix was wrong in a way that looked completely right. Break 1: hand-written timings never match…
The auto-captioner was misinterpreting the timing of clips, causing them to be out of sync. The initial approach of using hand-written timings resulted in mismatches due to differences in pacing between the AI-generated speech and human speech. The second issue arose when the transcript provided by the model, such as Whisper, contained inaccuracies like misheard words or missing words.
To resolve this, the solution was to prioritize the audio timing over the transcript and the script, by aligning the audio timing with the script's timings. However, even this solution had its flaws when dealing with sparse speech and long segments of dialogue. The third issue was that the ratio used to detect faulty timing was ineffective.
It failed to differentiate between a person speaking for six seconds versus one second of speech followed by five seconds of silence, as both would result in the same ratio. The root cause of the problem was that the ratio did not account for the speech rate, which is typically between 2 and 3 words per second. Therefore, a more accurate method would be to consider the speech rate instead of relying on duration alone.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
