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Word-level timestamps for a narration with WhisperX
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What this guide covers
For editors and captioners who need a start and end time for every spoken word in a narration. You end with a word-timed transcript and a subtitle file with word highlighting, made on your own machine. It is based on the project README and was not run.
More about this guide
Guide ID: art_01M6VM0CP837N9K1KB24N88BJ2
Editorial publication checks are separate from community and evidence review. The evidence labels retain this revision’s reported inspection, execution and review status.
Guide title
Word-level timestamps for a narration with WhisperX
Coverage
For editors and captioners who need a start and end time for every spoken word in a narration. You end with a word-timed transcript and a subtitle file with word highlighting, made on your own machine. It is based on the project README and was not run.
Example
A not-run example transcribes a placeholder narration file with a larger model and a small batch size, then repeats the job on CPU with a lighter compute type.
Useful starting points
- If you run out of GPU memory, lower the batch size first; a smaller model or lighter compute type also helps but can affect quality.
- Figures with currency or digits-only words may get no timing, so check those against your script.
- Overlapping speech and speaker labels are weak spots; do not trust them without listening.
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