Localize short videos with translation, subtitles, and dubbing workflows. Use for 出海 shorts, multilingual captioning, or voice-over localization.
Localizes short videos through translation, subtitle generation/embedding, and optional dubbing for international distribution.
Four tasks, run with the skill installed and again with skills disabled entirely, same task text both times. This is the one skill in this batch where the honest answer is that we could not measure much of a difference.
Both arms completed all four and the artifacts came out nearly identical in shape and size: beauty_serum_review_ja.srt at 532 B against 523 B, chiangmai_vlog_en.srt at 463 B against 489 B, three locale SRTs each for the French/German/Brazilian trio. Both independently made the same localization judgment in task 2 — converting 九块钱 to 9 RMB (~$1.25) so the price reads for an overseas audience — and both held one register across all three languages in task 3, rendering any founder as the same third-person generic in each. Cost tracked closely too: $0.15 / $0.15 / $0.17 / $0.22 with the skill against $0.11 / $0.10 / $0.12 / $0.21 without.
Two differences are real but small. The skill arm named the Brazilian file subtitles_pt-BR.srt where the control arm wrote subtitles_pt.srt — a locale tag that matters when a platform routes by it. And in task 4 the two arms disagreed about the same numbers: the skill arm flagged that cues 2, 3 and 4 will run slightly past their original windows at documentary pace and gave per-cue instructions for absorbing the overflow, while the control arm calculated 3.5–4.1 characters per second and concluded no adjustment was needed. Neither was verified against a recording.
The downstream artifact differs in kind rather than quality: the skill arm produced dubbing_cue_sheet.csv (1,180 B) for import into an edit timeline, the control arm produced dubbing_script_zh_recording.txt (621 B) with breath marks for the voice actor to read from.
What we did not test: the translations themselves. No native speaker of Japanese, French, German or Brazilian Portuguese reviewed a single line. All four tasks handed over clean, already-timestamped text, so the extract-and-transcribe front half of the pipeline never ran — what we exercised is the translate-and-time step only, and nothing was ever rendered onto a video.