aviz85/translate-video
skills/translate-video/SKILL.md
Translate video subtitles to any language with native-quality refinement. Full pipeline: transcribe → translate → refine → embed RTL-safe subtitles. Use for: translate video, תרגם סרטון, video translation, foreign subtitles, Hebrew subtitles, translated captions.
$ install --global
skillsauthnpx skillsauth add aviz85/claude-skills-library translate-videoInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Last scanned: Apr 3, 2026, 12:15 PM16.0s7 files scanned
SKILL.md
- name:
- translate-video
- description:
- Translate video subtitles to any language with native-quality refinement. Full pipeline: transcribe → translate → refine → embed RTL-safe subtitles. Use for: translate video, תרגם סרטון, video translation, foreign subtitles, Hebrew subtitles, translated captions.
- argument-hint:
- [video-path] [target-language] [--shorts|--regular]
Translate Video
End-to-end video translation pipeline: transcribe → translate → refine subtitles → embed.
Usage
/translate-video /path/to/video.mp4 he --regular
/translate-video /path/to/video.mp4 he --shorts
$1— video file path (required)$2— target language code (default:he). See references/languages.md$3—--shorts(TikTok/Reels) or--regular(YouTube/tutorials). If omitted, ask the user.
Pipeline
Step 1: Transcribe
If audio > 25MB, extract first:
ffmpeg -i "$VIDEO" -vn -acodec libmp3lame -ab 128k "$AUDIO.mp3" -y
Transcribe with word-level JSON (always include --json):
cd ~/.claude/skills/transcribe/scripts && [ -d node_modules ] || npm install --silent
npx ts-node transcribe.ts -i "$INPUT" -o "$BASENAME.srt" --json
Produces: {basename}.srt, {basename}.md, {basename}_transcript.json
Step 2: Translate
Read .md for full context. Translate the .srt — preserve all timestamps and index numbers exactly. See translation rules in references/modes.md.
Step 3: Refine Subtitles
Read references/modes.md for full rules.
--shorts: Fix text only, preserve all timestamps. No merging.
--regular: Merge into full sentences using word-level timestamps.
- Plan subtitle groups from
.md(word counts per group) - Fill
GROUP_SIZESin scripts/build-timestamps.py and run it - Replace English text in output SRT with translated text
Step 4: Post-process (both modes)
Enforces MAX 2 lines, MAX chars/line (38 for --shorts, 42 for --regular):
python3 ~/.claude/skills/translate-video/scripts/postprocess.py "$SRT" 42
Step 5: RTL Fix (Hebrew / Arabic / Farsi only)
python3 ~/.claude/skills/translate-video/scripts/rtl-fix.py "$SRT"
Step 6: Embed & Open
~/.local/bin/ffmpeg-ass -i "$VIDEO" \
-vf "subtitles=$SRT:force_style='FontSize=24,PrimaryColour=&H00FFFFFF,OutlineColour=&H00000000,Outline=2,Shadow=1,Alignment=2,MarginV=30'" \
-c:v libx264 -preset fast -crf 23 -c:a copy "$OUTPUT" -y
open "$OUTPUT"
⚠️ Do NOT use Docker ffmpeg for long videos on ARM Mac — x86 emulation is ~100x slower.
Output Files
| File | Description |
|------|-------------|
| {name}.srt | Original language SRT |
| {name}.md | Readable transcript |
| {name}_transcript.json | Word-level timestamps |
| {name}_{lang}.srt | Translated + refined SRT |
| {name}_{lang}_subtitled.mp4 | Final video |
Supporting Files
| File | Purpose | |------|---------| | references/modes.md | Detailed --shorts and --regular rules | | references/languages.md | Language codes + RTL flags | | scripts/build-timestamps.py | Word-index cursor for --regular timestamps | | scripts/postprocess.py | Enforce line limits on any SRT | | scripts/rtl-fix.py | Apply RTL Unicode markers |
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