From aviz85-claude-skills-library
End-to-end video subtitle translation pipeline: transcribe audio, translate to any language, refine subtitles, and embed into video. Supports RTL languages like Hebrew.
How this skill is triggered — by the user, by Claude, or both
Slash command
/aviz85-claude-skills-library:translate-video [video-path] [target-language] [--shorts|--regular][video-path] [target-language] [--shorts|--regular]The summary Claude sees in its skill listing — used to decide when to auto-load this skill
End-to-end video translation pipeline: transcribe → translate → refine subtitles → embed.
End-to-end video translation pipeline: transcribe → translate → refine subtitles → embed.
/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$3 — --shorts (TikTok/Reels) or --regular (YouTube/tutorials). If omitted, ask the user.If audio > 25MB, extract first:
ffmpeg -i "$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 "$BASENAME.srt" --json
Produces: {basename}.srt, {basename}.md, {basename}_transcript.json
Read .md for full context. Translate the .srt — preserve all timestamps and index numbers exactly. See translation rules in references/modes.md.
Read references/modes.md for full rules.
--shorts: Fix text only, preserve all timestamps. No merging.
--regular: Merge into full sentences using word-level timestamps.
.md (word counts per group)GROUP_SIZES in scripts/build-timestamps.py and run itEnforces MAX 2 lines, MAX chars/line (38 for --shorts, 42 for --regular):
python3 ~/.claude/skills/translate-video/scripts/postprocess.py "$SRT" 42
python3 ~/.claude/skills/translate-video/scripts/rtl-fix.py "$SRT"
~/.local/bin/ffmpeg-ass -i "$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.
| 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 |
| 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 |
npx claudepluginhub aviz85/claude-skills-library --plugin ten-by-tenTranslates video subtitles to any language (e.g., Hebrew, Arabic) via pipeline: transcribe audio, translate with context, refine semantically, embed RTL-safe subtitles.
Generates SRT subtitles from video/audio with transcription (Hebrew via ivrit.ai, English via Whisper) and translation. Supports burning subtitles into video for social media.
Generates SRT subtitles from video/audio with transcription (Hebrew via ivrit.ai, English via Whisper) and translation. Supports burning subtitles into video for social media.