lattifai/omni-captions-skills/omnicaptions-translate

Caption Translation

Default: Claude native translation (no API key needed)

Use Gemini API only when user explicitly requests it.

Default Workflow (Claude)

1. Read the caption file
2. Translate using Claude's native ability
3. Write output with _Claude_{lang} suffix

Gemini API (Optional)

Use CLI when user requests Gemini:

omnicaptions translate input.srt -l zh --bilingual

Output: input_Gemini_zh.srt

When to Use

• Translate SRT/VTT/ASS to another language
• Generate bilingual captions (original + translation)
• Translate YouTube video transcripts
• Need context-aware translation (not line-by-line)

When NOT to Use

• Need transcription (use /omnicaptions:transcribe)
• Just format conversion without translation (use /omnicaptions:convert)

Setup

pip install omni-captions-skills --extra-index-url https://lattifai.github.io/pypi/simple/

API Key

Priority: GEMINI_API_KEY env → .env file → ~/.config/omnicaptions/config.json

If not set, ask user: Please enter your Gemini API key (get from https://aistudio.google.com/apikey):

Then run with -k <key>. Key will be saved to config file automatically.

Context-Aware Translation

LLM-based translation is superior to traditional machine translation because it understands context across multiple lines:

Why Context Matters

How It Works

┌─────────────────────────────────────────┐
│  Batch size: 30 lines                   │
│  Context: 5 lines before/after          │
├─────────────────────────────────────────┤
│  [5 previous lines] → context           │
│  [30 current lines] → translate         │
│  [5 next lines]     → preview           │
└─────────────────────────────────────────┘

Benefits:

• Speaker continuity - maintains character voice
• Split sentences - handles dialogue spanning multiple lines
• Idioms & culture - adapts cultural references naturally
• Pronoun resolution - correct he/she/they based on context

Advanced Features

Bilingual Output

# Original + Translation (for language learning)
omnicaptions translate input.srt -l zh --bilingual

Output example:

1
00:00:01,000 --> 00:00:03,500
Welcome to the show.
欢迎来到节目。
 
2
00:00:03,500 --> 00:00:06,000
Thank you for having me.
感谢邀请我。

Custom Glossary (Coming Soon)

For domain-specific or branded content:

# Use glossary for consistent terminology
omnicaptions translate input.srt -l zh --glossary terms.json

Glossary format:

{
  "API": "接口",
  "Token": "令牌",
  "Machine Learning": "机器学习"
}

Benefits:

• Terminology consistency - "one term, one translation"
• Brand compliance - use official product names
• Domain accuracy - medical, legal, technical terms

Best Practices

1. Provide Context for Better Quality

For specialized content, use custom prompts:

from omnicaptions import GeminiCaption
gc = GeminiCaption()
gc._translation_prompt = """
You are translating captions for a medical documentary.
Use formal Chinese medical terminology.
Glossary: {glossary}
"""
gc.translate("input.srt", "output.srt", "zh")

2. Choose the Right Model

3. Review Bilingual Output

Bilingual captions let viewers verify translation quality - ideal for:

• Language learners
• Quality assurance
• Accessibility

CLI Usage

# Translate (auto-output to same directory)
omnicaptions translate input.srt -l zh         # → ./input_Gemini_zh.srt
 
# Specify output file or directory
omnicaptions translate input.srt -o output/ -l zh   # → output/input_Gemini_zh.srt
omnicaptions translate input.srt -o zh.srt -l zh    # → zh.srt
 
# Bilingual output (original + translation)
omnicaptions translate input.srt -l zh --bilingual
 
# Specify model
omnicaptions translate input.vtt -l ja -m gemini-3-pro-preview

Language Codes

Supported Formats

All formats from lattifai-captions: SRT, VTT, ASS, TTML, JSON, Gemini MD, etc.

Common Mistakes

References

• Caption LLM Translator - Context window approach
• Caption Translator - Batch processing
• Captions.Translate.Agent - Multi-agent workflow

Related Skills

Workflow Examples

Important: Generate bilingual captions AFTER LaiCut alignment.

File naming convention - preserve language tag and processing chain:

video.en.vtt → video.en_LaiCut.json → video.en_LaiCut.srt → video.en_LaiCut_Claude_zh.srt → video.en_LaiCut_Claude_zh_Color.ass

# 1. LaiCut 对齐 (保留词级时间)
omnicaptions LaiCut video.mp4 video.en.vtt
# → video.en_LaiCut.json
 
# 2. 转换为 SRT (翻译用，文件小)
omnicaptions convert video.en_LaiCut.json -o video.en_LaiCut.srt
 
# 3a. Claude 翻译 (默认)
# → video.en_LaiCut_Claude_zh.srt
 
# 3b. 或 Gemini 翻译
omnicaptions translate video.en_LaiCut.srt -l zh --bilingual
# → video.en_LaiCut_Gemini_zh.srt
 
# 4. 转换为带颜色的 ASS
omnicaptions convert video.en_LaiCut_Claude_zh.srt -o video.en_LaiCut_Claude_zh_Color.ass \
  --line1-color "#00FF00" --line2-color "#FFFF00"

Large JSON Files

LaiCut outputs JSON with word-level timing. For translation, convert to SRT first (much smaller):

# JSON (word-level, ~150KB) → SRT (segment-level, ~15KB)
omnicaptions convert video.en_LaiCut.json -o video.en_LaiCut.srt

Why? JSON preserves word timing for karaoke, but translation only needs segment text. SRT is 10-20x smaller.

Claude Translation Rules (Default)

1. Preserve format exactly - Keep all timing codes, formatting tags, style definitions
2. Context-aware - Consider surrounding lines for coherent dialogue
3. Speaker consistency - Maintain character voice and tone
4. Cultural adaptation - Adapt idioms and references naturally
5. Large files - Process in batches of 100 lines to maintain quality

Claude vs Gemini