translate-ppt

name	translate-ppt
version	1.0.0
description	Translate Chinese PowerPoint presentations to English while preserving all images, charts, shapes, and media content. Adjusts fonts to Calibri and optimizes layout for professional business presentations. Use when the user asks to translate a PPT/PPTX file from Chinese to English, or mentions PPT translation, slide translation, or presentation localization.
requires	{"tools":["python3"]}
triggers	["translate PPT","translate PPTX","translate PowerPoint","translate slides","Chinese to English PPT","presentation translation","slide translation","PPT localization"]

Translate PPT

Translate Chinese PowerPoint presentations (.pptx) to English with professional business styling.

Overview

This skill translates Chinese PPTX files to English using any OpenAI-compatible LLM endpoint (local or cloud). It preserves all non-text content while:

• Preserving all non-text content (images, charts, shapes, tables, SmartArt, media)
• Adjusting fonts to Calibri family for consistent business styling
• Optimizing text box sizing and layout for English text (typically longer than Chinese)
• Maintaining original slide masters, layouts, animations, and transitions

Prerequisites

• Python 3.8+
• Required packages: python-pptx, requests
• An LLM endpoint that supports OpenAI-compatible API (e.g., Qoderwork built-in models, Ollama, OpenAI, DeepSeek, etc.)

Quick Start

Option A: Use with Qoderwork (Easiest — No Extra Setup)

If you're running this skill within Qoderwork, models are already available. Just run:

pip install python-pptx requests
python .qoder/skills/translate-ppt/scripts/translate_ppt.py input.pptx --api-base <qoderwork-endpoint> --model <available-model>

Option B: Use with Local Ollama

1. Install Ollama: Download and install from https://ollama.com

2. Pull a recommended model:

   ollama pull qwen2.5:14b
   

3. Install Python dependencies:

   pip install python-pptx requests
   

4. Run translation:

   python .qoder/skills/translate-ppt/scripts/translate_ppt.py input.pptx
   

   If output path is not specified, defaults to <input_name>_en.pptx.

Option C: Use with Cloud API

Run with your cloud endpoint:

python .qoder/skills/translate-ppt/scripts/translate_ppt.py input.pptx --api-base https://api.openai.com/v1 --api-key sk-xxx --model gpt-4o

Translation Rules

• Translate: All text content (titles, body text, notes, table cells, grouped shape text)
• Preserve: Images, charts data, embedded media, hyperlinks, original formatting
• Mixed content: Only translate Chinese portions of mixed Chinese/English text

Font & Layout Adjustments

Element	Font	Style
Titles	Calibri	Bold
Body text	Calibri	Regular

• Maintain original font sizes (with auto-shrink if text overflows)
• Adjust text box width up to 20% if English text is significantly longer
• Preserve original color scheme and text formatting (bold, italic, underline)

Business Style Guidelines

• Consistent Calibri font family throughout
• Clean, professional spacing
• Preserved slide master/layout templates
• All animations and transitions intact

Recommended Models

Translation quality varies significantly between models. Choose based on your setup:

Note for Qoderwork users: You can use whatever models are already configured in your client environment — no additional setup required.

Model	Size	Quality	Speed	Command
qwen2.5:14b	~9GB	★★★★★ Best for Chinese	Fast	ollama pull qwen2.5:14b
qwen2.5:7b	~4.7GB	★★★★ Good balance	Faster	ollama pull qwen2.5:7b
llama3.1:8b	~4.7GB	★★★ Decent	Fast	ollama pull llama3.1:8b
gemma2:9b	~5.4GB	★★★ Decent	Fast	ollama pull gemma2:9b
qwen2.5:3b	~2GB	★★ Basic	Fastest	ollama pull qwen2.5:3b

Tip: For best results with Chinese-to-English business content, Qwen2.5 14B is strongly recommended as it has excellent Chinese language understanding. Smaller models may produce less accurate or less natural translations.

Command-Line Options

Option	Description	Default
--font	Override default font	Calibri
--model	LLM model to use	qwen2.5:14b
--api-base	OpenAI-compatible API base URL	http://localhost:11434/v1
--api-key	API key (optional, not needed for local models)	None
--batch-size	Text segments per API call	20
--verbose, -v	Enable detailed logging	False

Troubleshooting

Issue	Solution
Connection refused	Check your API endpoint URL. For Ollama: ensure ollama serve is running. For cloud APIs: verify the URL is correct.
Model not found	Verify the model name is correct for your endpoint. For Ollama: ollama pull <model>
Corrupt PPTX	Verify file opens in PowerPoint; try saving as new file first
Font not found	Ensure Calibri is installed on your system
API rate limits	Reduce --batch-size or add delay between calls

Reference

See reference.md for detailed API documentation and architecture.