Subtitle Correction Skill for Claude

Subtitle Correction Skill

This skill corrects speech recognition errors in subtitle files while strictly preserving timeline information.

Interactive Workflow

Step 1: Request Terminology from User

IMPORTANT: Before starting any correction, ALWAYS ask the user for domain-specific terms.

Prompt the user with:

在开始修正之前，请提供一些关键术语，帮助我更准确地识别和修正语音识别错误：

1. **专有名词**：人名、品牌名、产品名等（如：Anthropic、Claude）
2. **技术术语**：框架、库、工具名称（如：LangChain、OpenAI、PyTorch）
3. **领域词汇**：行业特定词汇（如：checkpointer、middleware、runtime）
4. **其他关键词**：视频中频繁出现的重要词汇

请用逗号分隔，例如：`LangChain, Agent, OpenAI, checkpointer`

For English users:

Before I begin correction, please provide key terms to help me accurately identify speech recognition errors:

1. **Proper nouns**: Names, brands, products (e.g., Anthropic, Claude)
2. **Technical terms**: Frameworks, libraries, tools (e.g., LangChain, PyTorch)
3. **Domain vocabulary**: Industry-specific terms (e.g., checkpointer, middleware)
4. **Other keywords**: Important words that appear frequently

Please separate with commas, e.g.: `LangChain, Agent, OpenAI, checkpointer`

Step 2: Confirm Understanding

After receiving terms, confirm by:

• Listing the terms received
• Identifying the likely domain/context (AI/ML tutorial, web dev, etc.)
• Asking if there are any additional terms before proceeding

Example response:

收到以下术语：
- 技术框架：LangChain, LangGraph, OpenAI
- 技术概念：Agent, checkpointer, runtime

看起来这是一个 LangChain 智能体开发的教程视频。

还有其他需要补充的术语吗？如果没有，我将开始修正字幕。

Step 3: Process with Terms

Use the provided terms to:

• Build a mental model of expected vocabulary
• Identify likely speech recognition errors
• Apply consistent corrections throughout

When User Doesn't Provide Terms

If user says "没有" / "no" / "直接开始":

• Proceed with correction using built-in patterns
• Flag uncertain corrections for user review
• After completion, ask if any terms were missed

Core Workflow

• Read the subtitle file - Load the .srt file provided by the user
• Identify error patterns - Recognize common speech recognition mistakes
• Apply corrections - Fix errors while preserving timestamps exactly
• Output corrected file - Return or save based on user's context

Strict Rules

Timeline Preservation

• NEVER modify timestamps - Keep all 00:00:00,000 --> 00:00:00,000 lines exactly as-is
• NEVER change subtitle numbering - Preserve sequence numbers
• NEVER merge or split subtitle entries - One-to-one correspondence

Error Categories

1. Phonetic Errors (同音字/谐音错误)

Common in Chinese speech recognition:

• 会话 ↔ 绘画 (huìhuà)
• 元数据 ↔ 源数据 (yuán shùjù)
• 本课 ↔ 本科 (běnkè)
• 示例 ↔ 事例 (shìlì)
• 实践 ↔ 时间 (shíjiàn)

2. Technical Term Errors

Speech recognition often fails on:

• Framework names: LangChain, LangGraph, OpenAI, PyTorch, TensorFlow
• Programming terms: API, SDK, runtime, checkpointer, middleware
• Code identifiers: snake_case names, function names, class names

3. English-Chinese Mixed Content

• Luncheon/lunch → langchain
• open EI/open Email → OpenAI
• land GRAPH → langgraph
• a memory Server → MemorySaver

4. Code-Related Terms

Convert spoken descriptions to proper format:

• "underscore" → "_" in variable names
• "dot" → "." in method calls
• Recognize camelCase, snake_case, PascalCase patterns

User-Provided Terminology

When users provide a terminology list, use it as the primary reference for corrections:

用户提供的术语：LangChain,Agent,OpenAI,LangGraph

These terms indicate:

• Expected proper spellings of technical terms
• Context about the content domain
• Hints for identifying speech recognition errors

Processing Strategy

For Long Files (>200 lines)

• Process in chunks using view_range parameter
• Maintain context across chunks
• Build complete corrected file incrementally

For Technical Content

• Identify the domain (AI/ML, web dev, etc.)
• Build mental model of expected terminology
• Apply domain-specific corrections consistently

Quality Checks

Before outputting:

• Verify all timestamps unchanged
• Verify subtitle count unchanged
• Check terminology consistency throughout
• Ensure no orphaned corrections (partial fixes)

Common Correction Patterns

Chinese AI/ML Course Content

| Error | Correction | Context |

|-------|------------|---------|

| 蓝犬/蓝卷/Lantern | LangChain | Framework name |

| 绘画 | 会话 | Session/conversation |

| 拖/tour | tool | Tool concept |

| checkpoint组件 | checkpointer组件 | Memory component |

| 源数据 | 元数据 | Metadata |

| 大约模型 | 大模型 | Large model |

| 中间键 | 中间件 | Middleware |

Code Identifiers

| Spoken | Written |

|--------|---------|

| user underscore 001 | user_001 |

| thread underscore id | thread_id |

| create underscore agent | create_agent |

| runtime dot state | runtime.state |

Output Format

When saving, use -corrected suffix:

• Input: filename.srt
• Output: filename-corrected.srt

Validation Script

Use scripts/subtitle_tool.py to validate and analyze subtitle files:

# Validate corrected file preserves structure
python scripts/subtitle_tool.py validate original.srt corrected.srt

# Show word-level diff with colored output (default, changes only)
python scripts/subtitle_tool.py diff original.srt corrected.srt

# Show ALL entries (changed and unchanged) in terminal
python scripts/subtitle_tool.py diff original.srt corrected.srt --all

# Generate HTML diff report (recommended for review)
python scripts/subtitle_tool.py diff original.srt corrected.srt --html report.html

# Show simple line-based diff (original/corrected lines)
python scripts/subtitle_tool.py diff original.srt corrected.srt --simple

# Disable colors for piping to files
python scripts/subtitle_tool.py diff original.srt corrected.srt --no-color

# Analyze file for potential speech recognition errors
python scripts/subtitle_tool.py analyze input.srt --terms "LangChain,OpenAI"

Diff Output Formats

Terminal Output (Default)

Shows word-level changes with colors:

[1] 00:00:01,500 --> 00:00:04,500
  今天我们来学习[-Luncheon-]{+LangChain+}框架

[3] 00:00:08,000 --> 00:00:12,500
  我们可以用它来创建[-绘画-]{+会话+}应用程序

• [-deleted-] - Text removed (shown in red in terminal)
• {+added+} - Text added (shown in green in terminal)
• Use --all to show unchanged entries too

HTML Report (Recommended for Review)

Generates a full comparison report with:

• All entries displayed (changed and unchanged)
• Side-by-side columns: Original | Corrected | Diff View
• Visual highlighting: Red strikethrough for deletions, green for additions
• Summary statistics: Total entries, changed count, unchanged count
• Jump links: Quick navigation to changed entries
• Change indicators: Yellow dot marks changed rows

python scripts/subtitle_tool.py diff original.srt corrected.srt --html diff_report.html

Then open diff_report.html in a browser.

Validation Workflow

• Before correction: Run analyze to identify potential errors
• After correction: Run validate to ensure structural integrity
• Review changes: Run diff --html report.html to generate a visual comparison report

Validation Checks

• Entry count matches original
• All timestamps unchanged
• All sequence numbers preserved
• No formatting corruption

Example Interaction

Interaction 1: User uploads file without terms

User: 帮我修正这个字幕文件

Claude:

在开始修正之前，请提供一些关键术语，帮助我更准确地识别语音识别错误：

1. **专有名词**：人名、品牌名、产品名
2. **技术术语**：框架、库、工具名称
3. **领域词汇**：行业特定词汇
4. **其他关键词**：视频中频繁出现的重要词汇

请用逗号分隔，例如：`LangChain, Agent, OpenAI`

User: LangChain, Agent, OpenAI, LangGraph, checkpointer

Claude:

收到以下术语：
- 技术框架：LangChain, LangGraph, OpenAI
- 技术概念：Agent, checkpointer

这是一个 LangChain 相关的技术教程。还有补充吗？

User: 没有了，开始吧

Claude: [Proceeds with correction workflow]

Interaction 2: User provides terms upfront

User: 帮我修正字幕，术语：LangChain, OpenAI, Agent

Claude: [Confirms terms, then proceeds directly]

Correction Process

• Read uploaded .srt file
• Run analyze to identify potential errors
• Apply corrections using provided terms as primary reference
• Run validate to confirm structural integrity
• Save corrected file with -corrected suffix
• Generate diff report and present summary of changes
• Offer HTML report: Ask user if they want an HTML diff report for easier review

Output: Provide categorized summary of corrections made.

After completion, prompt user:

修正完成！我可以生成一个 HTML 差异报告，方便您在浏览器中查看所有修改。
需要生成 HTML 报告吗？

Correction complete! I can generate an HTML diff report for easier review in your browser.
Would you like me to generate the HTML report?