bd0aa12004
## New Skill: transcript-fixer v1.0.0 Correct speech-to-text (ASR/STT) transcription errors through dictionary-based rules and AI-powered corrections with automatic pattern learning. **Features:** - Two-stage correction pipeline (dictionary + AI) - Automatic pattern detection and learning - Domain-specific dictionaries (general, embodied_ai, finance, medical) - SQLite-based correction repository - Team collaboration with import/export - GLM API integration for AI corrections - Cost optimization through dictionary promotion **Use cases:** - Correcting meeting notes, lecture recordings, or interview transcripts - Fixing Chinese/English homophone errors and technical terminology - Building domain-specific correction dictionaries - Improving transcript accuracy through iterative learning **Documentation:** - Complete workflow guides in references/ - SQL query templates - Troubleshooting guide - Team collaboration patterns - API setup instructions **Marketplace updates:** - Updated marketplace to v1.8.0 - Added transcript-fixer plugin (category: productivity) - Updated README.md with skill description and use cases - Updated CLAUDE.md with skill listing and counts 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
103 lines
3.0 KiB
Python
103 lines
3.0 KiB
Python
#!/usr/bin/env python3
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"""
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Change extraction and summarization
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SINGLE RESPONSIBILITY: Extract and summarize changes between text versions
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"""
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from __future__ import annotations
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import difflib
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from .text_splitter import split_into_words
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def extract_changes(original: str, fixed: str) -> list[dict]:
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"""
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Extract all changes and return change list
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Args:
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original: Original text
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fixed: Fixed text
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Returns:
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List of change dictionaries with type, context, and content
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"""
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original_words = split_into_words(original)
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fixed_words = split_into_words(fixed)
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diff = difflib.SequenceMatcher(None, original_words, fixed_words)
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changes = []
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for tag, i1, i2, j1, j2 in diff.get_opcodes():
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if tag == 'replace':
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original_text = ''.join(original_words[i1:i2])
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fixed_text = ''.join(fixed_words[j1:j2])
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changes.append({
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'type': 'replace',
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'original': original_text,
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'fixed': fixed_text,
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'context_before': ''.join(original_words[max(0, i1-5):i1]),
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'context_after': ''.join(original_words[i2:min(len(original_words), i2+5)])
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})
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elif tag == 'delete':
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original_text = ''.join(original_words[i1:i2])
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changes.append({
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'type': 'delete',
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'original': original_text,
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'fixed': '',
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'context_before': ''.join(original_words[max(0, i1-5):i1]),
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'context_after': ''.join(original_words[i2:min(len(original_words), i2+5)])
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})
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elif tag == 'insert':
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fixed_text = ''.join(fixed_words[j1:j2])
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changes.append({
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'type': 'insert',
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'original': '',
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'fixed': fixed_text,
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'context_before': ''.join(fixed_words[max(0, j1-5):j1]) if j1 > 0 else '',
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'context_after': ''.join(fixed_words[j2:min(len(fixed_words), j2+5)])
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})
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return changes
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def generate_change_summary(changes: list[dict]) -> str:
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"""
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Generate change summary
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Args:
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changes: List of change dictionaries
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Returns:
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Formatted summary string
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"""
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result = []
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result.append("=" * 80)
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result.append(f"修改摘要 (共 {len(changes)} 处修改)")
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result.append("=" * 80)
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result.append("")
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for i, change in enumerate(changes, 1):
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change_type = {
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'replace': '替换',
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'delete': '删除',
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'insert': '添加'
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}[change['type']]
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result.append(f"[{i}] {change_type}")
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if change['original']:
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result.append(f" 原文: {change['original']}")
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if change['fixed']:
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result.append(f" 修复: {change['fixed']}")
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# Show context
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context = change['context_before'] + "【修改处】" + change['context_after']
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if context.strip():
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result.append(f" 上下文: ...{context}...")
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result.append("")
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return '\n'.join(result)
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