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>
105 lines
2.5 KiB
Python
105 lines
2.5 KiB
Python
#!/usr/bin/env python3
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"""
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Markdown report generator
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SINGLE RESPONSIBILITY: Generate detailed Markdown comparison report
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"""
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from __future__ import annotations
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from datetime import datetime
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from pathlib import Path
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from .change_extractor import extract_changes, generate_change_summary
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def generate_markdown_report(
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original_file: str,
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stage1_file: str,
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stage2_file: str,
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original: str,
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stage1: str,
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stage2: str
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) -> str:
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"""
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Generate comprehensive Markdown comparison report
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Args:
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original_file: Original file path
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stage1_file: Stage 1 file path
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stage2_file: Stage 2 file path
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original: Original text content
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stage1: Stage 1 text content
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stage2: Stage 2 text content
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Returns:
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Formatted Markdown report string
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"""
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original_path = Path(original_file)
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stage1_path = Path(stage1_file)
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stage2_path = Path(stage2_file)
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# Extract changes for each stage
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changes_stage1 = extract_changes(original, stage1)
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changes_stage2 = extract_changes(stage1, stage2)
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changes_total = extract_changes(original, stage2)
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# Generate summaries
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summary_stage1 = generate_change_summary(changes_stage1)
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summary_stage2 = generate_change_summary(changes_stage2)
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summary_total = generate_change_summary(changes_total)
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# Build report
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report = f"""# 会议记录修复对比报告
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## 文件信息
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- **原始文件**: {original_path.name}
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- **阶段1修复**: {stage1_path.name}
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- **阶段2修复**: {stage2_path.name}
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- **生成时间**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
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## 修改统计
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| 阶段 | 修改数量 | 说明 |
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|------|---------|------|
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| 阶段1: 词典修复 | {len(changes_stage1)} | 基于预定义词典的批量替换 |
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| 阶段2: AI修复 | {len(changes_stage2)} | GLM-4.6智能纠错 |
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| **总计** | **{len(changes_total)}** | **原始→最终版本** |
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---
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# 阶段1: 词典修复详情
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{summary_stage1}
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---
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# 阶段2: AI智能修复详情
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{summary_stage2}
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---
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# 总体修改详情 (原始→最终)
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{summary_total}
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---
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## 使用说明
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1. **查看修改**: 每处修改都包含上下文,便于理解修改原因
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2. **人工审核**: 重点审核标记为"替换"的修改
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3. **专业术语**: 特别注意公司名、人名、技术术语的修改
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## 建议审核重点
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- [ ] 专业术语(具身智能、机器人等)
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- [ ] 人名和公司名
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- [ ] 数字(金额、时间等)
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- [ ] 上下文是否通顺
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"""
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return report
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