135a1873af
New scripts: - fix_transcript_timestamps.py: Repair malformed timestamps (HH:MM:SS format) - split_transcript_sections.py: Split transcript by keywords and rebase timestamps - Automated tests for both scripts Features: - Timestamp validation and repair (handle missing colons, invalid ranges) - Section splitting with custom names - Rebase timestamps to 00:00:00 for each section - Preserve speaker format and content integrity - In-place editing with backup Documentation updates: - Add usage examples to SKILL.md - Clarify dictionary iteration workflow (save stable patterns only) - Update workflow guides with new script references - Add script parameter documentation Use cases: - Fix ASR output with broken timestamps - Split long meetings into focused sections - Prepare sections for independent processing Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
134 lines
4.7 KiB
Markdown
134 lines
4.7 KiB
Markdown
# Dictionary Iteration Workflow
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The core value of transcript-fixer is building a personalized correction dictionary that improves over time.
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## The Core Loop
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```
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┌─────────────────────────────────────────────────┐
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│ 1. Fix transcript (manual or Stage 3) │
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│ ↓ │
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│ 2. Identify new ASR errors during fixing │
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│ ↓ │
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│ 3. IMMEDIATELY save to dictionary │
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│ ↓ │
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│ 4. Next time: Stage 1 auto-corrects these │
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└─────────────────────────────────────────────────┘
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```
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**Key principle**: Every stable, reusable ASR correction you make should be saved to the dictionary. This transforms one-time work into permanent value without polluting the database.
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## Workflow Checklist
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Copy this checklist when correcting transcripts:
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```
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Correction Progress:
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- [ ] Run correction: --input file.md --stage 3
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- [ ] Review output file for remaining ASR errors
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- [ ] Fix errors manually with Edit tool
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- [ ] Save EACH correction to dictionary with --add
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- [ ] Verify with --list that corrections were saved
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- [ ] Next time: Stage 1 handles these automatically
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```
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## Save Corrections Immediately
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After fixing any transcript, save stable corrections:
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```bash
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# Single correction
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uv run scripts/fix_transcription.py --add "错误词" "正确词" --domain general
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# Multiple corrections - run command for each
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uv run scripts/fix_transcription.py --add "片片总" "翩翩总" --domain general
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uv run scripts/fix_transcription.py --add "姐弟" "结业" --domain general
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uv run scripts/fix_transcription.py --add "自杀性" "自嗨性" --domain general
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uv run scripts/fix_transcription.py --add "被看" "被砍" --domain general
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uv run scripts/fix_transcription.py --add "单反过" "单访过" --domain general
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```
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## Verify Dictionary
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Always verify corrections were saved:
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```bash
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# List all corrections in current domain
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uv run scripts/fix_transcription.py --list
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# Direct database query
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sqlite3 ~/.transcript-fixer/corrections.db \
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"SELECT from_text, to_text, domain FROM active_corrections ORDER BY added_at DESC LIMIT 10;"
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```
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## Domain Selection
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Choose the right domain for corrections:
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| Domain | Use Case |
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|--------|----------|
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| `general` | Common ASR errors, names, general vocabulary |
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| `embodied_ai` | 具身智能、机器人、AI 相关术语 |
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| `finance` | 财务、投资、金融术语 |
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| `medical` | 医疗、健康相关术语 |
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| `火星加速器` | Custom Chinese domain name (any valid name works) |
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```bash
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# Domain-specific correction
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uv run scripts/fix_transcription.py --add "股价系统" "框架系统" --domain embodied_ai
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uv run scripts/fix_transcription.py --add "片片总" "翩翩总" --domain 火星加速器
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```
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## Common ASR Error Patterns
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Build your dictionary with these common patterns:
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| Type | Examples |
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|------|----------|
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| **Homophones** | 赢→营, 减→剪, 被看→被砍, 营业→营的 |
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| **Names** | 片片→翩翩, 亮亮→亮哥 |
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| **Technical** | 巨升智能→具身智能, 股价→框架 |
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| **English** | log→vlog |
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| **Broken words** | 姐弟→结业, 单反→单访 |
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## When GLM API Fails
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If you see `[CLAUDE_FALLBACK]` output, the GLM API is unavailable.
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Steps:
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1. Claude Code should analyze the text directly for ASR errors
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2. Fix using Edit tool
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3. **MUST save corrections to dictionary** - this is critical
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4. Dictionary corrections work even without AI
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## Auto-Learning Feature
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After running Stage 3 multiple times:
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```bash
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# Check learned patterns
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uv run scripts/fix_transcription.py --review-learned
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# Approve high-confidence patterns
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uv run scripts/fix_transcription.py --approve "错误词" "正确词"
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```
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Patterns appearing ≥3 times at ≥80% confidence are suggested for review.
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## Best Practices
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1. **Save immediately**: Don't batch corrections - save each one right after fixing
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2. **Be specific**: Use exact phrases, not partial words
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3. **Use domains**: Organize corrections by topic for better precision
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4. **Verify**: Always run --list to confirm saves
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5. **Review suggestions**: Periodically check --review-learned for auto-detected patterns
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## What NOT to Save to Dictionary
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Do **not** save these as reusable dictionary entries:
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- Full-sentence deletions
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- One-off section headers or meeting-specific boilerplate
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- Context-only disambiguations such as `cloud -> Claude` when `cloud` can also be legitimate
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- File-local cleanup after section splitting or timestamp rebasing
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