5c9eda4fbd
asr-transcribe-to-text: - Add local MLX transcription path (macOS Apple Silicon, 15-27x realtime) - Add bundled script transcribe_local_mlx.py with max_tokens=200000 - Add local_mlx_guide.md with benchmarks and truncation trap docs - Auto-detect platform and recommend local vs remote mode - Fix audio extraction format (MP3 → WAV 16kHz mono PCM) - Add Step 5: recommend transcript-fixer after transcription transcript-fixer: - Optimize SKILL.md from 289 → 153 lines (best practices compliance) - Move FALSE_POSITIVE_RISKS (40 lines) to references/false_positive_guide.md - Move Example Session to references/example_session.md - Improve description for better triggering (226 → 580 chars) - Add handoff to meeting-minutes-taker skill-creator: - Add "Pipeline Handoff" pattern to Skill Writing Guide - Add pipeline check reminder in Step 4 (Edit the Skill) Pipeline handoffs added to 8 skills forming 6 chains: - youtube-downloader → asr-transcribe-to-text → transcript-fixer → meeting-minutes-taker → pdf/ppt-creator - deep-research → fact-checker → pdf/ppt-creator - doc-to-markdown → docs-cleaner / fact-checker - claude-code-history-files-finder → continue-claude-work Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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False Positive Prevention Guide
Dictionary-based corrections are powerful but dangerous. Adding the wrong rule silently corrupts every future transcript. The --add command runs safety checks automatically, but you must understand the risks.
What is safe to add
- ASR-specific gibberish: "巨升智能" -> "具身智能" (no real word sounds like "巨升智能")
- Long compound errors: "语音是别" -> "语音识别" (4+ chars, unlikely to collide)
- English transliteration errors: "japanese 3 pro" -> "Gemini 3 Pro"
What is NEVER safe to add
- Common Chinese words: "仿佛", "正面", "犹豫", "传说", "增加", "教育" -- these appear correctly in normal text. Replacing them corrupts transcripts from better ASR models.
- Words <=2 characters: Almost any 2-char Chinese string is a valid word or part of one. "线数" inside "产线数据" becomes "产线束据".
- Both sides are real words: "仿佛->反复", "犹豫->抑郁" -- both forms are valid Chinese. The "error" is only an error for one specific ASR model.
When in doubt, use a context rule instead
Context rules use regex patterns that match only in specific surroundings, avoiding false positives:
# Instead of: --add "线数" "线束"
# Use a context rule in the database:
sqlite3 ~/.transcript-fixer/corrections.db "INSERT INTO context_rules (pattern, replacement, description, priority) VALUES ('(?<!产)线数(?!据)', '线束', 'ASR: 线数->线束 (not inside 产线数据)', 10);"
Auditing the dictionary
Run --audit periodically to scan all rules for false positive risks:
uv run scripts/fix_transcription.py --audit
uv run scripts/fix_transcription.py --audit --domain manufacturing
Forcing a risky addition
If you understand the risks and still want to add a flagged rule:
uv run scripts/fix_transcription.py --add "仿佛" "反复" --domain general --force