Update transcript-fixer guidance and hook paths
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daymade
@@ -17,15 +17,21 @@ All scripts use PEP 723 inline metadata — `uv run` auto-installs dependencies.
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# First time: Initialize database
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uv run scripts/fix_transcription.py --init
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# Phase 1: Dictionary corrections (instant, free)
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# Single file
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uv run scripts/fix_transcription.py --input meeting.md --stage 1
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# Batch: multiple files in parallel (use shell loop)
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for f in /path/to/*.txt; do
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uv run scripts/fix_transcription.py --input "$f" --stage 1
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done
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```
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After Stage 1, Claude reads the output and fixes remaining ASR errors natively (no API key needed):
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1. Read Stage 1 output in ~200-line chunks using the Read tool
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2. Identify ASR errors — homophones, garbled terms, broken sentences
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3. Present corrections in a table for user review before applying
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4. Save stable patterns to dictionary for future reuse
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1. Read all Stage 1 outputs — read **entire** transcript before proposing corrections (later context disambiguates earlier errors)
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2. Identify ASR errors — compile all corrections across files
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3. Apply fixes with sed in batch, verify each with diff
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4. Finalize: rename `_stage1.md` → `.md`, delete original `.txt`
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5. Save stable patterns to dictionary for future reuse
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See `references/example_session.md` for a concrete input/output walkthrough.
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@@ -51,6 +57,25 @@ Two-phase pipeline with persistent learning:
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**After fixing, always save reusable corrections to dictionary.** This is the skill's core value — see `references/iteration_workflow.md` for the complete checklist.
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### Dictionary Addition After Fixing
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After native AI correction, review all applied fixes and decide which to save. Use this decision matrix:
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| Pattern type | Example | Action |
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|-------------|---------|--------|
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| Non-word → correct term | 克劳锐→Claude, cloucode→Claude Code | ✅ Add (zero false positive risk) |
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| Rare word → correct term | 潜彩→前采, 维星→韦青 | ✅ Add (verify it's not a real word first) |
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| Person/company name ASR error | 宋天航→宋天生, 策马攀山→策马看山 | ✅ Add (stable, unique) |
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| Common word → context word | 争→蒸, 钱财→前采, 报纸→标品 | ❌ Skip (high false positive risk) |
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| Real brand → different brand | Xcode→Claude Code, Clover→Claude | ❌ Skip (real words in other contexts) |
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Batch add multiple corrections in one session:
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```bash
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uv run scripts/fix_transcription.py --add "错误1" "正确1" --domain tech
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uv run scripts/fix_transcription.py --add "错误2" "正确2" --domain business
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# Chain with && for efficiency
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```
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## False Positive Prevention
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Adding wrong dictionary rules silently corrupts future transcripts. **Read `references/false_positive_guide.md` before adding any correction rule**, especially for short words (≤2 chars) or common Chinese words that appear correctly in normal text.
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@@ -59,21 +84,46 @@ Adding wrong dictionary rules silently corrupts future transcripts. **Read `refe
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When running inside Claude Code, use Claude's own language understanding for Phase 2:
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1. Run Stage 1 (dictionary): `--input file.md --stage 1`
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1. Run Stage 1 (dictionary) on all files (parallel if multiple)
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2. Verify Stage 1 — diff original vs output. If dictionary introduced false positives, work from the **original** file
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3. Read the full text in ~200-line chunks. Read the entire transcript before proposing corrections — later context often disambiguates earlier errors
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4. Identify ASR errors:
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- Product/tool names: "close code" → "Claude Code", "get Hub" → "GitHub"
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- Technical terms: "Web coding" → "Vibe Coding", "happy pass" → "happy path"
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- Homophone errors: "上海文" → "上下文", "分值" → "分支"
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- English ASR garbling: "Pre top" → "prototype", "rapper" → "repo"
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- Broken sentences: "很大程。路上" → "很大程度上"
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5. Present corrections in high/medium confidence tables with line numbers
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6. Apply with sed on a copy, verify with diff, replace original
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7. Generate word diff: `uv run scripts/generate_word_diff.py original.md corrected.md diff.html`
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8. Save stable patterns to dictionary
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3. Read **all** Stage 1 outputs fully before proposing any corrections — later context often disambiguates earlier errors. For large files (>10k tokens), read in chunks but finish the entire file before identifying errors
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4. Identify ASR errors per file — classify by confidence:
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- **High confidence** (apply directly): non-words, obvious garbling, product name variants
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- **Medium confidence** (present for review): context-dependent homophones, person names
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5. Apply fixes efficiently:
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- **Global replacements** (unique non-words like "克劳锐"→"Claude"): use `sed -i ''` with `-e` flags, multiple patterns in one command
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- **Context-dependent** (common words like "争"→"蒸" only in distillation context): use sed with longer context phrases for uniqueness, or Edit tool
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6. Verify with diff: `diff original.txt corrected_stage1.md`
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7. Finalize files: rename `*_stage1.md` → `*.md`, delete original `.txt`
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8. Save stable patterns to dictionary (see "Dictionary Addition" below)
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9. Remove false positives if Stage 1 had any
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### Common ASR Error Patterns
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AI product names are frequently garbled. These patterns recur across transcripts:
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| Correct term | Common ASR variants |
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|-------------|-------------------|
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| Claude | cloud, Clou, calloc, 克劳锐, Clover, color |
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| Claude Code | cloud code, Xcode, call code, cloucode, cloudcode, color code |
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| Claude Agent SDK | cloud agent SDK |
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| Opus | Opaas |
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| Vibe Coding | web coding, Web coding |
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| GitHub | get Hub, Git Hub |
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| prototype | Pre top |
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Person names and company names also produce consistent ASR errors across sessions — always add confirmed name corrections to the dictionary.
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### Efficient Batch Fix Strategy
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When fixing multiple files (e.g., 5 transcripts from one day):
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1. **Stage 1 in parallel**: run all files through dictionary at once
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2. **Read all files first**: build a mental model of speakers, topics, and recurring terms before fixing anything
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3. **Compile a global correction list**: many errors repeat across files from the same session (same speakers, same topics)
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4. **Apply global corrections first** (sed with multiple `-e` flags), then per-file context-dependent fixes
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5. **Verify all diffs**, finalize all files, then do one dictionary addition pass
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### Enhanced Capabilities (Native Mode Only)
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- **Intelligent paragraph breaks**: Add `\n\n` at logical topic transitions
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