transcript-fixer by daymade

---
name: transcript-fixer
description: Corrects speech-to-text transcription errors using dictionary rules and AI-powered analysis. Builds personalized correction databases that learn from each fix. Triggers when working with ASR/STT output containing recognition errors, homophones, garbled technical terms, or Chinese/English mixed content. Also triggers on requests to clean up meeting notes, lecture transcripts, interview recordings, or any text produced by speech recognition. Use this skill even when the user just says "fix this transcript" or "clean up these meeting notes" without mentioning ASR specifically.
---

# Transcript Fixer

Two-phase correction pipeline: deterministic dictionary rules (instant, free) followed by AI-powered error detection. Corrections accumulate in `~/.transcript-fixer/corrections.db`, improving accuracy over time.

## Prerequisites

All scripts use PEP 723 inline metadata — `uv run` auto-installs dependencies. Requires `uv` ([install guide](https://docs.astral.sh/uv/getting-started/installation/)).

## Quick Start

```bash
# First time: Initialize database
uv run scripts/fix_transcription.py --init

# Single file
uv run scripts/fix_transcription.py --input meeting.md --stage 1

# Batch: multiple files in parallel (use shell loop)
for f in /path/to/*.txt; do
  uv run scripts/fix_transcription.py --input "$f" --stage 1
done
```

After Stage 1, Claude reads the output and fixes remaining ASR errors natively (no API key needed):
1. Read all Stage 1 outputs — read **entire** transcript before proposing corrections (later context disambiguates earlier errors)
2. Identify ASR errors — compile all corrections across files
3. Apply fixes with sed in batch, verify each with diff
4. Finalize: rename `_stage1.md` → `.md`, delete original `.txt`
5. Save stable patterns to dictionary for future reuse

See `references/example_session.md` for a concrete input/output walkthrough.

**Alternative: API batch processing** (for automation without Claude Code):
```bash
export GLM_API_KEY="<api-key>"  # From https://open.bigmodel.cn/
uv run scripts/fix_transcript_enhanced.py input.md --output ./corrected
```

## Core Workflow

Two-phase pipeline with persistent learning:

1. **Initialize** (once): `uv run scripts/fix_transcription.py --init`
2. **Add domain corrections**: `--add "错误词" "正确词" --domain <domain>`
3. **Phase 1 — Dictionary**: `--input file.md --stage 1` (instant, free)
4. **Phase 2 — AI Correction**: Claude reads output and fixes errors natively, or `--stage 3` with `GLM_API_KEY` for API mode
5. **Save stable patterns**: `--add "错误词" "正确词"` after each session
6. **Review learned patterns**: `--review-learned` and `--approve` high-confidence suggestions

**Domains**: `general`, `embodied_ai`, `finance`, `medical`, or custom (e.g., `火星加速器`)
**Learning**: Patterns appearing ≥3 times at ≥80% confidence auto-promote from AI to dictionary

**After fixing, always save reusable corrections to dictionary.** This is the skill's core value — see `references/iteration_workflow.md` for the complete checklist.

### Dictionary Addition After Fixing

After native AI correction, review all applied fixes and decide which to save. Use this decision matrix:

| Pattern type | Example | Action |
|-------------|---------|--------|
| Non-word → correct term | 克劳锐→Claude, cloucode→Claude Code | ✅ Add (zero false positive risk) |
| Rare word → correct term | 潜彩→前采, 维星→韦青 | ✅ Add (verify it's not a real word first) |
| Person/company name ASR error | 宋天航→宋天生, 策马攀山→策马看山 | ✅ Add (stable, unique) |
| Common word → context word | 争→蒸, 钱财→前采, 报纸→标品 | ❌ Skip (high false positive risk) |
| Real brand → different brand | Xcode→Claude Code, Clover→Claude | ❌ Skip (real words in other contexts) |

Batch add multiple corrections in one session:
```bash
uv run scripts/fix_transcription.py --add "错误1" "正确1" --domain tech
uv run scripts/fix_transcription.py --add "错误2" "正确2" --domain business
# Chain with && for efficiency
```

## False Positive Prevention

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.

## Native AI Correction (Default Mode)

When running inside Claude Code, use Claude's own language understanding for Phase 2:

1. Run Stage 1 (dictionary) on all files (parallel if multiple)
2. Verify Stage 1 — diff original vs output. If dictionary introduced false positives, work from the **original** file
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
4. Identify ASR errors per file — classify by confidence:
   - **High confidence** (apply directly): non-words, obvious garbling, product name variants
   - **Medium confidence** (present for review): context-dependent homophones, person names
5. Apply fixes efficiently:
   - **Global replacements** (unique non-words like "克劳锐"→"Claude"): use `sed -i ''` with `-e` flags, multiple patterns in one command
   - **Context-dependent** (common words like "争"→"蒸" only in distillation context): use sed with longer context phrases for uniqueness, or Edit tool
6. Verify with diff: `diff original.txt corrected_stage1.md`
7. Finalize files: rename `*_stage1.md` → `*.md`, delete original `.txt`
8. Save stable patterns to dictionary (see "Dictionary Addition" below)
9. Remove false positives if Stage 1 had any

### Common ASR Error Patterns

AI product names are frequently garbled. These patterns recur across transcripts:

| Correct term | Common ASR variants |
|-------------|-------------------|
| Claude | cloud, Clou, calloc, 克劳锐, Clover, color |
| Claude Code | cloud code, Xcode, call code, cloucode, cloudcode, color code |
| Claude Agent SDK | cloud agent SDK |
| Opus | Opaas |
| Vibe Coding | web coding, Web coding |
| GitHub | get Hub, Git Hub |
| prototype | Pre top |

Person names and company names also produce consistent ASR errors across sessions — always add confirmed name corrections to the dictionary.

### Efficient Batch Fix Strategy

When fixing multiple files (e.g., 5 transcripts from one day):

1. **Stage 1 in parallel**: run all files through dictionary at once
2. **Read all files first**: build a mental model of speakers, topics, and recurring terms before fixing anything
3. **Compile a global correction list**: many errors repeat across files from the same session (same speakers, same topics)
4. **Apply global corrections first** (sed with multiple `-e` flags), then per-file context-dependent fixes
5. **Verify all diffs**, finalize all files, then do one dictionary addition pass

### Enhanced Capabilities (Native Mode Only)

- **Intelligent paragraph breaks**: Add `\n\n` at logical topic transitions
- **Filler word reduction**: "这个这个这个" → "这个"
- **Interactive review**: Corrections confirmed before applying
- **Context-aware judgment**: Full document context resolves ambiguous errors

### When to Use API Mode Instead

Use `GLM_API_KEY` + Stage 3 for batch processing, standalone usage without Claude Code, or reproducible automated processing.

### Legacy Fallback

When the script outputs `[CLAUDE_FALLBACK]` (GLM API error), switch to native mode automatically.

## Utility Scripts

**Timestamp repair**:
```bash
uv run scripts/fix_transcript_timestamps.py meeting.txt --in-place
```

**Split transcript into sections** (rebase each to `00:00:00`):
```bash
uv run scripts/split_transcript_sections.py meeting.txt \
  --first-section-name "课前聊天" \
  --section "正式上课::好，无缝切换嘛。" \
  --rebase-to-zero
```

**Word-level diff** (recommended for reviewing corrections):
```bash
uv run scripts/generate_word_diff.py original.md corrected.md output.html
```

## Output Files

- `*_stage1.md` — Dictionary corrections applied
- `*_corrected.txt` — Final version (native mode) or `*_stage2.md` (API mode)
- `*_对比.html` — Visual diff (open in browser)

## Database Operations

**Read `references/database_schema.md` before any database operations.**

```bash
sqlite3 ~/.transcript-fixer/corrections.db "SELECT * FROM active_corrections;"
sqlite3 ~/.transcript-fixer/corrections.db "SELECT value FROM system_config WHERE key='schema_version';"
```

## Stages

| Stage | Description | Speed | Cost |
|-------|-------------|-------|------|
| 1 | Dictionary only | Instant | Free |
| 1 + Native | Dictionary + Claude AI (default) | ~1min | Free |
| 3 | Dictionary + API AI + diff report | ~10s | API calls |

## Bundled Resources

**Scripts:**
- `fix_transcription.py` — Core CLI (dictionary, add, audit, learning)
- `fix_transcript_enhanced.py` — Enhanced wrapper for interactive use
- `fix_transcript_timestamps.py` — Timestamp normalization and repair
- `generate_word_diff.py` — Word-level diff HTML generation
- `split_transcript_sections.py` — Split transcript by marker phrases

**References** (load as needed):
- **Safety**: `false_positive_guide.md` (read before adding rules), `database_schema.md` (read before DB ops)
- **Workflow**: `iteration_workflow.md`, `workflow_guide.md`, `example_session.md`
- **CLI**: `quick_reference.md`, `script_parameters.md`
- **Advanced**: `dictionary_guide.md`, `sql_queries.md`, `architecture.md`, `best_practices.md`
- **Operations**: `troubleshooting.md`, `installation_setup.md`, `glm_api_setup.md`, `team_collaboration.md`

## Troubleshooting

`uv run scripts/fix_transcription.py --validate` checks setup health. See `references/troubleshooting.md` for detailed resolution.

## Next Step: Structure into Meeting Minutes

After correcting a transcript, if the content is from a meeting, lecture, or interview, suggest structuring it:

```
Transcript corrected: [N] errors fixed, saved to [output_path].

Want to turn this into structured meeting minutes with decisions and action items?

Options:
A) Yes — run /meeting-minutes-taker (Recommended for meetings/lectures)
B) Export as PDF — run /pdf-creator on the corrected text
C) No thanks — the corrected transcript is all I need
```