transcript-fixer

Transcript Fixer

Correct speech-to-text transcription errors through dictionary-based rules, AI-powered corrections, and automatic pattern detection. Build a personalized knowledge base that learns from each correction.

When to Use This Skill

• Correcting ASR/STT errors in meeting notes, lectures, or interviews
• Building domain-specific correction dictionaries
• Fixing Chinese/English homophone errors or technical terminology
• Collaborating on shared correction knowledge bases

Prerequisites

Python execution must use uv - never use system Python directly.

If uv is not installed:

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows PowerShell
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Quick Start

Default: Native AI Correction (no API key needed)

When invoked from Claude Code, the skill uses a two-phase approach:

1. Dictionary phase (script): Apply 700+ learned correction rules instantly
2. AI phase (Claude native): Claude reads the text directly and fixes ASR errors, adds paragraph breaks, removes filler words

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

# Phase 1: Dictionary corrections (instant, free)
uv run scripts/fix_transcription.py --input meeting.md --stage 1

After Stage 1, Claude should:

1. Read the Stage 1 output in ~3000-char chunks
2. Identify ASR errors (homophones, technical terms, broken sentences)
3. Present corrections in a table for user review (high/medium confidence)
4. Apply confirmed corrections and save stable patterns to dictionary
5. Optionally: add paragraph breaks and remove excessive filler words

Alternative: API-Based Batch Processing (for automation or large volumes):

# Set API key for automated AI corrections
export GLM_API_KEY="<api-key>"  # From https://open.bigmodel.cn/

# Run full pipeline (dict + API AI + diff report)
uv run scripts/fix_transcript_enhanced.py input.md --output ./corrected

Timestamp repair:

uv run scripts/fix_transcript_timestamps.py meeting.txt --in-place

Split transcript into sections and rebase each section to 00:00:00:

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。对。那个曹总连上了吗？那个网页。" \
  --section "课后复盘::我们复盘一下。" \
  --rebase-to-zero

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 for best experience)

Generate word-level diff (recommended for reviewing corrections):

uv run scripts/generate_word_diff.py original.md corrected.md output.html

This creates an HTML file showing word-by-word differences with clear highlighting:

• 🔴 japanese 3 pro → 🟢 Gemini 3 Pro (complete word replacements)
• Easy to spot exactly what changed without character-level noise

Example Session

Input transcript (meeting.md):

今天我们讨论了巨升智能的最新进展。
股价系统需要优化，目前性能不够好。

After Stage 1 (meeting_stage1.md):

今天我们讨论了具身智能的最新进展。  ← "巨升"→"具身" corrected
股价系统需要优化,目前性能不够好。  ← Unchanged (not in dictionary)

After Stage 2 (meeting_stage2.md):

今天我们讨论了具身智能的最新进展。
框架系统需要优化，目前性能不够好。  ← "股价"→"框架" corrected by AI

Learned pattern detected:

✓ Detected: "股价" → "框架" (confidence: 85%, count: 1)
  Run --review-learned after 2 more occurrences to approve

Core Workflow

Two-phase pipeline stores corrections in ~/.transcript-fixer/corrections.db:

1. Initialize (first time): 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 ASR errors natively (default), or use --stage 3 with GLM_API_KEY for API mode
5. Save stable patterns: --add "错误词" "正确词" after each fix session
6. Review learned patterns: --review-learned and --approve high-confidence suggestions

Domains: general, embodied_ai, finance, medical, or custom names including Chinese (e.g., 火星加速器, 具身智能) Learning: Patterns appearing ≥3 times at ≥80% confidence move from AI to dictionary

See references/workflow_guide.md for detailed workflows, references/script_parameters.md for complete CLI reference, and references/team_collaboration.md for collaboration patterns.

Critical Workflow: Dictionary Iteration

Save stable, reusable ASR patterns after each fix. This is the skill's core value.

After fixing errors manually, immediately save stable corrections to dictionary:

uv run scripts/fix_transcription.py --add "错误词" "正确词" --domain general

Do not save one-off deletions, ambiguous context-only rewrites, or section-specific cleanup to the dictionary.

See references/iteration_workflow.md for complete iteration guide with checklist.

FALSE POSITIVE RISKS -- READ BEFORE ADDING CORRECTIONS

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

Native AI Correction (Default Mode)

Claude IS the AI. When running inside Claude Code, use Claude's own language understanding for Stage 2 corrections instead of calling an external API. This is the default behavior — no API key needed.

Workflow

1. Run Stage 1 (dictionary): uv run scripts/fix_transcription.py --input file.md --stage 1
2. Read the text in ~3000-character chunks (use cut -c<start>-<end> for single-line files)
3. Identify ASR errors — look for:
   • Homophone errors (同音字): "上海文" → "上下文", "扩种" → "扩充"
   • Broken sentence boundaries: "很大程。路上" → "很大程度上"
   • Technical terms: "Web coding" → "Vibe Coding"
   • Missing/extra characters: "沉沉默" → "沉默"
4. Present corrections in a table with confidence levels before applying:
   • High confidence: clear ASR errors with unambiguous corrections
   • Medium confidence: context-dependent, need user confirmation
5. Apply corrections to a copy of the file (never modify the original)
6. Save stable patterns to dictionary: --add "错误词" "正确词" --domain general
7. Generate word diff: uv run scripts/generate_word_diff.py original.md corrected.md diff.html

Enhanced AI Capabilities (Native Mode Only)

Native mode can do things the API mode cannot:

• Intelligent paragraph breaks: Add \n\n at logical topic transitions in continuous text
• Filler word reduction: Remove excessive repetition (这个这个这个 → 这个, 都都都都 → 都)
• Interactive review: Present corrections for user confirmation before applying
• Context-aware judgment: Use full document context to resolve ambiguous errors

When to Use API Mode Instead

Use GLM_API_KEY + Stage 3 for:

• Batch processing multiple files in automation
• When Claude Code is not available (standalone script usage)
• Consistent reproducible processing without interactive review

Legacy Fallback Marker

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

Database Operations

MUST read references/database_schema.md before any database operations.

Quick reference:

# View all corrections
sqlite3 ~/.transcript-fixer/corrections.db "SELECT * FROM active_corrections;"

# Check schema version
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:

• ensure_deps.py - Initialize shared virtual environment (run once, optional)
• fix_transcript_enhanced.py - Enhanced wrapper (recommended for interactive use)
• fix_transcription.py - Core CLI (for automation)
• fix_transcript_timestamps.py - Normalize/repair speaker timestamps and optionally rebase to zero
• generate_word_diff.py - Generate word-level diff HTML for reviewing corrections
• split_transcript_sections.py - Split a transcript by marker phrases and optionally rebase each section
• `exa

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