name: transcript-fixer description: Corrects speech-to-text transcription errors in meeting notes, lectures, and interviews using dictionary rules and AI. Learns patterns to build personalized correction databases. Use when working with transcripts containing ASR/STT errors, homophones, or Chinese/English mixed content requiring cleanup.

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

Recommended: Use Enhanced Wrapper (auto-detects API key, opens HTML diff):

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

# Process transcript with enhanced UX
uv run scripts/fix_transcript_enhanced.py input.md --output ./corrected

The enhanced wrapper automatically:

• Detects GLM API key from shell configs (checks lines near ANTHROPIC_BASE_URL)
• Moves output files to specified directory
• Opens HTML visual diff in browser for immediate feedback

Alternative: Use Core Script Directly:

# 1. Set API key (if not auto-detected)
export GLM_API_KEY="<api-key>"  # From https://open.bigmodel.cn/

# 2. Add common corrections (5-10 terms)
uv run scripts/fix_transcription.py --add "错误词" "正确词" --domain general

# 3. Run full correction pipeline
uv run scripts/fix_transcription.py --input meeting.md --stage 3

# 4. Review learned patterns after 3-5 runs
uv run scripts/fix_transcription.py --review-learned

Output files:

• *_stage1.md - Dictionary corrections applied
• *_stage2.md - AI corrections applied (final version)
• *_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

Three-stage 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. Process transcript: --input file.md --stage 3
4. Review learned patterns: --review-learned and --approve high-confidence suggestions

Stages: Dictionary (instant, free) → AI via GLM API (parallel) → Full pipeline 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

MUST save corrections after each fix. This is the skill's core value.

After fixing errors manually, immediately save to dictionary:

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

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

AI Fallback Strategy

When GLM API is unavailable (503, network issues), the script outputs [CLAUDE_FALLBACK] marker.

Claude Code should then:

1. Analyze the text directly for ASR errors
2. Fix using Edit tool
3. MUST save corrections to dictionary with --add

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

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)
• generate_word_diff.py - Generate word-level diff HTML for reviewing corrections
• examples/bulk_import.py - Bulk import example

References (load as needed):

• Critical: database_schema.md (read before DB operations), iteration_workflow.md (dictionary iteration best practices)
• Getting started: installation_setup.md, glm_api_setup.md, workflow_guide.md
• Daily use: quick_reference.md, script_parameters.md, dictionary_guide.md
• Advanced: sql_queries.md, file_formats.md, architecture.md, best_practices.md
• Operations: troubleshooting.md, team_collaboration.md

Troubleshooting

Verify setup health with uv run scripts/fix_transcription.py --validate. Common issues:

• Missing database → Run --init
• Missing API key → export GLM_API_KEY="<key>" (obtain from https://open.bigmodel.cn/)
• Permission errors → Check ~/.transcript-fixer/ ownership

See references/troubleshooting.md for detailed error resolution and references/glm_api_setup.md for API configuration.