Claude Code Plugins

Community-maintained marketplace

Feedback

transcript-fixer

@daymade/claude-code-skills
30
0

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.

Install Skill

1Download skill
2Enable skills in Claude

Open claude.ai/settings/capabilities and find the "Skills" section

3Upload to Claude

Click "Upload skill" and select the downloaded ZIP file

Note: Please verify skill by going through its instructions before using it.

SKILL.md

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

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)

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 (isolates corrections) 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.

Bundled Resources

Scripts:

  • fix_transcript_enhanced.py - Enhanced wrapper (recommended for interactive use)
  • fix_transcription.py - Core CLI (for automation)
  • examples/bulk_import.py - Bulk import example

References (load as needed):

  • 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.