bd0aa12004
## New Skill: transcript-fixer v1.0.0 Correct speech-to-text (ASR/STT) transcription errors through dictionary-based rules and AI-powered corrections with automatic pattern learning. **Features:** - Two-stage correction pipeline (dictionary + AI) - Automatic pattern detection and learning - Domain-specific dictionaries (general, embodied_ai, finance, medical) - SQLite-based correction repository - Team collaboration with import/export - GLM API integration for AI corrections - Cost optimization through dictionary promotion **Use cases:** - Correcting meeting notes, lecture recordings, or interview transcripts - Fixing Chinese/English homophone errors and technical terminology - Building domain-specific correction dictionaries - Improving transcript accuracy through iterative learning **Documentation:** - Complete workflow guides in references/ - SQL query templates - Troubleshooting guide - Team collaboration patterns - API setup instructions **Marketplace updates:** - Updated marketplace to v1.8.0 - Added transcript-fixer plugin (category: productivity) - Updated README.md with skill description and use cases - Updated CLAUDE.md with skill listing and counts 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
136 lines
3.0 KiB
Markdown
136 lines
3.0 KiB
Markdown
# Setup Guide
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Complete installation and configuration guide for transcript-fixer.
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## Table of Contents
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- [Installation](#installation)
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- [API Configuration](#api-configuration)
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- [Environment Setup](#environment-setup)
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- [Next Steps](#next-steps)
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## Installation
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### Dependencies
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Install required dependencies using uv:
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```bash
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uv pip install -r requirements.txt
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```
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Or sync the project environment:
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```bash
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uv sync
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```
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**Required packages**:
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- `anthropic` - For Claude API integration (future)
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- `requests` - For GLM API calls
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- `difflib` - Standard library for diff generation
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### Database Initialization
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Initialize the SQLite database (first time only):
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```bash
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uv run scripts/fix_transcription.py --init
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```
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This creates `~/.transcript-fixer/corrections.db` with the complete schema:
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- 8 tables (corrections, context_rules, history, suggestions, etc.)
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- 3 views (active_corrections, pending_suggestions, statistics)
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- ACID transactions enabled
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- Automatic backups before migrations
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See `file_formats.md` for complete database schema.
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## API Configuration
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### GLM API Key (Required for Stage 2)
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Stage 2 AI corrections require a GLM API key.
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1. **Obtain API key**: Visit https://open.bigmodel.cn/
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2. **Register** for an account
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3. **Generate** an API key from the dashboard
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4. **Set environment variable**:
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```bash
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export GLM_API_KEY="your-api-key-here"
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```
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**Persistence**: Add to shell profile for permanent access:
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```bash
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# For bash
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echo 'export GLM_API_KEY="your-key"' >> ~/.bashrc
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source ~/.bashrc
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# For zsh
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echo 'export GLM_API_KEY="your-key"' >> ~/.zshrc
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source ~/.zshrc
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```
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### Verify Configuration
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Run validation to check setup:
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```bash
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uv run scripts/fix_transcription.py --validate
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```
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**Expected output**:
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```
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🔍 Validating transcript-fixer configuration...
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✅ Configuration directory exists: ~/.transcript-fixer
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✅ Database valid: 0 corrections
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✅ All 8 tables present
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✅ GLM_API_KEY is set
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============================================================
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✅ All checks passed! Configuration is valid.
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============================================================
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```
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## Environment Setup
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### Python Environment
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**Required**: Python 3.8+
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**Recommended**: Use uv for all Python operations:
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```bash
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# Never use system python directly
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uv run scripts/fix_transcription.py # ✅ Correct
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# Don't use system python
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python scripts/fix_transcription.py # ❌ Wrong
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```
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### Directory Structure
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After initialization, the directory structure is:
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```
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~/.transcript-fixer/
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├── corrections.db # SQLite database
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├── corrections.YYYYMMDD.bak # Automatic backups
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└── (migration artifacts)
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```
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**Important**: The `.db` file should NOT be committed to Git. Export corrections to JSON for version control instead.
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## Next Steps
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After setup:
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1. Add initial corrections (5-10 terms)
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2. Run first correction on a test file
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3. Review learned suggestions after 3-5 runs
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4. Build domain-specific dictionaries
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See `workflow_guide.md` for detailed usage instructions.
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