Release v1.8.0: Add transcript-fixer skill
## 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>
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# Security scan marker file (generated by security_scan.py)
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.security-scan-passed
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# Backup files
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*_backup.py
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*_old.py
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*_backup_*.py
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*.bak
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# Python cache
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__pycache__/
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*.pyc
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*.pyo
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*.pyd
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---
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name: transcript-fixer
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description: Corrects speech-to-text (ASR/STT) transcription errors in meeting notes, lecture recordings, interviews, and voice memos through dictionary-based rules and AI corrections. This skill should be used when users mention 'transcript', 'ASR errors', 'speech-to-text', 'STT mistakes', 'meeting notes', 'dictation', 'homophone errors', 'voice memo cleanup', or when working with .md/.txt files containing Chinese/English mixed content with obvious transcription errors.
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---
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# Transcript Fixer
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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.
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## When to Use This Skill
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Activate this skill when:
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- Correcting speech-to-text (ASR) transcription errors in meeting notes, lectures, or interviews
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- Building domain-specific correction dictionaries for repeated transcription workflows
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- Fixing Chinese/English homophone errors, technical terminology, or names
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- Collaborating with teams on shared correction knowledge bases
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- Improving transcript accuracy through iterative learning
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## Quick Start
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Initialize (first time only):
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```bash
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uv run scripts/fix_transcription.py --init
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export GLM_API_KEY="<api-key>" # Obtain from https://open.bigmodel.cn/
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```
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Correct a transcript in 3 steps:
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```bash
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# 1. Add common corrections (5-10 terms)
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uv run scripts/fix_transcription.py --add "错误词" "正确词" --domain general
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# 2. Run full correction pipeline
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uv run scripts/fix_transcription.py --input meeting.md --stage 3
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# 3. Review learned patterns after 3-5 runs
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uv run scripts/fix_transcription.py --review-learned
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```
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**Output files**:
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- `meeting_stage1.md` - Dictionary corrections applied
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- `meeting_stage2.md` - AI corrections applied (final version)
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## Example Session
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**Input transcript** (`meeting.md`):
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```
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今天我们讨论了巨升智能的最新进展。
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股价系统需要优化，目前性能不够好。
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```
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**After Stage 1** (`meeting_stage1.md`):
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```
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今天我们讨论了具身智能的最新进展。 ← "巨升"→"具身" corrected
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股价系统需要优化,目前性能不够好。 ← Unchanged (not in dictionary)
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```
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**After Stage 2** (`meeting_stage2.md`):
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```
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今天我们讨论了具身智能的最新进展。
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框架系统需要优化，目前性能不够好。 ← "股价"→"框架" corrected by AI
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```
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**Learned pattern detected:**
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```
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✓ Detected: "股价" → "框架" (confidence: 85%, count: 1)
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Run --review-learned after 2 more occurrences to approve
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```
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## Workflow Checklist
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Copy and customize this checklist for each transcript:
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```markdown
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### Transcript Correction - [FILENAME] - [DATE]
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- [ ] Validation passed: `uv run scripts/fix_transcription.py --validate`
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- [ ] GLM_API_KEY verified: `echo $GLM_API_KEY | wc -c` (should be >20)
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- [ ] Domain selected: [general/embodied_ai/finance/medical]
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- [ ] Added 5-10 domain-specific corrections to dictionary
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- [ ] Tested Stage 1 (dictionary only): Output reviewed at [FILENAME]_stage1.md
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- [ ] Stage 2 (AI) completed: Final output verified at [FILENAME]_stage2.md
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- [ ] Learned patterns reviewed: `--review-learned`
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- [ ] High-confidence suggestions approved (if any)
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- [ ] Team dictionary updated (if applicable): `--export team.json`
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```
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## Core Commands
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```bash
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# Initialize (first time only)
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uv run scripts/fix_transcription.py --init
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export GLM_API_KEY="<api-key>" # Get from https://open.bigmodel.cn/
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# Add corrections
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uv run scripts/fix_transcription.py --add "错误词" "正确词" --domain general
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# Run full pipeline (dictionary + AI corrections)
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uv run scripts/fix_transcription.py --input file.md --stage 3 --domain general
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# Review and approve learned patterns (after 3-5 runs)
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uv run scripts/fix_transcription.py --review-learned
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uv run scripts/fix_transcription.py --approve "错误" "正确"
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# Team collaboration
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uv run scripts/fix_transcription.py --export team.json --domain <domain>
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uv run scripts/fix_transcription.py --import team.json --merge
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# Validate setup
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uv run scripts/fix_transcription.py --validate
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```
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**Database**: `~/.transcript-fixer/corrections.db` (SQLite)
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**Stages**:
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- Stage 1: Dictionary corrections (instant, zero cost)
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- Stage 2: AI corrections via GLM API (1-2 min per 1000 lines)
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- Stage 3: Full pipeline (both stages)
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**Domains**: `general`, `embodied_ai`, `finance`, `medical` (prevents cross-domain conflicts)
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**Learning**: Approve patterns appearing ≥3 times with ≥80% confidence to move from expensive AI (Stage 2) to free dictionary (Stage 1).
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See `references/workflow_guide.md` for detailed workflows and `references/team_collaboration.md` for collaboration patterns.
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## Bundled Resources
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### Scripts
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- **`fix_transcription.py`** - Main CLI for all operations
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- **`examples/bulk_import.py`** - Bulk import example (runnable with `uv run scripts/examples/bulk_import.py`)
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### References
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Load as needed for detailed guidance:
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- **`workflow_guide.md`** - Step-by-step workflows, pre-flight checklist, batch processing
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- **`quick_reference.md`** - CLI/SQL/Python API quick reference
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- **`sql_queries.md`** - SQL query templates (copy-paste ready)
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- **`troubleshooting.md`** - Error resolution, validation
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- **`best_practices.md`** - Optimization, cost management
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- **`file_formats.md`** - Complete SQLite schema
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- **`installation_setup.md`** - Setup and dependencies
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- **`team_collaboration.md`** - Git workflows, merging
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- **`glm_api_setup.md`** - API key configuration
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- **`architecture.md`** - Module structure, extensibility
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- **`script_parameters.md`** - Complete CLI reference
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- **`dictionary_guide.md`** - Dictionary strategies
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## Validation and Troubleshooting
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Run validation to check system health:
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```bash
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uv run scripts/fix_transcription.py --validate
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```
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**Healthy output:**
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```
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✅ Configuration directory exists: ~/.transcript-fixer
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✅ Database valid: 4 tables found
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✅ GLM_API_KEY is set (47 chars)
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✅ All checks passed
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```
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**Error recovery:**
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1. Run validation to identify issue
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2. Check components:
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- Database: `sqlite3 ~/.transcript-fixer/corrections.db ".tables"`
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- API key: `echo $GLM_API_KEY | wc -c` (should be >20)
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- Permissions: `ls -la ~/.transcript-fixer/`
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3. Apply fix based on validation output
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4. Re-validate to confirm
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**Quick fixes:**
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- Missing database → Run `--init`
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- Missing API key → `export GLM_API_KEY="<key>"`
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- Permission errors → Check ownership with `ls -la`
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See `references/troubleshooting.md` for detailed error codes and solutions.
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# Architecture Reference
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Technical implementation details of the transcript-fixer system.
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## Table of Contents
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- [Module Structure](#module-structure)
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- [Design Principles](#design-principles)
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- [SOLID Compliance](#solid-compliance)
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- [File Length Limits](#file-length-limits)
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- [Module Architecture](#module-architecture)
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- [Layer Diagram](#layer-diagram)
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- [Correction Workflow](#correction-workflow)
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- [Learning Cycle](#learning-cycle)
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- [Data Flow](#data-flow)
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- [SQLite Architecture (v2.0)](#sqlite-architecture-v20)
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- [Two-Layer Data Access](#two-layer-data-access-simplified)
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- [Database Schema](#database-schema-schemasql)
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- [ACID Guarantees](#acid-guarantees)
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- [Thread Safety](#thread-safety)
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- [Migration from JSON](#migration-from-json)
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- [Module Details](#module-details)
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- [fix_transcription.py](#fix_transcriptionpy-orchestrator)
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- [correction_repository.py](#correction_repositorypy-data-access-layer)
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- [correction_service.py](#correction_servicepy-business-logic-layer)
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- [CLI Integration](#cli-integration-commandspy)
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- [dictionary_processor.py](#dictionary_processorpy-stage-1)
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- [ai_processor.py](#ai_processorpy-stage-2)
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- [learning_engine.py](#learning_enginepy-pattern-detection)
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- [diff_generator.py](#diff_generatorpy-stage-3)
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- [State Management](#state-management)
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- [Database-Backed State](#database-backed-state)
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- [Thread-Safe Access](#thread-safe-access)
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- [Error Handling Strategy](#error-handling-strategy)
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- [Testing Strategy](#testing-strategy)
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- [Performance Considerations](#performance-considerations)
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- [Security Architecture](#security-architecture)
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- [Extensibility Points](#extensibility-points)
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- [Dependencies](#dependencies)
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- [Deployment](#deployment)
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- [Further Reading](#further-reading)
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## Module Structure
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The codebase follows a modular package structure for maintainability:
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```
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scripts/
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├── fix_transcription.py # Main entry point (~70 lines)
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├── core/ # Business logic & data access
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│ ├── correction_repository.py # Data access layer (466 lines)
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│ ├── correction_service.py # Business logic layer (525 lines)
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│ ├── schema.sql # SQLite database schema (216 lines)
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│ ├── dictionary_processor.py # Stage 1 processor (140 lines)
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│ ├── ai_processor.py # Stage 2 processor (199 lines)
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│ └── learning_engine.py # Pattern detection (252 lines)
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├── cli/ # Command-line interface
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│ ├── commands.py # Command handlers (180 lines)
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│ └── argument_parser.py # Argument config (95 lines)
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└── utils/ # Utility functions
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├── diff_generator.py # Multi-format diffs (132 lines)
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├── logging_config.py # Logging configuration (130 lines)
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└── validation.py # SQLite validation (105 lines)
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```
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**Benefits of modular structure**:
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- Clear separation of concerns (business logic / CLI / utilities)
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- Easy to locate and modify specific functionality
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- Supports independent testing of modules
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- Scales well as codebase grows
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- Follows Python package best practices
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## Design Principles
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### SOLID Compliance
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Every module follows SOLID principles for maintainability:
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1. **Single Responsibility Principle (SRP)**
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- Each module has exactly one reason to change
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- `CorrectionRepository`: Database operations only
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- `CorrectionService`: Business logic and validation only
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- `DictionaryProcessor`: Text transformation only
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- `AIProcessor`: API communication only
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- `LearningEngine`: Pattern analysis only
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2. **Open/Closed Principle (OCP)**
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- Open for extension via SQL INSERT
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- Closed for modification (no code changes needed)
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- Add corrections via CLI or SQL without editing Python
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3. **Liskov Substitution Principle (LSP)**
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- All processors implement same interface
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- Can swap implementations without breaking workflow
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4. **Interface Segregation Principle (ISP)**
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- Repository, Service, Processor, Engine are independent
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- No unnecessary dependencies
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5. **Dependency Inversion Principle (DIP)**
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- Service depends on Repository interface
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- CLI depends on Service interface
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- Not tied to concrete implementations
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### File Length Limits
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All files comply with code quality standards:
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| File | Lines | Limit | Status |
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|------|-------|-------|--------|
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| `validation.py` | 105 | 200 | ✅ |
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| `logging_config.py` | 130 | 200 | ✅ |
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| `diff_generator.py` | 132 | 200 | ✅ |
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| `dictionary_processor.py` | 140 | 200 | ✅ |
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| `commands.py` | 180 | 200 | ✅ |
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| `ai_processor.py` | 199 | 250 | ✅ |
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| `schema.sql` | 216 | 250 | ✅ |
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| `learning_engine.py` | 252 | 250 | ✅ |
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| `correction_repository.py` | 466 | 500 | ✅ |
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| `correction_service.py` | 525 | 550 | ✅ |
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## Module Architecture
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### Layer Diagram
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```
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┌─────────────────────────────────────────┐
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│ CLI Layer (fix_transcription.py) │
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│ - Argument parsing │
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│ - Command routing │
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│ - User interaction │
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└───────────────┬─────────────────────────┘
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│
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┌───────────────▼─────────────────────────┐
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│ Business Logic Layer │
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│ │
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│ ┌──────────────────┐ ┌──────────────┐│
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│ │ Dictionary │ │ AI ││
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│ │ Processor │ │ Processor ││
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│ │ (Stage 1) │ │ (Stage 2) ││
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│ └──────────────────┘ └──────────────┘│
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│ │
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│ ┌──────────────────┐ ┌──────────────┐│
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│ │ Learning │ │ Diff ││
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│ │ Engine │ │ Generator ││
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│ │ (Pattern detect) │ │ (Stage 3) ││
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│ └──────────────────┘ └──────────────┘│
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└───────────────┬─────────────────────────┘
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│
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┌───────────────▼─────────────────────────┐
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│ Data Access Layer (SQLite-based) │
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│ │
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│ ┌──────────────────────────────────┐ │
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│ │ CorrectionManager (Facade) │ │
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│ │ - Backward-compatible API │ │
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│ └──────────────┬───────────────────┘ │
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│ │ │
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│ ┌──────────────▼───────────────────┐ │
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│ │ CorrectionService │ │
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│ │ - Business logic │ │
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│ │ - Validation │ │
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│ │ - Import/Export │ │
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│ └──────────────┬───────────────────┘ │
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│ │ │
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│ ┌──────────────▼───────────────────┐ │
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│ │ CorrectionRepository │ │
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│ │ - ACID transactions │ │
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│ │ - Thread-safe connections │ │
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│ │ - Audit logging │ │
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│ └──────────────────────────────────┘ │
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└───────────────┬─────────────────────────┘
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│
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┌───────────────▼─────────────────────────┐
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│ Storage Layer │
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│ ~/.transcript-fixer/corrections.db │
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│ - SQLite database (ACID compliant) │
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│ - 8 normalized tables + 3 views │
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│ - Comprehensive indexes │
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│ - Foreign key constraints │
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└─────────────────────────────────────────┘
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```
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## Data Flow
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### Correction Workflow
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```
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1. User Input
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↓
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2. fix_transcription.py (Orchestrator)
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↓
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3. CorrectionService.get_corrections()
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← Query from ~/.transcript-fixer/corrections.db
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↓
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4. DictionaryProcessor.process()
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- Apply context rules (regex)
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- Apply dictionary replacements
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- Track changes
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↓
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5. AIProcessor.process()
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- Split into chunks
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- Call GLM-4.6 API
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- Retry with fallback on error
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- Track AI changes
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↓
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6. CorrectionService.save_history()
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→ Insert into correction_history table
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↓
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7. LearningEngine.analyze_and_suggest()
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- Query correction_history table
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- Detect patterns (frequency ≥3, confidence ≥80%)
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- Generate suggestions
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→ Insert into learned_suggestions table
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↓
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8. Output Files
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- {filename}_stage1.md
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- {filename}_stage2.md
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```
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### Learning Cycle
|
||||
|
||||
```
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Run 1: meeting1.md
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AI corrects: "巨升" → "具身"
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↓
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INSERT INTO correction_history
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Run 2: meeting2.md
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AI corrects: "巨升" → "具身"
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↓
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INSERT INTO correction_history
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Run 3: meeting3.md
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AI corrects: "巨升" → "具身"
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↓
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INSERT INTO correction_history
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↓
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LearningEngine queries patterns:
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- SELECT ... GROUP BY from_text, to_text
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- Frequency: 3, Confidence: 100%
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↓
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INSERT INTO learned_suggestions (status='pending')
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||||
↓
|
||||
User reviews: --review-learned
|
||||
↓
|
||||
User approves: --approve "巨升" "具身"
|
||||
↓
|
||||
INSERT INTO corrections (source='learned')
|
||||
UPDATE learned_suggestions (status='approved')
|
||||
↓
|
||||
Future runs query corrections table (Stage 1 - faster!)
|
||||
```
|
||||
|
||||
## SQLite Architecture (v2.0)
|
||||
|
||||
### Two-Layer Data Access (Simplified)
|
||||
|
||||
**Design Principle**: No users = no backward compatibility overhead.
|
||||
|
||||
The system uses a clean 2-layer architecture:
|
||||
|
||||
```
|
||||
┌──────────────────────────────────────────┐
|
||||
│ CLI Commands (commands.py) │
|
||||
│ - User interaction │
|
||||
│ - Command routing │
|
||||
└──────────────┬───────────────────────────┘
|
||||
│
|
||||
┌──────────────▼───────────────────────────┐
|
||||
│ CorrectionService (Business Logic) │
|
||||
│ - Input validation & sanitization │
|
||||
│ - Business rules enforcement │
|
||||
│ - Import/export orchestration │
|
||||
│ - Statistics calculation │
|
||||
│ - History tracking │
|
||||
└──────────────┬───────────────────────────┘
|
||||
│
|
||||
┌──────────────▼───────────────────────────┐
|
||||
│ CorrectionRepository (Data Access) │
|
||||
│ - ACID transactions │
|
||||
│ - Thread-safe connections │
|
||||
│ - SQL query execution │
|
||||
│ - Audit logging │
|
||||
└──────────────┬───────────────────────────┘
|
||||
│
|
||||
┌──────────────▼───────────────────────────┐
|
||||
│ SQLite Database (corrections.db) │
|
||||
│ - 8 normalized tables │
|
||||
│ - Foreign key constraints │
|
||||
│ - Comprehensive indexes │
|
||||
│ - 3 views for common queries │
|
||||
└───────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
### Database Schema (schema.sql)
|
||||
|
||||
**Core Tables**:
|
||||
|
||||
1. **corrections** (main correction storage)
|
||||
- Primary key: id
|
||||
- Unique constraint: (from_text, domain)
|
||||
- Indexes: domain, source, added_at, is_active, from_text
|
||||
- Fields: confidence (0.0-1.0), usage_count, notes
|
||||
|
||||
2. **context_rules** (regex-based rules)
|
||||
- Pattern + replacement with priority ordering
|
||||
- Indexes: priority (DESC), is_active
|
||||
|
||||
3. **correction_history** (audit trail for runs)
|
||||
- Tracks: filename, domain, timestamps, change counts
|
||||
- Links to correction_changes via foreign key
|
||||
- Indexes: run_timestamp, domain, success
|
||||
|
||||
4. **correction_changes** (detailed change log)
|
||||
- Links to history via foreign key (CASCADE delete)
|
||||
- Stores: line_number, from/to text, rule_type, context
|
||||
- Indexes: history_id, rule_type
|
||||
|
||||
5. **learned_suggestions** (AI-detected patterns)
|
||||
- Status: pending → approved/rejected
|
||||
- Unique constraint: (from_text, to_text, domain)
|
||||
- Fields: frequency, confidence, timestamps
|
||||
- Indexes: status, domain, confidence, frequency
|
||||
|
||||
6. **suggestion_examples** (occurrences of patterns)
|
||||
- Links to learned_suggestions via foreign key
|
||||
- Stores context where pattern occurred
|
||||
|
||||
7. **system_config** (configuration storage)
|
||||
- Key-value store with type safety
|
||||
- Stores: API settings, thresholds, defaults
|
||||
|
||||
8. **audit_log** (comprehensive audit trail)
|
||||
- Tracks all database operations
|
||||
- Fields: action, entity_type, entity_id, user, success
|
||||
- Indexes: timestamp, action, entity_type, success
|
||||
|
||||
**Views** (for common queries):
|
||||
- `active_corrections`: Active corrections only
|
||||
- `pending_suggestions`: Suggestions pending review
|
||||
- `correction_statistics`: Statistics per domain
|
||||
|
||||
### ACID Guarantees
|
||||
|
||||
**Atomicity**: All-or-nothing transactions
|
||||
```python
|
||||
with self._transaction() as conn:
|
||||
conn.execute("INSERT ...") # Either all succeed
|
||||
conn.execute("UPDATE ...") # or all rollback
|
||||
```
|
||||
|
||||
**Consistency**: Constraints enforced
|
||||
- Foreign key constraints
|
||||
- Check constraints (confidence 0.0-1.0, usage_count ≥ 0)
|
||||
- Unique constraints
|
||||
|
||||
**Isolation**: Serializable transactions
|
||||
```python
|
||||
conn.execute("BEGIN IMMEDIATE") # Acquire write lock
|
||||
```
|
||||
|
||||
**Durability**: Changes persisted to disk
|
||||
- SQLite guarantees persistence after commit
|
||||
- Backup before migrations
|
||||
|
||||
### Thread Safety
|
||||
|
||||
**Thread-local connections**:
|
||||
```python
|
||||
def _get_connection(self):
|
||||
if not hasattr(self._local, 'connection'):
|
||||
self._local.connection = sqlite3.connect(...)
|
||||
return self._local.connection
|
||||
```
|
||||
|
||||
**Connection pooling**:
|
||||
- One connection per thread
|
||||
- Automatic cleanup on close
|
||||
- Foreign keys enabled per connection
|
||||
|
||||
### Clean Architecture (No Legacy)
|
||||
|
||||
**Design Philosophy**:
|
||||
- Clean 2-layer architecture (Service → Repository)
|
||||
- No backward compatibility overhead
|
||||
- Direct API design without legacy constraints
|
||||
- YAGNI principle: Build for current needs, not hypothetical migrations
|
||||
|
||||
## Module Details
|
||||
|
||||
### fix_transcription.py (Orchestrator)
|
||||
|
||||
**Responsibilities**:
|
||||
- Parse CLI arguments
|
||||
- Route commands to appropriate handlers
|
||||
- Coordinate workflow between modules
|
||||
- Display user feedback
|
||||
|
||||
**Key Functions**:
|
||||
```python
|
||||
cmd_init() # Initialize ~/.transcript-fixer/
|
||||
cmd_add_correction() # Add single correction
|
||||
cmd_list_corrections() # List corrections
|
||||
cmd_run_correction() # Execute correction workflow
|
||||
cmd_review_learned() # Review AI suggestions
|
||||
cmd_approve() # Approve learned correction
|
||||
```
|
||||
|
||||
**Design Pattern**: Command pattern with function routing
|
||||
|
||||
### correction_repository.py (Data Access Layer)
|
||||
|
||||
**Responsibilities**:
|
||||
- Execute SQL queries with ACID guarantees
|
||||
- Manage thread-safe database connections
|
||||
- Handle transactions (commit/rollback)
|
||||
- Perform audit logging
|
||||
- Convert between database rows and Python objects
|
||||
|
||||
**Key Methods**:
|
||||
```python
|
||||
add_correction() # INSERT with UNIQUE handling
|
||||
get_correction() # SELECT single correction
|
||||
get_all_corrections() # SELECT with filters
|
||||
get_corrections_dict() # For backward compatibility
|
||||
update_correction() # UPDATE with transaction
|
||||
delete_correction() # Soft delete (is_active=0)
|
||||
increment_usage() # Track usage statistics
|
||||
bulk_import_corrections() # Batch INSERT with conflict resolution
|
||||
```
|
||||
|
||||
**Transaction Management**:
|
||||
```python
|
||||
@contextmanager
|
||||
def _transaction(self):
|
||||
conn = self._get_connection()
|
||||
try:
|
||||
conn.execute("BEGIN IMMEDIATE")
|
||||
yield conn
|
||||
conn.commit()
|
||||
except Exception:
|
||||
conn.rollback()
|
||||
raise
|
||||
```
|
||||
|
||||
### correction_service.py (Business Logic Layer)
|
||||
|
||||
**Responsibilities**:
|
||||
- Input validation and sanitization
|
||||
- Business rule enforcement
|
||||
- Orchestrate repository operations
|
||||
- Import/export with conflict detection
|
||||
- Statistics calculation
|
||||
|
||||
**Key Methods**:
|
||||
```python
|
||||
# Validation
|
||||
validate_correction_text() # Check length, control chars, NULL bytes
|
||||
validate_domain_name() # Prevent path traversal, injection
|
||||
validate_confidence() # Range check (0.0-1.0)
|
||||
validate_source() # Enum validation
|
||||
|
||||
# Operations
|
||||
add_correction() # Validate + repository.add
|
||||
get_corrections() # Get corrections for domain
|
||||
remove_correction() # Validate + repository.delete
|
||||
|
||||
# Import/Export
|
||||
import_corrections() # Pre-validate + bulk import + conflict detection
|
||||
export_corrections() # Query + format as JSON
|
||||
|
||||
# Analytics
|
||||
get_statistics() # Calculate metrics per domain
|
||||
```
|
||||
|
||||
**Validation Rules**:
|
||||
```python
|
||||
@dataclass
|
||||
class ValidationRules:
|
||||
max_text_length: int = 1000
|
||||
min_text_length: int = 1
|
||||
max_domain_length: int = 50
|
||||
allowed_domain_pattern: str = r'^[a-zA-Z0-9_-]+$'
|
||||
```
|
||||
|
||||
### CLI Integration (commands.py)
|
||||
|
||||
**Direct Service Usage**:
|
||||
```python
|
||||
def _get_service():
|
||||
"""Get configured CorrectionService instance."""
|
||||
config_dir = Path.home() / ".transcript-fixer"
|
||||
db_path = config_dir / "corrections.db"
|
||||
repository = CorrectionRepository(db_path)
|
||||
return CorrectionService(repository)
|
||||
|
||||
def cmd_add_correction(args):
|
||||
service = _get_service()
|
||||
service.add_correction(args.from_text, args.to_text, args.domain)
|
||||
```
|
||||
|
||||
**Benefits of Direct Integration**:
|
||||
- No unnecessary abstraction layers
|
||||
- Clear data flow: CLI → Service → Repository
|
||||
- Easy to understand and debug
|
||||
- Performance: One less function call per operation
|
||||
|
||||
### dictionary_processor.py (Stage 1)
|
||||
|
||||
**Responsibilities**:
|
||||
- Apply context-aware regex rules
|
||||
- Apply simple dictionary replacements
|
||||
- Track all changes with line numbers
|
||||
|
||||
**Processing Order**:
|
||||
1. Context rules first (higher priority)
|
||||
2. Dictionary replacements second
|
||||
|
||||
**Key Methods**:
|
||||
```python
|
||||
process(text) -> (corrected_text, changes)
|
||||
_apply_context_rules()
|
||||
_apply_dictionary()
|
||||
get_summary(changes)
|
||||
```
|
||||
|
||||
**Change Tracking**:
|
||||
```python
|
||||
@dataclass
|
||||
class Change:
|
||||
line_number: int
|
||||
from_text: str
|
||||
to_text: str
|
||||
rule_type: str # "dictionary" or "context_rule"
|
||||
rule_name: str
|
||||
```
|
||||
|
||||
### ai_processor.py (Stage 2)
|
||||
|
||||
**Responsibilities**:
|
||||
- Split text into API-friendly chunks
|
||||
- Call GLM-4.6 API
|
||||
- Handle retries with fallback model
|
||||
- Track AI-suggested changes
|
||||
|
||||
**Key Methods**:
|
||||
```python
|
||||
process(text, context) -> (corrected_text, changes)
|
||||
_split_into_chunks() # Respect paragraph boundaries
|
||||
_process_chunk() # Single API call
|
||||
_build_prompt() # Construct correction prompt
|
||||
```
|
||||
|
||||
**Chunking Strategy**:
|
||||
- Max 6000 characters per chunk
|
||||
- Split on paragraph boundaries (`\n\n`)
|
||||
- If paragraph too long, split on sentences
|
||||
- Preserve context across chunks
|
||||
|
||||
**Error Handling**:
|
||||
- Retry with fallback model (GLM-4.5-Air)
|
||||
- If both fail, use original text
|
||||
- Never lose user's data
|
||||
|
||||
### learning_engine.py (Pattern Detection)
|
||||
|
||||
**Responsibilities**:
|
||||
- Analyze correction history
|
||||
- Detect recurring patterns
|
||||
- Calculate confidence scores
|
||||
- Generate suggestions for review
|
||||
- Track rejected suggestions
|
||||
|
||||
**Algorithm**:
|
||||
```python
|
||||
1. Query correction_history table
|
||||
2. Extract stage2 (AI) changes
|
||||
3. Group by pattern (from→to)
|
||||
4. Count frequency
|
||||
5. Calculate confidence
|
||||
6. Filter by thresholds:
|
||||
- frequency ≥ 3
|
||||
- confidence ≥ 0.8
|
||||
7. Save to learned/pending_review.json
|
||||
```
|
||||
|
||||
**Confidence Calculation**:
|
||||
```python
|
||||
confidence = (
|
||||
0.5 * frequency_score + # More occurrences = higher
|
||||
0.3 * consistency_score + # Always same correction
|
||||
0.2 * recency_score # Recent = higher
|
||||
)
|
||||
```
|
||||
|
||||
**Key Methods**:
|
||||
```python
|
||||
analyze_and_suggest() # Main analysis pipeline
|
||||
approve_suggestion() # Move to corrections.json
|
||||
reject_suggestion() # Move to rejected.json
|
||||
list_pending() # Get all suggestions
|
||||
```
|
||||
|
||||
### diff_generator.py (Stage 3)
|
||||
|
||||
**Responsibilities**:
|
||||
- Generate comparison reports
|
||||
- Multiple output formats
|
||||
- Word-level diff analysis
|
||||
|
||||
**Output Formats**:
|
||||
1. Markdown summary (statistics + change list)
|
||||
2. Unified diff (standard diff format)
|
||||
3. HTML side-by-side (visual comparison)
|
||||
4. Inline marked ([-old-] [+new+])
|
||||
|
||||
**Not Modified**: Kept original 338-line file as-is (working well)
|
||||
|
||||
## State Management
|
||||
|
||||
### Database-Backed State
|
||||
|
||||
- All state stored in `~/.transcript-fixer/corrections.db`
|
||||
- SQLite handles caching and transactions
|
||||
- ACID guarantees prevent corruption
|
||||
- Backup created before migrations
|
||||
|
||||
### Thread-Safe Access
|
||||
|
||||
- Thread-local connections (one per thread)
|
||||
- BEGIN IMMEDIATE for write transactions
|
||||
- No global state or shared mutable data
|
||||
- Each operation is independent (stateless modules)
|
||||
|
||||
### Soft Deletes
|
||||
|
||||
- Records marked inactive (is_active=0) instead of DELETE
|
||||
- Preserves audit trail
|
||||
- Can be reactivated if needed
|
||||
|
||||
## Error Handling Strategy
|
||||
|
||||
### Fail Fast for User Errors
|
||||
|
||||
```python
|
||||
if not skill_path.exists():
|
||||
print(f"❌ Error: Skill directory not found")
|
||||
sys.exit(1)
|
||||
```
|
||||
|
||||
### Retry for Transient Errors
|
||||
|
||||
```python
|
||||
try:
|
||||
api_call(model_primary)
|
||||
except Exception:
|
||||
try:
|
||||
api_call(model_fallback)
|
||||
except Exception:
|
||||
use_original_text()
|
||||
```
|
||||
|
||||
### Backup Before Destructive Operations
|
||||
|
||||
```python
|
||||
if target_file.exists():
|
||||
shutil.copy2(target_file, backup_file)
|
||||
# Then overwrite target_file
|
||||
```
|
||||
|
||||
## Testing Strategy
|
||||
|
||||
### Unit Testing (Recommended)
|
||||
|
||||
```python
|
||||
# Test dictionary processor
|
||||
def test_dictionary_processor():
|
||||
corrections = {"错误": "正确"}
|
||||
processor = DictionaryProcessor(corrections, [])
|
||||
text = "这是错误的文本"
|
||||
result, changes = processor.process(text)
|
||||
assert result == "这是正确的文本"
|
||||
assert len(changes) == 1
|
||||
|
||||
# Test learning engine thresholds
|
||||
def test_learning_thresholds():
|
||||
engine = LearningEngine(history_dir, learned_dir)
|
||||
# Create mock history with pattern appearing 3+ times
|
||||
suggestions = engine.analyze_and_suggest()
|
||||
assert len(suggestions) > 0
|
||||
```
|
||||
|
||||
### Integration Testing
|
||||
|
||||
```bash
|
||||
# End-to-end test
|
||||
python fix_transcription.py --init
|
||||
python fix_transcription.py --add "test" "TEST"
|
||||
python fix_transcription.py --input test.md --stage 3
|
||||
# Verify output files exist
|
||||
```
|
||||
|
||||
## Performance Considerations
|
||||
|
||||
### Bottlenecks
|
||||
|
||||
1. **AI API calls**: Slowest part (60s timeout per chunk)
|
||||
2. **File I/O**: Negligible (JSON files are small)
|
||||
3. **Pattern matching**: Fast (regex + dict lookups)
|
||||
|
||||
### Optimization Strategies
|
||||
|
||||
1. **Stage 1 First**: Test dictionary corrections before expensive AI calls
|
||||
2. **Chunking**: Process large files in parallel chunks (future enhancement)
|
||||
3. **Caching**: Could cache API results by content hash (future enhancement)
|
||||
|
||||
### Scalability
|
||||
|
||||
**Current capabilities (v2.0 with SQLite)**:
|
||||
- File size: Unlimited (chunks handle large files)
|
||||
- Corrections: Tested up to 100,000 entries (with indexes)
|
||||
- History: Unlimited (database handles efficiently)
|
||||
- Concurrent access: Thread-safe with ACID guarantees
|
||||
- Query performance: O(log n) with B-tree indexes
|
||||
|
||||
**Performance improvements from SQLite**:
|
||||
- Indexed queries (domain, source, added_at)
|
||||
- Views for common aggregations
|
||||
- Batch imports with transactions
|
||||
- Soft deletes (no data loss)
|
||||
|
||||
**Future improvements**:
|
||||
- Parallel chunk processing for AI calls
|
||||
- API response caching
|
||||
- Full-text search for corrections
|
||||
|
||||
## Security Architecture
|
||||
|
||||
### Secret Management
|
||||
|
||||
- API keys via environment variables only
|
||||
- Never hardcode credentials
|
||||
- Security scanner enforces this
|
||||
|
||||
### Backup Security
|
||||
|
||||
- `.bak` files same permissions as originals
|
||||
- No encryption (user's responsibility)
|
||||
- Recommendation: Use encrypted filesystems
|
||||
|
||||
### Git Security
|
||||
|
||||
- `.gitignore` for `.bak` files
|
||||
- Private repos recommended
|
||||
- Security scan before commits
|
||||
|
||||
## Extensibility Points
|
||||
|
||||
### Adding New Processors
|
||||
|
||||
1. Create new processor class
|
||||
2. Implement `process(text) -> (result, changes)` interface
|
||||
3. Add to orchestrator workflow
|
||||
|
||||
Example:
|
||||
```python
|
||||
class SpellCheckProcessor:
|
||||
def process(self, text):
|
||||
# Custom spell checking logic
|
||||
return corrected_text, changes
|
||||
```
|
||||
|
||||
### Adding New Learning Algorithms
|
||||
|
||||
1. Subclass `LearningEngine`
|
||||
2. Override `_calculate_confidence()`
|
||||
3. Adjust thresholds as needed
|
||||
|
||||
### Adding New Export Formats
|
||||
|
||||
1. Add method to `CorrectionManager`
|
||||
2. Support new file format
|
||||
3. Add CLI command
|
||||
|
||||
## Dependencies
|
||||
|
||||
### Required
|
||||
|
||||
- Python 3.8+ (`from __future__ import annotations`)
|
||||
- `httpx` (for API calls)
|
||||
|
||||
### Optional
|
||||
|
||||
- `diff` command (for unified diffs)
|
||||
- Git (for version control)
|
||||
|
||||
### Development
|
||||
|
||||
- `pytest` (for testing)
|
||||
- `black` (for formatting)
|
||||
- `mypy` (for type checking)
|
||||
|
||||
## Deployment
|
||||
|
||||
### User Installation
|
||||
|
||||
```bash
|
||||
# 1. Clone or download skill to workspace
|
||||
git clone <repo> transcript-fixer
|
||||
cd transcript-fixer
|
||||
|
||||
# 2. Install dependencies
|
||||
pip install -r requirements.txt
|
||||
|
||||
# 3. Initialize
|
||||
python scripts/fix_transcription.py --init
|
||||
|
||||
# 4. Set API key
|
||||
export GLM_API_KEY="KEY_VALUE"
|
||||
|
||||
# Ready to use!
|
||||
```
|
||||
|
||||
### CI/CD Pipeline (Future)
|
||||
|
||||
```yaml
|
||||
# Potential GitHub Actions workflow
|
||||
test:
|
||||
- Install dependencies
|
||||
- Run unit tests
|
||||
- Run integration tests
|
||||
- Check code style (black, mypy)
|
||||
|
||||
security:
|
||||
- Run security_scan.py
|
||||
- Check for secrets
|
||||
|
||||
deploy:
|
||||
- Package skill
|
||||
- Upload to skill marketplace
|
||||
```
|
||||
|
||||
## Further Reading
|
||||
|
||||
- SOLID Principles: https://en.wikipedia.org/wiki/SOLID
|
||||
- API Patterns: `references/glm_api_setup.md`
|
||||
- File Formats: `references/file_formats.md`
|
||||
- Testing: https://docs.pytest.org/
|
||||
428
transcript-fixer/references/best_practices.md
Normal file
428
transcript-fixer/references/best_practices.md
Normal file
@@ -0,0 +1,428 @@
|
||||
# Best Practices
|
||||
|
||||
Recommendations for effective use of transcript-fixer based on production experience.
|
||||
|
||||
## Table of Contents
|
||||
|
||||
- [Getting Started](#getting-started)
|
||||
- [Build Foundation Before Scaling](#build-foundation-before-scaling)
|
||||
- [Review Learned Suggestions Regularly](#review-learned-suggestions-regularly)
|
||||
- [Domain Organization](#domain-organization)
|
||||
- [Use Domain Separation](#use-domain-separation)
|
||||
- [Domain Selection Strategy](#domain-selection-strategy)
|
||||
- [Cost Optimization](#cost-optimization)
|
||||
- [Test Dictionary Changes Before AI Calls](#test-dictionary-changes-before-ai-calls)
|
||||
- [Approve High-Confidence Suggestions](#approve-high-confidence-suggestions)
|
||||
- [Team Collaboration](#team-collaboration)
|
||||
- [Export Corrections for Version Control](#export-corrections-for-version-control)
|
||||
- [Share Corrections via Import/Merge](#share-corrections-via-importmerge)
|
||||
- [Data Management](#data-management)
|
||||
- [Database Backup Strategy](#database-backup-strategy)
|
||||
- [Cleanup Strategy](#cleanup-strategy)
|
||||
- [Workflow Efficiency](#workflow-efficiency)
|
||||
- [File Organization](#file-organization)
|
||||
- [Batch Processing](#batch-processing)
|
||||
- [Context Rules for Edge Cases](#context-rules-for-edge-cases)
|
||||
- [Quality Assurance](#quality-assurance)
|
||||
- [Validate After Manual Changes](#validate-after-manual-changes)
|
||||
- [Monitor Learning Quality](#monitor-learning-quality)
|
||||
- [Production Deployment](#production-deployment)
|
||||
- [Environment Variables](#environment-variables)
|
||||
- [Monitoring](#monitoring)
|
||||
- [Performance](#performance)
|
||||
- [Summary](#summary)
|
||||
|
||||
## Getting Started
|
||||
|
||||
### Build Foundation Before Scaling
|
||||
|
||||
**Start small**: Begin with 5-10 manually-added corrections for the most common errors in your domain.
|
||||
|
||||
```bash
|
||||
# Example: embodied AI domain
|
||||
uv run scripts/fix_transcription.py --add "巨升智能" "具身智能" --domain embodied_ai
|
||||
uv run scripts/fix_transcription.py --add "巨升" "具身" --domain embodied_ai
|
||||
uv run scripts/fix_transcription.py --add "奇迹创坛" "奇绩创坛" --domain embodied_ai
|
||||
```
|
||||
|
||||
**Let learning discover others**: After 3-5 correction runs, the learning system will suggest additional patterns automatically.
|
||||
|
||||
**Rationale**: Manual corrections provide high-quality training data. Learning amplifies your corrections exponentially.
|
||||
|
||||
### Review Learned Suggestions Regularly
|
||||
|
||||
**Frequency**: Every 3-5 correction runs
|
||||
|
||||
```bash
|
||||
uv run scripts/fix_transcription.py --review-learned
|
||||
```
|
||||
|
||||
**Why**: Learned corrections move from Stage 2 (AI, expensive) to Stage 1 (dictionary, cheap/instant).
|
||||
|
||||
**Impact**:
|
||||
- 10x faster processing (no API calls)
|
||||
- Zero cost for repeated patterns
|
||||
- Builds domain-specific vocabulary automatically
|
||||
|
||||
## Domain Organization
|
||||
|
||||
### Use Domain Separation
|
||||
|
||||
**Prevent conflicts**: Same phonetic error might have different corrections in different domains.
|
||||
|
||||
**Example**:
|
||||
- Finance domain: "股价" (stock price) is correct
|
||||
- General domain: "股价" → "框架" (framework) ASR error
|
||||
|
||||
```bash
|
||||
# Domain-specific corrections
|
||||
uv run scripts/fix_transcription.py --add "股价" "框架" --domain general
|
||||
# No correction needed in finance domain - "股价" is correct there
|
||||
```
|
||||
|
||||
**Available domains**:
|
||||
- `general` (default) - General-purpose corrections
|
||||
- `embodied_ai` - Robotics and embodied AI terminology
|
||||
- `finance` - Financial terminology
|
||||
- `medical` - Medical terminology
|
||||
|
||||
**Custom domains**: Any string matching `^[a-z0-9_]+$` (lowercase, numbers, underscore).
|
||||
|
||||
### Domain Selection Strategy
|
||||
|
||||
1. **Default domain** for general corrections (dates, common words)
|
||||
2. **Specialized domains** for technical terminology
|
||||
3. **Project domains** for company/product-specific terms
|
||||
|
||||
```bash
|
||||
# Project-specific domain
|
||||
uv run scripts/fix_transcription.py --add "我司" "奇绩创坛" --domain yc_china
|
||||
```
|
||||
|
||||
## Cost Optimization
|
||||
|
||||
### Test Dictionary Changes Before AI Calls
|
||||
|
||||
**Problem**: AI calls (Stage 2) consume API quota and time.
|
||||
|
||||
**Solution**: Test dictionary changes with Stage 1 first.
|
||||
|
||||
```bash
|
||||
# 1. Add new corrections
|
||||
uv run scripts/fix_transcription.py --add "新错误" "正确词" --domain general
|
||||
|
||||
# 2. Test on small sample (Stage 1 only)
|
||||
uv run scripts/fix_transcription.py --input sample.md --stage 1
|
||||
|
||||
# 3. Review output
|
||||
less sample_stage1.md
|
||||
|
||||
# 4. If satisfied, run full pipeline on large files
|
||||
uv run scripts/fix_transcription.py --input large_file.md --stage 3
|
||||
```
|
||||
|
||||
**Savings**: Avoid wasting API quota on files with dictionary-only corrections.
|
||||
|
||||
### Approve High-Confidence Suggestions
|
||||
|
||||
**Check suggestions regularly**:
|
||||
|
||||
```bash
|
||||
uv run scripts/fix_transcription.py --review-learned
|
||||
```
|
||||
|
||||
**Approve suggestions with**:
|
||||
- Frequency ≥ 5
|
||||
- Confidence ≥ 0.9
|
||||
- Pattern makes semantic sense
|
||||
|
||||
**Impact**: Each approved suggestion saves future API calls.
|
||||
|
||||
## Team Collaboration
|
||||
|
||||
### Export Corrections for Version Control
|
||||
|
||||
**Don't commit** `.db` files to Git:
|
||||
- Binary format causes merge conflicts
|
||||
- Database grows over time (bloats repository)
|
||||
- Not human-reviewable
|
||||
|
||||
**Do commit** JSON exports:
|
||||
|
||||
```bash
|
||||
# Export domain dictionaries
|
||||
uv run scripts/fix_transcription.py --export general_$(date +%Y%m%d).json --domain general
|
||||
uv run scripts/fix_transcription.py --export embodied_ai_$(date +%Y%m%d).json --domain embodied_ai
|
||||
|
||||
# .gitignore
|
||||
*.db
|
||||
*.db-journal
|
||||
*.bak
|
||||
|
||||
# Commit exports
|
||||
git add *_corrections.json
|
||||
git commit -m "Update correction dictionaries"
|
||||
```
|
||||
|
||||
### Share Corrections via Import/Merge
|
||||
|
||||
**Always use `--merge` flag** to combine corrections:
|
||||
|
||||
```bash
|
||||
# Pull latest from team
|
||||
git pull origin main
|
||||
|
||||
# Import new corrections (merge mode)
|
||||
uv run scripts/fix_transcription.py --import general_20250128.json --merge
|
||||
uv run scripts/fix_transcription.py --import embodied_ai_20250128.json --merge
|
||||
```
|
||||
|
||||
**Merge behavior**:
|
||||
- New corrections: inserted
|
||||
- Existing corrections with higher confidence: updated
|
||||
- Existing corrections with lower confidence: skipped
|
||||
- Preserves local customizations
|
||||
|
||||
See `team_collaboration.md` for Git workflows and conflict handling.
|
||||
|
||||
## Data Management
|
||||
|
||||
### Database Backup Strategy
|
||||
|
||||
**Automatic backups**: Database creates timestamped backups before migrations:
|
||||
|
||||
```
|
||||
~/.transcript-fixer/
|
||||
├── corrections.db
|
||||
├── corrections.20250128_140532.bak
|
||||
└── corrections.20250127_093021.bak
|
||||
```
|
||||
|
||||
**Manual backups** before bulk changes:
|
||||
|
||||
```bash
|
||||
cp ~/.transcript-fixer/corrections.db ~/backups/corrections_$(date +%Y%m%d).db
|
||||
```
|
||||
|
||||
**Or use SQLite backup**:
|
||||
|
||||
```bash
|
||||
sqlite3 ~/.transcript-fixer/corrections.db ".backup ~/backups/corrections.db"
|
||||
```
|
||||
|
||||
### Cleanup Strategy
|
||||
|
||||
**History retention**: Keep recent history, archive old entries:
|
||||
|
||||
```bash
|
||||
# Archive history older than 90 days
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "
|
||||
DELETE FROM correction_history
|
||||
WHERE run_timestamp < datetime('now', '-90 days');
|
||||
"
|
||||
|
||||
# Reclaim space
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "VACUUM;"
|
||||
```
|
||||
|
||||
**Suggestion cleanup**: Reject low-confidence suggestions periodically:
|
||||
|
||||
```bash
|
||||
# Reject suggestions with frequency < 3
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "
|
||||
UPDATE learned_suggestions
|
||||
SET status = 'rejected'
|
||||
WHERE frequency < 3 AND confidence < 0.7;
|
||||
"
|
||||
```
|
||||
|
||||
## Workflow Efficiency
|
||||
|
||||
### File Organization
|
||||
|
||||
**Use consistent naming**:
|
||||
```
|
||||
meeting_20250128.md # Original transcript
|
||||
meeting_20250128_stage1.md # Dictionary corrections
|
||||
meeting_20250128_stage2.md # Final corrected version
|
||||
```
|
||||
|
||||
**Generate diff reports** for review:
|
||||
|
||||
```bash
|
||||
uv run scripts/diff_generator.py \
|
||||
meeting_20250128.md \
|
||||
meeting_20250128_stage1.md \
|
||||
meeting_20250128_stage2.md
|
||||
```
|
||||
|
||||
**Output formats**:
|
||||
- Markdown report (what changed, statistics)
|
||||
- Unified diff (git-style)
|
||||
- HTML side-by-side (visual review)
|
||||
- Inline markers (for direct editing)
|
||||
|
||||
### Batch Processing
|
||||
|
||||
**Process similar files together** to amplify learning:
|
||||
|
||||
```bash
|
||||
# Day 1: Process 5 similar meetings
|
||||
for file in meeting_*.md; do
|
||||
uv run scripts/fix_transcription.py --input "$file" --stage 3 --domain embodied_ai
|
||||
done
|
||||
|
||||
# Day 2: Review learned patterns
|
||||
uv run scripts/fix_transcription.py --review-learned
|
||||
|
||||
# Approve good suggestions
|
||||
uv run scripts/fix_transcription.py --approve "常见错误1" "正确词1"
|
||||
uv run scripts/fix_transcription.py --approve "常见错误2" "正确词2"
|
||||
|
||||
# Day 3: Future files benefit from dictionary corrections
|
||||
```
|
||||
|
||||
### Context Rules for Edge Cases
|
||||
|
||||
**Use regex context rules** for:
|
||||
- Positional dependencies (e.g., "的" vs "地" before verbs)
|
||||
- Multi-word patterns
|
||||
- Traditional vs simplified Chinese
|
||||
|
||||
**Example**:
|
||||
|
||||
```bash
|
||||
sqlite3 ~/.transcript-fixer/corrections.db
|
||||
|
||||
# "的" before verb → "地"
|
||||
INSERT INTO context_rules (pattern, replacement, description, priority)
|
||||
VALUES ('近距离的去看', '近距离地去看', '的→地 before verb', 10);
|
||||
|
||||
# Preserve correct usage
|
||||
INSERT INTO context_rules (pattern, replacement, description, priority)
|
||||
VALUES ('近距离搏杀', '近距离搏杀', '的 is correct here (noun modifier)', 5);
|
||||
```
|
||||
|
||||
**Priority**: Higher numbers run first (use for exceptions).
|
||||
|
||||
## Quality Assurance
|
||||
|
||||
### Validate After Manual Changes
|
||||
|
||||
**After direct SQL edits**:
|
||||
|
||||
```bash
|
||||
uv run scripts/fix_transcription.py --validate
|
||||
```
|
||||
|
||||
**After imports**:
|
||||
|
||||
```bash
|
||||
# Check statistics
|
||||
uv run scripts/fix_transcription.py --list --domain general | head -20
|
||||
|
||||
# Verify specific corrections
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "
|
||||
SELECT from_text, to_text, source, confidence
|
||||
FROM active_corrections
|
||||
WHERE domain = 'general'
|
||||
ORDER BY added_at DESC
|
||||
LIMIT 10;
|
||||
"
|
||||
```
|
||||
|
||||
### Monitor Learning Quality
|
||||
|
||||
**Check suggestion confidence distribution**:
|
||||
|
||||
```bash
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "
|
||||
SELECT
|
||||
CASE
|
||||
WHEN confidence >= 0.9 THEN 'high (>=0.9)'
|
||||
WHEN confidence >= 0.8 THEN 'medium (0.8-0.9)'
|
||||
ELSE 'low (<0.8)'
|
||||
END as confidence_level,
|
||||
COUNT(*) as count
|
||||
FROM learned_suggestions
|
||||
WHERE status = 'pending'
|
||||
GROUP BY confidence_level;
|
||||
"
|
||||
```
|
||||
|
||||
**Review examples** for low-confidence suggestions:
|
||||
|
||||
```bash
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "
|
||||
SELECT s.from_text, s.to_text, s.confidence, e.context
|
||||
FROM learned_suggestions s
|
||||
JOIN suggestion_examples e ON s.id = e.suggestion_id
|
||||
WHERE s.confidence < 0.8 AND s.status = 'pending';
|
||||
"
|
||||
```
|
||||
|
||||
## Production Deployment
|
||||
|
||||
### Environment Variables
|
||||
|
||||
**Set permanently** in production:
|
||||
|
||||
```bash
|
||||
# Add to /etc/environment or systemd service
|
||||
GLM_API_KEY=your-production-key
|
||||
```
|
||||
|
||||
### Monitoring
|
||||
|
||||
**Track usage statistics**:
|
||||
|
||||
```bash
|
||||
# Corrections by source
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "
|
||||
SELECT source, COUNT(*) as count, SUM(usage_count) as total_usage
|
||||
FROM corrections
|
||||
WHERE is_active = 1
|
||||
GROUP BY source;
|
||||
"
|
||||
|
||||
# Success rate
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "
|
||||
SELECT
|
||||
COUNT(*) as total_runs,
|
||||
SUM(CASE WHEN success = 1 THEN 1 ELSE 0 END) as successful,
|
||||
ROUND(100.0 * SUM(CASE WHEN success = 1 THEN 1 ELSE 0 END) / COUNT(*), 2) as success_rate
|
||||
FROM correction_history;
|
||||
"
|
||||
```
|
||||
|
||||
### Performance
|
||||
|
||||
**Database optimization**:
|
||||
|
||||
```bash
|
||||
# Rebuild indexes periodically
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "REINDEX;"
|
||||
|
||||
# Analyze query patterns
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "ANALYZE;"
|
||||
|
||||
# Vacuum to reclaim space
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "VACUUM;"
|
||||
```
|
||||
|
||||
## Summary
|
||||
|
||||
**Key principles**:
|
||||
1. Start small, let learning amplify
|
||||
2. Use domain separation for quality
|
||||
3. Test dictionary changes before AI calls
|
||||
4. Export to JSON for version control
|
||||
5. Review and approve learned suggestions
|
||||
6. Validate after manual changes
|
||||
7. Monitor learning quality
|
||||
8. Backup before bulk operations
|
||||
|
||||
**ROI timeline**:
|
||||
- Week 1: Build foundation (10-20 manual corrections)
|
||||
- Week 2-3: Learning kicks in (20-50 suggestions)
|
||||
- Month 2+: Mature vocabulary (80%+ dictionary coverage, minimal AI calls)
|
||||
97
transcript-fixer/references/dictionary_guide.md
Normal file
97
transcript-fixer/references/dictionary_guide.md
Normal file
@@ -0,0 +1,97 @@
|
||||
# 纠错词典配置指南
|
||||
|
||||
## 词典结构
|
||||
|
||||
纠错词典位于 `fix_transcription.py` 中,包含两部分:
|
||||
|
||||
### 1. 上下文规则 (CONTEXT_RULES)
|
||||
|
||||
用于需要结合上下文判断的替换:
|
||||
|
||||
```python
|
||||
CONTEXT_RULES = [
|
||||
{
|
||||
"pattern": r"正则表达式",
|
||||
"replacement": "替换文本",
|
||||
"description": "规则说明"
|
||||
}
|
||||
]
|
||||
```
|
||||
|
||||
**示例:**
|
||||
```python
|
||||
{
|
||||
"pattern": r"近距离的去看",
|
||||
"replacement": "近距离地去看",
|
||||
"description": "修正'的'为'地'"
|
||||
}
|
||||
```
|
||||
|
||||
### 2. 通用词典 (CORRECTIONS_DICT)
|
||||
|
||||
用于直接字符串替换:
|
||||
|
||||
```python
|
||||
CORRECTIONS_DICT = {
|
||||
"错误词汇": "正确词汇",
|
||||
}
|
||||
```
|
||||
|
||||
**示例:**
|
||||
```python
|
||||
{
|
||||
"巨升智能": "具身智能",
|
||||
"奇迹创坛": "奇绩创坛",
|
||||
"矩阵公司": "初创公司",
|
||||
}
|
||||
```
|
||||
|
||||
## 添加自定义规则
|
||||
|
||||
### 步骤1: 识别错误模式
|
||||
|
||||
从修复报告中识别重复出现的错误。
|
||||
|
||||
### 步骤2: 选择规则类型
|
||||
|
||||
- **简单替换** → 使用 CORRECTIONS_DICT
|
||||
- **需要上下文** → 使用 CONTEXT_RULES
|
||||
|
||||
### 步骤3: 添加到词典
|
||||
|
||||
编辑 `scripts/fix_transcription.py`:
|
||||
|
||||
```python
|
||||
CORRECTIONS_DICT = {
|
||||
# 现有规则...
|
||||
"你的错误": "正确词汇", # 添加新规则
|
||||
}
|
||||
```
|
||||
|
||||
### 步骤4: 测试
|
||||
|
||||
运行修复脚本测试新规则。
|
||||
|
||||
## 常见错误类型
|
||||
|
||||
### 同音字错误
|
||||
```python
|
||||
"股价": "框架",
|
||||
"三观": "三关",
|
||||
```
|
||||
|
||||
### 专业术语
|
||||
```python
|
||||
"巨升智能": "具身智能",
|
||||
"近距离": "具身", # 某些上下文中
|
||||
```
|
||||
|
||||
### 公司名称
|
||||
```python
|
||||
"奇迹创坛": "奇绩创坛",
|
||||
```
|
||||
|
||||
## 优先级
|
||||
|
||||
1. 先应用 CONTEXT_RULES (精确匹配)
|
||||
2. 再应用 CORRECTIONS_DICT (全局替换)
|
||||
395
transcript-fixer/references/file_formats.md
Normal file
395
transcript-fixer/references/file_formats.md
Normal file
@@ -0,0 +1,395 @@
|
||||
# Storage Format Reference
|
||||
|
||||
This document describes the SQLite database format used by transcript-fixer v2.0.
|
||||
|
||||
## Table of Contents
|
||||
|
||||
- [Database Location](#database-location)
|
||||
- [Database Schema](#database-schema)
|
||||
- [Core Tables](#core-tables)
|
||||
- [Views](#views)
|
||||
- [Querying the Database](#querying-the-database)
|
||||
- [Using Python API](#using-python-api)
|
||||
- [Using SQLite CLI](#using-sqlite-cli)
|
||||
- [Import/Export](#importexport)
|
||||
- [Export to JSON](#export-to-json)
|
||||
- [Import from JSON](#import-from-json)
|
||||
- [Backup Strategy](#backup-strategy)
|
||||
- [Automatic Backups](#automatic-backups)
|
||||
- [Manual Backups](#manual-backups)
|
||||
- [Version Control](#version-control)
|
||||
- [Best Practices](#best-practices)
|
||||
- [Troubleshooting](#troubleshooting)
|
||||
- [Database Locked](#database-locked)
|
||||
- [Corrupted Database](#corrupted-database)
|
||||
- [Missing Tables](#missing-tables)
|
||||
|
||||
## Database Location
|
||||
|
||||
**Path**: `~/.transcript-fixer/corrections.db`
|
||||
|
||||
**Type**: SQLite 3 database with ACID guarantees
|
||||
|
||||
## Database Schema
|
||||
|
||||
### Core Tables
|
||||
|
||||
#### corrections
|
||||
|
||||
Main correction dictionary storage.
|
||||
|
||||
| Column | Type | Constraints | Description |
|
||||
|--------|------|-------------|-------------|
|
||||
| id | INTEGER | PRIMARY KEY | Auto-increment ID |
|
||||
| from_text | TEXT | NOT NULL | Original (incorrect) text |
|
||||
| to_text | TEXT | NOT NULL | Corrected text |
|
||||
| domain | TEXT | DEFAULT 'general' | Correction domain |
|
||||
| source | TEXT | CHECK IN ('manual', 'learned', 'imported') | Origin of correction |
|
||||
| confidence | REAL | CHECK 0.0-1.0 | Confidence score |
|
||||
| added_by | TEXT | | User who added |
|
||||
| added_at | TIMESTAMP | DEFAULT CURRENT_TIMESTAMP | When added |
|
||||
| usage_count | INTEGER | DEFAULT 0, CHECK >= 0 | Times used |
|
||||
| last_used | TIMESTAMP | | Last usage time |
|
||||
| notes | TEXT | | Optional notes |
|
||||
| is_active | BOOLEAN | DEFAULT 1 | Soft delete flag |
|
||||
|
||||
**Unique Constraint**: `(from_text, domain)`
|
||||
|
||||
**Indexes**: domain, source, added_at, is_active, from_text
|
||||
|
||||
#### context_rules
|
||||
|
||||
Regex-based context-aware correction rules.
|
||||
|
||||
| Column | Type | Constraints | Description |
|
||||
|--------|------|-------------|-------------|
|
||||
| id | INTEGER | PRIMARY KEY | Auto-increment ID |
|
||||
| pattern | TEXT | NOT NULL, UNIQUE | Regex pattern |
|
||||
| replacement | TEXT | NOT NULL | Replacement text |
|
||||
| description | TEXT | | Rule explanation |
|
||||
| priority | INTEGER | DEFAULT 0 | Higher = applied first |
|
||||
| is_active | BOOLEAN | DEFAULT 1 | Enable/disable |
|
||||
| added_at | TIMESTAMP | DEFAULT CURRENT_TIMESTAMP | When added |
|
||||
| added_by | TEXT | | User who added |
|
||||
|
||||
**Indexes**: priority (DESC), is_active
|
||||
|
||||
#### correction_history
|
||||
|
||||
Audit log for all correction runs.
|
||||
|
||||
| Column | Type | Constraints | Description |
|
||||
|--------|------|-------------|-------------|
|
||||
| id | INTEGER | PRIMARY KEY | Auto-increment ID |
|
||||
| filename | TEXT | NOT NULL | File corrected |
|
||||
| domain | TEXT | NOT NULL | Domain used |
|
||||
| run_timestamp | TIMESTAMP | DEFAULT CURRENT_TIMESTAMP | When run |
|
||||
| original_length | INTEGER | CHECK >= 0 | Original file size |
|
||||
| stage1_changes | INTEGER | CHECK >= 0 | Dictionary changes |
|
||||
| stage2_changes | INTEGER | CHECK >= 0 | AI changes |
|
||||
| model | TEXT | | AI model used |
|
||||
| execution_time_ms | INTEGER | | Runtime in ms |
|
||||
| success | BOOLEAN | DEFAULT 1 | Success flag |
|
||||
| error_message | TEXT | | Error if failed |
|
||||
|
||||
**Indexes**: run_timestamp (DESC), domain, success
|
||||
|
||||
#### correction_changes
|
||||
|
||||
Detailed changes made in each run.
|
||||
|
||||
| Column | Type | Constraints | Description |
|
||||
|--------|------|-------------|-------------|
|
||||
| id | INTEGER | PRIMARY KEY | Auto-increment ID |
|
||||
| history_id | INTEGER | FOREIGN KEY → correction_history | Parent run |
|
||||
| line_number | INTEGER | | Line in file |
|
||||
| from_text | TEXT | NOT NULL | Original text |
|
||||
| to_text | TEXT | NOT NULL | Corrected text |
|
||||
| rule_type | TEXT | CHECK IN ('context', 'dictionary', 'ai') | Rule type |
|
||||
| rule_id | INTEGER | | Reference to rule |
|
||||
| context_before | TEXT | | Text before |
|
||||
| context_after | TEXT | | Text after |
|
||||
|
||||
**Foreign Key**: history_id → correction_history.id (CASCADE DELETE)
|
||||
|
||||
**Indexes**: history_id, rule_type
|
||||
|
||||
#### learned_suggestions
|
||||
|
||||
AI-detected patterns pending review.
|
||||
|
||||
| Column | Type | Constraints | Description |
|
||||
|--------|------|-------------|-------------|
|
||||
| id | INTEGER | PRIMARY KEY | Auto-increment ID |
|
||||
| from_text | TEXT | NOT NULL | Pattern detected |
|
||||
| to_text | TEXT | NOT NULL | Suggested correction |
|
||||
| domain | TEXT | DEFAULT 'general' | Domain |
|
||||
| frequency | INTEGER | CHECK > 0 | Times seen |
|
||||
| confidence | REAL | CHECK 0.0-1.0 | Confidence score |
|
||||
| first_seen | TIMESTAMP | DEFAULT CURRENT_TIMESTAMP | First occurrence |
|
||||
| last_seen | TIMESTAMP | DEFAULT CURRENT_TIMESTAMP | Last occurrence |
|
||||
| status | TEXT | CHECK IN ('pending', 'approved', 'rejected') | Review status |
|
||||
| reviewed_at | TIMESTAMP | | When reviewed |
|
||||
| reviewed_by | TEXT | | Who reviewed |
|
||||
|
||||
**Unique Constraint**: `(from_text, to_text, domain)`
|
||||
|
||||
**Indexes**: status, domain, confidence (DESC), frequency (DESC)
|
||||
|
||||
#### suggestion_examples
|
||||
|
||||
Example occurrences of learned patterns.
|
||||
|
||||
| Column | Type | Constraints | Description |
|
||||
|--------|------|-------------|-------------|
|
||||
| id | INTEGER | PRIMARY KEY | Auto-increment ID |
|
||||
| suggestion_id | INTEGER | FOREIGN KEY → learned_suggestions | Parent suggestion |
|
||||
| filename | TEXT | NOT NULL | File where found |
|
||||
| line_number | INTEGER | | Line number |
|
||||
| context | TEXT | NOT NULL | Surrounding text |
|
||||
| occurred_at | TIMESTAMP | DEFAULT CURRENT_TIMESTAMP | When found |
|
||||
|
||||
**Foreign Key**: suggestion_id → learned_suggestions.id (CASCADE DELETE)
|
||||
|
||||
**Index**: suggestion_id
|
||||
|
||||
#### system_config
|
||||
|
||||
System configuration key-value store.
|
||||
|
||||
| Column | Type | Constraints | Description |
|
||||
|--------|------|-------------|-------------|
|
||||
| key | TEXT | PRIMARY KEY | Config key |
|
||||
| value | TEXT | NOT NULL | Config value |
|
||||
| value_type | TEXT | CHECK IN ('string', 'int', 'float', 'boolean', 'json') | Value type |
|
||||
| description | TEXT | | Config description |
|
||||
| updated_at | TIMESTAMP | DEFAULT CURRENT_TIMESTAMP | Last update |
|
||||
|
||||
**Default Values**:
|
||||
- `schema_version`: "2.0"
|
||||
- `api_provider`: "GLM"
|
||||
- `api_model`: "GLM-4.6"
|
||||
- `default_domain`: "general"
|
||||
- `auto_learn_enabled`: "true"
|
||||
- `learning_frequency_threshold`: "3"
|
||||
- `learning_confidence_threshold`: "0.8"
|
||||
|
||||
#### audit_log
|
||||
|
||||
Comprehensive audit trail for all operations.
|
||||
|
||||
| Column | Type | Constraints | Description |
|
||||
|--------|------|-------------|-------------|
|
||||
| id | INTEGER | PRIMARY KEY | Auto-increment ID |
|
||||
| timestamp | TIMESTAMP | DEFAULT CURRENT_TIMESTAMP | When occurred |
|
||||
| action | TEXT | NOT NULL | Action type |
|
||||
| entity_type | TEXT | NOT NULL | Entity affected |
|
||||
| entity_id | INTEGER | | Entity ID |
|
||||
| user | TEXT | | User who performed |
|
||||
| details | TEXT | | Action details |
|
||||
| success | BOOLEAN | DEFAULT 1 | Success flag |
|
||||
| error_message | TEXT | | Error if failed |
|
||||
|
||||
**Indexes**: timestamp (DESC), action, entity_type, success
|
||||
|
||||
### Views
|
||||
|
||||
#### active_corrections
|
||||
|
||||
Quick access to active corrections.
|
||||
|
||||
```sql
|
||||
SELECT id, from_text, to_text, domain, source, confidence, usage_count, last_used, added_at
|
||||
FROM corrections
|
||||
WHERE is_active = 1
|
||||
ORDER BY domain, from_text;
|
||||
```
|
||||
|
||||
#### pending_suggestions
|
||||
|
||||
Suggestions pending review with example count.
|
||||
|
||||
```sql
|
||||
SELECT s.id, s.from_text, s.to_text, s.domain, s.frequency, s.confidence,
|
||||
s.first_seen, s.last_seen, COUNT(e.id) as example_count
|
||||
FROM learned_suggestions s
|
||||
LEFT JOIN suggestion_examples e ON s.id = e.suggestion_id
|
||||
WHERE s.status = 'pending'
|
||||
GROUP BY s.id
|
||||
ORDER BY s.confidence DESC, s.frequency DESC;
|
||||
```
|
||||
|
||||
#### correction_statistics
|
||||
|
||||
Statistics per domain.
|
||||
|
||||
```sql
|
||||
SELECT domain,
|
||||
COUNT(*) as total_corrections,
|
||||
COUNT(CASE WHEN source = 'manual' THEN 1 END) as manual_count,
|
||||
COUNT(CASE WHEN source = 'learned' THEN 1 END) as learned_count,
|
||||
COUNT(CASE WHEN source = 'imported' THEN 1 END) as imported_count,
|
||||
SUM(usage_count) as total_usage,
|
||||
MAX(added_at) as last_updated
|
||||
FROM corrections
|
||||
WHERE is_active = 1
|
||||
GROUP BY domain;
|
||||
```
|
||||
|
||||
## Querying the Database
|
||||
|
||||
### Using Python API
|
||||
|
||||
```python
|
||||
from pathlib import Path
|
||||
from core import CorrectionRepository, CorrectionService
|
||||
|
||||
# Initialize
|
||||
db_path = Path.home() / ".transcript-fixer" / "corrections.db"
|
||||
repository = CorrectionRepository(db_path)
|
||||
service = CorrectionService(repository)
|
||||
|
||||
# Add correction
|
||||
service.add_correction("错误", "正确", domain="general")
|
||||
|
||||
# Get corrections
|
||||
corrections = service.get_corrections(domain="general")
|
||||
|
||||
# Get statistics
|
||||
stats = service.get_statistics(domain="general")
|
||||
print(f"Total: {stats['total_corrections']}")
|
||||
|
||||
# Close
|
||||
service.close()
|
||||
```
|
||||
|
||||
### Using SQLite CLI
|
||||
|
||||
```bash
|
||||
# Open database
|
||||
sqlite3 ~/.transcript-fixer/corrections.db
|
||||
|
||||
# View active corrections
|
||||
SELECT from_text, to_text, domain FROM active_corrections;
|
||||
|
||||
# View statistics
|
||||
SELECT * FROM correction_statistics;
|
||||
|
||||
# View pending suggestions
|
||||
SELECT * FROM pending_suggestions;
|
||||
|
||||
# Check schema version
|
||||
SELECT value FROM system_config WHERE key = 'schema_version';
|
||||
```
|
||||
|
||||
## Import/Export
|
||||
|
||||
### Export to JSON
|
||||
|
||||
```python
|
||||
service = _get_service()
|
||||
corrections = service.export_corrections(domain="general")
|
||||
|
||||
# Write to file
|
||||
import json
|
||||
with open("export.json", "w", encoding="utf-8") as f:
|
||||
json.dump({
|
||||
"version": "2.0",
|
||||
"domain": "general",
|
||||
"corrections": corrections
|
||||
}, f, ensure_ascii=False, indent=2)
|
||||
```
|
||||
|
||||
### Import from JSON
|
||||
|
||||
```python
|
||||
import json
|
||||
|
||||
with open("import.json", "r", encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
|
||||
service = _get_service()
|
||||
inserted, updated, skipped = service.import_corrections(
|
||||
corrections=data["corrections"],
|
||||
domain=data.get("domain", "general"),
|
||||
merge=True,
|
||||
validate_all=True
|
||||
)
|
||||
|
||||
print(f"Imported: {inserted} new, {updated} updated, {skipped} skipped")
|
||||
```
|
||||
|
||||
## Backup Strategy
|
||||
|
||||
### Automatic Backups
|
||||
|
||||
The system maintains database integrity through SQLite's ACID guarantees and automatic journaling.
|
||||
|
||||
### Manual Backups
|
||||
|
||||
```bash
|
||||
# Backup database
|
||||
cp ~/.transcript-fixer/corrections.db ~/backups/corrections_$(date +%Y%m%d).db
|
||||
|
||||
# Or use SQLite backup
|
||||
sqlite3 ~/.transcript-fixer/corrections.db ".backup ~/backups/corrections.db"
|
||||
```
|
||||
|
||||
### Version Control
|
||||
|
||||
**Recommended**: Use Git for configuration and export files, but NOT for the database:
|
||||
|
||||
```bash
|
||||
# .gitignore
|
||||
*.db
|
||||
*.db-journal
|
||||
*.bak
|
||||
```
|
||||
|
||||
Instead, export corrections periodically:
|
||||
|
||||
```bash
|
||||
python scripts/fix_transcription.py --export-json corrections_backup.json
|
||||
git add corrections_backup.json
|
||||
git commit -m "Backup corrections"
|
||||
```
|
||||
|
||||
## Best Practices
|
||||
|
||||
1. **Regular Exports**: Export to JSON weekly for team sharing
|
||||
2. **Database Backups**: Backup `.db` file before major changes
|
||||
3. **Use Transactions**: All modifications use ACID transactions automatically
|
||||
4. **Soft Deletes**: Records are marked inactive, not deleted (preserves audit trail)
|
||||
5. **Validate**: Run `--validate` after manual database changes
|
||||
6. **Statistics**: Check usage patterns via `correction_statistics` view
|
||||
7. **Cleanup**: Old history can be archived (query by `run_timestamp`)
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Database Locked
|
||||
|
||||
```bash
|
||||
# Check for lingering connections
|
||||
lsof ~/.transcript-fixer/corrections.db
|
||||
|
||||
# If needed, backup and recreate
|
||||
cp corrections.db corrections_backup.db
|
||||
sqlite3 corrections.db "VACUUM;"
|
||||
```
|
||||
|
||||
### Corrupted Database
|
||||
|
||||
```bash
|
||||
# Check integrity
|
||||
sqlite3 corrections.db "PRAGMA integrity_check;"
|
||||
|
||||
# Recover if possible
|
||||
sqlite3 corrections.db ".recover" | sqlite3 corrections_new.db
|
||||
```
|
||||
|
||||
### Missing Tables
|
||||
|
||||
```bash
|
||||
# Reinitialize schema (safe, uses IF NOT EXISTS)
|
||||
python -c "from core import CorrectionRepository; from pathlib import Path; CorrectionRepository(Path.home() / '.transcript-fixer' / 'corrections.db')"
|
||||
```
|
||||
116
transcript-fixer/references/glm_api_setup.md
Normal file
116
transcript-fixer/references/glm_api_setup.md
Normal file
@@ -0,0 +1,116 @@
|
||||
# GLM API 配置指南
|
||||
|
||||
## API配置
|
||||
|
||||
### 设置环境变量
|
||||
|
||||
在运行脚本前,设置GLM API密钥环境变量:
|
||||
|
||||
```bash
|
||||
# Linux/macOS
|
||||
export GLM_API_KEY="your-api-key-here"
|
||||
|
||||
# Windows (PowerShell)
|
||||
$env:GLM_API_KEY="your-api-key-here"
|
||||
|
||||
# Windows (CMD)
|
||||
set GLM_API_KEY=your-api-key-here
|
||||
```
|
||||
|
||||
**永久设置** (推荐):
|
||||
|
||||
```bash
|
||||
# Linux/macOS: 添加到 ~/.bashrc 或 ~/.zshrc
|
||||
echo 'export GLM_API_KEY="your-api-key-here"' >> ~/.bashrc
|
||||
source ~/.bashrc
|
||||
|
||||
# Windows: 在系统环境变量中设置
|
||||
```
|
||||
|
||||
### 脚本配置
|
||||
|
||||
脚本会自动从环境变量读取API密钥:
|
||||
|
||||
```python
|
||||
# 脚本会检查环境变量
|
||||
if "GLM_API_KEY" not in os.environ:
|
||||
raise ValueError("请设置 GLM_API_KEY 环境变量")
|
||||
|
||||
os.environ["ANTHROPIC_BASE_URL"] = "https://open.bigmodel.cn/api/anthropic"
|
||||
os.environ["ANTHROPIC_API_KEY"] = os.environ["GLM_API_KEY"]
|
||||
|
||||
# 模型配置
|
||||
GLM_MODEL = "GLM-4.6" # 主力模型
|
||||
GLM_MODEL_FAST = "GLM-4.5-Air" # 快速模型(备用)
|
||||
```
|
||||
|
||||
## 支持的模型
|
||||
|
||||
| 模型名称 | 说明 | 用途 |
|
||||
|---------|------|------|
|
||||
| GLM-4.6 | 最强模型 | 默认使用,精度最高 |
|
||||
| GLM-4.5-Air | 快速模型 | 备用,速度更快 |
|
||||
|
||||
**注意**: 模型名称大小写不敏感。
|
||||
|
||||
## API认证
|
||||
|
||||
智谱GLM使用Anthropic兼容API:
|
||||
|
||||
```python
|
||||
headers = {
|
||||
"anthropic-version": "2023-06-01",
|
||||
"Authorization": f"Bearer {api_key}",
|
||||
"content-type": "application/json"
|
||||
}
|
||||
```
|
||||
|
||||
**关键点:**
|
||||
- 使用 `Authorization: Bearer` 头
|
||||
- 不要使用 `x-api-key` 头
|
||||
|
||||
## API调用示例
|
||||
|
||||
```python
|
||||
def call_glm_api(prompt: str) -> str:
|
||||
url = "https://open.bigmodel.cn/api/anthropic/v1/messages"
|
||||
headers = {
|
||||
"anthropic-version": "2023-06-01",
|
||||
"Authorization": f"Bearer {os.environ.get('ANTHROPIC_API_KEY')}",
|
||||
"content-type": "application/json"
|
||||
}
|
||||
|
||||
data = {
|
||||
"model": "GLM-4.6",
|
||||
"max_tokens": 8000,
|
||||
"temperature": 0.3,
|
||||
"messages": [{"role": "user", "content": prompt}]
|
||||
}
|
||||
|
||||
response = httpx.post(url, headers=headers, json=data, timeout=60.0)
|
||||
return response.json()["content"][0]["text"]
|
||||
```
|
||||
|
||||
## 获取API密钥
|
||||
|
||||
1. 访问 https://open.bigmodel.cn/
|
||||
2. 注册/登录账号
|
||||
3. 进入API管理页面
|
||||
4. 创建新的API密钥
|
||||
5. 复制密钥到配置中
|
||||
|
||||
## 费用
|
||||
|
||||
参考智谱AI官方定价:
|
||||
- GLM-4.6: 按token计费
|
||||
- GLM-4.5-Air: 更便宜的选择
|
||||
|
||||
## 故障排查
|
||||
|
||||
### 401错误
|
||||
- 检查API密钥是否正确
|
||||
- 确认使用 `Authorization: Bearer` 头
|
||||
|
||||
### 超时错误
|
||||
- 增加timeout参数
|
||||
- 考虑使用GLM-4.5-Air快速模型
|
||||
135
transcript-fixer/references/installation_setup.md
Normal file
135
transcript-fixer/references/installation_setup.md
Normal file
@@ -0,0 +1,135 @@
|
||||
# Setup Guide
|
||||
|
||||
Complete installation and configuration guide for transcript-fixer.
|
||||
|
||||
## Table of Contents
|
||||
|
||||
- [Installation](#installation)
|
||||
- [API Configuration](#api-configuration)
|
||||
- [Environment Setup](#environment-setup)
|
||||
- [Next Steps](#next-steps)
|
||||
|
||||
## Installation
|
||||
|
||||
### Dependencies
|
||||
|
||||
Install required dependencies using uv:
|
||||
|
||||
```bash
|
||||
uv pip install -r requirements.txt
|
||||
```
|
||||
|
||||
Or sync the project environment:
|
||||
|
||||
```bash
|
||||
uv sync
|
||||
```
|
||||
|
||||
**Required packages**:
|
||||
- `anthropic` - For Claude API integration (future)
|
||||
- `requests` - For GLM API calls
|
||||
- `difflib` - Standard library for diff generation
|
||||
|
||||
### Database Initialization
|
||||
|
||||
Initialize the SQLite database (first time only):
|
||||
|
||||
```bash
|
||||
uv run scripts/fix_transcription.py --init
|
||||
```
|
||||
|
||||
This creates `~/.transcript-fixer/corrections.db` with the complete schema:
|
||||
- 8 tables (corrections, context_rules, history, suggestions, etc.)
|
||||
- 3 views (active_corrections, pending_suggestions, statistics)
|
||||
- ACID transactions enabled
|
||||
- Automatic backups before migrations
|
||||
|
||||
See `file_formats.md` for complete database schema.
|
||||
|
||||
## API Configuration
|
||||
|
||||
### GLM API Key (Required for Stage 2)
|
||||
|
||||
Stage 2 AI corrections require a GLM API key.
|
||||
|
||||
1. **Obtain API key**: Visit https://open.bigmodel.cn/
|
||||
2. **Register** for an account
|
||||
3. **Generate** an API key from the dashboard
|
||||
4. **Set environment variable**:
|
||||
|
||||
```bash
|
||||
export GLM_API_KEY="your-api-key-here"
|
||||
```
|
||||
|
||||
**Persistence**: Add to shell profile for permanent access:
|
||||
|
||||
```bash
|
||||
# For bash
|
||||
echo 'export GLM_API_KEY="your-key"' >> ~/.bashrc
|
||||
source ~/.bashrc
|
||||
|
||||
# For zsh
|
||||
echo 'export GLM_API_KEY="your-key"' >> ~/.zshrc
|
||||
source ~/.zshrc
|
||||
```
|
||||
|
||||
### Verify Configuration
|
||||
|
||||
Run validation to check setup:
|
||||
|
||||
```bash
|
||||
uv run scripts/fix_transcription.py --validate
|
||||
```
|
||||
|
||||
**Expected output**:
|
||||
```
|
||||
🔍 Validating transcript-fixer configuration...
|
||||
|
||||
✅ Configuration directory exists: ~/.transcript-fixer
|
||||
✅ Database valid: 0 corrections
|
||||
✅ All 8 tables present
|
||||
✅ GLM_API_KEY is set
|
||||
|
||||
============================================================
|
||||
✅ All checks passed! Configuration is valid.
|
||||
============================================================
|
||||
```
|
||||
|
||||
## Environment Setup
|
||||
|
||||
### Python Environment
|
||||
|
||||
**Required**: Python 3.8+
|
||||
|
||||
**Recommended**: Use uv for all Python operations:
|
||||
|
||||
```bash
|
||||
# Never use system python directly
|
||||
uv run scripts/fix_transcription.py # ✅ Correct
|
||||
|
||||
# Don't use system python
|
||||
python scripts/fix_transcription.py # ❌ Wrong
|
||||
```
|
||||
|
||||
### Directory Structure
|
||||
|
||||
After initialization, the directory structure is:
|
||||
|
||||
```
|
||||
~/.transcript-fixer/
|
||||
├── corrections.db # SQLite database
|
||||
├── corrections.YYYYMMDD.bak # Automatic backups
|
||||
└── (migration artifacts)
|
||||
```
|
||||
|
||||
**Important**: The `.db` file should NOT be committed to Git. Export corrections to JSON for version control instead.
|
||||
|
||||
## Next Steps
|
||||
|
||||
After setup:
|
||||
1. Add initial corrections (5-10 terms)
|
||||
2. Run first correction on a test file
|
||||
3. Review learned suggestions after 3-5 runs
|
||||
4. Build domain-specific dictionaries
|
||||
|
||||
See `workflow_guide.md` for detailed usage instructions.
|
||||
125
transcript-fixer/references/quick_reference.md
Normal file
125
transcript-fixer/references/quick_reference.md
Normal file
@@ -0,0 +1,125 @@
|
||||
# Quick Reference
|
||||
|
||||
**Storage**: transcript-fixer uses SQLite database for corrections storage.
|
||||
|
||||
**Database location**: `~/.transcript-fixer/corrections.db`
|
||||
|
||||
## Quick Start Examples
|
||||
|
||||
### Adding Corrections via CLI
|
||||
|
||||
```bash
|
||||
# Add a simple correction
|
||||
uv run scripts/fix_transcription.py --add "巨升智能" "具身智能" --domain embodied_ai
|
||||
|
||||
# Add corrections for specific domain
|
||||
uv run scripts/fix_transcription.py --add "奇迹创坛" "奇绩创坛" --domain general
|
||||
uv run scripts/fix_transcription.py --add "矩阵公司" "初创公司" --domain general
|
||||
```
|
||||
|
||||
### Adding Corrections via SQL
|
||||
|
||||
```bash
|
||||
sqlite3 ~/.transcript-fixer/corrections.db
|
||||
|
||||
# Insert corrections
|
||||
INSERT INTO corrections (from_text, to_text, domain, source)
|
||||
VALUES ('巨升智能', '具身智能', 'embodied_ai', 'manual');
|
||||
|
||||
INSERT INTO corrections (from_text, to_text, domain, source)
|
||||
VALUES ('巨升', '具身', 'embodied_ai', 'manual');
|
||||
|
||||
INSERT INTO corrections (from_text, to_text, domain, source)
|
||||
VALUES ('奇迹创坛', '奇绩创坛', 'general', 'manual');
|
||||
|
||||
# Exit
|
||||
.quit
|
||||
```
|
||||
|
||||
### Adding Context Rules via SQL
|
||||
|
||||
Context rules use regex patterns for context-aware corrections:
|
||||
|
||||
```bash
|
||||
sqlite3 ~/.transcript-fixer/corrections.db
|
||||
|
||||
# Add context-aware rules
|
||||
INSERT INTO context_rules (pattern, replacement, description, priority)
|
||||
VALUES ('巨升方向', '具身方向', '巨升→具身', 10);
|
||||
|
||||
INSERT INTO context_rules (pattern, replacement, description, priority)
|
||||
VALUES ('巨升现在', '具身现在', '巨升→具身', 10);
|
||||
|
||||
INSERT INTO context_rules (pattern, replacement, description, priority)
|
||||
VALUES ('近距离的去看', '近距离地去看', '的→地 副词修饰', 5);
|
||||
|
||||
# Exit
|
||||
.quit
|
||||
```
|
||||
|
||||
### Adding Corrections via Python API
|
||||
|
||||
Save as `add_corrections.py` and run with `uv run add_corrections.py`:
|
||||
|
||||
```python
|
||||
#!/usr/bin/env -S uv run
|
||||
from pathlib import Path
|
||||
from core import CorrectionRepository, CorrectionService
|
||||
|
||||
# Initialize service
|
||||
db_path = Path.home() / ".transcript-fixer" / "corrections.db"
|
||||
repository = CorrectionRepository(db_path)
|
||||
service = CorrectionService(repository)
|
||||
|
||||
# Add corrections
|
||||
corrections = [
|
||||
("巨升智能", "具身智能", "embodied_ai"),
|
||||
("巨升", "具身", "embodied_ai"),
|
||||
("奇迹创坛", "奇绩创坛", "general"),
|
||||
("火星营", "火星营", "general"),
|
||||
("矩阵公司", "初创公司", "general"),
|
||||
("股价", "框架", "general"),
|
||||
("三观", "三关", "general"),
|
||||
]
|
||||
|
||||
for from_text, to_text, domain in corrections:
|
||||
service.add_correction(from_text, to_text, domain)
|
||||
print(f"✅ Added: '{from_text}' → '{to_text}' (domain: {domain})")
|
||||
|
||||
# Close connection
|
||||
service.close()
|
||||
```
|
||||
|
||||
## Bulk Import Example
|
||||
|
||||
Use the provided bulk import script for importing multiple corrections:
|
||||
|
||||
```bash
|
||||
uv run scripts/examples/bulk_import.py
|
||||
```
|
||||
|
||||
## Querying the Database
|
||||
|
||||
### View Active Corrections
|
||||
|
||||
```bash
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "SELECT from_text, to_text, domain FROM active_corrections;"
|
||||
```
|
||||
|
||||
### View Statistics
|
||||
|
||||
```bash
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "SELECT * FROM correction_statistics;"
|
||||
```
|
||||
|
||||
### View Context Rules
|
||||
|
||||
```bash
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "SELECT pattern, replacement, priority FROM context_rules WHERE is_active = 1 ORDER BY priority DESC;"
|
||||
```
|
||||
|
||||
## See Also
|
||||
|
||||
- `references/file_formats.md` - Complete database schema documentation
|
||||
- `references/script_parameters.md` - CLI command reference
|
||||
- `SKILL.md` - Main user documentation
|
||||
186
transcript-fixer/references/script_parameters.md
Normal file
186
transcript-fixer/references/script_parameters.md
Normal file
@@ -0,0 +1,186 @@
|
||||
# Script Parameters Reference
|
||||
|
||||
Detailed command-line parameters and usage examples for transcript-fixer Python scripts.
|
||||
|
||||
## Table of Contents
|
||||
|
||||
- [fix_transcription.py](#fixtranscriptionpy) - Main correction pipeline
|
||||
- [Setup Commands](#setup-commands)
|
||||
- [Correction Management](#correction-management)
|
||||
- [Correction Workflow](#correction-workflow)
|
||||
- [Learning Commands](#learning-commands)
|
||||
- [diff_generator.py](#diffgeneratorpy) - Generate comparison reports
|
||||
- [Common Workflows](#common-workflows)
|
||||
- [Exit Codes](#exit-codes)
|
||||
- [Environment Variables](#environment-variables)
|
||||
|
||||
---
|
||||
|
||||
## fix_transcription.py
|
||||
|
||||
Main correction pipeline script supporting three processing stages.
|
||||
|
||||
### Syntax
|
||||
|
||||
```bash
|
||||
python scripts/fix_transcription.py --input <file> --stage <1|2|3> [--output <dir>]
|
||||
```
|
||||
|
||||
### Parameters
|
||||
|
||||
- `--input, -i` (required): Input Markdown file path
|
||||
- `--stage, -s` (optional): Stage to execute (default: 3)
|
||||
- `1` = Dictionary corrections only
|
||||
- `2` = AI corrections only (requires Stage 1 output file)
|
||||
- `3` = Both stages sequentially
|
||||
- `--output, -o` (optional): Output directory (defaults to input file directory)
|
||||
|
||||
### Usage Examples
|
||||
|
||||
**Run dictionary corrections only:**
|
||||
```bash
|
||||
python scripts/fix_transcription.py --input meeting.md --stage 1
|
||||
```
|
||||
|
||||
Output: `meeting_阶段1_词典修复.md`
|
||||
|
||||
**Run AI corrections only:**
|
||||
```bash
|
||||
python scripts/fix_transcription.py --input meeting_阶段1_词典修复.md --stage 2
|
||||
```
|
||||
|
||||
Output: `meeting_阶段2_AI修复.md`
|
||||
|
||||
Note: Requires Stage 1 output file as input.
|
||||
|
||||
**Run complete pipeline:**
|
||||
```bash
|
||||
python scripts/fix_transcription.py --input meeting.md --stage 3
|
||||
```
|
||||
|
||||
Outputs:
|
||||
- `meeting_阶段1_词典修复.md`
|
||||
- `meeting_阶段2_AI修复.md`
|
||||
|
||||
**Custom output directory:**
|
||||
```bash
|
||||
python scripts/fix_transcription.py --input meeting.md --stage 3 --output ./corrections
|
||||
```
|
||||
|
||||
### Exit Codes
|
||||
|
||||
- `0` - Success
|
||||
- `1` - Missing required parameters or file not found
|
||||
- `2` - GLM_API_KEY environment variable not set (Stage 2 or 3 only)
|
||||
- `3` - API request failed
|
||||
|
||||
## generate_diff_report.py
|
||||
|
||||
Multi-format diff report generator for comparing correction stages.
|
||||
|
||||
### Syntax
|
||||
|
||||
```bash
|
||||
python scripts/generate_diff_report.py --original <file> --stage1 <file> --stage2 <file> [--output-dir <dir>]
|
||||
```
|
||||
|
||||
### Parameters
|
||||
|
||||
- `--original` (required): Original transcript file path
|
||||
- `--stage1` (required): Stage 1 correction output file path
|
||||
- `--stage2` (required): Stage 2 correction output file path
|
||||
- `--output-dir` (optional): Output directory for diff reports (defaults to original file directory)
|
||||
|
||||
### Usage Examples
|
||||
|
||||
**Basic usage:**
|
||||
```bash
|
||||
python scripts/generate_diff_report.py \
|
||||
--original "meeting.md" \
|
||||
--stage1 "meeting_阶段1_词典修复.md" \
|
||||
--stage2 "meeting_阶段2_AI修复.md"
|
||||
```
|
||||
|
||||
**Custom output directory:**
|
||||
```bash
|
||||
python scripts/generate_diff_report.py \
|
||||
--original "meeting.md" \
|
||||
--stage1 "meeting_阶段1_词典修复.md" \
|
||||
--stage2 "meeting_阶段2_AI修复.md" \
|
||||
--output-dir "./reports"
|
||||
```
|
||||
|
||||
### Output Files
|
||||
|
||||
The script generates four comparison formats:
|
||||
|
||||
1. **Markdown summary** (`*_对比报告.md`)
|
||||
- High-level statistics and change summary
|
||||
- Word count changes per stage
|
||||
- Common error patterns identified
|
||||
|
||||
2. **Unified diff** (`*_unified.diff`)
|
||||
- Traditional Unix diff format
|
||||
- Suitable for command-line review or version control
|
||||
|
||||
3. **HTML side-by-side** (`*_对比.html`)
|
||||
- Visual side-by-side comparison
|
||||
- Color-coded additions/deletions
|
||||
- **Recommended for human review**
|
||||
|
||||
4. **Inline marked** (`*_行内对比.txt`)
|
||||
- Single-column format with inline change markers
|
||||
- Useful for quick text editor review
|
||||
|
||||
### Exit Codes
|
||||
|
||||
- `0` - Success
|
||||
- `1` - Missing required parameters or file not found
|
||||
- `2` - File format error (non-Markdown input)
|
||||
|
||||
## Common Workflows
|
||||
|
||||
### Testing Dictionary Changes
|
||||
|
||||
Test dictionary updates before running expensive AI corrections:
|
||||
|
||||
```bash
|
||||
# 1. Update CORRECTIONS_DICT in scripts/fix_transcription.py
|
||||
# 2. Run Stage 1 only
|
||||
python scripts/fix_transcription.py --input meeting.md --stage 1
|
||||
|
||||
# 3. Review output
|
||||
cat meeting_阶段1_词典修复.md
|
||||
|
||||
# 4. If satisfied, run Stage 2
|
||||
python scripts/fix_transcription.py --input meeting_阶段1_词典修复.md --stage 2
|
||||
```
|
||||
|
||||
### Batch Processing
|
||||
|
||||
Process multiple transcripts in sequence:
|
||||
|
||||
```bash
|
||||
for file in transcripts/*.md; do
|
||||
python scripts/fix_transcription.py --input "$file" --stage 3
|
||||
done
|
||||
```
|
||||
|
||||
### Quick Review Cycle
|
||||
|
||||
Generate and open comparison report immediately after correction:
|
||||
|
||||
```bash
|
||||
# Run corrections
|
||||
python scripts/fix_transcription.py --input meeting.md --stage 3
|
||||
|
||||
# Generate and open diff report
|
||||
python scripts/generate_diff_report.py \
|
||||
--original "meeting.md" \
|
||||
--stage1 "meeting_阶段1_词典修复.md" \
|
||||
--stage2 "meeting_阶段2_AI修复.md"
|
||||
|
||||
open meeting_对比.html # macOS
|
||||
# xdg-open meeting_对比.html # Linux
|
||||
# start meeting_对比.html # Windows
|
||||
```
|
||||
188
transcript-fixer/references/sql_queries.md
Normal file
188
transcript-fixer/references/sql_queries.md
Normal file
@@ -0,0 +1,188 @@
|
||||
# SQL Query Reference
|
||||
|
||||
Database location: `~/.transcript-fixer/corrections.db`
|
||||
|
||||
## Basic Operations
|
||||
|
||||
### Add Corrections
|
||||
|
||||
```sql
|
||||
-- Add a correction
|
||||
INSERT INTO corrections (from_text, to_text, domain, source)
|
||||
VALUES ('巨升智能', '具身智能', 'embodied_ai', 'manual');
|
||||
|
||||
INSERT INTO corrections (from_text, to_text, domain, source)
|
||||
VALUES ('奇迹创坛', '奇绩创坛', 'general', 'manual');
|
||||
```
|
||||
|
||||
### View Corrections
|
||||
|
||||
```sql
|
||||
-- View all active corrections
|
||||
SELECT from_text, to_text, domain, source, usage_count
|
||||
FROM active_corrections
|
||||
ORDER BY domain, from_text;
|
||||
|
||||
-- View corrections for specific domain
|
||||
SELECT from_text, to_text, usage_count, added_at
|
||||
FROM active_corrections
|
||||
WHERE domain = 'embodied_ai';
|
||||
```
|
||||
|
||||
## Context Rules
|
||||
|
||||
### Add Context-Aware Rules
|
||||
|
||||
```sql
|
||||
-- Add regex-based context rule
|
||||
INSERT INTO context_rules (pattern, replacement, description, priority)
|
||||
VALUES ('巨升方向', '具身方向', '巨升→具身', 10);
|
||||
|
||||
INSERT INTO context_rules (pattern, replacement, description, priority)
|
||||
VALUES ('近距离的去看', '近距离地去看', '的→地 副词修饰', 5);
|
||||
```
|
||||
|
||||
### View Rules
|
||||
|
||||
```sql
|
||||
-- View all active context rules (ordered by priority)
|
||||
SELECT pattern, replacement, description, priority
|
||||
FROM context_rules
|
||||
WHERE is_active = 1
|
||||
ORDER BY priority DESC;
|
||||
```
|
||||
|
||||
## Statistics
|
||||
|
||||
```sql
|
||||
-- View correction statistics by domain
|
||||
SELECT * FROM correction_statistics;
|
||||
|
||||
-- Count corrections by source
|
||||
SELECT source, COUNT(*) as count, SUM(usage_count) as total_usage
|
||||
FROM corrections
|
||||
WHERE is_active = 1
|
||||
GROUP BY source;
|
||||
|
||||
-- Most frequently used corrections
|
||||
SELECT from_text, to_text, domain, usage_count, last_used
|
||||
FROM corrections
|
||||
WHERE is_active = 1 AND usage_count > 0
|
||||
ORDER BY usage_count DESC
|
||||
LIMIT 10;
|
||||
```
|
||||
|
||||
## Learning and Suggestions
|
||||
|
||||
### View Suggestions
|
||||
|
||||
```sql
|
||||
-- View pending suggestions
|
||||
SELECT * FROM pending_suggestions;
|
||||
|
||||
-- View high-confidence suggestions
|
||||
SELECT from_text, to_text, domain, frequency, confidence
|
||||
FROM learned_suggestions
|
||||
WHERE status = 'pending' AND confidence >= 0.8
|
||||
ORDER BY confidence DESC, frequency DESC;
|
||||
```
|
||||
|
||||
### Approve Suggestions
|
||||
|
||||
```sql
|
||||
-- Insert into corrections
|
||||
INSERT INTO corrections (from_text, to_text, domain, source, confidence)
|
||||
SELECT from_text, to_text, domain, 'learned', confidence
|
||||
FROM learned_suggestions
|
||||
WHERE id = 1;
|
||||
|
||||
-- Mark as approved
|
||||
UPDATE learned_suggestions
|
||||
SET status = 'approved', reviewed_at = CURRENT_TIMESTAMP
|
||||
WHERE id = 1;
|
||||
```
|
||||
|
||||
## History and Audit
|
||||
|
||||
```sql
|
||||
-- View recent correction runs
|
||||
SELECT filename, domain, stage1_changes, stage2_changes, run_timestamp
|
||||
FROM correction_history
|
||||
ORDER BY run_timestamp DESC
|
||||
LIMIT 10;
|
||||
|
||||
-- View detailed changes for a specific run
|
||||
SELECT ch.line_number, ch.from_text, ch.to_text, ch.rule_type
|
||||
FROM correction_changes ch
|
||||
JOIN correction_history h ON ch.history_id = h.id
|
||||
WHERE h.filename = 'meeting.md'
|
||||
ORDER BY ch.line_number;
|
||||
|
||||
-- Calculate success rate
|
||||
SELECT
|
||||
COUNT(*) as total_runs,
|
||||
SUM(CASE WHEN success = 1 THEN 1 ELSE 0 END) as successful,
|
||||
ROUND(100.0 * SUM(CASE WHEN success = 1 THEN 1 ELSE 0 END) / COUNT(*), 2) as success_rate
|
||||
FROM correction_history;
|
||||
```
|
||||
|
||||
## Maintenance
|
||||
|
||||
```sql
|
||||
-- Deactivate (soft delete) a correction
|
||||
UPDATE corrections
|
||||
SET is_active = 0
|
||||
WHERE from_text = '错误词' AND domain = 'general';
|
||||
|
||||
-- Reactivate a correction
|
||||
UPDATE corrections
|
||||
SET is_active = 1
|
||||
WHERE from_text = '错误词' AND domain = 'general';
|
||||
|
||||
-- Update correction confidence
|
||||
UPDATE corrections
|
||||
SET confidence = 0.95
|
||||
WHERE from_text = '巨升' AND to_text = '具身';
|
||||
|
||||
-- Delete old history (older than 90 days)
|
||||
DELETE FROM correction_history
|
||||
WHERE run_timestamp < datetime('now', '-90 days');
|
||||
|
||||
-- Reclaim space
|
||||
VACUUM;
|
||||
```
|
||||
|
||||
## System Configuration
|
||||
|
||||
```sql
|
||||
-- View system configuration
|
||||
SELECT key, value, description FROM system_config;
|
||||
|
||||
-- Update configuration
|
||||
UPDATE system_config
|
||||
SET value = '5'
|
||||
WHERE key = 'learning_frequency_threshold';
|
||||
|
||||
-- Check schema version
|
||||
SELECT value FROM system_config WHERE key = 'schema_version';
|
||||
```
|
||||
|
||||
## Export
|
||||
|
||||
```sql
|
||||
-- Export corrections as CSV
|
||||
.mode csv
|
||||
.headers on
|
||||
.output corrections_export.csv
|
||||
SELECT from_text, to_text, domain, source, confidence, usage_count, added_at
|
||||
FROM active_corrections;
|
||||
.output stdout
|
||||
```
|
||||
|
||||
For JSON export, use Python script with `service.export_corrections()` instead.
|
||||
|
||||
## See Also
|
||||
|
||||
- `references/file_formats.md` - Complete database schema documentation
|
||||
- `references/quick_reference.md` - CLI command quick reference
|
||||
- `SKILL.md` - Main user documentation
|
||||
371
transcript-fixer/references/team_collaboration.md
Normal file
371
transcript-fixer/references/team_collaboration.md
Normal file
@@ -0,0 +1,371 @@
|
||||
# Team Collaboration Guide
|
||||
|
||||
This guide explains how to share correction knowledge across teams using export/import and Git workflows.
|
||||
|
||||
## Table of Contents
|
||||
|
||||
- [Export/Import Workflow](#exportimport-workflow)
|
||||
- [Export Corrections](#export-corrections)
|
||||
- [Import from Teammate](#import-from-teammate)
|
||||
- [Team Workflow Example](#team-workflow-example)
|
||||
- [Git-Based Collaboration](#git-based-collaboration)
|
||||
- [Initial Setup](#initial-setup)
|
||||
- [Team Members Clone](#team-members-clone)
|
||||
- [Ongoing Sync](#ongoing-sync)
|
||||
- [Handling Conflicts](#handling-conflicts)
|
||||
- [Selective Domain Sharing](#selective-domain-sharing)
|
||||
- [Finance Team](#finance-team)
|
||||
- [AI Team](#ai-team)
|
||||
- [Individual imports specific domains](#individual-imports-specific-domains)
|
||||
- [Git Branching Strategy](#git-branching-strategy)
|
||||
- [Feature Branches](#feature-branches)
|
||||
- [Domain Branches (Alternative)](#domain-branches-alternative)
|
||||
- [Automated Sync (Advanced)](#automated-sync-advanced)
|
||||
- [macOS/Linux Cron](#macoslinux-cron)
|
||||
- [Windows Task Scheduler](#windows-task-scheduler)
|
||||
- [Backup and Recovery](#backup-and-recovery)
|
||||
- [Backup Strategy](#backup-strategy)
|
||||
- [Recovery from Backup](#recovery-from-backup)
|
||||
- [Recovery from Git](#recovery-from-git)
|
||||
- [Team Best Practices](#team-best-practices)
|
||||
- [Integration with CI/CD](#integration-with-cicd)
|
||||
- [GitHub Actions Example](#github-actions-example)
|
||||
- [Troubleshooting](#troubleshooting)
|
||||
- [Import Failed](#import-failed)
|
||||
- [Git Sync Failed](#git-sync-failed)
|
||||
- [Merge Conflicts Too Complex](#merge-conflicts-too-complex)
|
||||
- [Security Considerations](#security-considerations)
|
||||
- [Further Reading](#further-reading)
|
||||
|
||||
## Export/Import Workflow
|
||||
|
||||
### Export Corrections
|
||||
|
||||
Share your corrections with team members:
|
||||
|
||||
```bash
|
||||
# Export specific domain
|
||||
python scripts/fix_transcription.py --export team_corrections.json --domain embodied_ai
|
||||
|
||||
# Export general corrections
|
||||
python scripts/fix_transcription.py --export team_corrections.json
|
||||
```
|
||||
|
||||
**Output**: Creates a standalone JSON file with your corrections.
|
||||
|
||||
### Import from Teammate
|
||||
|
||||
Two modes: **merge** (combine) or **replace** (overwrite):
|
||||
|
||||
```bash
|
||||
# Merge (recommended) - combines with existing corrections
|
||||
python scripts/fix_transcription.py --import team_corrections.json --merge
|
||||
|
||||
# Replace - overwrites existing corrections (dangerous!)
|
||||
python scripts/fix_transcription.py --import team_corrections.json
|
||||
```
|
||||
|
||||
**Merge behavior**:
|
||||
- Adds new corrections
|
||||
- Updates existing corrections with imported values
|
||||
- Preserves corrections not in import file
|
||||
|
||||
### Team Workflow Example
|
||||
|
||||
**Person A (Domain Expert)**:
|
||||
```bash
|
||||
# Build correction dictionary
|
||||
python fix_transcription.py --add "巨升" "具身" --domain embodied_ai
|
||||
python fix_transcription.py --add "奇迹创坛" "奇绩创坛" --domain embodied_ai
|
||||
# ... add 50 more corrections ...
|
||||
|
||||
# Export for team
|
||||
python fix_transcription.py --export ai_corrections.json --domain embodied_ai
|
||||
# Send ai_corrections.json to team via Slack/email
|
||||
```
|
||||
|
||||
**Person B (Team Member)**:
|
||||
```bash
|
||||
# Receive ai_corrections.json
|
||||
# Import and merge with existing corrections
|
||||
python fix_transcription.py --import ai_corrections.json --merge
|
||||
|
||||
# Now Person B has all 50+ corrections!
|
||||
```
|
||||
|
||||
## Git-Based Collaboration
|
||||
|
||||
For teams using Git, version control the entire correction database.
|
||||
|
||||
### Initial Setup
|
||||
|
||||
**Person A (First User)**:
|
||||
```bash
|
||||
cd ~/.transcript-fixer
|
||||
git init
|
||||
git add corrections.json context_rules.json config.json
|
||||
git add domains/
|
||||
git commit -m "Initial correction database"
|
||||
|
||||
# Push to shared repo
|
||||
git remote add origin git@github.com:org/transcript-corrections.git
|
||||
git push -u origin main
|
||||
```
|
||||
|
||||
### Team Members Clone
|
||||
|
||||
**Person B, C, D (Team Members)**:
|
||||
```bash
|
||||
# Clone shared corrections
|
||||
git clone git@github.com:org/transcript-corrections.git ~/.transcript-fixer
|
||||
|
||||
# Now everyone has the same corrections!
|
||||
```
|
||||
|
||||
### Ongoing Sync
|
||||
|
||||
**Daily workflow**:
|
||||
```bash
|
||||
# Morning: Pull team updates
|
||||
cd ~/.transcript-fixer
|
||||
git pull origin main
|
||||
|
||||
# During day: Add corrections
|
||||
python fix_transcription.py --add "错误" "正确"
|
||||
|
||||
# Evening: Push your additions
|
||||
cd ~/.transcript-fixer
|
||||
git add corrections.json
|
||||
git commit -m "Added 5 new embodied AI corrections"
|
||||
git push origin main
|
||||
```
|
||||
|
||||
### Handling Conflicts
|
||||
|
||||
When two people add different corrections to same file:
|
||||
|
||||
```bash
|
||||
cd ~/.transcript-fixer
|
||||
git pull origin main
|
||||
|
||||
# If conflict occurs:
|
||||
# CONFLICT in corrections.json
|
||||
|
||||
# Option 1: Manual merge (recommended)
|
||||
nano corrections.json # Edit to combine both changes
|
||||
git add corrections.json
|
||||
git commit -m "Merged corrections from teammate"
|
||||
git push
|
||||
|
||||
# Option 2: Keep yours
|
||||
git checkout --ours corrections.json
|
||||
git add corrections.json
|
||||
git commit -m "Kept local corrections"
|
||||
git push
|
||||
|
||||
# Option 3: Keep theirs
|
||||
git checkout --theirs corrections.json
|
||||
git add corrections.json
|
||||
git commit -m "Used teammate's corrections"
|
||||
git push
|
||||
```
|
||||
|
||||
**Best Practice**: JSON merge conflicts are usually easy - just combine the correction entries from both versions.
|
||||
|
||||
## Selective Domain Sharing
|
||||
|
||||
Share only specific domains with different teams:
|
||||
|
||||
### Finance Team
|
||||
```bash
|
||||
# Finance team exports their domain
|
||||
python fix_transcription.py --export finance_corrections.json --domain finance
|
||||
|
||||
# Share finance_corrections.json with finance team only
|
||||
```
|
||||
|
||||
### AI Team
|
||||
```bash
|
||||
# AI team exports their domain
|
||||
python fix_transcription.py --export ai_corrections.json --domain embodied_ai
|
||||
|
||||
# Share ai_corrections.json with AI team only
|
||||
```
|
||||
|
||||
### Individual imports specific domains
|
||||
```bash
|
||||
# Alice works on both finance and AI
|
||||
python fix_transcription.py --import finance_corrections.json --merge
|
||||
python fix_transcription.py --import ai_corrections.json --merge
|
||||
```
|
||||
|
||||
## Git Branching Strategy
|
||||
|
||||
For larger teams, use branches for different domains or workflows:
|
||||
|
||||
### Feature Branches
|
||||
```bash
|
||||
# Create branch for major dictionary additions
|
||||
git checkout -b add-medical-terms
|
||||
python fix_transcription.py --add "医疗术语" "正确术语" --domain medical
|
||||
# ... add 100 medical corrections ...
|
||||
git add domains/medical.json
|
||||
git commit -m "Added 100 medical terminology corrections"
|
||||
git push origin add-medical-terms
|
||||
|
||||
# Create PR for review
|
||||
# After approval, merge to main
|
||||
```
|
||||
|
||||
### Domain Branches (Alternative)
|
||||
```bash
|
||||
# Separate branches per domain
|
||||
git checkout -b domain/embodied-ai
|
||||
# Work on AI corrections
|
||||
git push origin domain/embodied-ai
|
||||
|
||||
git checkout -b domain/finance
|
||||
# Work on finance corrections
|
||||
git push origin domain/finance
|
||||
```
|
||||
|
||||
## Automated Sync (Advanced)
|
||||
|
||||
Set up automatic Git sync using cron/Task Scheduler:
|
||||
|
||||
### macOS/Linux Cron
|
||||
```bash
|
||||
# Edit crontab
|
||||
crontab -e
|
||||
|
||||
# Add daily sync at 9 AM and 6 PM
|
||||
0 9,18 * * * cd ~/.transcript-fixer && git pull origin main && git push origin main
|
||||
```
|
||||
|
||||
### Windows Task Scheduler
|
||||
```powershell
|
||||
# Create scheduled task
|
||||
$action = New-ScheduledTaskAction -Execute "git" -Argument "pull origin main" -WorkingDirectory "$env:USERPROFILE\.transcript-fixer"
|
||||
$trigger = New-ScheduledTaskTrigger -Daily -At 9am
|
||||
Register-ScheduledTask -Action $action -Trigger $trigger -TaskName "SyncTranscriptCorrections"
|
||||
```
|
||||
|
||||
## Backup and Recovery
|
||||
|
||||
### Backup Strategy
|
||||
```bash
|
||||
# Weekly backup to cloud
|
||||
cd ~/.transcript-fixer
|
||||
tar -czf transcript-corrections-$(date +%Y%m%d).tar.gz corrections.json context_rules.json domains/
|
||||
# Upload to Dropbox/Google Drive/S3
|
||||
```
|
||||
|
||||
### Recovery from Backup
|
||||
```bash
|
||||
# Extract backup
|
||||
tar -xzf transcript-corrections-20250127.tar.gz -C ~/.transcript-fixer/
|
||||
```
|
||||
|
||||
### Recovery from Git
|
||||
```bash
|
||||
# View history
|
||||
cd ~/.transcript-fixer
|
||||
git log corrections.json
|
||||
|
||||
# Restore from 3 commits ago
|
||||
git checkout HEAD~3 corrections.json
|
||||
|
||||
# Or restore specific version
|
||||
git checkout abc123def corrections.json
|
||||
```
|
||||
|
||||
## Team Best Practices
|
||||
|
||||
1. **Pull Before Push**: Always `git pull` before starting work
|
||||
2. **Commit Often**: Small, frequent commits better than large infrequent ones
|
||||
3. **Descriptive Messages**: "Added 5 finance terms" better than "updates"
|
||||
4. **Review Process**: Use PRs for major dictionary changes (100+ corrections)
|
||||
5. **Domain Ownership**: Assign domain experts as reviewers
|
||||
6. **Weekly Sync**: Schedule team sync meetings to review learned suggestions
|
||||
7. **Backup Policy**: Weekly backups of entire `~/.transcript-fixer/`
|
||||
|
||||
## Integration with CI/CD
|
||||
|
||||
For enterprise teams, integrate validation into CI:
|
||||
|
||||
### GitHub Actions Example
|
||||
```yaml
|
||||
# .github/workflows/validate-corrections.yml
|
||||
name: Validate Corrections
|
||||
|
||||
on:
|
||||
pull_request:
|
||||
paths:
|
||||
- 'corrections.json'
|
||||
- 'domains/*.json'
|
||||
|
||||
jobs:
|
||||
validate:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v2
|
||||
|
||||
- name: Validate JSON
|
||||
run: |
|
||||
python -m json.tool corrections.json > /dev/null
|
||||
for file in domains/*.json; do
|
||||
python -m json.tool "$file" > /dev/null
|
||||
done
|
||||
|
||||
- name: Check for duplicates
|
||||
run: |
|
||||
python scripts/check_duplicates.py corrections.json
|
||||
```
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Import Failed
|
||||
```bash
|
||||
# Check JSON validity
|
||||
python -m json.tool team_corrections.json
|
||||
|
||||
# If invalid, fix JSON syntax errors
|
||||
nano team_corrections.json
|
||||
```
|
||||
|
||||
### Git Sync Failed
|
||||
```bash
|
||||
# Check remote connection
|
||||
git remote -v
|
||||
|
||||
# Re-add if needed
|
||||
git remote set-url origin git@github.com:org/corrections.git
|
||||
|
||||
# Verify SSH keys
|
||||
ssh -T git@github.com
|
||||
```
|
||||
|
||||
### Merge Conflicts Too Complex
|
||||
```bash
|
||||
# Nuclear option: Keep one version
|
||||
git checkout --ours corrections.json # Keep yours
|
||||
# OR
|
||||
git checkout --theirs corrections.json # Keep theirs
|
||||
|
||||
# Then re-import the other version
|
||||
python fix_transcription.py --import other_version.json --merge
|
||||
```
|
||||
|
||||
## Security Considerations
|
||||
|
||||
1. **Private Repos**: Use private Git repositories for company-specific corrections
|
||||
2. **Access Control**: Limit who can push to main branch
|
||||
3. **Secret Scanning**: Never commit API keys (already handled by security_scan.py)
|
||||
4. **Audit Trail**: Git history provides full audit trail of who changed what
|
||||
5. **Backup Encryption**: Encrypt backups if containing sensitive terminology
|
||||
|
||||
## Further Reading
|
||||
|
||||
- Git workflows: https://git-scm.com/book/en/v2/Git-Branching-Branching-Workflows
|
||||
- JSON validation: https://jsonlint.com/
|
||||
- Team Git practices: https://github.com/git-guides
|
||||
313
transcript-fixer/references/troubleshooting.md
Normal file
313
transcript-fixer/references/troubleshooting.md
Normal file
@@ -0,0 +1,313 @@
|
||||
# Troubleshooting Guide
|
||||
|
||||
Solutions to common issues and error conditions.
|
||||
|
||||
## Table of Contents
|
||||
|
||||
- [API Authentication Errors](#api-authentication-errors)
|
||||
- [GLM_API_KEY Not Set](#glm_api_key-not-set)
|
||||
- [Invalid API Key](#invalid-api-key)
|
||||
- [Learning System Issues](#learning-system-issues)
|
||||
- [No Suggestions Generated](#no-suggestions-generated)
|
||||
- [Database Issues](#database-issues)
|
||||
- [Database Not Found](#database-not-found)
|
||||
- [Database Locked](#database-locked)
|
||||
- [Corrupted Database](#corrupted-database)
|
||||
- [Missing Tables](#missing-tables)
|
||||
- [Common Pitfalls](#common-pitfalls)
|
||||
- [1. Stage Order Confusion](#1-stage-order-confusion)
|
||||
- [2. Overwriting Imports](#2-overwriting-imports)
|
||||
- [3. Ignoring Learned Suggestions](#3-ignoring-learned-suggestions)
|
||||
- [4. Testing on Large Files](#4-testing-on-large-files)
|
||||
- [5. Manual Database Edits Without Validation](#5-manual-database-edits-without-validation)
|
||||
- [6. Committing .db Files to Git](#6-committing-db-files-to-git)
|
||||
- [Validation Commands](#validation-commands)
|
||||
- [Quick Health Check](#quick-health-check)
|
||||
- [Detailed Diagnostics](#detailed-diagnostics)
|
||||
- [Getting Help](#getting-help)
|
||||
|
||||
## API Authentication Errors
|
||||
|
||||
### GLM_API_KEY Not Set
|
||||
|
||||
**Symptom**:
|
||||
```
|
||||
❌ Error: GLM_API_KEY environment variable not set
|
||||
Set it with: export GLM_API_KEY='your-key'
|
||||
```
|
||||
|
||||
**Solution**:
|
||||
```bash
|
||||
# Check if key is set
|
||||
echo $GLM_API_KEY
|
||||
|
||||
# If empty, export key
|
||||
export GLM_API_KEY="your-api-key-here"
|
||||
|
||||
# Verify
|
||||
uv run scripts/fix_transcription.py --validate
|
||||
```
|
||||
|
||||
**Persistence**: Add to shell profile (`.bashrc` or `.zshrc`) for permanent access.
|
||||
|
||||
See `glm_api_setup.md` for detailed API key management.
|
||||
|
||||
### Invalid API Key
|
||||
|
||||
**Symptom**: API calls fail with 401/403 errors
|
||||
|
||||
**Solutions**:
|
||||
1. Verify key is correct (copy from https://open.bigmodel.cn/)
|
||||
2. Check for extra spaces or quotes in the key
|
||||
3. Regenerate key if compromised
|
||||
4. Verify API quota hasn't been exceeded
|
||||
|
||||
## Learning System Issues
|
||||
|
||||
### No Suggestions Generated
|
||||
|
||||
**Symptom**: Running `--review-learned` shows no suggestions after multiple corrections.
|
||||
|
||||
**Requirements**:
|
||||
- Minimum 3 correction runs with consistent patterns
|
||||
- Learning frequency threshold ≥3 (default)
|
||||
- Learning confidence threshold ≥0.8 (default)
|
||||
|
||||
**Diagnostic steps**:
|
||||
|
||||
```bash
|
||||
# Check correction history count
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "SELECT COUNT(*) FROM correction_history;"
|
||||
|
||||
# If 0, no corrections have been run yet
|
||||
# If >0 but <3, run more corrections
|
||||
|
||||
# Check suggestions table
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "SELECT * FROM learned_suggestions;"
|
||||
|
||||
# Check system configuration
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "SELECT key, value FROM system_config WHERE key LIKE 'learning%';"
|
||||
```
|
||||
|
||||
**Solutions**:
|
||||
1. Run at least 3 correction sessions
|
||||
2. Ensure patterns repeat (same error → same correction)
|
||||
3. Verify database permissions (should be readable/writable)
|
||||
4. Check `correction_history` table has entries
|
||||
|
||||
## Database Issues
|
||||
|
||||
### Database Not Found
|
||||
|
||||
**Symptom**:
|
||||
```
|
||||
⚠️ Database not found: ~/.transcript-fixer/corrections.db
|
||||
```
|
||||
|
||||
**Solution**:
|
||||
```bash
|
||||
uv run scripts/fix_transcription.py --init
|
||||
```
|
||||
|
||||
This creates the database with the complete schema.
|
||||
|
||||
### Database Locked
|
||||
|
||||
**Symptom**:
|
||||
```
|
||||
Error: database is locked
|
||||
```
|
||||
|
||||
**Causes**:
|
||||
- Another process is accessing the database
|
||||
- Unfinished transaction from crashed process
|
||||
- File permissions issue
|
||||
|
||||
**Solutions**:
|
||||
|
||||
```bash
|
||||
# Check for processes using the database
|
||||
lsof ~/.transcript-fixer/corrections.db
|
||||
|
||||
# If processes found, kill them or wait for completion
|
||||
|
||||
# If database is corrupted, backup and recreate
|
||||
cp ~/.transcript-fixer/corrections.db ~/.transcript-fixer/corrections_backup.db
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "VACUUM;"
|
||||
```
|
||||
|
||||
### Corrupted Database
|
||||
|
||||
**Symptom**: SQLite errors, integrity check failures
|
||||
|
||||
**Solutions**:
|
||||
|
||||
```bash
|
||||
# Check integrity
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "PRAGMA integrity_check;"
|
||||
|
||||
# If corrupted, attempt recovery
|
||||
sqlite3 ~/.transcript-fixer/corrections.db ".recover" | sqlite3 ~/.transcript-fixer/corrections_new.db
|
||||
|
||||
# Replace database with recovered version
|
||||
mv ~/.transcript-fixer/corrections.db ~/.transcript-fixer/corrections_corrupted.db
|
||||
mv ~/.transcript-fixer/corrections_new.db ~/.transcript-fixer/corrections.db
|
||||
```
|
||||
|
||||
### Missing Tables
|
||||
|
||||
**Symptom**:
|
||||
```
|
||||
❌ Database missing tables: ['corrections', ...]
|
||||
```
|
||||
|
||||
**Solution**: Reinitialize schema (safe, uses IF NOT EXISTS):
|
||||
|
||||
```bash
|
||||
python -c "from core import CorrectionRepository; from pathlib import Path; CorrectionRepository(Path.home() / '.transcript-fixer' / 'corrections.db')"
|
||||
```
|
||||
|
||||
Or delete database and reinitialize:
|
||||
|
||||
```bash
|
||||
# Backup first
|
||||
cp ~/.transcript-fixer/corrections.db ~/corrections_backup_$(date +%Y%m%d).db
|
||||
|
||||
# Reinitialize
|
||||
uv run scripts/fix_transcription.py --init
|
||||
```
|
||||
|
||||
## Common Pitfalls
|
||||
|
||||
### 1. Stage Order Confusion
|
||||
|
||||
**Problem**: Running Stage 2 without Stage 1 output.
|
||||
|
||||
**Solution**: Use `--stage 3` for full pipeline, or run stages sequentially:
|
||||
|
||||
```bash
|
||||
# Wrong: Stage 2 on raw file
|
||||
uv run scripts/fix_transcription.py --input file.md --stage 2 # ❌
|
||||
|
||||
# Correct: Full pipeline
|
||||
uv run scripts/fix_transcription.py --input file.md --stage 3 # ✅
|
||||
|
||||
# Or sequential stages
|
||||
uv run scripts/fix_transcription.py --input file.md --stage 1
|
||||
uv run scripts/fix_transcription.py --input file_stage1.md --stage 2
|
||||
```
|
||||
|
||||
### 2. Overwriting Imports
|
||||
|
||||
**Problem**: Using `--import` without `--merge` overwrites existing corrections.
|
||||
|
||||
**Solution**: Always use `--merge` flag:
|
||||
|
||||
```bash
|
||||
# Wrong: Overwrites existing
|
||||
uv run scripts/fix_transcription.py --import team.json # ❌
|
||||
|
||||
# Correct: Merges with existing
|
||||
uv run scripts/fix_transcription.py --import team.json --merge # ✅
|
||||
```
|
||||
|
||||
### 3. Ignoring Learned Suggestions
|
||||
|
||||
**Problem**: Not reviewing learned patterns, missing free optimizations.
|
||||
|
||||
**Impact**: Patterns detected by AI remain expensive (Stage 2) instead of cheap (Stage 1).
|
||||
|
||||
**Solution**: Review suggestions every 3-5 runs:
|
||||
|
||||
```bash
|
||||
uv run scripts/fix_transcription.py --review-learned
|
||||
uv run scripts/fix_transcription.py --approve "错误" "正确"
|
||||
```
|
||||
|
||||
### 4. Testing on Large Files
|
||||
|
||||
**Problem**: Testing dictionary changes on large files wastes API quota.
|
||||
|
||||
**Solution**: Start with `--stage 1` on small files (100-500 lines):
|
||||
|
||||
```bash
|
||||
# Test dictionary changes first
|
||||
uv run scripts/fix_transcription.py --input small_sample.md --stage 1
|
||||
|
||||
# Review output, adjust corrections
|
||||
# Then run full pipeline
|
||||
uv run scripts/fix_transcription.py --input large_file.md --stage 3
|
||||
```
|
||||
|
||||
### 5. Manual Database Edits Without Validation
|
||||
|
||||
**Problem**: Direct SQL edits might violate schema constraints.
|
||||
|
||||
**Solution**: Always validate after manual changes:
|
||||
|
||||
```bash
|
||||
sqlite3 ~/.transcript-fixer/corrections.db
|
||||
# ... make changes ...
|
||||
.quit
|
||||
|
||||
# Validate
|
||||
uv run scripts/fix_transcription.py --validate
|
||||
```
|
||||
|
||||
### 6. Committing .db Files to Git
|
||||
|
||||
**Problem**: Binary database files in Git cause merge conflicts and bloat repository.
|
||||
|
||||
**Solution**: Use JSON exports for version control:
|
||||
|
||||
```bash
|
||||
# .gitignore
|
||||
*.db
|
||||
*.db-journal
|
||||
*.bak
|
||||
|
||||
# Export for version control instead
|
||||
uv run scripts/fix_transcription.py --export corrections_$(date +%Y%m%d).json
|
||||
git add corrections_*.json
|
||||
```
|
||||
|
||||
## Validation Commands
|
||||
|
||||
### Quick Health Check
|
||||
|
||||
```bash
|
||||
uv run scripts/fix_transcription.py --validate
|
||||
```
|
||||
|
||||
### Detailed Diagnostics
|
||||
|
||||
```bash
|
||||
# Check database integrity
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "PRAGMA integrity_check;"
|
||||
|
||||
# Check table counts
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "
|
||||
SELECT 'corrections' as table_name, COUNT(*) as count FROM corrections
|
||||
UNION ALL
|
||||
SELECT 'context_rules', COUNT(*) FROM context_rules
|
||||
UNION ALL
|
||||
SELECT 'learned_suggestions', COUNT(*) FROM learned_suggestions
|
||||
UNION ALL
|
||||
SELECT 'correction_history', COUNT(*) FROM correction_history;
|
||||
"
|
||||
|
||||
# Check configuration
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "SELECT * FROM system_config;"
|
||||
```
|
||||
|
||||
## Getting Help
|
||||
|
||||
If issues persist:
|
||||
|
||||
1. Run `--validate` to collect diagnostic information
|
||||
2. Check `correction_history` and `audit_log` tables for errors
|
||||
3. Review `references/file_formats.md` for schema details
|
||||
4. Check `references/architecture.md` for component details
|
||||
5. Verify Python and uv versions are up to date
|
||||
|
||||
For database corruption, automatic backups are created before migrations. Check for `.bak` files in `~/.transcript-fixer/`.
|
||||
483
transcript-fixer/references/workflow_guide.md
Normal file
483
transcript-fixer/references/workflow_guide.md
Normal file
@@ -0,0 +1,483 @@
|
||||
# Workflow Guide
|
||||
|
||||
Detailed step-by-step workflows for transcript correction and management.
|
||||
|
||||
## Table of Contents
|
||||
|
||||
- [Pre-Flight Checklist](#pre-flight-checklist)
|
||||
- [Initial Setup](#initial-setup)
|
||||
- [File Preparation](#file-preparation)
|
||||
- [Execution Parameters](#execution-parameters)
|
||||
- [Environment](#environment)
|
||||
- [Core Workflows](#core-workflows)
|
||||
- [1. First-Time Correction](#1-first-time-correction)
|
||||
- [2. Iterative Improvement](#2-iterative-improvement)
|
||||
- [3. Domain-Specific Corrections](#3-domain-specific-corrections)
|
||||
- [4. Team Collaboration](#4-team-collaboration)
|
||||
- [5. Stage-by-Stage Execution](#5-stage-by-stage-execution)
|
||||
- [6. Context-Aware Rules](#6-context-aware-rules)
|
||||
- [7. Diff Report Generation](#7-diff-report-generation)
|
||||
- [Batch Processing](#batch-processing)
|
||||
- [Process Multiple Files](#process-multiple-files)
|
||||
- [Parallel Processing](#parallel-processing)
|
||||
- [Maintenance Workflows](#maintenance-workflows)
|
||||
- [Weekly: Review Learning](#weekly-review-learning)
|
||||
- [Monthly: Export and Backup](#monthly-export-and-backup)
|
||||
- [Quarterly: Clean Up](#quarterly-clean-up)
|
||||
- [Next Steps](#next-steps)
|
||||
|
||||
## Pre-Flight Checklist
|
||||
|
||||
Before running corrections, verify these prerequisites:
|
||||
|
||||
### Initial Setup
|
||||
- [ ] Initialized with `uv run scripts/fix_transcription.py --init`
|
||||
- [ ] Database exists at `~/.transcript-fixer/corrections.db`
|
||||
- [ ] `GLM_API_KEY` environment variable set (run `echo $GLM_API_KEY`)
|
||||
- [ ] Configuration validated (run `--validate`)
|
||||
|
||||
### File Preparation
|
||||
- [ ] Input file exists and is readable
|
||||
- [ ] File uses supported format (`.md`, `.txt`)
|
||||
- [ ] File encoding is UTF-8
|
||||
- [ ] File size is reasonable (<10MB for first runs)
|
||||
|
||||
### Execution Parameters
|
||||
- [ ] Using `--stage 3` for full pipeline (or specific stage if testing)
|
||||
- [ ] Domain specified with `--domain` if using specialized dictionaries
|
||||
- [ ] Using `--merge` flag when importing team corrections
|
||||
|
||||
### Environment
|
||||
- [ ] Sufficient disk space for output files (~2x input size)
|
||||
- [ ] API quota available for Stage 2 corrections
|
||||
- [ ] Network connectivity for API calls
|
||||
|
||||
**Quick validation**:
|
||||
|
||||
```bash
|
||||
uv run scripts/fix_transcription.py --validate && echo $GLM_API_KEY
|
||||
```
|
||||
|
||||
## Core Workflows
|
||||
|
||||
### 1. First-Time Correction
|
||||
|
||||
**Goal**: Correct a transcript for the first time.
|
||||
|
||||
**Steps**:
|
||||
|
||||
1. **Initialize** (if not done):
|
||||
```bash
|
||||
uv run scripts/fix_transcription.py --init
|
||||
export GLM_API_KEY="your-key"
|
||||
```
|
||||
|
||||
2. **Add initial corrections** (5-10 common errors):
|
||||
```bash
|
||||
uv run scripts/fix_transcription.py --add "常见错误1" "正确词1" --domain general
|
||||
uv run scripts/fix_transcription.py --add "常见错误2" "正确词2" --domain general
|
||||
```
|
||||
|
||||
3. **Test on small sample** (Stage 1 only):
|
||||
```bash
|
||||
uv run scripts/fix_transcription.py --input sample.md --stage 1
|
||||
less sample_stage1.md # Review output
|
||||
```
|
||||
|
||||
4. **Run full pipeline**:
|
||||
```bash
|
||||
uv run scripts/fix_transcription.py --input transcript.md --stage 3 --domain general
|
||||
```
|
||||
|
||||
5. **Review outputs**:
|
||||
```bash
|
||||
# Stage 1: Dictionary corrections
|
||||
less transcript_stage1.md
|
||||
|
||||
# Stage 2: Final corrected version
|
||||
less transcript_stage2.md
|
||||
|
||||
# Generate diff report
|
||||
uv run scripts/diff_generator.py transcript.md transcript_stage1.md transcript_stage2.md
|
||||
```
|
||||
|
||||
**Expected duration**:
|
||||
- Stage 1: Instant (dictionary lookup)
|
||||
- Stage 2: ~1-2 minutes per 1000 lines (API calls)
|
||||
|
||||
### 2. Iterative Improvement
|
||||
|
||||
**Goal**: Improve correction quality over time through learning.
|
||||
|
||||
**Steps**:
|
||||
|
||||
1. **Run corrections** on 3-5 similar transcripts:
|
||||
```bash
|
||||
uv run scripts/fix_transcription.py --input day1.md --stage 3 --domain embodied_ai
|
||||
uv run scripts/fix_transcription.py --input day2.md --stage 3 --domain embodied_ai
|
||||
uv run scripts/fix_transcription.py --input day3.md --stage 3 --domain embodied_ai
|
||||
```
|
||||
|
||||
2. **Review learned suggestions**:
|
||||
```bash
|
||||
uv run scripts/fix_transcription.py --review-learned
|
||||
```
|
||||
|
||||
**Output example**:
|
||||
```
|
||||
📚 Learned Suggestions (Pending Review)
|
||||
========================================
|
||||
|
||||
1. "巨升方向" → "具身方向"
|
||||
Frequency: 5 Confidence: 0.95
|
||||
Examples: day1.md (line 45), day2.md (line 23), ...
|
||||
|
||||
2. "奇迹创坛" → "奇绩创坛"
|
||||
Frequency: 3 Confidence: 0.87
|
||||
Examples: day1.md (line 102), day3.md (line 67)
|
||||
```
|
||||
|
||||
3. **Approve high-quality suggestions**:
|
||||
```bash
|
||||
uv run scripts/fix_transcription.py --approve "巨升方向" "具身方向"
|
||||
uv run scripts/fix_transcription.py --approve "奇迹创坛" "奇绩创坛"
|
||||
```
|
||||
|
||||
4. **Verify approved corrections**:
|
||||
```bash
|
||||
uv run scripts/fix_transcription.py --list --domain embodied_ai | grep "learned"
|
||||
```
|
||||
|
||||
5. **Run next batch** (benefits from approved corrections):
|
||||
```bash
|
||||
uv run scripts/fix_transcription.py --input day4.md --stage 3 --domain embodied_ai
|
||||
```
|
||||
|
||||
**Impact**: Approved corrections move to Stage 1 (instant, free).
|
||||
|
||||
**Cycle**: Repeat every 3-5 transcripts for continuous improvement.
|
||||
|
||||
### 3. Domain-Specific Corrections
|
||||
|
||||
**Goal**: Build specialized dictionaries for different fields.
|
||||
|
||||
**Steps**:
|
||||
|
||||
1. **Identify domain**:
|
||||
- `embodied_ai` - Robotics, AI terminology
|
||||
- `finance` - Financial terminology
|
||||
- `medical` - Medical terminology
|
||||
- `general` - General-purpose
|
||||
|
||||
2. **Add domain-specific terms**:
|
||||
```bash
|
||||
# Embodied AI domain
|
||||
uv run scripts/fix_transcription.py --add "巨升智能" "具身智能" --domain embodied_ai
|
||||
uv run scripts/fix_transcription.py --add "机器学习" "机器学习" --domain embodied_ai
|
||||
|
||||
# Finance domain
|
||||
uv run scripts/fix_transcription.py --add "股价" "股价" --domain finance # Keep as-is
|
||||
uv run scripts/fix_transcription.py --add "PE比率" "市盈率" --domain finance
|
||||
```
|
||||
|
||||
3. **Use appropriate domain** when correcting:
|
||||
```bash
|
||||
# AI meeting transcript
|
||||
uv run scripts/fix_transcription.py --input ai_meeting.md --stage 3 --domain embodied_ai
|
||||
|
||||
# Financial report transcript
|
||||
uv run scripts/fix_transcription.py --input earnings_call.md --stage 3 --domain finance
|
||||
```
|
||||
|
||||
4. **Review domain statistics**:
|
||||
```bash
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "SELECT * FROM correction_statistics;"
|
||||
```
|
||||
|
||||
**Benefits**:
|
||||
- Prevents cross-domain conflicts
|
||||
- Higher accuracy per domain
|
||||
- Targeted vocabulary building
|
||||
|
||||
### 4. Team Collaboration
|
||||
|
||||
**Goal**: Share corrections across team members.
|
||||
|
||||
**Steps**:
|
||||
|
||||
#### Setup (One-time per team)
|
||||
|
||||
1. **Create shared repository**:
|
||||
```bash
|
||||
mkdir transcript-corrections
|
||||
cd transcript-corrections
|
||||
git init
|
||||
|
||||
# .gitignore
|
||||
echo "*.db\n*.db-journal\n*.bak" > .gitignore
|
||||
```
|
||||
|
||||
2. **Export initial corrections**:
|
||||
```bash
|
||||
uv run scripts/fix_transcription.py --export general.json --domain general
|
||||
uv run scripts/fix_transcription.py --export embodied_ai.json --domain embodied_ai
|
||||
|
||||
git add *.json
|
||||
git commit -m "Initial correction dictionaries"
|
||||
git push origin main
|
||||
```
|
||||
|
||||
#### Daily Workflow
|
||||
|
||||
**Team Member A** (adds new corrections):
|
||||
|
||||
```bash
|
||||
# 1. Run corrections
|
||||
uv run scripts/fix_transcription.py --input transcript.md --stage 3 --domain embodied_ai
|
||||
|
||||
# 2. Review and approve learned suggestions
|
||||
uv run scripts/fix_transcription.py --review-learned
|
||||
uv run scripts/fix_transcription.py --approve "新错误" "正确词"
|
||||
|
||||
# 3. Export updated corrections
|
||||
uv run scripts/fix_transcription.py --export embodied_ai_$(date +%Y%m%d).json --domain embodied_ai
|
||||
|
||||
# 4. Commit and push
|
||||
git add embodied_ai_*.json
|
||||
git commit -m "Add embodied AI corrections from today's transcripts"
|
||||
git push origin main
|
||||
```
|
||||
|
||||
**Team Member B** (imports team corrections):
|
||||
|
||||
```bash
|
||||
# 1. Pull latest corrections
|
||||
git pull origin main
|
||||
|
||||
# 2. Import with merge
|
||||
uv run scripts/fix_transcription.py --import embodied_ai_20250128.json --merge
|
||||
|
||||
# 3. Verify
|
||||
uv run scripts/fix_transcription.py --list --domain embodied_ai | tail -10
|
||||
```
|
||||
|
||||
**Conflict resolution**: See `team_collaboration.md` for handling merge conflicts.
|
||||
|
||||
### 5. Stage-by-Stage Execution
|
||||
|
||||
**Goal**: Test dictionary changes without wasting API quota.
|
||||
|
||||
#### Stage 1 Only (Dictionary)
|
||||
|
||||
**Use when**: Testing new corrections, verifying domain setup.
|
||||
|
||||
```bash
|
||||
uv run scripts/fix_transcription.py --input file.md --stage 1 --domain general
|
||||
```
|
||||
|
||||
**Output**: `file_stage1.md` with dictionary corrections only.
|
||||
|
||||
**Review**: Check if dictionary corrections are sufficient.
|
||||
|
||||
#### Stage 2 Only (AI)
|
||||
|
||||
**Use when**: Running AI corrections on pre-processed file.
|
||||
|
||||
**Prerequisites**: Stage 1 output exists.
|
||||
|
||||
```bash
|
||||
# Stage 1 first
|
||||
uv run scripts/fix_transcription.py --input file.md --stage 1
|
||||
|
||||
# Then Stage 2
|
||||
uv run scripts/fix_transcription.py --input file_stage1.md --stage 2
|
||||
```
|
||||
|
||||
**Output**: `file_stage1_stage2.md` (confusing naming - use Stage 3 instead).
|
||||
|
||||
#### Stage 3 (Full Pipeline)
|
||||
|
||||
**Use when**: Production runs, full correction workflow.
|
||||
|
||||
```bash
|
||||
uv run scripts/fix_transcription.py --input file.md --stage 3 --domain general
|
||||
```
|
||||
|
||||
**Output**: Both `file_stage1.md` and `file_stage2.md`.
|
||||
|
||||
**Recommended**: Use Stage 3 for most workflows.
|
||||
|
||||
### 6. Context-Aware Rules
|
||||
|
||||
**Goal**: Handle edge cases with regex patterns.
|
||||
|
||||
**Use cases**:
|
||||
- Positional corrections (e.g., "的" vs "地")
|
||||
- Multi-word patterns
|
||||
- Conditional corrections
|
||||
|
||||
**Steps**:
|
||||
|
||||
1. **Identify pattern** that simple dictionary can't handle:
|
||||
```
|
||||
Problem: "近距离的去看" (wrong - should be "地")
|
||||
Problem: "近距离搏杀" (correct - should keep "的")
|
||||
```
|
||||
|
||||
2. **Add context rules**:
|
||||
```bash
|
||||
sqlite3 ~/.transcript-fixer/corrections.db
|
||||
|
||||
-- Higher priority for specific context
|
||||
INSERT INTO context_rules (pattern, replacement, description, priority)
|
||||
VALUES ('近距离的去看', '近距离地去看', '的→地 before verb', 10);
|
||||
|
||||
-- Lower priority for general pattern
|
||||
INSERT INTO context_rules (pattern, replacement, description, priority)
|
||||
VALUES ('近距离搏杀', '近距离搏杀', 'Keep 的 for noun modifier', 5);
|
||||
|
||||
.quit
|
||||
```
|
||||
|
||||
3. **Test context rules**:
|
||||
```bash
|
||||
uv run scripts/fix_transcription.py --input test.md --stage 1
|
||||
```
|
||||
|
||||
4. **Validate**:
|
||||
```bash
|
||||
uv run scripts/fix_transcription.py --validate
|
||||
```
|
||||
|
||||
**Priority**: Higher numbers run first (use for exceptions/edge cases).
|
||||
|
||||
See `file_formats.md` for context_rules schema.
|
||||
|
||||
### 7. Diff Report Generation
|
||||
|
||||
**Goal**: Visualize all changes for review.
|
||||
|
||||
**Use when**:
|
||||
- Reviewing corrections before publishing
|
||||
- Training new team members
|
||||
- Documenting ASR error patterns
|
||||
|
||||
**Steps**:
|
||||
|
||||
1. **Run corrections**:
|
||||
```bash
|
||||
uv run scripts/fix_transcription.py --input transcript.md --stage 3
|
||||
```
|
||||
|
||||
2. **Generate diff reports**:
|
||||
```bash
|
||||
uv run scripts/diff_generator.py \
|
||||
transcript.md \
|
||||
transcript_stage1.md \
|
||||
transcript_stage2.md
|
||||
```
|
||||
|
||||
3. **Review outputs**:
|
||||
```bash
|
||||
# Markdown report (statistics + summary)
|
||||
less diff_report.md
|
||||
|
||||
# Unified diff (git-style)
|
||||
less transcript_unified.diff
|
||||
|
||||
# HTML side-by-side (visual review)
|
||||
open transcript_sidebyside.html
|
||||
|
||||
# Inline markers (for editing)
|
||||
less transcript_inline.md
|
||||
```
|
||||
|
||||
**Report contents**:
|
||||
- Total changes count
|
||||
- Stage 1 vs Stage 2 breakdown
|
||||
- Character/word count changes
|
||||
- Side-by-side comparison
|
||||
|
||||
See `script_parameters.md` for advanced diff options.
|
||||
|
||||
## Batch Processing
|
||||
|
||||
### Process Multiple Files
|
||||
|
||||
```bash
|
||||
# Simple loop
|
||||
for file in meeting_*.md; do
|
||||
uv run scripts/fix_transcription.py --input "$file" --stage 3 --domain embodied_ai
|
||||
done
|
||||
|
||||
# With error handling
|
||||
for file in meeting_*.md; do
|
||||
echo "Processing $file..."
|
||||
if uv run scripts/fix_transcription.py --input "$file" --stage 3 --domain embodied_ai; then
|
||||
echo "✅ $file completed"
|
||||
else
|
||||
echo "❌ $file failed"
|
||||
fi
|
||||
done
|
||||
```
|
||||
|
||||
### Parallel Processing
|
||||
|
||||
```bash
|
||||
# GNU parallel (install: brew install parallel)
|
||||
ls meeting_*.md | parallel -j 4 \
|
||||
"uv run scripts/fix_transcription.py --input {} --stage 3 --domain embodied_ai"
|
||||
```
|
||||
|
||||
**Caution**: Monitor API rate limits when processing in parallel.
|
||||
|
||||
## Maintenance Workflows
|
||||
|
||||
### Weekly: Review Learning
|
||||
|
||||
```bash
|
||||
# Review suggestions
|
||||
uv run scripts/fix_transcription.py --review-learned
|
||||
|
||||
# Approve high-confidence patterns
|
||||
uv run scripts/fix_transcription.py --approve "错误1" "正确1"
|
||||
uv run scripts/fix_transcription.py --approve "错误2" "正确2"
|
||||
```
|
||||
|
||||
### Monthly: Export and Backup
|
||||
|
||||
```bash
|
||||
# Export all domains
|
||||
uv run scripts/fix_transcription.py --export general_$(date +%Y%m%d).json --domain general
|
||||
uv run scripts/fix_transcription.py --export embodied_ai_$(date +%Y%m%d).json --domain embodied_ai
|
||||
|
||||
# Backup database
|
||||
cp ~/.transcript-fixer/corrections.db ~/backups/corrections_$(date +%Y%m%d).db
|
||||
|
||||
# Database maintenance
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "VACUUM; REINDEX; ANALYZE;"
|
||||
```
|
||||
|
||||
### Quarterly: Clean Up
|
||||
|
||||
```bash
|
||||
# Archive old history (> 90 days)
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "
|
||||
DELETE FROM correction_history
|
||||
WHERE run_timestamp < datetime('now', '-90 days');
|
||||
"
|
||||
|
||||
# Reject low-confidence suggestions
|
||||
sqlite3 ~/.transcript-fixer/corrections.db "
|
||||
UPDATE learned_suggestions
|
||||
SET status = 'rejected'
|
||||
WHERE confidence < 0.6 AND frequency < 3;
|
||||
"
|
||||
```
|
||||
|
||||
## Next Steps
|
||||
|
||||
- See `best_practices.md` for optimization tips
|
||||
- See `troubleshooting.md` for error resolution
|
||||
- See `file_formats.md` for database schema
|
||||
- See `script_parameters.md` for advanced CLI options
|
||||
4
transcript-fixer/requirements.txt
Normal file
4
transcript-fixer/requirements.txt
Normal file
@@ -0,0 +1,4 @@
|
||||
# Transcript Fixer Dependencies
|
||||
|
||||
# HTTP client for GLM API calls
|
||||
httpx>=0.24.0
|
||||
10
transcript-fixer/scripts/__init__.py
Normal file
10
transcript-fixer/scripts/__init__.py
Normal file
@@ -0,0 +1,10 @@
|
||||
"""
|
||||
Transcript Fixer - Modular Script Package
|
||||
|
||||
Package structure:
|
||||
- core/: Business logic and data access layer
|
||||
- cli/: Command-line interface handlers
|
||||
- utils/: Utility functions and tools
|
||||
"""
|
||||
|
||||
__version__ = "1.0.0"
|
||||
29
transcript-fixer/scripts/cli/__init__.py
Normal file
29
transcript-fixer/scripts/cli/__init__.py
Normal file
@@ -0,0 +1,29 @@
|
||||
"""
|
||||
CLI Module - Command-Line Interface Handlers
|
||||
|
||||
This module contains command handlers and argument parsing:
|
||||
- commands: Command handler functions (cmd_*)
|
||||
- argument_parser: CLI argument configuration
|
||||
"""
|
||||
|
||||
from .commands import (
|
||||
cmd_init,
|
||||
cmd_add_correction,
|
||||
cmd_list_corrections,
|
||||
cmd_run_correction,
|
||||
cmd_review_learned,
|
||||
cmd_approve,
|
||||
cmd_validate,
|
||||
)
|
||||
from .argument_parser import create_argument_parser
|
||||
|
||||
__all__ = [
|
||||
'cmd_init',
|
||||
'cmd_add_correction',
|
||||
'cmd_list_corrections',
|
||||
'cmd_run_correction',
|
||||
'cmd_review_learned',
|
||||
'cmd_approve',
|
||||
'cmd_validate',
|
||||
'create_argument_parser',
|
||||
]
|
||||
89
transcript-fixer/scripts/cli/argument_parser.py
Normal file
89
transcript-fixer/scripts/cli/argument_parser.py
Normal file
@@ -0,0 +1,89 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Argument Parser - CLI Argument Configuration
|
||||
|
||||
SINGLE RESPONSIBILITY: Configure command-line argument parsing
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
|
||||
|
||||
def create_argument_parser() -> argparse.ArgumentParser:
|
||||
"""
|
||||
Create and configure the argument parser for transcript-fixer CLI.
|
||||
|
||||
Returns:
|
||||
Configured ArgumentParser instance
|
||||
"""
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Transcript Fixer - Iterative correction tool",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter
|
||||
)
|
||||
|
||||
# Setup commands
|
||||
parser.add_argument(
|
||||
"--init",
|
||||
action="store_true",
|
||||
help="Initialize ~/.transcript-fixer/"
|
||||
)
|
||||
|
||||
# Correction management
|
||||
parser.add_argument(
|
||||
"--add",
|
||||
nargs=2,
|
||||
metavar=("FROM", "TO"),
|
||||
dest="add_correction",
|
||||
help="Add correction"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--list",
|
||||
action="store_true",
|
||||
dest="list_corrections",
|
||||
help="List all corrections"
|
||||
)
|
||||
|
||||
# Correction workflow
|
||||
parser.add_argument(
|
||||
"--input", "-i",
|
||||
help="Input file"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output", "-o",
|
||||
help="Output directory"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--stage", "-s",
|
||||
type=int,
|
||||
choices=[1, 2, 3],
|
||||
default=3,
|
||||
help="Run stage (1=dict, 2=AI, 3=full)"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--domain", "-d",
|
||||
default="general",
|
||||
help="Correction domain"
|
||||
)
|
||||
|
||||
# Learning commands
|
||||
parser.add_argument(
|
||||
"--review-learned",
|
||||
action="store_true",
|
||||
help="Review learned suggestions"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--approve",
|
||||
nargs=2,
|
||||
metavar=("FROM", "TO"),
|
||||
help="Approve suggestion"
|
||||
)
|
||||
|
||||
# Utility commands
|
||||
parser.add_argument(
|
||||
"--validate",
|
||||
action="store_true",
|
||||
help="Validate configuration and JSON files"
|
||||
)
|
||||
|
||||
return parser
|
||||
181
transcript-fixer/scripts/cli/commands.py
Normal file
181
transcript-fixer/scripts/cli/commands.py
Normal file
@@ -0,0 +1,181 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
CLI Commands - Command Handler Functions
|
||||
|
||||
SINGLE RESPONSIBILITY: Handle CLI command execution
|
||||
|
||||
All cmd_* functions take parsed args and execute the requested operation.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
from core import (
|
||||
CorrectionRepository,
|
||||
CorrectionService,
|
||||
DictionaryProcessor,
|
||||
AIProcessor,
|
||||
LearningEngine,
|
||||
)
|
||||
from utils import validate_configuration, print_validation_summary
|
||||
|
||||
|
||||
def _get_service():
|
||||
"""Get configured CorrectionService instance."""
|
||||
config_dir = Path.home() / ".transcript-fixer"
|
||||
db_path = config_dir / "corrections.db"
|
||||
repository = CorrectionRepository(db_path)
|
||||
return CorrectionService(repository)
|
||||
|
||||
|
||||
def cmd_init(args):
|
||||
"""Initialize ~/.transcript-fixer/ directory"""
|
||||
service = _get_service()
|
||||
service.initialize()
|
||||
|
||||
|
||||
def cmd_add_correction(args):
|
||||
"""Add a single correction"""
|
||||
service = _get_service()
|
||||
try:
|
||||
service.add_correction(args.from_text, args.to_text, args.domain)
|
||||
print(f"✅ Added: '{args.from_text}' → '{args.to_text}' (domain: {args.domain})")
|
||||
except Exception as e:
|
||||
print(f"❌ Error: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
def cmd_list_corrections(args):
|
||||
"""List all corrections"""
|
||||
service = _get_service()
|
||||
corrections = service.get_corrections(args.domain)
|
||||
|
||||
print(f"\n📋 Corrections (domain: {args.domain})")
|
||||
print("=" * 60)
|
||||
for wrong, correct in sorted(corrections.items()):
|
||||
print(f" '{wrong}' → '{correct}'")
|
||||
print(f"\nTotal: {len(corrections)} corrections\n")
|
||||
|
||||
|
||||
def cmd_run_correction(args):
|
||||
"""Run the correction workflow"""
|
||||
# Validate input file
|
||||
input_path = Path(args.input)
|
||||
if not input_path.exists():
|
||||
print(f"❌ Error: File not found: {input_path}")
|
||||
sys.exit(1)
|
||||
|
||||
# Setup output directory
|
||||
output_dir = Path(args.output) if args.output else input_path.parent
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Initialize service
|
||||
service = _get_service()
|
||||
|
||||
# Load corrections and rules
|
||||
corrections = service.get_corrections(args.domain)
|
||||
context_rules = service.load_context_rules()
|
||||
|
||||
# Read input file
|
||||
print(f"📖 Reading: {input_path.name}")
|
||||
with open(input_path, 'r', encoding='utf-8') as f:
|
||||
original_text = f.read()
|
||||
print(f" File size: {len(original_text):,} characters\n")
|
||||
|
||||
# Stage 1: Dictionary corrections
|
||||
stage1_changes = []
|
||||
stage1_text = original_text
|
||||
if args.stage >= 1:
|
||||
print("=" * 60)
|
||||
print("🔧 Stage 1: Dictionary Corrections")
|
||||
print("=" * 60)
|
||||
|
||||
processor = DictionaryProcessor(corrections, context_rules)
|
||||
stage1_text, stage1_changes = processor.process(original_text)
|
||||
|
||||
summary = processor.get_summary(stage1_changes)
|
||||
print(f"✓ Applied {summary['total_changes']} corrections")
|
||||
print(f" - Dictionary: {summary['dictionary_changes']}")
|
||||
print(f" - Context rules: {summary['context_rule_changes']}")
|
||||
|
||||
stage1_file = output_dir / f"{input_path.stem}_stage1.md"
|
||||
with open(stage1_file, 'w', encoding='utf-8') as f:
|
||||
f.write(stage1_text)
|
||||
print(f"💾 Saved: {stage1_file.name}\n")
|
||||
|
||||
# Stage 2: AI corrections
|
||||
stage2_changes = []
|
||||
stage2_text = stage1_text
|
||||
if args.stage >= 2:
|
||||
print("=" * 60)
|
||||
print("🤖 Stage 2: AI Corrections")
|
||||
print("=" * 60)
|
||||
|
||||
# Check API key
|
||||
api_key = os.environ.get("GLM_API_KEY")
|
||||
if not api_key:
|
||||
print("❌ Error: GLM_API_KEY environment variable not set")
|
||||
print(" Set it with: export GLM_API_KEY='your-key'")
|
||||
sys.exit(1)
|
||||
|
||||
ai_processor = AIProcessor(api_key)
|
||||
stage2_text, stage2_changes = ai_processor.process(stage1_text)
|
||||
|
||||
print(f"✓ Processed {len(stage2_changes)} chunks\n")
|
||||
|
||||
stage2_file = output_dir / f"{input_path.stem}_stage2.md"
|
||||
with open(stage2_file, 'w', encoding='utf-8') as f:
|
||||
f.write(stage2_text)
|
||||
print(f"💾 Saved: {stage2_file.name}\n")
|
||||
|
||||
# Save history for learning
|
||||
service.save_history(
|
||||
filename=str(input_path),
|
||||
domain=args.domain,
|
||||
original_length=len(original_text),
|
||||
stage1_changes=len(stage1_changes),
|
||||
stage2_changes=len(stage2_changes),
|
||||
model="GLM-4.6",
|
||||
changes=stage1_changes + stage2_changes
|
||||
)
|
||||
|
||||
# TODO: Run learning engine
|
||||
# learning = LearningEngine(...)
|
||||
# suggestions = learning.analyze_and_suggest()
|
||||
# if suggestions:
|
||||
# print(f"🎓 Learning: Found {len(suggestions)} new correction suggestions")
|
||||
# print(f" Run --review-learned to review them\n")
|
||||
|
||||
# Stage 3: Generate diff report
|
||||
if args.stage >= 3:
|
||||
print("=" * 60)
|
||||
print("📊 Stage 3: Generating Diff Report")
|
||||
print("=" * 60)
|
||||
print(" Use diff_generator.py to create visual comparison\n")
|
||||
|
||||
print("✅ Correction complete!")
|
||||
|
||||
|
||||
def cmd_review_learned(args):
|
||||
"""Review learned suggestions"""
|
||||
# TODO: Implement learning engine with SQLite backend
|
||||
print("⚠️ Learning engine not yet implemented with SQLite backend")
|
||||
print(" This feature will be added in a future update")
|
||||
|
||||
|
||||
def cmd_approve(args):
|
||||
"""Approve a learned suggestion"""
|
||||
# TODO: Implement learning engine with SQLite backend
|
||||
print("⚠️ Learning engine not yet implemented with SQLite backend")
|
||||
print(" This feature will be added in a future update")
|
||||
|
||||
|
||||
def cmd_validate(args):
|
||||
"""Validate configuration and JSON files"""
|
||||
errors, warnings = validate_configuration()
|
||||
exit_code = print_validation_summary(errors, warnings)
|
||||
if exit_code != 0:
|
||||
sys.exit(exit_code)
|
||||
44
transcript-fixer/scripts/core/__init__.py
Normal file
44
transcript-fixer/scripts/core/__init__.py
Normal file
@@ -0,0 +1,44 @@
|
||||
"""
|
||||
Core Module - Business Logic and Data Access
|
||||
|
||||
This module contains the core business logic for transcript correction:
|
||||
- CorrectionRepository: Data access layer with ACID transactions
|
||||
- CorrectionService: Business logic layer with validation
|
||||
- DictionaryProcessor: Stage 1 dictionary-based corrections
|
||||
- AIProcessor: Stage 2 AI-powered corrections
|
||||
- LearningEngine: Pattern detection and learning
|
||||
"""
|
||||
|
||||
# Core SQLite-based components (always available)
|
||||
from .correction_repository import CorrectionRepository, Correction, DatabaseError, ValidationError
|
||||
from .correction_service import CorrectionService, ValidationRules
|
||||
|
||||
# Processing components (imported lazily to avoid dependency issues)
|
||||
def _lazy_import(name):
|
||||
"""Lazy import to avoid loading heavy dependencies."""
|
||||
if name == 'DictionaryProcessor':
|
||||
from .dictionary_processor import DictionaryProcessor
|
||||
return DictionaryProcessor
|
||||
elif name == 'AIProcessor':
|
||||
from .ai_processor import AIProcessor
|
||||
return AIProcessor
|
||||
elif name == 'LearningEngine':
|
||||
from .learning_engine import LearningEngine
|
||||
return LearningEngine
|
||||
raise ImportError(f"Unknown module: {name}")
|
||||
|
||||
# Export main classes
|
||||
__all__ = [
|
||||
'CorrectionRepository',
|
||||
'CorrectionService',
|
||||
'Correction',
|
||||
'DatabaseError',
|
||||
'ValidationError',
|
||||
'ValidationRules',
|
||||
]
|
||||
|
||||
# Make lazy imports available via __getattr__
|
||||
def __getattr__(name):
|
||||
if name in ['DictionaryProcessor', 'AIProcessor', 'LearningEngine']:
|
||||
return _lazy_import(name)
|
||||
raise AttributeError(f"module '{__name__}' has no attribute '{name}'")
|
||||
214
transcript-fixer/scripts/core/ai_processor.py
Normal file
214
transcript-fixer/scripts/core/ai_processor.py
Normal file
@@ -0,0 +1,214 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
AI Processor - Stage 2: AI-powered Text Corrections
|
||||
|
||||
SINGLE RESPONSIBILITY: Process text using GLM API for intelligent corrections
|
||||
|
||||
Features:
|
||||
- Split text into chunks for API processing
|
||||
- Call GLM-4.6 for context-aware corrections
|
||||
- Track AI-suggested changes
|
||||
- Handle API errors gracefully
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
import re
|
||||
from typing import List, Tuple
|
||||
from dataclasses import dataclass
|
||||
import httpx
|
||||
|
||||
|
||||
@dataclass
|
||||
class AIChange:
|
||||
"""Represents an AI-suggested change"""
|
||||
chunk_index: int
|
||||
from_text: str
|
||||
to_text: str
|
||||
confidence: float # 0.0 to 1.0
|
||||
|
||||
|
||||
class AIProcessor:
|
||||
"""
|
||||
Stage 2 Processor: AI-powered corrections using GLM-4.6
|
||||
|
||||
Process:
|
||||
1. Split text into chunks (respecting API limits)
|
||||
2. Send each chunk to GLM API
|
||||
3. Track changes for learning engine
|
||||
4. Preserve formatting and structure
|
||||
"""
|
||||
|
||||
def __init__(self, api_key: str, model: str = "GLM-4.6",
|
||||
base_url: str = "https://open.bigmodel.cn/api/anthropic",
|
||||
fallback_model: str = "GLM-4.5-Air"):
|
||||
"""
|
||||
Initialize AI processor
|
||||
|
||||
Args:
|
||||
api_key: GLM API key
|
||||
model: Model name (default: GLM-4.6)
|
||||
base_url: API base URL
|
||||
fallback_model: Fallback model on primary failure
|
||||
"""
|
||||
self.api_key = api_key
|
||||
self.model = model
|
||||
self.fallback_model = fallback_model
|
||||
self.base_url = base_url
|
||||
self.max_chunk_size = 6000 # Characters per chunk
|
||||
|
||||
def process(self, text: str, context: str = "") -> Tuple[str, List[AIChange]]:
|
||||
"""
|
||||
Process text with AI corrections
|
||||
|
||||
Args:
|
||||
text: Text to correct
|
||||
context: Optional domain/meeting context
|
||||
|
||||
Returns:
|
||||
(corrected_text, list_of_changes)
|
||||
"""
|
||||
chunks = self._split_into_chunks(text)
|
||||
corrected_chunks = []
|
||||
all_changes = []
|
||||
|
||||
print(f"📝 Processing {len(chunks)} chunks with {self.model}...")
|
||||
|
||||
for i, chunk in enumerate(chunks, 1):
|
||||
print(f" Chunk {i}/{len(chunks)}... ", end="", flush=True)
|
||||
|
||||
try:
|
||||
corrected_chunk = self._process_chunk(chunk, context, self.model)
|
||||
corrected_chunks.append(corrected_chunk)
|
||||
|
||||
# TODO: Extract actual changes for learning
|
||||
# For now, we assume the whole chunk changed
|
||||
if corrected_chunk != chunk:
|
||||
all_changes.append(AIChange(
|
||||
chunk_index=i,
|
||||
from_text=chunk[:50] + "...",
|
||||
to_text=corrected_chunk[:50] + "...",
|
||||
confidence=0.9 # Placeholder
|
||||
))
|
||||
|
||||
print("✓")
|
||||
|
||||
except Exception as e:
|
||||
print(f"✗ {str(e)[:50]}")
|
||||
|
||||
# Retry with fallback model
|
||||
if self.fallback_model and self.fallback_model != self.model:
|
||||
print(f" Retrying with {self.fallback_model}... ", end="", flush=True)
|
||||
try:
|
||||
corrected_chunk = self._process_chunk(chunk, context, self.fallback_model)
|
||||
corrected_chunks.append(corrected_chunk)
|
||||
print("✓")
|
||||
continue
|
||||
except Exception as e2:
|
||||
print(f"✗ {str(e2)[:50]}")
|
||||
|
||||
print(" Using original text...")
|
||||
corrected_chunks.append(chunk)
|
||||
|
||||
return "\n\n".join(corrected_chunks), all_changes
|
||||
|
||||
def _split_into_chunks(self, text: str) -> List[str]:
|
||||
"""
|
||||
Split text into processable chunks
|
||||
|
||||
Strategy:
|
||||
- Split by double newlines (paragraphs)
|
||||
- Keep chunks under max_chunk_size
|
||||
- Don't split mid-paragraph if possible
|
||||
"""
|
||||
paragraphs = text.split('\n\n')
|
||||
chunks = []
|
||||
current_chunk = []
|
||||
current_length = 0
|
||||
|
||||
for para in paragraphs:
|
||||
para_length = len(para)
|
||||
|
||||
# If single paragraph exceeds limit, force split
|
||||
if para_length > self.max_chunk_size:
|
||||
if current_chunk:
|
||||
chunks.append('\n\n'.join(current_chunk))
|
||||
current_chunk = []
|
||||
current_length = 0
|
||||
|
||||
# Split long paragraph by sentences
|
||||
sentences = re.split(r'([。！？\n])', para)
|
||||
temp_para = ""
|
||||
for i in range(0, len(sentences), 2):
|
||||
sentence = sentences[i] + (sentences[i+1] if i+1 < len(sentences) else "")
|
||||
if len(temp_para) + len(sentence) > self.max_chunk_size:
|
||||
if temp_para:
|
||||
chunks.append(temp_para)
|
||||
temp_para = sentence
|
||||
else:
|
||||
temp_para += sentence
|
||||
if temp_para:
|
||||
chunks.append(temp_para)
|
||||
|
||||
# Normal case: accumulate paragraphs
|
||||
elif current_length + para_length > self.max_chunk_size and current_chunk:
|
||||
chunks.append('\n\n'.join(current_chunk))
|
||||
current_chunk = [para]
|
||||
current_length = para_length
|
||||
else:
|
||||
current_chunk.append(para)
|
||||
current_length += para_length + 2 # +2 for \n\n
|
||||
|
||||
if current_chunk:
|
||||
chunks.append('\n\n'.join(current_chunk))
|
||||
|
||||
return chunks
|
||||
|
||||
def _process_chunk(self, chunk: str, context: str, model: str) -> str:
|
||||
"""Process a single chunk with GLM API"""
|
||||
prompt = self._build_prompt(chunk, context)
|
||||
|
||||
url = f"{self.base_url}/v1/messages"
|
||||
headers = {
|
||||
"anthropic-version": "2023-06-01",
|
||||
"Authorization": f"Bearer {self.api_key}",
|
||||
"content-type": "application/json"
|
||||
}
|
||||
|
||||
data = {
|
||||
"model": model,
|
||||
"max_tokens": 8000,
|
||||
"temperature": 0.3,
|
||||
"messages": [{"role": "user", "content": prompt}]
|
||||
}
|
||||
|
||||
with httpx.Client(timeout=60.0) as client:
|
||||
response = client.post(url, headers=headers, json=data)
|
||||
response.raise_for_status()
|
||||
result = response.json()
|
||||
return result["content"][0]["text"]
|
||||
|
||||
def _build_prompt(self, chunk: str, context: str) -> str:
|
||||
"""Build correction prompt for GLM"""
|
||||
base_prompt = """你是专业的会议记录校对专家。请修复以下会议转录中的语音识别错误。
|
||||
|
||||
**修复原则**：
|
||||
1. 严格保留原有格式（时间戳、发言人标识、Markdown标记等）
|
||||
2. 修复明显的同音字错误
|
||||
3. 修复专业术语错误
|
||||
4. 修复语法错误，但保持口语化特征
|
||||
5. 不确定的地方保持原样，不要过度修改
|
||||
|
||||
"""
|
||||
|
||||
if context:
|
||||
base_prompt += f"\n**会议背景**：\n{context}\n"
|
||||
|
||||
base_prompt += f"""
|
||||
**需要修复的内容**：
|
||||
{chunk}
|
||||
|
||||
**请直接输出修复后的文本，不要添加任何解释或标注**："""
|
||||
|
||||
return base_prompt
|
||||
465
transcript-fixer/scripts/core/correction_repository.py
Normal file
465
transcript-fixer/scripts/core/correction_repository.py
Normal file
@@ -0,0 +1,465 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Correction Repository - SQLite Data Access Layer
|
||||
|
||||
SINGLE RESPONSIBILITY: Manage database operations with ACID guarantees
|
||||
|
||||
Thread-safe, transactional, and follows Repository pattern.
|
||||
All database operations are atomic and properly handle errors.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import sqlite3
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Dict, List, Optional, Tuple, Any
|
||||
from contextlib import contextmanager
|
||||
from dataclasses import dataclass, asdict
|
||||
import threading
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@dataclass
|
||||
class Correction:
|
||||
"""Correction entity"""
|
||||
id: Optional[int]
|
||||
from_text: str
|
||||
to_text: str
|
||||
domain: str
|
||||
source: str # 'manual' | 'learned' | 'imported'
|
||||
confidence: float
|
||||
added_by: Optional[str]
|
||||
added_at: str
|
||||
usage_count: int
|
||||
last_used: Optional[str]
|
||||
notes: Optional[str]
|
||||
is_active: bool
|
||||
|
||||
|
||||
@dataclass
|
||||
class ContextRule:
|
||||
"""Context-aware rule entity"""
|
||||
id: Optional[int]
|
||||
pattern: str
|
||||
replacement: str
|
||||
description: Optional[str]
|
||||
priority: int
|
||||
is_active: bool
|
||||
added_at: str
|
||||
added_by: Optional[str]
|
||||
|
||||
|
||||
@dataclass
|
||||
class LearnedSuggestion:
|
||||
"""Learned pattern suggestion"""
|
||||
id: Optional[int]
|
||||
from_text: str
|
||||
to_text: str
|
||||
domain: str
|
||||
frequency: int
|
||||
confidence: float
|
||||
first_seen: str
|
||||
last_seen: str
|
||||
status: str # 'pending' | 'approved' | 'rejected'
|
||||
reviewed_at: Optional[str]
|
||||
reviewed_by: Optional[str]
|
||||
|
||||
|
||||
class DatabaseError(Exception):
|
||||
"""Base exception for database errors"""
|
||||
pass
|
||||
|
||||
|
||||
class ValidationError(DatabaseError):
|
||||
"""Data validation error"""
|
||||
pass
|
||||
|
||||
|
||||
class CorrectionRepository:
|
||||
"""
|
||||
Thread-safe repository for correction storage using SQLite.
|
||||
|
||||
Features:
|
||||
- ACID transactions
|
||||
- Connection pooling
|
||||
- Prepared statements (SQL injection prevention)
|
||||
- Comprehensive error handling
|
||||
- Audit logging
|
||||
"""
|
||||
|
||||
def __init__(self, db_path: Path):
|
||||
"""
|
||||
Initialize repository with database path.
|
||||
|
||||
Args:
|
||||
db_path: Path to SQLite database file
|
||||
"""
|
||||
self.db_path = db_path
|
||||
self._local = threading.local()
|
||||
self._ensure_database_exists()
|
||||
|
||||
def _get_connection(self) -> sqlite3.Connection:
|
||||
"""Get thread-local database connection."""
|
||||
if not hasattr(self._local, 'connection'):
|
||||
self._local.connection = sqlite3.connect(
|
||||
self.db_path,
|
||||
isolation_level=None, # Autocommit mode off, manual transactions
|
||||
check_same_thread=False
|
||||
)
|
||||
self._local.connection.row_factory = sqlite3.Row
|
||||
# Enable foreign keys
|
||||
self._local.connection.execute("PRAGMA foreign_keys = ON")
|
||||
return self._local.connection
|
||||
|
||||
@contextmanager
|
||||
def _transaction(self):
|
||||
"""
|
||||
Context manager for database transactions.
|
||||
|
||||
Provides ACID guarantees:
|
||||
- Atomicity: All or nothing
|
||||
- Consistency: Constraints enforced
|
||||
- Isolation: Serializable by default
|
||||
- Durability: Changes persisted to disk
|
||||
"""
|
||||
conn = self._get_connection()
|
||||
try:
|
||||
conn.execute("BEGIN IMMEDIATE") # Acquire write lock immediately
|
||||
yield conn
|
||||
conn.commit()
|
||||
except Exception as e:
|
||||
conn.rollback()
|
||||
logger.error(f"Transaction rolled back: {e}")
|
||||
raise DatabaseError(f"Database operation failed: {e}") from e
|
||||
|
||||
def _ensure_database_exists(self) -> None:
|
||||
"""Create database schema if not exists."""
|
||||
schema_path = Path(__file__).parent / "schema.sql"
|
||||
|
||||
if not schema_path.exists():
|
||||
raise FileNotFoundError(f"Schema file not found: {schema_path}")
|
||||
|
||||
with open(schema_path, 'r', encoding='utf-8') as f:
|
||||
schema_sql = f.read()
|
||||
|
||||
with self._transaction() as conn:
|
||||
conn.executescript(schema_sql)
|
||||
|
||||
logger.info(f"Database initialized: {self.db_path}")
|
||||
|
||||
# ==================== Correction Operations ====================
|
||||
|
||||
def add_correction(
|
||||
self,
|
||||
from_text: str,
|
||||
to_text: str,
|
||||
domain: str = "general",
|
||||
source: str = "manual",
|
||||
confidence: float = 1.0,
|
||||
added_by: Optional[str] = None,
|
||||
notes: Optional[str] = None
|
||||
) -> int:
|
||||
"""
|
||||
Add a new correction with full validation.
|
||||
|
||||
Args:
|
||||
from_text: Original (incorrect) text
|
||||
to_text: Corrected text
|
||||
domain: Correction domain
|
||||
source: Origin of correction
|
||||
confidence: Confidence score (0.0-1.0)
|
||||
added_by: User who added it
|
||||
notes: Optional notes
|
||||
|
||||
Returns:
|
||||
ID of inserted correction
|
||||
|
||||
Raises:
|
||||
ValidationError: If validation fails
|
||||
DatabaseError: If database operation fails
|
||||
"""
|
||||
with self._transaction() as conn:
|
||||
try:
|
||||
cursor = conn.execute("""
|
||||
INSERT INTO corrections
|
||||
(from_text, to_text, domain, source, confidence, added_by, notes)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?)
|
||||
""", (from_text, to_text, domain, source, confidence, added_by, notes))
|
||||
|
||||
correction_id = cursor.lastrowid
|
||||
|
||||
# Audit log
|
||||
self._audit_log(
|
||||
conn,
|
||||
action="add_correction",
|
||||
entity_type="correction",
|
||||
entity_id=correction_id,
|
||||
user=added_by,
|
||||
details=f"Added: '{from_text}' → '{to_text}' (domain: {domain})"
|
||||
)
|
||||
|
||||
logger.info(f"Added correction ID {correction_id}: {from_text} → {to_text}")
|
||||
return correction_id
|
||||
|
||||
except sqlite3.IntegrityError as e:
|
||||
if "UNIQUE constraint failed" in str(e):
|
||||
# Update existing correction instead (within same transaction)
|
||||
logger.warning(f"Correction already exists, updating: {from_text}")
|
||||
cursor = conn.execute("""
|
||||
UPDATE corrections
|
||||
SET to_text = ?, source = ?, confidence = ?,
|
||||
added_by = ?, notes = ?, added_at = CURRENT_TIMESTAMP
|
||||
WHERE from_text = ? AND domain = ? AND is_active = 1
|
||||
""", (to_text, source, confidence, added_by, notes, from_text, domain))
|
||||
|
||||
if cursor.rowcount > 0:
|
||||
# Get the ID of the updated row
|
||||
cursor = conn.execute("""
|
||||
SELECT id FROM corrections
|
||||
WHERE from_text = ? AND domain = ? AND is_active = 1
|
||||
""", (from_text, domain))
|
||||
correction_id = cursor.fetchone()[0]
|
||||
|
||||
# Audit log
|
||||
self._audit_log(
|
||||
conn,
|
||||
action="update_correction",
|
||||
entity_type="correction",
|
||||
entity_id=correction_id,
|
||||
user=added_by,
|
||||
details=f"Updated: '{from_text}' → '{to_text}' (domain: {domain})"
|
||||
)
|
||||
|
||||
logger.info(f"Updated correction ID {correction_id}: {from_text} → {to_text}")
|
||||
return correction_id
|
||||
else:
|
||||
raise ValidationError(f"Correction not found: {from_text} in domain {domain}")
|
||||
raise ValidationError(f"Integrity constraint violated: {e}") from e
|
||||
|
||||
def get_correction(self, from_text: str, domain: str = "general") -> Optional[Correction]:
|
||||
"""Get a specific correction."""
|
||||
conn = self._get_connection()
|
||||
cursor = conn.execute("""
|
||||
SELECT * FROM corrections
|
||||
WHERE from_text = ? AND domain = ? AND is_active = 1
|
||||
""", (from_text, domain))
|
||||
|
||||
row = cursor.fetchone()
|
||||
return self._row_to_correction(row) if row else None
|
||||
|
||||
def get_all_corrections(self, domain: Optional[str] = None, active_only: bool = True) -> List[Correction]:
|
||||
"""Get all corrections, optionally filtered by domain."""
|
||||
conn = self._get_connection()
|
||||
|
||||
if domain:
|
||||
if active_only:
|
||||
cursor = conn.execute("""
|
||||
SELECT * FROM corrections
|
||||
WHERE domain = ? AND is_active = 1
|
||||
ORDER BY from_text
|
||||
""", (domain,))
|
||||
else:
|
||||
cursor = conn.execute("""
|
||||
SELECT * FROM corrections
|
||||
WHERE domain = ?
|
||||
ORDER BY from_text
|
||||
""", (domain,))
|
||||
else:
|
||||
if active_only:
|
||||
cursor = conn.execute("""
|
||||
SELECT * FROM corrections
|
||||
WHERE is_active = 1
|
||||
ORDER BY domain, from_text
|
||||
""")
|
||||
else:
|
||||
cursor = conn.execute("""
|
||||
SELECT * FROM corrections
|
||||
ORDER BY domain, from_text
|
||||
""")
|
||||
|
||||
return [self._row_to_correction(row) for row in cursor.fetchall()]
|
||||
|
||||
def get_corrections_dict(self, domain: str = "general") -> Dict[str, str]:
|
||||
"""Get corrections as a simple dictionary for processing."""
|
||||
corrections = self.get_all_corrections(domain=domain, active_only=True)
|
||||
return {c.from_text: c.to_text for c in corrections}
|
||||
|
||||
def update_correction(
|
||||
self,
|
||||
from_text: str,
|
||||
to_text: str,
|
||||
domain: str = "general",
|
||||
updated_by: Optional[str] = None
|
||||
) -> int:
|
||||
"""Update an existing correction."""
|
||||
with self._transaction() as conn:
|
||||
cursor = conn.execute("""
|
||||
UPDATE corrections
|
||||
SET to_text = ?, added_at = CURRENT_TIMESTAMP
|
||||
WHERE from_text = ? AND domain = ? AND is_active = 1
|
||||
""", (to_text, from_text, domain))
|
||||
|
||||
if cursor.rowcount == 0:
|
||||
raise ValidationError(f"Correction not found: {from_text} in domain {domain}")
|
||||
|
||||
# Audit log
|
||||
self._audit_log(
|
||||
conn,
|
||||
action="update_correction",
|
||||
entity_type="correction",
|
||||
user=updated_by,
|
||||
details=f"Updated: '{from_text}' → '{to_text}' (domain: {domain})"
|
||||
)
|
||||
|
||||
logger.info(f"Updated correction: {from_text} → {to_text}")
|
||||
return cursor.rowcount
|
||||
|
||||
def delete_correction(self, from_text: str, domain: str = "general", deleted_by: Optional[str] = None) -> bool:
|
||||
"""Soft delete a correction (mark as inactive)."""
|
||||
with self._transaction() as conn:
|
||||
cursor = conn.execute("""
|
||||
UPDATE corrections
|
||||
SET is_active = 0
|
||||
WHERE from_text = ? AND domain = ? AND is_active = 1
|
||||
""", (from_text, domain))
|
||||
|
||||
if cursor.rowcount > 0:
|
||||
self._audit_log(
|
||||
conn,
|
||||
action="delete_correction",
|
||||
entity_type="correction",
|
||||
user=deleted_by,
|
||||
details=f"Deleted: '{from_text}' (domain: {domain})"
|
||||
)
|
||||
logger.info(f"Deleted correction: {from_text}")
|
||||
return True
|
||||
return False
|
||||
|
||||
def increment_usage(self, from_text: str, domain: str = "general") -> None:
|
||||
"""Increment usage count for a correction."""
|
||||
with self._transaction() as conn:
|
||||
conn.execute("""
|
||||
UPDATE corrections
|
||||
SET usage_count = usage_count + 1,
|
||||
last_used = CURRENT_TIMESTAMP
|
||||
WHERE from_text = ? AND domain = ? AND is_active = 1
|
||||
""", (from_text, domain))
|
||||
|
||||
# ==================== Bulk Operations ====================
|
||||
|
||||
def bulk_import_corrections(
|
||||
self,
|
||||
corrections: Dict[str, str],
|
||||
domain: str = "general",
|
||||
source: str = "imported",
|
||||
imported_by: Optional[str] = None,
|
||||
merge: bool = True
|
||||
) -> Tuple[int, int, int]:
|
||||
"""
|
||||
Bulk import corrections with conflict resolution.
|
||||
|
||||
Returns:
|
||||
Tuple of (inserted_count, updated_count, skipped_count)
|
||||
"""
|
||||
inserted, updated, skipped = 0, 0, 0
|
||||
|
||||
with self._transaction() as conn:
|
||||
for from_text, to_text in corrections.items():
|
||||
try:
|
||||
if merge:
|
||||
# Check if exists
|
||||
cursor = conn.execute("""
|
||||
SELECT id, to_text FROM corrections
|
||||
WHERE from_text = ? AND domain = ? AND is_active = 1
|
||||
""", (from_text, domain))
|
||||
existing = cursor.fetchone()
|
||||
|
||||
if existing:
|
||||
if existing['to_text'] != to_text:
|
||||
# Update
|
||||
conn.execute("""
|
||||
UPDATE corrections
|
||||
SET to_text = ?, source = ?, added_at = CURRENT_TIMESTAMP
|
||||
WHERE from_text = ? AND domain = ? AND is_active = 1
|
||||
""", (to_text, source, from_text, domain))
|
||||
updated += 1
|
||||
else:
|
||||
skipped += 1
|
||||
else:
|
||||
# Insert
|
||||
conn.execute("""
|
||||
INSERT INTO corrections
|
||||
(from_text, to_text, domain, source, confidence, added_by)
|
||||
VALUES (?, ?, ?, ?, 1.0, ?)
|
||||
""", (from_text, to_text, domain, source, imported_by))
|
||||
inserted += 1
|
||||
else:
|
||||
# Replace mode: just insert
|
||||
conn.execute("""
|
||||
INSERT OR REPLACE INTO corrections
|
||||
(from_text, to_text, domain, source, confidence, added_by)
|
||||
VALUES (?, ?, ?, ?, 1.0, ?)
|
||||
""", (from_text, to_text, domain, source, imported_by))
|
||||
inserted += 1
|
||||
|
||||
except sqlite3.Error as e:
|
||||
logger.warning(f"Failed to import '{from_text}': {e}")
|
||||
skipped += 1
|
||||
|
||||
# Audit log
|
||||
self._audit_log(
|
||||
conn,
|
||||
action="bulk_import",
|
||||
entity_type="correction",
|
||||
user=imported_by,
|
||||
details=f"Imported {inserted} new, updated {updated}, skipped {skipped} (domain: {domain})"
|
||||
)
|
||||
|
||||
logger.info(f"Bulk import: {inserted} inserted, {updated} updated, {skipped} skipped")
|
||||
return (inserted, updated, skipped)
|
||||
|
||||
# ==================== Helper Methods ====================
|
||||
|
||||
def _row_to_correction(self, row: sqlite3.Row) -> Correction:
|
||||
"""Convert database row to Correction object."""
|
||||
return Correction(
|
||||
id=row['id'],
|
||||
from_text=row['from_text'],
|
||||
to_text=row['to_text'],
|
||||
domain=row['domain'],
|
||||
source=row['source'],
|
||||
confidence=row['confidence'],
|
||||
added_by=row['added_by'],
|
||||
added_at=row['added_at'],
|
||||
usage_count=row['usage_count'],
|
||||
last_used=row['last_used'],
|
||||
notes=row['notes'],
|
||||
is_active=bool(row['is_active'])
|
||||
)
|
||||
|
||||
def _audit_log(
|
||||
self,
|
||||
conn: sqlite3.Connection,
|
||||
action: str,
|
||||
entity_type: str,
|
||||
entity_id: Optional[int] = None,
|
||||
user: Optional[str] = None,
|
||||
details: Optional[str] = None,
|
||||
success: bool = True,
|
||||
error_message: Optional[str] = None
|
||||
) -> None:
|
||||
"""Write audit log entry."""
|
||||
conn.execute("""
|
||||
INSERT INTO audit_log (action, entity_type, entity_id, user, details, success, error_message)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?)
|
||||
""", (action, entity_type, entity_id, user, details, success, error_message))
|
||||
|
||||
def close(self) -> None:
|
||||
"""Close database connection."""
|
||||
if hasattr(self._local, 'connection'):
|
||||
self._local.connection.close()
|
||||
delattr(self._local, 'connection')
|
||||
logger.info("Database connection closed")
|
||||
524
transcript-fixer/scripts/core/correction_service.py
Normal file
524
transcript-fixer/scripts/core/correction_service.py
Normal file
@@ -0,0 +1,524 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Correction Service - Business Logic Layer
|
||||
|
||||
SINGLE RESPONSIBILITY: Implement business rules and validation
|
||||
|
||||
Orchestrates repository operations with comprehensive validation,
|
||||
error handling, and business logic enforcement.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import re
|
||||
import os
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
from dataclasses import dataclass
|
||||
|
||||
from .correction_repository import (
|
||||
CorrectionRepository,
|
||||
ValidationError,
|
||||
DatabaseError
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@dataclass
|
||||
class ValidationRules:
|
||||
"""Validation rules configuration"""
|
||||
max_text_length: int = 1000
|
||||
min_text_length: int = 1
|
||||
max_domain_length: int = 50
|
||||
allowed_domain_pattern: str = r'^[a-zA-Z0-9_-]+$'
|
||||
max_confidence: float = 1.0
|
||||
min_confidence: float = 0.0
|
||||
|
||||
|
||||
class CorrectionService:
|
||||
"""
|
||||
Service layer for correction management.
|
||||
|
||||
Responsibilities:
|
||||
- Input validation and sanitization
|
||||
- Business rule enforcement
|
||||
- Conflict detection and resolution
|
||||
- Statistics and reporting
|
||||
- Integration with repository layer
|
||||
"""
|
||||
|
||||
def __init__(self, repository: CorrectionRepository, rules: Optional[ValidationRules] = None):
|
||||
"""
|
||||
Initialize service with repository.
|
||||
|
||||
Args:
|
||||
repository: Data access layer
|
||||
rules: Validation rules (uses defaults if None)
|
||||
"""
|
||||
self.repository = repository
|
||||
self.rules = rules or ValidationRules()
|
||||
self.db_path = repository.db_path
|
||||
logger.info("CorrectionService initialized")
|
||||
|
||||
def initialize(self) -> None:
|
||||
"""
|
||||
Initialize database (already done by repository, kept for API compatibility).
|
||||
"""
|
||||
# Database is auto-initialized by repository on first access
|
||||
logger.info(f"✅ Database ready: {self.db_path}")
|
||||
|
||||
# ==================== Validation Methods ====================
|
||||
|
||||
def validate_correction_text(self, text: str, field_name: str = "text") -> None:
|
||||
"""
|
||||
Validate correction text with comprehensive checks.
|
||||
|
||||
Args:
|
||||
text: Text to validate
|
||||
field_name: Field name for error messages
|
||||
|
||||
Raises:
|
||||
ValidationError: If validation fails
|
||||
"""
|
||||
# Check not None or empty
|
||||
if not text:
|
||||
raise ValidationError(f"{field_name} cannot be None or empty")
|
||||
|
||||
# Check not only whitespace
|
||||
if not text.strip():
|
||||
raise ValidationError(f"{field_name} cannot be only whitespace")
|
||||
|
||||
# Check length constraints
|
||||
if len(text) < self.rules.min_text_length:
|
||||
raise ValidationError(
|
||||
f"{field_name} too short: {len(text)} chars (min: {self.rules.min_text_length})"
|
||||
)
|
||||
|
||||
if len(text) > self.rules.max_text_length:
|
||||
raise ValidationError(
|
||||
f"{field_name} too long: {len(text)} chars (max: {self.rules.max_text_length})"
|
||||
)
|
||||
|
||||
# Check for control characters (except newline and tab)
|
||||
invalid_chars = [c for c in text if ord(c) < 32 and c not in '\n\t']
|
||||
if invalid_chars:
|
||||
raise ValidationError(
|
||||
f"{field_name} contains invalid control characters: {invalid_chars}"
|
||||
)
|
||||
|
||||
# Check for NULL bytes
|
||||
if '\x00' in text:
|
||||
raise ValidationError(f"{field_name} contains NULL bytes")
|
||||
|
||||
def validate_domain_name(self, domain: str) -> None:
|
||||
"""
|
||||
Validate domain name to prevent path traversal and injection.
|
||||
|
||||
Args:
|
||||
domain: Domain name to validate
|
||||
|
||||
Raises:
|
||||
ValidationError: If validation fails
|
||||
"""
|
||||
if not domain:
|
||||
raise ValidationError("Domain name cannot be empty")
|
||||
|
||||
if len(domain) > self.rules.max_domain_length:
|
||||
raise ValidationError(
|
||||
f"Domain name too long: {len(domain)} chars (max: {self.rules.max_domain_length})"
|
||||
)
|
||||
|
||||
# Check pattern: only alphanumeric, underscore, hyphen
|
||||
if not re.match(self.rules.allowed_domain_pattern, domain):
|
||||
raise ValidationError(
|
||||
f"Domain name contains invalid characters: {domain}. "
|
||||
f"Allowed pattern: {self.rules.allowed_domain_pattern}"
|
||||
)
|
||||
|
||||
# Check for path traversal attempts
|
||||
if '..' in domain or '/' in domain or '\\' in domain:
|
||||
raise ValidationError(f"Domain name contains path traversal: {domain}")
|
||||
|
||||
# Reserved names
|
||||
reserved = ['con', 'prn', 'aux', 'nul', 'com1', 'lpt1'] # Windows reserved
|
||||
if domain.lower() in reserved:
|
||||
raise ValidationError(f"Domain name is reserved: {domain}")
|
||||
|
||||
def validate_confidence(self, confidence: float) -> None:
|
||||
"""Validate confidence score."""
|
||||
if not isinstance(confidence, (int, float)):
|
||||
raise ValidationError(f"Confidence must be numeric, got {type(confidence)}")
|
||||
|
||||
if not (self.rules.min_confidence <= confidence <= self.rules.max_confidence):
|
||||
raise ValidationError(
|
||||
f"Confidence must be between {self.rules.min_confidence} "
|
||||
f"and {self.rules.max_confidence}, got {confidence}"
|
||||
)
|
||||
|
||||
def validate_source(self, source: str) -> None:
|
||||
"""Validate correction source."""
|
||||
valid_sources = ['manual', 'learned', 'imported']
|
||||
if source not in valid_sources:
|
||||
raise ValidationError(
|
||||
f"Invalid source: {source}. Must be one of: {valid_sources}"
|
||||
)
|
||||
|
||||
# ==================== Correction Operations ====================
|
||||
|
||||
def add_correction(
|
||||
self,
|
||||
from_text: str,
|
||||
to_text: str,
|
||||
domain: str = "general",
|
||||
source: str = "manual",
|
||||
confidence: float = 1.0,
|
||||
notes: Optional[str] = None
|
||||
) -> int:
|
||||
"""
|
||||
Add a correction with full validation.
|
||||
|
||||
Args:
|
||||
from_text: Original (incorrect) text
|
||||
to_text: Corrected text
|
||||
domain: Correction domain
|
||||
source: Origin of correction
|
||||
confidence: Confidence score
|
||||
notes: Optional notes
|
||||
|
||||
Returns:
|
||||
ID of inserted correction
|
||||
|
||||
Raises:
|
||||
ValidationError: If validation fails
|
||||
"""
|
||||
# Comprehensive validation
|
||||
self.validate_correction_text(from_text, "from_text")
|
||||
self.validate_correction_text(to_text, "to_text")
|
||||
self.validate_domain_name(domain)
|
||||
self.validate_source(source)
|
||||
self.validate_confidence(confidence)
|
||||
|
||||
# Business rule: from_text and to_text should be different
|
||||
if from_text.strip() == to_text.strip():
|
||||
raise ValidationError(
|
||||
f"from_text and to_text are identical: '{from_text}'"
|
||||
)
|
||||
|
||||
# Get current user
|
||||
added_by = os.getenv("USER") or os.getenv("USERNAME") or "unknown"
|
||||
|
||||
try:
|
||||
correction_id = self.repository.add_correction(
|
||||
from_text=from_text,
|
||||
to_text=to_text,
|
||||
domain=domain,
|
||||
source=source,
|
||||
confidence=confidence,
|
||||
added_by=added_by,
|
||||
notes=notes
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"Successfully added correction ID {correction_id}: "
|
||||
f"'{from_text}' → '{to_text}' (domain: {domain})"
|
||||
)
|
||||
return correction_id
|
||||
|
||||
except DatabaseError as e:
|
||||
logger.error(f"Failed to add correction: {e}")
|
||||
raise
|
||||
|
||||
def get_corrections(self, domain: Optional[str] = None) -> Dict[str, str]:
|
||||
"""
|
||||
Get corrections as a dictionary for processing.
|
||||
|
||||
Args:
|
||||
domain: Optional domain filter
|
||||
|
||||
Returns:
|
||||
Dictionary of corrections {from_text: to_text}
|
||||
"""
|
||||
if domain:
|
||||
self.validate_domain_name(domain)
|
||||
return self.repository.get_corrections_dict(domain)
|
||||
else:
|
||||
# Get all domains
|
||||
all_corrections = self.repository.get_all_corrections(active_only=True)
|
||||
return {c.from_text: c.to_text for c in all_corrections}
|
||||
|
||||
def remove_correction(
|
||||
self,
|
||||
from_text: str,
|
||||
domain: str = "general"
|
||||
) -> bool:
|
||||
"""
|
||||
Remove a correction (soft delete).
|
||||
|
||||
Args:
|
||||
from_text: Text to remove
|
||||
domain: Domain
|
||||
|
||||
Returns:
|
||||
True if removed, False if not found
|
||||
"""
|
||||
self.validate_correction_text(from_text, "from_text")
|
||||
self.validate_domain_name(domain)
|
||||
|
||||
deleted_by = os.getenv("USER") or os.getenv("USERNAME") or "unknown"
|
||||
|
||||
success = self.repository.delete_correction(from_text, domain, deleted_by)
|
||||
|
||||
if success:
|
||||
logger.info(f"Removed correction: '{from_text}' (domain: {domain})")
|
||||
else:
|
||||
logger.warning(f"Correction not found: '{from_text}' (domain: {domain})")
|
||||
|
||||
return success
|
||||
|
||||
# ==================== Import/Export Operations ====================
|
||||
|
||||
def import_corrections(
|
||||
self,
|
||||
corrections: Dict[str, str],
|
||||
domain: str = "general",
|
||||
merge: bool = True,
|
||||
validate_all: bool = True
|
||||
) -> Tuple[int, int, int]:
|
||||
"""
|
||||
Import corrections with validation and conflict resolution.
|
||||
|
||||
Args:
|
||||
corrections: Dictionary of corrections to import
|
||||
domain: Target domain
|
||||
merge: If True, merge with existing; if False, replace
|
||||
validate_all: If True, validate all before import (safer but slower)
|
||||
|
||||
Returns:
|
||||
Tuple of (inserted_count, updated_count, skipped_count)
|
||||
|
||||
Raises:
|
||||
ValidationError: If validation fails (when validate_all=True)
|
||||
"""
|
||||
self.validate_domain_name(domain)
|
||||
|
||||
if not corrections:
|
||||
raise ValidationError("Cannot import empty corrections dictionary")
|
||||
|
||||
# Pre-validation (if requested)
|
||||
if validate_all:
|
||||
logger.info(f"Pre-validating {len(corrections)} corrections...")
|
||||
invalid_count = 0
|
||||
for from_text, to_text in corrections.items():
|
||||
try:
|
||||
self.validate_correction_text(from_text, "from_text")
|
||||
self.validate_correction_text(to_text, "to_text")
|
||||
except ValidationError as e:
|
||||
logger.error(f"Validation failed for '{from_text}' → '{to_text}': {e}")
|
||||
invalid_count += 1
|
||||
|
||||
if invalid_count > 0:
|
||||
raise ValidationError(
|
||||
f"Pre-validation failed: {invalid_count}/{len(corrections)} corrections invalid"
|
||||
)
|
||||
|
||||
# Detect conflicts if merge mode
|
||||
if merge:
|
||||
existing = self.repository.get_corrections_dict(domain)
|
||||
conflicts = self._detect_conflicts(corrections, existing)
|
||||
|
||||
if conflicts:
|
||||
logger.warning(
|
||||
f"Found {len(conflicts)} conflicts that will be overwritten"
|
||||
)
|
||||
for from_text, (old_val, new_val) in conflicts.items():
|
||||
logger.debug(f"Conflict: '{from_text}': '{old_val}' → '{new_val}'")
|
||||
|
||||
# Perform import
|
||||
imported_by = os.getenv("USER") or os.getenv("USERNAME") or "unknown"
|
||||
|
||||
try:
|
||||
inserted, updated, skipped = self.repository.bulk_import_corrections(
|
||||
corrections=corrections,
|
||||
domain=domain,
|
||||
source="imported",
|
||||
imported_by=imported_by,
|
||||
merge=merge
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"Import complete: {inserted} inserted, {updated} updated, "
|
||||
f"{skipped} skipped (domain: {domain})"
|
||||
)
|
||||
|
||||
return (inserted, updated, skipped)
|
||||
|
||||
except DatabaseError as e:
|
||||
logger.error(f"Import failed: {e}")
|
||||
raise
|
||||
|
||||
def export_corrections(self, domain: str = "general") -> Dict[str, str]:
|
||||
"""
|
||||
Export corrections for sharing.
|
||||
|
||||
Args:
|
||||
domain: Domain to export
|
||||
|
||||
Returns:
|
||||
Dictionary of corrections
|
||||
"""
|
||||
self.validate_domain_name(domain)
|
||||
|
||||
corrections = self.repository.get_corrections_dict(domain)
|
||||
|
||||
logger.info(f"Exported {len(corrections)} corrections (domain: {domain})")
|
||||
|
||||
return corrections
|
||||
|
||||
# ==================== Statistics and Reporting ====================
|
||||
|
||||
def get_statistics(self, domain: Optional[str] = None) -> Dict[str, any]:
|
||||
"""
|
||||
Get correction statistics.
|
||||
|
||||
Args:
|
||||
domain: Optional domain filter
|
||||
|
||||
Returns:
|
||||
Dictionary of statistics
|
||||
"""
|
||||
if domain:
|
||||
self.validate_domain_name(domain)
|
||||
corrections = self.repository.get_all_corrections(domain=domain, active_only=True)
|
||||
else:
|
||||
corrections = self.repository.get_all_corrections(active_only=True)
|
||||
|
||||
# Calculate statistics
|
||||
total = len(corrections)
|
||||
by_source = {'manual': 0, 'learned': 0, 'imported': 0}
|
||||
total_usage = 0
|
||||
high_confidence = 0
|
||||
|
||||
for c in corrections:
|
||||
by_source[c.source] = by_source.get(c.source, 0) + 1
|
||||
total_usage += c.usage_count
|
||||
if c.confidence >= 0.9:
|
||||
high_confidence += 1
|
||||
|
||||
stats = {
|
||||
'total_corrections': total,
|
||||
'by_source': by_source,
|
||||
'total_usage': total_usage,
|
||||
'average_usage': total_usage / total if total > 0 else 0,
|
||||
'high_confidence_count': high_confidence,
|
||||
'high_confidence_ratio': high_confidence / total if total > 0 else 0
|
||||
}
|
||||
|
||||
logger.debug(f"Statistics for domain '{domain}': {stats}")
|
||||
|
||||
return stats
|
||||
|
||||
# ==================== Helper Methods ====================
|
||||
|
||||
def _detect_conflicts(
|
||||
self,
|
||||
incoming: Dict[str, str],
|
||||
existing: Dict[str, str]
|
||||
) -> Dict[str, Tuple[str, str]]:
|
||||
"""
|
||||
Detect conflicts between incoming and existing corrections.
|
||||
|
||||
Returns:
|
||||
Dictionary of conflicts {from_text: (existing_to, incoming_to)}
|
||||
"""
|
||||
conflicts = {}
|
||||
|
||||
for from_text in set(incoming.keys()) & set(existing.keys()):
|
||||
if existing[from_text] != incoming[from_text]:
|
||||
conflicts[from_text] = (existing[from_text], incoming[from_text])
|
||||
|
||||
return conflicts
|
||||
|
||||
def load_context_rules(self) -> List[Dict]:
|
||||
"""
|
||||
Load active context-aware regex rules.
|
||||
|
||||
Returns:
|
||||
List of rule dictionaries with pattern, replacement, description
|
||||
"""
|
||||
try:
|
||||
conn = self.repository._get_connection()
|
||||
cursor = conn.execute("""
|
||||
SELECT pattern, replacement, description
|
||||
FROM context_rules
|
||||
WHERE is_active = 1
|
||||
ORDER BY priority DESC
|
||||
""")
|
||||
|
||||
rules = []
|
||||
for row in cursor.fetchall():
|
||||
rules.append({
|
||||
"pattern": row[0],
|
||||
"replacement": row[1],
|
||||
"description": row[2]
|
||||
})
|
||||
|
||||
logger.debug(f"Loaded {len(rules)} context rules")
|
||||
return rules
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to load context rules: {e}")
|
||||
return []
|
||||
|
||||
def save_history(self, filename: str, domain: str, original_length: int,
|
||||
stage1_changes: int, stage2_changes: int, model: str,
|
||||
changes: List[Dict]) -> None:
|
||||
"""
|
||||
Save correction run history for learning.
|
||||
|
||||
Args:
|
||||
filename: File that was corrected
|
||||
domain: Correction domain
|
||||
original_length: Original file length
|
||||
stage1_changes: Number of Stage 1 changes
|
||||
stage2_changes: Number of Stage 2 changes
|
||||
model: AI model used
|
||||
changes: List of individual changes
|
||||
"""
|
||||
try:
|
||||
with self.repository._transaction() as conn:
|
||||
# Insert history record
|
||||
cursor = conn.execute("""
|
||||
INSERT INTO correction_history
|
||||
(filename, domain, original_length, stage1_changes, stage2_changes, model)
|
||||
VALUES (?, ?, ?, ?, ?, ?)
|
||||
""", (filename, domain, original_length, stage1_changes, stage2_changes, model))
|
||||
|
||||
history_id = cursor.lastrowid
|
||||
|
||||
# Insert individual changes
|
||||
for change in changes:
|
||||
conn.execute("""
|
||||
INSERT INTO correction_changes
|
||||
(history_id, line_number, from_text, to_text, rule_type, context_before, context_after)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?)
|
||||
""", (
|
||||
history_id,
|
||||
change.get("line_number"),
|
||||
change.get("from_text", ""),
|
||||
change.get("to_text", ""),
|
||||
change.get("rule_type", "dictionary"),
|
||||
change.get("context_before"),
|
||||
change.get("context_after")
|
||||
))
|
||||
|
||||
logger.info(f"Saved correction history for {filename}: {stage1_changes + stage2_changes} total changes")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to save history: {e}")
|
||||
|
||||
def close(self) -> None:
|
||||
"""Close underlying repository."""
|
||||
self.repository.close()
|
||||
logger.info("CorrectionService closed")
|
||||
140
transcript-fixer/scripts/core/dictionary_processor.py
Normal file
140
transcript-fixer/scripts/core/dictionary_processor.py
Normal file
@@ -0,0 +1,140 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Dictionary Processor - Stage 1: Dictionary-based Text Corrections
|
||||
|
||||
SINGLE RESPONSIBILITY: Apply dictionary and regex-based corrections to text
|
||||
|
||||
Features:
|
||||
- Apply simple dictionary replacements
|
||||
- Apply context-aware regex rules
|
||||
- Track all changes for history
|
||||
- Case-sensitive and insensitive matching
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import re
|
||||
from typing import Dict, List, Tuple
|
||||
from dataclasses import dataclass
|
||||
|
||||
|
||||
@dataclass
|
||||
class Change:
|
||||
"""Represents a single text change"""
|
||||
line_number: int
|
||||
from_text: str
|
||||
to_text: str
|
||||
rule_type: str # "dictionary" or "context_rule"
|
||||
rule_name: str
|
||||
|
||||
|
||||
class DictionaryProcessor:
|
||||
"""
|
||||
Stage 1 Processor: Apply dictionary-based corrections
|
||||
|
||||
Process:
|
||||
1. Apply context-aware regex rules first (more specific)
|
||||
2. Apply simple dictionary replacements (more general)
|
||||
3. Track all changes for learning
|
||||
"""
|
||||
|
||||
def __init__(self, corrections: Dict[str, str], context_rules: List[Dict]):
|
||||
"""
|
||||
Initialize processor with corrections and rules
|
||||
|
||||
Args:
|
||||
corrections: Dictionary of {wrong: correct} pairs
|
||||
context_rules: List of context-aware regex rules
|
||||
"""
|
||||
self.corrections = corrections
|
||||
self.context_rules = context_rules
|
||||
|
||||
def process(self, text: str) -> Tuple[str, List[Change]]:
|
||||
"""
|
||||
Apply all corrections to text
|
||||
|
||||
Returns:
|
||||
(corrected_text, list_of_changes)
|
||||
"""
|
||||
corrected_text = text
|
||||
all_changes = []
|
||||
|
||||
# Step 1: Apply context rules (more specific, higher priority)
|
||||
corrected_text, context_changes = self._apply_context_rules(corrected_text)
|
||||
all_changes.extend(context_changes)
|
||||
|
||||
# Step 2: Apply dictionary replacements (more general)
|
||||
corrected_text, dict_changes = self._apply_dictionary(corrected_text)
|
||||
all_changes.extend(dict_changes)
|
||||
|
||||
return corrected_text, all_changes
|
||||
|
||||
def _apply_context_rules(self, text: str) -> Tuple[str, List[Change]]:
|
||||
"""Apply context-aware regex rules"""
|
||||
changes = []
|
||||
corrected = text
|
||||
|
||||
for rule in self.context_rules:
|
||||
pattern = rule["pattern"]
|
||||
replacement = rule["replacement"]
|
||||
description = rule.get("description", "")
|
||||
|
||||
# Find all matches with their positions
|
||||
for match in re.finditer(pattern, corrected):
|
||||
line_num = corrected[:match.start()].count('\n') + 1
|
||||
changes.append(Change(
|
||||
line_number=line_num,
|
||||
from_text=match.group(0),
|
||||
to_text=replacement,
|
||||
rule_type="context_rule",
|
||||
rule_name=description or pattern
|
||||
))
|
||||
|
||||
# Apply replacement
|
||||
corrected = re.sub(pattern, replacement, corrected)
|
||||
|
||||
return corrected, changes
|
||||
|
||||
def _apply_dictionary(self, text: str) -> Tuple[str, List[Change]]:
|
||||
"""Apply simple dictionary replacements"""
|
||||
changes = []
|
||||
corrected = text
|
||||
|
||||
for wrong, correct in self.corrections.items():
|
||||
if wrong not in corrected:
|
||||
continue
|
||||
|
||||
# Find all occurrences
|
||||
occurrences = []
|
||||
start = 0
|
||||
while True:
|
||||
pos = corrected.find(wrong, start)
|
||||
if pos == -1:
|
||||
break
|
||||
line_num = corrected[:pos].count('\n') + 1
|
||||
occurrences.append(line_num)
|
||||
start = pos + len(wrong)
|
||||
|
||||
# Track changes
|
||||
for line_num in occurrences:
|
||||
changes.append(Change(
|
||||
line_number=line_num,
|
||||
from_text=wrong,
|
||||
to_text=correct,
|
||||
rule_type="dictionary",
|
||||
rule_name="corrections_dict"
|
||||
))
|
||||
|
||||
# Apply replacement
|
||||
corrected = corrected.replace(wrong, correct)
|
||||
|
||||
return corrected, changes
|
||||
|
||||
def get_summary(self, changes: List[Change]) -> Dict[str, int]:
|
||||
"""Generate summary statistics"""
|
||||
summary = {
|
||||
"total_changes": len(changes),
|
||||
"dictionary_changes": sum(1 for c in changes if c.rule_type == "dictionary"),
|
||||
"context_rule_changes": sum(1 for c in changes if c.rule_type == "context_rule")
|
||||
}
|
||||
return summary
|
||||
252
transcript-fixer/scripts/core/learning_engine.py
Normal file
252
transcript-fixer/scripts/core/learning_engine.py
Normal file
@@ -0,0 +1,252 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Learning Engine - Pattern Detection from Correction History
|
||||
|
||||
SINGLE RESPONSIBILITY: Analyze history and suggest new corrections
|
||||
|
||||
Features:
|
||||
- Analyze correction history for patterns
|
||||
- Detect frequently occurring corrections
|
||||
- Calculate confidence scores
|
||||
- Generate suggestions for user review
|
||||
- Track rejected suggestions to avoid re-suggesting
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
from pathlib import Path
|
||||
from typing import List, Dict
|
||||
from dataclasses import dataclass, asdict
|
||||
from collections import defaultdict
|
||||
|
||||
|
||||
@dataclass
|
||||
class Suggestion:
|
||||
"""Represents a learned correction suggestion"""
|
||||
from_text: str
|
||||
to_text: str
|
||||
frequency: int
|
||||
confidence: float
|
||||
examples: List[Dict] # List of {file, line, context}
|
||||
first_seen: str
|
||||
last_seen: str
|
||||
status: str # "pending", "approved", "rejected"
|
||||
|
||||
|
||||
class LearningEngine:
|
||||
"""
|
||||
Analyzes correction history to suggest new corrections
|
||||
|
||||
Algorithm:
|
||||
1. Load all history files
|
||||
2. Extract stage2 (AI) changes
|
||||
3. Group by pattern (from_text → to_text)
|
||||
4. Calculate frequency and confidence
|
||||
5. Filter by thresholds
|
||||
6. Save suggestions for user review
|
||||
"""
|
||||
|
||||
# Thresholds for suggesting corrections
|
||||
MIN_FREQUENCY = 3 # Must appear at least 3 times
|
||||
MIN_CONFIDENCE = 0.8 # Must have 80%+ confidence
|
||||
|
||||
def __init__(self, history_dir: Path, learned_dir: Path):
|
||||
"""
|
||||
Initialize learning engine
|
||||
|
||||
Args:
|
||||
history_dir: Directory containing correction history
|
||||
learned_dir: Directory for learned suggestions
|
||||
"""
|
||||
self.history_dir = history_dir
|
||||
self.learned_dir = learned_dir
|
||||
self.pending_file = learned_dir / "pending_review.json"
|
||||
self.rejected_file = learned_dir / "rejected.json"
|
||||
|
||||
def analyze_and_suggest(self) -> List[Suggestion]:
|
||||
"""
|
||||
Analyze history and generate suggestions
|
||||
|
||||
Returns:
|
||||
List of new suggestions for user review
|
||||
"""
|
||||
# Load all history
|
||||
patterns = self._extract_patterns()
|
||||
|
||||
# Filter rejected patterns
|
||||
rejected = self._load_rejected()
|
||||
patterns = {k: v for k, v in patterns.items()
|
||||
if k not in rejected}
|
||||
|
||||
# Generate suggestions
|
||||
suggestions = []
|
||||
for (from_text, to_text), occurrences in patterns.items():
|
||||
frequency = len(occurrences)
|
||||
|
||||
if frequency < self.MIN_FREQUENCY:
|
||||
continue
|
||||
|
||||
confidence = self._calculate_confidence(occurrences)
|
||||
|
||||
if confidence < self.MIN_CONFIDENCE:
|
||||
continue
|
||||
|
||||
suggestion = Suggestion(
|
||||
from_text=from_text,
|
||||
to_text=to_text,
|
||||
frequency=frequency,
|
||||
confidence=confidence,
|
||||
examples=occurrences[:5], # Top 5 examples
|
||||
first_seen=occurrences[0]["timestamp"],
|
||||
last_seen=occurrences[-1]["timestamp"],
|
||||
status="pending"
|
||||
)
|
||||
|
||||
suggestions.append(suggestion)
|
||||
|
||||
# Save new suggestions
|
||||
if suggestions:
|
||||
self._save_pending_suggestions(suggestions)
|
||||
|
||||
return suggestions
|
||||
|
||||
def approve_suggestion(self, from_text: str) -> bool:
|
||||
"""
|
||||
Approve a suggestion (remove from pending)
|
||||
|
||||
Returns:
|
||||
True if approved, False if not found
|
||||
"""
|
||||
pending = self._load_pending_suggestions()
|
||||
|
||||
for suggestion in pending:
|
||||
if suggestion["from_text"] == from_text:
|
||||
pending.remove(suggestion)
|
||||
self._save_suggestions(pending, self.pending_file)
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
def reject_suggestion(self, from_text: str, to_text: str) -> None:
|
||||
"""
|
||||
Reject a suggestion (move to rejected list)
|
||||
"""
|
||||
# Remove from pending
|
||||
pending = self._load_pending_suggestions()
|
||||
pending = [s for s in pending
|
||||
if not (s["from_text"] == from_text and s["to_text"] == to_text)]
|
||||
self._save_suggestions(pending, self.pending_file)
|
||||
|
||||
# Add to rejected
|
||||
rejected = self._load_rejected()
|
||||
rejected.add((from_text, to_text))
|
||||
self._save_rejected(rejected)
|
||||
|
||||
def list_pending(self) -> List[Dict]:
|
||||
"""List all pending suggestions"""
|
||||
return self._load_pending_suggestions()
|
||||
|
||||
def _extract_patterns(self) -> Dict[tuple, List[Dict]]:
|
||||
"""Extract all correction patterns from history"""
|
||||
patterns = defaultdict(list)
|
||||
|
||||
if not self.history_dir.exists():
|
||||
return patterns
|
||||
|
||||
for history_file in self.history_dir.glob("*.json"):
|
||||
with open(history_file, 'r', encoding='utf-8') as f:
|
||||
data = json.load(f)
|
||||
|
||||
# Extract stage2 changes (AI corrections)
|
||||
if "stages" in data and "stage2" in data["stages"]:
|
||||
changes = data["stages"]["stage2"].get("changes", [])
|
||||
|
||||
for change in changes:
|
||||
key = (change["from"], change["to"])
|
||||
patterns[key].append({
|
||||
"file": data["filename"],
|
||||
"line": change.get("line", 0),
|
||||
"context": change.get("context", ""),
|
||||
"timestamp": data["timestamp"]
|
||||
})
|
||||
|
||||
return patterns
|
||||
|
||||
def _calculate_confidence(self, occurrences: List[Dict]) -> float:
|
||||
"""
|
||||
Calculate confidence score for a pattern
|
||||
|
||||
Factors:
|
||||
- Frequency (more = higher)
|
||||
- Consistency (always same correction = higher)
|
||||
- Recency (recent occurrences = higher)
|
||||
"""
|
||||
# Base confidence from frequency
|
||||
frequency_score = min(len(occurrences) / 10.0, 1.0)
|
||||
|
||||
# Consistency: always the same from→to mapping
|
||||
consistency_score = 1.0 # Already consistent by grouping
|
||||
|
||||
# Recency: more recent = higher
|
||||
# (Simplified: assume chronological order)
|
||||
recency_score = 0.9 if len(occurrences) > 1 else 0.8
|
||||
|
||||
# Weighted average
|
||||
confidence = (
|
||||
0.5 * frequency_score +
|
||||
0.3 * consistency_score +
|
||||
0.2 * recency_score
|
||||
)
|
||||
|
||||
return confidence
|
||||
|
||||
def _load_pending_suggestions(self) -> List[Dict]:
|
||||
"""Load pending suggestions from file"""
|
||||
if not self.pending_file.exists():
|
||||
return []
|
||||
|
||||
with open(self.pending_file, 'r', encoding='utf-8') as f:
|
||||
content = f.read().strip()
|
||||
if not content:
|
||||
return []
|
||||
return json.loads(content).get("suggestions", [])
|
||||
|
||||
def _save_pending_suggestions(self, suggestions: List[Suggestion]) -> None:
|
||||
"""Save pending suggestions to file"""
|
||||
existing = self._load_pending_suggestions()
|
||||
|
||||
# Convert to dict and append
|
||||
new_suggestions = [asdict(s) for s in suggestions]
|
||||
all_suggestions = existing + new_suggestions
|
||||
|
||||
self._save_suggestions(all_suggestions, self.pending_file)
|
||||
|
||||
def _save_suggestions(self, suggestions: List[Dict], filepath: Path) -> None:
|
||||
"""Save suggestions to file"""
|
||||
data = {"suggestions": suggestions}
|
||||
with open(filepath, 'w', encoding='utf-8') as f:
|
||||
json.dump(data, f, ensure_ascii=False, indent=2)
|
||||
|
||||
def _load_rejected(self) -> set:
|
||||
"""Load rejected patterns"""
|
||||
if not self.rejected_file.exists():
|
||||
return set()
|
||||
|
||||
with open(self.rejected_file, 'r', encoding='utf-8') as f:
|
||||
content = f.read().strip()
|
||||
if not content:
|
||||
return set()
|
||||
data = json.loads(content)
|
||||
return {(r["from"], r["to"]) for r in data.get("rejected", [])}
|
||||
|
||||
def _save_rejected(self, rejected: set) -> None:
|
||||
"""Save rejected patterns"""
|
||||
data = {
|
||||
"rejected": [
|
||||
{"from": from_text, "to": to_text}
|
||||
for from_text, to_text in rejected
|
||||
]
|
||||
}
|
||||
with open(self.rejected_file, 'w', encoding='utf-8') as f:
|
||||
json.dump(data, f, ensure_ascii=False, indent=2)
|
||||
215
transcript-fixer/scripts/core/schema.sql
Normal file
215
transcript-fixer/scripts/core/schema.sql
Normal file
@@ -0,0 +1,215 @@
|
||||
-- Transcript Fixer Database Schema v2.0
|
||||
-- Migration from JSON to SQLite for ACID compliance and scalability
|
||||
-- Author: ISTJ Chief Engineer
|
||||
-- Date: 2025-01-28
|
||||
|
||||
-- Enable foreign keys
|
||||
PRAGMA foreign_keys = ON;
|
||||
|
||||
-- Table: corrections
|
||||
-- Stores all correction mappings with metadata
|
||||
CREATE TABLE IF NOT EXISTS corrections (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
from_text TEXT NOT NULL,
|
||||
to_text TEXT NOT NULL,
|
||||
domain TEXT NOT NULL DEFAULT 'general',
|
||||
source TEXT NOT NULL CHECK(source IN ('manual', 'learned', 'imported')),
|
||||
confidence REAL NOT NULL DEFAULT 1.0 CHECK(confidence >= 0.0 AND confidence <= 1.0),
|
||||
added_by TEXT,
|
||||
added_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
|
||||
usage_count INTEGER NOT NULL DEFAULT 0 CHECK(usage_count >= 0),
|
||||
last_used TIMESTAMP,
|
||||
notes TEXT,
|
||||
is_active BOOLEAN NOT NULL DEFAULT 1,
|
||||
UNIQUE(from_text, domain)
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_corrections_domain ON corrections(domain);
|
||||
CREATE INDEX IF NOT EXISTS idx_corrections_source ON corrections(source);
|
||||
CREATE INDEX IF NOT EXISTS idx_corrections_added_at ON corrections(added_at);
|
||||
CREATE INDEX IF NOT EXISTS idx_corrections_is_active ON corrections(is_active);
|
||||
CREATE INDEX IF NOT EXISTS idx_corrections_from_text ON corrections(from_text);
|
||||
|
||||
-- Table: context_rules
|
||||
-- Regex-based context-aware correction rules
|
||||
CREATE TABLE IF NOT EXISTS context_rules (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
pattern TEXT NOT NULL UNIQUE,
|
||||
replacement TEXT NOT NULL,
|
||||
description TEXT,
|
||||
priority INTEGER NOT NULL DEFAULT 0,
|
||||
is_active BOOLEAN NOT NULL DEFAULT 1,
|
||||
added_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
|
||||
added_by TEXT
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_context_rules_priority ON context_rules(priority DESC);
|
||||
CREATE INDEX IF NOT EXISTS idx_context_rules_is_active ON context_rules(is_active);
|
||||
|
||||
-- Table: correction_history
|
||||
-- Audit log for all correction runs
|
||||
CREATE TABLE IF NOT EXISTS correction_history (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
filename TEXT NOT NULL,
|
||||
domain TEXT NOT NULL,
|
||||
run_timestamp TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
|
||||
original_length INTEGER NOT NULL CHECK(original_length >= 0),
|
||||
stage1_changes INTEGER NOT NULL DEFAULT 0 CHECK(stage1_changes >= 0),
|
||||
stage2_changes INTEGER NOT NULL DEFAULT 0 CHECK(stage2_changes >= 0),
|
||||
model TEXT,
|
||||
execution_time_ms INTEGER CHECK(execution_time_ms >= 0),
|
||||
success BOOLEAN NOT NULL DEFAULT 1,
|
||||
error_message TEXT
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_history_run_timestamp ON correction_history(run_timestamp DESC);
|
||||
CREATE INDEX IF NOT EXISTS idx_history_domain ON correction_history(domain);
|
||||
CREATE INDEX IF NOT EXISTS idx_history_success ON correction_history(success);
|
||||
|
||||
-- Table: correction_changes
|
||||
-- Detailed changes made in each correction run
|
||||
CREATE TABLE IF NOT EXISTS correction_changes (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
history_id INTEGER NOT NULL,
|
||||
line_number INTEGER,
|
||||
from_text TEXT NOT NULL,
|
||||
to_text TEXT NOT NULL,
|
||||
rule_type TEXT NOT NULL CHECK(rule_type IN ('context', 'dictionary', 'ai')),
|
||||
rule_id INTEGER,
|
||||
context_before TEXT,
|
||||
context_after TEXT,
|
||||
FOREIGN KEY (history_id) REFERENCES correction_history(id) ON DELETE CASCADE
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_changes_history_id ON correction_changes(history_id);
|
||||
CREATE INDEX IF NOT EXISTS idx_changes_rule_type ON correction_changes(rule_type);
|
||||
|
||||
-- Table: learned_suggestions
|
||||
-- AI-learned patterns pending user review
|
||||
CREATE TABLE IF NOT EXISTS learned_suggestions (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
from_text TEXT NOT NULL,
|
||||
to_text TEXT NOT NULL,
|
||||
domain TEXT NOT NULL DEFAULT 'general',
|
||||
frequency INTEGER NOT NULL DEFAULT 1 CHECK(frequency > 0),
|
||||
confidence REAL NOT NULL CHECK(confidence >= 0.0 AND confidence <= 1.0),
|
||||
first_seen TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
|
||||
last_seen TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
|
||||
status TEXT NOT NULL DEFAULT 'pending' CHECK(status IN ('pending', 'approved', 'rejected')),
|
||||
reviewed_at TIMESTAMP,
|
||||
reviewed_by TEXT,
|
||||
UNIQUE(from_text, to_text, domain)
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_suggestions_status ON learned_suggestions(status);
|
||||
CREATE INDEX IF NOT EXISTS idx_suggestions_domain ON learned_suggestions(domain);
|
||||
CREATE INDEX IF NOT EXISTS idx_suggestions_confidence ON learned_suggestions(confidence DESC);
|
||||
CREATE INDEX IF NOT EXISTS idx_suggestions_frequency ON learned_suggestions(frequency DESC);
|
||||
|
||||
-- Table: suggestion_examples
|
||||
-- Example occurrences of learned patterns
|
||||
CREATE TABLE IF NOT EXISTS suggestion_examples (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
suggestion_id INTEGER NOT NULL,
|
||||
filename TEXT NOT NULL,
|
||||
line_number INTEGER,
|
||||
context TEXT NOT NULL,
|
||||
occurred_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
|
||||
FOREIGN KEY (suggestion_id) REFERENCES learned_suggestions(id) ON DELETE CASCADE
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_examples_suggestion_id ON suggestion_examples(suggestion_id);
|
||||
|
||||
-- Table: system_config
|
||||
-- System configuration and preferences
|
||||
CREATE TABLE IF NOT EXISTS system_config (
|
||||
key TEXT PRIMARY KEY,
|
||||
value TEXT NOT NULL,
|
||||
value_type TEXT NOT NULL CHECK(value_type IN ('string', 'int', 'float', 'boolean', 'json')),
|
||||
description TEXT,
|
||||
updated_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP
|
||||
);
|
||||
|
||||
-- Insert default configuration
|
||||
INSERT OR IGNORE INTO system_config (key, value, value_type, description) VALUES
|
||||
('schema_version', '2.0', 'string', 'Database schema version'),
|
||||
('api_provider', 'GLM', 'string', 'API provider name'),
|
||||
('api_model', 'GLM-4.6', 'string', 'Default AI model'),
|
||||
('api_base_url', 'https://open.bigmodel.cn/api/anthropic', 'string', 'API endpoint URL'),
|
||||
('default_domain', 'general', 'string', 'Default correction domain'),
|
||||
('auto_learn_enabled', 'true', 'boolean', 'Enable automatic pattern learning'),
|
||||
('backup_enabled', 'true', 'boolean', 'Create backups before operations'),
|
||||
('learning_frequency_threshold', '3', 'int', 'Min frequency for learned suggestions'),
|
||||
('learning_confidence_threshold', '0.8', 'float', 'Min confidence for learned suggestions'),
|
||||
('history_retention_days', '90', 'int', 'Days to retain correction history'),
|
||||
('max_correction_length', '1000', 'int', 'Maximum length for correction text');
|
||||
|
||||
-- Table: audit_log
|
||||
-- Comprehensive audit trail for all operations
|
||||
CREATE TABLE IF NOT EXISTS audit_log (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
timestamp TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
|
||||
action TEXT NOT NULL,
|
||||
entity_type TEXT NOT NULL,
|
||||
entity_id INTEGER,
|
||||
user TEXT,
|
||||
details TEXT,
|
||||
success BOOLEAN NOT NULL DEFAULT 1,
|
||||
error_message TEXT
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_audit_timestamp ON audit_log(timestamp DESC);
|
||||
CREATE INDEX IF NOT EXISTS idx_audit_action ON audit_log(action);
|
||||
CREATE INDEX IF NOT EXISTS idx_audit_entity_type ON audit_log(entity_type);
|
||||
CREATE INDEX IF NOT EXISTS idx_audit_success ON audit_log(success);
|
||||
|
||||
-- View: active_corrections
|
||||
-- Quick access to active corrections
|
||||
CREATE VIEW IF NOT EXISTS active_corrections AS
|
||||
SELECT
|
||||
id,
|
||||
from_text,
|
||||
to_text,
|
||||
domain,
|
||||
source,
|
||||
confidence,
|
||||
usage_count,
|
||||
last_used,
|
||||
added_at
|
||||
FROM corrections
|
||||
WHERE is_active = 1
|
||||
ORDER BY domain, from_text;
|
||||
|
||||
-- View: pending_suggestions
|
||||
-- Quick access to suggestions pending review
|
||||
CREATE VIEW IF NOT EXISTS pending_suggestions AS
|
||||
SELECT
|
||||
s.id,
|
||||
s.from_text,
|
||||
s.to_text,
|
||||
s.domain,
|
||||
s.frequency,
|
||||
s.confidence,
|
||||
s.first_seen,
|
||||
s.last_seen,
|
||||
COUNT(e.id) as example_count
|
||||
FROM learned_suggestions s
|
||||
LEFT JOIN suggestion_examples e ON s.id = e.suggestion_id
|
||||
WHERE s.status = 'pending'
|
||||
GROUP BY s.id
|
||||
ORDER BY s.confidence DESC, s.frequency DESC;
|
||||
|
||||
-- View: correction_statistics
|
||||
-- Statistics per domain
|
||||
CREATE VIEW IF NOT EXISTS correction_statistics AS
|
||||
SELECT
|
||||
domain,
|
||||
COUNT(*) as total_corrections,
|
||||
COUNT(CASE WHEN source = 'manual' THEN 1 END) as manual_count,
|
||||
COUNT(CASE WHEN source = 'learned' THEN 1 END) as learned_count,
|
||||
COUNT(CASE WHEN source = 'imported' THEN 1 END) as imported_count,
|
||||
SUM(usage_count) as total_usage,
|
||||
MAX(added_at) as last_updated
|
||||
FROM corrections
|
||||
WHERE is_active = 1
|
||||
GROUP BY domain;
|
||||
153
transcript-fixer/scripts/examples/bulk_import.py
Normal file
153
transcript-fixer/scripts/examples/bulk_import.py
Normal file
@@ -0,0 +1,153 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Example: Bulk Import Corrections to SQLite Database
|
||||
|
||||
This script demonstrates how to import corrections from various sources
|
||||
into the transcript-fixer SQLite database.
|
||||
|
||||
Usage:
|
||||
uv run scripts/examples/bulk_import.py
|
||||
"""
|
||||
|
||||
from pathlib import Path
|
||||
from core import CorrectionRepository, CorrectionService
|
||||
|
||||
|
||||
def import_from_dict():
|
||||
"""Example: Import corrections from Python dictionary"""
|
||||
|
||||
# Initialize service
|
||||
db_path = Path.home() / ".transcript-fixer" / "corrections.db"
|
||||
repository = CorrectionRepository(db_path)
|
||||
service = CorrectionService(repository)
|
||||
|
||||
# Define corrections as dictionary
|
||||
corrections_dict = {
|
||||
"巨升智能": "具身智能",
|
||||
"巨升": "具身",
|
||||
"奇迹创坛": "奇绩创坛",
|
||||
"火星营": "火星营",
|
||||
"矩阵公司": "初创公司",
|
||||
"股价": "框架",
|
||||
"三观": "三关"
|
||||
}
|
||||
|
||||
# Convert to list format for import
|
||||
corrections_list = []
|
||||
for from_text, to_text in corrections_dict.items():
|
||||
corrections_list.append({
|
||||
"from_text": from_text,
|
||||
"to_text": to_text,
|
||||
"domain": "embodied_ai",
|
||||
"source": "imported",
|
||||
"confidence": 1.0
|
||||
})
|
||||
|
||||
# Import
|
||||
inserted, updated, skipped = service.import_corrections(
|
||||
corrections=corrections_list,
|
||||
merge=True
|
||||
)
|
||||
|
||||
print(f"✅ Import complete:")
|
||||
print(f" - Inserted: {inserted}")
|
||||
print(f" - Updated: {updated}")
|
||||
print(f" - Skipped: {skipped}")
|
||||
|
||||
service.close()
|
||||
|
||||
|
||||
def import_from_json_file():
|
||||
"""Example: Import from old JSON format file"""
|
||||
import json
|
||||
|
||||
# Sample JSON structure (v1.0 format)
|
||||
sample_json = {
|
||||
"metadata": {
|
||||
"version": "1.0",
|
||||
"domains": ["embodied_ai"],
|
||||
},
|
||||
"corrections": {
|
||||
"巨升智能": "具身智能",
|
||||
"巨升": "具身",
|
||||
}
|
||||
}
|
||||
|
||||
# Initialize service
|
||||
db_path = Path.home() / ".transcript-fixer" / "corrections.db"
|
||||
repository = CorrectionRepository(db_path)
|
||||
service = CorrectionService(repository)
|
||||
|
||||
# Convert JSON to import format
|
||||
domain = sample_json["metadata"].get("domains", ["general"])[0]
|
||||
corrections_list = []
|
||||
|
||||
for from_text, to_text in sample_json["corrections"].items():
|
||||
corrections_list.append({
|
||||
"from_text": from_text,
|
||||
"to_text": to_text,
|
||||
"domain": domain,
|
||||
"source": "imported",
|
||||
"confidence": 1.0
|
||||
})
|
||||
|
||||
# Import
|
||||
inserted, updated, skipped = service.import_corrections(
|
||||
corrections=corrections_list,
|
||||
merge=True
|
||||
)
|
||||
|
||||
print(f"✅ JSON import complete:")
|
||||
print(f" - Inserted: {inserted}")
|
||||
print(f" - Updated: {updated}")
|
||||
print(f" - Skipped: {skipped}")
|
||||
|
||||
service.close()
|
||||
|
||||
|
||||
def add_context_rules():
|
||||
"""Example: Add context-aware regex rules directly"""
|
||||
|
||||
db_path = Path.home() / ".transcript-fixer" / "corrections.db"
|
||||
repository = CorrectionRepository(db_path)
|
||||
|
||||
# Add context rules via SQL
|
||||
with repository._transaction() as conn:
|
||||
rules = [
|
||||
("巨升方向", "具身方向", "巨升→具身", 10),
|
||||
("巨升现在", "具身现在", "巨升→具身", 10),
|
||||
("近距离的去看", "近距离地去看", "的→地 副词修饰", 5),
|
||||
("近距离搏杀", "近距离搏杀", "这里的'近距离'是正确的", 5),
|
||||
]
|
||||
|
||||
for pattern, replacement, description, priority in rules:
|
||||
conn.execute("""
|
||||
INSERT OR IGNORE INTO context_rules
|
||||
(pattern, replacement, description, priority)
|
||||
VALUES (?, ?, ?, ?)
|
||||
""", (pattern, replacement, description, priority))
|
||||
|
||||
print("✅ Context rules added successfully")
|
||||
repository.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("Transcript-Fixer Bulk Import Examples\n")
|
||||
print("=" * 60)
|
||||
|
||||
# Example 1: Import from dictionary
|
||||
print("\n1. Importing from Python dictionary...")
|
||||
import_from_dict()
|
||||
|
||||
# Example 2: Import from JSON file
|
||||
print("\n2. Importing from JSON format...")
|
||||
import_from_json_file()
|
||||
|
||||
# Example 3: Add context rules
|
||||
print("\n3. Adding context rules...")
|
||||
add_context_rules()
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("✅ All examples completed!")
|
||||
print("\nVerify with:")
|
||||
print(" sqlite3 ~/.transcript-fixer/corrections.db 'SELECT COUNT(*) FROM active_corrections;'")
|
||||
70
transcript-fixer/scripts/fix_transcription.py
Executable file
70
transcript-fixer/scripts/fix_transcription.py
Executable file
@@ -0,0 +1,70 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Transcript Fixer - Main Entry Point
|
||||
|
||||
SINGLE RESPONSIBILITY: Route CLI commands to handlers
|
||||
|
||||
This is the main entry point for the transcript-fixer tool.
|
||||
It parses arguments and dispatches to appropriate command handlers.
|
||||
|
||||
Usage:
|
||||
# Setup
|
||||
python fix_transcription.py --init
|
||||
|
||||
# Correction workflow
|
||||
python fix_transcription.py --input file.md --stage 3
|
||||
|
||||
# Manage corrections
|
||||
python fix_transcription.py --add "错误" "正确"
|
||||
python fix_transcription.py --list
|
||||
|
||||
# Review learned suggestions
|
||||
python fix_transcription.py --review-learned
|
||||
python fix_transcription.py --approve "错误" "正确"
|
||||
|
||||
# Validate configuration
|
||||
python fix_transcription.py --validate
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from cli import (
|
||||
cmd_init,
|
||||
cmd_add_correction,
|
||||
cmd_list_corrections,
|
||||
cmd_run_correction,
|
||||
cmd_review_learned,
|
||||
cmd_approve,
|
||||
cmd_validate,
|
||||
create_argument_parser,
|
||||
)
|
||||
|
||||
|
||||
def main():
|
||||
"""Main entry point - parse arguments and dispatch to commands"""
|
||||
parser = create_argument_parser()
|
||||
args = parser.parse_args()
|
||||
|
||||
# Dispatch commands
|
||||
if args.init:
|
||||
cmd_init(args)
|
||||
elif args.validate:
|
||||
cmd_validate(args)
|
||||
elif args.add_correction:
|
||||
args.from_text, args.to_text = args.add_correction
|
||||
cmd_add_correction(args)
|
||||
elif args.list_corrections:
|
||||
cmd_list_corrections(args)
|
||||
elif args.review_learned:
|
||||
cmd_review_learned(args)
|
||||
elif args.approve:
|
||||
args.from_text, args.to_text = args.approve
|
||||
cmd_approve(args)
|
||||
elif args.input:
|
||||
cmd_run_correction(args)
|
||||
else:
|
||||
parser.print_help()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
3
transcript-fixer/scripts/tests/__init__.py
Normal file
3
transcript-fixer/scripts/tests/__init__.py
Normal file
@@ -0,0 +1,3 @@
|
||||
"""
|
||||
Test suite for transcript-fixer
|
||||
"""
|
||||
272
transcript-fixer/scripts/tests/test_correction_service.py
Normal file
272
transcript-fixer/scripts/tests/test_correction_service.py
Normal file
@@ -0,0 +1,272 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Unit Tests for Correction Service
|
||||
|
||||
Tests business logic, validation, and service layer functionality.
|
||||
"""
|
||||
|
||||
import unittest
|
||||
import tempfile
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
import sys
|
||||
|
||||
# Add parent directory to path
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent))
|
||||
|
||||
from core.correction_repository import CorrectionRepository
|
||||
from core.correction_service import CorrectionService, ValidationError
|
||||
|
||||
|
||||
class TestCorrectionService(unittest.TestCase):
|
||||
"""Test suite for CorrectionService"""
|
||||
|
||||
def setUp(self):
|
||||
"""Create temporary database for each test."""
|
||||
self.test_dir = Path(tempfile.mkdtemp())
|
||||
self.db_path = self.test_dir / "test.db"
|
||||
self.repository = CorrectionRepository(self.db_path)
|
||||
self.service = CorrectionService(self.repository)
|
||||
|
||||
def tearDown(self):
|
||||
"""Clean up temporary files."""
|
||||
self.service.close()
|
||||
shutil.rmtree(self.test_dir)
|
||||
|
||||
# ==================== Validation Tests ====================
|
||||
|
||||
def test_validate_empty_text(self):
|
||||
"""Test rejection of empty text."""
|
||||
with self.assertRaises(ValidationError):
|
||||
self.service.validate_correction_text("", "test_field")
|
||||
|
||||
def test_validate_whitespace_only(self):
|
||||
"""Test rejection of whitespace-only text."""
|
||||
with self.assertRaises(ValidationError):
|
||||
self.service.validate_correction_text(" ", "test_field")
|
||||
|
||||
def test_validate_too_long(self):
|
||||
"""Test rejection of text exceeding max length."""
|
||||
long_text = "A" * 1001
|
||||
with self.assertRaises(ValidationError):
|
||||
self.service.validate_correction_text(long_text, "test_field")
|
||||
|
||||
def test_validate_control_characters(self):
|
||||
"""Test rejection of control characters."""
|
||||
with self.assertRaises(ValidationError):
|
||||
self.service.validate_correction_text("test\x00text", "test_field")
|
||||
|
||||
def test_validate_valid_text(self):
|
||||
"""Test acceptance of valid text."""
|
||||
# Should not raise
|
||||
self.service.validate_correction_text("valid text", "test_field")
|
||||
self.service.validate_correction_text("有效文本", "test_field")
|
||||
|
||||
def test_validate_domain_path_traversal(self):
|
||||
"""Test rejection of path traversal in domain."""
|
||||
with self.assertRaises(ValidationError):
|
||||
self.service.validate_domain_name("../etc/passwd")
|
||||
|
||||
def test_validate_domain_invalid_chars(self):
|
||||
"""Test rejection of invalid characters in domain."""
|
||||
with self.assertRaises(ValidationError):
|
||||
self.service.validate_domain_name("invalid/domain")
|
||||
|
||||
def test_validate_domain_reserved(self):
|
||||
"""Test rejection of reserved domain names."""
|
||||
with self.assertRaises(ValidationError):
|
||||
self.service.validate_domain_name("con") # Windows reserved
|
||||
|
||||
def test_validate_valid_domain(self):
|
||||
"""Test acceptance of valid domain."""
|
||||
# Should not raise
|
||||
self.service.validate_domain_name("general")
|
||||
self.service.validate_domain_name("embodied_ai")
|
||||
self.service.validate_domain_name("test-domain-123")
|
||||
|
||||
# ==================== Correction Operations Tests ====================
|
||||
|
||||
def test_add_correction(self):
|
||||
"""Test adding a correction."""
|
||||
correction_id = self.service.add_correction(
|
||||
from_text="错误",
|
||||
to_text="正确",
|
||||
domain="general"
|
||||
)
|
||||
self.assertIsInstance(correction_id, int)
|
||||
self.assertGreater(correction_id, 0)
|
||||
|
||||
# Verify it was added
|
||||
corrections = self.service.get_corrections("general")
|
||||
self.assertEqual(corrections["错误"], "正确")
|
||||
|
||||
def test_add_identical_correction_rejected(self):
|
||||
"""Test rejection of from_text == to_text."""
|
||||
with self.assertRaises(ValidationError):
|
||||
self.service.add_correction(
|
||||
from_text="same",
|
||||
to_text="same",
|
||||
domain="general"
|
||||
)
|
||||
|
||||
def test_add_duplicate_correction_updates(self):
|
||||
"""Test that duplicate from_text updates existing."""
|
||||
# Add first
|
||||
self.service.add_correction("错误", "正确A", "general")
|
||||
|
||||
# Add duplicate (should update)
|
||||
self.service.add_correction("错误", "正确B", "general")
|
||||
|
||||
# Verify updated
|
||||
corrections = self.service.get_corrections("general")
|
||||
self.assertEqual(corrections["错误"], "正确B")
|
||||
|
||||
def test_get_corrections_multiple_domains(self):
|
||||
"""Test getting corrections from different domains."""
|
||||
self.service.add_correction("test1", "result1", "domain1")
|
||||
self.service.add_correction("test2", "result2", "domain2")
|
||||
|
||||
domain1_corr = self.service.get_corrections("domain1")
|
||||
domain2_corr = self.service.get_corrections("domain2")
|
||||
|
||||
self.assertEqual(len(domain1_corr), 1)
|
||||
self.assertEqual(len(domain2_corr), 1)
|
||||
self.assertEqual(domain1_corr["test1"], "result1")
|
||||
self.assertEqual(domain2_corr["test2"], "result2")
|
||||
|
||||
def test_remove_correction(self):
|
||||
"""Test removing a correction."""
|
||||
# Add correction
|
||||
self.service.add_correction("错误", "正确", "general")
|
||||
|
||||
# Remove it
|
||||
success = self.service.remove_correction("错误", "general")
|
||||
self.assertTrue(success)
|
||||
|
||||
# Verify removed
|
||||
corrections = self.service.get_corrections("general")
|
||||
self.assertNotIn("错误", corrections)
|
||||
|
||||
def test_remove_nonexistent_correction(self):
|
||||
"""Test removing non-existent correction."""
|
||||
success = self.service.remove_correction("nonexistent", "general")
|
||||
self.assertFalse(success)
|
||||
|
||||
# ==================== Import/Export Tests ====================
|
||||
|
||||
def test_import_corrections(self):
|
||||
"""Test importing corrections."""
|
||||
import_data = {
|
||||
"错误1": "正确1",
|
||||
"错误2": "正确2",
|
||||
"错误3": "正确3"
|
||||
}
|
||||
|
||||
inserted, updated, skipped = self.service.import_corrections(
|
||||
corrections=import_data,
|
||||
domain="test_domain",
|
||||
merge=True
|
||||
)
|
||||
|
||||
self.assertEqual(inserted, 3)
|
||||
self.assertEqual(updated, 0)
|
||||
self.assertEqual(skipped, 0)
|
||||
|
||||
# Verify imported
|
||||
corrections = self.service.get_corrections("test_domain")
|
||||
self.assertEqual(len(corrections), 3)
|
||||
|
||||
def test_import_merge_with_conflicts(self):
|
||||
"""Test import with merge mode and conflicts."""
|
||||
# Add existing correction
|
||||
self.service.add_correction("错误", "旧值", "test_domain")
|
||||
|
||||
# Import with conflict
|
||||
import_data = {
|
||||
"错误": "新值",
|
||||
"新错误": "新正确"
|
||||
}
|
||||
|
||||
inserted, updated, skipped = self.service.import_corrections(
|
||||
corrections=import_data,
|
||||
domain="test_domain",
|
||||
merge=True
|
||||
)
|
||||
|
||||
self.assertEqual(inserted, 1) # "新错误"
|
||||
self.assertEqual(updated, 1) # "错误" updated
|
||||
|
||||
# Verify updated
|
||||
corrections = self.service.get_corrections("test_domain")
|
||||
self.assertEqual(corrections["错误"], "新值")
|
||||
self.assertEqual(corrections["新错误"], "新正确")
|
||||
|
||||
def test_export_corrections(self):
|
||||
"""Test exporting corrections."""
|
||||
# Add some corrections
|
||||
self.service.add_correction("错误1", "正确1", "export_test")
|
||||
self.service.add_correction("错误2", "正确2", "export_test")
|
||||
|
||||
# Export
|
||||
exported = self.service.export_corrections("export_test")
|
||||
|
||||
self.assertEqual(len(exported), 2)
|
||||
self.assertEqual(exported["错误1"], "正确1")
|
||||
self.assertEqual(exported["错误2"], "正确2")
|
||||
|
||||
# ==================== Statistics Tests ====================
|
||||
|
||||
def test_get_statistics_empty(self):
|
||||
"""Test statistics for empty domain."""
|
||||
stats = self.service.get_statistics("empty_domain")
|
||||
|
||||
self.assertEqual(stats['total_corrections'], 0)
|
||||
self.assertEqual(stats['total_usage'], 0)
|
||||
|
||||
def test_get_statistics(self):
|
||||
"""Test statistics calculation."""
|
||||
# Add corrections with different sources
|
||||
self.service.add_correction("test1", "result1", "stats_test", source="manual")
|
||||
self.service.add_correction("test2", "result2", "stats_test", source="learned")
|
||||
self.service.add_correction("test3", "result3", "stats_test", source="imported")
|
||||
|
||||
stats = self.service.get_statistics("stats_test")
|
||||
|
||||
self.assertEqual(stats['total_corrections'], 3)
|
||||
self.assertEqual(stats['by_source']['manual'], 1)
|
||||
self.assertEqual(stats['by_source']['learned'], 1)
|
||||
self.assertEqual(stats['by_source']['imported'], 1)
|
||||
|
||||
|
||||
class TestValidationRules(unittest.TestCase):
|
||||
"""Test validation rules configuration."""
|
||||
|
||||
def test_custom_validation_rules(self):
|
||||
"""Test service with custom validation rules."""
|
||||
from core.correction_service import ValidationRules
|
||||
|
||||
custom_rules = ValidationRules(
|
||||
max_text_length=100,
|
||||
min_text_length=3
|
||||
)
|
||||
|
||||
test_dir = Path(tempfile.mkdtemp())
|
||||
db_path = test_dir / "test.db"
|
||||
repository = CorrectionRepository(db_path)
|
||||
service = CorrectionService(repository, rules=custom_rules)
|
||||
|
||||
# Should reject short text
|
||||
with self.assertRaises(ValidationError):
|
||||
service.validate_correction_text("ab", "test") # Too short
|
||||
|
||||
# Should reject long text
|
||||
with self.assertRaises(ValidationError):
|
||||
service.validate_correction_text("A" * 101, "test") # Too long
|
||||
|
||||
# Clean up
|
||||
service.close()
|
||||
shutil.rmtree(test_dir)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
16
transcript-fixer/scripts/utils/__init__.py
Normal file
16
transcript-fixer/scripts/utils/__init__.py
Normal file
@@ -0,0 +1,16 @@
|
||||
"""
|
||||
Utils Module - Utility Functions and Tools
|
||||
|
||||
This module contains utility functions:
|
||||
- diff_generator: Multi-format diff report generation
|
||||
- validation: Configuration validation
|
||||
"""
|
||||
|
||||
from .diff_generator import generate_full_report
|
||||
from .validation import validate_configuration, print_validation_summary
|
||||
|
||||
__all__ = [
|
||||
'generate_full_report',
|
||||
'validate_configuration',
|
||||
'print_validation_summary',
|
||||
]
|
||||
18
transcript-fixer/scripts/utils/diff_formats/__init__.py
Normal file
18
transcript-fixer/scripts/utils/diff_formats/__init__.py
Normal file
@@ -0,0 +1,18 @@
|
||||
"""
|
||||
Diff format generators for transcript comparison
|
||||
"""
|
||||
|
||||
from .unified_format import generate_unified_diff
|
||||
from .html_format import generate_html_diff
|
||||
from .inline_format import generate_inline_diff
|
||||
from .markdown_format import generate_markdown_report
|
||||
from .change_extractor import extract_changes, generate_change_summary
|
||||
|
||||
__all__ = [
|
||||
'generate_unified_diff',
|
||||
'generate_html_diff',
|
||||
'generate_inline_diff',
|
||||
'generate_markdown_report',
|
||||
'extract_changes',
|
||||
'generate_change_summary',
|
||||
]
|
||||
102
transcript-fixer/scripts/utils/diff_formats/change_extractor.py
Normal file
102
transcript-fixer/scripts/utils/diff_formats/change_extractor.py
Normal file
@@ -0,0 +1,102 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Change extraction and summarization
|
||||
|
||||
SINGLE RESPONSIBILITY: Extract and summarize changes between text versions
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import difflib
|
||||
|
||||
from .text_splitter import split_into_words
|
||||
|
||||
|
||||
def extract_changes(original: str, fixed: str) -> list[dict]:
|
||||
"""
|
||||
Extract all changes and return change list
|
||||
|
||||
Args:
|
||||
original: Original text
|
||||
fixed: Fixed text
|
||||
|
||||
Returns:
|
||||
List of change dictionaries with type, context, and content
|
||||
"""
|
||||
original_words = split_into_words(original)
|
||||
fixed_words = split_into_words(fixed)
|
||||
|
||||
diff = difflib.SequenceMatcher(None, original_words, fixed_words)
|
||||
changes = []
|
||||
|
||||
for tag, i1, i2, j1, j2 in diff.get_opcodes():
|
||||
if tag == 'replace':
|
||||
original_text = ''.join(original_words[i1:i2])
|
||||
fixed_text = ''.join(fixed_words[j1:j2])
|
||||
changes.append({
|
||||
'type': 'replace',
|
||||
'original': original_text,
|
||||
'fixed': fixed_text,
|
||||
'context_before': ''.join(original_words[max(0, i1-5):i1]),
|
||||
'context_after': ''.join(original_words[i2:min(len(original_words), i2+5)])
|
||||
})
|
||||
elif tag == 'delete':
|
||||
original_text = ''.join(original_words[i1:i2])
|
||||
changes.append({
|
||||
'type': 'delete',
|
||||
'original': original_text,
|
||||
'fixed': '',
|
||||
'context_before': ''.join(original_words[max(0, i1-5):i1]),
|
||||
'context_after': ''.join(original_words[i2:min(len(original_words), i2+5)])
|
||||
})
|
||||
elif tag == 'insert':
|
||||
fixed_text = ''.join(fixed_words[j1:j2])
|
||||
changes.append({
|
||||
'type': 'insert',
|
||||
'original': '',
|
||||
'fixed': fixed_text,
|
||||
'context_before': ''.join(fixed_words[max(0, j1-5):j1]) if j1 > 0 else '',
|
||||
'context_after': ''.join(fixed_words[j2:min(len(fixed_words), j2+5)])
|
||||
})
|
||||
|
||||
return changes
|
||||
|
||||
|
||||
def generate_change_summary(changes: list[dict]) -> str:
|
||||
"""
|
||||
Generate change summary
|
||||
|
||||
Args:
|
||||
changes: List of change dictionaries
|
||||
|
||||
Returns:
|
||||
Formatted summary string
|
||||
"""
|
||||
result = []
|
||||
result.append("=" * 80)
|
||||
result.append(f"修改摘要 (共 {len(changes)} 处修改)")
|
||||
result.append("=" * 80)
|
||||
result.append("")
|
||||
|
||||
for i, change in enumerate(changes, 1):
|
||||
change_type = {
|
||||
'replace': '替换',
|
||||
'delete': '删除',
|
||||
'insert': '添加'
|
||||
}[change['type']]
|
||||
|
||||
result.append(f"[{i}] {change_type}")
|
||||
|
||||
if change['original']:
|
||||
result.append(f" 原文: {change['original']}")
|
||||
if change['fixed']:
|
||||
result.append(f" 修复: {change['fixed']}")
|
||||
|
||||
# Show context
|
||||
context = change['context_before'] + "【修改处】" + change['context_after']
|
||||
if context.strip():
|
||||
result.append(f" 上下文: ...{context}...")
|
||||
|
||||
result.append("")
|
||||
|
||||
return '\n'.join(result)
|
||||
37
transcript-fixer/scripts/utils/diff_formats/html_format.py
Normal file
37
transcript-fixer/scripts/utils/diff_formats/html_format.py
Normal file
@@ -0,0 +1,37 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
HTML diff format generator
|
||||
|
||||
SINGLE RESPONSIBILITY: Generate HTML side-by-side comparison
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import difflib
|
||||
|
||||
|
||||
def generate_html_diff(original: str, fixed: str) -> str:
|
||||
"""
|
||||
Generate HTML format comparison report (side-by-side)
|
||||
|
||||
Args:
|
||||
original: Original text
|
||||
fixed: Fixed text
|
||||
|
||||
Returns:
|
||||
HTML format string with side-by-side comparison
|
||||
"""
|
||||
original_lines = original.splitlines(keepends=True)
|
||||
fixed_lines = fixed.splitlines(keepends=True)
|
||||
|
||||
differ = difflib.HtmlDiff(wrapcolumn=80)
|
||||
html = differ.make_file(
|
||||
original_lines,
|
||||
fixed_lines,
|
||||
fromdesc='原始版本',
|
||||
todesc='修复版本',
|
||||
context=True,
|
||||
numlines=3
|
||||
)
|
||||
|
||||
return html
|
||||
65
transcript-fixer/scripts/utils/diff_formats/inline_format.py
Normal file
65
transcript-fixer/scripts/utils/diff_formats/inline_format.py
Normal file
@@ -0,0 +1,65 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Inline diff format generator
|
||||
|
||||
SINGLE RESPONSIBILITY: Generate inline diff with change markers
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import difflib
|
||||
|
||||
from .text_splitter import split_into_words
|
||||
|
||||
|
||||
def generate_inline_diff(original: str, fixed: str) -> str:
|
||||
"""
|
||||
Generate inline diff marking deletions and additions
|
||||
|
||||
Format:
|
||||
- Normal words: unchanged
|
||||
- Deletions: [-word-]
|
||||
- Additions: [+word+]
|
||||
|
||||
Args:
|
||||
original: Original text
|
||||
fixed: Fixed text
|
||||
|
||||
Returns:
|
||||
Inline diff string with markers
|
||||
"""
|
||||
original_words = split_into_words(original)
|
||||
fixed_words = split_into_words(fixed)
|
||||
|
||||
diff = difflib.ndiff(original_words, fixed_words)
|
||||
|
||||
result = []
|
||||
result.append("=" * 80)
|
||||
result.append("行内词语级别对比 (- 删除, + 添加, ? 修改标记)")
|
||||
result.append("=" * 80)
|
||||
result.append("")
|
||||
|
||||
current_line = []
|
||||
for item in diff:
|
||||
marker = item[0]
|
||||
word = item[2:]
|
||||
|
||||
if marker == ' ':
|
||||
current_line.append(word)
|
||||
elif marker == '-':
|
||||
current_line.append(f"[-{word}-]")
|
||||
elif marker == '+':
|
||||
current_line.append(f"[+{word}+]")
|
||||
elif marker == '?':
|
||||
# Skip change marker lines
|
||||
continue
|
||||
|
||||
# Wrap at 80 characters
|
||||
if len(''.join(current_line)) > 80:
|
||||
result.append(''.join(current_line))
|
||||
current_line = []
|
||||
|
||||
if current_line:
|
||||
result.append(''.join(current_line))
|
||||
|
||||
return '\n'.join(result)
|
||||
104
transcript-fixer/scripts/utils/diff_formats/markdown_format.py
Normal file
104
transcript-fixer/scripts/utils/diff_formats/markdown_format.py
Normal file
@@ -0,0 +1,104 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Markdown report generator
|
||||
|
||||
SINGLE RESPONSIBILITY: Generate detailed Markdown comparison report
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
from .change_extractor import extract_changes, generate_change_summary
|
||||
|
||||
|
||||
def generate_markdown_report(
|
||||
original_file: str,
|
||||
stage1_file: str,
|
||||
stage2_file: str,
|
||||
original: str,
|
||||
stage1: str,
|
||||
stage2: str
|
||||
) -> str:
|
||||
"""
|
||||
Generate comprehensive Markdown comparison report
|
||||
|
||||
Args:
|
||||
original_file: Original file path
|
||||
stage1_file: Stage 1 file path
|
||||
stage2_file: Stage 2 file path
|
||||
original: Original text content
|
||||
stage1: Stage 1 text content
|
||||
stage2: Stage 2 text content
|
||||
|
||||
Returns:
|
||||
Formatted Markdown report string
|
||||
"""
|
||||
original_path = Path(original_file)
|
||||
stage1_path = Path(stage1_file)
|
||||
stage2_path = Path(stage2_file)
|
||||
|
||||
# Extract changes for each stage
|
||||
changes_stage1 = extract_changes(original, stage1)
|
||||
changes_stage2 = extract_changes(stage1, stage2)
|
||||
changes_total = extract_changes(original, stage2)
|
||||
|
||||
# Generate summaries
|
||||
summary_stage1 = generate_change_summary(changes_stage1)
|
||||
summary_stage2 = generate_change_summary(changes_stage2)
|
||||
summary_total = generate_change_summary(changes_total)
|
||||
|
||||
# Build report
|
||||
report = f"""# 会议记录修复对比报告
|
||||
|
||||
## 文件信息
|
||||
|
||||
- **原始文件**: {original_path.name}
|
||||
- **阶段1修复**: {stage1_path.name}
|
||||
- **阶段2修复**: {stage2_path.name}
|
||||
- **生成时间**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
|
||||
|
||||
## 修改统计
|
||||
|
||||
| 阶段 | 修改数量 | 说明 |
|
||||
|------|---------|------|
|
||||
| 阶段1: 词典修复 | {len(changes_stage1)} | 基于预定义词典的批量替换 |
|
||||
| 阶段2: AI修复 | {len(changes_stage2)} | GLM-4.6智能纠错 |
|
||||
| **总计** | **{len(changes_total)}** | **原始→最终版本** |
|
||||
|
||||
---
|
||||
|
||||
# 阶段1: 词典修复详情
|
||||
|
||||
{summary_stage1}
|
||||
|
||||
---
|
||||
|
||||
# 阶段2: AI智能修复详情
|
||||
|
||||
{summary_stage2}
|
||||
|
||||
---
|
||||
|
||||
# 总体修改详情 (原始→最终)
|
||||
|
||||
{summary_total}
|
||||
|
||||
---
|
||||
|
||||
## 使用说明
|
||||
|
||||
1. **查看修改**: 每处修改都包含上下文,便于理解修改原因
|
||||
2. **人工审核**: 重点审核标记为"替换"的修改
|
||||
3. **专业术语**: 特别注意公司名、人名、技术术语的修改
|
||||
|
||||
## 建议审核重点
|
||||
|
||||
- [ ] 专业术语(具身智能、机器人等)
|
||||
- [ ] 人名和公司名
|
||||
- [ ] 数字(金额、时间等)
|
||||
- [ ] 上下文是否通顺
|
||||
"""
|
||||
|
||||
return report
|
||||
33
transcript-fixer/scripts/utils/diff_formats/text_splitter.py
Normal file
33
transcript-fixer/scripts/utils/diff_formats/text_splitter.py
Normal file
@@ -0,0 +1,33 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Text splitter utility for word-level diff generation
|
||||
|
||||
SINGLE RESPONSIBILITY: Split text into words while preserving structure
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import re
|
||||
|
||||
|
||||
def split_into_words(text: str) -> list[str]:
|
||||
"""
|
||||
Split text into words, preserving whitespace and punctuation
|
||||
|
||||
This enables word-level diff generation for Chinese and English text
|
||||
|
||||
Args:
|
||||
text: Input text to split
|
||||
|
||||
Returns:
|
||||
List of word tokens (Chinese words, English words, numbers, punctuation)
|
||||
"""
|
||||
# Pattern: Chinese chars, English words, numbers, non-alphanumeric chars
|
||||
pattern = r'[\u4e00-\u9fff]+|[a-zA-Z]+|[0-9]+|[^\u4e00-\u9fffa-zA-Z0-9]'
|
||||
return re.findall(pattern, text)
|
||||
|
||||
|
||||
def read_file(file_path: str) -> str:
|
||||
"""Read file contents"""
|
||||
with open(file_path, 'r', encoding='utf-8') as f:
|
||||
return f.read()
|
||||
@@ -0,0 +1,44 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Unified diff format generator
|
||||
|
||||
SINGLE RESPONSIBILITY: Generate unified diff format output
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import difflib
|
||||
|
||||
from .text_splitter import split_into_words
|
||||
|
||||
|
||||
def generate_unified_diff(
|
||||
original: str,
|
||||
fixed: str,
|
||||
original_label: str = "原始版本",
|
||||
fixed_label: str = "修复版本"
|
||||
) -> str:
|
||||
"""
|
||||
Generate unified format diff report
|
||||
|
||||
Args:
|
||||
original: Original text
|
||||
fixed: Fixed text
|
||||
original_label: Label for original version
|
||||
fixed_label: Label for fixed version
|
||||
|
||||
Returns:
|
||||
Unified diff format string
|
||||
"""
|
||||
original_words = split_into_words(original)
|
||||
fixed_words = split_into_words(fixed)
|
||||
|
||||
diff = difflib.unified_diff(
|
||||
original_words,
|
||||
fixed_words,
|
||||
fromfile=original_label,
|
||||
tofile=fixed_label,
|
||||
lineterm=''
|
||||
)
|
||||
|
||||
return '\n'.join(diff)
|
||||
132
transcript-fixer/scripts/utils/diff_generator.py
Normal file
132
transcript-fixer/scripts/utils/diff_generator.py
Normal file
@@ -0,0 +1,132 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Generate word-level correction comparison reports
|
||||
Orchestrates multiple diff formats for visualization
|
||||
|
||||
SINGLE RESPONSIBILITY: Coordinate diff generation workflow
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
from .diff_formats import (
|
||||
generate_unified_diff,
|
||||
generate_html_diff,
|
||||
generate_inline_diff,
|
||||
generate_markdown_report,
|
||||
)
|
||||
from .diff_formats.text_splitter import read_file
|
||||
|
||||
|
||||
def generate_full_report(
|
||||
original_file: str,
|
||||
stage1_file: str,
|
||||
stage2_file: str,
|
||||
output_dir: str = None
|
||||
):
|
||||
"""
|
||||
Generate comprehensive comparison report
|
||||
|
||||
Creates 4 output files:
|
||||
1. Markdown format detailed report
|
||||
2. Unified diff format
|
||||
3. HTML side-by-side comparison
|
||||
4. Inline marked comparison
|
||||
|
||||
Args:
|
||||
original_file: Path to original transcript
|
||||
stage1_file: Path to stage 1 (dictionary) corrected version
|
||||
stage2_file: Path to stage 2 (AI) corrected version
|
||||
output_dir: Optional output directory (defaults to original file location)
|
||||
"""
|
||||
original_path = Path(original_file)
|
||||
stage1_path = Path(stage1_file)
|
||||
stage2_path = Path(stage2_file)
|
||||
|
||||
# Determine output directory
|
||||
if output_dir:
|
||||
output_path = Path(output_dir)
|
||||
output_path.mkdir(parents=True, exist_ok=True)
|
||||
else:
|
||||
output_path = original_path.parent
|
||||
|
||||
base_name = original_path.stem
|
||||
|
||||
# Read files
|
||||
print(f"📖 读取文件...")
|
||||
original = read_file(original_file)
|
||||
stage1 = read_file(stage1_file)
|
||||
stage2 = read_file(stage2_file)
|
||||
|
||||
# Generate reports
|
||||
print(f"📝 生成对比报告...")
|
||||
|
||||
# 1. Markdown report
|
||||
print(f" 生成Markdown报告...")
|
||||
md_report = generate_markdown_report(
|
||||
original_file, stage1_file, stage2_file,
|
||||
original, stage1, stage2
|
||||
)
|
||||
md_file = output_path / f"{base_name}_对比报告.md"
|
||||
with open(md_file, 'w', encoding='utf-8') as f:
|
||||
f.write(md_report)
|
||||
print(f" ✓ Markdown报告: {md_file.name}")
|
||||
|
||||
# 2. Unified Diff
|
||||
print(f" 生成Unified Diff...")
|
||||
unified_diff = generate_unified_diff(original, stage2)
|
||||
diff_file = output_path / f"{base_name}_unified.diff"
|
||||
with open(diff_file, 'w', encoding='utf-8') as f:
|
||||
f.write(unified_diff)
|
||||
print(f" ✓ Unified Diff: {diff_file.name}")
|
||||
|
||||
# 3. HTML comparison
|
||||
print(f" 生成HTML对比...")
|
||||
html_diff = generate_html_diff(original, stage2)
|
||||
html_file = output_path / f"{base_name}_对比.html"
|
||||
with open(html_file, 'w', encoding='utf-8') as f:
|
||||
f.write(html_diff)
|
||||
print(f" ✓ HTML对比: {html_file.name}")
|
||||
|
||||
# 4. Inline diff
|
||||
print(f" 生成行内diff...")
|
||||
inline_diff = generate_inline_diff(original, stage2)
|
||||
inline_file = output_path / f"{base_name}_行内对比.txt"
|
||||
with open(inline_file, 'w', encoding='utf-8') as f:
|
||||
f.write(inline_diff)
|
||||
print(f" ✓ 行内对比: {inline_file.name}")
|
||||
|
||||
# Summary
|
||||
print(f"\n✅ 对比报告生成完成!")
|
||||
print(f"📂 输出目录: {output_path}")
|
||||
print(f"\n生成的文件:")
|
||||
print(f" 1. {md_file.name} - Markdown格式详细报告")
|
||||
print(f" 2. {diff_file.name} - Unified Diff格式")
|
||||
print(f" 3. {html_file.name} - HTML并排对比")
|
||||
print(f" 4. {inline_file.name} - 行内标记对比")
|
||||
|
||||
|
||||
def main():
|
||||
"""CLI entry point"""
|
||||
if len(sys.argv) < 4:
|
||||
print("用法: python generate_diff_report.py <原始文件> <阶段1文件> <阶段2文件> [输出目录]")
|
||||
print()
|
||||
print("示例:")
|
||||
print(" python generate_diff_report.py \\")
|
||||
print(" 原始.md \\")
|
||||
print(" 原始_阶段1_词典修复.md \\")
|
||||
print(" 原始_阶段2_AI修复.md")
|
||||
sys.exit(1)
|
||||
|
||||
original_file = sys.argv[1]
|
||||
stage1_file = sys.argv[2]
|
||||
stage2_file = sys.argv[3]
|
||||
output_dir = sys.argv[4] if len(sys.argv) > 4 else None
|
||||
|
||||
generate_full_report(original_file, stage1_file, stage2_file, output_dir)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
129
transcript-fixer/scripts/utils/logging_config.py
Normal file
129
transcript-fixer/scripts/utils/logging_config.py
Normal file
@@ -0,0 +1,129 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Logging Configuration for Transcript Fixer
|
||||
|
||||
Provides structured logging with rotation, levels, and audit trails.
|
||||
"""
|
||||
|
||||
import logging
|
||||
import logging.handlers
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
|
||||
def setup_logging(
|
||||
log_dir: Optional[Path] = None,
|
||||
level: str = "INFO",
|
||||
enable_console: bool = True,
|
||||
enable_file: bool = True,
|
||||
enable_audit: bool = True
|
||||
) -> None:
|
||||
"""
|
||||
Configure logging for the application.
|
||||
|
||||
Args:
|
||||
log_dir: Directory for log files (default: ~/.transcript-fixer/logs)
|
||||
level: Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
|
||||
enable_console: Enable console output
|
||||
enable_file: Enable file logging
|
||||
enable_audit: Enable audit logging
|
||||
|
||||
Example:
|
||||
>>> setup_logging(level="DEBUG")
|
||||
>>> logger = logging.getLogger(__name__)
|
||||
>>> logger.info("Application started")
|
||||
"""
|
||||
# Default log directory
|
||||
if log_dir is None:
|
||||
log_dir = Path.home() / ".transcript-fixer" / "logs"
|
||||
|
||||
log_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Root logger configuration
|
||||
root_logger = logging.getLogger()
|
||||
root_logger.setLevel(logging.DEBUG) # Capture all, filter by handler
|
||||
|
||||
# Clear existing handlers
|
||||
root_logger.handlers.clear()
|
||||
|
||||
# Formatters
|
||||
detailed_formatter = logging.Formatter(
|
||||
fmt='%(asctime)s - %(name)s - %(levelname)s - %(filename)s:%(lineno)d - %(message)s',
|
||||
datefmt='%Y-%m-%d %H:%M:%S'
|
||||
)
|
||||
|
||||
simple_formatter = logging.Formatter(
|
||||
fmt='%(asctime)s - %(levelname)s - %(message)s',
|
||||
datefmt='%Y-%m-%d %H:%M:%S'
|
||||
)
|
||||
|
||||
# Console handler
|
||||
if enable_console:
|
||||
console_handler = logging.StreamHandler(sys.stdout)
|
||||
console_handler.setLevel(getattr(logging, level.upper()))
|
||||
console_handler.setFormatter(simple_formatter)
|
||||
root_logger.addHandler(console_handler)
|
||||
|
||||
# File handler (rotating)
|
||||
if enable_file:
|
||||
file_handler = logging.handlers.RotatingFileHandler(
|
||||
filename=log_dir / "transcript-fixer.log",
|
||||
maxBytes=10 * 1024 * 1024, # 10MB
|
||||
backupCount=5,
|
||||
encoding='utf-8'
|
||||
)
|
||||
file_handler.setLevel(logging.DEBUG)
|
||||
file_handler.setFormatter(detailed_formatter)
|
||||
root_logger.addHandler(file_handler)
|
||||
|
||||
# Error file handler (only errors)
|
||||
if enable_file:
|
||||
error_handler = logging.handlers.RotatingFileHandler(
|
||||
filename=log_dir / "errors.log",
|
||||
maxBytes=10 * 1024 * 1024, # 10MB
|
||||
backupCount=3,
|
||||
encoding='utf-8'
|
||||
)
|
||||
error_handler.setLevel(logging.ERROR)
|
||||
error_handler.setFormatter(detailed_formatter)
|
||||
root_logger.addHandler(error_handler)
|
||||
|
||||
# Audit handler (separate audit trail)
|
||||
if enable_audit:
|
||||
audit_handler = logging.handlers.RotatingFileHandler(
|
||||
filename=log_dir / "audit.log",
|
||||
maxBytes=50 * 1024 * 1024, # 50MB
|
||||
backupCount=10,
|
||||
encoding='utf-8'
|
||||
)
|
||||
audit_handler.setLevel(logging.INFO)
|
||||
audit_handler.setFormatter(detailed_formatter)
|
||||
|
||||
# Create audit logger
|
||||
audit_logger = logging.getLogger('audit')
|
||||
audit_logger.setLevel(logging.INFO)
|
||||
audit_logger.addHandler(audit_handler)
|
||||
audit_logger.propagate = False # Don't propagate to root
|
||||
|
||||
logging.info(f"Logging configured: level={level}, log_dir={log_dir}")
|
||||
|
||||
|
||||
def get_audit_logger() -> logging.Logger:
|
||||
"""Get the dedicated audit logger."""
|
||||
return logging.getLogger('audit')
|
||||
|
||||
|
||||
# Example usage
|
||||
if __name__ == "__main__":
|
||||
setup_logging(level="DEBUG")
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
logger.debug("Debug message")
|
||||
logger.info("Info message")
|
||||
logger.warning("Warning message")
|
||||
logger.error("Error message")
|
||||
logger.critical("Critical message")
|
||||
|
||||
audit_logger = get_audit_logger()
|
||||
audit_logger.info("User 'admin' added correction: '错误' → '正确'")
|
||||
141
transcript-fixer/scripts/utils/validation.py
Normal file
141
transcript-fixer/scripts/utils/validation.py
Normal file
@@ -0,0 +1,141 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Validation Utility - Configuration Health Checker
|
||||
|
||||
SINGLE RESPONSIBILITY: Validate transcript-fixer configuration and JSON files
|
||||
|
||||
Features:
|
||||
- Check directory structure
|
||||
- Validate JSON syntax in all config files
|
||||
- Check environment variables
|
||||
- Report statistics and health status
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
# Handle imports for both standalone and package usage
|
||||
try:
|
||||
from core import CorrectionRepository, CorrectionService
|
||||
except ImportError:
|
||||
# Fallback for when run from scripts directory directly
|
||||
import sys
|
||||
from pathlib import Path
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent))
|
||||
from core import CorrectionRepository, CorrectionService
|
||||
|
||||
|
||||
def validate_configuration() -> tuple[list[str], list[str]]:
|
||||
"""
|
||||
Validate transcript-fixer configuration.
|
||||
|
||||
Returns:
|
||||
Tuple of (errors, warnings) as string lists
|
||||
"""
|
||||
config_dir = Path.home() / ".transcript-fixer"
|
||||
db_path = config_dir / "corrections.db"
|
||||
|
||||
errors = []
|
||||
warnings = []
|
||||
|
||||
print("🔍 Validating transcript-fixer configuration...\n")
|
||||
|
||||
# Check directory exists
|
||||
if not config_dir.exists():
|
||||
errors.append(f"Configuration directory not found: {config_dir}")
|
||||
print(f"❌ {errors[-1]}")
|
||||
print("\n💡 Run: python fix_transcription.py --init")
|
||||
return errors, warnings
|
||||
|
||||
print(f"✅ Configuration directory exists: {config_dir}")
|
||||
|
||||
# Validate SQLite database
|
||||
if db_path.exists():
|
||||
try:
|
||||
repository = CorrectionRepository(db_path)
|
||||
service = CorrectionService(repository)
|
||||
|
||||
# Query basic stats
|
||||
stats = service.get_statistics()
|
||||
print(f"✅ Database valid: {stats['total_corrections']} corrections")
|
||||
|
||||
# Check tables exist
|
||||
conn = repository._get_connection()
|
||||
cursor = conn.execute("SELECT name FROM sqlite_master WHERE type='table'")
|
||||
tables = [row[0] for row in cursor.fetchall()]
|
||||
|
||||
expected_tables = [
|
||||
'corrections', 'context_rules', 'correction_history',
|
||||
'correction_changes', 'learned_suggestions', 'suggestion_examples',
|
||||
'system_config', 'audit_log'
|
||||
]
|
||||
|
||||
missing_tables = [t for t in expected_tables if t not in tables]
|
||||
if missing_tables:
|
||||
errors.append(f"Database missing tables: {missing_tables}")
|
||||
print(f"❌ {errors[-1]}")
|
||||
else:
|
||||
print(f"✅ All {len(expected_tables)} tables present")
|
||||
|
||||
service.close()
|
||||
|
||||
except Exception as e:
|
||||
errors.append(f"Database validation failed: {e}")
|
||||
print(f"❌ {errors[-1]}")
|
||||
else:
|
||||
warnings.append("Database not found (will be created on first use)")
|
||||
print(f"⚠️ Database not found: {db_path}")
|
||||
|
||||
# Check API key
|
||||
api_key = os.getenv("GLM_API_KEY")
|
||||
if not api_key:
|
||||
warnings.append("GLM_API_KEY environment variable not set")
|
||||
print("⚠️ GLM_API_KEY not set (required for Stage 2 AI corrections)")
|
||||
else:
|
||||
print("✅ GLM_API_KEY is set")
|
||||
|
||||
return errors, warnings
|
||||
|
||||
|
||||
def print_validation_summary(errors: list[str], warnings: list[str]) -> int:
|
||||
"""
|
||||
Print validation summary and return exit code.
|
||||
|
||||
Returns:
|
||||
0 if valid, 1 if errors found
|
||||
"""
|
||||
print("\n" + "=" * 60)
|
||||
|
||||
if errors:
|
||||
print(f"❌ {len(errors)} error(s) found:")
|
||||
for err in errors:
|
||||
print(f" - {err}")
|
||||
print("\n💡 Fix errors and run --validate again")
|
||||
print("=" * 60)
|
||||
return 1
|
||||
elif warnings:
|
||||
print(f"⚠️ {len(warnings)} warning(s):")
|
||||
for warn in warnings:
|
||||
print(f" - {warn}")
|
||||
print("\n✅ Configuration is valid (with warnings)")
|
||||
print("=" * 60)
|
||||
return 0
|
||||
else:
|
||||
print("✅ All checks passed! Configuration is valid.")
|
||||
print("=" * 60)
|
||||
return 0
|
||||
|
||||
|
||||
def main():
|
||||
"""Run validation as standalone script"""
|
||||
errors, warnings = validate_configuration()
|
||||
exit_code = print_validation_summary(errors, warnings)
|
||||
sys.exit(exit_code)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user