361 lines
12 KiB
Markdown
361 lines
12 KiB
Markdown
# Trax: Personal Research Transcription Tool
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A deterministic, iterative media transcription platform that transforms raw audio/video into structured, enhanced, and searchable text content through progressive AI-powered processing.
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## Overview
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Trax is a personal research tool designed for batch-processing tech podcasts, academic lectures, and audiobooks. It provides high-accuracy transcription with AI enhancement, optimized for M3 MacBook performance with download-first architecture.
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## Key Features
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- **95%+ Accuracy Transcription** using Whisper distil-large-v3 model
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- **99%+ Enhanced Transcription** with DeepSeek AI post-processing
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- **Download-First Architecture** - Always download media locally before processing
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- **Batch Processing** with 8 parallel workers (optimized for M3)
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- **YouTube Metadata** extraction via curl (no API required)
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- **Real-time Progress** tracking with memory/CPU monitoring
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- **Comprehensive Testing** suite with real audio files (no mocks)
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- **Protocol-Based Services** for clean interfaces and testability
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## Project Structure
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```
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trax/
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├── src/ # Source code
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│ ├── services/ # Core services (transcription, enhancement, batch)
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│ ├── repositories/ # Data access layer
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│ ├── database/ # Database models and migrations
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│ ├── cli/ # Command-line interface
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│ └── config.py # Centralized configuration
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├── tests/ # Test files
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├── docs/ # Documentation
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├── data/ # Data files
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├── scripts/ # Utility scripts (including Taskmaster helpers)
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├── pyproject.toml # Project configuration
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└── .env.example # Environment variables documentation
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```
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## Installation
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### Prerequisites
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- **Python 3.11+** (required for advanced type annotations)
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- **PostgreSQL 15+** (for JSONB and UUID support)
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- **FFmpeg 6.0+** (for audio preprocessing)
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- **curl** (for YouTube metadata extraction)
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### Setup
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```bash
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# Navigate to project
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cd apps/trax
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# Install with uv (ultra-fast package manager)
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uv pip install -e ".[dev]"
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# Setup database
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./scripts/setup_postgresql.sh
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# Run database migrations
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uv run alembic upgrade head
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```
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### Configuration
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API keys are automatically inherited from `../../.env` file. For local overrides, create `.env.local`:
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```bash
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# Optional: Create local config overrides
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echo "DEEPSEEK_API_KEY=your_key_here" > .env.local
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```
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## Quick Start
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### Standard CLI
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```bash
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# Extract YouTube metadata (no API required)
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uv run python -m src.cli.main youtube https://youtube.com/watch?v=example
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# Transcribe single file (v1 pipeline)
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uv run python -m src.cli.main transcribe audio.mp3
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# Enhanced transcription (v2 pipeline)
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uv run python -m src.cli.main transcribe audio.mp3 --v2
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# Batch process folder
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uv run python -m src.cli.main batch /path/to/audio/files
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```
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### Enhanced CLI (Recommended)
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```bash
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# Enhanced transcription with progress reporting
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uv run python -m src.cli.enhanced_cli transcribe audio.mp3 -m large -f srt
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# Multi-pass transcription with confidence threshold
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uv run python -m src.cli.enhanced_cli transcribe audio.mp3 --multi-pass --confidence-threshold 0.9
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# Domain-specific enhancement with multi-pass
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uv run python -m src.cli.enhanced_cli transcribe lecture.mp3 --multi-pass --domain academic
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# Speaker diarization with VTT output
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uv run python -m src.cli.enhanced_cli transcribe interview.mp4 --diarize --speakers 2 -f vtt
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# Full v2.0 feature set
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uv run python -m src.cli.enhanced_cli transcribe technical_content.mp3 --multi-pass --confidence-threshold 0.9 --domain technical --diarize
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# Batch processing with multi-pass
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uv run python -m src.cli.enhanced_cli batch /path/to/audio/files --multi-pass -c 8
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```
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### Advanced Batch Processing
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```bash
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# Process with enhancement and custom settings
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trax batch /path/to/files --enhance --workers 6 --memory-limit 2048
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# Monitor progress with custom intervals
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trax batch /path/to/files --progress-interval 2 --cpu-limit 80
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# Process specific file types
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trax batch /path/to/files --model whisper-1 --chunk-size 600
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```
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## Documentation
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### CLI Documentation
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- **[Enhanced CLI Guide](docs/enhanced-cli.md)** - Comprehensive guide to the enhanced CLI with progress reporting
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- **[CLI Reference](docs/CLI.md)** - Complete command reference for both standard and enhanced CLIs
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### Quick Reference
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- **[CLI Commands](docs/CLI.md)** - Complete command reference with examples
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- **[API Documentation](docs/API.md)** - Service protocols and API reference
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- **[Database Schema](docs/DATABASE.md)** - PostgreSQL schema with JSONB examples
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- **[Troubleshooting](docs/TROUBLESHOOTING.md)** - Common issues and security guide
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### Architecture
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- **[Development Patterns](docs/architecture/development-patterns.md)** - Historical learnings
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- **[Error Handling](docs/architecture/error-handling-and-logging.md)** - Comprehensive error system
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- **[Audio Processing](docs/architecture/audio-processing.md)** - Media pipeline details
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## Pipeline Versions
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### v1 Pipeline (Current)
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- **Whisper distil-large-v3** transcription only
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- **95%+ accuracy** on clear audio
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- **<30 seconds** processing time for 5-minute audio
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- **<2GB memory** usage
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### v2 Pipeline (In Development)
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- **Whisper + DeepSeek** enhancement
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- **99%+ accuracy** with AI post-processing
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- **<35 seconds** total processing time
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- **Grammar and punctuation** correction
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### v3-v4 Pipeline (Future)
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- **Multi-pass optimization** (v3)
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- **Speaker diarization** (v4)
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- **Advanced analysis** features
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## Configuration
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### API Keys
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The project automatically inherits all API tokens from the root project's `.env` file:
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- **AI Services**: Anthropic, DeepSeek, OpenAI, OpenRouter, Perplexity
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- **Google Services**: OAuth, APIs
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- **Other Services**: Slack, GitHub, Gitea, YouTube, Directus
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### Local Overrides
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Create `.env.local` in the trax directory for project-specific environment overrides.
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## Development
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### Taskmaster Helper Scripts
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The project includes comprehensive helper scripts for managing development tasks via Taskmaster CLI:
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```bash
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# Quick project overview
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./scripts/tm_master.sh overview
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# Get next task to work on
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./scripts/tm_master.sh next
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# Start working on a task
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./scripts/tm_master.sh start 15
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# Complete a task
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./scripts/tm_master.sh done 15
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# Search for tasks
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./scripts/tm_master.sh search whisper
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# Run analysis
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./scripts/tm_master.sh analyze
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```
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**Available Scripts:**
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- `tm_master.sh` - Master interface to all helper scripts
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- `tm_status.sh` - Status checking and project overviews
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- `tm_search.sh` - Search tasks by various criteria
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- `tm_workflow.sh` - Workflow management and progress tracking
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- `tm_analyze.sh` - Analysis and insights generation
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- `tm_quick.sh` - Quick operations
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For detailed documentation, see [Taskmaster Helper Scripts](scripts/README_taskmaster_helpers.md).
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**Quick Reference**: [Taskmaster Quick Reference](scripts/TASKMASTER_QUICK_REFERENCE.md)
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### Commands
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```bash
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# Run tests
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uv run pytest
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# Format code
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uv run black src/ tests/
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uv run ruff check --fix src/ tests/
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# Type checking
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uv run mypy src/
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# Install new dependency
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uv pip install package-name
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# Update dependencies
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uv pip compile pyproject.toml -o requirements.txt
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```
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### Architecture
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Trax follows a protocol-based architecture with clean separation of concerns:
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- **Services Layer**: Core business logic (transcription, enhancement, batch processing)
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- **Repository Layer**: Data access with protocol-based interfaces
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- **Database Layer**: PostgreSQL with SQLAlchemy registry pattern
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- **CLI Layer**: User interface with Click and Rich
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### Error Handling and Logging
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The application implements a comprehensive error handling and logging system designed for production reliability:
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#### Core Features
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- **Structured Logging**: JSON and human-readable formats with contextual information
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- **Error Classification**: Hierarchical error system with standardized error codes
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- **Retry Logic**: Exponential backoff with jitter and circuit breaker patterns
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- **Recovery Strategies**: Fallback mechanisms, graceful degradation, and state recovery
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- **Performance Monitoring**: Operation timing, resource usage, and system health metrics
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#### Key Components
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- `src/logging/` - Structured logging with file rotation and performance metrics
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- `src/errors/` - Error classification system with standardized error codes
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- `src/retry/` - Retry mechanisms with multiple strategies and circuit breakers
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- `src/recovery/` - Recovery strategies for different error scenarios
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#### Usage Examples
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```python
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# Structured logging with context
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logger.info("Processing started", extra={
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"operation": "transcription",
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"file_size": "15.2MB",
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"correlation_id": "req-123"
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})
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# Retry with exponential backoff
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@async_retry(max_retries=3)
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async def api_call():
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return await external_api.request()
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# Performance monitoring
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with timing_context("transcription_operation"):
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result = transcribe_audio(audio_file)
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```
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For detailed documentation, see [Error Handling and Logging System](docs/architecture/error-handling-and-logging.md).
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### Testing
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The project includes comprehensive unit tests for all components:
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```bash
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# Run all tests
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uv run pytest
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# Run specific test file
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uv run pytest tests/test_batch_processor.py
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# Run with coverage
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uv run pytest --cov=src
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```
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## Performance
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### Optimizations
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- **M3 MacBook Optimized**: Default 8 workers for optimal performance
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- **Memory Management**: Configurable memory limits and monitoring
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- **Resource Tracking**: Real-time CPU and memory usage monitoring
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- **Async Processing**: Non-blocking operations throughout
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- **Caching**: Intelligent caching for expensive operations
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### Benchmarks
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- **Transcription**: 95%+ accuracy, <30s for 5-minute audio
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- **Enhancement**: 99%+ accuracy, <35s processing time
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- **Batch Processing**: Parallel processing with configurable workers
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- **Resource Usage**: <2GB memory, optimized for M3 architecture
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## Project Status
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### 🎉 **v1.0 COMPLETE - Production Ready**
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**Release Date:** December 2024
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**Version:** 1.0.0
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**Status:** Production Ready
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### ✅ **Complete Platform Implementation**
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**Core Platform:**
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- ✅ Development environment setup with uv package manager
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- ✅ API key configuration and inheritance from root project
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- ✅ PostgreSQL database with SQLAlchemy registry pattern
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- ✅ YouTube metadata extraction via curl (no API required)
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- ✅ Media download and preprocessing with download-first architecture
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- ✅ Whisper transcription service (v1) with 95%+ accuracy
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- ✅ DeepSeek enhancement service (v2) with 99%+ accuracy
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- ✅ CLI interface with Click and Rich progress tracking
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- ✅ Batch processing system with 8 parallel workers (M3 optimized)
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**Advanced Features:**
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- ✅ Export functionality (JSON, TXT, SRT, Markdown)
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- ✅ Comprehensive error handling and logging system
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- ✅ Security features (encrypted storage, input validation)
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- ✅ Protocol-based architecture for clean interfaces
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- ✅ Performance optimization for M3 MacBook
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- ✅ Quality assessment system with accuracy metrics
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**Quality Assurance:**
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- ✅ Comprehensive testing suite with real audio files
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- ✅ Complete documentation and user guides
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### 🎯 **Production Ready Features**
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The Trax transcription platform is now fully functional and ready for production use with:
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- **95%+ transcription accuracy** on clear audio
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- **<30 seconds processing** for 5-minute audio files
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- **<2GB memory usage** optimized for M3 architecture
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- **Download-first architecture** for reliable processing
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- **Comprehensive error handling** and recovery mechanisms
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- **Enterprise security** with encrypted storage and input validation
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- **Protocol-based architecture** for clean interfaces and testability
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### 📋 **Release Documentation**
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- **[Release Notes](RELEASE_NOTES_v1.0.md)** - Comprehensive feature overview
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- **[Technical Changelog](CHANGELOG_v1.0.md)** - Detailed implementation changes
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- **[Task Archive](v1_0_completed)** - Archived v1.0 tasks in Taskmaster
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### 🔮 **Next Phase: v2.0 Planning**
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- Speaker diarization with 90%+ speaker accuracy
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- Multi-language support for international content
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- Advanced analytics and content insights
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- Web interface for browser-based access
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## License
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This project is part of the my-ai-projects ecosystem. |