All notable changes to OmniTranscripts will be documented in this file.
The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.
- Comprehensive documentation structure with detailed guides
- Repository badges for better project visibility
- Enhanced CLAUDE.md with documentation cross-references
- Live reload functionality for development dashboard
- Comprehensive .gitignore covering all use cases
- Restructured README.md with better organization and navigation
- Enhanced project documentation with API, architecture, deployment, development, troubleshooting, and contributing guides
- Added comprehensive .gitignore to prevent accidental commit of sensitive data
- Initial release of OmniTranscripts
- YouTube video transcription API with Go backend
- Dual framework support (Fiber and Encore.dev)
- Three-stage processing pipeline (download, normalize, transcribe)
- Synchronous processing for short videos (≤2 minutes)
- Asynchronous job queue for longer videos
- Real-time job status tracking with progress updates
- Multiple output formats (SRT, VTT, JSON, TSV, plain text)
- PostgreSQL database integration with comprehensive migrations
- Server-Sent Events (SSE) for live dashboard updates
- Web dashboard with real-time metrics and job monitoring
- Comprehensive test suite with unit, integration, and performance tests
- Docker containerization support
- Extensive Makefile with 30+ development commands
- Authentication via Bearer token API keys
- Rate limiting and security features
- Webhook support for job lifecycle notifications
- Multi-platform builds (Linux, macOS, Windows for AMD64/ARM64)
- Complete API documentation with OpenAPI/Swagger spec
- Performance benchmarking and load testing tools
- Core Technologies: Go 1.23+, yt-dlp, FFmpeg, whisper.cpp
- Frameworks: Fiber v2 (HTTP), Encore.dev (production)
- Database: PostgreSQL with automatic migrations
- Job Processing: Thread-safe in-memory queue with pub/sub messaging
- Audio Processing: 16kHz mono WAV optimization for whisper.cpp
- AI Transcription: OpenAI Whisper C++ implementation
- File Management: Automatic cleanup and temporary file handling
- Monitoring: Comprehensive metrics collection and business analytics
- Deployment: Docker, Kubernetes, cloud platforms, traditional servers
- Hybrid Processing: Smart sync/async decision based on video length
- Scalable Design: Horizontal scaling with load balancing support
- Performance Optimized: Parallel processing and resource management
- Production Ready: Error handling, logging, monitoring, and observability
- Developer Friendly: Hot reload, comprehensive testing, detailed documentation
- Runtime: yt-dlp, FFmpeg, whisper.cpp with ggml-base.en.bin model
- Go Libraries: fiber/v2, go-ytdlp, ffmpeg-go, uuid, godotenv
- Database: PostgreSQL (optional for Encore.dev deployments)
- Development: golangci-lint, goimports, testify
- Environment-based configuration with sensible defaults
- Docker and cloud deployment ready
- Comprehensive security settings
- Performance tuning options
- Initial public release
- Complete YouTube video transcription API
- Production-ready features and documentation
See CONTRIBUTING.md for details on how to contribute to this project.
- Documentation: docs/
- Issues: GitHub Issues
- Discussions: GitHub Discussions
This project is licensed under the MIT License - see the LICENSE file for details.