# Clean-Tracks Audio Censorship System - Product Requirements Document

## Executive Summary
Clean-Tracks is an intelligent audio processing tool that automatically detects and censors explicit content in audio files. It features both a user-friendly web interface and powerful command-line tools, leveraging OpenAI Whisper for accurate speech recognition.

## Project Goals
- Build a reliable audio censorship system that processes files in under 30% of their duration
- Create an intuitive web UI that allows users to process audio with minimal technical knowledge
- Provide customizable word lists that users can manage and control
- Support multiple audio formats (MP3, WAV, FLAC, M4A, OGG)
- Achieve 95%+ accuracy in explicit word detection
- Ensure WCAG AA accessibility compliance

## Target Users
1. Content Creators - Need to quickly clean podcast/video audio for wider distribution
2. Educators - Make educational content appropriate for classroom use
3. Parents - Clean music and media for family consumption
4. Media Professionals - Prepare content for broadcast standards

## Core Features

### 1. Audio Processing Engine
- Integrate OpenAI Whisper for speech-to-text with word-level timestamps
- Support multiple Whisper model sizes (tiny, base, small, medium, large)
- Process audio files up to 500MB
- Handle long-form content with intelligent chunking
- Preserve audio quality during processing
- Support GPU acceleration for faster processing

### 2. Word Detection System
- Implement exact and fuzzy word matching using Levenshtein distance
- Add phonetic matching for similar-sounding words
- Context-aware detection to reduce false positives
- Confidence scoring for each detection
- Support for multiple languages (initially English)

### 3. Censorship Methods
- Silence insertion (mute detected words)
- Beep tone generation (customizable frequency)
- White noise replacement
- Fade in/out transitions for smooth audio
- Reversible censorship with undo capability

### 4. Word List Management
- User-controlled explicit word lists
- Severity levels (mild, moderate, severe)
- Category organization (profanity, slurs, custom)
- Import/export functionality (CSV, JSON)
- Shareable community word lists
- Default word lists for common use cases

### 5. Web User Interface
- Drag-and-drop file upload with Dropzone.js
- Real-time processing progress with WebSocket updates
- Audio waveform visualization showing detected words
- Interactive word list management interface
- Mobile-responsive design with touch optimization
- First-time user onboarding with tutorial
- Before/after preview player

### 6. Command-Line Interface
- Process single files: clean-tracks process <file>
- Batch processing: clean-tracks batch <pattern>
- Word list management: clean-tracks words <command>
- Configuration: clean-tracks config <setting>
- Server mode: clean-tracks server

### 7. Batch Processing
- Queue management for multiple files
- Priority system for processing order
- Parallel processing capability
- Progress tracking for all jobs
- Automatic retry on failures

### 8. Performance & Optimization
- Result caching to avoid reprocessing
- GPU acceleration support
- Lazy loading for web interface
- CDN for static assets
- Code splitting for faster initial load

## Technical Requirements

### Backend Stack
- Python 3.11+ for core processing
- Flask web framework
- SQLAlchemy for database ORM
- OpenAI Whisper for transcription
- PyDub and FFmpeg for audio manipulation
- Librosa for audio analysis
- Socket.io for real-time updates

### Frontend Stack
- HTML5 with semantic markup
- Bootstrap 5 for responsive design
- Vanilla JavaScript with modern ES6+
- WaveSurfer.js for waveform visualization
- DataTables.js for word list management
- Dropzone.js for file uploads

### Infrastructure
- SQLite database for word lists and history
- Redis for caching (optional)
- Celery for background jobs (optional)
- Docker for containerization
- GitHub Actions for CI/CD

## User Experience Requirements

### Onboarding Flow
1. Welcome modal with 3-step visual guide
2. Sample audio file for testing
3. Interactive tooltip tour
4. Quick start guide

### Key User Journeys
1. Upload → Process → Review → Download (< 3 minutes)
2. Manage Word Lists → Add/Edit/Remove words
3. Configure Settings → Choose censorship style
4. Batch Process → Queue multiple files

### Design Principles
- Simplicity first - complex features hidden by default
- Visual feedback for all actions
- Clear error messages with solutions
- Progressive disclosure of advanced features
- Mobile-first responsive design

## Success Metrics
- Processing speed: < 30% of audio duration
- Detection accuracy: > 95%
- User task completion: > 90%
- Time to first success: < 3 minutes
- System uptime: 99.9%
- Page load time: < 2 seconds
- User satisfaction: > 4.5/5 stars

## Constraints & Limitations
- Initial release English-only
- Maximum file size: 500MB
- Requires FFmpeg installation
- Internet connection needed for first model download
- 4GB RAM minimum, 8GB recommended

## Security & Privacy
- All processing done locally (no cloud uploads)
- Optional incognito mode
- One-click data clearing
- No user tracking without consent
- Secure API with rate limiting

## Testing Requirements
- Unit tests for all components (pytest)
- Integration tests for API endpoints
- End-to-end tests with Playwright
- Visual regression testing
- Accessibility testing (WCAG AA)
- Performance benchmarking
- Cross-browser testing

## Documentation Needs
- User guide with screenshots
- API documentation with examples
- CLI command reference
- Word list management guide
- Deployment instructions
- Contributing guidelines

## Future Enhancements (Post-MVP)
- Multi-language support
- Cloud processing option
- Mobile native apps
- Real-time streaming audio processing
- AI-powered context understanding
- Collaborative word list editing
- Advanced analytics dashboard
- Plugin system for custom processors

## Project Timeline
- Week 1: Foundation and setup
- Week 2: Core engine development
- Week 3: Word list management
- Week 4: Web UI implementation
- Week 5: Advanced features
- Week 6: CLI and API
- Week 7: Testing and deployment

## Risks & Mitigation
- Whisper accuracy: Provide multiple model options
- Processing speed: Implement GPU support and caching
- Large file handling: Use chunking and streaming
- Browser compatibility: Progressive enhancement approach
- User adoption: Focus on intuitive UX and onboarding