- Tests complete workflow from unmarked text to segmented audio
- Demonstrates AI role detection on narrative text
- Shows AI text segmentation with intelligent speaker assignment
- Validates dialogue detection and speaker distribution
- Provides quality analysis of segmentation results
- Added comprehensive documentation for AI speaker detection
- Documented AI text segmentation capabilities
- Added Ollama setup instructions
- Explained benefits of AI-powered vs pattern-based detection
- Updated API endpoint documentation with AI feature details
- Added segment_text() method to OllamaService for AI-based text splitting
- OllamaService now annotates each text segment with the appropriate speaker
- Updated TTSService to use AI segmentation when available
- Falls back to pattern-based segmentation when Ollama unavailable
- AI intelligently assigns dialogue to speakers and narrative to Narrator
- Added comprehensive test suite for AI segmentation
- Verified AI correctly splits unmarked dialogue between multiple speakers
This makes the tool much more powerful - users can now input plain narrative
text without explicit role markers and the AI will intelligently assign voices
to different characters and the narrator.
- Changed model from 'llama3:8b' to 'llama3:latest' to match available models
- Verified Ollama integration is working correctly
- AI detection now successfully identifies speakers in unmarked text
- Sample up to 4 chunks of 5000 characters from different text positions
- Distribute samples evenly across beginning, middle, and end of text
- Deduplicate speakers across all samples for comprehensive detection
- Log individual sample results for debugging
- Handle short texts efficiently (single sample)
- Updated tests to verify multi-sample behavior
This ensures speakers introduced anywhere in long texts are detected,
not just those appearing in the first 2000 characters.
- Added Requirement 14 for intelligent speaker detection using Ollama/llama3:8B
- Updated design document with OllamaService component and integration details
- Added 4 new correctness properties for Ollama functionality
- Added tasks 15-17 for implementing Ollama integration
- Updated technology stack to include httpx and Ollama
- Added error handling guidelines for Ollama integration
- Configured fallback to pattern-based detection when Ollama unavailable
This enhancement allows users to paste raw dialogue text without manual role markers.
The system will use AI to intelligently detect speakers, making the tool more user-friendly.
- Updated main README with detailed setup, Kokoro model configuration, and troubleshooting
- Enhanced backend README with API documentation, Docker setup, and testing instructions
- Improved frontend README with component details, deployment guide, and development tips
- Added workflow testing steps and common error solutions
- Documented all supported languages, voices, and configuration options
Implements task 14 from text-to-audiobook spec
Requirements: 13.1, 13.2, 13.3, 13.4, 13.5
- Applied Tailwind CSS responsive classes across all components
- Implemented mobile-first design approach with breakpoints (sm, md, lg)
- Added touch-friendly interactions with min-h-[44px] touch targets
- Enhanced App.tsx with responsive padding, headings, and alerts
- Updated TextInputSection with responsive textarea and full-width mobile button
- Improved VoiceAssignmentSection with flexible layouts and mobile-optimized controls
- Enhanced AudioPlayerSection with responsive sizing and touch support
- Added custom CSS for range slider with larger thumb for mobile
- Improved select dropdowns with visible arrows and proper padding
- Added touch-manipulation class to prevent double-tap zoom
- Added active states for better mobile feedback
- Updated index.html with proper title and meta description
- Created RESPONSIVE_IMPROVEMENTS.md documentation
Requirements: 12.1, 12.2, 12.3, 12.4
- Add complete state management for text, phases, roles, voice assignments
- Implement phase transitions: input → assignment → generated
- Wire up text input to role detection API
- Wire up voice assignment to audio generation API
- Add loading states for async operations (role detection and generation)
- Implement comprehensive error handling and display
- Add warning messages for long text (>5000 chars) in assignment and generation phases
- Ensure text persists across all workflow phases
- Validates Requirements: 1.5, 6.1, 8.1, 8.2, 8.3, 8.4
- Created VoiceAssignmentSection component with role-to-voice mapping
- Added language selector with American English, British English, French, Japanese
- Added speed slider (0.5x - 1.5x range)
- Implemented Generate Audiobook button with proper disabled state
- Integrated component into App.tsx with state management
- Added role detection API call on text submission
- Validates all roles have assigned voices before enabling generation
Requirements: 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 4.1, 4.2, 4.3, 4.4, 5.1, 5.2, 5.3
- Create TextInputSection component with large multiline textarea
- Add 'Next: Assign voices' button
- Implement text validation (non-empty)
- Show validation message for empty text
- Disable button when text is empty
- Update App.tsx to integrate TextInputSection
- Requirements: 1.1, 1.2, 1.3, 1.4
- Create TypeScript interfaces (Role, RoleWithVoice, RolesResponse, GenerateRequest)
- Implement detectRoles() function for role detection
- Implement generateAudiobook() function for audio generation
- Implement checkHealth() function for backend health checks
- Add comprehensive error handling for network failures
- Use VITE_API_BASE_URL environment variable for configuration
- Add ApiError class for structured error handling
Requirements: 9.6, 9.7
- Vite project with React and TypeScript initialized
- TypeScript configured with strict mode enabled
- Tailwind CSS set up for styling
- Component and API directory structure created
- Environment variables configured (.env with VITE_API_BASE_URL)
- Build and dev server verified working
Requirements: 9.6, 12.3
- Create Dockerfile with Python 3.10 base image
- Install system dependencies (espeak-ng) for Kokoro TTS
- Install Python dependencies from requirements.txt
- Copy backend code and expose port 8000
- Configure Uvicorn server startup
- Add docker-compose.yml for easy orchestration
- Include health checks and environment variables
- Add .dockerignore to optimize build context
Implements task 6 - Requirements 11.1-11.6
- Add POST /api/tts endpoint with full request validation
- Validate text, language code, speed range, and roles list
- Integrate TTSService to generate multi-voice audiobooks
- Fix Kokoro pipeline generator handling to properly collect audio
- Stream audio response with correct Content-Type (audio/wav)
- Add Content-Disposition header with filename (audiobook.wav)
- Handle errors gracefully with appropriate HTTP status codes
- Add comprehensive test suite for endpoint validation
Requirements: 6.1, 6.7, 6.8, 9.5
- Created TTSService class with pipeline caching per language
- Implemented text segmentation to identify role markers (colon and bracket patterns)
- Implemented segment synthesis with voice assignment
- Implemented audio concatenation at 24 kHz sample rate
- Added fallback to default voice (af_bella) for unmapped roles
- Supports language codes: a, b, f, j
- Handles long text by chunking on sentence boundaries
- Added comprehensive unit tests (13 tests, all passing)
Requirements: 10.1, 10.3, 10.4, 6.2, 6.3, 6.4, 6.5, 6.6
- Created RoleDetector class with colon and bracket pattern extraction
- Implemented role deduplication logic
- Added fallback to default 'Narrator' role when no patterns found
- Created /api/tts/roles endpoint using RoleDetector
- Added comprehensive test suite for role detection
- All tests passing (7/7)
Requirements: 2.1, 2.2, 2.3, 2.4, 2.5, 2.6
- Created FastAPI application with CORS middleware
- Defined Pydantic models (RoleDetectionRequest, RolesResponse, TTSRequest, ErrorResponse)
- Implemented /api/health endpoint
- Set up error handling middleware for validation and general exceptions
- Configured logging with INFO level
- Added test script to verify API functionality
- Validates Requirements: 9.1, 9.2, 9.4
- Created backend directory with FastAPI project structure
- Created frontend directory with React + TypeScript + Vite
- Configured Python virtual environment and installed dependencies (fastapi, uvicorn, kokoro, soundfile, python-multipart)
- Verified espeak-ng system dependency is installed
- Configured Node.js project with TypeScript, React, and Vite
- Set up Tailwind CSS for styling
- Set up environment variable configuration for both projects
- Created comprehensive README documentation
Requirements: 9.6, 10.2, 11.1
