- Added proper error handling for missing voice models in preview endpoint
- Return 404 with helpful message when Piper models not found
- Added user-friendly alert in frontend when preview fails
- Added prominent warning in UI when Piper is selected about manual setup
- Check for empty audio before returning to prevent decode errors
Users now get clear feedback when Piper voice models are missing
instead of cryptic 'media could not be decoded' errors.
- Fixed preview endpoint to work with both Kokoro and Piper engines
- Improved voice model download with curl fallback and SSL handling
- Added better error messages when models are missing
- Created PIPER_SETUP.md with manual download instructions
- Added test_piper_direct.py for testing Piper installation
- Updated README with voice model download requirements
Note: Piper voice models require manual download from GitHub releases
due to HuggingFace URL structure. See PIPER_SETUP.md for instructions.
- Created PIPER_TTS_INTEGRATION.md with complete integration summary
- Added test_engine_selection.py for testing engine switching
- Documented engine comparison, usage examples, and pitch guidelines
- Listed all modified/created files and git commits
- Included next steps for optional enhancements
Task 26 completed: Piper TTS integration with engine selection UI
- Created PiperTTSService with pitch shifting (-12 to +12 semitones)
- Created UnifiedTTSService to manage multiple TTS engines
- Added engine selection endpoints (GET/POST /api/tts/engines)
- Added voices listing endpoint (GET /api/tts/voices)
- Added pitch_adjustments parameter to TTS generation
- Created install_piper.sh for easy Piper installation
- Updated backend README with multi-engine documentation
- Created comprehensive PIPER_TTS_GUIDE.md
- Added TTS_ENGINE environment variable for engine selection
- Updated requirements.txt with piper-tts and pydub
This allows users to choose between Kokoro (high quality) and Piper
(fast with pitch control) TTS engines at runtime.
Features:
- Voice preview button to test voices before generation
- Age category selection (young, teen, adult, old)
- Age categories adjust voice speed automatically:
- Young: 1.15x (faster, energetic)
- Teen: 1.08x (slightly faster)
- Adult: 1.0x (normal)
- Old: 0.85x (slower, measured)
- Preview caching to avoid regenerating same voice
- Backend /api/tts/preview endpoint for voice samples
- Frontend previewVoice API function
This allows users to hear how each voice sounds and adjust
character ages to make them sound younger or older.
- Add purple/indigo gradient background throughout app
- Implement glass morphism effects on all cards
- Add gradient buttons with smooth hover animations
- Integrate emoji icons for better visual communication
- Enhance typography with improved font stack
- Add loading spinners and smooth transitions
- Improve color scheme with modern purple/indigo palette
- Add glowing shadows and depth effects
- Maintain responsive design and touch-friendly interactions
Updated the speaker detection prompt to explicitly instruct the AI to:
- Identify narrative text and assign it to 'Narrator'
- Detect character dialogue and list character names
- Include BOTH Narrator and characters when story has mixed content
This ensures stories with narrative descriptions get proper voice
assignments for both the narrator and speaking characters.
Example: Story with 'Once upon a time...' narrative and dialogue from
Lily, Benny, Sammy now correctly detects all 4 roles.
- Added ScriptRequest, ScriptResponse, and ScriptSegment Pydantic models
- Created /api/tts/script POST endpoint in main.py
- Integrated with TTSService to generate script segments using AI
- Added generate_script_segments method to TTSService
- Returns segments with unique IDs for frontend tracking
- Handles errors gracefully when AI segmentation fails
- Falls back to pattern-based segmentation when Ollama unavailable
- Added comprehensive tests for the endpoint
- Validates: Requirements 15.1, 15.2
- 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.
- 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
- 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
