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* Task completion checkpoint
* Task completion checkpoint
* Task completion checkpoint
* Add Qwen3-TTS research document for live TTS replacement
Research findings for replacing Chatterbox TTS with Qwen3-TTS-12Hz-0.6B-Base:
- Current TTS: Chatterbox-Turbo-ONNX with batch-only generation, no streaming
- Qwen3-TTS: 97ms end-to-end latency, streaming support, 3-second voice cloning
- Voice cloning: Requires 3s reference audio + transcript (Makima voice planned)
- Integration: Python service with WebSocket bridge (no ONNX export available)
- Languages: 10 supported including English and Japanese
Document includes:
- Current architecture analysis (makima/src/tts.rs)
- Qwen3-TTS capabilities and requirements
- Feasibility assessment for live/streaming TTS
- Audio clip requirements for voice cloning
- Preliminary technical approach with architecture diagrams
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* [WIP] Heartbeat checkpoint - 2026-01-27 03:11:15 UTC
* Add Qwen3-TTS research documentation
Comprehensive research on replacing Chatterbox TTS with Qwen3-TTS-12Hz-0.6B-Base:
- Current TTS implementation analysis (Chatterbox-Turbo-ONNX in makima/src/tts.rs)
- Qwen3-TTS capabilities: 97ms streaming latency, voice cloning with 3s reference
- Cross-lingual support: Japanese voice (Makima/Tomori Kusunoki) speaking English
- Python microservice architecture recommendation (FastAPI + WebSocket)
- Implementation phases and technical approach
- Hardware requirements and dependencies
Key findings:
- Live/streaming TTS is highly feasible with 97ms latency
- Voice cloning fully supported with 0.95 speaker similarity
- Recommended: Python microservice with WebSocket streaming
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* Add comprehensive Qwen3-TTS integration specification
This specification document defines the complete integration of
Qwen3-TTS-12Hz-0.6B-Base as a replacement for the existing Chatterbox-Turbo
TTS implementation. The document covers:
## Functional Requirements
- WebSocket endpoint /api/v1/speak for streaming TTS
- Voice cloning with default Makima voice (Japanese VA speaking English)
- Support for custom voice references
- Detailed client-to-server and server-to-client message protocols
- Integration with Listen page for bidirectional speech
## Non-Functional Requirements
- Latency targets: < 200ms first audio byte
- Audio quality: 24kHz, mono, PCM16/PCM32f
- Hardware requirements: CUDA GPU with 4-8GB VRAM
- Scalability: 10 concurrent sessions per GPU
## Architecture Specification
- Python TTS microservice with FastAPI/WebSocket
- Rust proxy endpoint in makima server
- Voice prompt caching mechanism (LRU cache)
- Error handling and recovery strategies
## API Contract
- Complete WebSocket message format definitions (TypeScript)
- Error codes and responses (TTS_UNAVAILABLE, SYNTHESIS_ERROR, etc.)
- Session state machine and lifecycle management
## Voice Asset Requirements
- Makima voice clip specifications (5-10s WAV, transcript required)
- Storage location: models/voices/makima/
- Metadata format for voice management
## Testing Strategy
- Unit tests for Python TTS service and Rust proxy
- Integration tests for WebSocket flow
- Latency benchmarks with performance targets
- Test data fixtures for various text lengths
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* Add Qwen3-TTS implementation plan
Comprehensive implementation plan for replacing Chatterbox-TTS with
Qwen3-TTS streaming TTS service, including:
- Task breakdown with estimated hours for each phase
- Phase 1: Python TTS microservice (FastAPI, WebSocket)
- Phase 2: Rust proxy integration (speak.rs, tts_client.rs)
- Detailed file changes and new module structure
- Testing plan with unit, integration, and latency benchmarks
- Risk assessment with mitigation strategies
- Success criteria for each phase
Based on specification in docs/specs/qwen3-tts-spec.md
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* Add author and research references to TTS implementation plan
Add links to research documentation and author attribution.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* [WIP] Heartbeat checkpoint - 2026-01-27 03:25:06 UTC
* Add Python TTS service project structure (Phase 1.1-1.3)
Create the initial makima-tts Python service directory structure with:
- pyproject.toml with FastAPI, Qwen-TTS, and torch dependencies
- config.py with pydantic-settings TTSConfig class
- models.py with Pydantic message models (Start, Speak, Stop, Ready, etc.)
This implements tasks P1.1, P1.2, and P1.3 from the Qwen3-TTS implementation plan.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* Add TTS engine and voice manager for Qwen3-TTS (Phase 1.4-1.5)
Implement core TTS functionality:
- tts_engine.py: Qwen3-TTS wrapper with streaming audio chunk generation
- voice_manager.py: Voice prompt caching with LRU eviction and TTL support
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* [WIP] Heartbeat checkpoint - 2026-01-27 03:30:06 UTC
* Add TTS proxy client and message types (Phase 2.1, 2.2, 2.4)
- Add tts_client.rs with TtsConfig, TtsCircuitBreaker, TtsError,
TtsProxyClient, and TtsConnection structs for WebSocket proxying
- Add TTS message types to messages.rs (TtsAudioEncoding, TtsPriority,
TtsStartMessage, TtsSpeakMessage, TtsStopMessage, TtsClientMessage,
TtsReadyMessage, TtsAudioChunkMessage, TtsCompleteMessage,
TtsErrorMessage, TtsStoppedMessage, TtsServerMessage)
- Export tts_client module from server mod.rs
- tokio-tungstenite already present in Cargo.toml
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* Add TTS WebSocket handler and route (Phase 2.3, 2.5, 2.6)
- Create speak.rs WebSocket handler that proxies to Python TTS service
- Add TtsState fields (tts_client, tts_config) to AppState
- Add with_tts() builder and is_tts_healthy() methods to AppState
- Register /api/v1/speak route in the router
- Add speak module export in handlers/mod.rs
The handler forwards WebSocket messages bidirectionally between
the client and the Python TTS microservice with proper error handling.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* Add Makima voice profile assets for TTS voice cloning
Creates the voice assets directory structure with:
- manifest.json containing voice configuration (voice_id, speaker,
language, reference audio path, and Japanese transcript placeholder)
- README.md with instructions for obtaining voice reference audio
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* Add Rust-native Qwen3-TTS integration research document
Research findings for integrating Qwen3-TTS-12Hz-0.6B-Base directly into
the makima Rust codebase without Python. Key conclusions:
- ONNX export is not viable (unsupported architecture)
- Candle (HF Rust ML framework) is the recommended approach
- Model weights available in safetensors format (2.52GB total)
- Three components needed: LM backbone, code predictor, speech tokenizer
- Crane project has Qwen3-TTS as highest priority (potential upstream)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* [WIP] Heartbeat checkpoint - 2026-01-27 11:21:43 UTC
* [WIP] Heartbeat checkpoint - 2026-01-27 11:24:19 UTC
* [WIP] Heartbeat checkpoint - 2026-01-27 11:26:43 UTC
* feat: implement Rust-native Qwen3-TTS using candle framework
Replace monolithic tts.rs with modular tts/ directory structure:
- tts/mod.rs: TtsEngine trait, TtsEngineFactory, shared types (AudioChunk,
TtsError), and utility functions (save_wav, resample, argmax)
- tts/chatterbox.rs: existing ONNX-based ChatterboxTTS adapted to implement
TtsEngine trait with Mutex-wrapped sessions for Send+Sync
- tts/qwen3/mod.rs: Qwen3Tts entry point with HuggingFace model loading
- tts/qwen3/config.rs: Qwen3TtsConfig parsing from HF config.json
- tts/qwen3/model.rs: 28-layer Qwen3 transformer with RoPE, GQA (16 heads,
8 KV heads), SiLU MLP, RMS norm, and KV cache
- tts/qwen3/code_predictor.rs: 5-layer MTP module predicting 16 codebooks
- tts/qwen3/speech_tokenizer.rs: ConvNet encoder/decoder with 16-layer RVQ
- tts/qwen3/generate.rs: autoregressive generation loop with streaming support
Add candle-core, candle-nn, candle-transformers, safetensors to Cargo.toml.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* feat: integrate TTS engine into speak WebSocket handler
- Update speak.rs handler to use TTS engine directly from SharedState
instead of returning a stub "not implemented" error
- Add TtsEngine (OnceCell lazy-loaded) to AppState in state.rs with
get_tts_engine() method for lazy initialization on first connection
- Implement full WebSocket protocol: client sends JSON speak/cancel/stop
messages, server streams binary PCM audio chunks and audio_end signals
- Create voices/makima/manifest.json for Makima voice profile configuration
- All files compile successfully with zero errors
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* feat: add /speak TTS page with WebSocket audio playback
Add a new /speak frontend page for text-to-speech via WebSocket.
The page accepts text input and streams synthesized PCM audio through
the Web Audio API. Includes model loading indicator, cancel support,
and connection status. Also adds a loading bar to the listen page
ControlPanel during WebSocket connection.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
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