From eabd1304cce0e053cd32ec910d2f0ea429e8af14 Mon Sep 17 00:00:00 2001 From: soryu Date: Wed, 28 Jan 2026 02:54:17 +0000 Subject: Add Qwen3-TTS streaming endpoint for voice synthesis (#40) * 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 * [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 * 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 * 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 * Add author and research references to TTS implementation plan Add links to research documentation and author attribution. Co-Authored-By: Claude Opus 4.5 * [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 * 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 * [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 * 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 * 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 * 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 * [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 * 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 * 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 --------- Co-authored-by: Claude Opus 4.5 --- makima/frontend/src/hooks/useSpeakWebSocket.ts | 329 +++++++++++++++++++++++++ 1 file changed, 329 insertions(+) create mode 100644 makima/frontend/src/hooks/useSpeakWebSocket.ts (limited to 'makima/frontend/src/hooks') diff --git a/makima/frontend/src/hooks/useSpeakWebSocket.ts b/makima/frontend/src/hooks/useSpeakWebSocket.ts new file mode 100644 index 0000000..3ef8851 --- /dev/null +++ b/makima/frontend/src/hooks/useSpeakWebSocket.ts @@ -0,0 +1,329 @@ +import { useState, useCallback, useRef, useEffect } from "react"; +import { SPEAK_ENDPOINT } from "../lib/api"; + +export type SpeakStatus = + | "disconnected" + | "connecting" + | "connected" + | "loading_model" + | "speaking" + | "error"; + +export interface SpeakWebSocketState { + status: SpeakStatus; + error: string | null; +} + +export function useSpeakWebSocket() { + const [state, setState] = useState({ + status: "disconnected", + error: null, + }); + + const wsRef = useRef(null); + const audioContextRef = useRef(null); + const audioQueueRef = useRef([]); + const isPlayingRef = useRef(false); + const modelLoadingTimerRef = useRef | null>(null); + const nextPlayTimeRef = useRef(0); + + // Clean up on unmount + useEffect(() => { + return () => { + if (wsRef.current) { + wsRef.current.close(); + wsRef.current = null; + } + if (audioContextRef.current) { + audioContextRef.current.close(); + audioContextRef.current = null; + } + if (modelLoadingTimerRef.current) { + clearTimeout(modelLoadingTimerRef.current); + modelLoadingTimerRef.current = null; + } + }; + }, []); + + const getAudioContext = useCallback((): AudioContext => { + if (!audioContextRef.current || audioContextRef.current.state === "closed") { + audioContextRef.current = new AudioContext({ sampleRate: 24000 }); + } + return audioContextRef.current; + }, []); + + const playAudioQueue = useCallback(() => { + if (isPlayingRef.current) return; + isPlayingRef.current = true; + + const ctx = getAudioContext(); + + function scheduleNext() { + const chunk = audioQueueRef.current.shift(); + if (!chunk) { + isPlayingRef.current = false; + return; + } + + const buffer = ctx.createBuffer(1, chunk.length, 24000); + buffer.copyToChannel(chunk, 0); + + const source = ctx.createBufferSource(); + source.buffer = buffer; + source.connect(ctx.destination); + + // Schedule playback at the right time to avoid gaps + const now = ctx.currentTime; + const startTime = Math.max(now, nextPlayTimeRef.current); + source.start(startTime); + nextPlayTimeRef.current = startTime + buffer.duration; + + source.onended = () => { + if (audioQueueRef.current.length > 0) { + scheduleNext(); + } else { + isPlayingRef.current = false; + } + }; + } + + scheduleNext(); + }, [getAudioContext]); + + const connect = useCallback((): Promise => { + return new Promise((resolve) => { + if (wsRef.current?.readyState === WebSocket.OPEN) { + resolve(true); + return; + } + + if (wsRef.current) { + wsRef.current.close(); + wsRef.current = null; + } + + setState({ status: "connecting", error: null }); + + try { + const ws = new WebSocket(SPEAK_ENDPOINT); + ws.binaryType = "arraybuffer"; + wsRef.current = ws; + + ws.onopen = () => { + setState({ status: "connected", error: null }); + resolve(true); + }; + + ws.onmessage = (event) => { + // Binary data = PCM audio chunk + if (event.data instanceof ArrayBuffer) { + // Clear model loading timer on first audio data + if (modelLoadingTimerRef.current) { + clearTimeout(modelLoadingTimerRef.current); + modelLoadingTimerRef.current = null; + } + + // Update status to speaking if not already + setState((s) => { + if (s.status === "loading_model" || s.status === "connected") { + return { ...s, status: "speaking" }; + } + return s; + }); + + // Convert PCM16 LE to Float32 + const pcm16 = new Int16Array(event.data); + const float32 = new Float32Array(pcm16.length); + for (let i = 0; i < pcm16.length; i++) { + float32[i] = pcm16[i] / 32768; + } + + audioQueueRef.current.push(float32); + playAudioQueue(); + return; + } + + // Text data = JSON message + try { + const message = JSON.parse(event.data); + + switch (message.type) { + case "audio_end": + // Clear model loading timer + if (modelLoadingTimerRef.current) { + clearTimeout(modelLoadingTimerRef.current); + modelLoadingTimerRef.current = null; + } + // Wait for audio queue to drain, then go back to connected + // Use a short delay to let buffered audio finish + { + const checkDone = () => { + if (audioQueueRef.current.length === 0 && !isPlayingRef.current) { + setState((s) => { + if (s.status === "speaking" || s.status === "loading_model") { + return { ...s, status: "connected" }; + } + return s; + }); + } else { + setTimeout(checkDone, 100); + } + }; + checkDone(); + } + break; + + case "error": + if (modelLoadingTimerRef.current) { + clearTimeout(modelLoadingTimerRef.current); + modelLoadingTimerRef.current = null; + } + setState({ + status: "error", + error: message.message || `Error: ${message.code}`, + }); + break; + } + } catch { + console.error("Failed to parse speak WebSocket message:", event.data); + } + }; + + ws.onerror = () => { + setState({ + status: "error", + error: "Failed to connect to speak server", + }); + resolve(false); + }; + + ws.onclose = (event) => { + if (modelLoadingTimerRef.current) { + clearTimeout(modelLoadingTimerRef.current); + modelLoadingTimerRef.current = null; + } + + let errorMessage: string | null = null; + if (event.code === 1006) { + errorMessage = "Connection failed - server may be unavailable"; + } else if (event.code !== 1000 && event.code !== 1001) { + errorMessage = `Connection closed unexpectedly (code: ${event.code})`; + } + + setState((s) => ({ + status: "disconnected", + error: errorMessage || s.error, + })); + wsRef.current = null; + }; + } catch (err) { + const message = + err instanceof Error ? err.message : "Failed to create WebSocket connection"; + setState({ status: "error", error: message }); + resolve(false); + } + }); + }, [playAudioQueue]); + + const speak = useCallback( + async (text: string) => { + if (!text.trim()) return; + + // Connect if not connected + if (!wsRef.current || wsRef.current.readyState !== WebSocket.OPEN) { + const connected = await connect(); + if (!connected) return; + } + + // Reset audio state + audioQueueRef.current = []; + isPlayingRef.current = false; + nextPlayTimeRef.current = 0; + + // Resume audio context if suspended (browser autoplay policy) + const ctx = getAudioContext(); + if (ctx.state === "suspended") { + await ctx.resume(); + } + + // Start loading timer - if no audio arrives in 2 seconds, show loading state + modelLoadingTimerRef.current = setTimeout(() => { + setState((s) => { + if (s.status === "connected" || s.status === "connecting") { + return { ...s, status: "loading_model" }; + } + return s; + }); + modelLoadingTimerRef.current = null; + }, 2000); + + // Send speak request + wsRef.current?.send( + JSON.stringify({ type: "speak", text }) + ); + + setState((s) => ({ ...s, error: null })); + }, + [connect, getAudioContext] + ); + + const cancel = useCallback(() => { + // Clear audio queue + audioQueueRef.current = []; + isPlayingRef.current = false; + nextPlayTimeRef.current = 0; + + // Clear model loading timer + if (modelLoadingTimerRef.current) { + clearTimeout(modelLoadingTimerRef.current); + modelLoadingTimerRef.current = null; + } + + // Send cancel message + if (wsRef.current?.readyState === WebSocket.OPEN) { + wsRef.current.send(JSON.stringify({ type: "cancel" })); + } + + setState((s) => ({ + ...s, + status: wsRef.current?.readyState === WebSocket.OPEN ? "connected" : "disconnected", + })); + }, []); + + const disconnect = useCallback(() => { + // Clear audio queue + audioQueueRef.current = []; + isPlayingRef.current = false; + nextPlayTimeRef.current = 0; + + if (modelLoadingTimerRef.current) { + clearTimeout(modelLoadingTimerRef.current); + modelLoadingTimerRef.current = null; + } + + if (wsRef.current) { + // Send stop message before closing + if (wsRef.current.readyState === WebSocket.OPEN) { + wsRef.current.send(JSON.stringify({ type: "stop" })); + } + wsRef.current.close(1000, "User disconnected"); + wsRef.current = null; + } + + setState({ status: "disconnected", error: null }); + }, []); + + return { + ...state, + isConnected: + state.status === "connected" || + state.status === "speaking" || + state.status === "loading_model", + isSpeaking: state.status === "speaking", + isModelLoading: state.status === "loading_model", + speak, + cancel, + connect, + disconnect, + }; +} -- cgit v1.2.3