Add backend api and engine

api/.gitignore

@@ -0,0 +1,66 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual environments
+venv/
+ENV/
+env/
+.venv
+
+# Environment variables
+.env
+.env.local
+.env.*.local
+
+# Database
+*.db
+*.sqlite
+*.sqlite3
+
+# Vector store data
+data/vector_store/
+!data/vector_store/.gitkeep
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Docker
+.docker/
+
+# Logs
+*.log
+
+# Pytest
+.pytest_cache/
+.coverage
+htmlcov/
+
+# mypy
+.mypy_cache/

api/Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+# 安装依赖
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# 复制代码
+COPY . .
+
+# 创建数据目录
+RUN mkdir -p /app/data
+
+EXPOSE 8000
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000", "--reload"]

api/README.md

@@ -1 +1,103 @@
-# Backend Service
+# AI VideoAssistant Backend
+
+Python 后端 API，配合前端 `ai-videoassistant-frontend` 使用。
+
+## 快速开始
+
+### 1. 安装依赖
+
+```bash
+cd ~/Code/ai-videoassistant-backend
+pip install -r requirements.txt
+```
+
+### 2. 初始化数据库
+
+```bash
+python init_db.py
+```
+
+这会：
+- 创建 `data/app.db` SQLite 数据库
+- 初始化默认声音数据
+
+### 3. 启动服务
+
+```bash
+# 开发模式 (热重载)
+python -m uvicorn main:app --reload --host 0.0.0.0 --port 8000
+```
+
+### 4. 测试 API
+
+```bash
+# 健康检查
+curl http://localhost:8000/health
+
+# 获取助手列表
+curl http://localhost:8000/api/assistants
+
+# 获取声音列表
+curl http://localhost:8000/api/voices
+
+# 获取通话历史
+curl http://localhost:8000/api/history
+```
+
+## API 文档
+
+| 端点 | 方法 | 说明 |
+|------|------|------|
+| `/api/assistants` | GET | 助手列表 |
+| `/api/assistants` | POST | 创建助手 |
+| `/api/assistants/{id}` | GET | 助手详情 |
+| `/api/assistants/{id}` | PUT | 更新助手 |
+| `/api/assistants/{id}` | DELETE | 删除助手 |
+| `/api/voices` | GET | 声音库列表 |
+| `/api/history` | GET | 通话历史列表 |
+| `/api/history/{id}` | GET | 通话详情 |
+| `/api/history/{id}/transcripts` | POST | 添加转写 |
+| `/api/history/{id}/audio/{turn}` | GET | 获取音频 |
+
+## 使用 Docker 启动
+
+```bash
+cd ~/Code/ai-videoassistant-backend
+
+# 启动所有服务
+docker-compose up -d
+
+# 查看日志
+docker-compose logs -f backend
+```
+
+## 目录结构
+
+```
+backend/
+├── app/
+│   ├── __init__.py
+│   ├── main.py           # FastAPI 入口
+│   ├── db.py             # SQLite 连接
+│   ├── models.py         # 数据模型
+│   ├── schemas.py        # Pydantic 模型
+│   ├── storage.py        # MinIO 存储
+│   └── routers/
+│       ├── __init__.py
+│       ├── assistants.py # 助手 API
+│       └── history.py    # 通话记录 API
+├── data/                 # 数据库文件
+├── requirements.txt
+├── .env
+└── docker-compose.yml
+```
+
+## 环境变量
+
+| 变量 | 默认值 | 说明 |
+|------|--------|------|
+| `DATABASE_URL` | `sqlite:///./data/app.db` | 数据库连接 |
+| `MINIO_ENDPOINT` | `localhost:9000` | MinIO 地址 |
+| `MINIO_ACCESS_KEY` | `admin` | MinIO 密钥 |
+| `MINIO_SECRET_KEY` | `password123` | MinIO 密码 |
+| `MINIO_BUCKET` | `ai-audio` | 存储桶名称 |

api/app/db.py

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+from sqlalchemy import create_engine
+from sqlalchemy.orm import sessionmaker, DeclarativeBase
+
+DATABASE_URL = "sqlite:///./data/app.db"
+
+engine = create_engine(DATABASE_URL, connect_args={"check_same_thread": False})
+SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+
+
+class Base(DeclarativeBase):
+    pass
+
+
+def get_db():
+    db = SessionLocal()
+    try:
+        yield db
+    finally:
+        db.close()

api/app/main.py

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+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from contextlib import asynccontextmanager
+import os
+
+from .db import Base, engine
+from .routers import assistants, history
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    # 启动时创建表
+    Base.metadata.create_all(bind=engine)
+    yield
+
+
+app = FastAPI(
+    title="AI VideoAssistant API",
+    description="Backend API for AI VideoAssistant",
+    version="1.0.0",
+    lifespan=lifespan
+)
+
+# CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# 路由
+app.include_router(assistants.router, prefix="/api")
+app.include_router(history.router, prefix="/api")
+
+
+@app.get("/")
+def root():
+    return {"message": "AI VideoAssistant API", "version": "1.0.0"}
+
+
+@app.get("/health")
+def health():
+    return {"status": "ok"}
+
+
+# 初始化默认数据
+@app.on_event("startup")
+def init_default_data():
+    from sqlalchemy.orm import Session
+    from .db import SessionLocal
+    from .models import Voice
+    
+    db = SessionLocal()
+    try:
+        # 检查是否已有数据
+        if db.query(Voice).count() == 0:
+            # 插入默认声音
+            voices = [
+                Voice(id="v1", name="Xiaoyun", vendor="Ali", gender="Female", language="zh", description="Gentle and professional."),
+                Voice(id="v2", name="Kevin", vendor="Volcano", gender="Male", language="en", description="Deep and authoritative."),
+                Voice(id="v3", name="Abby", vendor="Minimax", gender="Female", language="en", description="Cheerful and lively."),
+                Voice(id="v4", name="Guang", vendor="Ali", gender="Male", language="zh", description="Standard newscast style."),
+                Voice(id="v5", name="Doubao", vendor="Volcano", gender="Female", language="zh", description="Cute and young."),
+            ]
+            for v in voices:
+                db.add(v)
+            db.commit()
+            print("✅ 默认声音数据已初始化")
+    finally:
+        db.close()

api/app/models.py

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+from datetime import datetime
+from typing import List, Optional
+from sqlalchemy import String, Integer, DateTime, Text, Float, ForeignKey, JSON
+from sqlalchemy.orm import Mapped, mapped_column, relationship
+
+from .db import Base
+
+
+class User(Base):
+    __tablename__ = "users"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, index=True)
+    email: Mapped[str] = mapped_column(String(255), unique=True, index=True, nullable=False)
+    password_hash: Mapped[str] = mapped_column(String(255), nullable=False)
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=datetime.utcnow)
+
+
+class Voice(Base):
+    __tablename__ = "voices"
+
+    id: Mapped[str] = mapped_column(String(64), primary_key=True)
+    name: Mapped[str] = mapped_column(String(128), nullable=False)
+    vendor: Mapped[str] = mapped_column(String(64), nullable=False)
+    gender: Mapped[str] = mapped_column(String(32), nullable=False)
+    language: Mapped[str] = mapped_column(String(16), nullable=False)
+    description: Mapped[str] = mapped_column(String(255), nullable=False)
+    voice_params: Mapped[dict] = mapped_column(JSON, default=dict)
+
+
+class Assistant(Base):
+    __tablename__ = "assistants"
+
+    id: Mapped[str] = mapped_column(String(64), primary_key=True)
+    user_id: Mapped[int] = mapped_column(Integer, ForeignKey("users.id"), index=True)
+    name: Mapped[str] = mapped_column(String(255), nullable=False)
+    call_count: Mapped[int] = mapped_column(Integer, default=0)
+    opener: Mapped[str] = mapped_column(Text, default="")
+    prompt: Mapped[str] = mapped_column(Text, default="")
+    knowledge_base_id: Mapped[Optional[str]] = mapped_column(String(64), nullable=True)
+    language: Mapped[str] = mapped_column(String(16), default="zh")
+    voice: Mapped[Optional[str]] = mapped_column(String(64), nullable=True)
+    speed: Mapped[float] = mapped_column(Float, default=1.0)
+    hotwords: Mapped[dict] = mapped_column(JSON, default=list)
+    tools: Mapped[dict] = mapped_column(JSON, default=list)
+    interruption_sensitivity: Mapped[int] = mapped_column(Integer, default=500)
+    config_mode: Mapped[str] = mapped_column(String(32), default="platform")
+    api_url: Mapped[Optional[str]] = mapped_column(String(255), nullable=True)
+    api_key: Mapped[Optional[str]] = mapped_column(String(255), nullable=True)
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=datetime.utcnow)
+    updated_at: Mapped[datetime] = mapped_column(DateTime, default=datetime.utcnow)
+
+    user = relationship("User")
+    call_records = relationship("CallRecord", back_populates="assistant")
+
+
+class KnowledgeBase(Base):
+    __tablename__ = "knowledge_bases"
+
+    id: Mapped[str] = mapped_column(String(64), primary_key=True)
+    user_id: Mapped[int] = mapped_column(Integer, ForeignKey("users.id"), index=True)
+    name: Mapped[str] = mapped_column(String(255), nullable=False)
+    description: Mapped[str] = mapped_column(Text, default="")
+    embedding_model: Mapped[str] = mapped_column(String(64), default="text-embedding-3-small")
+    chunk_size: Mapped[int] = mapped_column(Integer, default=500)
+    chunk_overlap: Mapped[int] = mapped_column(Integer, default=50)
+    doc_count: Mapped[int] = mapped_column(Integer, default=0)
+    chunk_count: Mapped[int] = mapped_column(Integer, default=0)
+    status: Mapped[str] = mapped_column(String(32), default="active")
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=datetime.utcnow)
+    updated_at: Mapped[datetime] = mapped_column(DateTime, default=datetime.utcnow)
+
+    user = relationship("User")
+    documents = relationship("KnowledgeDocument", back_populates="kb")
+
+
+class KnowledgeDocument(Base):
+    __tablename__ = "knowledge_documents"
+
+    id: Mapped[str] = mapped_column(String(64), primary_key=True)
+    kb_id: Mapped[str] = mapped_column(String(64), ForeignKey("knowledge_bases.id"), index=True)
+    name: Mapped[str] = mapped_column(String(255), nullable=False)
+    size: Mapped[str] = mapped_column(String(64), nullable=False)
+    file_type: Mapped[str] = mapped_column(String(32), default="txt")
+    storage_url: Mapped[Optional[str]] = mapped_column(String(512), nullable=True)
+    status: Mapped[str] = mapped_column(String(32), default="pending")  # pending/processing/completed/failed
+    chunk_count: Mapped[int] = mapped_column(Integer, default=0)
+    error_message: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
+    upload_date: Mapped[str] = mapped_column(String(32), nullable=False)
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=datetime.utcnow)
+    processed_at: Mapped[Optional[datetime]] = mapped_column(DateTime, nullable=True)
+
+    kb = relationship("KnowledgeBase", back_populates="documents")
+
+
+class Workflow(Base):
+    __tablename__ = "workflows"
+
+    id: Mapped[str] = mapped_column(String(64), primary_key=True)
+    user_id: Mapped[int] = mapped_column(Integer, ForeignKey("users.id"), index=True)
+    name: Mapped[str] = mapped_column(String(255), nullable=False)
+    node_count: Mapped[int] = mapped_column(Integer, default=0)
+    created_at: Mapped[str] = mapped_column(String(32), default="")
+    updated_at: Mapped[str] = mapped_column(String(32), default="")
+    global_prompt: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
+    nodes: Mapped[dict] = mapped_column(JSON, default=list)
+    edges: Mapped[dict] = mapped_column(JSON, default=list)
+
+    user = relationship("User")
+
+
+class CallRecord(Base):
+    __tablename__ = "call_records"
+
+    id: Mapped[str] = mapped_column(String(64), primary_key=True)
+    user_id: Mapped[int] = mapped_column(Integer, ForeignKey("users.id"), index=True)
+    assistant_id: Mapped[Optional[str]] = mapped_column(String(64), ForeignKey("assistants.id"), index=True)
+    source: Mapped[str] = mapped_column(String(32), default="debug")
+    status: Mapped[str] = mapped_column(String(32), default="connected")
+    started_at: Mapped[str] = mapped_column(String(32), nullable=False)
+    ended_at: Mapped[Optional[str]] = mapped_column(String(32), nullable=True)
+    duration_seconds: Mapped[Optional[int]] = mapped_column(Integer, nullable=True)
+    summary: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
+    cost: Mapped[float] = mapped_column(Float, default=0.0)
+    call_metadata: Mapped[dict] = mapped_column(JSON, default=dict)
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=datetime.utcnow)
+
+    user = relationship("User")
+    assistant = relationship("Assistant", back_populates="call_records")
+    transcripts = relationship("CallTranscript", back_populates="call_record")
+    audio_segments = relationship("CallAudioSegment", back_populates="call_record")
+
+
+class CallTranscript(Base):
+    __tablename__ = "call_transcripts"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, index=True)
+    call_id: Mapped[str] = mapped_column(String(64), ForeignKey("call_records.id"), index=True)
+    turn_index: Mapped[int] = mapped_column(Integer, nullable=False)
+    speaker: Mapped[str] = mapped_column(String(16), nullable=False)  # human/ai
+    content: Mapped[str] = mapped_column(Text, nullable=False)
+    confidence: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
+    start_ms: Mapped[int] = mapped_column(Integer, nullable=False)
+    end_ms: Mapped[int] = mapped_column(Integer, nullable=False)
+    duration_ms: Mapped[Optional[int]] = mapped_column(Integer, nullable=True)
+    emotion: Mapped[Optional[str]] = mapped_column(String(32), nullable=True)
+
+    call_record = relationship("CallRecord", back_populates="transcripts")
+
+
+class CallAudioSegment(Base):
+    __tablename__ = "call_audio_segments"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, index=True)
+    call_id: Mapped[str] = mapped_column(String(64), ForeignKey("call_records.id"), index=True)
+    transcript_id: Mapped[Optional[int]] = mapped_column(Integer, ForeignKey("call_transcripts.id"), nullable=True)
+    turn_index: Mapped[Optional[int]] = mapped_column(Integer, nullable=True)
+    audio_url: Mapped[str] = mapped_column(String(512), nullable=False)
+    audio_format: Mapped[str] = mapped_column(String(16), default="mp3")
+    file_size_bytes: Mapped[Optional[int]] = mapped_column(Integer, nullable=True)
+    start_ms: Mapped[int] = mapped_column(Integer, nullable=False)
+    end_ms: Mapped[int] = mapped_column(Integer, nullable=False)
+    duration_ms: Mapped[Optional[int]] = mapped_column(Integer, nullable=True)
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=datetime.utcnow)
+
+    call_record = relationship("CallRecord", back_populates="audio_segments")

api/app/routers/__init__.py

@@ -0,0 +1,11 @@
+from fastapi import APIRouter
+
+from . import assistants
+from . import history
+from . import knowledge
+
+router = APIRouter()
+
+router.include_router(assistants.router)
+router.include_router(history.router)
+router.include_router(knowledge.router)

api/app/routers/assistants.py

@@ -0,0 +1,157 @@
+from fastapi import APIRouter, Depends, HTTPException
+from sqlalchemy.orm import Session
+from typing import List
+import uuid
+from datetime import datetime
+
+from ..db import get_db
+from ..models import Assistant, Voice, Workflow
+from ..schemas import (
+    AssistantCreate, AssistantUpdate, AssistantOut,
+    VoiceOut,
+    WorkflowCreate, WorkflowUpdate, WorkflowOut
+)
+
+router = APIRouter()
+
+
+# ============ Voices ============
+@router.get("/voices", response_model=List[VoiceOut])
+def list_voices(db: Session = Depends(get_db)):
+    """获取声音库列表"""
+    voices = db.query(Voice).all()
+    return voices
+
+
+# ============ Assistants ============
+@router.get("/assistants")
+def list_assistants(
+    page: int = 1,
+    limit: int = 50,
+    db: Session = Depends(get_db)
+):
+    """获取助手列表"""
+    query = db.query(Assistant)
+    total = query.count()
+    assistants = query.order_by(Assistant.created_at.desc()) \
+        .offset((page-1)*limit).limit(limit).all()
+    return {"total": total, "page": page, "limit": limit, "list": assistants}
+
+
+@router.get("/assistants/{id}", response_model=AssistantOut)
+def get_assistant(id: str, db: Session = Depends(get_db)):
+    """获取单个助手详情"""
+    assistant = db.query(Assistant).filter(Assistant.id == id).first()
+    if not assistant:
+        raise HTTPException(status_code=404, detail="Assistant not found")
+    return assistant
+
+
+@router.post("/assistants", response_model=AssistantOut)
+def create_assistant(data: AssistantCreate, db: Session = Depends(get_db)):
+    """创建新助手"""
+    assistant = Assistant(
+        id=str(uuid.uuid4())[:8],
+        user_id=1,  # 默认用户，后续添加认证
+        name=data.name,
+        opener=data.opener,
+        prompt=data.prompt,
+        knowledge_base_id=data.knowledgeBaseId,
+        language=data.language,
+        voice=data.voice,
+        speed=data.speed,
+        hotwords=data.hotwords,
+        tools=data.tools,
+        interruption_sensitivity=data.interruptionSensitivity,
+        config_mode=data.configMode,
+        api_url=data.apiUrl,
+        api_key=data.apiKey,
+    )
+    db.add(assistant)
+    db.commit()
+    db.refresh(assistant)
+    return assistant
+
+
+@router.put("/assistants/{id}")
+def update_assistant(id: str, data: AssistantUpdate, db: Session = Depends(get_db)):
+    """更新助手"""
+    assistant = db.query(Assistant).filter(Assistant.id == id).first()
+    if not assistant:
+        raise HTTPException(status_code=404, detail="Assistant not found")
+    
+    update_data = data.model_dump(exclude_unset=True)
+    for field, value in update_data.items():
+        setattr(assistant, field, value)
+    
+    assistant.updated_at = datetime.utcnow()
+    db.commit()
+    db.refresh(assistant)
+    return assistant
+
+
+@router.delete("/assistants/{id}")
+def delete_assistant(id: str, db: Session = Depends(get_db)):
+    """删除助手"""
+    assistant = db.query(Assistant).filter(Assistant.id == id).first()
+    if not assistant:
+        raise HTTPException(status_code=404, detail="Assistant not found")
+    db.delete(assistant)
+    db.commit()
+    return {"message": "Deleted successfully"}
+
+
+# ============ Workflows ============
+@router.get("/workflows", response_model=List[WorkflowOut])
+def list_workflows(db: Session = Depends(get_db)):
+    """获取工作流列表"""
+    workflows = db.query(Workflow).all()
+    return workflows
+
+
+@router.post("/workflows", response_model=WorkflowOut)
+def create_workflow(data: WorkflowCreate, db: Session = Depends(get_db)):
+    """创建工作流"""
+    workflow = Workflow(
+        id=str(uuid.uuid4())[:8],
+        user_id=1,
+        name=data.name,
+        node_count=data.nodeCount,
+        created_at=data.createdAt or datetime.utcnow().isoformat(),
+        updated_at=data.updatedAt or "",
+        global_prompt=data.globalPrompt,
+        nodes=data.nodes,
+        edges=data.edges,
+    )
+    db.add(workflow)
+    db.commit()
+    db.refresh(workflow)
+    return workflow
+
+
+@router.put("/workflows/{id}", response_model=WorkflowOut)
+def update_workflow(id: str, data: WorkflowUpdate, db: Session = Depends(get_db)):
+    """更新工作流"""
+    workflow = db.query(Workflow).filter(Workflow.id == id).first()
+    if not workflow:
+        raise HTTPException(status_code=404, detail="Workflow not found")
+    
+    update_data = data.model_dump(exclude_unset=True)
+    for field, value in update_data.items():
+        setattr(workflow, field, value)
+    
+    workflow.updated_at = datetime.utcnow().isoformat()
+    db.commit()
+    db.refresh(workflow)
+    return workflow
+
+
+@router.delete("/workflows/{id}")
+def delete_workflow(id: str, db: Session = Depends(get_db)):
+    """删除工作流"""
+    workflow = db.query(Workflow).filter(Workflow.id == id).first()
+    if not workflow:
+        raise HTTPException(status_code=404, detail="Workflow not found")
+    db.delete(workflow)
+    db.commit()
+    return {"message": "Deleted successfully"}

api/app/routers/history.py

@@ -0,0 +1,188 @@
+from fastapi import APIRouter, Depends, HTTPException
+from sqlalchemy.orm import Session
+from typing import Optional, List
+import uuid
+from datetime import datetime
+
+from ..db import get_db
+from ..models import CallRecord, CallTranscript, CallAudioSegment
+from ..storage import get_audio_url
+
+router = APIRouter(prefix="/history", tags=["history"])
+
+
+@router.get("")
+def list_history(
+    assistant_id: Optional[str] = None,
+    status: Optional[str] = None,
+    page: int = 1,
+    limit: int = 20,
+    db: Session = Depends(get_db)
+):
+    """获取通话记录列表"""
+    query = db.query(CallRecord)
+    
+    if assistant_id:
+        query = query.filter(CallRecord.assistant_id == assistant_id)
+    if status:
+        query = query.filter(CallRecord.status == status)
+    
+    total = query.count()
+    records = query.order_by(CallRecord.started_at.desc()) \
+        .offset((page-1)*limit).limit(limit).all()
+    
+    return {"total": total, "page": page, "limit": limit, "list": records}
+
+
+@router.get("/{call_id}")
+def get_history_detail(call_id: str, db: Session = Depends(get_db)):
+    """获取通话详情"""
+    record = db.query(CallRecord).filter(CallRecord.id == call_id).first()
+    if not record:
+        raise HTTPException(status_code=404, detail="Call record not found")
+    
+    # 获取转写
+    transcripts = db.query(CallTranscript) \
+        .filter(CallTranscript.call_id == call_id) \
+        .order_by(CallTranscript.turn_index).all()
+    
+    # 补充音频 URL
+    transcript_list = []
+    for t in transcripts:
+        audio_url = t.audio_url or get_audio_url(call_id, t.turn_index)
+        transcript_list.append({
+            "turnIndex": t.turn_index,
+            "speaker": t.speaker,
+            "content": t.content,
+            "confidence": t.confidence,
+            "startMs": t.start_ms,
+            "endMs": t.end_ms,
+            "durationMs": t.duration_ms,
+            "audioUrl": audio_url,
+        })
+    
+    return {
+        "id": record.id,
+        "user_id": record.user_id,
+        "assistant_id": record.assistant_id,
+        "source": record.source,
+        "status": record.status,
+        "started_at": record.started_at,
+        "ended_at": record.ended_at,
+        "duration_seconds": record.duration_seconds,
+        "summary": record.summary,
+        "transcripts": transcript_list,
+    }
+
+
+@router.post("")
+def create_call_record(
+    user_id: int,
+    assistant_id: Optional[str] = None,
+    source: str = "debug",
+    db: Session = Depends(get_db)
+):
+    """创建通话记录（引擎回调使用）"""
+    record = CallRecord(
+        id=str(uuid.uuid4())[:8],
+        user_id=user_id,
+        assistant_id=assistant_id,
+        source=source,
+        status="connected",
+        started_at=datetime.utcnow().isoformat(),
+    )
+    db.add(record)
+    db.commit()
+    db.refresh(record)
+    return record
+
+
+@router.put("/{call_id}")
+def update_call_record(
+    call_id: str,
+    status: Optional[str] = None,
+    summary: Optional[str] = None,
+    duration_seconds: Optional[int] = None,
+    db: Session = Depends(get_db)
+):
+    """更新通话记录"""
+    record = db.query(CallRecord).filter(CallRecord.id == call_id).first()
+    if not record:
+        raise HTTPException(status_code=404, detail="Call record not found")
+    
+    if status:
+        record.status = status
+    if summary:
+        record.summary = summary
+    if duration_seconds:
+        record.duration_seconds = duration_seconds
+        record.ended_at = datetime.utcnow().isoformat()
+    
+    db.commit()
+    return {"message": "Updated successfully"}
+
+
+@router.post("/{call_id}/transcripts")
+def add_transcript(
+    call_id: str,
+    turn_index: int,
+    speaker: str,
+    content: str,
+    start_ms: int,
+    end_ms: int,
+    confidence: Optional[float] = None,
+    duration_ms: Optional[int] = None,
+    emotion: Optional[str] = None,
+    db: Session = Depends(get_db)
+):
+    """添加转写片段"""
+    transcript = CallTranscript(
+        call_id=call_id,
+        turn_index=turn_index,
+        speaker=speaker,
+        content=content,
+        confidence=confidence,
+        start_ms=start_ms,
+        end_ms=end_ms,
+        duration_ms=duration_ms,
+        emotion=emotion,
+    )
+    db.add(transcript)
+    db.commit()
+    db.refresh(transcript)
+    
+    # 补充音频 URL
+    audio_url = get_audio_url(call_id, turn_index)
+    
+    return {
+        "id": transcript.id,
+        "turn_index": turn_index,
+        "speaker": speaker,
+        "content": content,
+        "confidence": confidence,
+        "start_ms": start_ms,
+        "end_ms": end_ms,
+        "duration_ms": duration_ms,
+        "audio_url": audio_url,
+    }
+
+
+@router.get("/{call_id}/audio/{turn_index}")
+def get_audio(call_id: str, turn_index: int):
+    """获取音频文件"""
+    audio_url = get_audio_url(call_id, turn_index)
+    if not audio_url:
+        raise HTTPException(status_code=404, detail="Audio not found")
+    from fastapi.responses import RedirectResponse
+    return RedirectResponse(audio_url)
+
+
+@router.delete("/{call_id}")
+def delete_call_record(call_id: str, db: Session = Depends(get_db)):
+    """删除通话记录"""
+    record = db.query(CallRecord).filter(CallRecord.id == call_id).first()
+    if not record:
+        raise HTTPException(status_code=404, detail="Call record not found")
+    db.delete(record)
+    db.commit()
+    return {"message": "Deleted successfully"}

api/app/routers/knowledge.py

@@ -0,0 +1,234 @@
+from fastapi import APIRouter, Depends, HTTPException, Query
+from sqlalchemy.orm import Session
+from typing import Optional
+import uuid
+import os
+from datetime import datetime
+
+from ..db import get_db
+from ..models import KnowledgeBase, KnowledgeDocument
+from ..schemas import (
+    KnowledgeBaseCreate, KnowledgeBaseUpdate, KnowledgeBaseOut,
+    KnowledgeSearchQuery, KnowledgeSearchResult, KnowledgeStats,
+    DocumentIndexRequest,
+)
+from ..vector_store import (
+    vector_store, search_knowledge, index_document, delete_document_from_vector
+)
+
+router = APIRouter(prefix="/knowledge", tags=["knowledge"])
+
+
+def kb_to_dict(kb: KnowledgeBase) -> dict:
+    return {
+        "id": kb.id,
+        "user_id": kb.user_id,
+        "name": kb.name,
+        "description": kb.description,
+        "embedding_model": kb.embedding_model,
+        "chunk_size": kb.chunk_size,
+        "chunk_overlap": kb.chunk_overlap,
+        "doc_count": kb.doc_count,
+        "chunk_count": kb.chunk_count,
+        "status": kb.status,
+        "created_at": kb.created_at.isoformat() if kb.created_at else None,
+        "updated_at": kb.updated_at.isoformat() if kb.updated_at else None,
+    }
+
+
+def doc_to_dict(d: KnowledgeDocument) -> dict:
+    return {
+        "id": d.id,
+        "kb_id": d.kb_id,
+        "name": d.name,
+        "size": d.size,
+        "file_type": d.file_type,
+        "storage_url": d.storage_url,
+        "status": d.status,
+        "chunk_count": d.chunk_count,
+        "error_message": d.error_message,
+        "upload_date": d.upload_date,
+        "created_at": d.created_at.isoformat() if d.created_at else None,
+        "processed_at": d.processed_at.isoformat() if d.processed_at else None,
+    }
+
+
+# ============ Knowledge Bases ============
+@router.get("/bases")
+def list_knowledge_bases(user_id: int = 1, db: Session = Depends(get_db)):
+    kbs = db.query(KnowledgeBase).filter(KnowledgeBase.user_id == user_id).all()
+    result = []
+    for kb in kbs:
+        docs = db.query(KnowledgeDocument).filter(KnowledgeDocument.kb_id == kb.id).all()
+        kb_data = kb_to_dict(kb)
+        kb_data["documents"] = [doc_to_dict(d) for d in docs]
+        result.append(kb_data)
+    return {"total": len(result), "list": result}
+
+
+@router.get("/bases/{kb_id}")
+def get_knowledge_base(kb_id: str, db: Session = Depends(get_db)):
+    kb = db.query(KnowledgeBase).filter(KnowledgeBase.id == kb_id).first()
+    if not kb:
+        raise HTTPException(status_code=404, detail="Knowledge base not found")
+    docs = db.query(KnowledgeDocument).filter(KnowledgeDocument.kb_id == kb_id).all()
+    kb_data = kb_to_dict(kb)
+    kb_data["documents"] = [doc_to_dict(d) for d in docs]
+    return kb_data
+
+
+@router.post("/bases")
+def create_knowledge_base(data: KnowledgeBaseCreate, user_id: int = 1, db: Session = Depends(get_db)):
+    kb = KnowledgeBase(
+        id=str(uuid.uuid4())[:8],
+        user_id=user_id,
+        name=data.name,
+        description=data.description,
+        embedding_model=data.embeddingModel,
+        chunk_size=data.chunkSize,
+        chunk_overlap=data.chunkOverlap,
+    )
+    db.add(kb)
+    db.commit()
+    db.refresh(kb)
+    vector_store.create_collection(kb.id, data.embeddingModel)
+    return kb_to_dict(kb)
+
+
+@router.put("/bases/{kb_id}")
+def update_knowledge_base(kb_id: str, data: KnowledgeBaseUpdate, db: Session = Depends(get_db)):
+    kb = db.query(KnowledgeBase).filter(KnowledgeBase.id == kb_id).first()
+    if not kb:
+        raise HTTPException(status_code=404, detail="Knowledge base not found")
+    update_data = data.model_dump(exclude_unset=True)
+    for field, value in update_data.items():
+        setattr(kb, field, value)
+    kb.updated_at = datetime.utcnow()
+    db.commit()
+    db.refresh(kb)
+    return kb_to_dict(kb)
+
+
+@router.delete("/bases/{kb_id}")
+def delete_knowledge_base(kb_id: str, db: Session = Depends(get_db)):
+    kb = db.query(KnowledgeBase).filter(KnowledgeBase.id == kb_id).first()
+    if not kb:
+        raise HTTPException(status_code=404, detail="Knowledge base not found")
+    vector_store.delete_collection(kb_id)
+    docs = db.query(KnowledgeDocument).filter(KnowledgeDocument.kb_id == kb_id).all()
+    for doc in docs:
+        db.delete(doc)
+    db.delete(kb)
+    db.commit()
+    return {"message": "Deleted successfully"}
+
+
+# ============ Documents ============
+@router.post("/bases/{kb_id}/documents")
+def upload_document(
+    kb_id: str,
+    name: str = Query(...),
+    size: str = Query(...),
+    file_type: str = Query("txt"),
+    storage_url: Optional[str] = Query(None),
+    db: Session = Depends(get_db)
+):
+    kb = db.query(KnowledgeBase).filter(KnowledgeBase.id == kb_id).first()
+    if not kb:
+        raise HTTPException(status_code=404, detail="Knowledge base not found")
+    doc = KnowledgeDocument(
+        id=str(uuid.uuid4())[:8],
+        kb_id=kb_id,
+        name=name,
+        size=size,
+        file_type=file_type,
+        storage_url=storage_url,
+        status="pending",
+        upload_date=datetime.utcnow().isoformat()
+    )
+    db.add(doc)
+    db.commit()
+    db.refresh(doc)
+    return {"id": doc.id, "name": doc.name, "status": doc.status, "message": "Document created"}
+
+
+@router.post("/bases/{kb_id}/documents/{doc_id}/index")
+def index_document_content(kb_id: str, doc_id: str, request: DocumentIndexRequest, db: Session = Depends(get_db)):
+    # 检查文档是否存在，不存在则创建
+    doc = db.query(KnowledgeDocument).filter(
+        KnowledgeDocument.id == doc_id,
+        KnowledgeDocument.kb_id == kb_id
+    ).first()
+    
+    if not doc:
+        doc = KnowledgeDocument(
+            id=doc_id,
+            kb_id=kb_id,
+            name=f"doc-{doc_id}.txt",
+            size=str(len(request.content)),
+            file_type="txt",
+            status="pending",
+            upload_date=datetime.utcnow().isoformat()
+        )
+        db.add(doc)
+        db.commit()
+        db.refresh(doc)
+    else:
+        # 更新已有文档
+        doc.size = str(len(request.content))
+        doc.status = "pending"
+        db.commit()
+    
+    try:
+        chunk_count = index_document(kb_id, doc_id, request.content)
+        doc.status = "completed"
+        doc.chunk_count = chunk_count
+        doc.processed_at = datetime.utcnow()
+        kb = db.query(KnowledgeBase).filter(KnowledgeBase.id == kb_id).first()
+        kb.doc_count = db.query(KnowledgeDocument).filter(
+            KnowledgeDocument.kb_id == kb_id,
+            KnowledgeDocument.status == "completed"
+        ).count()
+        kb.chunk_count += chunk_count
+        db.commit()
+        return {"message": "Document indexed", "chunkCount": chunk_count}
+    except Exception as e:
+        doc.status = "failed"
+        doc.error_message = str(e)
+        db.commit()
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.delete("/bases/{kb_id}/documents/{doc_id}")
+def delete_document(kb_id: str, doc_id: str, db: Session = Depends(get_db)):
+    doc = db.query(KnowledgeDocument).filter(
+        KnowledgeDocument.id == doc_id,
+        KnowledgeDocument.kb_id == kb_id
+    ).first()
+    if not doc:
+        raise HTTPException(status_code=404, detail="Document not found")
+    try:
+        delete_document_from_vector(kb_id, doc_id)
+    except Exception:
+        pass
+    kb = db.query(KnowledgeBase).filter(KnowledgeBase.id == kb_id).first()
+    kb.chunk_count -= doc.chunk_count
+    kb.doc_count -= 1
+    db.delete(doc)
+    db.commit()
+    return {"message": "Deleted successfully"}
+
+
+# ============ Search ============
+@router.post("/search")
+def search_knowledge_base(query: KnowledgeSearchQuery):
+    return search_knowledge(kb_id=query.kb_id, query=query.query, n_results=query.nResults)
+
+
+# ============ Stats ============
+@router.get("/bases/{kb_id}/stats")
+def get_knowledge_stats(kb_id: str, db: Session = Depends(get_db)):
+    kb = db.query(KnowledgeBase).filter(KnowledgeBase.id == kb_id).first()
+    if not kb:
+        raise HTTPException(status_code=404, detail="Knowledge base not found")
+    return {"kb_id": kb_id, "docCount": kb.doc_count, "chunkCount": kb.chunk_count}

api/app/schemas.py

@@ -0,0 +1,271 @@
+from datetime import datetime
+from typing import List, Optional
+from pydantic import BaseModel
+
+
+# ============ Voice ============
+class VoiceBase(BaseModel):
+    name: str
+    vendor: str
+    gender: str
+    language: str
+    description: str
+
+
+class VoiceOut(VoiceBase):
+    id: str
+
+    class Config:
+        from_attributes = True
+
+
+# ============ Assistant ============
+class AssistantBase(BaseModel):
+    name: str
+    opener: str = ""
+    prompt: str = ""
+    knowledgeBaseId: Optional[str] = None
+    language: str = "zh"
+    voice: Optional[str] = None
+    speed: float = 1.0
+    hotwords: List[str] = []
+    tools: List[str] = []
+    interruptionSensitivity: int = 500
+    configMode: str = "platform"
+    apiUrl: Optional[str] = None
+    apiKey: Optional[str] = None
+
+
+class AssistantCreate(AssistantBase):
+    pass
+
+
+class AssistantUpdate(AssistantBase):
+    name: Optional[str] = None
+
+
+class AssistantOut(AssistantBase):
+    id: str
+    callCount: int = 0
+    created_at: Optional[datetime] = None
+
+    class Config:
+        from_attributes = True
+
+
+# ============ Knowledge Base ============
+class KnowledgeDocument(BaseModel):
+    id: str
+    name: str
+    size: str
+    fileType: str = "txt"
+    storageUrl: Optional[str] = None
+    status: str = "pending"
+    chunkCount: int = 0
+    uploadDate: str
+
+
+class KnowledgeDocumentCreate(BaseModel):
+    name: str
+    size: str
+    fileType: str = "txt"
+    storageUrl: Optional[str] = None
+
+
+class KnowledgeDocumentUpdate(BaseModel):
+    status: Optional[str] = None
+    chunkCount: Optional[int] = None
+    errorMessage: Optional[str] = None
+
+
+class KnowledgeBaseBase(BaseModel):
+    name: str
+    description: str = ""
+    embeddingModel: str = "text-embedding-3-small"
+    chunkSize: int = 500
+    chunkOverlap: int = 50
+
+
+class KnowledgeBaseCreate(KnowledgeBaseBase):
+    pass
+
+
+class KnowledgeBaseUpdate(BaseModel):
+    name: Optional[str] = None
+    description: Optional[str] = None
+    embeddingModel: Optional[str] = None
+    chunkSize: Optional[int] = None
+    chunkOverlap: Optional[int] = None
+    status: Optional[str] = None
+
+
+class KnowledgeBaseOut(KnowledgeBaseBase):
+    id: str
+    docCount: int = 0
+    chunkCount: int = 0
+    status: str = "active"
+    createdAt: Optional[datetime] = None
+    updatedAt: Optional[datetime] = None
+    documents: List[KnowledgeDocument] = []
+
+    class Config:
+        from_attributes = True
+
+
+# ============ Knowledge Search ============
+class KnowledgeSearchQuery(BaseModel):
+    query: str
+    kb_id: str
+    nResults: int = 5
+
+
+class KnowledgeSearchResult(BaseModel):
+    query: str
+    results: List[dict]
+
+
+class DocumentIndexRequest(BaseModel):
+    document_id: str
+    content: str
+
+
+class KnowledgeStats(BaseModel):
+    kb_id: str
+    docCount: int
+    chunkCount: int
+
+
+# ============ Workflow ============
+class WorkflowNode(BaseModel):
+    name: str
+    type: str
+    isStart: Optional[bool] = None
+    metadata: dict
+    prompt: Optional[str] = None
+    messagePlan: Optional[dict] = None
+    variableExtractionPlan: Optional[dict] = None
+    tool: Optional[dict] = None
+    globalNodePlan: Optional[dict] = None
+
+
+class WorkflowEdge(BaseModel):
+    from_: str
+    to: str
+    label: Optional[str] = None
+
+    class Config:
+        populate_by_name = True
+
+
+class WorkflowBase(BaseModel):
+    name: str
+    nodeCount: int = 0
+    createdAt: str = ""
+    updatedAt: str = ""
+    globalPrompt: Optional[str] = None
+    nodes: List[dict] = []
+    edges: List[dict] = []
+
+
+class WorkflowCreate(WorkflowBase):
+    pass
+
+
+class WorkflowUpdate(BaseModel):
+    name: Optional[str] = None
+    nodeCount: Optional[int] = None
+    nodes: Optional[List[dict]] = None
+    edges: Optional[List[dict]] = None
+    globalPrompt: Optional[str] = None
+
+
+class WorkflowOut(WorkflowBase):
+    id: str
+
+    class Config:
+        from_attributes = True
+
+
+# ============ Call Record ============
+class TranscriptSegment(BaseModel):
+    turnIndex: int
+    speaker: str  # human/ai
+    content: str
+    confidence: Optional[float] = None
+    startMs: int
+    endMs: int
+    durationMs: Optional[int] = None
+    audioUrl: Optional[str] = None
+
+
+class CallRecordCreate(BaseModel):
+    user_id: int
+    assistant_id: Optional[str] = None
+    source: str = "debug"
+
+
+class CallRecordUpdate(BaseModel):
+    status: Optional[str] = None
+    summary: Optional[str] = None
+    duration_seconds: Optional[int] = None
+
+
+class CallRecordOut(BaseModel):
+    id: str
+    user_id: int
+    assistant_id: Optional[str] = None
+    source: str
+    status: str
+    started_at: str
+    ended_at: Optional[str] = None
+    duration_seconds: Optional[int] = None
+    summary: Optional[str] = None
+    transcripts: List[TranscriptSegment] = []
+
+    class Config:
+        from_attributes = True
+
+
+# ============ Call Transcript ============
+class TranscriptCreate(BaseModel):
+    turn_index: int
+    speaker: str
+    content: str
+    confidence: Optional[float] = None
+    start_ms: int
+    end_ms: int
+    duration_ms: Optional[int] = None
+    emotion: Optional[str] = None
+
+
+class TranscriptOut(TranscriptCreate):
+    id: int
+    audio_url: Optional[str] = None
+
+    class Config:
+        from_attributes = True
+
+
+# ============ Dashboard ============
+class DashboardStats(BaseModel):
+    totalCalls: int
+    answerRate: int
+    avgDuration: str
+    humanTransferCount: int
+    trend: List[dict]
+
+
+# ============ API Response ============
+class Message(BaseModel):
+    message: str
+
+
+class DocumentIndexRequest(BaseModel):
+    content: str
+
+
+class ListResponse(BaseModel):
+    total: int
+    page: int
+    limit: int
+    list: List

api/app/storage.py

@@ -0,0 +1,56 @@
+import os
+from datetime import datetime
+from minio import Minio
+import uuid
+
+# MinIO 配置
+MINIO_ENDPOINT = os.getenv("MINIO_ENDPOINT", "localhost:9000")
+MINIO_ACCESS_KEY = os.getenv("MINIO_ACCESS_KEY", "admin")
+MINIO_SECRET_KEY = os.getenv("MINIO_SECRET_KEY", "password123")
+MINIO_BUCKET = os.getenv("MINIO_BUCKET", "ai-audio")
+
+# 初始化客户端
+minio_client = Minio(
+    MINIO_ENDPOINT,
+    access_key=MINIO_ACCESS_KEY,
+    secret_key=MINIO_SECRET_KEY,
+    secure=False
+)
+
+
+def ensure_bucket():
+    """确保 Bucket 存在"""
+    try:
+        if not minio_client.bucket_exists(MINIO_BUCKET):
+            minio_client.make_bucket(MINIO_BUCKET)
+    except Exception as e:
+        print(f"Warning: MinIO bucket check failed: {e}")
+
+
+def upload_audio(file_path: str, call_id: str, turn_index: int) -> str:
+    """上传音频片段，返回访问 URL"""
+    ensure_bucket()
+    
+    ext = os.path.splitext(file_path)[1] or ".mp3"
+    object_name = f"{call_id}/{call_id}-{turn_index:03d}{ext}"
+    
+    try:
+        minio_client.fput_object(MINIO_BUCKET, object_name, file_path)
+        return minio_client.presigned_get_object(MINIO_BUCKET, object_name, expires=604800)
+    except Exception as e:
+        print(f"Warning: MinIO upload failed: {e}")
+        return ""
+
+
+def get_audio_url(call_id: str, turn_index: int) -> str:
+    """获取音频 URL"""
+    object_name = f"{call_id}/{call_id}-{turn_index:03d}.mp3"
+    try:
+        return minio_client.presigned_get_object(MINIO_BUCKET, object_name, expires=604800)
+    except Exception:
+        return ""
+
+
+def generate_local_url(call_id: str, turn_index: int) -> str:
+    """生成本地 URL（如果不用 MinIO）"""
+    return f"/api/history/{call_id}/audio/{turn_index}"

api/app/vector_store.py

@@ -0,0 +1,311 @@
+"""
+向量数据库服务 (ChromaDB)
+"""
+import os
+from typing import List, Dict, Optional
+import chromadb
+from chromadb.config import Settings
+
+# 配置
+VECTOR_STORE_PATH = os.getenv("VECTOR_STORE_PATH", "./data/vector_store")
+COLLECTION_NAME_PREFIX = "kb_"
+
+
+class VectorStore:
+    """向量存储服务"""
+    
+    def __init__(self):
+        os.makedirs(VECTOR_STORE_PATH, exist_ok=True)
+        self.client = chromadb.PersistentClient(
+            path=VECTOR_STORE_PATH,
+            settings=Settings(anonymized_telemetry=False)
+        )
+    
+    def get_collection(self, kb_id: str):
+        """获取知识库集合"""
+        collection_name = f"{COLLECTION_NAME_PREFIX}{kb_id}"
+        try:
+            return self.client.get_collection(name=collection_name)
+        except (ValueError, chromadb.errors.NotFoundError):
+            return None
+    
+    def create_collection(self, kb_id: str, embedding_model: str = "text-embedding-3-small"):
+        """创建知识库向量集合"""
+        collection_name = f"{COLLECTION_NAME_PREFIX}{kb_id}"
+        try:
+            self.client.get_collection(name=collection_name)
+            return collection_name
+        except (ValueError, chromadb.errors.NotFoundError):
+            self.client.create_collection(
+                name=collection_name,
+                metadata={
+                    "kb_id": kb_id,
+                    "embedding_model": embedding_model
+                }
+            )
+            return collection_name
+    
+    def delete_collection(self, kb_id: str):
+        """删除知识库向量集合"""
+        collection_name = f"{COLLECTION_NAME_PREFIX}{kb_id}"
+        try:
+            self.client.delete_collection(name=collection_name)
+            return True
+        except (ValueError, chromadb.errors.NotFoundError):
+            return False
+    
+    def add_documents(
+        self,
+        kb_id: str,
+        documents: List[str],
+        embeddings: Optional[List[List[float]]] = None,
+        ids: Optional[List[str]] = None,
+        metadatas: Optional[List[Dict]] = None
+    ):
+        """添加文档片段到向量库"""
+        collection = self.get_collection(kb_id)
+        
+        if ids is None:
+            ids = [f"chunk-{i}" for i in range(len(documents))]
+        
+        if embeddings is not None:
+            collection.add(
+                documents=documents,
+                embeddings=embeddings,
+                ids=ids,
+                metadatas=metadatas
+            )
+        else:
+            collection.add(
+                documents=documents,
+                ids=ids,
+                metadatas=metadatas
+            )
+        
+        return len(documents)
+    
+    def search(
+        self,
+        kb_id: str,
+        query: str,
+        n_results: int = 5,
+        where: Optional[Dict] = None
+    ) -> Dict:
+        """检索相似文档"""
+        collection = self.get_collection(kb_id)
+        
+        # 生成查询向量
+        query_embedding = embedding_service.embed_query(query)
+        
+        results = collection.query(
+            query_embeddings=[query_embedding],
+            n_results=n_results,
+            where=where
+        )
+        
+        return results
+    
+    def get_stats(self, kb_id: str) -> Dict:
+        """获取向量库统计"""
+        collection = self.get_collection(kb_id)
+        return {
+            "count": collection.count(),
+            "kb_id": kb_id
+        }
+    
+    def delete_documents(self, kb_id: str, ids: List[str]):
+        """删除指定文档片段"""
+        collection = self.get_collection(kb_id)
+        collection.delete(ids=ids)
+    
+    def delete_by_metadata(self, kb_id: str, document_id: str):
+        """根据文档 ID 删除所有片段"""
+        collection = self.get_collection(kb_id)
+        results = collection.get(where={"document_id": document_id})
+        if results["ids"]:
+            collection.delete(ids=results["ids"])
+
+
+class EmbeddingService:
+    """ embedding 服务（支持多种模型）"""
+    
+    def __init__(self, model: str = "text-embedding-3-small"):
+        self.model = model
+        self._client = None
+    
+    def _get_client(self):
+        """获取 OpenAI 客户端"""
+        if self._client is None:
+            try:
+                from openai import OpenAI
+                api_key = os.getenv("OPENAI_API_KEY")
+                if api_key:
+                    self._client = OpenAI(api_key=api_key)
+            except ImportError:
+                pass
+        return self._client
+    
+    def embed(self, texts: List[str]) -> List[List[float]]:
+        """生成 embedding 向量"""
+        client = self._get_client()
+        
+        if client is None:
+            # 返回随机向量（仅用于测试）
+            import random
+            import math
+            dim = 1536 if "3-small" in self.model else 1024
+            return [[random.uniform(-1, 1) for _ in range(dim)] for _ in texts]
+        
+        response = client.embeddings.create(
+            model=self.model,
+            input=texts
+        )
+        return [data.embedding for data in response.data]
+    
+    def embed_query(self, query: str) -> List[float]:
+        """生成查询向量"""
+        return self.embed([query])[0]
+
+
+class DocumentProcessor:
+    """文档处理服务"""
+    
+    def __init__(self, chunk_size: int = 500, chunk_overlap: int = 50):
+        self.chunk_size = chunk_size
+        self.chunk_overlap = chunk_overlap
+    
+    def chunk_text(self, text: str, document_id: str = "") -> List[Dict]:
+        """将文本分块"""
+        # 简单分块（按句子/段落）
+        import re
+        
+        # 按句子分割
+        sentences = re.split(r'[。！？\n]', text)
+        
+        chunks = []
+        current_chunk = ""
+        current_size = 0
+        
+        for i, sentence in enumerate(sentences):
+            sentence = sentence.strip()
+            if not sentence:
+                continue
+            
+            sentence_len = len(sentence)
+            
+            if current_size + sentence_len > self.chunk_size and current_chunk:
+                # 保存当前块
+                chunks.append({
+                    "content": current_chunk.strip(),
+                    "document_id": document_id,
+                    "chunk_index": len(chunks),
+                    "metadata": {
+                        "source": "text"
+                    }
+                })
+                
+                # 处理重叠
+                if self.chunk_overlap > 0:
+                    # 保留末尾部分
+                    overlap_chars = current_chunk[-self.chunk_overlap:]
+                    current_chunk = overlap_chars + " " + sentence
+                    current_size = len(overlap_chars) + sentence_len + 1
+                else:
+                    current_chunk = sentence
+                    current_size = sentence_len
+            else:
+                if current_chunk:
+                    current_chunk += " "
+                current_chunk += sentence
+                current_size += sentence_len + 1
+        
+        # 保存最后一个块
+        if current_chunk.strip():
+            chunks.append({
+                "content": current_chunk.strip(),
+                "document_id": document_id,
+                "chunk_index": len(chunks),
+                "metadata": {
+                    "source": "text"
+                }
+            })
+        
+        return chunks
+    
+    def process_document(self, text: str, document_id: str = "") -> List[Dict]:
+        """完整处理文档"""
+        return self.chunk_text(text, document_id)
+
+
+# 全局实例
+vector_store = VectorStore()
+embedding_service = EmbeddingService()
+
+
+def search_knowledge(kb_id: str, query: str, n_results: int = 5) -> Dict:
+    """知识库检索"""
+    # 生成查询向量
+    query_vector = embedding_service.embed_query(query)
+    
+    # 检索
+    results = vector_store.search(
+        kb_id=kb_id,
+        query=query,
+        n_results=n_results
+    )
+    
+    return {
+        "query": query,
+        "results": [
+            {
+                "content": doc,
+                "metadata": meta,
+                "distance": dist
+            }
+            for doc, meta, dist in zip(
+                results.get("documents", [[]])[0] if results.get("documents") else [],
+                results.get("metadatas", [[]])[0] if results.get("metadatas") else [],
+                results.get("distances", [[]])[0] if results.get("distances") else []
+            )
+        ]
+    }
+
+
+def index_document(kb_id: str, document_id: str, text: str) -> int:
+    """索引文档到向量库"""
+    # 分块
+    processor = DocumentProcessor()
+    chunks = processor.process_document(text, document_id)
+    
+    if not chunks:
+        return 0
+    
+    # 生成向量
+    contents = [c["content"] for c in chunks]
+    embeddings = embedding_service.embed(contents)
+    
+    # 添加到向量库
+    ids = [f"{document_id}-{c['chunk_index']}" for c in chunks]
+    metadatas = [
+        {
+            "document_id": c["document_id"],
+            "chunk_index": c["chunk_index"],
+            "kb_id": kb_id
+        }
+        for c in chunks
+    ]
+    
+    vector_store.add_documents(
+        kb_id=kb_id,
+        documents=contents,
+        embeddings=embeddings,
+        ids=ids,
+        metadatas=metadatas
+    )
+    
+    return len(chunks)
+
+
+def delete_document_from_vector(kb_id: str, document_id: str):
+    """从向量库删除文档"""
+    vector_store.delete_by_metadata(kb_id, document_id)

api/init_db.py

@@ -0,0 +1,52 @@
+#!/usr/bin/env python3
+"""初始化数据库"""
+import os
+import sys
+
+# 添加路径
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+from app.db import Base, engine
+from app.models import Voice
+
+
+def init_db():
+    """创建所有表"""
+    print("📦 创建数据库表...")
+    Base.metadata.drop_all(bind=engine)  # 删除旧表
+    Base.metadata.create_all(bind=engine)
+    print("✅ 数据库表创建完成")
+
+
+def init_default_voices():
+    """初始化默认声音"""
+    from app.db import SessionLocal
+    
+    db = SessionLocal()
+    try:
+        if db.query(Voice).count() == 0:
+            voices = [
+                Voice(id="v1", name="Xiaoyun", vendor="Ali", gender="Female", language="zh", description="Gentle and professional."),
+                Voice(id="v2", name="Kevin", vendor="Volcano", gender="Male", language="en", description="Deep and authoritative."),
+                Voice(id="v3", name="Abby", vendor="Minimax", gender="Female", language="en", description="Cheerful and lively."),
+                Voice(id="v4", name="Guang", vendor="Ali", gender="Male", language="zh", description="Standard newscast style."),
+                Voice(id="v5", name="Doubao", vendor="Volcano", gender="Female", language="zh", description="Cute and young."),
+            ]
+            for v in voices:
+                db.add(v)
+            db.commit()
+            print("✅ 默认声音数据已初始化")
+        else:
+            print("ℹ️ 声音数据已存在，跳过初始化")
+    finally:
+        db.close()
+
+
+if __name__ == "__main__":
+    # 确保 data 目录存在
+    data_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "data")
+    os.makedirs(data_dir, exist_ok=True)
+    
+    init_db()
+    init_default_voices()
+    print("🎉 数据库初始化完成！")

api/main.py

@@ -0,0 +1,73 @@
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from contextlib import asynccontextmanager
+import os
+
+from app.db import Base, engine
+from app.routers import assistants, history, knowledge
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    # 启动时创建表
+    Base.metadata.create_all(bind=engine)
+    yield
+
+
+app = FastAPI(
+    title="AI VideoAssistant API",
+    description="Backend API for AI VideoAssistant",
+    version="1.0.0",
+    lifespan=lifespan
+)
+
+# CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# 路由
+app.include_router(assistants.router, prefix="/api")
+app.include_router(history.router, prefix="/api")
+app.include_router(knowledge.router, prefix="/api")
+
+
+@app.get("/")
+def root():
+    return {"message": "AI VideoAssistant API", "version": "1.0.0"}
+
+
+@app.get("/health")
+def health():
+    return {"status": "ok"}
+
+
+# 初始化默认数据
+@app.on_event("startup")
+def init_default_data():
+    from sqlalchemy.orm import Session
+    from app.db import SessionLocal
+    from app.models import Voice
+    
+    db = SessionLocal()
+    try:
+        # 检查是否已有数据
+        if db.query(Voice).count() == 0:
+            # 插入默认声音
+            voices = [
+                Voice(id="v1", name="Xiaoyun", vendor="Ali", gender="Female", language="zh", description="Gentle and professional."),
+                Voice(id="v2", name="Kevin", vendor="Volcano", gender="Male", language="en", description="Deep and authoritative."),
+                Voice(id="v3", name="Abby", vendor="Minimax", gender="Female", language="en", description="Cheerful and lively."),
+                Voice(id="v4", name="Guang", vendor="Ali", gender="Male", language="zh", description="Standard newscast style."),
+                Voice(id="v5", name="Doubao", vendor="Volcano", gender="Female", language="zh", description="Cute and young."),
+            ]
+            for v in voices:
+                db.add(v)
+            db.commit()
+            print("✅ 默认声音数据已初始化")
+    finally:
+        db.close()

api/requirements.txt

@@ -0,0 +1,11 @@
+aiosqlite==0.19.0
+fastapi==0.109.0
+uvicorn==0.27.0
+python-multipart==0.0.6
+python-dotenv==1.0.0
+pydantic==2.5.3
+sqlalchemy==2.0.25
+minio==7.2.0
+httpx==0.26.0
+chromadb==0.4.22
+openai==1.12.0

docker/docker-compose.yml

@@ -0,0 +1,49 @@
+version: '3.8'
+
+services:
+  # 后端 API
+  backend:
+    build:
+      context: ./backend
+      dockerfile: Dockerfile
+    ports:
+      - "8000:8000"
+    environment:
+      - DATABASE_URL=sqlite:///./data/app.db
+      - MINIO_ENDPOINT=minio:9000
+      - MINIO_ACCESS_KEY=admin
+      - MINIO_SECRET_KEY=password123
+      - MINIO_BUCKET=ai-audio
+    volumes:
+      - ./backend:/app
+      - ./backend/data:/app/data
+    depends_on:
+      - minio
+
+  # 对话引擎 (py-active-call)
+  engine:
+    build:
+      context: ../py-active-call
+      dockerfile: Dockerfile
+    ports:
+      - "8001:8001"
+    environment:
+      - BACKEND_URL=http://backend:8000
+    depends_on:
+      - backend
+
+  # MinIO (S3 兼容存储)
+  minio:
+    image: minio/minio
+    ports:
+      - "9000:9000"
+      - "9001:9001"
+    volumes:
+      - ./storage/minio/data:/data
+    environment:
+      MINIO_ROOT_USER: admin
+      MINIO_ROOT_PASSWORD: password123
+    command: server /data --console-address ":9001"
+
+volumes:
+  minio-data:

engine/.gitignore

@@ -0,0 +1,148 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+Pipfile.lock
+
+# poetry
+poetry.lock
+
+# pdm
+.pdm.toml
+
+# PEP 582
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# IDEs
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# Project specific
+recordings/
+logs/
+running/

engine/README.md

@@ -0,0 +1,25 @@
+# py-active-call-cc
+
+Python Active-Call: real-time audio streaming with WebSocket and WebRTC.
+
+This repo contains a Python 3.11+ codebase for building low-latency voice
+pipelines (capture, stream, and process audio) using WebRTC and WebSockets.
+It is currently in an early, experimental stage.
+
+# Usage
+
+启动
+
+```
+uvicorn app.main:app --reload --host 0.0.0.0 --port 8000
+```
+
+测试
+
+```
+python examples/test_websocket.py
+```
+
+```
+python mic_client.py
+```

engine/app/__init__.py

@@ -0,0 +1 @@
+"""Active-Call Application Package"""

engine/app/config.py

@@ -0,0 +1,120 @@
+"""Configuration management using Pydantic settings."""
+
+from typing import List, Optional
+from pydantic import Field
+from pydantic_settings import BaseSettings, SettingsConfigDict
+import json
+
+
+class Settings(BaseSettings):
+    """Application settings loaded from environment variables."""
+
+    model_config = SettingsConfigDict(
+        env_file=".env",
+        env_file_encoding="utf-8",
+        case_sensitive=False,
+        extra="ignore"
+    )
+
+    # Server Configuration
+    host: str = Field(default="0.0.0.0", description="Server host address")
+    port: int = Field(default=8000, description="Server port")
+    external_ip: Optional[str] = Field(default=None, description="External IP for NAT traversal")
+
+    # Audio Configuration
+    sample_rate: int = Field(default=16000, description="Audio sample rate in Hz")
+    chunk_size_ms: int = Field(default=20, description="Audio chunk duration in milliseconds")
+    default_codec: str = Field(default="pcm", description="Default audio codec")
+
+    # VAD Configuration
+    vad_type: str = Field(default="silero", description="VAD algorithm type")
+    vad_model_path: str = Field(default="data/vad/silero_vad.onnx", description="Path to VAD model")
+    vad_threshold: float = Field(default=0.5, description="VAD detection threshold")
+    vad_min_speech_duration_ms: int = Field(default=250, description="Minimum speech duration in milliseconds")
+    vad_eou_threshold_ms: int = Field(default=800, description="End of utterance (silence) threshold in milliseconds")
+
+    # OpenAI / LLM Configuration
+    openai_api_key: Optional[str] = Field(default=None, description="OpenAI API key")
+    openai_api_url: Optional[str] = Field(default=None, description="OpenAI API base URL (for Azure/compatible)")
+    llm_model: str = Field(default="gpt-4o-mini", description="LLM model name")
+    llm_temperature: float = Field(default=0.7, description="LLM temperature for response generation")
+    
+    # TTS Configuration
+    tts_provider: str = Field(default="siliconflow", description="TTS provider (edge, siliconflow)")
+    tts_voice: str = Field(default="anna", description="TTS voice name")
+    tts_speed: float = Field(default=1.0, description="TTS speech speed multiplier")
+    
+    # SiliconFlow Configuration
+    siliconflow_api_key: Optional[str] = Field(default=None, description="SiliconFlow API key")
+    siliconflow_tts_model: str = Field(default="FunAudioLLM/CosyVoice2-0.5B", description="SiliconFlow TTS model")
+    
+    # ASR Configuration
+    asr_provider: str = Field(default="siliconflow", description="ASR provider (siliconflow, buffered)")
+    siliconflow_asr_model: str = Field(default="FunAudioLLM/SenseVoiceSmall", description="SiliconFlow ASR model")
+    asr_interim_interval_ms: int = Field(default=500, description="Interval for interim ASR results in ms")
+    asr_min_audio_ms: int = Field(default=300, description="Minimum audio duration before first ASR result")
+    
+    # Duplex Pipeline Configuration
+    duplex_enabled: bool = Field(default=True, description="Enable duplex voice pipeline")
+    duplex_greeting: Optional[str] = Field(default=None, description="Optional greeting message")
+    duplex_system_prompt: Optional[str] = Field(
+        default="You are a helpful, friendly voice assistant. Keep your responses concise and conversational.",
+        description="System prompt for LLM"
+    )
+    
+    # Barge-in (interruption) Configuration
+    barge_in_min_duration_ms: int = Field(
+        default=200, 
+        description="Minimum speech duration (ms) required to trigger barge-in. Lower=more sensitive."
+    )
+
+    # Logging
+    log_level: str = Field(default="INFO", description="Logging level")
+    log_format: str = Field(default="json", description="Log format (json or text)")
+
+    # CORS
+    cors_origins: str = Field(
+        default='["http://localhost:3000", "http://localhost:8080"]',
+        description="CORS allowed origins"
+    )
+
+    # ICE Servers (WebRTC)
+    ice_servers: str = Field(
+        default='[{"urls": "stun:stun.l.google.com:19302"}]',
+        description="ICE servers configuration"
+    )
+
+    # WebSocket heartbeat and inactivity
+    inactivity_timeout_sec: int = Field(default=60, description="Close connection after no message from client (seconds)")
+    heartbeat_interval_sec: int = Field(default=50, description="Send heartBeat event to client every N seconds")
+
+    @property
+    def chunk_size_bytes(self) -> int:
+        """Calculate chunk size in bytes based on sample rate and duration."""
+        # 16-bit (2 bytes) per sample, mono channel
+        return int(self.sample_rate * 2 * (self.chunk_size_ms / 1000.0))
+
+    @property
+    def cors_origins_list(self) -> List[str]:
+        """Parse CORS origins from JSON string."""
+        try:
+            return json.loads(self.cors_origins)
+        except json.JSONDecodeError:
+            return ["http://localhost:3000", "http://localhost:8080"]
+
+    @property
+    def ice_servers_list(self) -> List[dict]:
+        """Parse ICE servers from JSON string."""
+        try:
+            return json.loads(self.ice_servers)
+        except json.JSONDecodeError:
+            return [{"urls": "stun:stun.l.google.com:19302"}]
+
+
+# Global settings instance
+settings = Settings()
+
+
+def get_settings() -> Settings:
+    """Get application settings instance."""
+    return settings

engine/app/main.py

@@ -0,0 +1,390 @@
+"""FastAPI application with WebSocket and WebRTC endpoints."""
+
+import asyncio
+import json
+import time
+import uuid
+from pathlib import Path
+from typing import Dict, Any, Optional, List
+from fastapi import FastAPI, WebSocket, WebSocketDisconnect, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse, FileResponse
+from loguru import logger
+
+# Try to import aiortc (optional for WebRTC functionality)
+try:
+    from aiortc import RTCPeerConnection, RTCSessionDescription
+    AIORTC_AVAILABLE = True
+except ImportError:
+    AIORTC_AVAILABLE = False
+    logger.warning("aiortc not available - WebRTC endpoint will be disabled")
+
+from app.config import settings
+from core.transports import SocketTransport, WebRtcTransport, BaseTransport
+from core.session import Session
+from processors.tracks import Resampled16kTrack
+from core.events import get_event_bus, reset_event_bus
+
+# Check interval for heartbeat/timeout (seconds)
+_HEARTBEAT_CHECK_INTERVAL_SEC = 5
+
+
+async def heartbeat_and_timeout_task(
+    transport: BaseTransport,
+    session: Session,
+    session_id: str,
+    last_received_at: List[float],
+    last_heartbeat_at: List[float],
+    inactivity_timeout_sec: int,
+    heartbeat_interval_sec: int,
+) -> None:
+    """
+    Background task: send heartBeat every ~heartbeat_interval_sec and close
+    connection if no message from client for inactivity_timeout_sec.
+    """
+    while True:
+        await asyncio.sleep(_HEARTBEAT_CHECK_INTERVAL_SEC)
+        if transport.is_closed:
+            break
+        now = time.monotonic()
+        if now - last_received_at[0] > inactivity_timeout_sec:
+            logger.info(f"Session {session_id}: {inactivity_timeout_sec}s no message, closing")
+            await session.cleanup()
+            break
+        if now - last_heartbeat_at[0] >= heartbeat_interval_sec:
+            try:
+                await transport.send_event({
+                    "event": "heartBeat",
+                    "timestamp": int(time.time() * 1000),
+                })
+                last_heartbeat_at[0] = now
+            except Exception as e:
+                logger.debug(f"Session {session_id}: heartbeat send failed: {e}")
+                break
+
+
+# Initialize FastAPI
+app = FastAPI(title="Python Active-Call", version="0.1.0")
+_WEB_CLIENT_PATH = Path(__file__).resolve().parent.parent / "examples" / "web_client.html"
+
+# Configure CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=settings.cors_origins_list,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Active sessions storage
+active_sessions: Dict[str, Session] = {}
+
+# Configure logging
+logger.remove()
+logger.add(
+    "./logs/active_call_{time}.log",
+    rotation="1 day",
+    retention="7 days",
+    level=settings.log_level,
+    format="{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {name}:{function}:{line} - {message}"
+)
+logger.add(
+    lambda msg: print(msg, end=""),
+    level=settings.log_level,
+    format="{time:HH:mm:ss} | {level: <8} | {message}"
+)
+
+
+@app.get("/health")
+async def health_check():
+    """Health check endpoint."""
+    return {"status": "healthy", "sessions": len(active_sessions)}
+
+
+@app.get("/")
+async def web_client_root():
+    """Serve the web client."""
+    if not _WEB_CLIENT_PATH.exists():
+        raise HTTPException(status_code=404, detail="Web client not found")
+    return FileResponse(_WEB_CLIENT_PATH)
+
+
+@app.get("/client")
+async def web_client_alias():
+    """Alias for the web client."""
+    if not _WEB_CLIENT_PATH.exists():
+        raise HTTPException(status_code=404, detail="Web client not found")
+    return FileResponse(_WEB_CLIENT_PATH)
+
+
+
+
+@app.get("/iceservers")
+async def get_ice_servers():
+    """Get ICE servers configuration for WebRTC."""
+    return settings.ice_servers_list
+
+
+@app.get("/call/lists")
+async def list_calls():
+    """List all active calls."""
+    return {
+        "calls": [
+            {
+                "id": session_id,
+                "state": session.state,
+                "created_at": session.created_at
+            }
+            for session_id, session in active_sessions.items()
+        ]
+    }
+
+
+@app.post("/call/kill/{session_id}")
+async def kill_call(session_id: str):
+    """Kill a specific active call."""
+    if session_id not in active_sessions:
+        raise HTTPException(status_code=404, detail="Session not found")
+
+    session = active_sessions[session_id]
+    await session.cleanup()
+    del active_sessions[session_id]
+
+    return True
+
+
+@app.websocket("/ws")
+async def websocket_endpoint(websocket: WebSocket):
+    """
+    WebSocket endpoint for raw audio streaming.
+
+    Accepts mixed text/binary frames:
+    - Text frames: JSON commands
+    - Binary frames: PCM audio data (16kHz, 16-bit, mono)
+    """
+    await websocket.accept()
+    session_id = str(uuid.uuid4())
+
+    # Create transport and session
+    transport = SocketTransport(websocket)
+    session = Session(session_id, transport)
+    active_sessions[session_id] = session
+
+    logger.info(f"WebSocket connection established: {session_id}")
+
+    last_received_at: List[float] = [time.monotonic()]
+    last_heartbeat_at: List[float] = [0.0]
+    hb_task = asyncio.create_task(
+        heartbeat_and_timeout_task(
+            transport,
+            session,
+            session_id,
+            last_received_at,
+            last_heartbeat_at,
+            settings.inactivity_timeout_sec,
+            settings.heartbeat_interval_sec,
+        )
+    )
+
+    try:
+        # Receive loop
+        while True:
+            message = await websocket.receive()
+            last_received_at[0] = time.monotonic()
+
+            # Handle binary audio data
+            if "bytes" in message:
+                await session.handle_audio(message["bytes"])
+
+            # Handle text commands
+            elif "text" in message:
+                await session.handle_text(message["text"])
+
+    except WebSocketDisconnect:
+        logger.info(f"WebSocket disconnected: {session_id}")
+
+    except Exception as e:
+        logger.error(f"WebSocket error: {e}", exc_info=True)
+
+    finally:
+        hb_task.cancel()
+        try:
+            await hb_task
+        except asyncio.CancelledError:
+            pass
+        # Cleanup session
+        if session_id in active_sessions:
+            await session.cleanup()
+            del active_sessions[session_id]
+
+        logger.info(f"Session {session_id} removed")
+
+
+@app.websocket("/webrtc")
+async def webrtc_endpoint(websocket: WebSocket):
+    """
+    WebRTC endpoint for WebRTC audio streaming.
+
+    Uses WebSocket for signaling (SDP exchange) and WebRTC for media transport.
+    """
+    # Check if aiortc is available
+    if not AIORTC_AVAILABLE:
+        await websocket.close(code=1011, reason="WebRTC not available - aiortc/av not installed")
+        logger.warning("WebRTC connection attempted but aiortc is not available")
+        return
+    await websocket.accept()
+    session_id = str(uuid.uuid4())
+
+    # Create WebRTC peer connection
+    pc = RTCPeerConnection()
+
+    # Create transport and session
+    transport = WebRtcTransport(websocket, pc)
+    session = Session(session_id, transport)
+    active_sessions[session_id] = session
+
+    logger.info(f"WebRTC connection established: {session_id}")
+
+    last_received_at: List[float] = [time.monotonic()]
+    last_heartbeat_at: List[float] = [0.0]
+    hb_task = asyncio.create_task(
+        heartbeat_and_timeout_task(
+            transport,
+            session,
+            session_id,
+            last_received_at,
+            last_heartbeat_at,
+            settings.inactivity_timeout_sec,
+            settings.heartbeat_interval_sec,
+        )
+    )
+
+    # Track handler for incoming audio
+    @pc.on("track")
+    def on_track(track):
+        logger.info(f"Track received: {track.kind}")
+
+        if track.kind == "audio":
+            # Wrap track with resampler
+            wrapped_track = Resampled16kTrack(track)
+
+            # Create task to pull audio from track
+            async def pull_audio():
+                try:
+                    while True:
+                        frame = await wrapped_track.recv()
+                        # Convert frame to bytes
+                        pcm_bytes = frame.to_ndarray().tobytes()
+                        # Feed to session
+                        await session.handle_audio(pcm_bytes)
+                except Exception as e:
+                    logger.error(f"Error pulling audio from track: {e}")
+
+            asyncio.create_task(pull_audio())
+
+    @pc.on("connectionstatechange")
+    async def on_connectionstatechange():
+        logger.info(f"Connection state: {pc.connectionState}")
+        if pc.connectionState == "failed" or pc.connectionState == "closed":
+            await session.cleanup()
+
+    try:
+        # Signaling loop
+        while True:
+            message = await websocket.receive()
+
+            if "text" not in message:
+                continue
+
+            last_received_at[0] = time.monotonic()
+            data = json.loads(message["text"])
+
+            # Handle SDP offer/answer
+            if "sdp" in data and "type" in data:
+                logger.info(f"Received SDP {data['type']}")
+
+                # Set remote description
+                offer = RTCSessionDescription(sdp=data["sdp"], type=data["type"])
+                await pc.setRemoteDescription(offer)
+
+                # Create and set local description
+                if data["type"] == "offer":
+                    answer = await pc.createAnswer()
+                    await pc.setLocalDescription(answer)
+
+                    # Send answer back
+                    await websocket.send_text(json.dumps({
+                        "event": "answer",
+                        "trackId": session_id,
+                        "timestamp": int(asyncio.get_event_loop().time() * 1000),
+                        "sdp": pc.localDescription.sdp
+                    }))
+
+                    logger.info(f"Sent SDP answer")
+
+            else:
+                # Handle other commands
+                await session.handle_text(message["text"])
+
+    except WebSocketDisconnect:
+        logger.info(f"WebRTC WebSocket disconnected: {session_id}")
+
+    except Exception as e:
+        logger.error(f"WebRTC error: {e}", exc_info=True)
+
+    finally:
+        hb_task.cancel()
+        try:
+            await hb_task
+        except asyncio.CancelledError:
+            pass
+        # Cleanup
+        await pc.close()
+        if session_id in active_sessions:
+            await session.cleanup()
+            del active_sessions[session_id]
+
+        logger.info(f"WebRTC session {session_id} removed")
+
+
+@app.on_event("startup")
+async def startup_event():
+    """Run on application startup."""
+    logger.info("Starting Python Active-Call server")
+    logger.info(f"Server: {settings.host}:{settings.port}")
+    logger.info(f"Sample rate: {settings.sample_rate} Hz")
+    logger.info(f"VAD model: {settings.vad_model_path}")
+
+
+@app.on_event("shutdown")
+async def shutdown_event():
+    """Run on application shutdown."""
+    logger.info("Shutting down Python Active-Call server")
+
+    # Cleanup all sessions
+    for session_id, session in active_sessions.items():
+        await session.cleanup()
+
+    # Close event bus
+    event_bus = get_event_bus()
+    await event_bus.close()
+    reset_event_bus()
+
+    logger.info("Server shutdown complete")
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    # Create logs directory
+    import os
+    os.makedirs("logs", exist_ok=True)
+
+    # Run server
+    uvicorn.run(
+        "app.main:app",
+        host=settings.host,
+        port=settings.port,
+        reload=True,
+        log_level=settings.log_level.lower()
+    )

engine/core/__init__.py

@@ -0,0 +1,20 @@
+"""Core Components Package"""
+
+from core.events import EventBus, get_event_bus
+from core.transports import BaseTransport, SocketTransport, WebRtcTransport
+from core.session import Session
+from core.conversation import ConversationManager, ConversationState, ConversationTurn
+from core.duplex_pipeline import DuplexPipeline
+
+__all__ = [
+    "EventBus",
+    "get_event_bus",
+    "BaseTransport",
+    "SocketTransport",
+    "WebRtcTransport",
+    "Session",
+    "ConversationManager",
+    "ConversationState",
+    "ConversationTurn",
+    "DuplexPipeline",
+]

engine/core/conversation.py

@@ -0,0 +1,255 @@
+"""Conversation management for voice AI.
+
+Handles conversation context, turn-taking, and message history
+for multi-turn voice conversations.
+"""
+
+import asyncio
+from typing import List, Optional, Dict, Any, Callable, Awaitable
+from dataclasses import dataclass, field
+from enum import Enum
+from loguru import logger
+
+from services.base import LLMMessage
+
+
+class ConversationState(Enum):
+    """State of the conversation."""
+    IDLE = "idle"                    # Waiting for user input
+    LISTENING = "listening"          # User is speaking
+    PROCESSING = "processing"        # Processing user input (LLM)
+    SPEAKING = "speaking"            # Bot is speaking
+    INTERRUPTED = "interrupted"      # Bot was interrupted
+
+
+@dataclass
+class ConversationTurn:
+    """A single turn in the conversation."""
+    role: str  # "user" or "assistant"
+    text: str
+    audio_duration_ms: Optional[int] = None
+    timestamp: float = field(default_factory=lambda: asyncio.get_event_loop().time())
+    was_interrupted: bool = False
+
+
+class ConversationManager:
+    """
+    Manages conversation state and history.
+    
+    Provides:
+    - Message history for LLM context
+    - Turn management
+    - State tracking
+    - Event callbacks for state changes
+    """
+    
+    def __init__(
+        self,
+        system_prompt: Optional[str] = None,
+        max_history: int = 20,
+        greeting: Optional[str] = None
+    ):
+        """
+        Initialize conversation manager.
+        
+        Args:
+            system_prompt: System prompt for LLM
+            max_history: Maximum number of turns to keep
+            greeting: Optional greeting message when conversation starts
+        """
+        self.system_prompt = system_prompt or (
+            "You are a helpful, friendly voice assistant. "
+            "Keep your responses concise and conversational. "
+            "Respond naturally as if having a phone conversation. "
+            "If you don't understand something, ask for clarification."
+        )
+        self.max_history = max_history
+        self.greeting = greeting
+        
+        # State
+        self.state = ConversationState.IDLE
+        self.turns: List[ConversationTurn] = []
+        
+        # Callbacks
+        self._state_callbacks: List[Callable[[ConversationState, ConversationState], Awaitable[None]]] = []
+        self._turn_callbacks: List[Callable[[ConversationTurn], Awaitable[None]]] = []
+        
+        # Current turn tracking
+        self._current_user_text: str = ""
+        self._current_assistant_text: str = ""
+        
+        logger.info("ConversationManager initialized")
+    
+    def on_state_change(
+        self,
+        callback: Callable[[ConversationState, ConversationState], Awaitable[None]]
+    ) -> None:
+        """Register callback for state changes."""
+        self._state_callbacks.append(callback)
+    
+    def on_turn_complete(
+        self,
+        callback: Callable[[ConversationTurn], Awaitable[None]]
+    ) -> None:
+        """Register callback for turn completion."""
+        self._turn_callbacks.append(callback)
+    
+    async def set_state(self, new_state: ConversationState) -> None:
+        """Set conversation state and notify listeners."""
+        if new_state != self.state:
+            old_state = self.state
+            self.state = new_state
+            logger.debug(f"Conversation state: {old_state.value} -> {new_state.value}")
+            
+            for callback in self._state_callbacks:
+                try:
+                    await callback(old_state, new_state)
+                except Exception as e:
+                    logger.error(f"State callback error: {e}")
+    
+    def get_messages(self) -> List[LLMMessage]:
+        """
+        Get conversation history as LLM messages.
+        
+        Returns:
+            List of LLMMessage objects including system prompt
+        """
+        messages = [LLMMessage(role="system", content=self.system_prompt)]
+        
+        # Add conversation history
+        for turn in self.turns[-self.max_history:]:
+            messages.append(LLMMessage(role=turn.role, content=turn.text))
+        
+        # Add current user text if any
+        if self._current_user_text:
+            messages.append(LLMMessage(role="user", content=self._current_user_text))
+        
+        return messages
+    
+    async def start_user_turn(self) -> None:
+        """Signal that user has started speaking."""
+        await self.set_state(ConversationState.LISTENING)
+        self._current_user_text = ""
+    
+    async def update_user_text(self, text: str, is_final: bool = False) -> None:
+        """
+        Update current user text (from ASR).
+        
+        Args:
+            text: Transcribed text
+            is_final: Whether this is the final transcript
+        """
+        self._current_user_text = text
+    
+    async def end_user_turn(self, text: str) -> None:
+        """
+        End user turn and add to history.
+        
+        Args:
+            text: Final user text
+        """
+        if text.strip():
+            turn = ConversationTurn(role="user", text=text.strip())
+            self.turns.append(turn)
+            
+            for callback in self._turn_callbacks:
+                try:
+                    await callback(turn)
+                except Exception as e:
+                    logger.error(f"Turn callback error: {e}")
+            
+            logger.info(f"User: {text[:50]}...")
+        
+        self._current_user_text = ""
+        await self.set_state(ConversationState.PROCESSING)
+    
+    async def start_assistant_turn(self) -> None:
+        """Signal that assistant has started speaking."""
+        await self.set_state(ConversationState.SPEAKING)
+        self._current_assistant_text = ""
+    
+    async def update_assistant_text(self, text: str) -> None:
+        """
+        Update current assistant text (streaming).
+        
+        Args:
+            text: Text chunk from LLM
+        """
+        self._current_assistant_text += text
+    
+    async def end_assistant_turn(self, was_interrupted: bool = False) -> None:
+        """
+        End assistant turn and add to history.
+        
+        Args:
+            was_interrupted: Whether the turn was interrupted by user
+        """
+        text = self._current_assistant_text.strip()
+        if text:
+            turn = ConversationTurn(
+                role="assistant",
+                text=text,
+                was_interrupted=was_interrupted
+            )
+            self.turns.append(turn)
+            
+            for callback in self._turn_callbacks:
+                try:
+                    await callback(turn)
+                except Exception as e:
+                    logger.error(f"Turn callback error: {e}")
+            
+            status = " (interrupted)" if was_interrupted else ""
+            logger.info(f"Assistant{status}: {text[:50]}...")
+        
+        self._current_assistant_text = ""
+        
+        if was_interrupted:
+            await self.set_state(ConversationState.INTERRUPTED)
+        else:
+            await self.set_state(ConversationState.IDLE)
+    
+    async def interrupt(self) -> None:
+        """Handle interruption (barge-in)."""
+        if self.state == ConversationState.SPEAKING:
+            await self.end_assistant_turn(was_interrupted=True)
+    
+    def reset(self) -> None:
+        """Reset conversation history."""
+        self.turns = []
+        self._current_user_text = ""
+        self._current_assistant_text = ""
+        self.state = ConversationState.IDLE
+        logger.info("Conversation reset")
+    
+    @property
+    def turn_count(self) -> int:
+        """Get number of turns in conversation."""
+        return len(self.turns)
+    
+    @property
+    def last_user_text(self) -> Optional[str]:
+        """Get last user text."""
+        for turn in reversed(self.turns):
+            if turn.role == "user":
+                return turn.text
+        return None
+    
+    @property
+    def last_assistant_text(self) -> Optional[str]:
+        """Get last assistant text."""
+        for turn in reversed(self.turns):
+            if turn.role == "assistant":
+                return turn.text
+        return None
+    
+    def get_context_summary(self) -> Dict[str, Any]:
+        """Get a summary of conversation context."""
+        return {
+            "state": self.state.value,
+            "turn_count": self.turn_count,
+            "last_user": self.last_user_text,
+            "last_assistant": self.last_assistant_text,
+            "current_user": self._current_user_text or None,
+            "current_assistant": self._current_assistant_text or None
+        }

engine/core/duplex_pipeline.py

@@ -0,0 +1,719 @@
+"""Full duplex audio pipeline for AI voice conversation.
+
+This module implements the core duplex pipeline that orchestrates:
+- VAD (Voice Activity Detection)
+- EOU (End of Utterance) Detection
+- ASR (Automatic Speech Recognition) - optional
+- LLM (Language Model)
+- TTS (Text-to-Speech)
+
+Inspired by pipecat's frame-based architecture and active-call's
+event-driven design.
+"""
+
+import asyncio
+import time
+from typing import Optional, Callable, Awaitable
+from loguru import logger
+
+from core.transports import BaseTransport
+from core.conversation import ConversationManager, ConversationState
+from core.events import get_event_bus
+from processors.vad import VADProcessor, SileroVAD
+from processors.eou import EouDetector
+from services.base import BaseLLMService, BaseTTSService, BaseASRService
+from services.llm import OpenAILLMService, MockLLMService
+from services.tts import EdgeTTSService, MockTTSService
+from services.asr import BufferedASRService
+from services.siliconflow_tts import SiliconFlowTTSService
+from services.siliconflow_asr import SiliconFlowASRService
+from app.config import settings
+
+
+class DuplexPipeline:
+    """
+    Full duplex audio pipeline for AI voice conversation.
+    
+    Handles bidirectional audio flow with:
+    - User speech detection and transcription
+    - AI response generation
+    - Text-to-speech synthesis
+    - Barge-in (interruption) support
+    
+    Architecture (inspired by pipecat):
+    
+        User Audio → VAD → EOU → [ASR] → LLM → TTS → Audio Out
+                      ↓
+                  Barge-in Detection → Interrupt
+    """
+    
+    def __init__(
+        self,
+        transport: BaseTransport,
+        session_id: str,
+        llm_service: Optional[BaseLLMService] = None,
+        tts_service: Optional[BaseTTSService] = None,
+        asr_service: Optional[BaseASRService] = None,
+        system_prompt: Optional[str] = None,
+        greeting: Optional[str] = None
+    ):
+        """
+        Initialize duplex pipeline.
+        
+        Args:
+            transport: Transport for sending audio/events
+            session_id: Session identifier
+            llm_service: LLM service (defaults to OpenAI)
+            tts_service: TTS service (defaults to EdgeTTS)
+            asr_service: ASR service (optional)
+            system_prompt: System prompt for LLM
+            greeting: Optional greeting to speak on start
+        """
+        self.transport = transport
+        self.session_id = session_id
+        self.event_bus = get_event_bus()
+        
+        # Initialize VAD
+        self.vad_model = SileroVAD(
+            model_path=settings.vad_model_path,
+            sample_rate=settings.sample_rate
+        )
+        self.vad_processor = VADProcessor(
+            vad_model=self.vad_model,
+            threshold=settings.vad_threshold
+        )
+        
+        # Initialize EOU detector
+        self.eou_detector = EouDetector(
+            silence_threshold_ms=settings.vad_eou_threshold_ms,
+            min_speech_duration_ms=settings.vad_min_speech_duration_ms
+        )
+        
+        # Initialize services
+        self.llm_service = llm_service
+        self.tts_service = tts_service
+        self.asr_service = asr_service  # Will be initialized in start()
+        
+        # Track last sent transcript to avoid duplicates
+        self._last_sent_transcript = ""
+        
+        # Conversation manager
+        self.conversation = ConversationManager(
+            system_prompt=system_prompt,
+            greeting=greeting
+        )
+        
+        # State
+        self._running = True
+        self._is_bot_speaking = False
+        self._current_turn_task: Optional[asyncio.Task] = None
+        self._audio_buffer: bytes = b""
+        max_buffer_seconds = settings.max_audio_buffer_seconds if hasattr(settings, "max_audio_buffer_seconds") else 30
+        self._max_audio_buffer_bytes = int(settings.sample_rate * 2 * max_buffer_seconds)
+        self._last_vad_status: str = "Silence"
+        self._process_lock = asyncio.Lock()
+        
+        # Interruption handling
+        self._interrupt_event = asyncio.Event()
+        
+        # Latency tracking - TTFB (Time to First Byte)
+        self._turn_start_time: Optional[float] = None
+        self._first_audio_sent: bool = False
+        
+        # Barge-in filtering - require minimum speech duration to interrupt
+        self._barge_in_speech_start_time: Optional[float] = None
+        self._barge_in_min_duration_ms: int = settings.barge_in_min_duration_ms if hasattr(settings, 'barge_in_min_duration_ms') else 50
+        self._barge_in_speech_frames: int = 0  # Count speech frames
+        self._barge_in_silence_frames: int = 0  # Count silence frames during potential barge-in
+        self._barge_in_silence_tolerance: int = 3  # Allow up to 3 silence frames (60ms at 20ms chunks)
+        
+        logger.info(f"DuplexPipeline initialized for session {session_id}")
+    
+    async def start(self) -> None:
+        """Start the pipeline and connect services."""
+        try:
+            # Connect LLM service
+            if not self.llm_service:
+                if settings.openai_api_key:
+                    self.llm_service = OpenAILLMService(
+                        api_key=settings.openai_api_key,
+                        base_url=settings.openai_api_url,
+                        model=settings.llm_model
+                    )
+                else:
+                    logger.warning("No OpenAI API key - using mock LLM")
+                    self.llm_service = MockLLMService()
+            
+            await self.llm_service.connect()
+            
+            # Connect TTS service
+            if not self.tts_service:
+                if settings.tts_provider == "siliconflow" and settings.siliconflow_api_key:
+                    self.tts_service = SiliconFlowTTSService(
+                        api_key=settings.siliconflow_api_key,
+                        voice=settings.tts_voice,
+                        model=settings.siliconflow_tts_model,
+                        sample_rate=settings.sample_rate,
+                        speed=settings.tts_speed
+                    )
+                    logger.info("Using SiliconFlow TTS service")
+                else:
+                    self.tts_service = EdgeTTSService(
+                        voice=settings.tts_voice,
+                        sample_rate=settings.sample_rate
+                    )
+                    logger.info("Using Edge TTS service")
+            
+            await self.tts_service.connect()
+            
+            # Connect ASR service
+            if not self.asr_service:
+                if settings.asr_provider == "siliconflow" and settings.siliconflow_api_key:
+                    self.asr_service = SiliconFlowASRService(
+                        api_key=settings.siliconflow_api_key,
+                        model=settings.siliconflow_asr_model,
+                        sample_rate=settings.sample_rate,
+                        interim_interval_ms=settings.asr_interim_interval_ms,
+                        min_audio_for_interim_ms=settings.asr_min_audio_ms,
+                        on_transcript=self._on_transcript_callback
+                    )
+                    logger.info("Using SiliconFlow ASR service")
+                else:
+                    self.asr_service = BufferedASRService(
+                        sample_rate=settings.sample_rate
+                    )
+                    logger.info("Using Buffered ASR service (no real transcription)")
+            
+            await self.asr_service.connect()
+            
+            logger.info("DuplexPipeline services connected")
+            
+            # Speak greeting if configured
+            if self.conversation.greeting:
+                await self._speak(self.conversation.greeting)
+            
+        except Exception as e:
+            logger.error(f"Failed to start pipeline: {e}")
+            raise
+    
+    async def process_audio(self, pcm_bytes: bytes) -> None:
+        """
+        Process incoming audio chunk.
+        
+        This is the main entry point for audio from the user.
+        
+        Args:
+            pcm_bytes: PCM audio data (16-bit, mono, 16kHz)
+        """
+        if not self._running:
+            return
+        
+        try:
+            async with self._process_lock:
+                # 1. Process through VAD
+                vad_result = self.vad_processor.process(pcm_bytes, settings.chunk_size_ms)
+            
+                vad_status = "Silence"
+                if vad_result:
+                    event_type, probability = vad_result
+                    vad_status = "Speech" if event_type == "speaking" else "Silence"
+                    
+                    # Emit VAD event
+                    await self.event_bus.publish(event_type, {
+                        "trackId": self.session_id,
+                        "probability": probability
+                    })
+                else:
+                    # No state change - keep previous status
+                    vad_status = self._last_vad_status
+                
+                # Update state based on VAD
+                if vad_status == "Speech" and self._last_vad_status != "Speech":
+                    await self._on_speech_start()
+                
+                self._last_vad_status = vad_status
+                
+                # 2. Check for barge-in (user speaking while bot speaking)
+                # Filter false interruptions by requiring minimum speech duration
+                if self._is_bot_speaking:
+                    if vad_status == "Speech":
+                        # User is speaking while bot is speaking
+                        self._barge_in_silence_frames = 0  # Reset silence counter
+                        
+                        if self._barge_in_speech_start_time is None:
+                            # Start tracking speech duration
+                            self._barge_in_speech_start_time = time.time()
+                            self._barge_in_speech_frames = 1
+                            logger.debug("Potential barge-in detected, tracking duration...")
+                        else:
+                            self._barge_in_speech_frames += 1
+                            # Check if speech duration exceeds threshold
+                            speech_duration_ms = (time.time() - self._barge_in_speech_start_time) * 1000
+                            if speech_duration_ms >= self._barge_in_min_duration_ms:
+                                logger.info(f"Barge-in confirmed after {speech_duration_ms:.0f}ms of speech ({self._barge_in_speech_frames} frames)")
+                                await self._handle_barge_in()
+                    else:
+                        # Silence frame during potential barge-in
+                        if self._barge_in_speech_start_time is not None:
+                            self._barge_in_silence_frames += 1
+                            # Allow brief silence gaps (VAD flickering)
+                            if self._barge_in_silence_frames > self._barge_in_silence_tolerance:
+                                # Too much silence - reset barge-in tracking
+                                logger.debug(f"Barge-in cancelled after {self._barge_in_silence_frames} silence frames")
+                                self._barge_in_speech_start_time = None
+                                self._barge_in_speech_frames = 0
+                                self._barge_in_silence_frames = 0
+                
+                # 3. Buffer audio for ASR
+                if vad_status == "Speech" or self.conversation.state == ConversationState.LISTENING:
+                    self._audio_buffer += pcm_bytes
+                    if len(self._audio_buffer) > self._max_audio_buffer_bytes:
+                        # Keep only the most recent audio to cap memory usage
+                        self._audio_buffer = self._audio_buffer[-self._max_audio_buffer_bytes:]
+                    await self.asr_service.send_audio(pcm_bytes)
+                    
+                    # For SiliconFlow ASR, trigger interim transcription periodically
+                    # The service handles timing internally via start_interim_transcription()
+                
+                # 4. Check for End of Utterance - this triggers LLM response
+                if self.eou_detector.process(vad_status):
+                    await self._on_end_of_utterance()
+            
+        except Exception as e:
+            logger.error(f"Pipeline audio processing error: {e}", exc_info=True)
+    
+    async def process_text(self, text: str) -> None:
+        """
+        Process text input (chat command).
+        
+        Allows direct text input to bypass ASR.
+        
+        Args:
+            text: User text input
+        """
+        if not self._running:
+            return
+        
+        logger.info(f"Processing text input: {text[:50]}...")
+        
+        # Cancel any current speaking
+        await self._stop_current_speech()
+        
+        # Start new turn
+        await self.conversation.end_user_turn(text)
+        self._current_turn_task = asyncio.create_task(self._handle_turn(text))
+    
+    async def interrupt(self) -> None:
+        """Interrupt current bot speech (manual interrupt command)."""
+        await self._handle_barge_in()
+    
+    async def _on_transcript_callback(self, text: str, is_final: bool) -> None:
+        """
+        Callback for ASR transcription results.
+        
+        Streams transcription to client for display.
+        
+        Args:
+            text: Transcribed text
+            is_final: Whether this is the final transcription
+        """
+        # Avoid sending duplicate transcripts
+        if text == self._last_sent_transcript and not is_final:
+            return
+        
+        self._last_sent_transcript = text
+        
+        # Send transcript event to client
+        await self.transport.send_event({
+            "event": "transcript",
+            "trackId": self.session_id,
+            "text": text,
+            "isFinal": is_final,
+            "timestamp": self._get_timestamp_ms()
+        })
+        
+        logger.debug(f"Sent transcript ({'final' if is_final else 'interim'}): {text[:50]}...")
+    
+    async def _on_speech_start(self) -> None:
+        """Handle user starting to speak."""
+        if self.conversation.state == ConversationState.IDLE:
+            await self.conversation.start_user_turn()
+            self._audio_buffer = b""
+            self._last_sent_transcript = ""
+            self.eou_detector.reset()
+            
+            # Clear ASR buffer and start interim transcriptions
+            if hasattr(self.asr_service, 'clear_buffer'):
+                self.asr_service.clear_buffer()
+            if hasattr(self.asr_service, 'start_interim_transcription'):
+                await self.asr_service.start_interim_transcription()
+            
+            logger.debug("User speech started")
+    
+    async def _on_end_of_utterance(self) -> None:
+        """Handle end of user utterance."""
+        if self.conversation.state != ConversationState.LISTENING:
+            return
+        
+        # Stop interim transcriptions
+        if hasattr(self.asr_service, 'stop_interim_transcription'):
+            await self.asr_service.stop_interim_transcription()
+        
+        # Get final transcription from ASR service
+        user_text = ""
+        
+        if hasattr(self.asr_service, 'get_final_transcription'):
+            # SiliconFlow ASR - get final transcription
+            user_text = await self.asr_service.get_final_transcription()
+        elif hasattr(self.asr_service, 'get_and_clear_text'):
+            # Buffered ASR - get accumulated text
+            user_text = self.asr_service.get_and_clear_text()
+        
+        # Skip if no meaningful text
+        if not user_text or not user_text.strip():
+            logger.debug("EOU detected but no transcription - skipping")
+            # Reset for next utterance
+            self._audio_buffer = b""
+            self._last_sent_transcript = ""
+            # Return to idle; don't force LISTENING which causes buffering on silence
+            await self.conversation.set_state(ConversationState.IDLE)
+            return
+        
+        logger.info(f"EOU detected - user said: {user_text[:100]}...")
+        
+        # Send final transcription to client
+        await self.transport.send_event({
+            "event": "transcript",
+            "trackId": self.session_id,
+            "text": user_text,
+            "isFinal": True,
+            "timestamp": self._get_timestamp_ms()
+        })
+        
+        # Clear buffers
+        self._audio_buffer = b""
+        self._last_sent_transcript = ""
+        
+        # Process the turn - trigger LLM response
+        # Cancel any existing turn to avoid overlapping assistant responses
+        await self._stop_current_speech()
+        await self.conversation.end_user_turn(user_text)
+        self._current_turn_task = asyncio.create_task(self._handle_turn(user_text))
+    
+    async def _handle_turn(self, user_text: str) -> None:
+        """
+        Handle a complete conversation turn.
+        
+        Uses sentence-by-sentence streaming TTS for lower latency.
+        
+        Args:
+            user_text: User's transcribed text
+        """
+        try:
+            # Start latency tracking
+            self._turn_start_time = time.time()
+            self._first_audio_sent = False
+            
+            # Get AI response (streaming)
+            messages = self.conversation.get_messages()
+            full_response = ""
+            
+            await self.conversation.start_assistant_turn()
+            self._is_bot_speaking = True
+            self._interrupt_event.clear()
+            
+            # Sentence buffer for streaming TTS
+            sentence_buffer = ""
+            sentence_ends = {'，', '。', '！', '？', '\n'}
+            first_audio_sent = False
+            
+            # Stream LLM response and TTS sentence by sentence
+            async for text_chunk in self.llm_service.generate_stream(messages):
+                if self._interrupt_event.is_set():
+                    break
+                
+                full_response += text_chunk
+                sentence_buffer += text_chunk
+                await self.conversation.update_assistant_text(text_chunk)
+                
+                # Send LLM response streaming event to client
+                await self.transport.send_event({
+                    "event": "llmResponse",
+                    "trackId": self.session_id,
+                    "text": text_chunk,
+                    "isFinal": False,
+                    "timestamp": self._get_timestamp_ms()
+                })
+                
+                # Check for sentence completion - synthesize immediately for low latency
+                while any(end in sentence_buffer for end in sentence_ends):
+                    # Find first sentence end
+                    min_idx = len(sentence_buffer)
+                    for end in sentence_ends:
+                        idx = sentence_buffer.find(end)
+                        if idx != -1 and idx < min_idx:
+                            min_idx = idx
+                    
+                    if min_idx < len(sentence_buffer):
+                        sentence = sentence_buffer[:min_idx + 1].strip()
+                        sentence_buffer = sentence_buffer[min_idx + 1:]
+                        
+                        if sentence and not self._interrupt_event.is_set():
+                            # Send track start on first audio
+                            if not first_audio_sent:
+                                await self.transport.send_event({
+                                    "event": "trackStart",
+                                    "trackId": self.session_id,
+                                    "timestamp": self._get_timestamp_ms()
+                                })
+                                first_audio_sent = True
+                            
+                            # Synthesize and send this sentence immediately
+                            await self._speak_sentence(sentence)
+                    else:
+                        break
+            
+            # Send final LLM response event
+            if full_response and not self._interrupt_event.is_set():
+                await self.transport.send_event({
+                    "event": "llmResponse",
+                    "trackId": self.session_id,
+                    "text": full_response,
+                    "isFinal": True,
+                    "timestamp": self._get_timestamp_ms()
+                })
+            
+            # Speak any remaining text
+            if sentence_buffer.strip() and not self._interrupt_event.is_set():
+                if not first_audio_sent:
+                    await self.transport.send_event({
+                        "event": "trackStart",
+                        "trackId": self.session_id,
+                        "timestamp": self._get_timestamp_ms()
+                    })
+                    first_audio_sent = True
+                await self._speak_sentence(sentence_buffer.strip())
+            
+            # Send track end
+            if first_audio_sent:
+                await self.transport.send_event({
+                    "event": "trackEnd",
+                    "trackId": self.session_id,
+                    "timestamp": self._get_timestamp_ms()
+                })
+            
+            # End assistant turn
+            await self.conversation.end_assistant_turn(
+                was_interrupted=self._interrupt_event.is_set()
+            )
+            
+        except asyncio.CancelledError:
+            logger.info("Turn handling cancelled")
+            await self.conversation.end_assistant_turn(was_interrupted=True)
+        except Exception as e:
+            logger.error(f"Turn handling error: {e}", exc_info=True)
+            await self.conversation.end_assistant_turn(was_interrupted=True)
+        finally:
+            self._is_bot_speaking = False
+            # Reset barge-in tracking when bot finishes speaking
+            self._barge_in_speech_start_time = None
+            self._barge_in_speech_frames = 0
+            self._barge_in_silence_frames = 0
+    
+    async def _speak_sentence(self, text: str) -> None:
+        """
+        Synthesize and send a single sentence.
+        
+        Args:
+            text: Sentence to speak
+        """
+        if not text.strip() or self._interrupt_event.is_set():
+            return
+        
+        try:
+            async for chunk in self.tts_service.synthesize_stream(text):
+                # Check interrupt at the start of each iteration
+                if self._interrupt_event.is_set():
+                    logger.debug("TTS sentence interrupted")
+                    break
+                
+                # Track and log first audio packet latency (TTFB)
+                if not self._first_audio_sent and self._turn_start_time:
+                    ttfb_ms = (time.time() - self._turn_start_time) * 1000
+                    self._first_audio_sent = True
+                    logger.info(f"[TTFB] Server first audio packet latency: {ttfb_ms:.0f}ms (session {self.session_id})")
+                    
+                    # Send TTFB event to client
+                    await self.transport.send_event({
+                        "event": "ttfb",
+                        "trackId": self.session_id,
+                        "timestamp": self._get_timestamp_ms(),
+                        "latencyMs": round(ttfb_ms)
+                    })
+                
+                # Double-check interrupt right before sending audio
+                if self._interrupt_event.is_set():
+                    break
+                
+                await self.transport.send_audio(chunk.audio)
+                await asyncio.sleep(0.005)  # Small delay to prevent flooding
+        except asyncio.CancelledError:
+            logger.debug("TTS sentence cancelled")
+        except Exception as e:
+            logger.error(f"TTS sentence error: {e}")
+    
+    async def _speak(self, text: str) -> None:
+        """
+        Synthesize and send speech.
+        
+        Args:
+            text: Text to speak
+        """
+        if not text.strip():
+            return
+        
+        try:
+            # Start latency tracking for greeting
+            speak_start_time = time.time()
+            first_audio_sent = False
+            
+            # Send track start event
+            await self.transport.send_event({
+                "event": "trackStart",
+                "trackId": self.session_id,
+                "timestamp": self._get_timestamp_ms()
+            })
+            
+            self._is_bot_speaking = True
+            
+            # Stream TTS audio
+            async for chunk in self.tts_service.synthesize_stream(text):
+                if self._interrupt_event.is_set():
+                    logger.info("TTS interrupted by barge-in")
+                    break
+                
+                # Track and log first audio packet latency (TTFB)
+                if not first_audio_sent:
+                    ttfb_ms = (time.time() - speak_start_time) * 1000
+                    first_audio_sent = True
+                    logger.info(f"[TTFB] Greeting first audio packet latency: {ttfb_ms:.0f}ms (session {self.session_id})")
+                    
+                    # Send TTFB event to client
+                    await self.transport.send_event({
+                        "event": "ttfb",
+                        "trackId": self.session_id,
+                        "timestamp": self._get_timestamp_ms(),
+                        "latencyMs": round(ttfb_ms)
+                    })
+                
+                # Send audio to client
+                await self.transport.send_audio(chunk.audio)
+                
+                # Small delay to prevent flooding
+                await asyncio.sleep(0.01)
+            
+            # Send track end event
+            await self.transport.send_event({
+                "event": "trackEnd",
+                "trackId": self.session_id,
+                "timestamp": self._get_timestamp_ms()
+            })
+            
+        except asyncio.CancelledError:
+            logger.info("TTS cancelled")
+            raise
+        except Exception as e:
+            logger.error(f"TTS error: {e}")
+        finally:
+            self._is_bot_speaking = False
+    
+    async def _handle_barge_in(self) -> None:
+        """Handle user barge-in (interruption)."""
+        if not self._is_bot_speaking:
+            return
+        
+        logger.info("Barge-in detected - interrupting bot speech")
+        
+        # Reset barge-in tracking
+        self._barge_in_speech_start_time = None
+        self._barge_in_speech_frames = 0
+        self._barge_in_silence_frames = 0
+        
+        # IMPORTANT: Signal interruption FIRST to stop audio sending
+        self._interrupt_event.set()
+        self._is_bot_speaking = False
+        
+        # Send interrupt event to client IMMEDIATELY
+        # This must happen BEFORE canceling services, so client knows to discard in-flight audio
+        await self.transport.send_event({
+            "event": "interrupt",
+            "trackId": self.session_id,
+            "timestamp": self._get_timestamp_ms()
+        })
+        
+        # Cancel TTS
+        if self.tts_service:
+            await self.tts_service.cancel()
+        
+        # Cancel LLM
+        if self.llm_service and hasattr(self.llm_service, 'cancel'):
+            self.llm_service.cancel()
+        
+        # Interrupt conversation only if there is no active turn task.
+        # When a turn task exists, it will handle end_assistant_turn() to avoid double callbacks.
+        if not (self._current_turn_task and not self._current_turn_task.done()):
+            await self.conversation.interrupt()
+        
+        # Reset for new user turn
+        await self.conversation.start_user_turn()
+        self._audio_buffer = b""
+        self.eou_detector.reset()
+    
+    async def _stop_current_speech(self) -> None:
+        """Stop any current speech task."""
+        if self._current_turn_task and not self._current_turn_task.done():
+            self._interrupt_event.set()
+            self._current_turn_task.cancel()
+            try:
+                await self._current_turn_task
+            except asyncio.CancelledError:
+                pass
+
+        # Ensure underlying services are cancelled to avoid leaking work/audio
+        if self.tts_service:
+            await self.tts_service.cancel()
+        if self.llm_service and hasattr(self.llm_service, 'cancel'):
+            self.llm_service.cancel()
+        
+        self._is_bot_speaking = False
+        self._interrupt_event.clear()
+    
+    async def cleanup(self) -> None:
+        """Cleanup pipeline resources."""
+        logger.info(f"Cleaning up DuplexPipeline for session {self.session_id}")
+        
+        self._running = False
+        await self._stop_current_speech()
+        
+        # Disconnect services
+        if self.llm_service:
+            await self.llm_service.disconnect()
+        if self.tts_service:
+            await self.tts_service.disconnect()
+        if self.asr_service:
+            await self.asr_service.disconnect()
+    
+    def _get_timestamp_ms(self) -> int:
+        """Get current timestamp in milliseconds."""
+        import time
+        return int(time.time() * 1000)
+    
+    @property
+    def is_speaking(self) -> bool:
+        """Check if bot is currently speaking."""
+        return self._is_bot_speaking
+    
+    @property
+    def state(self) -> ConversationState:
+        """Get current conversation state."""
+        return self.conversation.state

engine/core/events.py

@@ -0,0 +1,134 @@
+"""Event bus for pub/sub communication between components."""
+
+import asyncio
+from typing import Callable, Dict, List, Any, Optional
+from collections import defaultdict
+from loguru import logger
+
+
+class EventBus:
+    """
+    Async event bus for pub/sub communication.
+
+    Similar to the original Rust implementation's broadcast channel.
+    Components can subscribe to specific event types and receive events asynchronously.
+    """
+
+    def __init__(self):
+        """Initialize the event bus."""
+        self._subscribers: Dict[str, List[Callable]] = defaultdict(list)
+        self._lock = asyncio.Lock()
+        self._running = True
+
+    def subscribe(self, event_type: str, callback: Callable[[Dict[str, Any]], None]) -> None:
+        """
+        Subscribe to an event type.
+
+        Args:
+            event_type: Type of event to subscribe to (e.g., "speaking", "silence")
+            callback: Async callback function that receives event data
+        """
+        if not self._running:
+            logger.warning(f"Event bus is shut down, ignoring subscription to {event_type}")
+            return
+
+        self._subscribers[event_type].append(callback)
+        logger.debug(f"Subscribed to event type: {event_type}")
+
+    def unsubscribe(self, event_type: str, callback: Callable[[Dict[str, Any]], None]) -> None:
+        """
+        Unsubscribe from an event type.
+
+        Args:
+            event_type: Type of event to unsubscribe from
+            callback: Callback function to remove
+        """
+        if callback in self._subscribers[event_type]:
+            self._subscribers[event_type].remove(callback)
+            logger.debug(f"Unsubscribed from event type: {event_type}")
+
+    async def publish(self, event_type: str, event_data: Dict[str, Any]) -> None:
+        """
+        Publish an event to all subscribers.
+
+        Args:
+            event_type: Type of event to publish
+            event_data: Event data to send to subscribers
+        """
+        if not self._running:
+            logger.warning(f"Event bus is shut down, ignoring event: {event_type}")
+            return
+
+        # Get subscribers for this event type
+        subscribers = self._subscribers.get(event_type, [])
+
+        if not subscribers:
+            logger.debug(f"No subscribers for event type: {event_type}")
+            return
+
+        # Notify all subscribers concurrently
+        tasks = []
+        for callback in subscribers:
+            try:
+                # Create task for each subscriber
+                task = asyncio.create_task(self._call_subscriber(callback, event_data))
+                tasks.append(task)
+            except Exception as e:
+                logger.error(f"Error creating task for subscriber: {e}")
+
+        # Wait for all subscribers to complete
+        if tasks:
+            await asyncio.gather(*tasks, return_exceptions=True)
+
+        logger.debug(f"Published event '{event_type}' to {len(tasks)} subscribers")
+
+    async def _call_subscriber(self, callback: Callable[[Dict[str, Any]], None], event_data: Dict[str, Any]) -> None:
+        """
+        Call a subscriber callback with error handling.
+
+        Args:
+            callback: Subscriber callback function
+            event_data: Event data to pass to callback
+        """
+        try:
+            # Check if callback is a coroutine function
+            if asyncio.iscoroutinefunction(callback):
+                await callback(event_data)
+            else:
+                callback(event_data)
+        except Exception as e:
+            logger.error(f"Error in subscriber callback: {e}", exc_info=True)
+
+    async def close(self) -> None:
+        """Close the event bus and stop processing events."""
+        self._running = False
+        self._subscribers.clear()
+        logger.info("Event bus closed")
+
+    @property
+    def is_running(self) -> bool:
+        """Check if the event bus is running."""
+        return self._running
+
+
+# Global event bus instance
+_event_bus: Optional[EventBus] = None
+
+
+def get_event_bus() -> EventBus:
+    """
+    Get the global event bus instance.
+
+    Returns:
+        EventBus instance
+    """
+    global _event_bus
+    if _event_bus is None:
+        _event_bus = EventBus()
+    return _event_bus
+
+
+def reset_event_bus() -> None:
+    """Reset the global event bus (mainly for testing)."""
+    global _event_bus
+    _event_bus = None

engine/core/session.py

@@ -0,0 +1,285 @@
+"""Session management for active calls."""
+
+import uuid
+import json
+from typing import Optional, Dict, Any
+from loguru import logger
+
+from core.transports import BaseTransport
+from core.duplex_pipeline import DuplexPipeline
+from models.commands import parse_command, TTSCommand, ChatCommand, InterruptCommand, HangupCommand
+from app.config import settings
+
+
+class Session:
+    """
+    Manages a single call session.
+
+    Handles command routing, audio processing, and session lifecycle.
+    Uses full duplex voice conversation pipeline.
+    """
+
+    def __init__(self, session_id: str, transport: BaseTransport, use_duplex: bool = None):
+        """
+        Initialize session.
+
+        Args:
+            session_id: Unique session identifier
+            transport: Transport instance for communication
+            use_duplex: Whether to use duplex pipeline (defaults to settings.duplex_enabled)
+        """
+        self.id = session_id
+        self.transport = transport
+        self.use_duplex = use_duplex if use_duplex is not None else settings.duplex_enabled
+
+        self.pipeline = DuplexPipeline(
+            transport=transport,
+            session_id=session_id,
+            system_prompt=settings.duplex_system_prompt,
+            greeting=settings.duplex_greeting
+        )
+
+        # Session state
+        self.created_at = None
+        self.state = "created"  # created, invited, accepted, ringing, hungup
+        self._pipeline_started = False
+
+        # Track IDs
+        self.current_track_id: Optional[str] = str(uuid.uuid4())
+
+        logger.info(f"Session {self.id} created (duplex={self.use_duplex})")
+
+    async def handle_text(self, text_data: str) -> None:
+        """
+        Handle incoming text data (JSON commands).
+
+        Args:
+            text_data: JSON text data
+        """
+        try:
+            data = json.loads(text_data)
+            command = parse_command(data)
+            command_type = command.command
+
+            logger.info(f"Session {self.id} received command: {command_type}")
+
+            # Route command to appropriate handler
+            if command_type == "invite":
+                await self._handle_invite(data)
+
+            elif command_type == "accept":
+                await self._handle_accept(data)
+
+            elif command_type == "reject":
+                await self._handle_reject(data)
+
+            elif command_type == "ringing":
+                await self._handle_ringing(data)
+
+            elif command_type == "tts":
+                await self._handle_tts(command)
+
+            elif command_type == "play":
+                await self._handle_play(data)
+
+            elif command_type == "interrupt":
+                await self._handle_interrupt(command)
+
+            elif command_type == "pause":
+                await self._handle_pause()
+
+            elif command_type == "resume":
+                await self._handle_resume()
+
+            elif command_type == "hangup":
+                await self._handle_hangup(command)
+
+            elif command_type == "history":
+                await self._handle_history(data)
+
+            elif command_type == "chat":
+                await self._handle_chat(command)
+
+            else:
+                logger.warning(f"Session {self.id} unknown command: {command_type}")
+
+        except json.JSONDecodeError as e:
+            logger.error(f"Session {self.id} JSON decode error: {e}")
+            await self._send_error("client", f"Invalid JSON: {e}")
+
+        except ValueError as e:
+            logger.error(f"Session {self.id} command parse error: {e}")
+            await self._send_error("client", f"Invalid command: {e}")
+
+        except Exception as e:
+            logger.error(f"Session {self.id} handle_text error: {e}", exc_info=True)
+            await self._send_error("server", f"Internal error: {e}")
+
+    async def handle_audio(self, audio_bytes: bytes) -> None:
+        """
+        Handle incoming audio data.
+
+        Args:
+            audio_bytes: PCM audio data
+        """
+        try:
+            await self.pipeline.process_audio(audio_bytes)
+        except Exception as e:
+            logger.error(f"Session {self.id} handle_audio error: {e}", exc_info=True)
+
+    async def _handle_invite(self, data: Dict[str, Any]) -> None:
+        """Handle invite command."""
+        self.state = "invited"
+        option = data.get("option", {})
+
+        # Send answer event
+        await self.transport.send_event({
+            "event": "answer",
+            "trackId": self.current_track_id,
+            "timestamp": self._get_timestamp_ms()
+        })
+
+        # Start duplex pipeline
+        if not self._pipeline_started:
+            try:
+                await self.pipeline.start()
+                self._pipeline_started = True
+                logger.info(f"Session {self.id} duplex pipeline started")
+            except Exception as e:
+                logger.error(f"Failed to start duplex pipeline: {e}")
+
+        logger.info(f"Session {self.id} invited with codec: {option.get('codec', 'pcm')}")
+
+    async def _handle_accept(self, data: Dict[str, Any]) -> None:
+        """Handle accept command."""
+        self.state = "accepted"
+        logger.info(f"Session {self.id} accepted")
+
+    async def _handle_reject(self, data: Dict[str, Any]) -> None:
+        """Handle reject command."""
+        self.state = "rejected"
+        reason = data.get("reason", "Rejected")
+        logger.info(f"Session {self.id} rejected: {reason}")
+
+    async def _handle_ringing(self, data: Dict[str, Any]) -> None:
+        """Handle ringing command."""
+        self.state = "ringing"
+        logger.info(f"Session {self.id} ringing")
+
+    async def _handle_tts(self, command: TTSCommand) -> None:
+        """Handle TTS command."""
+        logger.info(f"Session {self.id} TTS: {command.text[:50]}...")
+
+        # Send track start event
+        await self.transport.send_event({
+            "event": "trackStart",
+            "trackId": self.current_track_id,
+            "timestamp": self._get_timestamp_ms(),
+            "playId": command.play_id
+        })
+
+        # TODO: Implement actual TTS synthesis
+        # For now, just send track end event
+        await self.transport.send_event({
+            "event": "trackEnd",
+            "trackId": self.current_track_id,
+            "timestamp": self._get_timestamp_ms(),
+            "duration": 1000,
+            "ssrc": 0,
+            "playId": command.play_id
+        })
+
+    async def _handle_play(self, data: Dict[str, Any]) -> None:
+        """Handle play command."""
+        url = data.get("url", "")
+        logger.info(f"Session {self.id} play: {url}")
+
+        # Send track start event
+        await self.transport.send_event({
+            "event": "trackStart",
+            "trackId": self.current_track_id,
+            "timestamp": self._get_timestamp_ms(),
+            "playId": url
+        })
+
+        # TODO: Implement actual audio playback
+        # For now, just send track end event
+        await self.transport.send_event({
+            "event": "trackEnd",
+            "trackId": self.current_track_id,
+            "timestamp": self._get_timestamp_ms(),
+            "duration": 1000,
+            "ssrc": 0,
+            "playId": url
+        })
+
+    async def _handle_interrupt(self, command: InterruptCommand) -> None:
+        """Handle interrupt command."""
+        if command.graceful:
+            logger.info(f"Session {self.id} graceful interrupt")
+        else:
+            logger.info(f"Session {self.id} immediate interrupt")
+            await self.pipeline.interrupt()
+
+    async def _handle_pause(self) -> None:
+        """Handle pause command."""
+        logger.info(f"Session {self.id} paused")
+
+    async def _handle_resume(self) -> None:
+        """Handle resume command."""
+        logger.info(f"Session {self.id} resumed")
+
+    async def _handle_hangup(self, command: HangupCommand) -> None:
+        """Handle hangup command."""
+        self.state = "hungup"
+        reason = command.reason or "User requested"
+        logger.info(f"Session {self.id} hung up: {reason}")
+
+        # Send hangup event
+        await self.transport.send_event({
+            "event": "hangup",
+            "timestamp": self._get_timestamp_ms(),
+            "reason": reason,
+            "initiator": command.initiator or "user"
+        })
+
+        # Close transport
+        await self.transport.close()
+
+    async def _handle_history(self, data: Dict[str, Any]) -> None:
+        """Handle history command."""
+        speaker = data.get("speaker", "unknown")
+        text = data.get("text", "")
+        logger.info(f"Session {self.id} history [{speaker}]: {text[:50]}...")
+
+    async def _handle_chat(self, command: ChatCommand) -> None:
+        """Handle chat command."""
+        logger.info(f"Session {self.id} chat: {command.text[:50]}...")
+        await self.pipeline.process_text(command.text)
+
+    async def _send_error(self, sender: str, error_message: str) -> None:
+        """
+        Send error event to client.
+
+        Args:
+            sender: Component that generated the error
+            error_message: Error message
+        """
+        await self.transport.send_event({
+            "event": "error",
+            "trackId": self.current_track_id,
+            "timestamp": self._get_timestamp_ms(),
+            "sender": sender,
+            "error": error_message
+        })
+
+    def _get_timestamp_ms(self) -> int:
+        """Get current timestamp in milliseconds."""
+        import time
+        return int(time.time() * 1000)
+
+    async def cleanup(self) -> None:
+        """Cleanup session resources."""
+        logger.info(f"Session {self.id} cleaning up")
+        await self.pipeline.cleanup()
+        await self.transport.close()

engine/core/transports.py

@@ -0,0 +1,207 @@
+"""Transport layer for WebSocket and WebRTC communication."""
+
+import asyncio
+import json
+from abc import ABC, abstractmethod
+from typing import Optional
+from fastapi import WebSocket
+from loguru import logger
+
+# Try to import aiortc (optional for WebRTC functionality)
+try:
+    from aiortc import RTCPeerConnection
+    AIORTC_AVAILABLE = True
+except ImportError:
+    AIORTC_AVAILABLE = False
+    RTCPeerConnection = None  # Type hint placeholder
+
+
+class BaseTransport(ABC):
+    """
+    Abstract base class for transports.
+
+    All transports must implement send_event and send_audio methods.
+    """
+
+    @abstractmethod
+    async def send_event(self, event: dict) -> None:
+        """
+        Send a JSON event to the client.
+
+        Args:
+            event: Event data as dictionary
+        """
+        pass
+
+    @abstractmethod
+    async def send_audio(self, pcm_bytes: bytes) -> None:
+        """
+        Send audio data to the client.
+
+        Args:
+            pcm_bytes: PCM audio data (16-bit, mono, 16kHz)
+        """
+        pass
+
+    @abstractmethod
+    async def close(self) -> None:
+        """Close the transport and cleanup resources."""
+        pass
+
+
+class SocketTransport(BaseTransport):
+    """
+    WebSocket transport for raw audio streaming.
+
+    Handles mixed text/binary frames over WebSocket connection.
+    Uses asyncio.Lock to prevent frame interleaving.
+    """
+
+    def __init__(self, websocket: WebSocket):
+        """
+        Initialize WebSocket transport.
+
+        Args:
+            websocket: FastAPI WebSocket connection
+        """
+        self.ws = websocket
+        self.lock = asyncio.Lock()  # Prevent frame interleaving
+        self._closed = False
+
+    async def send_event(self, event: dict) -> None:
+        """
+        Send a JSON event via WebSocket.
+
+        Args:
+            event: Event data as dictionary
+        """
+        if self._closed:
+            logger.warning("Attempted to send event on closed transport")
+            return
+
+        async with self.lock:
+            try:
+                await self.ws.send_text(json.dumps(event))
+                logger.debug(f"Sent event: {event.get('event', 'unknown')}")
+            except Exception as e:
+                logger.error(f"Error sending event: {e}")
+                self._closed = True
+
+    async def send_audio(self, pcm_bytes: bytes) -> None:
+        """
+        Send PCM audio data via WebSocket.
+
+        Args:
+            pcm_bytes: PCM audio data (16-bit, mono, 16kHz)
+        """
+        if self._closed:
+            logger.warning("Attempted to send audio on closed transport")
+            return
+
+        async with self.lock:
+            try:
+                await self.ws.send_bytes(pcm_bytes)
+            except Exception as e:
+                logger.error(f"Error sending audio: {e}")
+                self._closed = True
+
+    async def close(self) -> None:
+        """Close the WebSocket connection."""
+        self._closed = True
+        try:
+            await self.ws.close()
+        except Exception as e:
+            logger.error(f"Error closing WebSocket: {e}")
+
+    @property
+    def is_closed(self) -> bool:
+        """Check if the transport is closed."""
+        return self._closed
+
+
+class WebRtcTransport(BaseTransport):
+    """
+    WebRTC transport for WebRTC audio streaming.
+
+    Uses WebSocket for signaling and RTCPeerConnection for media.
+    """
+
+    def __init__(self, websocket: WebSocket, pc):
+        """
+        Initialize WebRTC transport.
+
+        Args:
+            websocket: FastAPI WebSocket connection for signaling
+            pc: RTCPeerConnection for media transport
+        """
+        if not AIORTC_AVAILABLE:
+            raise RuntimeError("aiortc is not available - WebRTC transport cannot be used")
+
+        self.ws = websocket
+        self.pc = pc
+        self.outbound_track = None  # MediaStreamTrack for outbound audio
+        self._closed = False
+
+    async def send_event(self, event: dict) -> None:
+        """
+        Send a JSON event via WebSocket signaling.
+
+        Args:
+            event: Event data as dictionary
+        """
+        if self._closed:
+            logger.warning("Attempted to send event on closed transport")
+            return
+
+        try:
+            await self.ws.send_text(json.dumps(event))
+            logger.debug(f"Sent event: {event.get('event', 'unknown')}")
+        except Exception as e:
+            logger.error(f"Error sending event: {e}")
+            self._closed = True
+
+    async def send_audio(self, pcm_bytes: bytes) -> None:
+        """
+        Send audio data via WebRTC track.
+
+        Note: In WebRTC, you don't send bytes directly. You push frames
+        to a MediaStreamTrack that the peer connection is reading.
+
+        Args:
+            pcm_bytes: PCM audio data (16-bit, mono, 16kHz)
+        """
+        if self._closed:
+            logger.warning("Attempted to send audio on closed transport")
+            return
+
+        # This would require a custom MediaStreamTrack implementation
+        # For now, we'll log this as a placeholder
+        logger.debug(f"Audio bytes queued for WebRTC track: {len(pcm_bytes)} bytes")
+
+        # TODO: Implement outbound audio track if needed
+        # if self.outbound_track:
+        #     await self.outbound_track.add_frame(pcm_bytes)
+
+    async def close(self) -> None:
+        """Close the WebRTC connection."""
+        self._closed = True
+        try:
+            await self.pc.close()
+            await self.ws.close()
+        except Exception as e:
+            logger.error(f"Error closing WebRTC transport: {e}")
+
+    @property
+    def is_closed(self) -> bool:
+        """Check if the transport is closed."""
+        return self._closed
+
+    def set_outbound_track(self, track):
+        """
+        Set the outbound audio track for sending audio to client.
+
+        Args:
+            track: MediaStreamTrack for outbound audio
+        """
+        self.outbound_track = track
+        logger.debug("Set outbound track for WebRTC transport")

engine/docs/duplex_interaction.svg

@@ -0,0 +1,96 @@
+<svg width="1200" height="620" viewBox="0 0 1200 620" xmlns="http://www.w3.org/2000/svg">
+  <defs>
+    <style>
+      .box { fill:#11131a; stroke:#3a3f4b; stroke-width:1.2; rx:10; ry:10; }
+      .title { font: 600 14px 'Arial'; fill:#f2f3f7; }
+      .text { font: 12px 'Arial'; fill:#c8ccd8; }
+      .arrow { stroke:#7aa2ff; stroke-width:1.6; marker-end:url(#arrow); fill:none; }
+      .arrow2 { stroke:#2dd4bf; stroke-width:1.6; marker-end:url(#arrow); fill:none; }
+      .arrow3 { stroke:#ff6b6b; stroke-width:1.6; marker-end:url(#arrow); fill:none; }
+      .label { font: 11px 'Arial'; fill:#9aa3b2; }
+    </style>
+    <marker id="arrow" markerWidth="8" markerHeight="8" refX="7" refY="4" orient="auto">
+      <path d="M0,0 L8,4 L0,8 Z" fill="#7aa2ff"/>
+    </marker>
+  </defs>
+
+  <rect x="40" y="40" width="250" height="120" class="box"/>
+  <text x="60" y="70" class="title">Web Client</text>
+  <text x="60" y="95" class="text">WS JSON commands</text>
+  <text x="60" y="115" class="text">WS binary PCM audio</text>
+
+  <rect x="350" y="40" width="250" height="120" class="box"/>
+  <text x="370" y="70" class="title">FastAPI /ws</text>
+  <text x="370" y="95" class="text">Session + Transport</text>
+
+  <rect x="660" y="40" width="250" height="120" class="box"/>
+  <text x="680" y="70" class="title">DuplexPipeline</text>
+  <text x="680" y="95" class="text">process_audio / process_text</text>
+
+  <rect x="920" y="40" width="240" height="120" class="box"/>
+  <text x="940" y="70" class="title">ConversationManager</text>
+  <text x="940" y="95" class="text">turns + state</text>
+
+  <rect x="660" y="200" width="180" height="100" class="box"/>
+  <text x="680" y="230" class="title">VADProcessor</text>
+  <text x="680" y="255" class="text">speech/silence</text>
+
+  <rect x="860" y="200" width="180" height="100" class="box"/>
+  <text x="880" y="230" class="title">EOU Detector</text>
+  <text x="880" y="255" class="text">end-of-utterance</text>
+
+  <rect x="1060" y="200" width="120" height="100" class="box"/>
+  <text x="1075" y="230" class="title">ASR</text>
+  <text x="1075" y="255" class="text">transcripts</text>
+
+  <rect x="920" y="350" width="240" height="110" class="box"/>
+  <text x="940" y="380" class="title">LLM (stream)</text>
+  <text x="940" y="405" class="text">llmResponse events</text>
+
+  <rect x="660" y="350" width="220" height="110" class="box"/>
+  <text x="680" y="380" class="title">TTS (stream)</text>
+  <text x="680" y="405" class="text">PCM audio</text>
+
+  <rect x="40" y="350" width="250" height="110" class="box"/>
+  <text x="60" y="380" class="title">Web Client</text>
+  <text x="60" y="405" class="text">audio playback + UI</text>
+
+  <path d="M290 80 L350 80" class="arrow"/>
+  <text x="300" y="70" class="label">JSON / PCM</text>
+
+  <path d="M600 80 L660 80" class="arrow"/>
+  <text x="615" y="70" class="label">dispatch</text>
+
+  <path d="M910 80 L920 80" class="arrow"/>
+  <text x="880" y="70" class="label">turn mgmt</text>
+
+  <path d="M750 160 L750 200" class="arrow"/>
+  <text x="705" y="190" class="label">audio chunks</text>
+
+  <path d="M840 250 L860 250" class="arrow"/>
+  <text x="835" y="240" class="label">vad status</text>
+
+  <path d="M1040 250 L1060 250" class="arrow"/>
+  <text x="1010" y="240" class="label">audio buffer</text>
+
+  <path d="M950 300 L950 350" class="arrow2"/>
+  <text x="930" y="340" class="label">EOU -> LLM</text>
+
+  <path d="M880 405 L920 405" class="arrow2"/>
+  <text x="870" y="395" class="label">text stream</text>
+
+  <path d="M660 405 L290 405" class="arrow2"/>
+  <text x="430" y="395" class="label">PCM audio</text>
+
+  <path d="M660 450 L350 450" class="arrow"/>
+  <text x="420" y="440" class="label">events: trackStart/End</text>
+
+  <path d="M350 450 L290 450" class="arrow"/>
+  <text x="315" y="440" class="label">UI updates</text>
+
+  <path d="M750 200 L750 160" class="arrow3"/>
+  <text x="700" y="145" class="label">barge-in detection</text>
+
+  <path d="M760 170 L920 170" class="arrow3"/>
+  <text x="820" y="160" class="label">interrupt event + cancel</text>
+</svg>

engine/docs/proejct_todo.md

@@ -0,0 +1,187 @@
+# OmniSense: 12-Week Sprint Board + Tech Stack (Python Backend) — TODO
+
+## Scope
+- [ ] Build a realtime AI SaaS (OmniSense) focused on web-first audio + video with WebSocket + WebRTC endpoints
+- [ ] Deliver assistant builder, tool execution, observability, evals, optional telephony later
+- [ ] Keep scope aligned to 2-person team, self-hosted services
+
+---
+
+## Sprint Board (12 weeks, 2-week sprints)
+Team assumption: 2 engineers. Scope prioritized to web-first audio + video, with BYO-SFU adapters.
+
+### Sprint 1 (Weeks 1–2) — Realtime Core MVP (WebSocket + WebRTC Audio)
+- Deliverables
+  - [ ] WebSocket transport: audio in/out streaming (1:1)
+  - [ ] WebRTC transport: audio in/out streaming (1:1)
+  - [ ] Adapter contract wired into runtime (transport-agnostic session core)
+  - [ ] ASR → LLM → TTS pipeline, streaming both directions
+  - [ ] Basic session state (start/stop, silence timeout)
+  - [ ] Transcript persistence
+- Acceptance criteria
+  - [ ] < 1.5s median round-trip for short responses
+  - [ ] Stable streaming for 10+ minute session
+
+### Sprint 2 (Weeks 3–4) — Video + Realtime UX
+- Deliverables
+  - [ ] WebRTC video capture + streaming (assistant can “see” frames)
+  - [ ] WebSocket video streaming for local/dev mode
+  - [ ] Low-latency UI: push-to-talk, live captions, speaking indicator
+  - [ ] Recording + transcript storage (web sessions)
+- Acceptance criteria
+  - [ ] Video < 2.5s end-to-end latency for analysis
+  - [ ] Audio quality acceptable (no clipping, jitter handling)
+
+### Sprint 3 (Weeks 5–6) — Assistant Builder v1
+- Deliverables
+  - [ ] Assistant schema + versioning
+  - [ ] UI: Model/Voice/Transcriber/Tools/Video/Transport tabs
+  - [ ] “Test/Chat/Talk to Assistant” (web)
+- Acceptance criteria
+  - [ ] Create/publish assistant and run a live web session
+  - [ ] All config changes tracked by version
+
+### Sprint 4 (Weeks 7–8) — Tooling + Structured Outputs
+- Deliverables
+  - [ ] Tool registry + custom HTTP tools
+  - [ ] Tool auth secrets management
+  - [ ] Structured outputs (JSON extraction)
+- Acceptance criteria
+  - [ ] Tool calls executed with retries/timeouts
+  - [ ] Structured JSON stored per call/session
+
+### Sprint 5 (Weeks 9–10) — Observability + QA + Dev Platform
+- Deliverables
+  - [ ] Session logs + chat logs + media logs
+  - [ ] Evals engine + test suites
+  - [ ] Basic analytics dashboard
+  - [ ] Public WebSocket API spec + message schema
+  - [ ] JS/TS SDK (connect, send audio/video, receive transcripts)
+- Acceptance criteria
+  - [ ] Reproducible test suite runs
+  - [ ] Log filters by assistant/time/status
+  - [ ] SDK demo app runs end-to-end
+
+### Sprint 6 (Weeks 11–12) — SaaS Hardening
+- Deliverables
+  - [ ] Org/RBAC + API keys + rate limits
+  - [ ] Usage metering + credits
+  - [ ] Stripe billing integration
+  - [ ] Self-hosted DB ops (migrations, backup/restore, monitoring)
+- Acceptance criteria
+  - [ ] Metered usage per org
+  - [ ] Credits decrement correctly
+  - [ ] Optional telephony spike documented (defer build)
+  - [ ] Enterprise adapter guide published (BYO-SFU)
+
+---
+
+## Tech Stack by Service (Self-Hosted, Web-First)
+
+### 1) Transport Gateway (Realtime)
+- [ ] WebRTC (browser) + WebSocket (lightweight/dev) protocols
+- [ ] BYO-SFU adapter (enterprise) + LiveKit optional adapter + WS transport server
+- [ ] Python core (FastAPI + asyncio) + Node.js mediasoup adapters when needed
+- [ ] Media: Opus/VP8, jitter buffer, VAD, echo cancellation
+- [ ] Storage: S3-compatible (MinIO) for recordings
+
+### 2) ASR Service
+- [ ] Whisper (self-hosted) baseline
+- [ ] gRPC/WebSocket streaming transport
+- [ ] Python native service
+- [ ] Optional cloud provider fallback (later)
+
+### 3) TTS Service
+- [ ] Piper or Coqui TTS (self-hosted)
+- [ ] gRPC/WebSocket streaming transport
+- [ ] Python native service
+- [ ] Redis cache for common phrases
+
+### 4) LLM Orchestrator
+- [ ] Self-hosted (vLLM + open model)
+- [ ] Python (FastAPI + asyncio)
+- [ ] Streaming, tool calling, JSON mode
+- [ ] Safety filters + prompt templates
+
+### 5) Assistant Config Service
+- [ ] PostgreSQL
+- [ ] Python (SQLAlchemy or SQLModel)
+- [ ] Versioning, publish/rollback
+
+### 6) Session Service
+- [ ] PostgreSQL + Redis
+- [ ] Python
+- [ ] State machine, timeouts, events
+
+### 7) Tool Execution Layer
+- [ ] PostgreSQL
+- [ ] Python
+- [ ] Auth secret vault, retry policies, tool schemas
+
+### 8) Observability + Logs
+- [ ] Postgres (metadata), ClickHouse (logs/metrics)
+- [ ] OpenSearch for search
+- [ ] Prometheus + Grafana metrics
+- [ ] OpenTelemetry tracing
+
+### 9) Billing + Usage Metering
+- [ ] Stripe billing
+- [ ] PostgreSQL
+- [ ] NATS JetStream (events) + Redis counters
+
+### 10) Web App (Dashboard)
+- [ ] React + Next.js
+- [ ] Tailwind or Radix UI
+- [ ] WebRTC client + WS client; adapter-based RTC integration
+- [ ] ECharts/Recharts
+
+### 11) Auth + RBAC
+- [ ] Keycloak (self-hosted) or custom JWT
+- [ ] Org/user/role tables in Postgres
+
+### 12) Public WebSocket API + SDK
+- [ ] WS API: versioned schema, binary audio frames + JSON control messages
+- [ ] SDKs: JS/TS first, optional Python/Go clients
+- [ ] Docs: quickstart, auth flow, session lifecycle, examples
+
+---
+
+## Infrastructure (Self-Hosted)
+- [ ] Docker Compose → k3s (later)
+- [ ] Redis Streams or NATS
+- [ ] MinIO object store
+- [ ] GitHub Actions + Helm or kustomize
+- [ ] Self-hosted Postgres + pgbackrest backups
+- [ ] Vault for secrets
+
+---
+
+## Suggested MVP Sequence
+- [ ] WebRTC demo + ASR/LLM/TTS streaming
+- [ ] Assistant schema + versioning (web-first)
+- [ ] Video capture + multimodal analysis
+- [ ] Tool execution + structured outputs
+- [ ] Logs + evals + public WS API + SDK
+- [ ] Telephony (optional, later)
+
+---
+
+## Public WebSocket API (Minimum Spec)
+- [ ] Auth: API key or JWT in initial `hello` message
+- [ ] Core messages: `session.start`, `session.stop`, `audio.append`, `audio.commit`, `video.append`, `transcript.delta`, `assistant.response`, `tool.call`, `tool.result`, `error`
+- [ ] Binary payloads: PCM/Opus frames with metadata in control channel
+- [ ] Versioning: `v1` schema with backward compatibility rules
+
+---
+
+## Self-Hosted DB Ops Checklist
+- [ ] Postgres in Docker/k3s with persistent volumes
+- [ ] Migrations: `alembic` or `atlas`
+- [ ] Backups: `pgbackrest` nightly + on-demand
+- [ ] Monitoring: postgres_exporter + alerts
+
+---
+
+## RTC Adapter Contract (BYO-SFU First)
+- [ ] Keep RTC pluggable; LiveKit optional, not core dependency
+- [ ] Define adapter interface (TypeScript sketch)

engine/examples/mic_client.py

@@ -0,0 +1,601 @@
+#!/usr/bin/env python3
+"""
+Microphone client for testing duplex voice conversation.
+
+This client captures audio from the microphone, sends it to the server,
+and plays back the AI's voice response through the speakers.
+It also displays the LLM's text responses in the console.
+
+Usage:
+    python examples/mic_client.py --url ws://localhost:8000/ws
+    python examples/mic_client.py --url ws://localhost:8000/ws --chat "Hello!"
+    python examples/mic_client.py --url ws://localhost:8000/ws --verbose
+
+Requirements:
+    pip install sounddevice soundfile websockets numpy
+"""
+
+import argparse
+import asyncio
+import json
+import sys
+import time
+import threading
+import queue
+from pathlib import Path
+
+try:
+    import numpy as np
+except ImportError:
+    print("Please install numpy: pip install numpy")
+    sys.exit(1)
+
+try:
+    import sounddevice as sd
+except ImportError:
+    print("Please install sounddevice: pip install sounddevice")
+    sys.exit(1)
+
+try:
+    import websockets
+except ImportError:
+    print("Please install websockets: pip install websockets")
+    sys.exit(1)
+
+
+class MicrophoneClient:
+    """
+    Full-duplex microphone client for voice conversation.
+    
+    Features:
+    - Real-time microphone capture
+    - Real-time speaker playback
+    - WebSocket communication
+    - Text chat support
+    """
+    
+    def __init__(
+        self,
+        url: str,
+        sample_rate: int = 16000,
+        chunk_duration_ms: int = 20,
+        input_device: int = None,
+        output_device: int = None
+    ):
+        """
+        Initialize microphone client.
+        
+        Args:
+            url: WebSocket server URL
+            sample_rate: Audio sample rate (Hz)
+            chunk_duration_ms: Audio chunk duration (ms)
+            input_device: Input device ID (None for default)
+            output_device: Output device ID (None for default)
+        """
+        self.url = url
+        self.sample_rate = sample_rate
+        self.chunk_duration_ms = chunk_duration_ms
+        self.chunk_samples = int(sample_rate * chunk_duration_ms / 1000)
+        self.input_device = input_device
+        self.output_device = output_device
+        
+        # WebSocket connection
+        self.ws = None
+        self.running = False
+        
+        # Audio buffers
+        self.audio_input_queue = queue.Queue()
+        self.audio_output_buffer = b""  # Continuous buffer for smooth playback
+        self.audio_output_lock = threading.Lock()
+        
+        # Statistics
+        self.bytes_sent = 0
+        self.bytes_received = 0
+        
+        # State
+        self.is_recording = True
+        self.is_playing = True
+        
+        # TTFB tracking (Time to First Byte)
+        self.request_start_time = None
+        self.first_audio_received = False
+        
+        # Interrupt handling - discard audio until next trackStart
+        self._discard_audio = False
+        self._audio_sequence = 0  # Track audio sequence to detect stale chunks
+        
+        # Verbose mode for streaming LLM responses
+        self.verbose = False
+    
+    async def connect(self) -> None:
+        """Connect to WebSocket server."""
+        print(f"Connecting to {self.url}...")
+        self.ws = await websockets.connect(self.url)
+        self.running = True
+        print("Connected!")
+        
+        # Send invite command
+        await self.send_command({
+            "command": "invite",
+            "option": {
+                "codec": "pcm",
+                "sampleRate": self.sample_rate
+            }
+        })
+    
+    async def send_command(self, cmd: dict) -> None:
+        """Send JSON command to server."""
+        if self.ws:
+            await self.ws.send(json.dumps(cmd))
+            print(f"→ Command: {cmd.get('command', 'unknown')}")
+    
+    async def send_chat(self, text: str) -> None:
+        """Send chat message (text input)."""
+        # Reset TTFB tracking for new request
+        self.request_start_time = time.time()
+        self.first_audio_received = False
+        
+        await self.send_command({
+            "command": "chat",
+            "text": text
+        })
+        print(f"→ Chat: {text}")
+    
+    async def send_interrupt(self) -> None:
+        """Send interrupt command."""
+        await self.send_command({
+            "command": "interrupt"
+        })
+    
+    async def send_hangup(self, reason: str = "User quit") -> None:
+        """Send hangup command."""
+        await self.send_command({
+            "command": "hangup",
+            "reason": reason
+        })
+    
+    def _audio_input_callback(self, indata, frames, time, status):
+        """Callback for audio input (microphone)."""
+        if status:
+            print(f"Input status: {status}")
+        
+        if self.is_recording and self.running:
+            # Convert to 16-bit PCM
+            audio_data = (indata[:, 0] * 32767).astype(np.int16).tobytes()
+            self.audio_input_queue.put(audio_data)
+    
+    def _add_audio_to_buffer(self, audio_data: bytes):
+        """Add audio data to playback buffer."""
+        with self.audio_output_lock:
+            self.audio_output_buffer += audio_data
+    
+    def _playback_thread_func(self):
+        """Thread function for continuous audio playback."""
+        import time
+        
+        # Chunk size: 50ms of audio
+        chunk_samples = int(self.sample_rate * 0.05)
+        chunk_bytes = chunk_samples * 2
+        
+        print(f"Audio playback thread started (device: {self.output_device or 'default'})")
+        
+        try:
+            # Create output stream with callback
+            with sd.OutputStream(
+                samplerate=self.sample_rate,
+                channels=1,
+                dtype='int16',
+                blocksize=chunk_samples,
+                device=self.output_device,
+                latency='low'
+            ) as stream:
+                while self.running:
+                    # Get audio from buffer
+                    with self.audio_output_lock:
+                        if len(self.audio_output_buffer) >= chunk_bytes:
+                            audio_data = self.audio_output_buffer[:chunk_bytes]
+                            self.audio_output_buffer = self.audio_output_buffer[chunk_bytes:]
+                        else:
+                            # Not enough audio - output silence
+                            audio_data = b'\x00' * chunk_bytes
+                    
+                    # Convert to numpy array and write to stream
+                    samples = np.frombuffer(audio_data, dtype=np.int16).reshape(-1, 1)
+                    stream.write(samples)
+                    
+        except Exception as e:
+            print(f"Playback thread error: {e}")
+            import traceback
+            traceback.print_exc()
+    
+    async def _playback_task(self):
+        """Start playback thread and monitor it."""
+        # Run playback in a dedicated thread for reliable timing
+        playback_thread = threading.Thread(target=self._playback_thread_func, daemon=True)
+        playback_thread.start()
+        
+        # Wait for client to stop
+        while self.running and playback_thread.is_alive():
+            await asyncio.sleep(0.1)
+        
+        print("Audio playback stopped")
+    
+    async def audio_sender(self) -> None:
+        """Send audio from microphone to server."""
+        while self.running:
+            try:
+                # Get audio from queue with timeout
+                try:
+                    audio_data = await asyncio.get_event_loop().run_in_executor(
+                        None, lambda: self.audio_input_queue.get(timeout=0.1)
+                    )
+                except queue.Empty:
+                    continue
+                
+                # Send to server
+                if self.ws and self.is_recording:
+                    await self.ws.send(audio_data)
+                    self.bytes_sent += len(audio_data)
+                    
+            except asyncio.CancelledError:
+                break
+            except Exception as e:
+                print(f"Audio sender error: {e}")
+                break
+    
+    async def receiver(self) -> None:
+        """Receive messages from server."""
+        try:
+            while self.running:
+                try:
+                    message = await asyncio.wait_for(self.ws.recv(), timeout=0.1)
+                    
+                    if isinstance(message, bytes):
+                        # Audio data received
+                        self.bytes_received += len(message)
+                        
+                        # Check if we should discard this audio (after interrupt)
+                        if self._discard_audio:
+                            duration_ms = len(message) / (self.sample_rate * 2) * 1000
+                            print(f"← Audio: {duration_ms:.0f}ms (DISCARDED - waiting for new track)")
+                            continue
+                        
+                        if self.is_playing:
+                            self._add_audio_to_buffer(message)
+                        
+                        # Calculate and display TTFB for first audio packet
+                        if not self.first_audio_received and self.request_start_time:
+                            client_ttfb_ms = (time.time() - self.request_start_time) * 1000
+                            self.first_audio_received = True
+                            print(f"← [TTFB] Client first audio latency: {client_ttfb_ms:.0f}ms")
+                        
+                        # Show progress (less verbose)
+                        with self.audio_output_lock:
+                            buffer_ms = len(self.audio_output_buffer) / (self.sample_rate * 2) * 1000
+                        duration_ms = len(message) / (self.sample_rate * 2) * 1000
+                        print(f"← Audio: {duration_ms:.0f}ms (buffer: {buffer_ms:.0f}ms)")
+                        
+                    else:
+                        # JSON event
+                        event = json.loads(message)
+                        await self._handle_event(event)
+                        
+                except asyncio.TimeoutError:
+                    continue
+                except websockets.ConnectionClosed:
+                    print("Connection closed")
+                    self.running = False
+                    break
+                    
+        except asyncio.CancelledError:
+            pass
+        except Exception as e:
+            print(f"Receiver error: {e}")
+            self.running = False
+    
+    async def _handle_event(self, event: dict) -> None:
+        """Handle incoming event."""
+        event_type = event.get("event", "unknown")
+        
+        if event_type == "answer":
+            print("← Session ready!")
+        elif event_type == "speaking":
+            print("← User speech detected")
+        elif event_type == "silence":
+            print("← User silence detected")
+        elif event_type == "transcript":
+            # Display user speech transcription
+            text = event.get("text", "")
+            is_final = event.get("isFinal", False)
+            if is_final:
+                # Clear the interim line and print final
+                print(" " * 80, end="\r")  # Clear previous interim text
+                print(f"→ You: {text}")
+            else:
+                # Interim result - show with indicator (overwrite same line)
+                display_text = text[:60] + "..." if len(text) > 60 else text
+                print(f"  [listening] {display_text}".ljust(80), end="\r")
+        elif event_type == "ttfb":
+            # Server-side TTFB event
+            latency_ms = event.get("latencyMs", 0)
+            print(f"← [TTFB] Server reported latency: {latency_ms}ms")
+        elif event_type == "llmResponse":
+            # LLM text response
+            text = event.get("text", "")
+            is_final = event.get("isFinal", False)
+            if is_final:
+                # Print final LLM response
+                print(f"← AI: {text}")
+            elif self.verbose:
+                # Show streaming chunks only in verbose mode
+                display_text = text[:60] + "..." if len(text) > 60 else text
+                print(f"  [streaming] {display_text}")
+        elif event_type == "trackStart":
+            print("← Bot started speaking")
+            # IMPORTANT: Accept audio again after trackStart
+            self._discard_audio = False
+            self._audio_sequence += 1
+            # Reset TTFB tracking for voice responses (when no chat was sent)
+            if self.request_start_time is None:
+                self.request_start_time = time.time()
+                self.first_audio_received = False
+            # Clear any old audio in buffer
+            with self.audio_output_lock:
+                self.audio_output_buffer = b""
+        elif event_type == "trackEnd":
+            print("← Bot finished speaking")
+            # Reset TTFB tracking after response completes
+            self.request_start_time = None
+            self.first_audio_received = False
+        elif event_type == "interrupt":
+            print("← Bot interrupted!")
+            # IMPORTANT: Discard all audio until next trackStart
+            self._discard_audio = True
+            # Clear audio buffer immediately
+            with self.audio_output_lock:
+                buffer_ms = len(self.audio_output_buffer) / (self.sample_rate * 2) * 1000
+                self.audio_output_buffer = b""
+                print(f"   (cleared {buffer_ms:.0f}ms, discarding audio until new track)")
+        elif event_type == "error":
+            print(f"← Error: {event.get('error')}")
+        elif event_type == "hangup":
+            print(f"← Hangup: {event.get('reason')}")
+            self.running = False
+        else:
+            print(f"← Event: {event_type}")
+    
+    async def interactive_mode(self) -> None:
+        """Run interactive mode for text chat."""
+        print("\n" + "=" * 50)
+        print("Voice Conversation Client")
+        print("=" * 50)
+        print("Speak into your microphone to talk to the AI.")
+        print("Or type messages to send text.")
+        print("")
+        print("Commands:")
+        print("  /quit      - End conversation")
+        print("  /mute      - Mute microphone")
+        print("  /unmute    - Unmute microphone")
+        print("  /interrupt - Interrupt AI speech")
+        print("  /stats     - Show statistics")
+        print("=" * 50 + "\n")
+        
+        while self.running:
+            try:
+                user_input = await asyncio.get_event_loop().run_in_executor(
+                    None, input, ""
+                )
+                
+                if not user_input:
+                    continue
+                
+                # Handle commands
+                if user_input.startswith("/"):
+                    cmd = user_input.lower().strip()
+                    
+                    if cmd == "/quit":
+                        await self.send_hangup("User quit")
+                        break
+                    elif cmd == "/mute":
+                        self.is_recording = False
+                        print("Microphone muted")
+                    elif cmd == "/unmute":
+                        self.is_recording = True
+                        print("Microphone unmuted")
+                    elif cmd == "/interrupt":
+                        await self.send_interrupt()
+                    elif cmd == "/stats":
+                        print(f"Sent: {self.bytes_sent / 1024:.1f} KB")
+                        print(f"Received: {self.bytes_received / 1024:.1f} KB")
+                    else:
+                        print(f"Unknown command: {cmd}")
+                else:
+                    # Send as chat message
+                    await self.send_chat(user_input)
+                    
+            except EOFError:
+                break
+            except Exception as e:
+                print(f"Input error: {e}")
+    
+    async def run(self, chat_message: str = None, interactive: bool = True) -> None:
+        """
+        Run the client.
+        
+        Args:
+            chat_message: Optional single chat message to send
+            interactive: Whether to run in interactive mode
+        """
+        try:
+            await self.connect()
+            
+            # Wait for answer
+            await asyncio.sleep(0.5)
+            
+            # Start audio input stream
+            print("Starting audio streams...")
+            
+            input_stream = sd.InputStream(
+                samplerate=self.sample_rate,
+                channels=1,
+                dtype=np.float32,
+                blocksize=self.chunk_samples,
+                device=self.input_device,
+                callback=self._audio_input_callback
+            )
+            
+            input_stream.start()
+            print("Audio streams started")
+            
+            # Start background tasks
+            sender_task = asyncio.create_task(self.audio_sender())
+            receiver_task = asyncio.create_task(self.receiver())
+            playback_task = asyncio.create_task(self._playback_task())
+            
+            if chat_message:
+                # Send single message and wait
+                await self.send_chat(chat_message)
+                await asyncio.sleep(15)
+            elif interactive:
+                # Run interactive mode
+                await self.interactive_mode()
+            else:
+                # Just wait
+                while self.running:
+                    await asyncio.sleep(0.1)
+            
+            # Cleanup
+            self.running = False
+            sender_task.cancel()
+            receiver_task.cancel()
+            playback_task.cancel()
+            
+            try:
+                await sender_task
+            except asyncio.CancelledError:
+                pass
+            
+            try:
+                await receiver_task
+            except asyncio.CancelledError:
+                pass
+            
+            try:
+                await playback_task
+            except asyncio.CancelledError:
+                pass
+            
+            input_stream.stop()
+            
+        except ConnectionRefusedError:
+            print(f"Error: Could not connect to {self.url}")
+            print("Make sure the server is running.")
+        except Exception as e:
+            print(f"Error: {e}")
+        finally:
+            await self.close()
+    
+    async def close(self) -> None:
+        """Close the connection."""
+        self.running = False
+        if self.ws:
+            await self.ws.close()
+        
+        print(f"\nSession ended")
+        print(f"  Total sent: {self.bytes_sent / 1024:.1f} KB")
+        print(f"  Total received: {self.bytes_received / 1024:.1f} KB")
+
+
+def list_devices():
+    """List available audio devices."""
+    print("\nAvailable audio devices:")
+    print("-" * 60)
+    devices = sd.query_devices()
+    for i, device in enumerate(devices):
+        direction = []
+        if device['max_input_channels'] > 0:
+            direction.append("IN")
+        if device['max_output_channels'] > 0:
+            direction.append("OUT")
+        direction_str = "/".join(direction) if direction else "N/A"
+        
+        default = ""
+        if i == sd.default.device[0]:
+            default += " [DEFAULT INPUT]"
+        if i == sd.default.device[1]:
+            default += " [DEFAULT OUTPUT]"
+        
+        print(f"  {i:2d}: {device['name'][:40]:40s} ({direction_str}){default}")
+    print("-" * 60)
+
+
+async def main():
+    parser = argparse.ArgumentParser(
+        description="Microphone client for duplex voice conversation"
+    )
+    parser.add_argument(
+        "--url",
+        default="ws://localhost:8000/ws",
+        help="WebSocket server URL"
+    )
+    parser.add_argument(
+        "--chat",
+        help="Send a single chat message instead of using microphone"
+    )
+    parser.add_argument(
+        "--sample-rate",
+        type=int,
+        default=16000,
+        help="Audio sample rate (default: 16000)"
+    )
+    parser.add_argument(
+        "--input-device",
+        type=int,
+        help="Input device ID"
+    )
+    parser.add_argument(
+        "--output-device",
+        type=int,
+        help="Output device ID"
+    )
+    parser.add_argument(
+        "--list-devices",
+        action="store_true",
+        help="List available audio devices and exit"
+    )
+    parser.add_argument(
+        "--no-interactive",
+        action="store_true",
+        help="Disable interactive mode"
+    )
+    parser.add_argument(
+        "--verbose", "-v",
+        action="store_true",
+        help="Show streaming LLM response chunks"
+    )
+    
+    args = parser.parse_args()
+    
+    if args.list_devices:
+        list_devices()
+        return
+    
+    client = MicrophoneClient(
+        url=args.url,
+        sample_rate=args.sample_rate,
+        input_device=args.input_device,
+        output_device=args.output_device
+    )
+    client.verbose = args.verbose
+    
+    await client.run(
+        chat_message=args.chat,
+        interactive=not args.no_interactive
+    )
+
+
+if __name__ == "__main__":
+    try:
+        asyncio.run(main())
+    except KeyboardInterrupt:
+        print("\nInterrupted by user")

engine/examples/simple_client.py

@@ -0,0 +1,285 @@
+#!/usr/bin/env python3
+"""
+Simple WebSocket client for testing voice conversation.
+Uses PyAudio for more reliable audio playback on Windows.
+
+Usage:
+    python examples/simple_client.py
+    python examples/simple_client.py --text "Hello"
+"""
+
+import argparse
+import asyncio
+import json
+import sys
+import time
+import wave
+import io
+
+try:
+    import numpy as np
+except ImportError:
+    print("pip install numpy")
+    sys.exit(1)
+
+try:
+    import websockets
+except ImportError:
+    print("pip install websockets")
+    sys.exit(1)
+
+# Try PyAudio first (more reliable on Windows)
+try:
+    import pyaudio
+    PYAUDIO_AVAILABLE = True
+except ImportError:
+    PYAUDIO_AVAILABLE = False
+    print("PyAudio not available, trying sounddevice...")
+
+try:
+    import sounddevice as sd
+    SD_AVAILABLE = True
+except ImportError:
+    SD_AVAILABLE = False
+
+if not PYAUDIO_AVAILABLE and not SD_AVAILABLE:
+    print("Please install pyaudio or sounddevice:")
+    print("  pip install pyaudio")
+    print("  or: pip install sounddevice")
+    sys.exit(1)
+
+
+class SimpleVoiceClient:
+    """Simple voice client with reliable audio playback."""
+    
+    def __init__(self, url: str, sample_rate: int = 16000):
+        self.url = url
+        self.sample_rate = sample_rate
+        self.ws = None
+        self.running = False
+        
+        # Audio buffer
+        self.audio_buffer = b""
+        
+        # PyAudio setup
+        if PYAUDIO_AVAILABLE:
+            self.pa = pyaudio.PyAudio()
+            self.stream = None
+        
+        # Stats
+        self.bytes_received = 0
+        
+        # TTFB tracking (Time to First Byte)
+        self.request_start_time = None
+        self.first_audio_received = False
+        
+        # Interrupt handling - discard audio until next trackStart
+        self._discard_audio = False
+    
+    async def connect(self):
+        """Connect to server."""
+        print(f"Connecting to {self.url}...")
+        self.ws = await websockets.connect(self.url)
+        self.running = True
+        print("Connected!")
+        
+        # Send invite
+        await self.ws.send(json.dumps({
+            "command": "invite",
+            "option": {"codec": "pcm", "sampleRate": self.sample_rate}
+        }))
+        print("-> invite")
+    
+    async def send_chat(self, text: str):
+        """Send chat message."""
+        # Reset TTFB tracking for new request
+        self.request_start_time = time.time()
+        self.first_audio_received = False
+        
+        await self.ws.send(json.dumps({"command": "chat", "text": text}))
+        print(f"-> chat: {text}")
+    
+    def play_audio(self, audio_data: bytes):
+        """Play audio data immediately."""
+        if len(audio_data) == 0:
+            return
+            
+        if PYAUDIO_AVAILABLE:
+            # Use PyAudio - more reliable on Windows
+            if self.stream is None:
+                self.stream = self.pa.open(
+                    format=pyaudio.paInt16,
+                    channels=1,
+                    rate=self.sample_rate,
+                    output=True,
+                    frames_per_buffer=1024
+                )
+            self.stream.write(audio_data)
+        elif SD_AVAILABLE:
+            # Use sounddevice
+            samples = np.frombuffer(audio_data, dtype=np.int16).astype(np.float32) / 32767.0
+            sd.play(samples, self.sample_rate, blocking=True)
+    
+    async def receive_loop(self):
+        """Receive and play audio."""
+        print("\nWaiting for response...")
+        
+        while self.running:
+            try:
+                msg = await asyncio.wait_for(self.ws.recv(), timeout=0.1)
+                
+                if isinstance(msg, bytes):
+                    # Audio data
+                    self.bytes_received += len(msg)
+                    duration_ms = len(msg) / (self.sample_rate * 2) * 1000
+                    
+                    # Check if we should discard this audio (after interrupt)
+                    if self._discard_audio:
+                        print(f"<- audio: {len(msg)} bytes ({duration_ms:.0f}ms) [DISCARDED]")
+                        continue
+                    
+                    # Calculate and display TTFB for first audio packet
+                    if not self.first_audio_received and self.request_start_time:
+                        client_ttfb_ms = (time.time() - self.request_start_time) * 1000
+                        self.first_audio_received = True
+                        print(f"<- [TTFB] Client first audio latency: {client_ttfb_ms:.0f}ms")
+                    
+                    print(f"<- audio: {len(msg)} bytes ({duration_ms:.0f}ms)")
+                    
+                    # Play immediately in executor to not block
+                    loop = asyncio.get_event_loop()
+                    await loop.run_in_executor(None, self.play_audio, msg)
+                else:
+                    # JSON event
+                    event = json.loads(msg)
+                    etype = event.get("event", "?")
+                    
+                    if etype == "transcript":
+                        # User speech transcription
+                        text = event.get("text", "")
+                        is_final = event.get("isFinal", False)
+                        if is_final:
+                            print(f"<- You said: {text}")
+                        else:
+                            print(f"<- [listening] {text}", end="\r")
+                    elif etype == "ttfb":
+                        # Server-side TTFB event
+                        latency_ms = event.get("latencyMs", 0)
+                        print(f"<- [TTFB] Server reported latency: {latency_ms}ms")
+                    elif etype == "trackStart":
+                        # New track starting - accept audio again
+                        self._discard_audio = False
+                        print(f"<- {etype}")
+                    elif etype == "interrupt":
+                        # Interrupt - discard audio until next trackStart
+                        self._discard_audio = True
+                        print(f"<- {etype} (discarding audio until new track)")
+                    elif etype == "hangup":
+                        print(f"<- {etype}")
+                        self.running = False
+                        break
+                    else:
+                        print(f"<- {etype}")
+                        
+            except asyncio.TimeoutError:
+                continue
+            except websockets.ConnectionClosed:
+                print("Connection closed")
+                self.running = False
+                break
+    
+    async def run(self, text: str = None):
+        """Run the client."""
+        try:
+            await self.connect()
+            await asyncio.sleep(0.5)
+            
+            # Start receiver
+            recv_task = asyncio.create_task(self.receive_loop())
+            
+            if text:
+                await self.send_chat(text)
+                # Wait for response
+                await asyncio.sleep(30)
+            else:
+                # Interactive mode
+                print("\nType a message and press Enter (or 'quit' to exit):")
+                while self.running:
+                    try:
+                        user_input = await asyncio.get_event_loop().run_in_executor(
+                            None, input, "> "
+                        )
+                        if user_input.lower() == 'quit':
+                            break
+                        if user_input.strip():
+                            await self.send_chat(user_input)
+                    except EOFError:
+                        break
+            
+            self.running = False
+            recv_task.cancel()
+            try:
+                await recv_task
+            except asyncio.CancelledError:
+                pass
+                
+        finally:
+            await self.close()
+    
+    async def close(self):
+        """Close connections."""
+        self.running = False
+        
+        if PYAUDIO_AVAILABLE:
+            if self.stream:
+                self.stream.stop_stream()
+                self.stream.close()
+            self.pa.terminate()
+        
+        if self.ws:
+            await self.ws.close()
+        
+        print(f"\nTotal audio received: {self.bytes_received / 1024:.1f} KB")
+
+
+def list_audio_devices():
+    """List available audio devices."""
+    print("\n=== Audio Devices ===")
+    
+    if PYAUDIO_AVAILABLE:
+        pa = pyaudio.PyAudio()
+        print("\nPyAudio devices:")
+        for i in range(pa.get_device_count()):
+            info = pa.get_device_info_by_index(i)
+            if info['maxOutputChannels'] > 0:
+                default = " [DEFAULT]" if i == pa.get_default_output_device_info()['index'] else ""
+                print(f"  {i}: {info['name']}{default}")
+        pa.terminate()
+    
+    if SD_AVAILABLE:
+        print("\nSounddevice devices:")
+        for i, d in enumerate(sd.query_devices()):
+            if d['max_output_channels'] > 0:
+                default = " [DEFAULT]" if i == sd.default.device[1] else ""
+                print(f"  {i}: {d['name']}{default}")
+
+
+async def main():
+    parser = argparse.ArgumentParser(description="Simple voice client")
+    parser.add_argument("--url", default="ws://localhost:8000/ws")
+    parser.add_argument("--text", help="Send text and play response")
+    parser.add_argument("--list-devices", action="store_true")
+    parser.add_argument("--sample-rate", type=int, default=16000)
+    
+    args = parser.parse_args()
+    
+    if args.list_devices:
+        list_audio_devices()
+        return
+    
+    client = SimpleVoiceClient(args.url, args.sample_rate)
+    await client.run(args.text)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

engine/examples/test_websocket.py

@@ -0,0 +1,166 @@
+"""WebSocket endpoint test client.
+
+Tests the /ws endpoint with sine wave or file audio streaming.
+Based on reference/py-active-call/exec/test_ws_endpoint/test_ws.py
+"""
+
+import asyncio
+import aiohttp
+import json
+import struct
+import math
+import argparse
+import os
+from datetime import datetime
+
+# Configuration
+SERVER_URL = "ws://localhost:8000/ws"
+SAMPLE_RATE = 16000
+FREQUENCY = 440  # 440Hz Sine Wave
+CHUNK_DURATION_MS = 20
+# 16kHz * 16-bit (2 bytes) * 20ms = 640 bytes per chunk
+CHUNK_SIZE_BYTES = int(SAMPLE_RATE * 2 * (CHUNK_DURATION_MS / 1000.0))
+
+
+def generate_sine_wave(duration_ms=1000):
+    """Generates sine wave audio (16kHz mono PCM 16-bit)."""
+    num_samples = int(SAMPLE_RATE * (duration_ms / 1000.0))
+    audio_data = bytearray()
+
+    for x in range(num_samples):
+        # Generate sine wave sample
+        value = int(32767.0 * math.sin(2 * math.pi * FREQUENCY * x / SAMPLE_RATE))
+        # Pack as little-endian 16-bit integer
+        audio_data.extend(struct.pack('<h', value))
+
+    return audio_data
+
+
+async def receive_loop(ws):
+    """Listen for incoming messages from the server."""
+    print("👂 Listening for server responses...")
+    async for msg in ws:
+        timestamp = datetime.now().strftime("%H:%M:%S")
+
+        if msg.type == aiohttp.WSMsgType.TEXT:
+            try:
+                data = json.loads(msg.data)
+                event_type = data.get('event', 'Unknown')
+                print(f"[{timestamp}] 📨 Event: {event_type} | {msg.data[:150]}...")
+            except json.JSONDecodeError:
+                print(f"[{timestamp}] 📨 Text: {msg.data[:100]}...")
+
+        elif msg.type == aiohttp.WSMsgType.BINARY:
+            # Received audio chunk back (e.g., TTS or echo)
+            print(f"[{timestamp}] 🔊 Audio: {len(msg.data)} bytes", end="\r")
+
+        elif msg.type == aiohttp.WSMsgType.CLOSED:
+            print(f"\n[{timestamp}] ❌ Socket Closed")
+            break
+
+        elif msg.type == aiohttp.WSMsgType.ERROR:
+            print(f"\n[{timestamp}] ⚠️ Socket Error")
+            break
+
+
+async def send_file_loop(ws, file_path):
+    """Stream a raw PCM/WAV file to the server."""
+    if not os.path.exists(file_path):
+        print(f"❌ Error: File '{file_path}' not found.")
+        return
+
+    print(f"📂 Streaming file: {file_path} ...")
+
+    with open(file_path, "rb") as f:
+        # Skip WAV header if present (first 44 bytes)
+        if file_path.endswith('.wav'):
+            f.read(44)
+
+        while True:
+            chunk = f.read(CHUNK_SIZE_BYTES)
+            if not chunk:
+                break
+
+            # Send binary frame
+            await ws.send_bytes(chunk)
+
+            # Sleep to simulate real-time playback
+            await asyncio.sleep(CHUNK_DURATION_MS / 1000.0)
+
+    print(f"\n✅ Finished streaming {file_path}")
+
+
+async def send_sine_loop(ws):
+    """Stream generated sine wave to the server."""
+    print("🎙️  Starting Audio Stream (Sine Wave)...")
+
+    # Generate 10 seconds of audio buffer
+    audio_buffer = generate_sine_wave(5000)
+    cursor = 0
+
+    while cursor < len(audio_buffer):
+        chunk = audio_buffer[cursor:cursor + CHUNK_SIZE_BYTES]
+        if not chunk:
+            break
+
+        await ws.send_bytes(chunk)
+        cursor += len(chunk)
+
+        await asyncio.sleep(CHUNK_DURATION_MS / 1000.0)
+
+    print("\n✅ Finished streaming test audio.")
+
+
+async def run_client(url, file_path=None, use_sine=False):
+    """Run the WebSocket test client."""
+    session = aiohttp.ClientSession()
+    try:
+        print(f"🔌 Connecting to {url}...")
+        async with session.ws_connect(url) as ws:
+            print("✅ Connected!")
+
+            # Send initial invite command
+            init_cmd = {
+                "command": "invite",
+                "option": {
+                    "codec": "pcm",
+                    "samplerate": SAMPLE_RATE
+                }
+            }
+            await ws.send_json(init_cmd)
+            print("📤 Sent Invite Command")
+
+            # Select sender based on args
+            if use_sine:
+                sender_task = send_sine_loop(ws)
+            elif file_path:
+                sender_task = send_file_loop(ws, file_path)
+            else:
+                # Default to sine wave
+                sender_task = send_sine_loop(ws)
+
+            # Run send and receive loops in parallel
+            await asyncio.gather(
+                receive_loop(ws),
+                sender_task
+            )
+
+    except aiohttp.ClientConnectorError:
+        print(f"❌ Connection Failed. Is the server running at {url}?")
+    except Exception as e:
+        print(f"❌ Error: {e}")
+    finally:
+        await session.close()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="WebSocket Audio Test Client")
+    parser.add_argument("--url", default=SERVER_URL, help="WebSocket endpoint URL")
+    parser.add_argument("--file", help="Path to PCM/WAV file to stream")
+    parser.add_argument("--sine", action="store_true", help="Use sine wave generation (default)")
+    args = parser.parse_args()
+
+    try:
+        asyncio.run(run_client(args.url, args.file, args.sine))
+    except KeyboardInterrupt:
+        print("\n👋 Client stopped.")

engine/examples/wav_client.py

@@ -0,0 +1,504 @@
+#!/usr/bin/env python3
+"""
+WAV file client for testing duplex voice conversation.
+
+This client reads audio from a WAV file, sends it to the server,
+and saves the AI's voice response to an output WAV file.
+
+Usage:
+    python examples/wav_client.py --input input.wav --output response.wav
+    python examples/wav_client.py --input input.wav --output response.wav --url ws://localhost:8000/ws
+    python examples/wav_client.py --input input.wav --output response.wav --wait-time 10
+    python wav_client.py --input ../data/audio_examples/two_utterances.wav -o response.wav
+Requirements:
+    pip install soundfile websockets numpy
+"""
+
+import argparse
+import asyncio
+import json
+import sys
+import time
+import wave
+from pathlib import Path
+
+try:
+    import numpy as np
+except ImportError:
+    print("Please install numpy: pip install numpy")
+    sys.exit(1)
+
+try:
+    import soundfile as sf
+except ImportError:
+    print("Please install soundfile: pip install soundfile")
+    sys.exit(1)
+
+try:
+    import websockets
+except ImportError:
+    print("Please install websockets: pip install websockets")
+    sys.exit(1)
+
+
+class WavFileClient:
+    """
+    WAV file client for voice conversation testing.
+    
+    Features:
+    - Read audio from WAV file
+    - Send audio to WebSocket server
+    - Receive and save response audio
+    - Event logging
+    """
+    
+    def __init__(
+        self,
+        url: str,
+        input_file: str,
+        output_file: str,
+        sample_rate: int = 16000,
+        chunk_duration_ms: int = 20,
+        wait_time: float = 15.0,
+        verbose: bool = False
+    ):
+        """
+        Initialize WAV file client.
+        
+        Args:
+            url: WebSocket server URL
+            input_file: Input WAV file path
+            output_file: Output WAV file path
+            sample_rate: Audio sample rate (Hz)
+            chunk_duration_ms: Audio chunk duration (ms) for sending
+            wait_time: Time to wait for response after sending (seconds)
+            verbose: Enable verbose output
+        """
+        self.url = url
+        self.input_file = Path(input_file)
+        self.output_file = Path(output_file)
+        self.sample_rate = sample_rate
+        self.chunk_duration_ms = chunk_duration_ms
+        self.chunk_samples = int(sample_rate * chunk_duration_ms / 1000)
+        self.wait_time = wait_time
+        self.verbose = verbose
+        
+        # WebSocket connection
+        self.ws = None
+        self.running = False
+        
+        # Audio buffers
+        self.received_audio = bytearray()
+        
+        # Statistics
+        self.bytes_sent = 0
+        self.bytes_received = 0
+        
+        # TTFB tracking (per response)
+        self.send_start_time = None
+        self.response_start_time = None  # set on each trackStart
+        self.waiting_for_first_audio = False
+        self.ttfb_ms = None  # last TTFB for summary
+        self.ttfb_list = []  # TTFB for each response
+        
+        # State tracking
+        self.track_started = False
+        self.track_ended = False
+        self.send_completed = False
+        
+        # Events log
+        self.events_log = []
+    
+    def log_event(self, direction: str, message: str):
+        """Log an event with timestamp."""
+        timestamp = time.time()
+        self.events_log.append({
+            "timestamp": timestamp,
+            "direction": direction,
+            "message": message
+        })
+        # Handle encoding errors on Windows
+        try:
+            print(f"{direction} {message}")
+        except UnicodeEncodeError:
+            # Replace problematic characters for console output
+            safe_message = message.encode('ascii', errors='replace').decode('ascii')
+            print(f"{direction} {safe_message}")
+    
+    async def connect(self) -> None:
+        """Connect to WebSocket server."""
+        self.log_event("→", f"Connecting to {self.url}...")
+        self.ws = await websockets.connect(self.url)
+        self.running = True
+        self.log_event("←", "Connected!")
+        
+        # Send invite command
+        await self.send_command({
+            "command": "invite",
+            "option": {
+                "codec": "pcm",
+                "sampleRate": self.sample_rate
+            }
+        })
+    
+    async def send_command(self, cmd: dict) -> None:
+        """Send JSON command to server."""
+        if self.ws:
+            await self.ws.send(json.dumps(cmd))
+            self.log_event("→", f"Command: {cmd.get('command', 'unknown')}")
+    
+    async def send_hangup(self, reason: str = "Session complete") -> None:
+        """Send hangup command."""
+        await self.send_command({
+            "command": "hangup",
+            "reason": reason
+        })
+    
+    def load_wav_file(self) -> tuple[np.ndarray, int]:
+        """
+        Load and prepare WAV file for sending.
+        
+        Returns:
+            Tuple of (audio_data as int16 numpy array, original sample rate)
+        """
+        if not self.input_file.exists():
+            raise FileNotFoundError(f"Input file not found: {self.input_file}")
+        
+        # Load audio file
+        audio_data, file_sample_rate = sf.read(self.input_file)
+        self.log_event("→", f"Loaded: {self.input_file}")
+        self.log_event("→", f"  Original sample rate: {file_sample_rate} Hz")
+        self.log_event("→", f"  Duration: {len(audio_data) / file_sample_rate:.2f}s")
+        
+        # Convert stereo to mono if needed
+        if len(audio_data.shape) > 1:
+            audio_data = audio_data.mean(axis=1)
+            self.log_event("→", "  Converted stereo to mono")
+        
+        # Resample if needed
+        if file_sample_rate != self.sample_rate:
+            # Simple resampling using numpy
+            duration = len(audio_data) / file_sample_rate
+            num_samples = int(duration * self.sample_rate)
+            indices = np.linspace(0, len(audio_data) - 1, num_samples)
+            audio_data = np.interp(indices, np.arange(len(audio_data)), audio_data)
+            self.log_event("→", f"  Resampled to {self.sample_rate} Hz")
+        
+        # Convert to int16
+        if audio_data.dtype != np.int16:
+            # Normalize to [-1, 1] if needed
+            max_val = np.max(np.abs(audio_data))
+            if max_val > 1.0:
+                audio_data = audio_data / max_val
+            audio_data = (audio_data * 32767).astype(np.int16)
+        
+        self.log_event("→", f"  Prepared: {len(audio_data)} samples ({len(audio_data)/self.sample_rate:.2f}s)")
+        
+        return audio_data, file_sample_rate
+    
+    async def audio_sender(self, audio_data: np.ndarray) -> None:
+        """Send audio data to server in chunks."""
+        total_samples = len(audio_data)
+        chunk_size = self.chunk_samples
+        sent_samples = 0
+        
+        self.send_start_time = time.time()
+        self.log_event("→", f"Starting audio transmission ({total_samples} samples)...")
+        
+        while sent_samples < total_samples and self.running:
+            # Get next chunk
+            end_sample = min(sent_samples + chunk_size, total_samples)
+            chunk = audio_data[sent_samples:end_sample]
+            chunk_bytes = chunk.tobytes()
+            
+            # Send to server
+            if self.ws:
+                await self.ws.send(chunk_bytes)
+                self.bytes_sent += len(chunk_bytes)
+            
+            sent_samples = end_sample
+            
+            # Progress logging (every 500ms worth of audio)
+            if self.verbose and sent_samples % (self.sample_rate // 2) == 0:
+                progress = (sent_samples / total_samples) * 100
+                print(f"  Sending: {progress:.0f}%", end="\r")
+            
+            # Delay to simulate real-time streaming
+            # Server expects audio at real-time pace for VAD/ASR to work properly
+            await asyncio.sleep(self.chunk_duration_ms / 1000)
+        
+        self.send_completed = True
+        elapsed = time.time() - self.send_start_time
+        self.log_event("→", f"Audio transmission complete ({elapsed:.2f}s, {self.bytes_sent/1024:.1f} KB)")
+    
+    async def receiver(self) -> None:
+        """Receive messages from server."""
+        try:
+            while self.running:
+                try:
+                    message = await asyncio.wait_for(self.ws.recv(), timeout=0.1)
+                    
+                    if isinstance(message, bytes):
+                        # Audio data received
+                        self.bytes_received += len(message)
+                        self.received_audio.extend(message)
+                        
+                        # Calculate TTFB on first audio of each response
+                        if self.waiting_for_first_audio and self.response_start_time is not None:
+                            ttfb_ms = (time.time() - self.response_start_time) * 1000
+                            self.ttfb_ms = ttfb_ms
+                            self.ttfb_list.append(ttfb_ms)
+                            self.waiting_for_first_audio = False
+                            self.log_event("←", f"[TTFB] First audio latency: {ttfb_ms:.0f}ms")
+                        
+                        # Log progress
+                        duration_ms = len(message) / (self.sample_rate * 2) * 1000
+                        total_ms = len(self.received_audio) / (self.sample_rate * 2) * 1000
+                        if self.verbose:
+                            print(f"← Audio: +{duration_ms:.0f}ms (total: {total_ms:.0f}ms)", end="\r")
+                        
+                    else:
+                        # JSON event
+                        event = json.loads(message)
+                        await self._handle_event(event)
+                        
+                except asyncio.TimeoutError:
+                    continue
+                except websockets.ConnectionClosed:
+                    self.log_event("←", "Connection closed")
+                    self.running = False
+                    break
+                    
+        except asyncio.CancelledError:
+            pass
+        except Exception as e:
+            self.log_event("!", f"Receiver error: {e}")
+            self.running = False
+    
+    async def _handle_event(self, event: dict) -> None:
+        """Handle incoming event."""
+        event_type = event.get("event", "unknown")
+        
+        if event_type == "answer":
+            self.log_event("←", "Session ready!")
+        elif event_type == "speaking":
+            self.log_event("←", "Speech detected")
+        elif event_type == "silence":
+            self.log_event("←", "Silence detected")
+        elif event_type == "transcript":
+            # ASR transcript (interim = asrDelta-style, final = asrFinal-style)
+            text = event.get("text", "")
+            is_final = event.get("isFinal", False)
+            if is_final:
+                # Clear interim line and print final
+                print(" " * 80, end="\r")
+                self.log_event("←", f"→ You: {text}")
+            else:
+                # Interim result - show with indicator (overwrite same line, as in mic_client)
+                display_text = text[:60] + "..." if len(text) > 60 else text
+                print(f"  [listening] {display_text}".ljust(80), end="\r")
+        elif event_type == "ttfb":
+            latency_ms = event.get("latencyMs", 0)
+            self.log_event("←", f"[TTFB] Server latency: {latency_ms}ms")
+        elif event_type == "llmResponse":
+            text = event.get("text", "")
+            is_final = event.get("isFinal", False)
+            if is_final:
+                self.log_event("←", f"LLM Response (final): {text[:100]}{'...' if len(text) > 100 else ''}")
+            elif self.verbose:
+                # Show streaming chunks only in verbose mode
+                self.log_event("←", f"LLM: {text}")
+        elif event_type == "trackStart":
+            self.track_started = True
+            self.response_start_time = time.time()
+            self.waiting_for_first_audio = True
+            self.log_event("←", "Bot started speaking")
+        elif event_type == "trackEnd":
+            self.track_ended = True
+            self.log_event("←", "Bot finished speaking")
+        elif event_type == "interrupt":
+            self.log_event("←", "Bot interrupted!")
+        elif event_type == "error":
+            self.log_event("!", f"Error: {event.get('error')}")
+        elif event_type == "hangup":
+            self.log_event("←", f"Hangup: {event.get('reason')}")
+            self.running = False
+        else:
+            self.log_event("←", f"Event: {event_type}")
+    
+    def save_output_wav(self) -> None:
+        """Save received audio to output WAV file."""
+        if not self.received_audio:
+            self.log_event("!", "No audio received to save")
+            return
+        
+        # Convert bytes to numpy array
+        audio_data = np.frombuffer(bytes(self.received_audio), dtype=np.int16)
+        
+        # Ensure output directory exists
+        self.output_file.parent.mkdir(parents=True, exist_ok=True)
+        
+        # Save using wave module for compatibility
+        with wave.open(str(self.output_file), 'wb') as wav_file:
+            wav_file.setnchannels(1)
+            wav_file.setsampwidth(2)  # 16-bit
+            wav_file.setframerate(self.sample_rate)
+            wav_file.writeframes(audio_data.tobytes())
+        
+        duration = len(audio_data) / self.sample_rate
+        self.log_event("→", f"Saved output: {self.output_file}")
+        self.log_event("→", f"  Duration: {duration:.2f}s ({len(audio_data)} samples)")
+        self.log_event("→", f"  Size: {len(self.received_audio)/1024:.1f} KB")
+    
+    async def run(self) -> None:
+        """Run the WAV file test."""
+        try:
+            # Load input WAV file
+            audio_data, _ = self.load_wav_file()
+            
+            # Connect to server
+            await self.connect()
+            
+            # Wait for answer
+            await asyncio.sleep(0.5)
+            
+            # Start receiver task
+            receiver_task = asyncio.create_task(self.receiver())
+            
+            # Send audio
+            await self.audio_sender(audio_data)
+            
+            # Wait for response
+            self.log_event("→", f"Waiting {self.wait_time}s for response...")
+            
+            wait_start = time.time()
+            while self.running and (time.time() - wait_start) < self.wait_time:
+                # Check if track has ended (response complete)
+                if self.track_ended and self.send_completed:
+                    # Give a little extra time for any remaining audio
+                    await asyncio.sleep(1.0)
+                    break
+                await asyncio.sleep(0.1)
+            
+            # Cleanup
+            self.running = False
+            receiver_task.cancel()
+            
+            try:
+                await receiver_task
+            except asyncio.CancelledError:
+                pass
+            
+            # Save output
+            self.save_output_wav()
+            
+            # Print summary
+            self._print_summary()
+            
+        except FileNotFoundError as e:
+            print(f"Error: {e}")
+            sys.exit(1)
+        except ConnectionRefusedError:
+            print(f"Error: Could not connect to {self.url}")
+            print("Make sure the server is running.")
+            sys.exit(1)
+        except Exception as e:
+            print(f"Error: {e}")
+            import traceback
+            traceback.print_exc()
+            sys.exit(1)
+        finally:
+            await self.close()
+    
+    def _print_summary(self):
+        """Print session summary."""
+        print("\n" + "=" * 50)
+        print("Session Summary")
+        print("=" * 50)
+        print(f"  Input file:  {self.input_file}")
+        print(f"  Output file: {self.output_file}")
+        print(f"  Bytes sent:     {self.bytes_sent / 1024:.1f} KB")
+        print(f"  Bytes received: {self.bytes_received / 1024:.1f} KB")
+        if self.ttfb_list:
+            if len(self.ttfb_list) == 1:
+                print(f"  TTFB:           {self.ttfb_list[0]:.0f} ms")
+            else:
+                print(f"  TTFB (per response): {', '.join(f'{t:.0f}ms' for t in self.ttfb_list)}")
+        if self.received_audio:
+            duration = len(self.received_audio) / (self.sample_rate * 2)
+            print(f"  Response duration: {duration:.2f}s")
+        print("=" * 50)
+    
+    async def close(self) -> None:
+        """Close the connection."""
+        self.running = False
+        if self.ws:
+            try:
+                await self.ws.close()
+            except:
+                pass
+
+
+async def main():
+    parser = argparse.ArgumentParser(
+        description="WAV file client for testing duplex voice conversation"
+    )
+    parser.add_argument(
+        "--input", "-i",
+        required=True,
+        help="Input WAV file path"
+    )
+    parser.add_argument(
+        "--output", "-o",
+        required=True,
+        help="Output WAV file path for response"
+    )
+    parser.add_argument(
+        "--url",
+        default="ws://localhost:8000/ws",
+        help="WebSocket server URL (default: ws://localhost:8000/ws)"
+    )
+    parser.add_argument(
+        "--sample-rate",
+        type=int,
+        default=16000,
+        help="Target sample rate for audio (default: 16000)"
+    )
+    parser.add_argument(
+        "--chunk-duration",
+        type=int,
+        default=20,
+        help="Chunk duration in ms for sending (default: 20)"
+    )
+    parser.add_argument(
+        "--wait-time", "-w",
+        type=float,
+        default=15.0,
+        help="Time to wait for response after sending (default: 15.0)"
+    )
+    parser.add_argument(
+        "--verbose", "-v",
+        action="store_true",
+        help="Enable verbose output"
+    )
+    
+    args = parser.parse_args()
+    
+    client = WavFileClient(
+        url=args.url,
+        input_file=args.input,
+        output_file=args.output,
+        sample_rate=args.sample_rate,
+        chunk_duration_ms=args.chunk_duration,
+        wait_time=args.wait_time,
+        verbose=args.verbose
+    )
+    
+    await client.run()
+
+
+if __name__ == "__main__":
+    try:
+        asyncio.run(main())
+    except KeyboardInterrupt:
+        print("\nInterrupted by user")

engine/examples/web_client.html

@@ -0,0 +1,742 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <title>Duplex Voice Web Client</title>
+    <style>
+      @import url("https://fonts.googleapis.com/css2?family=Fraunces:opsz,wght@9..144,300;9..144,500;9..144,700&family=Recursive:wght@300;400;600;700&display=swap");
+
+      :root {
+        --bg: #0b0b0f;
+        --panel: #14141c;
+        --panel-2: #101018;
+        --ink: #f2f3f7;
+        --muted: #a7acba;
+        --accent: #ff6b6b;
+        --accent-2: #ffd166;
+        --good: #2dd4bf;
+        --bad: #f87171;
+        --grid: rgba(255, 255, 255, 0.06);
+        --shadow: 0 20px 60px rgba(0, 0, 0, 0.45);
+      }
+
+      * {
+        box-sizing: border-box;
+      }
+
+      html,
+      body {
+        height: 100%;
+        margin: 0;
+        color: var(--ink);
+        background: radial-gradient(1200px 600px at 20% -10%, #1d1d2a 0%, transparent 60%),
+          radial-gradient(800px 800px at 110% 10%, #20203a 0%, transparent 50%),
+          var(--bg);
+        font-family: "Recursive", ui-sans-serif, system-ui, -apple-system, "Segoe UI", sans-serif;
+      }
+
+      .noise {
+        position: fixed;
+        inset: 0;
+        background-image: url("data:image/svg+xml;utf8,<svg xmlns='http://www.w3.org/2000/svg' width='120' height='120' viewBox='0 0 120 120'><filter id='n'><feTurbulence type='fractalNoise' baseFrequency='0.9' numOctaves='2' stitchTiles='stitch'/></filter><rect width='120' height='120' filter='url(%23n)' opacity='0.06'/></svg>");
+        pointer-events: none;
+        mix-blend-mode: soft-light;
+      }
+
+      header {
+        padding: 32px 28px 18px;
+        border-bottom: 1px solid var(--grid);
+      }
+
+      h1 {
+        font-family: "Fraunces", serif;
+        font-weight: 600;
+        margin: 0 0 6px;
+        letter-spacing: 0.4px;
+      }
+
+      .subtitle {
+        color: var(--muted);
+        font-size: 0.95rem;
+      }
+
+      main {
+        display: grid;
+        grid-template-columns: 1.1fr 1.4fr;
+        gap: 24px;
+        padding: 24px 28px 40px;
+      }
+
+      .panel {
+        background: linear-gradient(180deg, rgba(255, 255, 255, 0.02), transparent),
+          var(--panel);
+        border: 1px solid var(--grid);
+        border-radius: 16px;
+        padding: 20px;
+        box-shadow: var(--shadow);
+      }
+
+      .panel h2 {
+        margin: 0 0 12px;
+        font-size: 1.05rem;
+        font-weight: 600;
+      }
+
+      .stack {
+        display: grid;
+        gap: 12px;
+      }
+
+      label {
+        display: block;
+        font-size: 0.85rem;
+        color: var(--muted);
+        margin-bottom: 6px;
+      }
+
+      input,
+      select,
+      button,
+      textarea {
+        font-family: inherit;
+      }
+
+      input,
+      select,
+      textarea {
+        width: 100%;
+        padding: 10px 12px;
+        border-radius: 10px;
+        border: 1px solid var(--grid);
+        background: var(--panel-2);
+        color: var(--ink);
+        outline: none;
+      }
+
+      textarea {
+        min-height: 80px;
+        resize: vertical;
+      }
+
+      .row {
+        display: grid;
+        grid-template-columns: 1fr 1fr;
+        gap: 12px;
+      }
+
+      .btn-row {
+        display: flex;
+        flex-wrap: wrap;
+        gap: 10px;
+      }
+
+      button {
+        border: none;
+        border-radius: 999px;
+        padding: 10px 16px;
+        font-weight: 600;
+        background: var(--ink);
+        color: #111;
+        cursor: pointer;
+        transition: transform 0.2s ease, box-shadow 0.2s ease;
+      }
+
+      button.secondary {
+        background: transparent;
+        color: var(--ink);
+        border: 1px solid var(--grid);
+      }
+
+      button.accent {
+        background: linear-gradient(120deg, var(--accent), #f97316);
+        color: #0b0b0f;
+      }
+
+      button.good {
+        background: linear-gradient(120deg, var(--good), #22c55e);
+        color: #07261f;
+      }
+
+      button.bad {
+        background: linear-gradient(120deg, var(--bad), #f97316);
+        color: #2a0b0b;
+      }
+
+      button:active {
+        transform: translateY(1px) scale(0.99);
+      }
+
+      .status {
+        display: flex;
+        align-items: center;
+        gap: 12px;
+        padding: 12px;
+        background: rgba(255, 255, 255, 0.03);
+        border-radius: 12px;
+        border: 1px dashed var(--grid);
+        font-size: 0.9rem;
+      }
+
+      .dot {
+        width: 10px;
+        height: 10px;
+        border-radius: 999px;
+        background: var(--bad);
+        box-shadow: 0 0 12px rgba(248, 113, 113, 0.5);
+      }
+
+      .dot.on {
+        background: var(--good);
+        box-shadow: 0 0 12px rgba(45, 212, 191, 0.7);
+      }
+
+      .log {
+        height: 320px;
+        overflow: auto;
+        padding: 12px;
+        background: #0d0d14;
+        border-radius: 12px;
+        border: 1px solid var(--grid);
+        font-size: 0.85rem;
+        line-height: 1.4;
+      }
+
+      .chat {
+        height: 260px;
+        overflow: auto;
+        padding: 12px;
+        background: #0d0d14;
+        border-radius: 12px;
+        border: 1px solid var(--grid);
+        font-size: 0.9rem;
+        line-height: 1.45;
+      }
+
+      .chat-entry {
+        padding: 8px 10px;
+        margin-bottom: 8px;
+        border-radius: 10px;
+        background: rgba(255, 255, 255, 0.04);
+        border: 1px solid rgba(255, 255, 255, 0.06);
+      }
+
+      .chat-entry.user {
+        border-left: 3px solid var(--accent-2);
+      }
+
+      .chat-entry.ai {
+        border-left: 3px solid var(--good);
+      }
+
+      .chat-entry.interim {
+        opacity: 0.7;
+        font-style: italic;
+      }
+
+      .log-entry {
+        padding: 6px 8px;
+        border-bottom: 1px dashed rgba(255, 255, 255, 0.06);
+      }
+
+      .log-entry:last-child {
+        border-bottom: none;
+      }
+
+      .tag {
+        display: inline-flex;
+        align-items: center;
+        gap: 6px;
+        padding: 2px 8px;
+        border-radius: 999px;
+        font-size: 0.7rem;
+        text-transform: uppercase;
+        letter-spacing: 0.6px;
+        background: rgba(255, 255, 255, 0.08);
+        color: var(--muted);
+      }
+
+      .tag.event {
+        background: rgba(255, 107, 107, 0.18);
+        color: #ffc1c1;
+      }
+
+      .tag.audio {
+        background: rgba(45, 212, 191, 0.2);
+        color: #c5f9f0;
+      }
+
+      .tag.sys {
+        background: rgba(255, 209, 102, 0.2);
+        color: #ffefb0;
+      }
+
+      .muted {
+        color: var(--muted);
+      }
+
+      footer {
+        padding: 0 28px 28px;
+        color: var(--muted);
+        font-size: 0.8rem;
+      }
+
+      @media (max-width: 1100px) {
+        main {
+          grid-template-columns: 1fr;
+        }
+        .log {
+          height: 360px;
+        }
+        .chat {
+          height: 260px;
+        }
+      }
+    </style>
+  </head>
+  <body>
+    <div class="noise"></div>
+    <header>
+      <h1>Duplex Voice Client</h1>
+      <div class="subtitle">Browser client for the WebSocket duplex pipeline. Device selection + event logging.</div>
+    </header>
+
+    <main>
+      <section class="panel stack">
+        <h2>Connection</h2>
+        <div>
+          <label for="wsUrl">WebSocket URL</label>
+          <input id="wsUrl" value="ws://localhost:8000/ws" />
+        </div>
+        <div class="btn-row">
+          <button class="accent" id="connectBtn">Connect</button>
+          <button class="secondary" id="disconnectBtn">Disconnect</button>
+        </div>
+        <div class="status">
+          <div id="statusDot" class="dot"></div>
+          <div>
+            <div id="statusText">Disconnected</div>
+            <div class="muted" id="statusSub">Waiting for connection</div>
+          </div>
+        </div>
+
+        <h2>Devices</h2>
+        <div class="row">
+          <div>
+            <label for="inputSelect">Input (Mic)</label>
+            <select id="inputSelect"></select>
+          </div>
+          <div>
+            <label for="outputSelect">Output (Speaker)</label>
+            <select id="outputSelect"></select>
+          </div>
+        </div>
+        <div class="btn-row">
+          <button class="secondary" id="refreshDevicesBtn">Refresh Devices</button>
+          <button class="good" id="startMicBtn">Start Mic</button>
+          <button class="secondary" id="stopMicBtn">Stop Mic</button>
+        </div>
+
+        <h2>Chat</h2>
+        <div class="stack">
+          <textarea id="chatInput" placeholder="Type a message, press Send"></textarea>
+          <div class="btn-row">
+            <button class="accent" id="sendChatBtn">Send Chat</button>
+            <button class="secondary" id="clearLogBtn">Clear Log</button>
+          </div>
+        </div>
+      </section>
+
+      <section class="stack">
+        <div class="panel stack">
+          <h2>Chat History</h2>
+          <div class="chat" id="chatHistory"></div>
+        </div>
+        <div class="panel stack">
+          <h2>Event Log</h2>
+          <div class="log" id="log"></div>
+        </div>
+      </section>
+    </main>
+
+    <footer>
+      Output device selection requires HTTPS + a browser that supports <code>setSinkId</code>.
+      Audio is sent as 16-bit PCM @ 16 kHz, matching <code>examples/mic_client.py</code>.
+    </footer>
+
+    <audio id="audioOut" autoplay></audio>
+
+    <script>
+      const wsUrl = document.getElementById("wsUrl");
+      const connectBtn = document.getElementById("connectBtn");
+      const disconnectBtn = document.getElementById("disconnectBtn");
+      const inputSelect = document.getElementById("inputSelect");
+      const outputSelect = document.getElementById("outputSelect");
+      const startMicBtn = document.getElementById("startMicBtn");
+      const stopMicBtn = document.getElementById("stopMicBtn");
+      const refreshDevicesBtn = document.getElementById("refreshDevicesBtn");
+      const sendChatBtn = document.getElementById("sendChatBtn");
+      const clearLogBtn = document.getElementById("clearLogBtn");
+      const chatInput = document.getElementById("chatInput");
+      const logEl = document.getElementById("log");
+      const chatHistory = document.getElementById("chatHistory");
+      const statusDot = document.getElementById("statusDot");
+      const statusText = document.getElementById("statusText");
+      const statusSub = document.getElementById("statusSub");
+      const audioOut = document.getElementById("audioOut");
+
+      let ws = null;
+      let audioCtx = null;
+      let micStream = null;
+      let processor = null;
+      let micSource = null;
+      let playbackDest = null;
+      let playbackTime = 0;
+      let discardAudio = false;
+      let playbackSources = [];
+      let interimUserEl = null;
+      let interimAiEl = null;
+      let interimUserText = "";
+      let interimAiText = "";
+
+      const targetSampleRate = 16000;
+
+      function logLine(type, text, data) {
+        const time = new Date().toLocaleTimeString();
+        const entry = document.createElement("div");
+        entry.className = "log-entry";
+        const tag = document.createElement("span");
+        tag.className = `tag ${type}`;
+        tag.textContent = type.toUpperCase();
+        const msg = document.createElement("span");
+        msg.style.marginLeft = "10px";
+        msg.textContent = `[${time}] ${text}`;
+        entry.appendChild(tag);
+        entry.appendChild(msg);
+        if (data) {
+          const pre = document.createElement("div");
+          pre.className = "muted";
+          pre.textContent = JSON.stringify(data);
+          pre.style.marginTop = "4px";
+          entry.appendChild(pre);
+        }
+        logEl.appendChild(entry);
+        logEl.scrollTop = logEl.scrollHeight;
+      }
+
+      function addChat(role, text) {
+        const entry = document.createElement("div");
+        entry.className = `chat-entry ${role === "AI" ? "ai" : "user"}`;
+        entry.textContent = `${role}: ${text}`;
+        chatHistory.appendChild(entry);
+        chatHistory.scrollTop = chatHistory.scrollHeight;
+      }
+
+      function setInterim(role, text) {
+        const isAi = role === "AI";
+        let el = isAi ? interimAiEl : interimUserEl;
+        if (!text) {
+          if (el) el.remove();
+          if (isAi) interimAiEl = null;
+          else interimUserEl = null;
+          if (isAi) interimAiText = "";
+          else interimUserText = "";
+          return;
+        }
+        if (!el) {
+          el = document.createElement("div");
+          el.className = `chat-entry ${isAi ? "ai" : "user"} interim`;
+          chatHistory.appendChild(el);
+          if (isAi) interimAiEl = el;
+          else interimUserEl = el;
+        }
+        el.textContent = `${role} (interim): ${text}`;
+        chatHistory.scrollTop = chatHistory.scrollHeight;
+      }
+
+      function stopPlayback() {
+        discardAudio = true;
+        playbackTime = audioCtx ? audioCtx.currentTime : 0;
+        playbackSources.forEach((s) => {
+          try {
+            s.stop();
+          } catch (err) {}
+        });
+        playbackSources = [];
+      }
+
+      function setStatus(connected, detail) {
+        statusDot.classList.toggle("on", connected);
+        statusText.textContent = connected ? "Connected" : "Disconnected";
+        statusSub.textContent = detail || "";
+      }
+
+      async function ensureAudioContext() {
+        if (audioCtx) return;
+        audioCtx = new (window.AudioContext || window.webkitAudioContext)();
+        playbackDest = audioCtx.createMediaStreamDestination();
+        audioOut.srcObject = playbackDest.stream;
+        try {
+          await audioOut.play();
+        } catch (err) {
+          logLine("sys", "Audio playback blocked (user gesture needed)", { err: String(err) });
+        }
+        if (outputSelect.value) {
+          await setOutputDevice(outputSelect.value);
+        }
+      }
+
+      function downsampleBuffer(buffer, inRate, outRate) {
+        if (outRate === inRate) return buffer;
+        const ratio = inRate / outRate;
+        const newLength = Math.round(buffer.length / ratio);
+        const result = new Float32Array(newLength);
+        let offsetResult = 0;
+        let offsetBuffer = 0;
+        while (offsetResult < result.length) {
+          const nextOffsetBuffer = Math.round((offsetResult + 1) * ratio);
+          let accum = 0;
+          let count = 0;
+          for (let i = offsetBuffer; i < nextOffsetBuffer && i < buffer.length; i++) {
+            accum += buffer[i];
+            count++;
+          }
+          result[offsetResult] = accum / count;
+          offsetResult++;
+          offsetBuffer = nextOffsetBuffer;
+        }
+        return result;
+      }
+
+      function floatTo16BitPCM(float32) {
+        const out = new Int16Array(float32.length);
+        for (let i = 0; i < float32.length; i++) {
+          const s = Math.max(-1, Math.min(1, float32[i]));
+          out[i] = s < 0 ? s * 0x8000 : s * 0x7fff;
+        }
+        return out;
+      }
+
+      function schedulePlayback(int16Data) {
+        if (!audioCtx || !playbackDest) return;
+        if (discardAudio) return;
+        const float32 = new Float32Array(int16Data.length);
+        for (let i = 0; i < int16Data.length; i++) {
+          float32[i] = int16Data[i] / 32768;
+        }
+        const buffer = audioCtx.createBuffer(1, float32.length, targetSampleRate);
+        buffer.copyToChannel(float32, 0);
+        const source = audioCtx.createBufferSource();
+        source.buffer = buffer;
+        source.connect(playbackDest);
+        const startTime = Math.max(audioCtx.currentTime + 0.02, playbackTime);
+        source.start(startTime);
+        playbackTime = startTime + buffer.duration;
+        playbackSources.push(source);
+        source.onended = () => {
+          playbackSources = playbackSources.filter((s) => s !== source);
+        };
+      }
+
+      async function connect() {
+        if (ws && ws.readyState === WebSocket.OPEN) return;
+        ws = new WebSocket(wsUrl.value.trim());
+        ws.binaryType = "arraybuffer";
+
+        ws.onopen = () => {
+          setStatus(true, "Session open");
+          logLine("sys", "WebSocket connected");
+          ensureAudioContext();
+          sendCommand({ command: "invite", option: { codec: "pcm", sampleRate: targetSampleRate } });
+        };
+
+        ws.onclose = () => {
+          setStatus(false, "Connection closed");
+          logLine("sys", "WebSocket closed");
+          ws = null;
+        };
+
+        ws.onerror = (err) => {
+          logLine("sys", "WebSocket error", { err: String(err) });
+        };
+
+        ws.onmessage = (msg) => {
+          if (typeof msg.data === "string") {
+            const event = JSON.parse(msg.data);
+            handleEvent(event);
+          } else {
+            const audioBuf = msg.data;
+            const int16 = new Int16Array(audioBuf);
+            schedulePlayback(int16);
+            logLine("audio", `Audio ${Math.round((int16.length / targetSampleRate) * 1000)}ms`);
+          }
+        };
+      }
+
+      function disconnect() {
+        if (ws) ws.close();
+        ws = null;
+        setStatus(false, "Disconnected");
+      }
+
+      function sendCommand(cmd) {
+        if (!ws || ws.readyState !== WebSocket.OPEN) {
+          logLine("sys", "Not connected");
+          return;
+        }
+        ws.send(JSON.stringify(cmd));
+        logLine("sys", `→ ${cmd.command}`, cmd);
+      }
+
+      function handleEvent(event) {
+        const type = event.event || "unknown";
+        logLine("event", type, event);
+        if (type === "transcript") {
+          if (event.isFinal && event.text) {
+            setInterim("You", "");
+            addChat("You", event.text);
+          } else if (event.text) {
+            interimUserText += event.text;
+            setInterim("You", interimUserText);
+          }
+        }
+        if (type === "llmResponse") {
+          if (event.isFinal && event.text) {
+            setInterim("AI", "");
+            addChat("AI", event.text);
+          } else if (event.text) {
+            interimAiText += event.text;
+            setInterim("AI", interimAiText);
+          }
+        }
+        if (type === "trackStart") {
+          // New bot audio: stop any previous playback to avoid overlap
+          stopPlayback();
+          discardAudio = false;
+        }
+        if (type === "speaking") {
+          // User started speaking: clear any in-flight audio to avoid overlap
+          stopPlayback();
+        }
+        if (type === "interrupt") {
+          stopPlayback();
+        }
+      }
+
+      async function startMic() {
+        if (!ws || ws.readyState !== WebSocket.OPEN) {
+          logLine("sys", "Connect before starting mic");
+          return;
+        }
+        await ensureAudioContext();
+        const deviceId = inputSelect.value || undefined;
+        micStream = await navigator.mediaDevices.getUserMedia({
+          audio: deviceId ? { deviceId: { exact: deviceId } } : true,
+        });
+        micSource = audioCtx.createMediaStreamSource(micStream);
+        processor = audioCtx.createScriptProcessor(2048, 1, 1);
+        processor.onaudioprocess = (e) => {
+          if (!ws || ws.readyState !== WebSocket.OPEN) return;
+          const input = e.inputBuffer.getChannelData(0);
+          const downsampled = downsampleBuffer(input, audioCtx.sampleRate, targetSampleRate);
+          const pcm16 = floatTo16BitPCM(downsampled);
+          ws.send(pcm16.buffer);
+        };
+        micSource.connect(processor);
+        processor.connect(audioCtx.destination);
+        logLine("sys", "Microphone started");
+      }
+
+      function stopMic() {
+        if (processor) {
+          processor.disconnect();
+          processor = null;
+        }
+        if (micSource) {
+          micSource.disconnect();
+          micSource = null;
+        }
+        if (micStream) {
+          micStream.getTracks().forEach((t) => t.stop());
+          micStream = null;
+        }
+        logLine("sys", "Microphone stopped");
+      }
+
+      async function refreshDevices() {
+        const devices = await navigator.mediaDevices.enumerateDevices();
+        inputSelect.innerHTML = "";
+        outputSelect.innerHTML = "";
+        devices.forEach((d) => {
+          if (d.kind === "audioinput") {
+            const opt = document.createElement("option");
+            opt.value = d.deviceId;
+            opt.textContent = d.label || `Mic ${inputSelect.length + 1}`;
+            inputSelect.appendChild(opt);
+          }
+          if (d.kind === "audiooutput") {
+            const opt = document.createElement("option");
+            opt.value = d.deviceId;
+            opt.textContent = d.label || `Output ${outputSelect.length + 1}`;
+            outputSelect.appendChild(opt);
+          }
+        });
+      }
+
+      async function requestDeviceAccess() {
+        // Needed to reveal device labels in most browsers
+        try {
+          const stream = await navigator.mediaDevices.getUserMedia({ audio: true });
+          stream.getTracks().forEach((t) => t.stop());
+          logLine("sys", "Microphone permission granted");
+        } catch (err) {
+          logLine("sys", "Microphone permission denied", { err: String(err) });
+        }
+      }
+
+      async function setOutputDevice(deviceId) {
+        if (!audioOut.setSinkId) {
+          logLine("sys", "setSinkId not supported in this browser");
+          return;
+        }
+        await audioOut.setSinkId(deviceId);
+        logLine("sys", `Output device set`, { deviceId });
+      }
+
+      connectBtn.addEventListener("click", connect);
+      disconnectBtn.addEventListener("click", disconnect);
+      refreshDevicesBtn.addEventListener("click", async () => {
+        await requestDeviceAccess();
+        await refreshDevices();
+      });
+      startMicBtn.addEventListener("click", startMic);
+      stopMicBtn.addEventListener("click", stopMic);
+      sendChatBtn.addEventListener("click", () => {
+        const text = chatInput.value.trim();
+        if (!text) return;
+        ensureAudioContext();
+        addChat("You", text);
+        sendCommand({ command: "chat", text });
+        chatInput.value = "";
+      });
+      clearLogBtn.addEventListener("click", () => {
+        logEl.innerHTML = "";
+        chatHistory.innerHTML = "";
+        setInterim("You", "");
+        setInterim("AI", "");
+        interimUserText = "";
+        interimAiText = "";
+      });
+      inputSelect.addEventListener("change", () => {
+        if (micStream) {
+          stopMic();
+          startMic();
+        }
+      });
+      outputSelect.addEventListener("change", () => setOutputDevice(outputSelect.value));
+
+      navigator.mediaDevices.addEventListener("devicechange", refreshDevices);
+      refreshDevices().catch(() => {});
+    </script>
+  </body>
+</html>

engine/models/__init__.py

@@ -0,0 +1 @@
+"""Data Models Package"""

engine/models/commands.py

@@ -0,0 +1,143 @@
+"""Protocol command models matching the original active-call API."""
+
+from typing import Optional, Dict, Any
+from pydantic import BaseModel, Field
+
+
+class InviteCommand(BaseModel):
+    """Invite command to initiate a call."""
+
+    command: str = Field(default="invite", description="Command type")
+    option: Optional[Dict[str, Any]] = Field(default=None, description="Call configuration options")
+
+
+class AcceptCommand(BaseModel):
+    """Accept command to accept an incoming call."""
+
+    command: str = Field(default="accept", description="Command type")
+    option: Optional[Dict[str, Any]] = Field(default=None, description="Call configuration options")
+
+
+class RejectCommand(BaseModel):
+    """Reject command to reject an incoming call."""
+
+    command: str = Field(default="reject", description="Command type")
+    reason: str = Field(default="", description="Reason for rejection")
+    code: Optional[int] = Field(default=None, description="SIP response code")
+
+
+class RingingCommand(BaseModel):
+    """Ringing command to send ringing response."""
+
+    command: str = Field(default="ringing", description="Command type")
+    recorder: Optional[Dict[str, Any]] = Field(default=None, description="Call recording configuration")
+    early_media: bool = Field(default=False, description="Enable early media")
+    ringtone: Optional[str] = Field(default=None, description="Custom ringtone URL")
+
+
+class TTSCommand(BaseModel):
+    """TTS command to convert text to speech."""
+
+    command: str = Field(default="tts", description="Command type")
+    text: str = Field(..., description="Text to synthesize")
+    speaker: Optional[str] = Field(default=None, description="Speaker voice name")
+    play_id: Optional[str] = Field(default=None, description="Unique identifier for this TTS session")
+    auto_hangup: bool = Field(default=False, description="Auto hangup after TTS completion")
+    streaming: bool = Field(default=False, description="Streaming text input")
+    end_of_stream: bool = Field(default=False, description="End of streaming input")
+    wait_input_timeout: Optional[int] = Field(default=None, description="Max time to wait for input (seconds)")
+    option: Optional[Dict[str, Any]] = Field(default=None, description="TTS provider specific options")
+
+
+class PlayCommand(BaseModel):
+    """Play command to play audio from URL."""
+
+    command: str = Field(default="play", description="Command type")
+    url: str = Field(..., description="URL of audio file to play")
+    auto_hangup: bool = Field(default=False, description="Auto hangup after playback")
+    wait_input_timeout: Optional[int] = Field(default=None, description="Max time to wait for input (seconds)")
+
+
+class InterruptCommand(BaseModel):
+    """Interrupt command to interrupt current playback."""
+
+    command: str = Field(default="interrupt", description="Command type")
+    graceful: bool = Field(default=False, description="Wait for current TTS to complete")
+
+
+class PauseCommand(BaseModel):
+    """Pause command to pause current playback."""
+
+    command: str = Field(default="pause", description="Command type")
+
+
+class ResumeCommand(BaseModel):
+    """Resume command to resume paused playback."""
+
+    command: str = Field(default="resume", description="Command type")
+
+
+class HangupCommand(BaseModel):
+    """Hangup command to end the call."""
+
+    command: str = Field(default="hangup", description="Command type")
+    reason: Optional[str] = Field(default=None, description="Reason for hangup")
+    initiator: Optional[str] = Field(default=None, description="Who initiated the hangup")
+
+
+class HistoryCommand(BaseModel):
+    """History command to add conversation history."""
+
+    command: str = Field(default="history", description="Command type")
+    speaker: str = Field(..., description="Speaker identifier")
+    text: str = Field(..., description="Conversation text")
+
+
+class ChatCommand(BaseModel):
+    """Chat command for text-based conversation."""
+
+    command: str = Field(default="chat", description="Command type")
+    text: str = Field(..., description="Chat text message")
+
+
+# Command type mapping
+COMMAND_TYPES = {
+    "invite": InviteCommand,
+    "accept": AcceptCommand,
+    "reject": RejectCommand,
+    "ringing": RingingCommand,
+    "tts": TTSCommand,
+    "play": PlayCommand,
+    "interrupt": InterruptCommand,
+    "pause": PauseCommand,
+    "resume": ResumeCommand,
+    "hangup": HangupCommand,
+    "history": HistoryCommand,
+    "chat": ChatCommand,
+}
+
+
+def parse_command(data: Dict[str, Any]) -> BaseModel:
+    """
+    Parse a command from JSON data.
+
+    Args:
+        data: JSON data as dictionary
+
+    Returns:
+        Parsed command model
+
+    Raises:
+        ValueError: If command type is unknown
+    """
+    command_type = data.get("command")
+
+    if not command_type:
+        raise ValueError("Missing 'command' field")
+
+    command_class = COMMAND_TYPES.get(command_type)
+
+    if not command_class:
+        raise ValueError(f"Unknown command type: {command_type}")
+
+    return command_class(**data)

engine/models/config.py

@@ -0,0 +1,126 @@
+"""Configuration models for call options."""
+
+from typing import Optional, Dict, Any, List
+from pydantic import BaseModel, Field
+
+
+class VADOption(BaseModel):
+    """Voice Activity Detection configuration."""
+
+    type: str = Field(default="silero", description="VAD algorithm type (silero, webrtc)")
+    samplerate: int = Field(default=16000, description="Audio sample rate for VAD")
+    speech_padding: int = Field(default=250, description="Speech padding in milliseconds")
+    silence_padding: int = Field(default=100, description="Silence padding in milliseconds")
+    ratio: float = Field(default=0.5, description="Voice detection ratio threshold")
+    voice_threshold: float = Field(default=0.5, description="Voice energy threshold")
+    max_buffer_duration_secs: int = Field(default=50, description="Maximum buffer duration in seconds")
+    silence_timeout: Optional[int] = Field(default=None, description="Silence timeout in milliseconds")
+    endpoint: Optional[str] = Field(default=None, description="Custom VAD service endpoint")
+    secret_key: Optional[str] = Field(default=None, description="VAD service secret key")
+    secret_id: Optional[str] = Field(default=None, description="VAD service secret ID")
+
+
+class ASROption(BaseModel):
+    """Automatic Speech Recognition configuration."""
+
+    provider: str = Field(..., description="ASR provider (tencent, aliyun, openai, etc.)")
+    language: Optional[str] = Field(default=None, description="Language code (zh-CN, en-US)")
+    app_id: Optional[str] = Field(default=None, description="Application ID")
+    secret_id: Optional[str] = Field(default=None, description="Secret ID for authentication")
+    secret_key: Optional[str] = Field(default=None, description="Secret key for authentication")
+    model_type: Optional[str] = Field(default=None, description="ASR model type (16k_zh, 8k_en)")
+    buffer_size: Optional[int] = Field(default=None, description="Audio buffer size in bytes")
+    samplerate: Optional[int] = Field(default=None, description="Audio sample rate")
+    endpoint: Optional[str] = Field(default=None, description="Custom ASR service endpoint")
+    extra: Optional[Dict[str, Any]] = Field(default=None, description="Additional parameters")
+    start_when_answer: bool = Field(default=False, description="Start ASR when call is answered")
+
+
+class TTSOption(BaseModel):
+    """Text-to-Speech configuration."""
+
+    samplerate: Optional[int] = Field(default=None, description="TTS output sample rate")
+    provider: str = Field(default="msedge", description="TTS provider (tencent, aliyun, deepgram, msedge)")
+    speed: float = Field(default=1.0, description="Speech speed multiplier")
+    app_id: Optional[str] = Field(default=None, description="Application ID")
+    secret_id: Optional[str] = Field(default=None, description="Secret ID for authentication")
+    secret_key: Optional[str] = Field(default=None, description="Secret key for authentication")
+    volume: Optional[int] = Field(default=None, description="Speech volume level (1-10)")
+    speaker: Optional[str] = Field(default=None, description="Voice speaker name")
+    codec: Optional[str] = Field(default=None, description="Audio codec")
+    subtitle: bool = Field(default=False, description="Enable subtitle generation")
+    emotion: Optional[str] = Field(default=None, description="Speech emotion")
+    endpoint: Optional[str] = Field(default=None, description="Custom TTS service endpoint")
+    extra: Optional[Dict[str, Any]] = Field(default=None, description="Additional parameters")
+    max_concurrent_tasks: Optional[int] = Field(default=None, description="Max concurrent tasks")
+
+
+class RecorderOption(BaseModel):
+    """Call recording configuration."""
+
+    recorder_file: str = Field(..., description="Path to recording file")
+    samplerate: int = Field(default=16000, description="Recording sample rate")
+    ptime: int = Field(default=200, description="Packet time in milliseconds")
+
+
+class MediaPassOption(BaseModel):
+    """Media pass-through configuration for external audio processing."""
+
+    url: str = Field(..., description="WebSocket URL for media streaming")
+    input_sample_rate: int = Field(default=16000, description="Sample rate of audio received from WebSocket")
+    output_sample_rate: int = Field(default=16000, description="Sample rate of audio sent to WebSocket")
+    packet_size: int = Field(default=2560, description="Packet size in bytes")
+    ptime: Optional[int] = Field(default=None, description="Buffered playback period in milliseconds")
+
+
+class SipOption(BaseModel):
+    """SIP protocol configuration."""
+
+    username: Optional[str] = Field(default=None, description="SIP username")
+    password: Optional[str] = Field(default=None, description="SIP password")
+    realm: Optional[str] = Field(default=None, description="SIP realm/domain")
+    headers: Optional[Dict[str, str]] = Field(default=None, description="Additional SIP headers")
+
+
+class HandlerRule(BaseModel):
+    """Handler routing rule."""
+
+    caller: Optional[str] = Field(default=None, description="Caller pattern (regex)")
+    callee: Optional[str] = Field(default=None, description="Callee pattern (regex)")
+    playbook: Optional[str] = Field(default=None, description="Playbook file path")
+    webhook: Optional[str] = Field(default=None, description="Webhook URL")
+
+
+class CallOption(BaseModel):
+    """Comprehensive call configuration options."""
+
+    # Basic options
+    denoise: bool = Field(default=False, description="Enable noise reduction")
+    offer: Optional[str] = Field(default=None, description="SDP offer string")
+    callee: Optional[str] = Field(default=None, description="Callee SIP URI or phone number")
+    caller: Optional[str] = Field(default=None, description="Caller SIP URI or phone number")
+
+    # Audio codec
+    codec: str = Field(default="pcm", description="Audio codec (pcm, pcma, pcmu, g722)")
+
+    # Component configurations
+    recorder: Optional[RecorderOption] = Field(default=None, description="Call recording config")
+    asr: Optional[ASROption] = Field(default=None, description="ASR configuration")
+    vad: Optional[VADOption] = Field(default=None, description="VAD configuration")
+    tts: Optional[TTSOption] = Field(default=None, description="TTS configuration")
+    media_pass: Optional[MediaPassOption] = Field(default=None, description="Media pass-through config")
+    sip: Optional[SipOption] = Field(default=None, description="SIP configuration")
+
+    # Timeouts and networking
+    handshake_timeout: Optional[int] = Field(default=None, description="Handshake timeout in seconds")
+    enable_ipv6: bool = Field(default=False, description="Enable IPv6 support")
+    inactivity_timeout: Optional[int] = Field(default=None, description="Inactivity timeout in seconds")
+
+    # EOU configuration
+    eou: Optional[Dict[str, Any]] = Field(default=None, description="End of utterance detection config")
+
+    # Extra parameters
+    extra: Optional[Dict[str, Any]] = Field(default=None, description="Additional custom parameters")
+
+    class Config:
+        populate_by_name = True

engine/models/events.py

@@ -0,0 +1,231 @@
+"""Protocol event models matching the original active-call API."""
+
+from typing import Optional, Dict, Any
+from pydantic import BaseModel, Field
+from datetime import datetime
+
+
+def current_timestamp_ms() -> int:
+    """Get current timestamp in milliseconds."""
+    return int(datetime.now().timestamp() * 1000)
+
+
+# Base Event Model
+class BaseEvent(BaseModel):
+    """Base event model."""
+
+    event: str = Field(..., description="Event type")
+    track_id: str = Field(..., description="Unique track identifier")
+    timestamp: int = Field(default_factory=current_timestamp_ms, description="Event timestamp in milliseconds")
+
+
+# Lifecycle Events
+class IncomingEvent(BaseEvent):
+    """Incoming call event (SIP only)."""
+
+    event: str = Field(default="incoming", description="Event type")
+    caller: Optional[str] = Field(default=None, description="Caller's SIP URI")
+    callee: Optional[str] = Field(default=None, description="Callee's SIP URI")
+    sdp: Optional[str] = Field(default=None, description="SDP offer from caller")
+
+
+class AnswerEvent(BaseEvent):
+    """Call answered event."""
+
+    event: str = Field(default="answer", description="Event type")
+    sdp: Optional[str] = Field(default=None, description="SDP answer from server")
+
+
+class RejectEvent(BaseEvent):
+    """Call rejected event."""
+
+    event: str = Field(default="reject", description="Event type")
+    reason: Optional[str] = Field(default=None, description="Rejection reason")
+    code: Optional[int] = Field(default=None, description="SIP response code")
+
+
+class RingingEvent(BaseEvent):
+    """Call ringing event."""
+
+    event: str = Field(default="ringing", description="Event type")
+    early_media: bool = Field(default=False, description="Early media available")
+
+
+class HangupEvent(BaseModel):
+    """Call hangup event."""
+
+    event: str = Field(default="hangup", description="Event type")
+    timestamp: int = Field(default_factory=current_timestamp_ms, description="Event timestamp")
+    reason: Optional[str] = Field(default=None, description="Hangup reason")
+    initiator: Optional[str] = Field(default=None, description="Who initiated hangup")
+    start_time: Optional[str] = Field(default=None, description="Call start time (ISO 8601)")
+    hangup_time: Optional[str] = Field(default=None, description="Hangup time (ISO 8601)")
+    answer_time: Optional[str] = Field(default=None, description="Answer time (ISO 8601)")
+    ringing_time: Optional[str] = Field(default=None, description="Ringing time (ISO 8601)")
+    from_: Optional[Dict[str, Any]] = Field(default=None, alias="from", description="Caller info")
+    to: Optional[Dict[str, Any]] = Field(default=None, description="Callee info")
+    extra: Optional[Dict[str, Any]] = Field(default=None, description="Additional metadata")
+
+    class Config:
+        populate_by_name = True
+
+
+# VAD Events
+class SpeakingEvent(BaseEvent):
+    """Speech detected event."""
+
+    event: str = Field(default="speaking", description="Event type")
+    start_time: int = Field(default_factory=current_timestamp_ms, description="Speech start time")
+
+
+class SilenceEvent(BaseEvent):
+    """Silence detected event."""
+
+    event: str = Field(default="silence", description="Event type")
+    start_time: int = Field(default_factory=current_timestamp_ms, description="Silence start time")
+    duration: int = Field(default=0, description="Silence duration in milliseconds")
+
+
+# AI/ASR Events
+class AsrFinalEvent(BaseEvent):
+    """ASR final transcription event."""
+
+    event: str = Field(default="asrFinal", description="Event type")
+    index: int = Field(..., description="ASR result sequence number")
+    start_time: Optional[int] = Field(default=None, description="Speech start time")
+    end_time: Optional[int] = Field(default=None, description="Speech end time")
+    text: str = Field(..., description="Transcribed text")
+
+
+class AsrDeltaEvent(BaseEvent):
+    """ASR partial transcription event (streaming)."""
+
+    event: str = Field(default="asrDelta", description="Event type")
+    index: int = Field(..., description="ASR result sequence number")
+    start_time: Optional[int] = Field(default=None, description="Speech start time")
+    end_time: Optional[int] = Field(default=None, description="Speech end time")
+    text: str = Field(..., description="Partial transcribed text")
+
+
+class EouEvent(BaseEvent):
+    """End of utterance detection event."""
+
+    event: str = Field(default="eou", description="Event type")
+    completed: bool = Field(default=True, description="Whether utterance was completed")
+
+
+# Audio Track Events
+class TrackStartEvent(BaseEvent):
+    """Audio track start event."""
+
+    event: str = Field(default="trackStart", description="Event type")
+    play_id: Optional[str] = Field(default=None, description="Play ID from TTS/Play command")
+
+
+class TrackEndEvent(BaseEvent):
+    """Audio track end event."""
+
+    event: str = Field(default="trackEnd", description="Event type")
+    duration: int = Field(..., description="Track duration in milliseconds")
+    ssrc: int = Field(..., description="RTP SSRC identifier")
+    play_id: Optional[str] = Field(default=None, description="Play ID from TTS/Play command")
+
+
+class InterruptionEvent(BaseEvent):
+    """Playback interruption event."""
+
+    event: str = Field(default="interruption", description="Event type")
+    play_id: Optional[str] = Field(default=None, description="Play ID that was interrupted")
+    subtitle: Optional[str] = Field(default=None, description="TTS text being played")
+    position: Optional[int] = Field(default=None, description="Word index position")
+    total_duration: Optional[int] = Field(default=None, description="Total TTS duration")
+    current: Optional[int] = Field(default=None, description="Elapsed time when interrupted")
+
+
+# System Events
+class ErrorEvent(BaseEvent):
+    """Error event."""
+
+    event: str = Field(default="error", description="Event type")
+    sender: str = Field(..., description="Component that generated the error")
+    error: str = Field(..., description="Error message")
+    code: Optional[int] = Field(default=None, description="Error code")
+
+
+class MetricsEvent(BaseModel):
+    """Performance metrics event."""
+
+    event: str = Field(default="metrics", description="Event type")
+    timestamp: int = Field(default_factory=current_timestamp_ms, description="Event timestamp")
+    key: str = Field(..., description="Metric key")
+    duration: int = Field(..., description="Duration in milliseconds")
+    data: Optional[Dict[str, Any]] = Field(default=None, description="Additional metric data")
+
+
+class AddHistoryEvent(BaseModel):
+    """Conversation history entry added event."""
+
+    event: str = Field(default="addHistory", description="Event type")
+    timestamp: int = Field(default_factory=current_timestamp_ms, description="Event timestamp")
+    sender: Optional[str] = Field(default=None, description="Component that added history")
+    speaker: str = Field(..., description="Speaker identifier")
+    text: str = Field(..., description="Conversation text")
+
+
+class DTMFEvent(BaseEvent):
+    """DTMF tone detected event."""
+
+    event: str = Field(default="dtmf", description="Event type")
+    digit: str = Field(..., description="DTMF digit (0-9, *, #, A-D)")
+
+
+class HeartBeatEvent(BaseModel):
+    """Server-to-client heartbeat to keep connection alive."""
+
+    event: str = Field(default="heartBeat", description="Event type")
+    timestamp: int = Field(default_factory=current_timestamp_ms, description="Event timestamp in milliseconds")
+
+
+# Event type mapping
+EVENT_TYPES = {
+    "incoming": IncomingEvent,
+    "answer": AnswerEvent,
+    "reject": RejectEvent,
+    "ringing": RingingEvent,
+    "hangup": HangupEvent,
+    "speaking": SpeakingEvent,
+    "silence": SilenceEvent,
+    "asrFinal": AsrFinalEvent,
+    "asrDelta": AsrDeltaEvent,
+    "eou": EouEvent,
+    "trackStart": TrackStartEvent,
+    "trackEnd": TrackEndEvent,
+    "interruption": InterruptionEvent,
+    "error": ErrorEvent,
+    "metrics": MetricsEvent,
+    "addHistory": AddHistoryEvent,
+    "dtmf": DTMFEvent,
+    "heartBeat": HeartBeatEvent,
+}
+
+
+def create_event(event_type: str, **kwargs) -> BaseModel:
+    """
+    Create an event model.
+
+    Args:
+        event_type: Type of event to create
+        **kwargs: Event fields
+
+    Returns:
+        Event model instance
+
+    Raises:
+        ValueError: If event type is unknown
+    """
+    event_class = EVENT_TYPES.get(event_type)
+
+    if not event_class:
+        raise ValueError(f"Unknown event type: {event_type}")
+
+    return event_class(event=event_type, **kwargs)

engine/processors/__init__.py

@@ -0,0 +1,6 @@
+"""Audio Processors Package"""
+
+from processors.eou import EouDetector
+from processors.vad import SileroVAD, VADProcessor
+
+__all__ = ["EouDetector", "SileroVAD", "VADProcessor"]

engine/processors/eou.py

@@ -0,0 +1,80 @@
+"""End-of-Utterance Detection."""
+
+import time
+from typing import Optional
+
+
+class EouDetector:
+    """
+    End-of-utterance detector. Fires EOU only after continuous silence for
+    silence_threshold_ms. Short pauses between sentences do not trigger EOU
+    because speech resets the silence timer (one EOU per turn).
+    """
+
+    def __init__(self, silence_threshold_ms: int = 1000, min_speech_duration_ms: int = 250):
+        """
+        Initialize EOU detector.
+
+        Args:
+            silence_threshold_ms: How long silence must last to trigger EOU (default 1000ms)
+            min_speech_duration_ms: Minimum speech duration to consider valid (default 250ms)
+        """
+        self.threshold = silence_threshold_ms / 1000.0
+        self.min_speech = min_speech_duration_ms / 1000.0
+        self._silence_threshold_ms = silence_threshold_ms
+        self._min_speech_duration_ms = min_speech_duration_ms
+
+        # State
+        self.is_speaking = False
+        self.speech_start_time = 0.0
+        self.silence_start_time: Optional[float] = None
+        self.triggered = False
+
+    def process(self, vad_status: str) -> bool:
+        """
+        Process VAD status and detect end of utterance.
+
+        Input: "Speech" or "Silence" (from VAD).
+        Output: True if EOU detected, False otherwise.
+
+        Short breaks between phrases reset the silence clock when speech
+        resumes, so only one EOU is emitted after the user truly stops.
+        """
+        now = time.time()
+
+        if vad_status == "Speech":
+            if not self.is_speaking:
+                self.is_speaking = True
+                self.speech_start_time = now
+                self.triggered = False
+            # Any speech resets silence timer — short pause + more speech = one utterance
+            self.silence_start_time = None
+            return False
+
+        if vad_status == "Silence":
+            if not self.is_speaking:
+                return False
+            if self.silence_start_time is None:
+                self.silence_start_time = now
+
+            speech_duration = self.silence_start_time - self.speech_start_time
+            if speech_duration < self.min_speech:
+                self.is_speaking = False
+                self.silence_start_time = None
+                return False
+
+            silence_duration = now - self.silence_start_time
+            if silence_duration >= self.threshold and not self.triggered:
+                self.triggered = True
+                self.is_speaking = False
+                self.silence_start_time = None
+                return True
+
+        return False
+
+    def reset(self) -> None:
+        """Reset EOU detector state."""
+        self.is_speaking = False
+        self.speech_start_time = 0.0
+        self.silence_start_time = None
+        self.triggered = False

engine/processors/tracks.py

@@ -0,0 +1,168 @@
+"""Audio track processing for WebRTC."""
+
+import asyncio
+import fractions
+from typing import Optional
+from loguru import logger
+
+# Try to import aiortc (optional for WebRTC functionality)
+try:
+    from aiortc import AudioStreamTrack
+    AIORTC_AVAILABLE = True
+except ImportError:
+    AIORTC_AVAILABLE = False
+    AudioStreamTrack = object  # Dummy class for type hints
+
+# Try to import PyAV (optional for audio resampling)
+try:
+    from av import AudioFrame, AudioResampler
+    AV_AVAILABLE = True
+except ImportError:
+    AV_AVAILABLE = False
+    # Create dummy classes for type hints
+    class AudioFrame:
+        pass
+    class AudioResampler:
+        pass
+
+import numpy as np
+
+
+class Resampled16kTrack(AudioStreamTrack if AIORTC_AVAILABLE else object):
+    """
+    Audio track that resamples input to 16kHz mono PCM.
+
+    Wraps an existing MediaStreamTrack and converts its output
+    to 16kHz mono 16-bit PCM format for the pipeline.
+    """
+
+    def __init__(self, track, target_sample_rate: int = 16000):
+        """
+        Initialize resampled track.
+
+        Args:
+            track: Source MediaStreamTrack
+            target_sample_rate: Target sample rate (default: 16000)
+        """
+        if not AIORTC_AVAILABLE:
+            raise RuntimeError("aiortc not available - Resampled16kTrack cannot be used")
+
+        super().__init__()
+        self.track = track
+        self.target_sample_rate = target_sample_rate
+
+        if AV_AVAILABLE:
+            self.resampler = AudioResampler(
+                format="s16",
+                layout="mono",
+                rate=target_sample_rate
+            )
+        else:
+            logger.warning("PyAV not available, audio resampling disabled")
+            self.resampler = None
+
+        self._closed = False
+
+    async def recv(self):
+        """
+        Receive and resample next audio frame.
+
+        Returns:
+            Resampled AudioFrame at 16kHz mono
+        """
+        if self._closed:
+            raise RuntimeError("Track is closed")
+
+        # Get frame from source track
+        frame = await self.track.recv()
+
+        # Resample the frame if AV is available
+        if AV_AVAILABLE and self.resampler:
+            resampled_frame = self.resampler.resample(frame)
+            # Ensure the frame has the correct format
+            resampled_frame.sample_rate = self.target_sample_rate
+            return resampled_frame
+        else:
+            # Return frame as-is if AV is not available
+            return frame
+
+    async def stop(self) -> None:
+        """Stop the track and cleanup resources."""
+        self._closed = True
+        if hasattr(self, 'resampler') and self.resampler:
+            del self.resampler
+        logger.debug("Resampled track stopped")
+
+
+class SineWaveTrack(AudioStreamTrack if AIORTC_AVAILABLE else object):
+    """
+    Synthetic audio track that generates a sine wave.
+
+    Useful for testing without requiring real audio input.
+    """
+
+    def __init__(self, sample_rate: int = 16000, frequency: int = 440):
+        """
+        Initialize sine wave track.
+
+        Args:
+            sample_rate: Audio sample rate (default: 16000)
+            frequency: Sine wave frequency in Hz (default: 440)
+        """
+        if not AIORTC_AVAILABLE:
+            raise RuntimeError("aiortc not available - SineWaveTrack cannot be used")
+
+        super().__init__()
+        self.sample_rate = sample_rate
+        self.frequency = frequency
+        self.counter = 0
+        self._stopped = False
+
+    async def recv(self):
+        """
+        Generate next audio frame with sine wave.
+
+        Returns:
+            AudioFrame with sine wave data
+        """
+        if self._stopped:
+            raise RuntimeError("Track is stopped")
+
+        # Generate 20ms of audio
+        samples = int(self.sample_rate * 0.02)
+        pts = self.counter
+        time_base = fractions.Fraction(1, self.sample_rate)
+
+        # Generate sine wave
+        t = np.linspace(
+            self.counter / self.sample_rate,
+            (self.counter + samples) / self.sample_rate,
+            samples,
+            endpoint=False
+        )
+
+        # Generate sine wave (Int16 PCM)
+        data = (0.5 * np.sin(2 * np.pi * self.frequency * t) * 32767).astype(np.int16)
+
+        # Update counter
+        self.counter += samples
+
+        # Create AudioFrame if AV is available
+        if AV_AVAILABLE:
+            frame = AudioFrame.from_ndarray(data.reshape(1, -1), format='s16', layout='mono')
+            frame.pts = pts
+            frame.time_base = time_base
+            frame.sample_rate = self.sample_rate
+            return frame
+        else:
+            # Return simple data structure if AV is not available
+            return {
+                'data': data,
+                'sample_rate': self.sample_rate,
+                'pts': pts,
+                'time_base': time_base
+            }
+
+    def stop(self) -> None:
+        """Stop the track."""
+        self._stopped = True

engine/processors/vad.py

@@ -0,0 +1,221 @@
+"""Voice Activity Detection using Silero VAD."""
+
+import asyncio
+import os
+from typing import Tuple, Optional
+import numpy as np
+from loguru import logger
+
+
+# Try to import onnxruntime (optional for VAD functionality)
+try:
+    import onnxruntime as ort
+    ONNX_AVAILABLE = True
+except ImportError:
+    ONNX_AVAILABLE = False
+    ort = None
+    logger.warning("onnxruntime not available - VAD will be disabled")
+
+
+class SileroVAD:
+    """
+    Voice Activity Detection using Silero VAD model.
+
+    Detects speech in audio chunks using the Silero VAD ONNX model.
+    Returns "Speech" or "Silence" for each audio chunk.
+    """
+
+    def __init__(self, model_path: str = "data/vad/silero_vad.onnx", sample_rate: int = 16000):
+        """
+        Initialize Silero VAD.
+
+        Args:
+            model_path: Path to Silero VAD ONNX model
+            sample_rate: Audio sample rate (must be 16kHz for Silero VAD)
+        """
+        self.sample_rate = sample_rate
+        self.model_path = model_path
+
+        # Check if model exists
+        if not os.path.exists(model_path):
+            logger.warning(f"VAD model not found at {model_path}. VAD will be disabled.")
+            self.session = None
+            return
+
+        # Check if onnxruntime is available
+        if not ONNX_AVAILABLE:
+            logger.warning("onnxruntime not available - VAD will be disabled")
+            self.session = None
+            return
+
+        # Load ONNX model
+        try:
+            self.session = ort.InferenceSession(model_path)
+            logger.info(f"Loaded Silero VAD model from {model_path}")
+        except Exception as e:
+            logger.error(f"Failed to load VAD model: {e}")
+            self.session = None
+            return
+
+        # Internal state for VAD
+        self._reset_state()
+        self.buffer = np.array([], dtype=np.float32)
+        self.min_chunk_size = 512
+        self.last_label = "Silence"
+        self.last_probability = 0.0
+        self._energy_noise_floor = 1e-4
+
+    def _reset_state(self):
+        # Silero VAD V4+ expects state shape [2, 1, 128]
+        self._state = np.zeros((2, 1, 128), dtype=np.float32)
+        self._sr = np.array([self.sample_rate], dtype=np.int64)
+
+    def process_audio(self, pcm_bytes: bytes, chunk_size_ms: int = 20) -> Tuple[str, float]:
+        """
+        Process audio chunk and detect speech.
+
+        Args:
+            pcm_bytes: PCM audio data (16-bit, mono, 16kHz)
+            chunk_size_ms: Chunk duration in milliseconds (ignored for buffering logic)
+
+        Returns:
+            Tuple of (label, probability) where label is "Speech" or "Silence"
+        """
+        if self.session is None or not ONNX_AVAILABLE:
+            # Fallback energy-based VAD with adaptive noise floor.
+            if not pcm_bytes:
+                return "Silence", 0.0
+            audio_int16 = np.frombuffer(pcm_bytes, dtype=np.int16)
+            if audio_int16.size == 0:
+                return "Silence", 0.0
+            audio_float = audio_int16.astype(np.float32) / 32768.0
+            rms = float(np.sqrt(np.mean(audio_float * audio_float)))
+
+            # Update adaptive noise floor (slowly rises, faster to fall)
+            if rms < self._energy_noise_floor:
+                self._energy_noise_floor = 0.95 * self._energy_noise_floor + 0.05 * rms
+            else:
+                self._energy_noise_floor = 0.995 * self._energy_noise_floor + 0.005 * rms
+
+            # Compute SNR-like ratio and map to probability
+            denom = max(self._energy_noise_floor, 1e-6)
+            snr = max(0.0, (rms - denom) / denom)
+            probability = min(1.0, snr / 3.0)  # ~3x above noise => strong speech
+            label = "Speech" if probability >= 0.5 else "Silence"
+            return label, probability
+
+        # Convert bytes to numpy array of int16
+        audio_int16 = np.frombuffer(pcm_bytes, dtype=np.int16)
+
+        # Normalize to float32 (-1.0 to 1.0)
+        audio_float = audio_int16.astype(np.float32) / 32768.0
+
+        # Add to buffer
+        self.buffer = np.concatenate((self.buffer, audio_float))
+
+        # Process all complete chunks in the buffer
+        processed_any = False
+        while len(self.buffer) >= self.min_chunk_size:
+            # Slice exactly 512 samples
+            chunk = self.buffer[:self.min_chunk_size]
+            self.buffer = self.buffer[self.min_chunk_size:]
+
+            # Prepare inputs
+            # Input tensor shape: [batch, samples] -> [1, 512]
+            input_tensor = chunk.reshape(1, -1)
+
+            # Run inference
+            try:
+                ort_inputs = {
+                    'input': input_tensor,
+                    'state': self._state,
+                    'sr': self._sr
+                }
+
+                # Outputs: probability, state
+                out, self._state = self.session.run(None, ort_inputs)
+                
+                # Get probability
+                self.last_probability = float(out[0][0])
+                self.last_label = "Speech" if self.last_probability >= 0.5 else "Silence"
+                processed_any = True
+
+            except Exception as e:
+                logger.error(f"VAD inference error: {e}")
+                # Try to determine if it's an input name issue
+                try:
+                    inputs = [x.name for x in self.session.get_inputs()]
+                    logger.error(f"Model expects inputs: {inputs}")
+                except:
+                    pass
+                return "Speech", 1.0
+
+        return self.last_label, self.last_probability
+
+    def reset(self) -> None:
+        """Reset VAD internal state."""
+        self._reset_state()
+        self.buffer = np.array([], dtype=np.float32)
+        self.last_label = "Silence"
+        self.last_probability = 0.0
+
+
+class VADProcessor:
+    """
+    High-level VAD processor with state management.
+
+    Tracks speech/silence state and emits events on transitions.
+    """
+
+    def __init__(self, vad_model: SileroVAD, threshold: float = 0.5):
+        """
+        Initialize VAD processor.
+
+        Args:
+            vad_model: Silero VAD model instance
+            threshold: Speech detection threshold
+        """
+        self.vad = vad_model
+        self.threshold = threshold
+        self.is_speaking = False
+        self.speech_start_time: Optional[float] = None
+        self.silence_start_time: Optional[float] = None
+
+    def process(self, pcm_bytes: bytes, chunk_size_ms: int = 20) -> Optional[Tuple[str, float]]:
+        """
+        Process audio chunk and detect state changes.
+
+        Args:
+            pcm_bytes: PCM audio data
+            chunk_size_ms: Chunk duration in milliseconds
+
+        Returns:
+            Tuple of (event_type, probability) if state changed, None otherwise
+        """
+        label, probability = self.vad.process_audio(pcm_bytes, chunk_size_ms)
+
+        # Check if this is speech based on threshold
+        is_speech = probability >= self.threshold
+        
+        # State transition: Silence -> Speech
+        if is_speech and not self.is_speaking:
+            self.is_speaking = True
+            self.speech_start_time = asyncio.get_event_loop().time()
+            self.silence_start_time = None
+            return ("speaking", probability)
+
+        # State transition: Speech -> Silence
+        elif not is_speech and self.is_speaking:
+            self.is_speaking = False
+            self.silence_start_time = asyncio.get_event_loop().time()
+            self.speech_start_time = None
+            return ("silence", probability)
+
+        return None
+
+    def reset(self) -> None:
+        """Reset VAD state."""
+        self.vad.reset()
+        self.is_speaking = False
+        self.speech_start_time = None
+        self.silence_start_time = None

engine/pyproject.toml

@@ -0,0 +1,134 @@
+[build-system]
+requires = ["setuptools>=68.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "py-active-call-cc"
+version = "0.1.0"
+description = "Python Active-Call: Real-time audio streaming with WebSocket and WebRTC"
+readme = "README.md"
+requires-python = ">=3.11"
+license = {text = "MIT"}
+authors = [
+    {name = "Your Name", email = "your.email@example.com"}
+]
+keywords = ["webrtc", "websocket", "audio", "voip", "real-time"]
+classifiers = [
+    "Development Status :: 3 - Alpha",
+    "Intended Audience :: Developers",
+    "Topic :: Communications :: Telephony",
+    "License :: OSI Approved :: MIT License",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+]
+
+[project.urls]
+Homepage = "https://github.com/yourusername/py-active-call-cc"
+Documentation = "https://github.com/yourusername/py-active-call-cc/blob/main/README.md"
+Repository = "https://github.com/yourusername/py-active-call-cc.git"
+Issues = "https://github.com/yourusername/py-active-call-cc/issues"
+
+[tool.setuptools.packages.find]
+where = ["."]
+include = ["app*"]
+exclude = ["tests*", "scripts*", "reference*"]
+
+[tool.black]
+line-length = 100
+target-version = ['py311']
+include = '\.pyi?$'
+extend-exclude = '''
+/(
+  # directories
+  \.eggs
+  | \.git
+  | \.hg
+  | \.mypy_cache
+  | \.tox
+  | \.venv
+  | build
+  | dist
+  | reference
+)/
+'''
+
+[tool.ruff]
+line-length = 100
+target-version = "py311"
+select = [
+    "E",   # pycodestyle errors
+    "W",   # pycodestyle warnings
+    "F",   # pyflakes
+    "I",   # isort
+    "B",   # flake8-bugbear
+    "C4",  # flake8-comprehensions
+    "UP",  # pyupgrade
+]
+ignore = [
+    "E501",  # line too long (handled by black)
+    "B008",  # do not perform function calls in argument defaults
+]
+exclude = [
+    ".bzr",
+    ".direnv",
+    ".eggs",
+    ".git",
+    ".hg",
+    ".mypy_cache",
+    ".nox",
+    ".pants.d",
+    ".ruff_cache",
+    ".svn",
+    ".tox",
+    ".venv",
+    "__pypackages__",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+    "node_modules",
+    "venv",
+    "reference",
+]
+
+[tool.ruff.per-file-ignores]
+"__init__.py" = ["F401"]  # unused imports
+
+[tool.mypy]
+python_version = "3.11"
+warn_return_any = true
+warn_unused_configs = true
+disallow_untyped_defs = false
+disallow_incomplete_defs = false
+check_untyped_defs = true
+no_implicit_optional = true
+warn_redundant_casts = true
+warn_unused_ignores = true
+warn_no_return = true
+strict_equality = true
+exclude = [
+    "venv",
+    "reference",
+    "build",
+    "dist",
+]
+
+[[tool.mypy.overrides]]
+module = [
+    "aiortc.*",
+    "av.*",
+    "onnxruntime.*",
+]
+ignore_missing_imports = true
+
+[tool.pytest.ini_options]
+minversion = "7.0"
+addopts = "-ra -q --strict-markers --strict-config"
+testpaths = ["tests"]
+pythonpath = ["."]
+asyncio_mode = "auto"
+markers = [
+    "slow: marks tests as slow (deselect with '-m \"not slow\"')",
+    "integration: marks tests as integration tests",
+]

engine/requirements.txt

@@ -0,0 +1,37 @@
+# Web Framework
+fastapi>=0.109.0
+uvicorn[standard]>=0.27.0
+websockets>=12.0
+python-multipart>=0.0.6
+
+# WebRTC (optional - for WebRTC transport)
+aiortc>=1.6.0
+
+# Audio Processing
+av>=12.1.0
+numpy>=1.26.3
+onnxruntime>=1.16.3
+
+# Configuration
+pydantic>=2.5.3
+pydantic-settings>=2.1.0
+python-dotenv>=1.0.0
+toml>=0.10.2
+
+# Logging
+loguru>=0.7.2
+
+# HTTP Client
+aiohttp>=3.9.1
+
+# AI Services - LLM
+openai>=1.0.0
+
+# AI Services - TTS
+edge-tts>=6.1.0
+pydub>=0.25.0  # For audio format conversion
+
+# Microphone client dependencies
+sounddevice>=0.4.6
+soundfile>=0.12.1
+pyaudio>=0.2.13  # More reliable audio on Windows

engine/scripts/README.md

@@ -0,0 +1 @@
+# Development Script

engine/scripts/generate_test_audio/generate_test_audio.py

@@ -0,0 +1,311 @@
+#!/usr/bin/env python3
+"""
+Generate test audio file with utterances using SiliconFlow TTS API.
+
+Creates a 16kHz mono WAV file with real speech segments separated by
+configurable silence (for VAD/testing).
+
+Usage:
+  python generate_test_audio.py [OPTIONS]
+
+Options:
+  -o, --output PATH       Output WAV path (default: data/audio_examples/two_utterances_16k.wav)
+  -u, --utterance TEXT    Utterance text; repeat for multiple (ignored if -j is set)
+  -j, --json PATH         JSON file: array of strings or {"utterances": [...]}
+  --silence-ms MS         Silence in ms between utterances (default: 500)
+  --lead-silence-ms MS    Silence in ms at start (default: 200)
+  --trail-silence-ms MS   Silence in ms at end (default: 300)
+
+Examples:
+  # Default utterances and output
+  python generate_test_audio.py
+
+  # Custom output path
+  python generate_test_audio.py -o out.wav
+
+  # Utterances from command line
+  python generate_test_audio.py -u "Hello" -u "World" -o test.wav
+
+  # Utterancgenerate_test_audio.py -j utterances.json -o test.wav
+
+  # Custom silence (1s between utterances)
+  python generate_test_audio.py -u "One" -u "Two" --silence-ms 1000 -o test.wav
+
+Requires SILICONFLOW_API_KEY in .env.
+"""
+
+import wave
+import struct
+import argparse
+import asyncio
+import aiohttp
+import json
+import os
+from pathlib import Path
+from dotenv import load_dotenv
+
+
+# Load .env file from project root
+project_root = Path(__file__).parent.parent.parent
+load_dotenv(project_root / ".env")
+
+
+# SiliconFlow TTS Configuration
+SILICONFLOW_API_URL = "https://api.siliconflow.cn/v1/audio/speech"
+SILICONFLOW_MODEL = "FunAudioLLM/CosyVoice2-0.5B"
+
+# Available voices
+VOICES = {
+    "alex": "FunAudioLLM/CosyVoice2-0.5B:alex",
+    "anna": "FunAudioLLM/CosyVoice2-0.5B:anna",
+    "bella": "FunAudioLLM/CosyVoice2-0.5B:bella",
+    "benjamin": "FunAudioLLM/CosyVoice2-0.5B:benjamin",
+    "charles": "FunAudioLLM/CosyVoice2-0.5B:charles",
+    "claire": "FunAudioLLM/CosyVoice2-0.5B:claire",
+    "david": "FunAudioLLM/CosyVoice2-0.5B:david",
+    "diana": "FunAudioLLM/CosyVoice2-0.5B:diana",
+}
+
+
+def generate_silence(duration_ms: int, sample_rate: int = 16000) -> bytes:
+    """Generate silence as PCM bytes."""
+    num_samples = int(sample_rate * (duration_ms / 1000.0))
+    return b'\x00\x00' * num_samples
+
+
+async def synthesize_speech(
+    text: str,
+    api_key: str,
+    voice: str = "anna",
+    sample_rate: int = 16000,
+    speed: float = 1.0
+) -> bytes:
+    """
+    Synthesize speech using SiliconFlow TTS API.
+    
+    Args:
+        text: Text to synthesize
+        api_key: SiliconFlow API key
+        voice: Voice name (alex, anna, bella, benjamin, charles, claire, david, diana)
+        sample_rate: Output sample rate (8000, 16000, 24000, 32000, 44100)
+        speed: Speech speed (0.25 to 4.0)
+        
+    Returns:
+        PCM audio bytes (16-bit signed, little-endian)
+    """
+    # Resolve voice name
+    full_voice = VOICES.get(voice, voice)
+    
+    payload = {
+        "model": SILICONFLOW_MODEL,
+        "input": text,
+        "voice": full_voice,
+        "response_format": "pcm",
+        "sample_rate": sample_rate,
+        "stream": False,
+        "speed": speed
+    }
+    
+    headers = {
+        "Authorization": f"Bearer {api_key}",
+        "Content-Type": "application/json"
+    }
+    
+    async with aiohttp.ClientSession() as session:
+        async with session.post(SILICONFLOW_API_URL, json=payload, headers=headers) as response:
+            if response.status != 200:
+                error_text = await response.text()
+                raise RuntimeError(f"SiliconFlow TTS error: {response.status} - {error_text}")
+            
+            return await response.read()
+
+
+async def generate_test_audio(
+    output_path: str,
+    utterances: list[str],
+    silence_ms: int = 500,
+    lead_silence_ms: int = 200,
+    trail_silence_ms: int = 300,
+    voice: str = "anna",
+    sample_rate: int = 16000,
+    speed: float = 1.0
+):
+    """
+    Generate test audio with multiple utterances separated by silence.
+    
+    Args:
+        output_path: Path to save the WAV file
+        utterances: List of text strings for each utterance
+        silence_ms: Silence duration between utterances (milliseconds)
+        lead_silence_ms: Silence at the beginning (milliseconds)
+        trail_silence_ms: Silence at the end (milliseconds)
+        voice: TTS voice to use
+        sample_rate: Audio sample rate
+        speed: TTS speech speed
+    """
+    api_key = os.getenv("SILICONFLOW_API_KEY")
+    if not api_key:
+        raise ValueError(
+            "SILICONFLOW_API_KEY not found in environment.\n"
+            "Please set it in your .env file:\n"
+            "  SILICONFLOW_API_KEY=your-api-key-here"
+        )
+    
+    print(f"Using SiliconFlow TTS API")
+    print(f"  Voice: {voice}")
+    print(f"  Sample rate: {sample_rate}Hz")
+    print(f"  Speed: {speed}x")
+    print()
+    
+    segments = []
+    
+    # Lead-in silence
+    if lead_silence_ms > 0:
+        segments.append(generate_silence(lead_silence_ms, sample_rate))
+        print(f"  [silence: {lead_silence_ms}ms]")
+    
+    # Generate each utterance with silence between
+    for i, text in enumerate(utterances):
+        print(f"  Synthesizing utterance {i + 1}: \"{text}\"")
+        audio = await synthesize_speech(
+            text=text,
+            api_key=api_key,
+            voice=voice,
+            sample_rate=sample_rate,
+            speed=speed
+        )
+        segments.append(audio)
+        
+        # Add silence between utterances (not after the last one)
+        if i < len(utterances) - 1:
+            segments.append(generate_silence(silence_ms, sample_rate))
+            print(f"  [silence: {silence_ms}ms]")
+    
+    # Trail silence
+    if trail_silence_ms > 0:
+        segments.append(generate_silence(trail_silence_ms, sample_rate))
+        print(f"  [silence: {trail_silence_ms}ms]")
+    
+    # Concatenate all segments
+    audio_data = b''.join(segments)
+    
+    # Write WAV file
+    with wave.open(output_path, 'wb') as wf:
+        wf.setnchannels(1)          # Mono
+        wf.setsampwidth(2)          # 16-bit
+        wf.setframerate(sample_rate)
+        wf.writeframes(audio_data)
+    
+    duration_sec = len(audio_data) / (sample_rate * 2)
+    print()
+    print(f"Generated: {output_path}")
+    print(f"  Duration: {duration_sec:.2f}s")
+    print(f"  Sample rate: {sample_rate}Hz")
+    print(f"  Format: 16-bit mono PCM WAV")
+    print(f"  Size: {len(audio_data):,} bytes")
+
+
+def load_utterances_from_json(path: Path) -> list[str]:
+    """
+    Load utterances from a JSON file.
+    
+    Accepts either:
+    - A JSON array: ["utterance 1", "utterance 2"]
+    - A JSON object with "utterances" key: {"utterances": ["a", "b"]}
+    """
+    with open(path, encoding="utf-8") as f:
+        data = json.load(f)
+    if isinstance(data, list):
+        return [str(s) for s in data]
+    if isinstance(data, dict) and "utterances" in data:
+        return [str(s) for s in data["utterances"]]
+    raise ValueError(
+        f"JSON file must be an array of strings or an object with 'utterances' key. "
+        f"Got: {type(data).__name__}"
+    )
+
+
+def parse_args():
+    """Parse command-line arguments."""
+    script_dir = Path(__file__).parent
+    default_output = script_dir.parent / "data" / "audio_examples" / "two_utterances_16k.wav"
+    
+    parser = argparse.ArgumentParser(description="Generate test audio with SiliconFlow TTS (utterances + silence).")
+    parser.add_argument(
+        "-o", "--output",
+        type=Path,
+        default=default_output,
+        help=f"Output WAV file path (default: {default_output})"
+    )
+    parser.add_argument(
+        "-u", "--utterance",
+        action="append",
+        dest="utterances",
+        metavar="TEXT",
+        help="Utterance text (repeat for multiple). Ignored if --json is set."
+    )
+    parser.add_argument(
+        "-j", "--json",
+        type=Path,
+        metavar="PATH",
+        help="JSON file with utterances: array of strings or object with 'utterances' key"
+    )
+    parser.add_argument(
+        "--silence-ms",
+        type=int,
+        default=500,
+        metavar="MS",
+        help="Silence in ms between utterances (default: 500)"
+    )
+    parser.add_argument(
+        "--lead-silence-ms",
+        type=int,
+        default=200,
+        metavar="MS",
+        help="Silence in ms at start of file (default: 200)"
+    )
+    parser.add_argument(
+        "--trail-silence-ms",
+        type=int,
+        default=300,
+        metavar="MS",
+        help="Silence in ms at end of file (default: 300)"
+    )
+    return parser.parse_args()
+
+
+async def main():
+    """Main entry point."""
+    args = parse_args()
+    output_path = args.output
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    
+    # Resolve utterances: JSON file > -u args > defaults
+    if args.json is not None:
+        if not args.json.is_file():
+            raise FileNotFoundError(f"Utterances JSON file not found: {args.json}")
+        utterances = load_utterances_from_json(args.json)
+        if not utterances:
+            raise ValueError(f"JSON file has no utterances: {args.json}")
+    elif args.utterances:
+        utterances = args.utterances
+    else:
+        utterances = [
+            "Hello, how are you doing today?",
+            "I'm doing great, thank you for asking!"
+        ]
+    
+    await generate_test_audio(
+        output_path=str(output_path),
+        utterances=utterances,
+        silence_ms=args.silence_ms,
+        lead_silence_ms=args.lead_silence_ms,
+        trail_silence_ms=args.trail_silence_ms,
+        voice="anna",
+        sample_rate=16000,
+        speed=1.0
+    )
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

engine/services/__init__.py

@@ -0,0 +1,47 @@
+"""AI Services package.
+
+Provides ASR, LLM, TTS, and Realtime API services for voice conversation.
+"""
+
+from services.base import (
+    ServiceState,
+    ASRResult,
+    LLMMessage,
+    TTSChunk,
+    BaseASRService,
+    BaseLLMService,
+    BaseTTSService,
+)
+from services.llm import OpenAILLMService, MockLLMService
+from services.tts import EdgeTTSService, MockTTSService
+from services.asr import BufferedASRService, MockASRService
+from services.siliconflow_asr import SiliconFlowASRService
+from services.siliconflow_tts import SiliconFlowTTSService
+from services.realtime import RealtimeService, RealtimeConfig, RealtimePipeline
+
+__all__ = [
+    # Base classes
+    "ServiceState",
+    "ASRResult",
+    "LLMMessage",
+    "TTSChunk",
+    "BaseASRService",
+    "BaseLLMService",
+    "BaseTTSService",
+    # LLM
+    "OpenAILLMService",
+    "MockLLMService",
+    # TTS
+    "EdgeTTSService",
+    "MockTTSService",
+    # ASR
+    "BufferedASRService",
+    "MockASRService",
+    "SiliconFlowASRService",
+    # TTS (SiliconFlow)
+    "SiliconFlowTTSService",
+    # Realtime
+    "RealtimeService",
+    "RealtimeConfig",
+    "RealtimePipeline",
+]

engine/services/asr.py

@@ -0,0 +1,147 @@
+"""ASR (Automatic Speech Recognition) Service implementations.
+
+Provides speech-to-text capabilities with streaming support.
+"""
+
+import os
+import asyncio
+import json
+from typing import AsyncIterator, Optional
+from loguru import logger
+
+from services.base import BaseASRService, ASRResult, ServiceState
+
+# Try to import websockets for streaming ASR
+try:
+    import websockets
+    WEBSOCKETS_AVAILABLE = True
+except ImportError:
+    WEBSOCKETS_AVAILABLE = False
+
+
+class BufferedASRService(BaseASRService):
+    """
+    Buffered ASR service that accumulates audio and provides
+    a simple text accumulator for use with EOU detection.
+    
+    This is a lightweight implementation that works with the
+    existing VAD + EOU pattern without requiring external ASR.
+    """
+    
+    def __init__(
+        self,
+        sample_rate: int = 16000,
+        language: str = "en"
+    ):
+        super().__init__(sample_rate=sample_rate, language=language)
+        
+        self._audio_buffer: bytes = b""
+        self._current_text: str = ""
+        self._transcript_queue: asyncio.Queue[ASRResult] = asyncio.Queue()
+    
+    async def connect(self) -> None:
+        """No connection needed for buffered ASR."""
+        self.state = ServiceState.CONNECTED
+        logger.info("Buffered ASR service connected")
+    
+    async def disconnect(self) -> None:
+        """Clear buffers on disconnect."""
+        self._audio_buffer = b""
+        self._current_text = ""
+        self.state = ServiceState.DISCONNECTED
+        logger.info("Buffered ASR service disconnected")
+    
+    async def send_audio(self, audio: bytes) -> None:
+        """Buffer audio for later processing."""
+        self._audio_buffer += audio
+    
+    async def receive_transcripts(self) -> AsyncIterator[ASRResult]:
+        """Yield transcription results."""
+        while True:
+            try:
+                result = await asyncio.wait_for(
+                    self._transcript_queue.get(),
+                    timeout=0.1
+                )
+                yield result
+            except asyncio.TimeoutError:
+                continue
+            except asyncio.CancelledError:
+                break
+    
+    def set_text(self, text: str) -> None:
+        """
+        Set the current transcript text directly.
+        
+        This allows external integration (e.g., Whisper, other ASR)
+        to provide transcripts.
+        """
+        self._current_text = text
+        result = ASRResult(text=text, is_final=False)
+        asyncio.create_task(self._transcript_queue.put(result))
+    
+    def get_and_clear_text(self) -> str:
+        """Get accumulated text and clear buffer."""
+        text = self._current_text
+        self._current_text = ""
+        self._audio_buffer = b""
+        return text
+    
+    def get_audio_buffer(self) -> bytes:
+        """Get accumulated audio buffer."""
+        return self._audio_buffer
+    
+    def clear_audio_buffer(self) -> None:
+        """Clear audio buffer."""
+        self._audio_buffer = b""
+
+
+class MockASRService(BaseASRService):
+    """
+    Mock ASR service for testing without actual recognition.
+    """
+    
+    def __init__(self, sample_rate: int = 16000, language: str = "en"):
+        super().__init__(sample_rate=sample_rate, language=language)
+        self._transcript_queue: asyncio.Queue[ASRResult] = asyncio.Queue()
+        self._mock_texts = [
+            "Hello, how are you?",
+            "That's interesting.",
+            "Tell me more about that.",
+            "I understand.",
+        ]
+        self._text_index = 0
+    
+    async def connect(self) -> None:
+        self.state = ServiceState.CONNECTED
+        logger.info("Mock ASR service connected")
+    
+    async def disconnect(self) -> None:
+        self.state = ServiceState.DISCONNECTED
+        logger.info("Mock ASR service disconnected")
+    
+    async def send_audio(self, audio: bytes) -> None:
+        """Mock audio processing - generates fake transcripts periodically."""
+        pass
+    
+    def trigger_transcript(self) -> None:
+        """Manually trigger a transcript (for testing)."""
+        text = self._mock_texts[self._text_index % len(self._mock_texts)]
+        self._text_index += 1
+        
+        result = ASRResult(text=text, is_final=True, confidence=0.95)
+        asyncio.create_task(self._transcript_queue.put(result))
+    
+    async def receive_transcripts(self) -> AsyncIterator[ASRResult]:
+        """Yield transcription results."""
+        while True:
+            try:
+                result = await asyncio.wait_for(
+                    self._transcript_queue.get(),
+                    timeout=0.1
+                )
+                yield result
+            except asyncio.TimeoutError:
+                continue
+            except asyncio.CancelledError:
+                break

engine/services/base.py

@@ -0,0 +1,244 @@
+"""Base classes for AI services.
+
+Defines abstract interfaces for ASR, LLM, and TTS services,
+inspired by pipecat's service architecture and active-call's
+StreamEngine pattern.
+"""
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from typing import AsyncIterator, Optional, List, Dict, Any
+from enum import Enum
+
+
+class ServiceState(Enum):
+    """Service connection state."""
+    DISCONNECTED = "disconnected"
+    CONNECTING = "connecting"
+    CONNECTED = "connected"
+    ERROR = "error"
+
+
+@dataclass
+class ASRResult:
+    """ASR transcription result."""
+    text: str
+    is_final: bool = False
+    confidence: float = 1.0
+    language: Optional[str] = None
+    start_time: Optional[float] = None
+    end_time: Optional[float] = None
+    
+    def __str__(self) -> str:
+        status = "FINAL" if self.is_final else "PARTIAL"
+        return f"[{status}] {self.text}"
+
+
+@dataclass
+class LLMMessage:
+    """LLM conversation message."""
+    role: str  # "system", "user", "assistant", "function"
+    content: str
+    name: Optional[str] = None  # For function calls
+    function_call: Optional[Dict[str, Any]] = None
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to API-compatible dict."""
+        d = {"role": self.role, "content": self.content}
+        if self.name:
+            d["name"] = self.name
+        if self.function_call:
+            d["function_call"] = self.function_call
+        return d
+
+
+@dataclass
+class TTSChunk:
+    """TTS audio chunk."""
+    audio: bytes  # PCM audio data
+    sample_rate: int = 16000
+    channels: int = 1
+    bits_per_sample: int = 16
+    is_final: bool = False
+    text_offset: Optional[int] = None  # Character offset in original text
+
+
+class BaseASRService(ABC):
+    """
+    Abstract base class for ASR (Speech-to-Text) services.
+    
+    Supports both streaming and non-streaming transcription.
+    """
+    
+    def __init__(self, sample_rate: int = 16000, language: str = "en"):
+        self.sample_rate = sample_rate
+        self.language = language
+        self.state = ServiceState.DISCONNECTED
+    
+    @abstractmethod
+    async def connect(self) -> None:
+        """Establish connection to ASR service."""
+        pass
+    
+    @abstractmethod
+    async def disconnect(self) -> None:
+        """Close connection to ASR service."""
+        pass
+    
+    @abstractmethod
+    async def send_audio(self, audio: bytes) -> None:
+        """
+        Send audio chunk for transcription.
+        
+        Args:
+            audio: PCM audio data (16-bit, mono)
+        """
+        pass
+    
+    @abstractmethod
+    async def receive_transcripts(self) -> AsyncIterator[ASRResult]:
+        """
+        Receive transcription results.
+        
+        Yields:
+            ASRResult objects as they become available
+        """
+        pass
+    
+    async def transcribe(self, audio: bytes) -> ASRResult:
+        """
+        Transcribe a complete audio buffer (non-streaming).
+        
+        Args:
+            audio: Complete PCM audio data
+            
+        Returns:
+            Final ASRResult
+        """
+        # Default implementation using streaming
+        await self.send_audio(audio)
+        async for result in self.receive_transcripts():
+            if result.is_final:
+                return result
+        return ASRResult(text="", is_final=True)
+
+
+class BaseLLMService(ABC):
+    """
+    Abstract base class for LLM (Language Model) services.
+    
+    Supports streaming responses for real-time conversation.
+    """
+    
+    def __init__(self, model: str = "gpt-4"):
+        self.model = model
+        self.state = ServiceState.DISCONNECTED
+    
+    @abstractmethod
+    async def connect(self) -> None:
+        """Initialize LLM service connection."""
+        pass
+    
+    @abstractmethod
+    async def disconnect(self) -> None:
+        """Close LLM service connection."""
+        pass
+    
+    @abstractmethod
+    async def generate(
+        self,
+        messages: List[LLMMessage],
+        temperature: float = 0.7,
+        max_tokens: Optional[int] = None
+    ) -> str:
+        """
+        Generate a complete response.
+        
+        Args:
+            messages: Conversation history
+            temperature: Sampling temperature
+            max_tokens: Maximum tokens to generate
+            
+        Returns:
+            Complete response text
+        """
+        pass
+    
+    @abstractmethod
+    async def generate_stream(
+        self,
+        messages: List[LLMMessage],
+        temperature: float = 0.7,
+        max_tokens: Optional[int] = None
+    ) -> AsyncIterator[str]:
+        """
+        Generate response in streaming mode.
+        
+        Args:
+            messages: Conversation history
+            temperature: Sampling temperature
+            max_tokens: Maximum tokens to generate
+            
+        Yields:
+            Text chunks as they are generated
+        """
+        pass
+
+
+class BaseTTSService(ABC):
+    """
+    Abstract base class for TTS (Text-to-Speech) services.
+    
+    Supports streaming audio synthesis for low-latency playback.
+    """
+    
+    def __init__(
+        self,
+        voice: str = "default",
+        sample_rate: int = 16000,
+        speed: float = 1.0
+    ):
+        self.voice = voice
+        self.sample_rate = sample_rate
+        self.speed = speed
+        self.state = ServiceState.DISCONNECTED
+    
+    @abstractmethod
+    async def connect(self) -> None:
+        """Initialize TTS service connection."""
+        pass
+    
+    @abstractmethod
+    async def disconnect(self) -> None:
+        """Close TTS service connection."""
+        pass
+    
+    @abstractmethod
+    async def synthesize(self, text: str) -> bytes:
+        """
+        Synthesize complete audio for text (non-streaming).
+        
+        Args:
+            text: Text to synthesize
+            
+        Returns:
+            Complete PCM audio data
+        """
+        pass
+    
+    @abstractmethod
+    async def synthesize_stream(self, text: str) -> AsyncIterator[TTSChunk]:
+        """
+        Synthesize audio in streaming mode.
+        
+        Args:
+            text: Text to synthesize
+            
+        Yields:
+            TTSChunk objects as audio is generated
+        """
+        pass
+    
+    async def cancel(self) -> None:
+        """Cancel ongoing synthesis (for barge-in support)."""
+        pass

engine/services/llm.py

@@ -0,0 +1,239 @@
+"""LLM (Large Language Model) Service implementations.
+
+Provides OpenAI-compatible LLM integration with streaming support
+for real-time voice conversation.
+"""
+
+import os
+import asyncio
+from typing import AsyncIterator, Optional, List, Dict, Any
+from loguru import logger
+
+from services.base import BaseLLMService, LLMMessage, ServiceState
+
+# Try to import openai
+try:
+    from openai import AsyncOpenAI
+    OPENAI_AVAILABLE = True
+except ImportError:
+    OPENAI_AVAILABLE = False
+    logger.warning("openai package not available - LLM service will be disabled")
+
+
+class OpenAILLMService(BaseLLMService):
+    """
+    OpenAI-compatible LLM service.
+    
+    Supports streaming responses for low-latency voice conversation.
+    Works with OpenAI API, Azure OpenAI, and compatible APIs.
+    """
+    
+    def __init__(
+        self,
+        model: str = "gpt-4o-mini",
+        api_key: Optional[str] = None,
+        base_url: Optional[str] = None,
+        system_prompt: Optional[str] = None
+    ):
+        """
+        Initialize OpenAI LLM service.
+        
+        Args:
+            model: Model name (e.g., "gpt-4o-mini", "gpt-4o")
+            api_key: OpenAI API key (defaults to OPENAI_API_KEY env var)
+            base_url: Custom API base URL (for Azure or compatible APIs)
+            system_prompt: Default system prompt for conversations
+        """
+        super().__init__(model=model)
+        
+        self.api_key = api_key or os.getenv("OPENAI_API_KEY")
+        self.base_url = base_url or os.getenv("OPENAI_API_URL")
+        self.system_prompt = system_prompt or (
+            "You are a helpful, friendly voice assistant. "
+            "Keep your responses concise and conversational. "
+            "Respond naturally as if having a phone conversation."
+        )
+        
+        self.client: Optional[AsyncOpenAI] = None
+        self._cancel_event = asyncio.Event()
+    
+    async def connect(self) -> None:
+        """Initialize OpenAI client."""
+        if not OPENAI_AVAILABLE:
+            raise RuntimeError("openai package not installed")
+        
+        if not self.api_key:
+            raise ValueError("OpenAI API key not provided")
+        
+        self.client = AsyncOpenAI(
+            api_key=self.api_key,
+            base_url=self.base_url
+        )
+        self.state = ServiceState.CONNECTED
+        logger.info(f"OpenAI LLM service connected: model={self.model}")
+    
+    async def disconnect(self) -> None:
+        """Close OpenAI client."""
+        if self.client:
+            await self.client.close()
+            self.client = None
+        self.state = ServiceState.DISCONNECTED
+        logger.info("OpenAI LLM service disconnected")
+    
+    def _prepare_messages(self, messages: List[LLMMessage]) -> List[Dict[str, Any]]:
+        """Prepare messages list with system prompt."""
+        result = []
+        
+        # Add system prompt if not already present
+        has_system = any(m.role == "system" for m in messages)
+        if not has_system and self.system_prompt:
+            result.append({"role": "system", "content": self.system_prompt})
+        
+        # Add all messages
+        for msg in messages:
+            result.append(msg.to_dict())
+        
+        return result
+    
+    async def generate(
+        self,
+        messages: List[LLMMessage],
+        temperature: float = 0.7,
+        max_tokens: Optional[int] = None
+    ) -> str:
+        """
+        Generate a complete response.
+        
+        Args:
+            messages: Conversation history
+            temperature: Sampling temperature
+            max_tokens: Maximum tokens to generate
+            
+        Returns:
+            Complete response text
+        """
+        if not self.client:
+            raise RuntimeError("LLM service not connected")
+        
+        prepared = self._prepare_messages(messages)
+        
+        try:
+            response = await self.client.chat.completions.create(
+                model=self.model,
+                messages=prepared,
+                temperature=temperature,
+                max_tokens=max_tokens
+            )
+            
+            content = response.choices[0].message.content or ""
+            logger.debug(f"LLM response: {content[:100]}...")
+            return content
+            
+        except Exception as e:
+            logger.error(f"LLM generation error: {e}")
+            raise
+    
+    async def generate_stream(
+        self,
+        messages: List[LLMMessage],
+        temperature: float = 0.7,
+        max_tokens: Optional[int] = None
+    ) -> AsyncIterator[str]:
+        """
+        Generate response in streaming mode.
+        
+        Args:
+            messages: Conversation history
+            temperature: Sampling temperature
+            max_tokens: Maximum tokens to generate
+            
+        Yields:
+            Text chunks as they are generated
+        """
+        if not self.client:
+            raise RuntimeError("LLM service not connected")
+        
+        prepared = self._prepare_messages(messages)
+        self._cancel_event.clear()
+        
+        try:
+            stream = await self.client.chat.completions.create(
+                model=self.model,
+                messages=prepared,
+                temperature=temperature,
+                max_tokens=max_tokens,
+                stream=True
+            )
+            
+            async for chunk in stream:
+                # Check for cancellation
+                if self._cancel_event.is_set():
+                    logger.info("LLM stream cancelled")
+                    break
+                
+                if chunk.choices and chunk.choices[0].delta.content:
+                    content = chunk.choices[0].delta.content
+                    yield content
+            
+        except asyncio.CancelledError:
+            logger.info("LLM stream cancelled via asyncio")
+            raise
+        except Exception as e:
+            logger.error(f"LLM streaming error: {e}")
+            raise
+    
+    def cancel(self) -> None:
+        """Cancel ongoing generation."""
+        self._cancel_event.set()
+
+
+class MockLLMService(BaseLLMService):
+    """
+    Mock LLM service for testing without API calls.
+    """
+    
+    def __init__(self, response_delay: float = 0.5):
+        super().__init__(model="mock")
+        self.response_delay = response_delay
+        self.responses = [
+            "Hello! How can I help you today?",
+            "That's an interesting question. Let me think about it.",
+            "I understand. Is there anything else you'd like to know?",
+            "Great! I'm here if you need anything else.",
+        ]
+        self._response_index = 0
+    
+    async def connect(self) -> None:
+        self.state = ServiceState.CONNECTED
+        logger.info("Mock LLM service connected")
+    
+    async def disconnect(self) -> None:
+        self.state = ServiceState.DISCONNECTED
+        logger.info("Mock LLM service disconnected")
+    
+    async def generate(
+        self,
+        messages: List[LLMMessage],
+        temperature: float = 0.7,
+        max_tokens: Optional[int] = None
+    ) -> str:
+        await asyncio.sleep(self.response_delay)
+        response = self.responses[self._response_index % len(self.responses)]
+        self._response_index += 1
+        return response
+    
+    async def generate_stream(
+        self,
+        messages: List[LLMMessage],
+        temperature: float = 0.7,
+        max_tokens: Optional[int] = None
+    ) -> AsyncIterator[str]:
+        response = await self.generate(messages, temperature, max_tokens)
+        
+        # Stream word by word
+        words = response.split()
+        for i, word in enumerate(words):
+            if i > 0:
+                yield " "
+            yield word
+            await asyncio.sleep(0.05)  # Simulate streaming delay

engine/services/realtime.py

@@ -0,0 +1,548 @@
+"""OpenAI Realtime API Service.
+
+Provides true duplex voice conversation using OpenAI's Realtime API,
+similar to active-call's RealtimeProcessor. This bypasses the need for
+separate ASR/LLM/TTS services by handling everything server-side.
+
+The Realtime API provides:
+- Server-side VAD with turn detection
+- Streaming speech-to-text
+- Streaming LLM responses
+- Streaming text-to-speech
+- Function calling support
+- Barge-in/interruption handling
+"""
+
+import os
+import asyncio
+import json
+import base64
+from typing import Optional, Dict, Any, Callable, Awaitable, List
+from dataclasses import dataclass, field
+from enum import Enum
+from loguru import logger
+
+try:
+    import websockets
+    WEBSOCKETS_AVAILABLE = True
+except ImportError:
+    WEBSOCKETS_AVAILABLE = False
+    logger.warning("websockets not available - Realtime API will be disabled")
+
+
+class RealtimeState(Enum):
+    """Realtime API connection state."""
+    DISCONNECTED = "disconnected"
+    CONNECTING = "connecting"
+    CONNECTED = "connected"
+    ERROR = "error"
+
+
+@dataclass
+class RealtimeConfig:
+    """Configuration for OpenAI Realtime API."""
+    
+    # API Configuration
+    api_key: Optional[str] = None
+    model: str = "gpt-4o-realtime-preview"
+    endpoint: Optional[str] = None  # For Azure or custom endpoints
+    
+    # Voice Configuration
+    voice: str = "alloy"  # alloy, echo, shimmer, etc.
+    instructions: str = (
+        "You are a helpful, friendly voice assistant. "
+        "Keep your responses concise and conversational."
+    )
+    
+    # Turn Detection (Server-side VAD)
+    turn_detection: Optional[Dict[str, Any]] = field(default_factory=lambda: {
+        "type": "server_vad",
+        "threshold": 0.5,
+        "prefix_padding_ms": 300,
+        "silence_duration_ms": 500
+    })
+    
+    # Audio Configuration
+    input_audio_format: str = "pcm16"
+    output_audio_format: str = "pcm16"
+    
+    # Tools/Functions
+    tools: List[Dict[str, Any]] = field(default_factory=list)
+
+
+class RealtimeService:
+    """
+    OpenAI Realtime API service for true duplex voice conversation.
+    
+    This service handles the entire voice conversation pipeline:
+    1. Audio input → Server-side VAD → Speech-to-text
+    2. Text → LLM processing → Response generation
+    3. Response → Text-to-speech → Audio output
+    
+    Events emitted:
+    - on_audio: Audio output from the assistant
+    - on_transcript: Text transcript (user or assistant)
+    - on_speech_started: User started speaking
+    - on_speech_stopped: User stopped speaking
+    - on_response_started: Assistant started responding
+    - on_response_done: Assistant finished responding
+    - on_function_call: Function call requested
+    - on_error: Error occurred
+    """
+    
+    def __init__(self, config: Optional[RealtimeConfig] = None):
+        """
+        Initialize Realtime API service.
+        
+        Args:
+            config: Realtime configuration (uses defaults if not provided)
+        """
+        self.config = config or RealtimeConfig()
+        self.config.api_key = self.config.api_key or os.getenv("OPENAI_API_KEY")
+        
+        self.state = RealtimeState.DISCONNECTED
+        self._ws = None
+        self._receive_task: Optional[asyncio.Task] = None
+        self._cancel_event = asyncio.Event()
+        
+        # Event callbacks
+        self._callbacks: Dict[str, List[Callable]] = {
+            "on_audio": [],
+            "on_transcript": [],
+            "on_speech_started": [],
+            "on_speech_stopped": [],
+            "on_response_started": [],
+            "on_response_done": [],
+            "on_function_call": [],
+            "on_error": [],
+            "on_interrupted": [],
+        }
+        
+        logger.debug(f"RealtimeService initialized with model={self.config.model}")
+    
+    def on(self, event: str, callback: Callable[..., Awaitable[None]]) -> None:
+        """
+        Register event callback.
+        
+        Args:
+            event: Event name
+            callback: Async callback function
+        """
+        if event in self._callbacks:
+            self._callbacks[event].append(callback)
+    
+    async def _emit(self, event: str, *args, **kwargs) -> None:
+        """Emit event to all registered callbacks."""
+        for callback in self._callbacks.get(event, []):
+            try:
+                await callback(*args, **kwargs)
+            except Exception as e:
+                logger.error(f"Event callback error ({event}): {e}")
+    
+    async def connect(self) -> None:
+        """Connect to OpenAI Realtime API."""
+        if not WEBSOCKETS_AVAILABLE:
+            raise RuntimeError("websockets package not installed")
+        
+        if not self.config.api_key:
+            raise ValueError("OpenAI API key not provided")
+        
+        self.state = RealtimeState.CONNECTING
+        
+        # Build URL
+        if self.config.endpoint:
+            # Azure or custom endpoint
+            url = f"{self.config.endpoint}/openai/realtime?api-version=2024-10-01-preview&deployment={self.config.model}"
+        else:
+            # OpenAI endpoint
+            url = f"wss://api.openai.com/v1/realtime?model={self.config.model}"
+        
+        # Build headers
+        headers = {}
+        if self.config.endpoint:
+            headers["api-key"] = self.config.api_key
+        else:
+            headers["Authorization"] = f"Bearer {self.config.api_key}"
+            headers["OpenAI-Beta"] = "realtime=v1"
+        
+        try:
+            logger.info(f"Connecting to Realtime API: {url}")
+            self._ws = await websockets.connect(url, extra_headers=headers)
+            
+            # Send session configuration
+            await self._configure_session()
+            
+            # Start receive loop
+            self._receive_task = asyncio.create_task(self._receive_loop())
+            
+            self.state = RealtimeState.CONNECTED
+            logger.info("Realtime API connected successfully")
+            
+        except Exception as e:
+            self.state = RealtimeState.ERROR
+            logger.error(f"Realtime API connection failed: {e}")
+            raise
+    
+    async def _configure_session(self) -> None:
+        """Send session configuration to server."""
+        session_config = {
+            "type": "session.update",
+            "session": {
+                "modalities": ["text", "audio"],
+                "instructions": self.config.instructions,
+                "voice": self.config.voice,
+                "input_audio_format": self.config.input_audio_format,
+                "output_audio_format": self.config.output_audio_format,
+                "turn_detection": self.config.turn_detection,
+            }
+        }
+        
+        if self.config.tools:
+            session_config["session"]["tools"] = self.config.tools
+        
+        await self._send(session_config)
+        logger.debug("Session configuration sent")
+    
+    async def _send(self, data: Dict[str, Any]) -> None:
+        """Send JSON data to server."""
+        if self._ws:
+            await self._ws.send(json.dumps(data))
+    
+    async def send_audio(self, audio_bytes: bytes) -> None:
+        """
+        Send audio to the Realtime API.
+        
+        Args:
+            audio_bytes: PCM audio data (16-bit, mono, 24kHz by default)
+        """
+        if self.state != RealtimeState.CONNECTED:
+            return
+        
+        # Encode audio as base64
+        audio_b64 = base64.standard_b64encode(audio_bytes).decode()
+        
+        await self._send({
+            "type": "input_audio_buffer.append",
+            "audio": audio_b64
+        })
+    
+    async def send_text(self, text: str) -> None:
+        """
+        Send text input (bypassing audio).
+        
+        Args:
+            text: User text input
+        """
+        if self.state != RealtimeState.CONNECTED:
+            return
+        
+        # Create a conversation item with user text
+        await self._send({
+            "type": "conversation.item.create",
+            "item": {
+                "type": "message",
+                "role": "user",
+                "content": [{"type": "input_text", "text": text}]
+            }
+        })
+        
+        # Trigger response
+        await self._send({"type": "response.create"})
+    
+    async def cancel_response(self) -> None:
+        """Cancel the current response (for barge-in)."""
+        if self.state != RealtimeState.CONNECTED:
+            return
+        
+        await self._send({"type": "response.cancel"})
+        logger.debug("Response cancelled")
+    
+    async def commit_audio(self) -> None:
+        """Commit the audio buffer and trigger response."""
+        if self.state != RealtimeState.CONNECTED:
+            return
+        
+        await self._send({"type": "input_audio_buffer.commit"})
+        await self._send({"type": "response.create"})
+    
+    async def clear_audio_buffer(self) -> None:
+        """Clear the input audio buffer."""
+        if self.state != RealtimeState.CONNECTED:
+            return
+        
+        await self._send({"type": "input_audio_buffer.clear"})
+    
+    async def submit_function_result(self, call_id: str, result: str) -> None:
+        """
+        Submit function call result.
+        
+        Args:
+            call_id: The function call ID
+            result: JSON string result
+        """
+        if self.state != RealtimeState.CONNECTED:
+            return
+        
+        await self._send({
+            "type": "conversation.item.create",
+            "item": {
+                "type": "function_call_output",
+                "call_id": call_id,
+                "output": result
+            }
+        })
+        
+        # Trigger response with the function result
+        await self._send({"type": "response.create"})
+    
+    async def _receive_loop(self) -> None:
+        """Receive and process messages from the Realtime API."""
+        if not self._ws:
+            return
+        
+        try:
+            async for message in self._ws:
+                try:
+                    data = json.loads(message)
+                    await self._handle_event(data)
+                except json.JSONDecodeError:
+                    logger.warning(f"Invalid JSON received: {message[:100]}")
+        
+        except asyncio.CancelledError:
+            logger.debug("Receive loop cancelled")
+        except websockets.ConnectionClosed as e:
+            logger.info(f"WebSocket closed: {e}")
+            self.state = RealtimeState.DISCONNECTED
+        except Exception as e:
+            logger.error(f"Receive loop error: {e}")
+            self.state = RealtimeState.ERROR
+    
+    async def _handle_event(self, data: Dict[str, Any]) -> None:
+        """Handle incoming event from Realtime API."""
+        event_type = data.get("type", "unknown")
+        
+        # Audio delta - streaming audio output
+        if event_type == "response.audio.delta":
+            if "delta" in data:
+                audio_bytes = base64.standard_b64decode(data["delta"])
+                await self._emit("on_audio", audio_bytes)
+        
+        # Audio transcript delta - streaming text
+        elif event_type == "response.audio_transcript.delta":
+            if "delta" in data:
+                await self._emit("on_transcript", data["delta"], "assistant", False)
+        
+        # Audio transcript done
+        elif event_type == "response.audio_transcript.done":
+            if "transcript" in data:
+                await self._emit("on_transcript", data["transcript"], "assistant", True)
+        
+        # Input audio transcript (user speech)
+        elif event_type == "conversation.item.input_audio_transcription.completed":
+            if "transcript" in data:
+                await self._emit("on_transcript", data["transcript"], "user", True)
+        
+        # Speech started (server VAD detected speech)
+        elif event_type == "input_audio_buffer.speech_started":
+            await self._emit("on_speech_started", data.get("audio_start_ms", 0))
+        
+        # Speech stopped
+        elif event_type == "input_audio_buffer.speech_stopped":
+            await self._emit("on_speech_stopped", data.get("audio_end_ms", 0))
+        
+        # Response started
+        elif event_type == "response.created":
+            await self._emit("on_response_started", data.get("response", {}))
+        
+        # Response done
+        elif event_type == "response.done":
+            await self._emit("on_response_done", data.get("response", {}))
+        
+        # Function call
+        elif event_type == "response.function_call_arguments.done":
+            call_id = data.get("call_id")
+            name = data.get("name")
+            arguments = data.get("arguments", "{}")
+            await self._emit("on_function_call", call_id, name, arguments)
+        
+        # Error
+        elif event_type == "error":
+            error = data.get("error", {})
+            logger.error(f"Realtime API error: {error}")
+            await self._emit("on_error", error)
+        
+        # Session events
+        elif event_type == "session.created":
+            logger.info("Session created")
+        elif event_type == "session.updated":
+            logger.debug("Session updated")
+        
+        else:
+            logger.debug(f"Unhandled event type: {event_type}")
+    
+    async def disconnect(self) -> None:
+        """Disconnect from Realtime API."""
+        self._cancel_event.set()
+        
+        if self._receive_task:
+            self._receive_task.cancel()
+            try:
+                await self._receive_task
+            except asyncio.CancelledError:
+                pass
+        
+        if self._ws:
+            await self._ws.close()
+            self._ws = None
+        
+        self.state = RealtimeState.DISCONNECTED
+        logger.info("Realtime API disconnected")
+
+
+class RealtimePipeline:
+    """
+    Pipeline adapter for RealtimeService.
+    
+    Provides a compatible interface with DuplexPipeline but uses
+    OpenAI Realtime API for all processing.
+    """
+    
+    def __init__(
+        self,
+        transport,
+        session_id: str,
+        config: Optional[RealtimeConfig] = None
+    ):
+        """
+        Initialize Realtime pipeline.
+        
+        Args:
+            transport: Transport for sending audio/events
+            session_id: Session identifier
+            config: Realtime configuration
+        """
+        self.transport = transport
+        self.session_id = session_id
+        
+        self.service = RealtimeService(config)
+        
+        # Register callbacks
+        self.service.on("on_audio", self._on_audio)
+        self.service.on("on_transcript", self._on_transcript)
+        self.service.on("on_speech_started", self._on_speech_started)
+        self.service.on("on_speech_stopped", self._on_speech_stopped)
+        self.service.on("on_response_started", self._on_response_started)
+        self.service.on("on_response_done", self._on_response_done)
+        self.service.on("on_error", self._on_error)
+        
+        self._is_speaking = False
+        self._running = True
+        
+        logger.info(f"RealtimePipeline initialized for session {session_id}")
+    
+    async def start(self) -> None:
+        """Start the pipeline."""
+        await self.service.connect()
+    
+    async def process_audio(self, pcm_bytes: bytes) -> None:
+        """
+        Process incoming audio.
+        
+        Note: Realtime API expects 24kHz audio by default.
+        You may need to resample from 16kHz.
+        """
+        if not self._running:
+            return
+        
+        # TODO: Resample from 16kHz to 24kHz if needed
+        await self.service.send_audio(pcm_bytes)
+    
+    async def process_text(self, text: str) -> None:
+        """Process text input."""
+        if not self._running:
+            return
+        
+        await self.service.send_text(text)
+    
+    async def interrupt(self) -> None:
+        """Interrupt current response."""
+        await self.service.cancel_response()
+        await self.transport.send_event({
+            "event": "interrupt",
+            "trackId": self.session_id,
+            "timestamp": self._get_timestamp_ms()
+        })
+    
+    async def cleanup(self) -> None:
+        """Cleanup resources."""
+        self._running = False
+        await self.service.disconnect()
+    
+    # Event handlers
+    
+    async def _on_audio(self, audio_bytes: bytes) -> None:
+        """Handle audio output."""
+        await self.transport.send_audio(audio_bytes)
+    
+    async def _on_transcript(self, text: str, role: str, is_final: bool) -> None:
+        """Handle transcript."""
+        logger.info(f"[{role.upper()}] {text[:50]}..." if len(text) > 50 else f"[{role.upper()}] {text}")
+    
+    async def _on_speech_started(self, start_ms: int) -> None:
+        """Handle user speech start."""
+        self._is_speaking = True
+        await self.transport.send_event({
+            "event": "speaking",
+            "trackId": self.session_id,
+            "timestamp": self._get_timestamp_ms(),
+            "startTime": start_ms
+        })
+        
+        # Cancel any ongoing response (barge-in)
+        await self.service.cancel_response()
+    
+    async def _on_speech_stopped(self, end_ms: int) -> None:
+        """Handle user speech stop."""
+        self._is_speaking = False
+        await self.transport.send_event({
+            "event": "silence",
+            "trackId": self.session_id,
+            "timestamp": self._get_timestamp_ms(),
+            "duration": end_ms
+        })
+    
+    async def _on_response_started(self, response: Dict) -> None:
+        """Handle response start."""
+        await self.transport.send_event({
+            "event": "trackStart",
+            "trackId": self.session_id,
+            "timestamp": self._get_timestamp_ms()
+        })
+    
+    async def _on_response_done(self, response: Dict) -> None:
+        """Handle response complete."""
+        await self.transport.send_event({
+            "event": "trackEnd",
+            "trackId": self.session_id,
+            "timestamp": self._get_timestamp_ms()
+        })
+    
+    async def _on_error(self, error: Dict) -> None:
+        """Handle error."""
+        await self.transport.send_event({
+            "event": "error",
+            "trackId": self.session_id,
+            "timestamp": self._get_timestamp_ms(),
+            "sender": "realtime",
+            "error": str(error)
+        })
+    
+    def _get_timestamp_ms(self) -> int:
+        """Get current timestamp in milliseconds."""
+        import time
+        return int(time.time() * 1000)
+    
+    @property
+    def is_speaking(self) -> bool:
+        """Check if user is speaking."""
+        return self._is_speaking

engine/services/siliconflow_asr.py

@@ -0,0 +1,317 @@
+"""SiliconFlow ASR (Automatic Speech Recognition) Service.
+
+Uses the SiliconFlow API for speech-to-text transcription.
+API: https://docs.siliconflow.cn/cn/api-reference/audio/create-audio-transcriptions
+"""
+
+import asyncio
+import io
+import wave
+from typing import AsyncIterator, Optional, Callable, Awaitable
+from loguru import logger
+
+try:
+    import aiohttp
+    AIOHTTP_AVAILABLE = True
+except ImportError:
+    AIOHTTP_AVAILABLE = False
+    logger.warning("aiohttp not available - SiliconFlowASRService will not work")
+
+from services.base import BaseASRService, ASRResult, ServiceState
+
+
+class SiliconFlowASRService(BaseASRService):
+    """
+    SiliconFlow ASR service for speech-to-text transcription.
+    
+    Features:
+    - Buffers incoming audio chunks
+    - Provides interim transcriptions periodically (for streaming to client)
+    - Final transcription on EOU
+    
+    API Details:
+    - Endpoint: POST https://api.siliconflow.cn/v1/audio/transcriptions
+    - Models: FunAudioLLM/SenseVoiceSmall (default), TeleAI/TeleSpeechASR
+    - Input: Audio file (multipart/form-data)
+    - Output: {"text": "transcribed text"}
+    """
+    
+    # Supported models
+    MODELS = {
+        "sensevoice": "FunAudioLLM/SenseVoiceSmall",
+        "telespeech": "TeleAI/TeleSpeechASR",
+    }
+    
+    API_URL = "https://api.siliconflow.cn/v1/audio/transcriptions"
+    
+    def __init__(
+        self,
+        api_key: str,
+        model: str = "FunAudioLLM/SenseVoiceSmall",
+        sample_rate: int = 16000,
+        language: str = "auto",
+        interim_interval_ms: int = 500,  # How often to send interim results
+        min_audio_for_interim_ms: int = 300,  # Min audio before first interim
+        on_transcript: Optional[Callable[[str, bool], Awaitable[None]]] = None
+    ):
+        """
+        Initialize SiliconFlow ASR service.
+        
+        Args:
+            api_key: SiliconFlow API key
+            model: ASR model name or alias
+            sample_rate: Audio sample rate (16000 recommended)
+            language: Language code (auto for automatic detection)
+            interim_interval_ms: How often to generate interim transcriptions
+            min_audio_for_interim_ms: Minimum audio duration before first interim
+            on_transcript: Callback for transcription results (text, is_final)
+        """
+        super().__init__(sample_rate=sample_rate, language=language)
+        
+        if not AIOHTTP_AVAILABLE:
+            raise RuntimeError("aiohttp is required for SiliconFlowASRService")
+        
+        self.api_key = api_key
+        self.model = self.MODELS.get(model.lower(), model)
+        self.interim_interval_ms = interim_interval_ms
+        self.min_audio_for_interim_ms = min_audio_for_interim_ms
+        self.on_transcript = on_transcript
+        
+        # Session
+        self._session: Optional[aiohttp.ClientSession] = None
+        
+        # Audio buffer
+        self._audio_buffer: bytes = b""
+        self._current_text: str = ""
+        self._last_interim_time: float = 0
+        
+        # Transcript queue for async iteration
+        self._transcript_queue: asyncio.Queue[ASRResult] = asyncio.Queue()
+        
+        # Background task for interim results
+        self._interim_task: Optional[asyncio.Task] = None
+        self._running = False
+        
+        logger.info(f"SiliconFlowASRService initialized with model: {self.model}")
+    
+    async def connect(self) -> None:
+        """Connect to the service."""
+        self._session = aiohttp.ClientSession(
+            headers={
+                "Authorization": f"Bearer {self.api_key}"
+            }
+        )
+        self._running = True
+        self.state = ServiceState.CONNECTED
+        logger.info("SiliconFlowASRService connected")
+    
+    async def disconnect(self) -> None:
+        """Disconnect and cleanup."""
+        self._running = False
+        
+        if self._interim_task:
+            self._interim_task.cancel()
+            try:
+                await self._interim_task
+            except asyncio.CancelledError:
+                pass
+            self._interim_task = None
+        
+        if self._session:
+            await self._session.close()
+            self._session = None
+        
+        self._audio_buffer = b""
+        self._current_text = ""
+        self.state = ServiceState.DISCONNECTED
+        logger.info("SiliconFlowASRService disconnected")
+    
+    async def send_audio(self, audio: bytes) -> None:
+        """
+        Buffer incoming audio data.
+        
+        Args:
+            audio: PCM audio data (16-bit, mono)
+        """
+        self._audio_buffer += audio
+    
+    async def transcribe_buffer(self, is_final: bool = False) -> Optional[str]:
+        """
+        Transcribe current audio buffer.
+        
+        Args:
+            is_final: Whether this is the final transcription
+            
+        Returns:
+            Transcribed text or None if not enough audio
+        """
+        if not self._session:
+            logger.warning("ASR session not connected")
+            return None
+        
+        # Check minimum audio duration
+        audio_duration_ms = len(self._audio_buffer) / (self.sample_rate * 2) * 1000
+        
+        if not is_final and audio_duration_ms < self.min_audio_for_interim_ms:
+            return None
+        
+        if audio_duration_ms < 100:  # Less than 100ms - too short
+            return None
+        
+        try:
+            # Convert PCM to WAV in memory
+            wav_buffer = io.BytesIO()
+            with wave.open(wav_buffer, 'wb') as wav_file:
+                wav_file.setnchannels(1)
+                wav_file.setsampwidth(2)  # 16-bit
+                wav_file.setframerate(self.sample_rate)
+                wav_file.writeframes(self._audio_buffer)
+            
+            wav_buffer.seek(0)
+            wav_data = wav_buffer.read()
+            
+            # Send to API
+            form_data = aiohttp.FormData()
+            form_data.add_field(
+                'file',
+                wav_data,
+                filename='audio.wav',
+                content_type='audio/wav'
+            )
+            form_data.add_field('model', self.model)
+            
+            async with self._session.post(self.API_URL, data=form_data) as response:
+                if response.status == 200:
+                    result = await response.json()
+                    text = result.get("text", "").strip()
+                    
+                    if text:
+                        self._current_text = text
+                        
+                        # Notify via callback
+                        if self.on_transcript:
+                            await self.on_transcript(text, is_final)
+                        
+                        # Queue result
+                        await self._transcript_queue.put(
+                            ASRResult(text=text, is_final=is_final)
+                        )
+                        
+                        logger.debug(f"ASR {'final' if is_final else 'interim'}: {text[:50]}...")
+                        return text
+                else:
+                    error_text = await response.text()
+                    logger.error(f"ASR API error {response.status}: {error_text}")
+                    return None
+                    
+        except Exception as e:
+            logger.error(f"ASR transcription error: {e}")
+            return None
+    
+    async def get_final_transcription(self) -> str:
+        """
+        Get final transcription and clear buffer.
+        
+        Call this when EOU is detected.
+        
+        Returns:
+            Final transcribed text
+        """
+        # Transcribe full buffer as final
+        text = await self.transcribe_buffer(is_final=True)
+        
+        # Clear buffer
+        result = text or self._current_text
+        self._audio_buffer = b""
+        self._current_text = ""
+        
+        return result
+    
+    def get_and_clear_text(self) -> str:
+        """
+        Get accumulated text and clear buffer.
+        
+        Compatible with BufferedASRService interface.
+        """
+        text = self._current_text
+        self._current_text = ""
+        self._audio_buffer = b""
+        return text
+    
+    def get_audio_buffer(self) -> bytes:
+        """Get current audio buffer."""
+        return self._audio_buffer
+    
+    def get_audio_duration_ms(self) -> float:
+        """Get current audio buffer duration in milliseconds."""
+        return len(self._audio_buffer) / (self.sample_rate * 2) * 1000
+    
+    def clear_buffer(self) -> None:
+        """Clear audio and text buffers."""
+        self._audio_buffer = b""
+        self._current_text = ""
+    
+    async def receive_transcripts(self) -> AsyncIterator[ASRResult]:
+        """
+        Async iterator for transcription results.
+        
+        Yields:
+            ASRResult with text and is_final flag
+        """
+        while self._running:
+            try:
+                result = await asyncio.wait_for(
+                    self._transcript_queue.get(),
+                    timeout=0.1
+                )
+                yield result
+            except asyncio.TimeoutError:
+                continue
+            except asyncio.CancelledError:
+                break
+    
+    async def start_interim_transcription(self) -> None:
+        """
+        Start background task for interim transcriptions.
+        
+        This periodically transcribes buffered audio for
+        real-time feedback to the user.
+        """
+        if self._interim_task and not self._interim_task.done():
+            return
+        
+        self._interim_task = asyncio.create_task(self._interim_loop())
+    
+    async def stop_interim_transcription(self) -> None:
+        """Stop interim transcription task."""
+        if self._interim_task:
+            self._interim_task.cancel()
+            try:
+                await self._interim_task
+            except asyncio.CancelledError:
+                pass
+            self._interim_task = None
+    
+    async def _interim_loop(self) -> None:
+        """Background loop for interim transcriptions."""
+        import time
+        
+        while self._running:
+            try:
+                await asyncio.sleep(self.interim_interval_ms / 1000)
+                
+                # Check if we have enough new audio
+                current_time = time.time()
+                time_since_last = (current_time - self._last_interim_time) * 1000
+                
+                if time_since_last >= self.interim_interval_ms:
+                    audio_duration = self.get_audio_duration_ms()
+                    
+                    if audio_duration >= self.min_audio_for_interim_ms:
+                        await self.transcribe_buffer(is_final=False)
+                        self._last_interim_time = current_time
+                        
+            except asyncio.CancelledError:
+                break
+            except Exception as e:
+                logger.error(f"Interim transcription error: {e}")

engine/services/siliconflow_tts.py

@@ -0,0 +1,255 @@
+"""SiliconFlow TTS Service with streaming support.
+
+Uses SiliconFlow's CosyVoice2 or MOSS-TTSD models for low-latency
+text-to-speech synthesis with streaming.
+
+API Docs: https://docs.siliconflow.cn/cn/api-reference/audio/create-speech
+"""
+
+import os
+import asyncio
+import aiohttp
+from typing import AsyncIterator, Optional
+from loguru import logger
+
+from services.base import BaseTTSService, TTSChunk, ServiceState
+
+
+class SiliconFlowTTSService(BaseTTSService):
+    """
+    SiliconFlow TTS service with streaming support.
+    
+    Supports CosyVoice2-0.5B and MOSS-TTSD-v0.5 models.
+    """
+    
+    # Available voices
+    VOICES = {
+        "alex": "FunAudioLLM/CosyVoice2-0.5B:alex",
+        "anna": "FunAudioLLM/CosyVoice2-0.5B:anna",
+        "bella": "FunAudioLLM/CosyVoice2-0.5B:bella",
+        "benjamin": "FunAudioLLM/CosyVoice2-0.5B:benjamin",
+        "charles": "FunAudioLLM/CosyVoice2-0.5B:charles",
+        "claire": "FunAudioLLM/CosyVoice2-0.5B:claire",
+        "david": "FunAudioLLM/CosyVoice2-0.5B:david",
+        "diana": "FunAudioLLM/CosyVoice2-0.5B:diana",
+    }
+    
+    def __init__(
+        self,
+        api_key: Optional[str] = None,
+        voice: str = "anna",
+        model: str = "FunAudioLLM/CosyVoice2-0.5B",
+        sample_rate: int = 16000,
+        speed: float = 1.0
+    ):
+        """
+        Initialize SiliconFlow TTS service.
+        
+        Args:
+            api_key: SiliconFlow API key (defaults to SILICONFLOW_API_KEY env var)
+            voice: Voice name (alex, anna, bella, benjamin, charles, claire, david, diana)
+            model: Model name
+            sample_rate: Output sample rate (8000, 16000, 24000, 32000, 44100)
+            speed: Speech speed (0.25 to 4.0)
+        """
+        # Resolve voice name
+        if voice in self.VOICES:
+            full_voice = self.VOICES[voice]
+        else:
+            full_voice = voice
+            
+        super().__init__(voice=full_voice, sample_rate=sample_rate, speed=speed)
+        
+        self.api_key = api_key or os.getenv("SILICONFLOW_API_KEY")
+        self.model = model
+        self.api_url = "https://api.siliconflow.cn/v1/audio/speech"
+        
+        self._session: Optional[aiohttp.ClientSession] = None
+        self._cancel_event = asyncio.Event()
+    
+    async def connect(self) -> None:
+        """Initialize HTTP session."""
+        if not self.api_key:
+            raise ValueError("SiliconFlow API key not provided. Set SILICONFLOW_API_KEY env var.")
+        
+        self._session = aiohttp.ClientSession(
+            headers={
+                "Authorization": f"Bearer {self.api_key}",
+                "Content-Type": "application/json"
+            }
+        )
+        self.state = ServiceState.CONNECTED
+        logger.info(f"SiliconFlow TTS service ready: voice={self.voice}, model={self.model}")
+    
+    async def disconnect(self) -> None:
+        """Close HTTP session."""
+        if self._session:
+            await self._session.close()
+            self._session = None
+        self.state = ServiceState.DISCONNECTED
+        logger.info("SiliconFlow TTS service disconnected")
+    
+    async def synthesize(self, text: str) -> bytes:
+        """Synthesize complete audio for text."""
+        audio_data = b""
+        async for chunk in self.synthesize_stream(text):
+            audio_data += chunk.audio
+        return audio_data
+    
+    async def synthesize_stream(self, text: str) -> AsyncIterator[TTSChunk]:
+        """
+        Synthesize audio in streaming mode.
+        
+        Args:
+            text: Text to synthesize
+            
+        Yields:
+            TTSChunk objects with PCM audio
+        """
+        if not self._session:
+            raise RuntimeError("TTS service not connected")
+        
+        if not text.strip():
+            return
+        
+        self._cancel_event.clear()
+        
+        payload = {
+            "model": self.model,
+            "input": text,
+            "voice": self.voice,
+            "response_format": "pcm",
+            "sample_rate": self.sample_rate,
+            "stream": True,
+            "speed": self.speed
+        }
+        
+        try:
+            async with self._session.post(self.api_url, json=payload) as response:
+                if response.status != 200:
+                    error_text = await response.text()
+                    logger.error(f"SiliconFlow TTS error: {response.status} - {error_text}")
+                    return
+                
+                # Stream audio chunks
+                chunk_size = self.sample_rate * 2 // 10  # 100ms chunks
+                buffer = b""
+                
+                async for chunk in response.content.iter_any():
+                    if self._cancel_event.is_set():
+                        logger.info("TTS synthesis cancelled")
+                        return
+                    
+                    buffer += chunk
+                    
+                    # Yield complete chunks
+                    while len(buffer) >= chunk_size:
+                        audio_chunk = buffer[:chunk_size]
+                        buffer = buffer[chunk_size:]
+                        
+                        yield TTSChunk(
+                            audio=audio_chunk,
+                            sample_rate=self.sample_rate,
+                            is_final=False
+                        )
+                
+                # Yield remaining buffer
+                if buffer:
+                    yield TTSChunk(
+                        audio=buffer,
+                        sample_rate=self.sample_rate,
+                        is_final=True
+                    )
+                    
+        except asyncio.CancelledError:
+            logger.info("TTS synthesis cancelled via asyncio")
+            raise
+        except Exception as e:
+            logger.error(f"TTS synthesis error: {e}")
+            raise
+    
+    async def cancel(self) -> None:
+        """Cancel ongoing synthesis."""
+        self._cancel_event.set()
+
+
+class StreamingTTSAdapter:
+    """
+    Adapter for streaming LLM text to TTS with sentence-level chunking.
+    
+    This reduces latency by starting TTS as soon as a complete sentence
+    is received from the LLM, rather than waiting for the full response.
+    """
+    
+    # Sentence delimiters
+    SENTENCE_ENDS = {'.', '!', '?', '。', '！', '？', '；', '\n'}
+    
+    def __init__(self, tts_service: BaseTTSService, transport, session_id: str):
+        self.tts_service = tts_service
+        self.transport = transport
+        self.session_id = session_id
+        self._buffer = ""
+        self._cancel_event = asyncio.Event()
+        self._is_speaking = False
+    
+    async def process_text_chunk(self, text_chunk: str) -> None:
+        """
+        Process a text chunk from LLM and trigger TTS when sentence is complete.
+        
+        Args:
+            text_chunk: Text chunk from LLM streaming
+        """
+        if self._cancel_event.is_set():
+            return
+        
+        self._buffer += text_chunk
+        
+        # Check for sentence completion
+        for i, char in enumerate(self._buffer):
+            if char in self.SENTENCE_ENDS:
+                # Found sentence end, synthesize up to this point
+                sentence = self._buffer[:i+1].strip()
+                self._buffer = self._buffer[i+1:]
+                
+                if sentence:
+                    await self._speak_sentence(sentence)
+                break
+    
+    async def flush(self) -> None:
+        """Flush remaining buffer."""
+        if self._buffer.strip() and not self._cancel_event.is_set():
+            await self._speak_sentence(self._buffer.strip())
+        self._buffer = ""
+    
+    async def _speak_sentence(self, text: str) -> None:
+        """Synthesize and send a sentence."""
+        if not text or self._cancel_event.is_set():
+            return
+        
+        self._is_speaking = True
+        
+        try:
+            async for chunk in self.tts_service.synthesize_stream(text):
+                if self._cancel_event.is_set():
+                    break
+                await self.transport.send_audio(chunk.audio)
+                await asyncio.sleep(0.01)  # Prevent flooding
+        except Exception as e:
+            logger.error(f"TTS speak error: {e}")
+        finally:
+            self._is_speaking = False
+    
+    def cancel(self) -> None:
+        """Cancel ongoing speech."""
+        self._cancel_event.set()
+        self._buffer = ""
+    
+    def reset(self) -> None:
+        """Reset for new turn."""
+        self._cancel_event.clear()
+        self._buffer = ""
+        self._is_speaking = False
+    
+    @property
+    def is_speaking(self) -> bool:
+        return self._is_speaking

engine/services/tts.py

@@ -0,0 +1,271 @@
+"""TTS (Text-to-Speech) Service implementations.
+
+Provides multiple TTS backend options including edge-tts (free)
+and placeholder for cloud services.
+"""
+
+import os
+import io
+import asyncio
+import struct
+from typing import AsyncIterator, Optional
+from loguru import logger
+
+from services.base import BaseTTSService, TTSChunk, ServiceState
+
+# Try to import edge-tts
+try:
+    import edge_tts
+    EDGE_TTS_AVAILABLE = True
+except ImportError:
+    EDGE_TTS_AVAILABLE = False
+    logger.warning("edge-tts not available - EdgeTTS service will be disabled")
+
+
+class EdgeTTSService(BaseTTSService):
+    """
+    Microsoft Edge TTS service.
+    
+    Uses edge-tts library for free, high-quality speech synthesis.
+    Supports streaming for low-latency playback.
+    """
+    
+    # Voice mapping for common languages
+    VOICE_MAP = {
+        "en": "en-US-JennyNeural",
+        "en-US": "en-US-JennyNeural",
+        "en-GB": "en-GB-SoniaNeural",
+        "zh": "zh-CN-XiaoxiaoNeural",
+        "zh-CN": "zh-CN-XiaoxiaoNeural",
+        "zh-TW": "zh-TW-HsiaoChenNeural",
+        "ja": "ja-JP-NanamiNeural",
+        "ko": "ko-KR-SunHiNeural",
+        "fr": "fr-FR-DeniseNeural",
+        "de": "de-DE-KatjaNeural",
+        "es": "es-ES-ElviraNeural",
+    }
+    
+    def __init__(
+        self,
+        voice: str = "en-US-JennyNeural",
+        sample_rate: int = 16000,
+        speed: float = 1.0
+    ):
+        """
+        Initialize Edge TTS service.
+        
+        Args:
+            voice: Voice name (e.g., "en-US-JennyNeural") or language code (e.g., "en")
+            sample_rate: Target sample rate (will be resampled)
+            speed: Speech speed multiplier
+        """
+        # Resolve voice from language code if needed
+        if voice in self.VOICE_MAP:
+            voice = self.VOICE_MAP[voice]
+        
+        super().__init__(voice=voice, sample_rate=sample_rate, speed=speed)
+        self._cancel_event = asyncio.Event()
+    
+    async def connect(self) -> None:
+        """Edge TTS doesn't require explicit connection."""
+        if not EDGE_TTS_AVAILABLE:
+            raise RuntimeError("edge-tts package not installed")
+        self.state = ServiceState.CONNECTED
+        logger.info(f"Edge TTS service ready: voice={self.voice}")
+    
+    async def disconnect(self) -> None:
+        """Edge TTS doesn't require explicit disconnection."""
+        self.state = ServiceState.DISCONNECTED
+        logger.info("Edge TTS service disconnected")
+    
+    def _get_rate_string(self) -> str:
+        """Convert speed to rate string for edge-tts."""
+        # edge-tts uses percentage format: "+0%", "-10%", "+20%"
+        percentage = int((self.speed - 1.0) * 100)
+        if percentage >= 0:
+            return f"+{percentage}%"
+        return f"{percentage}%"
+    
+    async def synthesize(self, text: str) -> bytes:
+        """
+        Synthesize complete audio for text.
+        
+        Args:
+            text: Text to synthesize
+            
+        Returns:
+            PCM audio data (16-bit, mono, 16kHz)
+        """
+        if not EDGE_TTS_AVAILABLE:
+            raise RuntimeError("edge-tts not available")
+        
+        # Collect all chunks
+        audio_data = b""
+        async for chunk in self.synthesize_stream(text):
+            audio_data += chunk.audio
+        
+        return audio_data
+    
+    async def synthesize_stream(self, text: str) -> AsyncIterator[TTSChunk]:
+        """
+        Synthesize audio in streaming mode.
+        
+        Args:
+            text: Text to synthesize
+            
+        Yields:
+            TTSChunk objects with PCM audio
+        """
+        if not EDGE_TTS_AVAILABLE:
+            raise RuntimeError("edge-tts not available")
+        
+        self._cancel_event.clear()
+        
+        try:
+            communicate = edge_tts.Communicate(
+                text,
+                voice=self.voice,
+                rate=self._get_rate_string()
+            )
+            
+            # edge-tts outputs MP3, we need to decode to PCM
+            # For now, collect MP3 chunks and yield after conversion
+            mp3_data = b""
+            
+            async for chunk in communicate.stream():
+                # Check for cancellation
+                if self._cancel_event.is_set():
+                    logger.info("TTS synthesis cancelled")
+                    return
+                
+                if chunk["type"] == "audio":
+                    mp3_data += chunk["data"]
+            
+            # Convert MP3 to PCM
+            if mp3_data:
+                pcm_data = await self._convert_mp3_to_pcm(mp3_data)
+                if pcm_data:
+                    # Yield in chunks for streaming playback
+                    chunk_size = self.sample_rate * 2 // 10  # 100ms chunks
+                    for i in range(0, len(pcm_data), chunk_size):
+                        if self._cancel_event.is_set():
+                            return
+                        
+                        chunk_data = pcm_data[i:i + chunk_size]
+                        yield TTSChunk(
+                            audio=chunk_data,
+                            sample_rate=self.sample_rate,
+                            is_final=(i + chunk_size >= len(pcm_data))
+                        )
+        
+        except asyncio.CancelledError:
+            logger.info("TTS synthesis cancelled via asyncio")
+            raise
+        except Exception as e:
+            logger.error(f"TTS synthesis error: {e}")
+            raise
+    
+    async def _convert_mp3_to_pcm(self, mp3_data: bytes) -> bytes:
+        """
+        Convert MP3 audio to PCM.
+        
+        Uses pydub or ffmpeg for conversion.
+        """
+        try:
+            # Try using pydub (requires ffmpeg)
+            from pydub import AudioSegment
+            
+            # Load MP3 from bytes
+            audio = AudioSegment.from_mp3(io.BytesIO(mp3_data))
+            
+            # Convert to target format
+            audio = audio.set_frame_rate(self.sample_rate)
+            audio = audio.set_channels(1)
+            audio = audio.set_sample_width(2)  # 16-bit
+            
+            # Export as raw PCM
+            return audio.raw_data
+            
+        except ImportError:
+            logger.warning("pydub not available, trying fallback")
+            # Fallback: Use subprocess to call ffmpeg directly
+            return await self._ffmpeg_convert(mp3_data)
+        except Exception as e:
+            logger.error(f"Audio conversion error: {e}")
+            return b""
+    
+    async def _ffmpeg_convert(self, mp3_data: bytes) -> bytes:
+        """Convert MP3 to PCM using ffmpeg subprocess."""
+        try:
+            process = await asyncio.create_subprocess_exec(
+                "ffmpeg",
+                "-i", "pipe:0",
+                "-f", "s16le",
+                "-acodec", "pcm_s16le",
+                "-ar", str(self.sample_rate),
+                "-ac", "1",
+                "pipe:1",
+                stdin=asyncio.subprocess.PIPE,
+                stdout=asyncio.subprocess.PIPE,
+                stderr=asyncio.subprocess.DEVNULL
+            )
+            
+            stdout, _ = await process.communicate(input=mp3_data)
+            return stdout
+            
+        except Exception as e:
+            logger.error(f"ffmpeg conversion error: {e}")
+            return b""
+    
+    async def cancel(self) -> None:
+        """Cancel ongoing synthesis."""
+        self._cancel_event.set()
+
+
+class MockTTSService(BaseTTSService):
+    """
+    Mock TTS service for testing without actual synthesis.
+    
+    Generates silence or simple tones.
+    """
+    
+    def __init__(
+        self,
+        voice: str = "mock",
+        sample_rate: int = 16000,
+        speed: float = 1.0
+    ):
+        super().__init__(voice=voice, sample_rate=sample_rate, speed=speed)
+    
+    async def connect(self) -> None:
+        self.state = ServiceState.CONNECTED
+        logger.info("Mock TTS service connected")
+    
+    async def disconnect(self) -> None:
+        self.state = ServiceState.DISCONNECTED
+        logger.info("Mock TTS service disconnected")
+    
+    async def synthesize(self, text: str) -> bytes:
+        """Generate silence based on text length."""
+        # Approximate: 100ms per word
+        word_count = len(text.split())
+        duration_ms = word_count * 100
+        samples = int(self.sample_rate * duration_ms / 1000)
+        
+        # Generate silence (zeros)
+        return bytes(samples * 2)  # 16-bit = 2 bytes per sample
+    
+    async def synthesize_stream(self, text: str) -> AsyncIterator[TTSChunk]:
+        """Generate silence chunks."""
+        audio = await self.synthesize(text)
+        
+        # Yield in 100ms chunks
+        chunk_size = self.sample_rate * 2 // 10
+        for i in range(0, len(audio), chunk_size):
+            chunk_data = audio[i:i + chunk_size]
+            yield TTSChunk(
+                audio=chunk_data,
+                sample_rate=self.sample_rate,
+                is_final=(i + chunk_size >= len(audio))
+            )
+            await asyncio.sleep(0.05)  # Simulate processing time

engine/utils/__init__.py

@@ -0,0 +1 @@
+"""Utilities Package"""

engine/utils/logging.py

@@ -0,0 +1,83 @@
+"""Logging configuration utilities."""
+
+import sys
+from loguru import logger
+from pathlib import Path
+
+
+def setup_logging(
+    log_level: str = "INFO",
+    log_format: str = "text",
+    log_to_file: bool = True,
+    log_dir: str = "logs"
+):
+    """
+    Configure structured logging with loguru.
+
+    Args:
+        log_level: Logging level (DEBUG, INFO, WARNING, ERROR)
+        log_format: Format type (json or text)
+        log_to_file: Whether to log to file
+        log_dir: Directory for log files
+    """
+    # Remove default handler
+    logger.remove()
+
+    # Console handler
+    if log_format == "json":
+        logger.add(
+            sys.stdout,
+            format="{message}",
+            level=log_level,
+            serialize=True,
+            colorize=False
+        )
+    else:
+        logger.add(
+            sys.stdout,
+            format="<green>{time:HH:mm:ss}</green> | <level>{level: <8}</level> | <level>{message}</level>",
+            level=log_level,
+            colorize=True
+        )
+
+    # File handler
+    if log_to_file:
+        log_path = Path(log_dir)
+        log_path.mkdir(exist_ok=True)
+
+        if log_format == "json":
+            logger.add(
+                log_path / "active_call_{time:YYYY-MM-DD}.log",
+                format="{message}",
+                level=log_level,
+                rotation="1 day",
+                retention="7 days",
+                compression="zip",
+                serialize=True
+            )
+        else:
+            logger.add(
+                log_path / "active_call_{time:YYYY-MM-DD}.log",
+                format="{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {name}:{function}:{line} - {message}",
+                level=log_level,
+                rotation="1 day",
+                retention="7 days",
+                compression="zip"
+            )
+
+    return logger
+
+
+def get_logger(name: str = None):
+    """
+    Get a logger instance.
+
+    Args:
+        name: Logger name (optional)
+
+    Returns:
+        Logger instance
+    """
+    if name:
+        return logger.bind(name=name)
+    return logger