Init commit

services/__init__.py

@@ -0,0 +1,51 @@
+"""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.openai_compatible_asr import OpenAICompatibleASRService, SiliconFlowASRService
+from services.openai_compatible_tts import OpenAICompatibleTTSService, SiliconFlowTTSService
+from services.streaming_tts_adapter import StreamingTTSAdapter
+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",
+    "OpenAICompatibleASRService",
+    "SiliconFlowASRService",
+    # TTS (SiliconFlow)
+    "OpenAICompatibleTTSService",
+    "SiliconFlowTTSService",
+    "StreamingTTSAdapter",
+    # Realtime
+    "RealtimeService",
+    "RealtimeConfig",
+    "RealtimePipeline",
+]

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

services/base.py

@@ -0,0 +1,253 @@
+"""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, Literal
+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 LLMStreamEvent:
+    """Structured LLM stream event."""
+
+    type: Literal["text_delta", "tool_call", "done"]
+    text: Optional[str] = None
+    tool_call: Optional[Dict[str, Any]] = None
+
+
+@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[LLMStreamEvent]:
+        """
+        Generate response in streaming mode.
+        
+        Args:
+            messages: Conversation history
+            temperature: Sampling temperature
+            max_tokens: Maximum tokens to generate
+            
+        Yields:
+            Stream events (text delta/tool call/done)
+        """
+        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

services/llm.py

@@ -0,0 +1,443 @@
+"""LLM (Large Language Model) Service implementations.
+
+Provides OpenAI-compatible LLM integration with streaming support
+for real-time voice conversation.
+"""
+
+import os
+import asyncio
+import uuid
+from typing import AsyncIterator, Optional, List, Dict, Any
+from loguru import logger
+
+from app.backend_client import search_knowledge_context
+from services.base import BaseLLMService, LLMMessage, LLMStreamEvent, 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,
+        knowledge_config: Optional[Dict[str, Any]] = 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()
+        self._knowledge_config: Dict[str, Any] = knowledge_config or {}
+        self._tool_schemas: List[Dict[str, Any]] = []
+
+    _RAG_DEFAULT_RESULTS = 5
+    _RAG_MAX_RESULTS = 8
+    _RAG_MAX_CONTEXT_CHARS = 4000
+    
+    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
+
+    def set_knowledge_config(self, config: Optional[Dict[str, Any]]) -> None:
+        """Update runtime knowledge retrieval config."""
+        self._knowledge_config = config or {}
+
+    def set_tool_schemas(self, schemas: Optional[List[Dict[str, Any]]]) -> None:
+        """Update runtime tool schemas."""
+        self._tool_schemas = []
+        if not isinstance(schemas, list):
+            return
+        for item in schemas:
+            if not isinstance(item, dict):
+                continue
+            fn = item.get("function")
+            if isinstance(fn, dict) and fn.get("name"):
+                self._tool_schemas.append(item)
+            elif item.get("name"):
+                self._tool_schemas.append(
+                    {
+                        "type": "function",
+                        "function": {
+                            "name": str(item.get("name")),
+                            "description": str(item.get("description") or ""),
+                            "parameters": item.get("parameters") or {"type": "object", "properties": {}},
+                        },
+                    }
+                )
+
+    @staticmethod
+    def _coerce_int(value: Any, default: int) -> int:
+        try:
+            return int(value)
+        except (TypeError, ValueError):
+            return default
+
+    def _resolve_kb_id(self) -> Optional[str]:
+        cfg = self._knowledge_config if isinstance(self._knowledge_config, dict) else {}
+        kb_id = str(
+            cfg.get("kbId")
+            or cfg.get("knowledgeBaseId")
+            or cfg.get("knowledge_base_id")
+            or ""
+        ).strip()
+        return kb_id or None
+
+    def _build_knowledge_prompt(self, results: List[Dict[str, Any]]) -> Optional[str]:
+        if not results:
+            return None
+
+        lines = [
+            "You have retrieved the following knowledge base snippets.",
+            "Use them only when relevant to the latest user request.",
+            "If snippets are insufficient, say you are not sure instead of guessing.",
+            "",
+        ]
+
+        used_chars = 0
+        used_count = 0
+        for item in results:
+            content = str(item.get("content") or "").strip()
+            if not content:
+                continue
+            if used_chars >= self._RAG_MAX_CONTEXT_CHARS:
+                break
+
+            metadata = item.get("metadata") if isinstance(item.get("metadata"), dict) else {}
+            doc_id = metadata.get("document_id")
+            chunk_index = metadata.get("chunk_index")
+            distance = item.get("distance")
+
+            source_parts = []
+            if doc_id:
+                source_parts.append(f"doc={doc_id}")
+            if chunk_index is not None:
+                source_parts.append(f"chunk={chunk_index}")
+            source = f" ({', '.join(source_parts)})" if source_parts else ""
+
+            distance_text = ""
+            try:
+                if distance is not None:
+                    distance_text = f", distance={float(distance):.4f}"
+            except (TypeError, ValueError):
+                distance_text = ""
+
+            remaining = self._RAG_MAX_CONTEXT_CHARS - used_chars
+            snippet = content[:remaining].strip()
+            if not snippet:
+                continue
+
+            used_count += 1
+            lines.append(f"[{used_count}{source}{distance_text}] {snippet}")
+            used_chars += len(snippet)
+
+        if used_count == 0:
+            return None
+
+        return "\n".join(lines)
+
+    async def _with_knowledge_context(self, messages: List[LLMMessage]) -> List[LLMMessage]:
+        cfg = self._knowledge_config if isinstance(self._knowledge_config, dict) else {}
+        enabled = cfg.get("enabled", True)
+        if isinstance(enabled, str):
+            enabled = enabled.strip().lower() not in {"false", "0", "off", "no"}
+        if not enabled:
+            return messages
+
+        kb_id = self._resolve_kb_id()
+        if not kb_id:
+            return messages
+
+        latest_user = ""
+        for msg in reversed(messages):
+            if msg.role == "user":
+                latest_user = (msg.content or "").strip()
+                break
+        if not latest_user:
+            return messages
+
+        n_results = self._coerce_int(cfg.get("nResults"), self._RAG_DEFAULT_RESULTS)
+        n_results = max(1, min(n_results, self._RAG_MAX_RESULTS))
+
+        results = await search_knowledge_context(
+            kb_id=kb_id,
+            query=latest_user,
+            n_results=n_results,
+        )
+        prompt = self._build_knowledge_prompt(results)
+        if not prompt:
+            return messages
+
+        logger.debug(f"RAG context injected (kb_id={kb_id}, chunks={len(results)})")
+        rag_system = LLMMessage(role="system", content=prompt)
+        if messages and messages[0].role == "system":
+            return [messages[0], rag_system, *messages[1:]]
+        return [rag_system, *messages]
+    
+    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")
+        
+        rag_messages = await self._with_knowledge_context(messages)
+        prepared = self._prepare_messages(rag_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[LLMStreamEvent]:
+        """
+        Generate response in streaming mode.
+        
+        Args:
+            messages: Conversation history
+            temperature: Sampling temperature
+            max_tokens: Maximum tokens to generate
+            
+        Yields:
+            Structured stream events
+        """
+        if not self.client:
+            raise RuntimeError("LLM service not connected")
+        
+        rag_messages = await self._with_knowledge_context(messages)
+        prepared = self._prepare_messages(rag_messages)
+        self._cancel_event.clear()
+        tool_accumulator: Dict[int, Dict[str, str]] = {}
+        openai_tools = self._tool_schemas or None
+        
+        try:
+            create_args: Dict[str, Any] = dict(
+                model=self.model,
+                messages=prepared,
+                temperature=temperature,
+                max_tokens=max_tokens,
+                stream=True,
+            )
+            if openai_tools:
+                create_args["tools"] = openai_tools
+                create_args["tool_choice"] = "auto"
+            stream = await self.client.chat.completions.create(**create_args)
+            
+            async for chunk in stream:
+                # Check for cancellation
+                if self._cancel_event.is_set():
+                    logger.info("LLM stream cancelled")
+                    break
+
+                if not chunk.choices:
+                    continue
+
+                choice = chunk.choices[0]
+                delta = getattr(choice, "delta", None)
+                if delta and getattr(delta, "content", None):
+                    content = delta.content
+                    yield LLMStreamEvent(type="text_delta", text=content)
+
+                # OpenAI streams function calls via incremental tool_calls deltas.
+                tool_calls = getattr(delta, "tool_calls", None) if delta else None
+                if tool_calls:
+                    for tc in tool_calls:
+                        index = getattr(tc, "index", 0) or 0
+                        item = tool_accumulator.setdefault(
+                            int(index),
+                            {"id": "", "name": "", "arguments": ""},
+                        )
+                        tc_id = getattr(tc, "id", None)
+                        if tc_id:
+                            item["id"] = str(tc_id)
+                        fn = getattr(tc, "function", None)
+                        if fn:
+                            fn_name = getattr(fn, "name", None)
+                            if fn_name:
+                                item["name"] = str(fn_name)
+                            fn_args = getattr(fn, "arguments", None)
+                            if fn_args:
+                                item["arguments"] += str(fn_args)
+
+                finish_reason = getattr(choice, "finish_reason", None)
+                if finish_reason == "tool_calls" and tool_accumulator:
+                    for _, payload in sorted(tool_accumulator.items(), key=lambda row: row[0]):
+                        call_name = payload.get("name", "").strip()
+                        if not call_name:
+                            continue
+                        call_id = payload.get("id", "").strip() or f"call_{uuid.uuid4().hex[:10]}"
+                        yield LLMStreamEvent(
+                            type="tool_call",
+                            tool_call={
+                                "id": call_id,
+                                "type": "function",
+                                "function": {
+                                    "name": call_name,
+                                    "arguments": payload.get("arguments", "") or "{}",
+                                },
+                            },
+                        )
+                    yield LLMStreamEvent(type="done")
+                    return
+
+                if finish_reason in {"stop", "length", "content_filter"}:
+                    yield LLMStreamEvent(type="done")
+                    return
+            
+        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[LLMStreamEvent]:
+        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 LLMStreamEvent(type="text_delta", text=" ")
+            yield LLMStreamEvent(type="text_delta", text=word)
+            await asyncio.sleep(0.05)  # Simulate streaming delay
+        yield LLMStreamEvent(type="done")

services/openai_compatible_asr.py

@@ -0,0 +1,321 @@
+"""OpenAI-compatible 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 - OpenAICompatibleASRService will not work")
+
+from services.base import BaseASRService, ASRResult, ServiceState
+
+
+class OpenAICompatibleASRService(BaseASRService):
+    """
+    OpenAI-compatible 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 OpenAI-compatible ASR service.
+        
+        Args:
+            api_key: Provider 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 OpenAICompatibleASRService")
+        
+        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"OpenAICompatibleASRService 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("OpenAICompatibleASRService 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("OpenAICompatibleASRService 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}")
+
+
+# Backward-compatible alias
+SiliconFlowASRService = OpenAICompatibleASRService

services/openai_compatible_tts.py

@@ -0,0 +1,324 @@
+"""OpenAI-compatible 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
+from services.streaming_tts_adapter import StreamingTTSAdapter  # backward-compatible re-export
+
+
+class OpenAICompatibleTTSService(BaseTTSService):
+    """
+    OpenAI-compatible 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 OpenAI-compatible TTS service.
+        
+        Args:
+            api_key: Provider 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 (case-insensitive), and normalize "model:VoiceId" suffix.
+        resolved_voice = (voice or "").strip()
+        voice_lookup = resolved_voice.lower()
+        if voice_lookup in self.VOICES:
+            full_voice = self.VOICES[voice_lookup]
+        elif ":" in resolved_voice:
+            model_part, voice_part = resolved_voice.split(":", 1)
+            normalized_voice_part = voice_part.strip().lower()
+            if normalized_voice_part in self.VOICES:
+                full_voice = f"{(model_part or model).strip()}:{normalized_voice_part}"
+            else:
+                full_voice = resolved_voice
+        else:
+            full_voice = resolved_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""
+                pending_chunk = None
+                
+                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:]
+
+                        # Keep one full chunk buffered so we can always tag the true
+                        # last full chunk as final when stream length is an exact multiple.
+                        if pending_chunk is not None:
+                            yield TTSChunk(
+                                audio=pending_chunk,
+                                sample_rate=self.sample_rate,
+                                is_final=False
+                            )
+                        pending_chunk = audio_chunk
+                
+                # Flush pending chunk(s) and remaining tail.
+                if pending_chunk is not None:
+                    if buffer:
+                        yield TTSChunk(
+                            audio=pending_chunk,
+                            sample_rate=self.sample_rate,
+                            is_final=False
+                        )
+                        pending_chunk = None
+                    else:
+                        yield TTSChunk(
+                            audio=pending_chunk,
+                            sample_rate=self.sample_rate,
+                            is_final=True
+                        )
+                        pending_chunk = None
+
+                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
+
+    def _is_non_sentence_period(self, text: str, idx: int) -> bool:
+        """Check whether '.' should NOT be treated as a sentence delimiter."""
+        if text[idx] != ".":
+            return False
+
+        # Decimal/version segment: 1.2, v1.2.3
+        if idx > 0 and idx < len(text) - 1 and text[idx - 1].isdigit() and text[idx + 1].isdigit():
+            return True
+
+        # Number abbreviations: No.1 / No. 1
+        left_start = idx - 1
+        while left_start >= 0 and text[left_start].isalpha():
+            left_start -= 1
+        left_token = text[left_start + 1:idx].lower()
+        if left_token == "no":
+            j = idx + 1
+            while j < len(text) and text[j].isspace():
+                j += 1
+            if j < len(text) and text[j].isdigit():
+                return True
+
+        return 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
+        while True:
+            split_idx = -1
+            for i, char in enumerate(self._buffer):
+                if char == "." and self._is_non_sentence_period(self._buffer, i):
+                    continue
+                if char in self.SENTENCE_ENDS:
+                    split_idx = i
+                    break
+            if split_idx < 0:
+                break
+
+            end_idx = split_idx + 1
+            while end_idx < len(self._buffer) and self._buffer[end_idx] in self.SENTENCE_ENDS:
+                end_idx += 1
+
+            sentence = self._buffer[:end_idx].strip()
+            self._buffer = self._buffer[end_idx:]
+
+            if sentence and any(ch.isalnum() for ch in sentence):
+                await self._speak_sentence(sentence)
+    
+    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
+
+
+# Backward-compatible alias
+SiliconFlowTTSService = OpenAICompatibleTTSService

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

services/siliconflow_asr.py

@@ -0,0 +1,8 @@
+"""Backward-compatible imports for legacy siliconflow_asr module."""
+
+from services.openai_compatible_asr import OpenAICompatibleASRService
+
+# Backward-compatible alias
+SiliconFlowASRService = OpenAICompatibleASRService
+
+__all__ = ["OpenAICompatibleASRService", "SiliconFlowASRService"]

services/siliconflow_tts.py

@@ -0,0 +1,8 @@
+"""Backward-compatible imports for legacy siliconflow_tts module."""
+
+from services.openai_compatible_tts import OpenAICompatibleTTSService, StreamingTTSAdapter
+
+# Backward-compatible alias
+SiliconFlowTTSService = OpenAICompatibleTTSService
+
+__all__ = ["OpenAICompatibleTTSService", "SiliconFlowTTSService", "StreamingTTSAdapter"]

services/streaming_text.py

@@ -0,0 +1,86 @@
+"""Shared text chunking helpers for streaming TTS."""
+
+from typing import Optional
+
+
+def is_non_sentence_period(text: str, idx: int) -> bool:
+    """Check whether '.' should NOT be treated as a sentence delimiter."""
+    if idx < 0 or idx >= len(text) or text[idx] != ".":
+        return False
+
+    # Decimal/version segment: 1.2, v1.2.3
+    if idx > 0 and idx < len(text) - 1 and text[idx - 1].isdigit() and text[idx + 1].isdigit():
+        return True
+
+    # Number abbreviations: No.1 / No. 1
+    left_start = idx - 1
+    while left_start >= 0 and text[left_start].isalpha():
+        left_start -= 1
+    left_token = text[left_start + 1:idx].lower()
+    if left_token == "no":
+        j = idx + 1
+        while j < len(text) and text[j].isspace():
+            j += 1
+        if j < len(text) and text[j].isdigit():
+            return True
+
+    return False
+
+
+def has_spoken_content(text: str) -> bool:
+    """Check whether text contains pronounceable content (not punctuation-only)."""
+    return any(char.isalnum() for char in text)
+
+
+def extract_tts_sentence(
+    text_buffer: str,
+    *,
+    end_chars: frozenset[str],
+    trailing_chars: frozenset[str],
+    closers: frozenset[str],
+    min_split_spoken_chars: int = 0,
+    hold_trailing_at_buffer_end: bool = False,
+    force: bool = False,
+) -> Optional[tuple[str, str]]:
+    """Extract one TTS sentence from text buffer."""
+    if not text_buffer:
+        return None
+
+    search_start = 0
+    while True:
+        split_idx = -1
+        for idx in range(search_start, len(text_buffer)):
+            char = text_buffer[idx]
+            if char == "." and is_non_sentence_period(text_buffer, idx):
+                continue
+            if char in end_chars:
+                split_idx = idx
+                break
+
+        if split_idx == -1:
+            return None
+
+        end_idx = split_idx + 1
+        while end_idx < len(text_buffer) and text_buffer[end_idx] in trailing_chars:
+            end_idx += 1
+
+        while end_idx < len(text_buffer) and text_buffer[end_idx] in closers:
+            end_idx += 1
+
+        if hold_trailing_at_buffer_end and not force and end_idx >= len(text_buffer):
+            return None
+
+        sentence = text_buffer[:end_idx].strip()
+        spoken_chars = sum(1 for ch in sentence if ch.isalnum())
+
+        if (
+            not force
+            and min_split_spoken_chars > 0
+            and 0 < spoken_chars < min_split_spoken_chars
+            and end_idx < len(text_buffer)
+        ):
+            search_start = end_idx
+            continue
+
+        remainder = text_buffer[end_idx:]
+        return sentence, remainder

services/streaming_tts_adapter.py

@@ -0,0 +1,95 @@
+"""Backend-agnostic streaming adapter from LLM text to TTS audio."""
+
+import asyncio
+
+from loguru import logger
+
+from services.base import BaseTTSService
+from services.streaming_text import extract_tts_sentence, has_spoken_content
+
+
+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_ENDS = {"。", "！", "？", ".", "!", "?", "\n"}
+    SENTENCE_CLOSERS = frozenset()
+
+    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
+        while True:
+            split_result = extract_tts_sentence(
+                self._buffer,
+                end_chars=frozenset(self.SENTENCE_ENDS),
+                trailing_chars=frozenset(self.SENTENCE_ENDS),
+                closers=self.SENTENCE_CLOSERS,
+                force=False,
+            )
+            if not split_result:
+                break
+
+            sentence, self._buffer = split_result
+            if sentence and has_spoken_content(sentence):
+                await self._speak_sentence(sentence)
+
+    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

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