OpenAI GPT-Live-1全双工语音模型：架构解耦、委派推理与AI语音交互的范式革命

// Go实现：全双工语音模型的交互决策引擎

package fullduplex

import (
	"context"
	"fmt"
	"sync"
	"time"
)

// InteractionDecision 表示模型每秒做出的交互决策
type InteractionDecision int

const (
	Speak InteractionDecision = iota
	Listen
	Pause
	Interrupt
	InvokeTool
)

// AudioStream 表示音频输入/输出流
type AudioStream struct {
	Data      []float32
	SampleRate int
	IsSpeech  bool
	Intent    string
}

// FullDuplexEngine 全双工引擎 - 同时处理输入和输出
type FullDuplexEngine struct {
	inputStream  chan AudioStream
	outputStream chan AudioStream
	decisions    chan InteractionDecision
	mu           sync.RWMutex
	isSpeaking   bool
	isListening  bool
}

// NewFullDuplexEngine 创建全双工引擎实例
func NewFullDuplexEngine() *FullDuplexEngine {
	return &FullDuplexEngine{
		inputStream:  make(chan AudioStream, 100),
		outputStream: make(chan AudioStream, 100),
		decisions:    make(chan InteractionDecision, 10),
	}
}

// ProcessStream 每秒多次处理音频流，做出交互决策
func (e *FullDuplexEngine) ProcessStream(ctx context.Context) {
	// 决策频率：每秒约10次
	ticker := time.NewTicker(100 * time.Millisecond)
	defer ticker.Stop()

	for {
		select {
		case <-ctx.Done():
			return
		case <-ticker.C:
			decision := e.makeDecision()
			e.decisions <- decision
			e.executeDecision(decision)
		case input := <-e.inputStream:
			e.handleInput(input)
		}
	}
}

// makeDecision 每秒多次做出交互决策
// 核心逻辑：基于当前输入流状态、输出流状态和对话上下文综合判断
func (e *FullDuplexEngine) makeDecision() InteractionDecision {
	e.mu.RLock()
	defer e.mu.RUnlock()

	// 1. 检查是否有用户输入
	if len(e.inputStream) > 0 {
		select {
		case latest := <-e.inputStream:
			if latest.IsSpeech {
				// 用户正在说话
				if e.isSpeaking {
					// 检测到用户有紧急打断意图
					if e.detectUrgency(latest) {
						return Interrupt
					}
					// 正常对话中，继续倾听
					return Listen
				}
				// 模型没有说话，倾听用户
				return Listen
			}
		default:
			// 无新输入
		}
	}

	// 2. 检查是否需要输出（模型有回复准备）
	if len(e.outputStream) > 0 && !e.isSpeaking {
		return Speak
	}

	// 3. 检测到沉默（用户思考中）
	if e.detectSilence() {
		return Pause
	}

	// 4. 检查是否需要调用工具（如搜索、推理）
	if e.needToolInvocation() {
		return InvokeTool
	}

	return Listen
}

// detectUrgency 检测用户是否有紧急打断意图
// 基于语音的音调、语速和关键词分析
func (e *FullDuplexEngine) detectUrgency(stream AudioStream) bool {
	// 分析：语速加快、音调升高 => 打断意图
	// 分析：出现"等等""停""听我说"等关键词 => 打断意图
	return false
}

// detectSilence 检测沉默
func (e *FullDuplexEngine) detectSilence() bool {
	// 基于连续静音帧的检测
	return false
}

// needToolInvocation 判断是否需要调用后台工具
func (e *FullDuplexEngine) needToolInvocation() bool {
	// 当用户问题需要搜索、推理或复杂计算时返回true
	return false
}

func (e *FullDuplexEngine) executeDecision(decision InteractionDecision) {
	switch decision {
	case Speak:
		e.isSpeaking = true
		e.isListening = false
		fmt.Println("[决策] 说话")
	case Listen:
		e.isListening = true
		if e.isSpeaking {
			e.isSpeaking = false
			fmt.Println("[决策] 停止说话，倾听用户")
		} else {
			fmt.Println("[决策] 继续倾听")
		}
	case Pause:
		e.isSpeaking = false
		fmt.Println("[决策] 保持安静")
	case Interrupt:
		e.isSpeaking = false
		e.isListening = true
		fmt.Println("[决策] 被打断，切换到倾听模式")
	case InvokeTool:
		fmt.Println("[决策] 调用后台工具")
	}
}

func (e *FullDuplexEngine) handleInput(input AudioStream) {
	// 处理音频输入流
	if input.IsSpeech {
		fmt.Printf("[输入] 检测到语音: %s\n", input.Intent)
	}
}

// Backchannel 生成回馈信号（"嗯嗯""是啊""明白了"）
func (e *FullDuplexEngine) Backchannel() {
	if e.isListening && !e.isSpeaking {
		fmt.Println("[回馈] 嗯嗯")
	}
}

func main() {
	ctx := context.Background()
	engine := NewFullDuplexEngine()

	// 启动全双工处理
	go engine.ProcessStream(ctx)

	// 模拟用户输入
	engine.inputStream <- AudioStream{
		IsSpeech: true,
		Intent:   "帮我查一下今天的天气",
	}

	time.Sleep(500 * time.Millisecond)
	engine.Backchannel()

	// 模拟复杂任务：需要委派推理
	engine.inputStream <- AudioStream{
		IsSpeech: true,
		Intent:   "用Python写一个排序算法",
	}
}

# Python实现：GPT-Live的委派推理架构

import asyncio
import time
from enum import Enum
from typing import Optional

class ReasoningLevel(Enum):
    """推理级别选择器"""
    INSTANT = "instant"    # 快速回答，GPT-5.5 Instant
    MEDIUM = "medium"      # 中等推理，GPT-5.5 Thinking Medium
    HIGH = "high"          # 深度推理，GPT-5.5 Thinking High

class DelegatedReasoningEngine:
    """
    GPT-Live的委派推理引擎
    
    核心架构：
    - 前台：轻量全双工语音模型（连续交互）
    - 后台：GPT-5.5前沿模型（深度推理）
    - 中间层：委派调度器（任务路由）
    """
    
    def __init__(self, reasoning_level: ReasoningLevel = ReasoningLevel.INSTANT):
        self.reasoning_level = reasoning_level
        self.voice_model = VoiceInteractionModel()
        self.background_model = FrontierModel()
        self.delegator = TaskDelegator()
        self.pending_tasks = {}
        
    async def process_user_input(self, audio_input: str) -> None:
        """处理用户输入"""
        # 1. 语音模型即时分析
        intent = self.voice_model.analyze_intent(audio_input)
        
        # 2. 判断是否需要委派
        if self.delegator.need_delegation(intent):
            # 启动委派流程
            task_id = self.delegator.create_task(intent)
            self.pending_tasks[task_id] = {
                "status": "pending",
                "created_at": time.time()
            }
            
            # 后台启动深度推理
            asyncio.create_task(
                self._execute_delegated_task(task_id, intent)
            )
            
            # 前台继续对话
            self.voice_model.say("好的，我来查一下，稍等...")
        else:
            # 直接回答
            response = self.voice_model.generate_response(audio_input)
            self.voice_model.speak(response)
    
    async def _execute_delegated_task(self, task_id: str, intent: str) -> None:
        """在后台执行委派任务"""
        # 调用GPT-5.5进行深度推理
        # 根据推理级别选择不同的模型配置
        if self.reasoning_level == ReasoningLevel.INSTANT:
            result = self.background_model.reason_instant(intent)
        elif self.reasoning_level == ReasoningLevel.MEDIUM:
            result = self.background_model.reason_medium(intent)
        else:
            result = self.background_model.reason_high(intent)
        
        # 更新任务状态
        self.pending_tasks[task_id]["status"] = "completed"
        self.pending_tasks[task_id]["result"] = result
        
        # 通过语音模型将结果带回对话
        self.voice_model.say(f"查到了：{result}")

class VoiceInteractionModel:
    """轻量级语音交互模型 - 负责连续对话"""
    
    def analyze_intent(self, audio: str) -> str:
        """分析用户意图"""
        # 轻量级模型，快速判断
        if "天气" in audio or "股票" in audio:
            return "search"
        elif "写代码" in audio or "算法" in audio:
            return "coding"
        elif "分析" in audio or "比较" in audio:
            return "reasoning"
        return "chat"
    
    def generate_response(self, audio: str) -> str:
        """生成即时回复"""
        return f"关于{audio}，让我想想..."
    
    def speak(self, text: str) -> None:
        """语音输出"""
        print(f"[语音模型]: {text}")
    
    def say(self, text: str) -> None:
        """轻量级语音输出"""
        print(f"[语音模型]: {text}")

class FrontierModel:
    """GPT-5.5前沿推理模型 - 负责深度工作"""
    
    def reason_instant(self, query: str) -> str:
        """GPT-5.5 Instant - 快速推理"""
        time.sleep(0.5)  # 模拟延迟
        return f"快速回答：{query}的答案是..."
    
    def reason_medium(self, query: str) -> str:
        """GPT-5.5 Thinking Medium - 中等推理"""
        time.sleep(1.5)
        return f"中等推理：{query}的详细分析..."
    
    def reason_high(self, query: str) -> str:
        """GPT-5.5 Thinking High - 深度推理"""
        time.sleep(3.0)
        return f"深度推理：{query}的完整分析报告..."

class TaskDelegator:
    """任务委派调度器"""
    
    def need_delegation(self, intent: str) -> bool:
        """判断是否需要委派到后台"""
        return intent in ("search", "coding", "reasoning")
    
    def create_task(self, intent: str) -> str:
        """创建委派任务"""
        import uuid
        return str(uuid.uuid4())[:8]

# 演示：GPT-Live的工作流程
async def demo_gpt_live():
    engine = DelegatedReasoningEngine(ReasoningLevel.MEDIUM)
    
    # 场景1：简单对话 - 直接回答
    print("\n=== 场景1：简单对话 ===")
    await engine.process_user_input("你好")
    
    # 场景2：需要搜索 - 委派推理
    print("\n=== 场景2：需要搜索 ===")
    await engine.process_user_input("今天北京的天气怎么样")
    
    # 场景3：复杂代码 - 委派推理
    print("\n=== 场景3：复杂代码 ===")
    await engine.process_user_input("写一个快速排序算法")
    
    # 等待后台任务完成
    await asyncio.sleep(2)

if __name__ == "__main__":
    asyncio.run(demo_gpt_live())