# config/asr.yaml
asr:
  provider: "whisper"
  model: "large-v3"
  language: "auto"  # 自动检测语言
  vad: "silero"
  
  # 或使用 Azure
  provider: "azure"
  speech_key: "${AZURE_SPEECH_KEY}"
  speech_region: "eastus"

# LLM 配置示例
llm:
  provider: "openai"
  model: "gpt-4o-audio-preview"  # 支持音频的 GPT-4o
  temperature: 0.7
  streaming: true
  
  # 或使用 Claude
  provider: "anthropic"
  model: "claude-sonnet-4-20260219"
  audio_output: true  # Claude 的音频输出模式

# skill_example.py
from vibevoice.skills import Skill, register

@register("weather")
class WeatherSkill(Skill):
    name = "天气查询"
    description = "查询指定城市的天气情况"
    
    async def execute(self, context: dict) -> str:
        city = context.get("params", {}).get("city", "北京")
        # 调用天气 API
        weather = await self.call_api(f"/weather?city={city}")
        return f"{city}今天天气：{weather['desc']}，气温{weather['temp']}度"
    
    def get_schema(self) -> dict:
        return {
            "name": "weather",
            "description": "查询城市天气",
            "parameters": {
                "city": {"type": "string", "description": "城市名称"}
            }
        }

# 创建配置文件
mkdir -p ~/.vibevoice
cat > ~/.vibevoice/config.yaml << EOF
llm:
  provider: "openai"
  api_key: "${OPENAI_API_KEY}"
  
asr:
  provider: "whisper"
  model: "large-v3"

tts:
  provider: "edge"
  voice: "zh-CN-XiaoxiaoNeural"
EOF

import asyncio
from vibevoice import VibeVoice

async def main():
    # 初始化
    vv = VibeVoice(config_path="~/.vibevoice/config.yaml")
    
    # 创建对话会话
    session = await vv.create_session(
        user_id="user_001",
        system_prompt="你是一个专业的健身教练。"
    )
    
    # 语音对话
    print("开始对话（按 Ctrl+C 退出）...")
    async for result in session.voice_loop():
        print(f"用户：{result.user_text}")
        print(f"AI：{result.response_text}")
        print(f"置信度：{result.confidence:.2%}")

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

from vibevoice.asr.base import BaseASR

class MyASR(BaseASR):
    name = "my_asr"
    
    async def recognize(self, audio_chunk: bytes) -> str:
        # 实现自己的 ASR 逻辑
        result = await self.my_asr_api(audio_chunk)
        return result.text
    
    async def detect_speech_end(self, audio_chunk: bytes) -> bool:
        # 实现 VAD 逻辑
        return await self.my_vad_model(audio_chunk)

# 注册引擎
vv.register_asr("my_asr", MyASR())

from vibevoice.tts.base import BaseTTS

class MyTTS(BaseTTS):
    name = "my_tts"
    
    async def synthesize(self, text: str) -> bytes:
        # 返回 WAV/MP3 格式音频
        audio = await self.my_tts_api(text)
        return audio
    
    def stream_audio(self, text: str):
        # 流式音频生成
        for chunk in self.my_streaming_tts(text):
            yield chunk

vv.register_tts("my_tts", MyTTS())

from vibevoice.llm.base import BaseLLM

class MyLLM(BaseLLM):
    name = "my_llm"
    
    async def generate(self, messages: list, **kwargs):
        response = await self.my_llm_api(messages)
        return response.text
    
    async def stream_generate(self, messages: list):
        async for chunk in self.my_streaming_api(messages):
            yield chunk.text

vv.register_llm("my_llm", MyLLM())

# 开发自定义 WebSocket 接口
from vibevoice.api.websocket import WebSocketHandler

class CustomWSHandler(WebSocketHandler):
    async def on_voice_frame(self, frame: bytes):
        # 处理自定义音频帧
        pass
    
    async def on_llm_token(self, token: str):
        # 处理 LLM 流式输出
        await self.send_json({"token": token})

# 注册到 API 服务器
vv.api_server.register("/custom", CustomWSHandler)

# 生产环境安全配置
security:
  api_key_required: true
  rate_limit:
    requests_per_minute: 60
    sessions_per_user: 5
    
  audio:
    max_duration_seconds: 300  # 最大语音时长
    allowed_formats: ["pcm", "wav"]
    
  logging:
    log_audio: false  # 生产环境关闭音频日志

# 接入监控系统
vv.monitor = {
    "prometheus_port": 9090,
    "metrics": [
        "latency.asr",
        "latency.llm",
        "latency.tts",
        "latency.end_to_end",
        "sessions.active",
        "sessions.total"
    ]
}

# docker-compose.yml
version: '3.8'
services:
  vibevoice:
    image: vibevoice/vibevoice:latest
    ports:
      - "8080:8080"
    volumes:
      - ./config:/root/.vibevoice
      - ./data:/root/.vibevoice/data
    devices:
      - /dev/snd:/dev/snd
    environment:
      - CUDA_VISIBLE_DEVICES=0
    restart: unless-stopped
    
  # 可选：本地 LLM
  ollama:
    image: ollama/ollama:latest
    ports:
      - "11434:11434"
    volumes:
      - ollama:/root/.ollama
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

# 微信公众平台对接示例
from vibevoice.integrations import WeChatAdapter

adapter = WeChatAdapter(
    app_id="your_app_id",
    app_secret="your_secret"
)

@adapter.on_voice_message()
async def handle_voice(msg):
    audio = await msg.download_voice()
    response = await vibevoice.process(audio)
    await msg.reply_voice(response.audio)

# 安装
pip install vibevoice

# 配置
vibevoice config init
vibevoice config set llm.provider openai
vibevoice config set llm.api_key YOUR_KEY

# 运行
vibevoice web --port 8080      # Web UI
vibevoice chat --voice         # 语音对话
vibevoice chat --text          # 文本对话

# 开发
vibevoice dev server           # 开发服务器
vibevoice dev test            # 运行测试

# 管理
vibevoice sessions list        # 查看会话
vibevoice sessions kill SESSION_ID  # 关闭会话