大模型服务平台百炼：Qwen-Omni-Realtime

DashScope Python SDK

• 准备运行环境

您的 Python 版本需要不低于 3.10。

首先根据您的操作系统安装 pyaudio。

brew install portaudio && pip install pyaudio

sudo yum install -y portaudio portaudio-devel && pip install pyaudio

pip install pyaudio

安装完成后，通过 pip 安装依赖：

pip install websocket-client dashscope

• 选择交互模式

  • VAD 模式（Voice Activity Detection，自动检测语音起止）

    服务端自动判断用户何时开始与停止说话并作出回应。

  • Manual 模式（按下即说，松开即发送）

    客户端控制语音起止。用户说话结束后，客户端需主动发送消息至服务端。

  VAD 模式

  新建一个 python 文件，命名为vad_dash.py，并将以下代码复制到文件中：

  vad_dash.py

  # 依赖：dashscope >= 1.23.9，pyaudio
  import os
  import base64
  import time
  import pyaudio
  from dashscope.audio.qwen_omni import MultiModality, AudioFormat,OmniRealtimeCallback,OmniRealtimeConversation
  import dashscope
  
  # 配置参数：地址、API Key、音色、模型、模型角色
  # 指定地域，设为intl表示国际（新加坡）,设为cn表示中国内地（北京）
  region = 'intl'
  base_domain = 'dashscope-intl.aliyuncs.com' if region == 'intl' else 'dashscope.aliyuncs.com'
  url = f'wss://{base_domain}/api-ws/v1/realtime'
  # 配置 API Key，若没有设置环境变量，请用 API Key 将下行替换为 dashscope.api_key = "sk-xxx"
  dashscope.api_key = os.getenv('DASHSCOPE_API_KEY')
  # 指定音色
  voice = 'Cherry'
  # 指定模型
  model = 'qwen3.5-omni-plus-realtime'
  # 指定模型角色
  instructions = "你是个人助理小云，请用幽默风趣的方式回答用户的问题"
  class SimpleCallback(OmniRealtimeCallback):
      def __init__(self, pya):
          self.pya = pya
          self.out = None
      def on_open(self):
          # 初始化音频输出流
          self.out = self.pya.open(
              format=pyaudio.paInt16,
              channels=1,
              rate=24000,
              output=True
          )
      def on_event(self, response):
          if response['type'] == 'response.audio.delta':
              # 播放音频
              self.out.write(base64.b64decode(response['delta']))
          elif response['type'] == 'conversation.item.input_audio_transcription.completed':
              # 打印转录文本
              print(f"[User] {response['transcript']}")
          elif response['type'] == 'response.audio_transcript.done':
              # 打印助手回复文本
              print(f"[LLM] {response['transcript']}")
  
  # 1. 初始化音频设备
  pya = pyaudio.PyAudio()
  # 2. 创建回调函数和会话
  callback = SimpleCallback(pya)
  conv = OmniRealtimeConversation(model=model, callback=callback, url=url)
  # 3. 建立连接并配置会话
  conv.connect()
  conv.update_session(output_modalities=[MultiModality.AUDIO, MultiModality.TEXT], voice=voice, instructions=instructions)
  # 4. 初始化音频输入流
  mic = pya.open(format=pyaudio.paInt16, channels=1, rate=16000, input=True)
  # 5. 主循环处理音频输入
  print("对话已开始，对着麦克风说话 (Ctrl+C 退出)...")
  try:
      while True:
          audio_data = mic.read(3200, exception_on_overflow=False)
          conv.append_audio(base64.b64encode(audio_data).decode())
          time.sleep(0.01)
  except KeyboardInterrupt:
      # 清理资源
      conv.close()
      mic.close()
      callback.out.close()
      pya.terminate()
      print("\n对话结束")

  运行vad_dash.py，通过麦克风即可与 Qwen-Omni-Realtime 模型实时对话，系统会检测您的音频起始位置并自动发送到服务器，无需您手动发送。

  Manual 模式

  新建一个 python 文件，命名为manual_dash.py，并将以下代码复制进文件中：

  manual_dash.py

  # 依赖：dashscope >= 1.23.9，pyaudio。
  import os
  import base64
  import sys
  import threading
  import pyaudio
  from dashscope.audio.qwen_omni import *
  import dashscope
  
  # 如果没有设置环境变量，请用您的 API Key 将下行替换为 dashscope.api_key = "sk-xxx"
  dashscope.api_key = os.getenv('DASHSCOPE_API_KEY')
  voice = 'Cherry'
  
  class MyCallback(OmniRealtimeCallback):
      """最简回调：建立连接时初始化扬声器，事件中直接播放返回音频。"""
      def __init__(self, ctx):
          super().__init__()
          self.ctx = ctx
  
      def on_open(self) -> None:
          # 连接建立后初始化 PyAudio 与扬声器(24k/mono/16bit)
          print('connection opened')
          try:
              self.ctx['pya'] = pyaudio.PyAudio()
              self.ctx['out'] = self.ctx['pya'].open(
                  format=pyaudio.paInt16,
                  channels=1,
                  rate=24000,
                  output=True
              )
              print('audio output initialized')
          except Exception as e:
              print('[Error] audio init failed: {}'.format(e))
  
      def on_close(self, close_status_code, close_msg) -> None:
          print('connection closed with code: {}, msg: {}'.format(close_status_code, close_msg))
          sys.exit(0)
  
      def on_event(self, response: str) -> None:
          try:
              t = response['type']
              handlers = {
                  'session.created': lambda r: print('start session: {}'.format(r['session']['id'])),
                  'conversation.item.input_audio_transcription.completed': lambda r: print('question: {}'.format(r['transcript'])),
                  'response.audio_transcript.delta': lambda r: print('llm text: {}'.format(r['delta'])),
                  'response.audio.delta': self._play_audio,
                  'response.done': self._response_done,
              }
              h = handlers.get(t)
              if h:
                  h(response)
          except Exception as e:
              print('[Error] {}'.format(e))
  
      def _play_audio(self, response):
          # 直接解码base64并写入输出流进行播放
          if self.ctx['out'] is None:
              return
          try:
              data = base64.b64decode(response['delta'])
              self.ctx['out'].write(data)
          except Exception as e:
              print('[Error] audio playback failed: {}'.format(e))
  
      def _response_done(self, response):
          # 标记本轮对话完成，用于主循环等待
          if self.ctx['conv'] is not None:
              print('[Metric] response: {}, first text delay: {}, first audio delay: {}'.format(
                  self.ctx['conv'].get_last_response_id(),
                  self.ctx['conv'].get_last_first_text_delay(),
                  self.ctx['conv'].get_last_first_audio_delay(),
              ))
          if self.ctx['resp_done'] is not None:
              self.ctx['resp_done'].set()
  
  def shutdown_ctx(ctx):
      """安全释放音频与PyAudio资源。"""
      try:
          if ctx['out'] is not None:
              ctx['out'].close()
              ctx['out'] = None
      except Exception:
          pass
      try:
          if ctx['pya'] is not None:
              ctx['pya'].terminate()
              ctx['pya'] = None
      except Exception:
          pass
  
  
  def record_until_enter(pya_inst: pyaudio.PyAudio, sample_rate=16000, chunk_size=3200):
      """按 Enter 停止录音，返回PCM字节。"""
      frames = []
      stop_evt = threading.Event()
  
      stream = pya_inst.open(
          format=pyaudio.paInt16,
          channels=1,
          rate=sample_rate,
          input=True,
          frames_per_buffer=chunk_size
      )
  
      def _reader():
          while not stop_evt.is_set():
              try:
                  frames.append(stream.read(chunk_size, exception_on_overflow=False))
              except Exception:
                  break
  
      t = threading.Thread(target=_reader, daemon=True)
      t.start()
      input()  # 用户再次按 Enter 停止录音
      stop_evt.set()
      t.join(timeout=1.0)
      try:
          stream.close()
      except Exception:
          pass
      return b''.join(frames)
  
  
  if __name__  == '__main__':
      print('Initializing ...')
      # 运行时上下文：存放音频与会话句柄
      ctx = {'pya': None, 'out': None, 'conv': None, 'resp_done': threading.Event()}
      callback = MyCallback(ctx)
      conversation = OmniRealtimeConversation(
          model='qwen3.5-omni-plus-realtime',
          callback=callback,
          # 以下为国际（新加坡）地域url，若使用中国内地（北京）地域的模型，需将url替换为：wss://dashscope.aliyuncs.com/api-ws/v1/realtime
          url="wss://dashscope-intl.aliyuncs.com/api-ws/v1/realtime",
      )
      try:
          conversation.connect()
      except Exception as e:
          print('[Error] connect failed: {}'.format(e))
          sys.exit(1)
  
      ctx['conv'] = conversation
      # 会话配置：启用文本+音频输出（禁用服务端VAD，改为手动录音）
      conversation.update_session(
          output_modalities=[MultiModality.AUDIO, MultiModality.TEXT],
          voice=voice,
          enable_input_audio_transcription=True,
          # 对输入音频做语音转录的模型，仅支持gummy-realtime-v1
          input_audio_transcription_model='gummy-realtime-v1',
          enable_turn_detection=False,
          instructions="你是个人助理小云，请你准确且友好地解答用户的问题，始终以乐于助人的态度回应。"
      )
  
      try:
          turn = 1
          while True:
              print(f"\n--- 第 {turn} 轮对话 ---")
              print("按 Enter 开始录音（输入 q 回车退出）...")
              user_input = input()
              if user_input.strip().lower() in ['q', 'quit']:
                  print("用户请求退出...")
                  break
              print("录音中... 再次按 Enter 停止录音。")
              if ctx['pya'] is None:
                  ctx['pya'] = pyaudio.PyAudio()
              recorded = record_until_enter(ctx['pya'])
              if not recorded:
                  print("未录制到有效音频，请重试。")
                  continue
              print(f"成功录制音频: {len(recorded)} 字节，发送中...")
  
              # 以3200字节为块发送（对应16k/16bit/100ms）
              chunk_size = 3200
              for i in range(0, len(recorded), chunk_size):
                  chunk = recorded[i:i+chunk_size]
                  conversation.append_audio(base64.b64encode(chunk).decode('ascii'))
  
              print("发送完成，等待模型响应...")
              ctx['resp_done'].clear()
              conversation.commit()
              conversation.create_response()
              ctx['resp_done'].wait()
              print('播放音频完成')
              turn += 1
      except KeyboardInterrupt:
          print("\n程序被用户中断")
      finally:
          shutdown_ctx(ctx)
          print("程序退出")

  运行manual_dash.py，按 Enter 键开始说话，再按一次获取模型响应的音频。

DashScope Java SDK

选择交互模式

• VAD 模式（Voice Activity Detection，自动检测语音起止）

  Realtime API 自动判断用户何时开始与停止说话并作出回应。

• Manual 模式（按下即说，松开即发送）

  客户端控制语音起止。用户说话结束后，客户端需主动发送消息至服务端。

import com.alibaba.dashscope.audio.omni.*;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.google.gson.JsonObject;
import javax.sound.sampled.*;
import java.nio.ByteBuffer;
import java.util.Arrays;
import java.util.Base64;
import java.util.Map;
import java.util.Queue;
import java.util.concurrent.ConcurrentLinkedQueue;
import java.util.concurrent.atomic.AtomicBoolean;

public class OmniServerVad {
    static class SequentialAudioPlayer {
        private final SourceDataLine line;
        private final Queue<byte[]> audioQueue = new ConcurrentLinkedQueue<>();
        private final Thread playerThread;
        private final AtomicBoolean shouldStop = new AtomicBoolean(false);

        public SequentialAudioPlayer() throws LineUnavailableException {
            AudioFormat format = new AudioFormat(24000, 16, 1, true, false);
            line = AudioSystem.getSourceDataLine(format);
            line.open(format);
            line.start();

            playerThread = new Thread(() -> {
                while (!shouldStop.get()) {
                    byte[] audio = audioQueue.poll();
                    if (audio != null) {
                        line.write(audio, 0, audio.length);
                    } else {
                        try { Thread.sleep(10); } catch (InterruptedException ignored) {}
                    }
                }
            }, "AudioPlayer");
            playerThread.start();
        }

        public void play(String base64Audio) {
            try {
                byte[] audio = Base64.getDecoder().decode(base64Audio);
                audioQueue.add(audio);
            } catch (Exception e) {
                System.err.println("音频解码失败: " + e.getMessage());
            }
        }

        public void cancel() {
            audioQueue.clear();
            line.flush();
        }

        public void close() {
            shouldStop.set(true);
            try { playerThread.join(1000); } catch (InterruptedException ignored) {}
            line.drain();
            line.close();
        }
    }

    public static void main(String[] args) {
        try {
            SequentialAudioPlayer player = new SequentialAudioPlayer();
            AtomicBoolean userIsSpeaking = new AtomicBoolean(false);
            AtomicBoolean shouldStop = new AtomicBoolean(false);

            OmniRealtimeParam param = OmniRealtimeParam.builder()
                    .model("qwen3.5-omni-plus-realtime")
                    .apikey(System.getenv("DASHSCOPE_API_KEY"))
                    // 以下为国际（新加坡）地域url，若使用中国内地（北京）地域的模型，需将url替换为：wss://dashscope.aliyuncs.com/api-ws/v1/realtime
                    .url("wss://dashscope-intl.aliyuncs.com/api-ws/v1/realtime")
                    .build();

            OmniRealtimeConversation conversation = new OmniRealtimeConversation(param, new OmniRealtimeCallback() {
                @Override public void onOpen() {
                    System.out.println("连接已建立");
                }
                @Override public void onClose(int code, String reason) {
                    System.out.println("连接已关闭 (" + code + "): " + reason);
                    shouldStop.set(true);
                }
                @Override public void onEvent(JsonObject event) {
                    handleEvent(event, player, userIsSpeaking);
                }
            });

            conversation.connect();
            conversation.updateSession(OmniRealtimeConfig.builder()
                    .modalities(Arrays.asList(OmniRealtimeModality.AUDIO, OmniRealtimeModality.TEXT))
                    .voice("Cherry")
                    .enableTurnDetection(true)
                    .enableInputAudioTranscription(true)
                    .parameters(Map.of("instructions",
                            "你是五星级酒店的AI客服专员，请准确且友好地解答客户关于房型、设施、价格、预订政策的咨询。请始终以专业和乐于助人的态度回应，杜绝提供未经证实或超出酒店服务范围的信息。"))
                    .build()
            );

            System.out.println("请开始说话（自动检测语音开始/结束，按Ctrl+C退出）...");
            AudioFormat format = new AudioFormat(16000, 16, 1, true, false);
            TargetDataLine mic = AudioSystem.getTargetDataLine(format);
            mic.open(format);
            mic.start();

            ByteBuffer buffer = ByteBuffer.allocate(3200);
            while (!shouldStop.get()) {
                int bytesRead = mic.read(buffer.array(), 0, buffer.capacity());
                if (bytesRead > 0) {
                    try {
                        conversation.appendAudio(Base64.getEncoder().encodeToString(buffer.array()));
                    } catch (Exception e) {
                        if (e.getMessage() != null && e.getMessage().contains("closed")) {
                            System.out.println("对话已关闭，停止录音");
                            break;
                        }
                    }
                }
                Thread.sleep(20);
            }

            conversation.close(1000, "正常结束");
            player.close();
            mic.close();
            System.out.println("\n程序已退出");

        } catch (NoApiKeyException e) {
            System.err.println("未找到API KEY: 请设置环境变量 DASHSCOPE_API_KEY");
            System.exit(1);
        } catch (Exception e) {
            e.printStackTrace();
        }
    }

    private static void handleEvent(JsonObject event, SequentialAudioPlayer player, AtomicBoolean userIsSpeaking) {
        String type = event.get("type").getAsString();
        switch (type) {
            case "input_audio_buffer.speech_started":
                System.out.println("\n[用户开始说话]");
                player.cancel();
                userIsSpeaking.set(true);
                break;
            case "input_audio_buffer.speech_stopped":
                System.out.println("[用户停止说话]");
                userIsSpeaking.set(false);
                break;
            case "response.audio.delta":
                if (!userIsSpeaking.get()) {
                    player.play(event.get("delta").getAsString());
                }
                break;
            case "conversation.item.input_audio_transcription.completed":
                System.out.println("用户: " + event.get("transcript").getAsString());
                break;
            case "response.audio_transcript.delta":
                System.out.print(event.get("delta").getAsString());
                break;
            case "response.done":
                System.out.println("回复完成");
                break;
        }
    }
}

// DashScope Java SDK 版本不低于2.20.9

import com.alibaba.dashscope.audio.omni.*;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.google.gson.JsonObject;
import javax.sound.sampled.*;
import java.io.ByteArrayOutputStream;
import java.io.IOException;
import java.util.Arrays;
import java.util.Base64;
import java.util.HashMap;
import java.util.Queue;
import java.util.concurrent.ConcurrentLinkedQueue;
import java.util.concurrent.CountDownLatch;
import java.util.concurrent.atomic.AtomicBoolean;
import java.util.concurrent.atomic.AtomicReference;

public class Main {
    // RealtimePcmPlayer 类定义开始
    public static class RealtimePcmPlayer {
        private int sampleRate;
        private SourceDataLine line;
        private AudioFormat audioFormat;
        private Thread decoderThread;
        private Thread playerThread;
        private AtomicBoolean stopped = new AtomicBoolean(false);
        private Queue<String> b64AudioBuffer = new ConcurrentLinkedQueue<>();
        private Queue<byte[]> RawAudioBuffer = new ConcurrentLinkedQueue<>();

        // 构造函数初始化音频格式和音频线路
        public RealtimePcmPlayer(int sampleRate) throws LineUnavailableException {
            this.sampleRate = sampleRate;
            this.audioFormat = new AudioFormat(this.sampleRate, 16, 1, true, false);
            DataLine.Info info = new DataLine.Info(SourceDataLine.class, audioFormat);
            line = (SourceDataLine) AudioSystem.getLine(info);
            line.open(audioFormat);
            line.start();
            decoderThread = new Thread(new Runnable() {
                @Override
                public void run() {
                    while (!stopped.get()) {
                        String b64Audio = b64AudioBuffer.poll();
                        if (b64Audio != null) {
                            byte[] rawAudio = Base64.getDecoder().decode(b64Audio);
                            RawAudioBuffer.add(rawAudio);
                        } else {
                            try {
                                Thread.sleep(100);
                            } catch (InterruptedException e) {
                                throw new RuntimeException(e);
                            }
                        }
                    }
                }
            });
            playerThread = new Thread(new Runnable() {
                @Override
                public void run() {
                    while (!stopped.get()) {
                        byte[] rawAudio = RawAudioBuffer.poll();
                        if (rawAudio != null) {
                            try {
                                playChunk(rawAudio);
                            } catch (IOException e) {
                                throw new RuntimeException(e);
                            } catch (InterruptedException e) {
                                throw new RuntimeException(e);
                            }
                        } else {
                            try {
                                Thread.sleep(100);
                            } catch (InterruptedException e) {
                                throw new RuntimeException(e);
                            }
                        }
                    }
                }
            });
            decoderThread.start();
            playerThread.start();
        }

        // 播放一个音频块并阻塞直到播放完成
        private void playChunk(byte[] chunk) throws IOException, InterruptedException {
            if (chunk == null || chunk.length == 0) return;

            int bytesWritten = 0;
            while (bytesWritten < chunk.length) {
                bytesWritten += line.write(chunk, bytesWritten, chunk.length - bytesWritten);
            }
            int audioLength = chunk.length / (this.sampleRate*2/1000);
            // 等待缓冲区中的音频播放完成
            Thread.sleep(audioLength - 10);
        }

        public void write(String b64Audio) {
            b64AudioBuffer.add(b64Audio);
        }

        public void cancel() {
            b64AudioBuffer.clear();
            RawAudioBuffer.clear();
        }

        public void waitForComplete() throws InterruptedException {
            while (!b64AudioBuffer.isEmpty() || !RawAudioBuffer.isEmpty()) {
                Thread.sleep(100);
            }
            line.drain();
        }

        public void shutdown() throws InterruptedException {
            stopped.set(true);
            decoderThread.join();
            playerThread.join();
            if (line != null && line.isRunning()) {
                line.drain();
                line.close();
            }
        }
    } // RealtimePcmPlayer 类定义结束
    // 新增录音方法
    private static void recordAndSend(TargetDataLine line, OmniRealtimeConversation conversation) throws IOException {
        ByteArrayOutputStream out = new ByteArrayOutputStream();
        byte[] buffer = new byte[3200];
        AtomicBoolean stopRecording = new AtomicBoolean(false);

        // 启动监听Enter键的线程
        Thread enterKeyListener = new Thread(() -> {
            try {
                System.in.read();
                stopRecording.set(true);
            } catch (IOException e) {
                e.printStackTrace();
            }
        });
        enterKeyListener.start();

        // 录音循环
        while (!stopRecording.get()) {
            int count = line.read(buffer, 0, buffer.length);
            if (count > 0) {
                out.write(buffer, 0, count);
            }
        }

        // 发送录音数据
        byte[] audioData = out.toByteArray();
        String audioB64 = Base64.getEncoder().encodeToString(audioData);
        conversation.appendAudio(audioB64);
        out.close();
    }

    public static void main(String[] args) throws InterruptedException, LineUnavailableException {
        OmniRealtimeParam param = OmniRealtimeParam.builder()
                .model("qwen3.5-omni-plus-realtime")
                // 新加坡和北京地域的API Key不同。获取API Key：https://www.alibabacloud.com/help/zh/model-studio/get-api-key
                // 若没有配置环境变量，请用百炼API Key将下行替换为：.apikey("sk-xxx")
                .apikey(System.getenv("DASHSCOPE_API_KEY"))
                //以下为新加坡地域url，若使用北京地域的模型，需将url替换为：wss://dashscope.aliyuncs.com/api-ws/v1/realtime
                .url("wss://dashscope-intl.aliyuncs.com/api-ws/v1/realtime")
                .build();
        AtomicReference<CountDownLatch> responseDoneLatch = new AtomicReference<>(null);
        responseDoneLatch.set(new CountDownLatch(1));

        RealtimePcmPlayer audioPlayer = new RealtimePcmPlayer(24000);
        final AtomicReference<OmniRealtimeConversation> conversationRef = new AtomicReference<>(null);
        OmniRealtimeConversation conversation = new OmniRealtimeConversation(param, new OmniRealtimeCallback() {
            @Override
            public void onOpen() {
                System.out.println("connection opened");
            }
            @Override
            public void onEvent(JsonObject message) {
                String type = message.get("type").getAsString();
                switch(type) {
                    case "session.created":
                        System.out.println("start session: " + message.get("session").getAsJsonObject().get("id").getAsString());
                        break;
                    case "conversation.item.input_audio_transcription.completed":
                        System.out.println("question: " + message.get("transcript").getAsString());
                        break;
                    case "response.audio_transcript.delta":
                        System.out.println("got llm response delta: " + message.get("delta").getAsString());
                        break;
                    case "response.audio.delta":
                        String recvAudioB64 = message.get("delta").getAsString();
                        audioPlayer.write(recvAudioB64);
                        break;
                    case "response.done":
                        System.out.println("======RESPONSE DONE======");
                        if (conversationRef.get() != null) {
                            System.out.println("[Metric] response: " + conversationRef.get().getResponseId() +
                                    ", first text delay: " + conversationRef.get().getFirstTextDelay() +
                                    " ms, first audio delay: " + conversationRef.get().getFirstAudioDelay() + " ms");
                        }
                        responseDoneLatch.get().countDown();
                        break;
                    default:
                        break;
                }
            }
            @Override
            public void onClose(int code, String reason) {
                System.out.println("connection closed code: " + code + ", reason: " + reason);
            }
        });
        conversationRef.set(conversation);
        try {
            conversation.connect();
        } catch (NoApiKeyException e) {
            throw new RuntimeException(e);
        }
        OmniRealtimeConfig config = OmniRealtimeConfig.builder()
                .modalities(Arrays.asList(OmniRealtimeModality.AUDIO, OmniRealtimeModality.TEXT))
                .voice("Cherry")
                .enableTurnDetection(false)
                // 设定模型角色
                .parameters(new HashMap<String, Object>() {{
                    put("instructions","你是个人助理小云，请你准确且友好地解答用户的问题，始终以乐于助人的态度回应。");
                }})
                .build();
        conversation.updateSession(config);

        // 新增麦克风录音功能
        AudioFormat format = new AudioFormat(16000, 16, 1, true, false);
        DataLine.Info info = new DataLine.Info(TargetDataLine.class, format);

        if (!AudioSystem.isLineSupported(info)) {
            System.out.println("Line not supported");
            return;
        }

        TargetDataLine line = null;
        try {
            line = (TargetDataLine) AudioSystem.getLine(info);
            line.open(format);
            line.start();

            while (true) {
                System.out.println("按Enter开始录音...");
                System.in.read();
                System.out.println("开始录音，请说话...再次按Enter停止录音并发送");
                recordAndSend(line, conversation);
                conversation.commit();
                conversation.createResponse(null, null);
                // 重置latch以便下次等待
                responseDoneLatch.set(new CountDownLatch(1));
            }
        } catch (LineUnavailableException | IOException e) {
            e.printStackTrace();
        } finally {
            if (line != null) {
                line.stop();
                line.close();
            }
        }
    }}

WebSocket（Python）

• 准备运行环境

  您的 Python 版本需要不低于 3.10。

  首先根据您的操作系统来安装 pyaudio。

  macOS

  brew install portaudio && pip install pyaudio

  Debian/Ubuntu

  sudo apt-get install python3-pyaudio
  
  或者
  
  pip install pyaudio

  推荐使用pip install pyaudio。如果安装失败，请先根据您的操作系统安装portaudio依赖。

  CentOS

  sudo yum install -y portaudio portaudio-devel && pip install pyaudio

  Windows

  pip install pyaudio

  安装完成后，通过 pip 安装 websocket 相关的依赖：

  pip install websockets==15.0.1

• 创建客户端

  在本地新建一个 python 文件，命名为omni_realtime_client.py，并将以下代码复制进文件中：

  omni_realtime_client.py

  import asyncio
  import websockets
  import json
  import base64
  import time
  from typing import Optional, Callable, List, Dict, Any
  from enum import Enum
  
  class TurnDetectionMode(Enum):
      SERVER_VAD = "server_vad"
      MANUAL = "manual"
  
  class OmniRealtimeClient:
  
      def __init__(
              self,
              base_url,
              api_key: str,
              model: str = "",
              voice: str = "Ethan",
              instructions: str = "You are a helpful assistant.",
              turn_detection_mode: TurnDetectionMode = TurnDetectionMode.SERVER_VAD,
              on_text_delta: Optional[Callable[[str], None]] = None,
              on_audio_delta: Optional[Callable[[bytes], None]] = None,
              on_input_transcript: Optional[Callable[[str], None]] = None,
              on_output_transcript: Optional[Callable[[str], None]] = None,
              extra_event_handlers: Optional[Dict[str, Callable[[Dict[str, Any]], None]]] = None
      ):
          self.base_url = base_url
          self.api_key = api_key
          self.model = model
          self.voice = voice
          self.instructions = instructions
          self.ws = None
          self.on_text_delta = on_text_delta
          self.on_audio_delta = on_audio_delta
          self.on_input_transcript = on_input_transcript
          self.on_output_transcript = on_output_transcript
          self.turn_detection_mode = turn_detection_mode
          self.extra_event_handlers = extra_event_handlers or {}
  
          # 当前回复状态
          self._current_response_id = None
          self._current_item_id = None
          self._is_responding = False
          # 输入/输出转录打印状态
          self._print_input_transcript = True
          self._output_transcript_buffer = ""
  
      async def connect(self) -> None:
          """与 Realtime API 建立 WebSocket 连接。"""
          url = f"{self.base_url}?model={self.model}"
          headers = {
              "Authorization": f"Bearer {self.api_key}"
          }
          self.ws = await websockets.connect(url, additional_headers=headers)
  
          # 会话配置
          session_config = {
              "modalities": ["text", "audio"],
              "voice": self.voice,
              "instructions": self.instructions,
              "input_audio_format": "pcm",
              "output_audio_format": "pcm",
              "input_audio_transcription": {
                  "model": "gummy-realtime-v1"
              }
          }
  
          if self.turn_detection_mode == TurnDetectionMode.MANUAL:
              session_config['turn_detection'] = None
              await self.update_session(session_config)
          elif self.turn_detection_mode == TurnDetectionMode.SERVER_VAD:
              session_config['turn_detection'] = {
                  "type": "server_vad",
                  "threshold": 0.1,
                  "prefix_padding_ms": 500,
                  "silence_duration_ms": 900
              }
              await self.update_session(session_config)
          else:
              raise ValueError(f"Invalid turn detection mode: {self.turn_detection_mode}")
  
      async def send_event(self, event) -> None:
          event['event_id'] = "event_" + str(int(time.time() * 1000))
          await self.ws.send(json.dumps(event))
  
      async def update_session(self, config: Dict[str, Any]) -> None:
          """更新会话配置。"""
          event = {
              "type": "session.update",
              "session": config
          }
          await self.send_event(event)
  
      async def stream_audio(self, audio_chunk: bytes) -> None:
          """向 API 流式发送原始音频数据。"""
          # 仅支持 16bit 16kHz 单声道 PCM
          audio_b64 = base64.b64encode(audio_chunk).decode()
          append_event = {
              "type": "input_audio_buffer.append",
              "audio": audio_b64
          }
          await self.send_event(append_event)
  
      async def commit_audio_buffer(self) -> None:
          """提交音频缓冲区以触发处理。"""
          event = {
              "type": "input_audio_buffer.commit"
          }
          await self.send_event(event)
  
      async def append_image(self, image_chunk: bytes) -> None:
          """向图像缓冲区追加图像数据。
          图像数据可以来自本地文件，也可以来自实时视频流。
          注意:
              - 图像格式必须为 JPG 或 JPEG。推荐分辨率为 480P 或 720P，最高支持 1080P。
              - 单张图片大小不应超过 500KB。
              - 将图像数据编码为 Base64 后再发送。
              - 建议以 1张/秒 的频率向服务端发送图像。
              - 在发送图像数据之前，需要至少发送过一次音频数据。
          """
          image_b64 = base64.b64encode(image_chunk).decode()
          event = {
              "type": "input_image_buffer.append",
              "image": image_b64
          }
          await self.send_event(event)
  
      async def create_response(self) -> None:
          """向 API 请求生成回复（仅在手动模式下需要调用）。"""
          event = {
              "type": "response.create"
          }
          await self.send_event(event)
  
      async def cancel_response(self) -> None:
          """取消当前回复。"""
          event = {
              "type": "response.cancel"
          }
          await self.send_event(event)
  
      async def handle_interruption(self):
          """处理用户对当前回复的打断。"""
          if not self._is_responding:
              return
          # 1. 取消当前回复
          if self._current_response_id:
              await self.cancel_response()
  
          self._is_responding = False
          self._current_response_id = None
          self._current_item_id = None
  
      async def handle_messages(self) -> None:
          try:
              async for message in self.ws:
                  event = json.loads(message)
                  event_type = event.get("type")
                  if event_type == "error":
                      print(" Error: ", event['error'])
                      continue
                  elif event_type == "response.created":
                      self._current_response_id = event.get("response", {}).get("id")
                      self._is_responding = True
                  elif event_type == "response.output_item.added":
                      self._current_item_id = event.get("item", {}).get("id")
                  elif event_type == "response.done":
                      self._is_responding = False
                      self._current_response_id = None
                      self._current_item_id = None
                  elif event_type == "input_audio_buffer.speech_started":
                      print("检测到语音开始")
                      if self._is_responding:
                          print("处理打断")
                          await self.handle_interruption()
                  elif event_type == "input_audio_buffer.speech_stopped":
                      print("检测到语音结束")
                  elif event_type == "response.text.delta":
                      if self.on_text_delta:
                          self.on_text_delta(event["delta"])
                  elif event_type == "response.audio.delta":
                      if self.on_audio_delta:
                          audio_bytes = base64.b64decode(event["delta"])
                          self.on_audio_delta(audio_bytes)
                  elif event_type == "conversation.item.input_audio_transcription.completed":
                      transcript = event.get("transcript", "")
                      print(f"用户: {transcript}")
                      if self.on_input_transcript:
                          await asyncio.to_thread(self.on_input_transcript, transcript)
                          self._print_input_transcript = True
                  elif event_type == "response.audio_transcript.delta":
                      if self.on_output_transcript:
                          delta = event.get("delta", "")
                          if not self._print_input_transcript:
                              self._output_transcript_buffer += delta
                          else:
                              if self._output_transcript_buffer:
                                  await asyncio.to_thread(self.on_output_transcript, self._output_transcript_buffer)
                                  self._output_transcript_buffer = ""
                              await asyncio.to_thread(self.on_output_transcript, delta)
                  elif event_type == "response.audio_transcript.done":
                      print(f"大模型: {event.get('transcript', '')}")
                      self._print_input_transcript = False
                  elif event_type in self.extra_event_handlers:
                      self.extra_event_handlers[event_type](event)
          except websockets.exceptions.ConnectionClosed:
              print(" Connection closed")
          except Exception as e:
              print(" Error in message handling: ", str(e))
      async def close(self) -> None:
          """关闭 WebSocket 连接。"""
          if self.ws:
              await self.ws.close()

• 选择交互模式

  • VAD 模式（Voice Activity Detection，自动检测语音起止）

    Realtime API 自动判断用户何时开始与停止说话并作出回应。

  • Manual 模式（按下即说，松开即发送）

    客户端控制语音起止。用户说话结束后，客户端需主动发送消息至服务端。

  VAD 模式

  在omni_realtime_client.py的同级目录下新建另一个 python 文件，命名为vad_mode.py，并将以下代码复制进文件中：

  vad_mode.py

  # -- coding: utf-8 --
  import os, asyncio, pyaudio, queue, threading
  from omni_realtime_client import OmniRealtimeClient, TurnDetectionMode
  
  # 音频播放器类（处理中断）
  class AudioPlayer:
      def __init__(self, pyaudio_instance, rate=24000):
          self.stream = pyaudio_instance.open(format=pyaudio.paInt16, channels=1, rate=rate, output=True)
          self.queue = queue.Queue()
          self.stop_evt = threading.Event()
          self.interrupt_evt = threading.Event()
          threading.Thread(target=self._run, daemon=True).start()
  
      def _run(self):
          while not self.stop_evt.is_set():
              try:
                  data = self.queue.get(timeout=0.5)
                  if data is None: break
                  if not self.interrupt_evt.is_set(): self.stream.write(data)
                  self.queue.task_done()
              except queue.Empty: continue
  
      def add_audio(self, data): self.queue.put(data)
      def handle_interrupt(self): self.interrupt_evt.set(); self.queue.queue.clear()
      def stop(self): self.stop_evt.set(); self.queue.put(None); self.stream.stop_stream(); self.stream.close()
  
  # 麦克风录音并发送
  async def record_and_send(client):
      p = pyaudio.PyAudio()
      stream = p.open(format=pyaudio.paInt16, channels=1, rate=16000, input=True, frames_per_buffer=3200)
      print("开始录音，请讲话...")
      try:
          while True:
              audio_data = stream.read(3200)
              await client.stream_audio(audio_data)
              await asyncio.sleep(0.02)
      finally:
          stream.stop_stream(); stream.close(); p.terminate()
  
  async def main():
      p = pyaudio.PyAudio()
      player = AudioPlayer(pyaudio_instance=p)
  
      client = OmniRealtimeClient(
          # 以下是国际（新加坡）地域 base_url，中国内地（北京）地域base_url为wss://dashscope.aliyuncs.com/api-ws/v1/realtime
          base_url="wss://dashscope-intl.aliyuncs.com/api-ws/v1/realtime",
          api_key=os.environ.get("DASHSCOPE_API_KEY"),
          model="qwen3.5-omni-plus-realtime",
          voice="Cherry",
          instructions="你是小云，风趣幽默的好助手",
          turn_detection_mode=TurnDetectionMode.SERVER_VAD,
          on_text_delta=lambda t: print(f"\nAssistant: {t}", end="", flush=True),
          on_audio_delta=player.add_audio,
      )
  
      await client.connect()
      print("连接成功，开始实时对话...")
  
      # 并发运行
      await asyncio.gather(client.handle_messages(), record_and_send(client))
  
  if __name__ == "__main__":
      try:
          asyncio.run(main())
      except KeyboardInterrupt:
          print("\n程序已退出。")

  运行vad_mode.py，通过麦克风即可与 Realtime 模型实时对话，系统会检测您的音频起始位置并自动发送到服务器，无需您手动发送。

  Manual 模式

  在omni_realtime_client.py的同级目录下新建另一个 python 文件，命名为manual_mode.py，并将以下代码复制进文件中：

  manual_mode.py

  # -- coding: utf-8 --
  import os
  import asyncio
  import time
  import threading
  import queue
  import pyaudio
  from omni_realtime_client import OmniRealtimeClient, TurnDetectionMode
  
  
  class AudioPlayer:
      """实时音频播放器类"""
  
      def __init__(self, sample_rate=24000, channels=1, sample_width=2):
          self.sample_rate = sample_rate
          self.channels = channels
          self.sample_width = sample_width  # 2 bytes for 16-bit
          self.audio_queue = queue.Queue()
          self.is_playing = False
          self.play_thread = None
          self.pyaudio_instance = None
          self.stream = None
          self._lock = threading.Lock()  # 添加锁来同步访问
          self._last_data_time = time.time()  # 记录最后接收数据的时间
          self._response_done = False  # 添加响应完成标志
          self._waiting_for_response = False  # 标记是否正在等待服务器响应
          # 记录最后一次向音频流写入数据的时间及最近一次音频块的时长，用于更精确地判断播放结束
          self._last_play_time = time.time()
          self._last_chunk_duration = 0.0
  
      def start(self):
          """启动音频播放器"""
          with self._lock:
              if self.is_playing:
                  return
  
              self.is_playing = True
  
              try:
                  self.pyaudio_instance = pyaudio.PyAudio()
  
                  # 创建音频输出流
                  self.stream = self.pyaudio_instance.open(
                      format=pyaudio.paInt16,  # 16-bit
                      channels=self.channels,
                      rate=self.sample_rate,
                      output=True,
                      frames_per_buffer=1024
                  )
  
                  # 启动播放线程
                  self.play_thread = threading.Thread(target=self._play_audio)
                  self.play_thread.daemon = True
                  self.play_thread.start()
  
                  print("音频播放器已启动")
              except Exception as e:
                  print(f"启动音频播放器失败: {e}")
                  self._cleanup_resources()
                  raise
  
      def stop(self):
          """停止音频播放器"""
          with self._lock:
              if not self.is_playing:
                  return
  
              self.is_playing = False
  
          # 清空队列
          while not self.audio_queue.empty():
              try:
                  self.audio_queue.get_nowait()
              except queue.Empty:
                  break
  
          # 等待播放线程结束（在锁外面等待，避免死锁）
          if self.play_thread and self.play_thread.is_alive():
              self.play_thread.join(timeout=2.0)
  
          # 再次获取锁来清理资源
          with self._lock:
              self._cleanup_resources()
  
          print("音频播放器已停止")
  
      def _cleanup_resources(self):
          """清理音频资源（必须在锁内调用）"""
          try:
              # 关闭音频流
              if self.stream:
                  if not self.stream.is_stopped():
                      self.stream.stop_stream()
                  self.stream.close()
                  self.stream = None
          except Exception as e:
              print(f"关闭音频流时出错: {e}")
  
          try:
              if self.pyaudio_instance:
                  self.pyaudio_instance.terminate()
                  self.pyaudio_instance = None
          except Exception as e:
              print(f"终止PyAudio时出错: {e}")
  
      def add_audio_data(self, audio_data):
          """添加音频数据到播放队列"""
          if self.is_playing and audio_data:
              self.audio_queue.put(audio_data)
              with self._lock:
                  self._last_data_time = time.time()  # 更新最后接收数据的时间
                  self._waiting_for_response = False  # 收到数据，不再等待
  
      def stop_receiving_data(self):
          """标记不再接收新的音频数据"""
          with self._lock:
              self._response_done = True
              self._waiting_for_response = False  # 响应结束，不再等待
  
      def prepare_for_next_turn(self):
          """为下一轮对话重置播放器状态。"""
          with self._lock:
              self._response_done = False
              self._last_data_time = time.time()
              self._last_play_time = time.time()
              self._last_chunk_duration = 0.0
              self._waiting_for_response = True  # 开始等待下一轮响应
  
          # 清空上一轮可能残留的音频数据
          while not self.audio_queue.empty():
              try:
                  self.audio_queue.get_nowait()
              except queue.Empty:
                  break
  
      def is_finished_playing(self):
          """检查是否已经播放完所有音频数据"""
          with self._lock:
              queue_size = self.audio_queue.qsize()
              time_since_last_data = time.time() - self._last_data_time
              time_since_last_play = time.time() - self._last_play_time
  
              # ---------------------- 智能结束判定 ----------------------
              # 1. 首选：如果服务器已标记完成且播放队列为空
              #    进一步等待最近一块音频播放完毕（音频块时长 + 0.1s 容错）。
              if self._response_done and queue_size == 0:
                  min_wait = max(self._last_chunk_duration + 0.1, 0.5)  # 至少等待 0.5s
                  if time_since_last_play >= min_wait:
                      return True
  
              # 2. 备用：如果长时间没有新数据且播放队列为空
              #    当服务器没有明确发出 `response.done` 时，此逻辑作为保障
              if not self._waiting_for_response and queue_size == 0 and time_since_last_data > 1.0:
                  print("\n(超时未收到新音频，判定播放结束)")
                  return True
  
              return False
  
      def _play_audio(self):
          """播放音频数据的工作线程"""
          while True:
              # 检查是否应该停止
              with self._lock:
                  if not self.is_playing:
                      break
                  stream_ref = self.stream  # 获取流的引用
  
              try:
                  # 从队列中获取音频数据，超时0.1秒
                  audio_data = self.audio_queue.get(timeout=0.1)
  
                  # 再次检查状态和流的有效性
                  with self._lock:
                      if self.is_playing and stream_ref and not stream_ref.is_stopped():
                          try:
                              # 播放音频数据
                              stream_ref.write(audio_data)
                              # 更新最近播放信息
                              self._last_play_time = time.time()
                              self._last_chunk_duration = len(audio_data) / (
                                          self.channels * self.sample_width) / self.sample_rate
                          except Exception as e:
                              print(f"写入音频流时出错: {e}")
                              break
  
                  # 标记该数据块已处理完成
                  self.audio_queue.task_done()
  
              except queue.Empty:
                  # 队列为空时继续等待
                  continue
              except Exception as e:
                  print(f"播放音频时出错: {e}")
                  break
  
  
  class MicrophoneRecorder:
      """实时麦克风录音器"""
  
      def __init__(self, sample_rate=16000, channels=1, chunk_size=3200):
          self.sample_rate = sample_rate
          self.channels = channels
          self.chunk_size = chunk_size
          self.pyaudio_instance = None
          self.stream = None
          self.frames = []
          self._is_recording = False
          self._record_thread = None
  
      def _recording_thread(self):
          """录音工作线程"""
          # 在 _is_recording 为 True 期间，持续从音频流中读取数据
          while self._is_recording:
              try:
                  # 使用 exception_on_overflow=False 避免因缓冲区溢出而崩溃
                  data = self.stream.read(self.chunk_size, exception_on_overflow=False)
                  self.frames.append(data)
              except (IOError, OSError) as e:
                  # 当流被关闭时，读取操作可能会引发错误
                  print(f"录音流读取错误，可能已关闭: {e}")
                  break
  
      def start(self):
          """开始录音"""
          if self._is_recording:
              print("录音已在进行中。")
              return
  
          self.frames = []
          self._is_recording = True
  
          try:
              self.pyaudio_instance = pyaudio.PyAudio()
              self.stream = self.pyaudio_instance.open(
                  format=pyaudio.paInt16,
                  channels=self.channels,
                  rate=self.sample_rate,
                  input=True,
                  frames_per_buffer=self.chunk_size
              )
  
              self._record_thread = threading.Thread(target=self._recording_thread)
              self._record_thread.daemon = True
              self._record_thread.start()
              print("麦克风录音已开始...")
          except Exception as e:
              print(f"启动麦克风失败: {e}")
              self._is_recording = False
              self._cleanup()
              raise
  
      def stop(self):
          """停止录音并返回音频数据"""
          if not self._is_recording:
              return None
  
          self._is_recording = False
  
          # 等待录音线程安全退出
          if self._record_thread:
              self._record_thread.join(timeout=1.0)
  
          self._cleanup()
  
          print("麦克风录音已停止。")
          return b''.join(self.frames)
  
      def _cleanup(self):
          """安全地清理 PyAudio 资源"""
          if self.stream:
              try:
                  if self.stream.is_active():
                      self.stream.stop_stream()
                  self.stream.close()
              except Exception as e:
                  print(f"关闭音频流时出错: {e}")
  
          if self.pyaudio_instance:
              try:
                  self.pyaudio_instance.terminate()
              except Exception as e:
                  print(f"终止 PyAudio 实例时出错: {e}")
  
          self.stream = None
          self.pyaudio_instance = None
  
  
  async def interactive_test():
      """
      交互式测试脚本：允许多轮连续对话，每轮可以发送音频和图片。
      """
      # ------------------- 1. 初始化和连接 (一次性) -------------------
      # 新加坡和北京地域的API Key不同。获取API Key：https://www.alibabacloud.com/help/zh/model-studio/get-api-key
      api_key = os.environ.get("DASHSCOPE_API_KEY")
      if not api_key:
          print("请设置DASHSCOPE_API_KEY环境变量")
          return
  
      print("--- 实时多轮音视频对话客户端 ---")
      print("正在初始化音频播放器和客户端...")
  
      audio_player = AudioPlayer()
      audio_player.start()
  
      def on_audio_received(audio_data):
          audio_player.add_audio_data(audio_data)
  
      def on_response_done(event):
          print("\n(收到响应结束标记)")
          audio_player.stop_receiving_data()
  
      realtime_client = OmniRealtimeClient(
          # 以下为新加坡地域base_url，若使用北京地域的模型，需将base_url替换为：wss://dashscope.aliyuncs.com/api-ws/v1/realtime
          base_url="wss://dashscope-intl.aliyuncs.com/api-ws/v1/realtime",
          api_key=api_key,
          model="qwen3.5-omni-plus-realtime",
          voice="Ethan",
          instructions="你是个人助理小云，请你准确且友好地解答用户的问题，始终以乐于助人的态度回应。", # 设定模型角色
          on_text_delta=lambda text: print(f"助手回复: {text}", end="", flush=True),
          on_audio_delta=on_audio_received,
          turn_detection_mode=TurnDetectionMode.MANUAL,
          extra_event_handlers={"response.done": on_response_done}
      )
  
      message_handler_task = None
      try:
          await realtime_client.connect()
          print("已连接到服务器。输入 'q' 或 'quit' 可随时退出程序。")
          message_handler_task = asyncio.create_task(realtime_client.handle_messages())
          await asyncio.sleep(0.5)
  
          turn_counter = 1
          # ------------------- 2. 多轮对话循环 -------------------
          while True:
              print(f"\n--- 第 {turn_counter} 轮对话 ---")
              audio_player.prepare_for_next_turn()
  
              recorded_audio = None
              image_paths = []
  
              # --- 获取用户输入：从麦克风录音 ---
              loop = asyncio.get_event_loop()
              recorder = MicrophoneRecorder(sample_rate=16000)  # 推荐使用16k采样率进行语音识别
  
              print("准备录音。按 Enter 键开始录音 (或输入 'q' 退出)...")
              user_input = await loop.run_in_executor(None, input)
              if user_input.strip().lower() in ['q', 'quit']:
                  print("用户请求退出...")
                  return
  
              try:
                  recorder.start()
              except Exception:
                  print("无法启动录音，请检查您的麦克风权限和设备。跳过本轮。")
                  continue
  
              print("录音中... 再次按 Enter 键停止录音。")
              await loop.run_in_executor(None, input)
  
              recorded_audio = recorder.stop()
  
              if not recorded_audio or len(recorded_audio) == 0:
                  print("未录制到有效音频，请重新开始本轮对话。")
                  continue
  
              # --- 获取图片输入 (可选) ---
              # 以下图片输入功能已被注释，暂时禁用。若需启用请取消下方代码注释。
              # print("\n请逐行输入【图片文件】的绝对路径 (可选)。完成后，输入 's' 或按 Enter 发送请求。")
              # while True:
              #     path = input("图片路径: ").strip()
              #     if path.lower() == 's' or path == '':
              #         break
              #     if path.lower() in ['q', 'quit']:
              #         print("用户请求退出...")
              #         return
              #
              #     if not os.path.isabs(path):
              #         print("错误: 请输入绝对路径。")
              #         continue
              #     if not os.path.exists(path):
              #         print(f"错误: 文件不存在 -> {path}")
              #         continue
              #     image_paths.append(path)
              #     print(f"已添加图片: {os.path.basename(path)}")
  
              # --- 3. 发送数据并获取响应 ---
              print("\n--- 输入确认 ---")
              print(f"待处理音频: 1个 (来自麦克风), 图片: {len(image_paths)}个")
              print("------------------")
  
              # 3.1 发送录制的音频
              try:
                  print(f"发送麦克风录音 ({len(recorded_audio)}字节)")
                  await realtime_client.stream_audio(recorded_audio)
                  await asyncio.sleep(0.1)
              except Exception as e:
                  print(f"发送麦克风录音失败: {e}")
                  continue
  
              # 3.2 发送所有图片文件
              # 以下图片发送代码已被注释，暂时禁用。
              # for i, path in enumerate(image_paths):
              #     try:
              #         with open(path, "rb") as f:
              #             data = f.read()
              #         print(f"发送图片 {i+1}: {os.path.basename(path)} ({len(data)}字节)")
              #         await realtime_client.append_image(data)
              #         await asyncio.sleep(0.1)
              #     except Exception as e:
              #         print(f"发送图片 {os.path.basename(path)} 失败: {e}")
  
              # 3.3 提交并等待响应
              print("提交所有输入，请求服务器响应...")
              await realtime_client.commit_audio_buffer()
              await realtime_client.create_response()
  
              print("等待并播放服务器响应音频...")
              start_time = time.time()
              max_wait_time = 60
              while not audio_player.is_finished_playing():
                  if time.time() - start_time > max_wait_time:
                      print(f"\n等待超时 ({max_wait_time}秒), 进入下一轮。")
                      break
                  await asyncio.sleep(0.2)
  
              print("\n本轮音频播放完成！")
              turn_counter += 1
  
      except (asyncio.CancelledError, KeyboardInterrupt):
          print("\n程序被中断。")
      except Exception as e:
          print(f"发生未处理的错误: {e}")
      finally:
          # ------------------- 4. 清理资源 -------------------
          print("\n正在关闭连接并清理资源...")
          if message_handler_task and not message_handler_task.done():
              message_handler_task.cancel()
  
          if 'realtime_client' in locals() and realtime_client.ws and not realtime_client.ws.close:
              await realtime_client.close()
              print("连接已关闭。")
  
          audio_player.stop()
          print("程序退出。")
  
  
  if __name__ == "__main__":
      try:
          asyncio.run(interactive_test())
      except KeyboardInterrupt:
          print("\n程序被用户强制退出。")

  运行manual_mode.py，按 Enter 键开始说话，再按一次获取模型响应的音频。

VAD 模式

将session.update事件的session.turn_detection 设为"server_vad"以启用 VAD 模式。此模式下，服务端自动检测语音起止并进行响应。适用于语音通话场景。

交互流程如下：

1. 服务端检测到语音开始，发送input_audio_buffer.speech_started 事件。

2. 客户端随时发送 input_audio_buffer.append与input_image_buffer.append 事件追加音频与图片至缓冲区。

   发送 input_image_buffer.append 事件前，至少发送过一次 input_audio_buffer.append 事件。

3. 服务端检测到语音结束，发送input_audio_buffer.speech_stopped 事件。

4. 服务端发送input_audio_buffer.committed 事件提交音频缓冲区。

5. 服务端发送 conversation.item.created 事件，包含从缓冲区创建的用户消息项。

Manual 模式

将session.update事件的session.turn_detection 设为 null 以启用 Manual 模式。此模式下，客户端通过显式发送input_audio_buffer.commit 和response.create事件请求服务器响应。适用于按下即说场景，如聊天软件中的发送语音。

交互流程如下：

1. 客户端随时发送 input_audio_buffer.append与input_image_buffer.append事件追加音频与图片至缓冲区。

   发送 input_image_buffer.append 事件前，至少发送过一次 input_audio_buffer.append 事件。

2. 客户端发送input_audio_buffer.commit事件提交音频缓冲区与图像缓冲区，告知服务端本轮的用户输入（音频及图片）已全部发送完毕。

3. 服务端响应 input_audio_buffer.committed事件。

4. 客户端发送response.create事件，等待服务端返回模型的输出。

5. 服务端响应conversation.item.created事件。

qwen3.5-omni-realtime模型

qwen3-omni-flash-realtime-2025-12-01模型

qwen3-omni-flash-realtime、qwen3-omni-flash-realtime-2025-09-15模型

Qwen-Omni-Turbo-Realtime模型