Alibaba Cloud Model Studio：Qwen-Omni-Realtime

DashScope Python SDK

• 準備運行環境

您的 Python 版本需要不低於 3.10。

首先根據您的作業系統安裝 pyaudio。

brew install portaudio && pip install pyaudio

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

pip install pyaudio

安裝完成後，通過 pip 安裝依賴：

pip install websocket-client dashscope

• 選擇互動模式

  • VAD 模式（Voice Activity Detection，自動檢測語音起止）

    服務端自動判斷使用者何時開始與停止說話並作出回應。

  • Manual 模式（按下即說，鬆開即發送）

    用戶端控制語音起止。使用者說話結束後，用戶端需主動發送訊息至服務端。

  VAD 模式

  建立一個 python 檔案，命名為vad_dash.py，並將以下代碼複製到檔案中：

  vad_dash.py

  # 依賴：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
  
  # 配置參數：地址、API Key、音色、模型、模型角色
  # 指定地區，設為intl表示國際（新加坡）,設為cn表示中國內地（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，若沒有設定環境變數，請用 API Key 將下行替換為 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 = "你是個人助理小雲，請用幽默風趣的方式回答使用者的問題"
  class SimpleCallback(OmniRealtimeCallback):
      def __init__(self, pya):
          self.pya = pya
          self.out = None
      def on_open(self):
          # 初始化音訊輸出流
          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':
              # 播放音頻
              self.out.write(base64.b64decode(response['delta']))
          elif response['type'] == 'conversation.item.input_audio_transcription.completed':
              # 列印轉錄文本
              print(f"[User] {response['transcript']}")
          elif response['type'] == 'response.audio_transcript.done':
              # 列印助手回複文本
              print(f"[LLM] {response['transcript']}")
  
  # 1. 初始化音訊裝置
  pya = pyaudio.PyAudio()
  # 2. 建立回呼函數和會話
  callback = SimpleCallback(pya)
  conv = OmniRealtimeConversation(model=model, callback=callback, url=url)
  # 3. 建立串連並配置會話
  conv.connect()
  conv.update_session(output_modalities=[MultiModality.AUDIO, MultiModality.TEXT], voice=voice, instructions=instructions)
  # 4. 初始化音頻輸入資料流
  mic = pya.open(format=pyaudio.paInt16, channels=1, rate=16000, input=True)
  # 5. 主迴圈處理音頻輸入
  print("對話已開始，對著麥克風說話 (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:
      # 清理資源
      conv.close()
      mic.close()
      callback.out.close()
      pya.terminate()
      print("\n對話結束")

  運行vad_dash.py，通過麥克風即可與 Qwen-Omni-Realtime 模型即時對話，系統會檢測您的音頻起始位置並自動發送到伺服器，無需您手動發送。

  Manual 模式

  建立一個 python 檔案，命名為manual_dash.py，並將以下代碼複製進檔案中：

  manual_dash.py

  # 依賴：dashscope >= 1.23.9，pyaudio。
  import os
  import base64
  import sys
  import threading
  import pyaudio
  from dashscope.audio.qwen_omni import *
  import dashscope
  
  # 如果沒有設定環境變數，請用您的 API Key 將下行替換為 dashscope.api_key = "sk-xxx"
  dashscope.api_key = os.getenv('DASHSCOPE_API_KEY')
  voice = 'Cherry'
  
  class MyCallback(OmniRealtimeCallback):
      """最簡回調：建立串連時初始化擴音器，事件中直接播放返迴音頻。"""
      def __init__(self, ctx):
          super().__init__()
          self.ctx = ctx
  
      def on_open(self) -> None:
          # 串連建立後初始化 PyAudio 與擴音器(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):
          # 直接解碼base64並寫入輸出資料流進行播放
          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):
          # 標記本輪對話完成，用於主迴圈等待
          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):
      """安全釋放音頻與PyAudio資源。"""
      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 停止錄音，返回PCM位元組。"""
      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()  # 使用者再次按 Enter 停止錄音
      stop_evt.set()
      t.join(timeout=1.0)
      try:
          stream.close()
      except Exception:
          pass
      return b''.join(frames)
  
  
  if __name__  == '__main__':
      print('Initializing ...')
      # 運行時上下文：存放音頻與交談控制代碼
      ctx = {'pya': None, 'out': None, 'conv': None, 'resp_done': threading.Event()}
      callback = MyCallback(ctx)
      conversation = OmniRealtimeConversation(
          model='qwen3.5-omni-plus-realtime',
          callback=callback,
          # 以下為國際（新加坡）地區url，若使用中國內地（北京）地區的模型，需將url替換為：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
      # 會話配置：啟用文本+音訊輸出（禁用服務端VAD，改為手動錄音）
      conversation.update_session(
          output_modalities=[MultiModality.AUDIO, MultiModality.TEXT],
          voice=voice,
          enable_input_audio_transcription=True,
          # 對輸入音頻做語音轉錄的模型，僅支援gummy-realtime-v1
          input_audio_transcription_model='gummy-realtime-v1',
          enable_turn_detection=False,
          instructions="你是個人助理小雲，請你準確且友好地解答使用者的問題，始終以樂於助人的態度回應。"
      )
  
      try:
          turn = 1
          while True:
              print(f"\n--- 第 {turn} 輪對話 ---")
              print("按 Enter 開始錄音（輸入 q 斷行符號退出）...")
              user_input = input()
              if user_input.strip().lower() in ['q', 'quit']:
                  print("使用者請求退出...")
                  break
              print("錄音中... 再次按 Enter 停止錄音。")
              if ctx['pya'] is None:
                  ctx['pya'] = pyaudio.PyAudio()
              recorded = record_until_enter(ctx['pya'])
              if not recorded:
                  print("未錄製到有效音頻，請重試。")
                  continue
              print(f"成功錄製音頻: {len(recorded)} 位元組，發送中...")
  
              # 以3200位元組為塊發送（對應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("發送完成，等待模型響應...")
              ctx['resp_done'].clear()
              conversation.commit()
              conversation.create_response()
              ctx['resp_done'].wait()
              print('播放音頻完成')
              turn += 1
      except KeyboardInterrupt:
          print("\n程式被使用者中斷")
      finally:
          shutdown_ctx(ctx)
          print("程式退出")

  運行manual_dash.py，按 Enter 鍵開始說話，再按一次擷取模型響應的音頻。

DashScope Java SDK

選擇互動模式

• VAD 模式（Voice Activity Detection，自動檢測語音起止）

  Realtime API 自動判斷使用者何時開始與停止說話並作出回應。

• Manual 模式（按下即說，鬆開即發送）

  用戶端控制語音起止。使用者說話結束後，用戶端需主動發送訊息至服務端。

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("音頻解碼失敗: " + 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"))
                    // 以下為國際（新加坡）地區url，若使用中國內地（北京）地區的模型，需將url替換為：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("串連已建立");
                }
                @Override public void onClose(int code, String reason) {
                    System.out.println("串連已關閉 (" + 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",
                            "你是五星級酒店的AI客服專員，請準確且友好地解答客戶關於房型、設施、價格、預訂政策的諮詢。請始終以專業和樂於助人的態度回應，杜絕提供未經證實或超出酒店服務涵蓋範圍的資訊。"))
                    .build()
            );

            System.out.println("請開始說話（自動檢測語音開始/結束，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("對話已關閉，停止錄音");
                            break;
                        }
                    }
                }
                Thread.sleep(20);
            }

            conversation.close(1000, "正常結束");
            player.close();
            mic.close();
            System.out.println("\n程式已退出");

        } catch (NoApiKeyException e) {
            System.err.println("未找到API KEY: 請設定環境變數 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[使用者開始說話]");
                player.cancel();
                userIsSpeaking.set(true);
                break;
            case "input_audio_buffer.speech_stopped":
                System.out.println("[使用者停止說話]");
                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("使用者: " + event.get("transcript").getAsString());
                break;
            case "response.audio_transcript.delta":
                System.out.print(event.get("delta").getAsString());
                break;
            case "response.done":
                System.out.println("回複完成");
                break;
        }
    }
}

// DashScope Java SDK 版本不低於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 類定義開始
    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<>();

        // 建構函式初始化音頻格式和音頻線路
        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();
        }

        // 播放一個音頻塊並阻塞直到播放完成
        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);
            // 等待緩衝區中的音頻播放完成
            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 類定義結束
    // 新增錄音方法
    private static void recordAndSend(TargetDataLine line, OmniRealtimeConversation conversation) throws IOException {
        ByteArrayOutputStream out = new ByteArrayOutputStream();
        byte[] buffer = new byte[3200];
        AtomicBoolean stopRecording = new AtomicBoolean(false);

        // 啟動監聽Enter鍵的線程
        Thread enterKeyListener = new Thread(() -> {
            try {
                System.in.read();
                stopRecording.set(true);
            } catch (IOException e) {
                e.printStackTrace();
            }
        });
        enterKeyListener.start();

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

        // 發送錄音資料
        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")
                // 新加坡和北京地區的API Key不同。擷取API Key：https://www.alibabacloud.com/help/zh/model-studio/get-api-key
                // 若沒有配置環境變數，請用百鍊API Key將下行替換為：.apikey("sk-xxx")
                .apikey(System.getenv("DASHSCOPE_API_KEY"))
                //以下為新加坡地區url，若使用北京地區的模型，需將url替換為：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)
                // 設定模型角色
                .parameters(new HashMap<String, Object>() {{
                    put("instructions","你是個人助理小雲，請你準確且友好地解答使用者的問題，始終以樂於助人的態度回應。");
                }})
                .build();
        conversation.updateSession(config);

        // 新增麥克風錄音功能
        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開始錄音...");
                System.in.read();
                System.out.println("開始錄音，請說話...再次按Enter停止錄音並發送");
                recordAndSend(line, conversation);
                conversation.commit();
                conversation.createResponse(null, null);
                // 重設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）

• 準備運行環境

  您的 Python 版本需要不低於 3.10。

  首先根據您的作業系統來安裝 pyaudio。

  macOS

  brew install portaudio && pip install pyaudio

  Debian/Ubuntu

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

  推薦使用pip install pyaudio。如果安裝失敗，請先根據您的作業系統安裝portaudio依賴。

  CentOS

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

  Windows

  pip install pyaudio

  安裝完成後，通過 pip 安裝 websocket 相關的依賴：

  pip install websockets==15.0.1

• 建立用戶端

  在本地建立一個 python 檔案，命名為omni_realtime_client.py，並將以下代碼複製進檔案中：

  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 {}
  
          # 當前回複狀態
          self._current_response_id = None
          self._current_item_id = None
          self._is_responding = False
          # 輸入/輸出轉錄列印狀態
          self._print_input_transcript = True
          self._output_transcript_buffer = ""
  
      async def connect(self) -> None:
          """與 Realtime API 建立 WebSocket 串連。"""
          url = f"{self.base_url}?model={self.model}"
          headers = {
              "Authorization": f"Bearer {self.api_key}"
          }
          self.ws = await websockets.connect(url, additional_headers=headers)
  
          # 會話配置
          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:
          """更新會話配置。"""
          event = {
              "type": "session.update",
              "session": config
          }
          await self.send_event(event)
  
      async def stream_audio(self, audio_chunk: bytes) -> None:
          """向 API 流式發送原始音頻資料。"""
          # 僅支援 16bit 16kHz 單聲道 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:
          """提交音頻緩衝區以觸發處理。"""
          event = {
              "type": "input_audio_buffer.commit"
          }
          await self.send_event(event)
  
      async def append_image(self, image_chunk: bytes) -> None:
          """向映像緩衝區追加映像資料。
          映像資料可以來自本地檔案，也可以來自即時視頻流。
          注意:
              - 映像格式必須為 JPG 或 JPEG。推薦解析度為 480P 或 720P，最高支援 1080P。
              - 單張圖片大小不應超過 500KB。
              - 將映像資料編碼為 Base64 後再發送。
              - 建議以 1張/秒 的頻率向服務端發送映像。
              - 在發送映像資料之前，需要至少發送過一次音頻資料。
          """
          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 請求產生回複（僅在手動模式下需要調用）。"""
          event = {
              "type": "response.create"
          }
          await self.send_event(event)
  
      async def cancel_response(self) -> None:
          """取消當前回複。"""
          event = {
              "type": "response.cancel"
          }
          await self.send_event(event)
  
      async def handle_interruption(self):
          """處理使用者對當前回複的打斷。"""
          if not self._is_responding:
              return
          # 1. 取消當前回複
          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("檢測到語音開始")
                      if self._is_responding:
                          print("處理打斷")
                          await self.handle_interruption()
                  elif event_type == "input_audio_buffer.speech_stopped":
                      print("檢測到語音結束")
                  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"使用者: {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:
          """關閉 WebSocket 串連。"""
          if self.ws:
              await self.ws.close()

• 選擇互動模式

  • VAD 模式（Voice Activity Detection，自動檢測語音起止）

    Realtime API 自動判斷使用者何時開始與停止說話並作出回應。

  • Manual 模式（按下即說，鬆開即發送）

    用戶端控制語音起止。使用者說話結束後，用戶端需主動發送訊息至服務端。

  VAD 模式

  在omni_realtime_client.py的同級目錄下建立另一個 python 檔案，命名為vad_mode.py，並將以下代碼複製進檔案中：

  vad_mode.py

  # -- coding: utf-8 --
  import os, asyncio, pyaudio, queue, threading
  from omni_realtime_client import OmniRealtimeClient, TurnDetectionMode
  
  # 音頻播放器類（處理中斷）
  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()
  
  # 麥克風錄音並發送
  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("開始錄音，請講話...")
      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(
          # 以下是國際（新加坡）地區 base_url，中國內地（北京）地區base_url為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="你是小雲，風趣幽默的好助手",
          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("串連成功，開始即時對話...")
  
      # 並發運行
      await asyncio.gather(client.handle_messages(), record_and_send(client))
  
  if __name__ == "__main__":
      try:
          asyncio.run(main())
      except KeyboardInterrupt:
          print("\n程式已退出。")

  運行vad_mode.py，通過麥克風即可與 Realtime 模型即時對話，系統會檢測您的音頻起始位置並自動發送到伺服器，無需您手動發送。

  Manual 模式

  在omni_realtime_client.py的同級目錄下建立另一個 python 檔案，命名為manual_mode.py，並將以下代碼複製進檔案中：

  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:
      """即時音頻播放器類"""
  
      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()  # 添加鎖來同步訪問
          self._last_data_time = time.time()  # 記錄最後接收資料的時間
          self._response_done = False  # 添加響應完成標誌
          self._waiting_for_response = False  # 標記是否正在等待伺服器響應
          # 記錄最後一次向音頻流寫入資料的時間及最近一次音頻塊的時間長度，用於更精確地判斷播放結束
          self._last_play_time = time.time()
          self._last_chunk_duration = 0.0
  
      def start(self):
          """啟動音頻播放器"""
          with self._lock:
              if self.is_playing:
                  return
  
              self.is_playing = True
  
              try:
                  self.pyaudio_instance = pyaudio.PyAudio()
  
                  # 建立音訊輸出流
                  self.stream = self.pyaudio_instance.open(
                      format=pyaudio.paInt16,  # 16-bit
                      channels=self.channels,
                      rate=self.sample_rate,
                      output=True,
                      frames_per_buffer=1024
                  )
  
                  # 啟動播放線程
                  self.play_thread = threading.Thread(target=self._play_audio)
                  self.play_thread.daemon = True
                  self.play_thread.start()
  
                  print("音頻播放器已啟動")
              except Exception as e:
                  print(f"啟動音頻播放器失敗: {e}")
                  self._cleanup_resources()
                  raise
  
      def stop(self):
          """停止音頻播放器"""
          with self._lock:
              if not self.is_playing:
                  return
  
              self.is_playing = False
  
          # 清空隊列
          while not self.audio_queue.empty():
              try:
                  self.audio_queue.get_nowait()
              except queue.Empty:
                  break
  
          # 等待播放線程結束（在鎖外面等待，避免死結）
          if self.play_thread and self.play_thread.is_alive():
              self.play_thread.join(timeout=2.0)
  
          # 再次擷取鎖來清理資源
          with self._lock:
              self._cleanup_resources()
  
          print("音頻播放器已停止")
  
      def _cleanup_resources(self):
          """清理音頻資源（必須在鎖內調用）"""
          try:
              # 關閉音頻流
              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"關閉音頻流時出錯: {e}")
  
          try:
              if self.pyaudio_instance:
                  self.pyaudio_instance.terminate()
                  self.pyaudio_instance = None
          except Exception as e:
              print(f"終止PyAudio時出錯: {e}")
  
      def add_audio_data(self, audio_data):
          """添加音頻資料到播放隊列"""
          if self.is_playing and audio_data:
              self.audio_queue.put(audio_data)
              with self._lock:
                  self._last_data_time = time.time()  # 更新最後接收資料的時間
                  self._waiting_for_response = False  # 收到資料，不再等待
  
      def stop_receiving_data(self):
          """標記不再接收新的音頻資料"""
          with self._lock:
              self._response_done = True
              self._waiting_for_response = False  # 響應結束，不再等待
  
      def prepare_for_next_turn(self):
          """為下一輪對話重設播放器狀態。"""
          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  # 開始等待下一輪響應
  
          # 清空上一輪可能殘留的音頻資料
          while not self.audio_queue.empty():
              try:
                  self.audio_queue.get_nowait()
              except queue.Empty:
                  break
  
      def is_finished_playing(self):
          """檢查是否已經播放完所有音頻資料"""
          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
  
              # ---------------------- 智能結束判定 ----------------------
              # 1. 首選：如果伺服器已標記完成且播放隊列為空白
              #    進一步等待最近一塊音頻播放完畢（音頻塊時間長度 + 0.1s 容錯）。
              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. 備用：如果長時間沒有新資料且播放隊列為空白
              #    當伺服器沒有明確發出 `response.done` 時，此邏輯作為保障
              if not self._waiting_for_response and queue_size == 0 and time_since_last_data > 1.0:
                  print("\n(逾時未收到新音頻，判定播放結束)")
                  return True
  
              return False
  
      def _play_audio(self):
          """播放音頻資料的背景工作執行緒"""
          while True:
              # 檢查是否應該停止
              with self._lock:
                  if not self.is_playing:
                      break
                  stream_ref = self.stream  # 擷取流的引用
  
              try:
                  # 從隊列中擷取音頻資料，逾時0.1秒
                  audio_data = self.audio_queue.get(timeout=0.1)
  
                  # 再次檢查狀態和流的有效性
                  with self._lock:
                      if self.is_playing and stream_ref and not stream_ref.is_stopped():
                          try:
                              # 播放音頻資料
                              stream_ref.write(audio_data)
                              # 更新最近播放資訊
                              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"寫入音頻流時出錯: {e}")
                              break
  
                  # 標記該資料區塊已處理完成
                  self.audio_queue.task_done()
  
              except queue.Empty:
                  # 隊列為空白時繼續等待
                  continue
              except Exception as e:
                  print(f"播放音頻時出錯: {e}")
                  break
  
  
  class MicrophoneRecorder:
      """即時麥克風錄音器"""
  
      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):
          """錄音背景工作執行緒"""
          # 在 _is_recording 為 True 期間，持續從音頻流中讀取資料
          while self._is_recording:
              try:
                  # 使用 exception_on_overflow=False 避免因緩衝區溢位而崩潰
                  data = self.stream.read(self.chunk_size, exception_on_overflow=False)
                  self.frames.append(data)
              except (IOError, OSError) as e:
                  # 當流被關閉時，讀取操作可能會引發錯誤
                  print(f"錄音流讀取錯誤，可能已關閉: {e}")
                  break
  
      def start(self):
          """開始錄音"""
          if self._is_recording:
              print("錄音已在進行中。")
              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("麥克風錄音已開始...")
          except Exception as e:
              print(f"啟動麥克風失敗: {e}")
              self._is_recording = False
              self._cleanup()
              raise
  
      def stop(self):
          """停止錄音並返迴音頻資料"""
          if not self._is_recording:
              return None
  
          self._is_recording = False
  
          # 等待錄音安全執行緒退出
          if self._record_thread:
              self._record_thread.join(timeout=1.0)
  
          self._cleanup()
  
          print("麥克風錄音已停止。")
          return b''.join(self.frames)
  
      def _cleanup(self):
          """安全地清理 PyAudio 資源"""
          if self.stream:
              try:
                  if self.stream.is_active():
                      self.stream.stop_stream()
                  self.stream.close()
              except Exception as e:
                  print(f"關閉音頻流時出錯: {e}")
  
          if self.pyaudio_instance:
              try:
                  self.pyaudio_instance.terminate()
              except Exception as e:
                  print(f"終止 PyAudio 執行個體時出錯: {e}")
  
          self.stream = None
          self.pyaudio_instance = None
  
  
  async def interactive_test():
      """
      互動式測試指令碼：允許多輪連續對話，每輪可以發送音頻和圖片。
      """
      # ------------------- 1. 初始化和串連 (一次性) -------------------
      # 新加坡和北京地區的API Key不同。擷取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("請設定DASHSCOPE_API_KEY環境變數")
          return
  
      print("--- 即時多輪音視訊交談用戶端 ---")
      print("正在初始化音頻播放器和用戶端...")
  
      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(收到響應結束標記)")
          audio_player.stop_receiving_data()
  
      realtime_client = OmniRealtimeClient(
          # 以下為新加坡地區base_url，若使用北京地區的模型，需將base_url替換為：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="你是個人助理小雲，請你準確且友好地解答使用者的問題，始終以樂於助人的態度回應。", # 設定模型角色
          on_text_delta=lambda text: print(f"助手回複: {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("已串連到伺服器。輸入 'q' 或 'quit' 可隨時退出程式。")
          message_handler_task = asyncio.create_task(realtime_client.handle_messages())
          await asyncio.sleep(0.5)
  
          turn_counter = 1
          # ------------------- 2. 多輪對話迴圈 -------------------
          while True:
              print(f"\n--- 第 {turn_counter} 輪對話 ---")
              audio_player.prepare_for_next_turn()
  
              recorded_audio = None
              image_paths = []
  
              # --- 擷取使用者輸入：從麥克風錄音 ---
              loop = asyncio.get_event_loop()
              recorder = MicrophoneRecorder(sample_rate=16000)  # 推薦使用16k採樣率進行語音辨識
  
              print("準備錄音。按 Enter 鍵開始錄音 (或輸入 'q' 退出)...")
              user_input = await loop.run_in_executor(None, input)
              if user_input.strip().lower() in ['q', 'quit']:
                  print("使用者請求退出...")
                  return
  
              try:
                  recorder.start()
              except Exception:
                  print("無法啟動錄音，請檢查您的麥克風許可權和裝置。跳過本輪。")
                  continue
  
              print("錄音中... 再次按 Enter 鍵停止錄音。")
              await loop.run_in_executor(None, input)
  
              recorded_audio = recorder.stop()
  
              if not recorded_audio or len(recorded_audio) == 0:
                  print("未錄製到有效音頻，請重新開始本輪對話。")
                  continue
  
              # --- 擷取圖片輸入 (可選) ---
              # 以下圖片輸入功能已被注釋，暫時禁用。若需啟用請取消下方代碼注釋。
              # print("\n請逐行輸入【圖片檔案】的絕對路徑 (可選)。完成後，輸入 's' 或按 Enter 發送請求。")
              # while True:
              #     path = input("圖片路徑: ").strip()
              #     if path.lower() == 's' or path == '':
              #         break
              #     if path.lower() in ['q', 'quit']:
              #         print("使用者請求退出...")
              #         return
              #
              #     if not os.path.isabs(path):
              #         print("錯誤: 請輸入絕對路徑。")
              #         continue
              #     if not os.path.exists(path):
              #         print(f"錯誤: 檔案不存在 -> {path}")
              #         continue
              #     image_paths.append(path)
              #     print(f"已添加圖片: {os.path.basename(path)}")
  
              # --- 3. 發送資料並擷取響應 ---
              print("\n--- 輸入確認 ---")
              print(f"待處理音頻: 1個 (來自麥克風), 圖片: {len(image_paths)}個")
              print("------------------")
  
              # 3.1 發送錄製的音頻
              try:
                  print(f"發送麥克風錄音 ({len(recorded_audio)}位元組)")
                  await realtime_client.stream_audio(recorded_audio)
                  await asyncio.sleep(0.1)
              except Exception as e:
                  print(f"發送麥克風錄音失敗: {e}")
                  continue
  
              # 3.2 發送所有圖片檔案
              # 以下圖片發送代碼已被注釋，暫時禁用。
              # for i, path in enumerate(image_paths):
              #     try:
              #         with open(path, "rb") as f:
              #             data = f.read()
              #         print(f"發送圖片 {i+1}: {os.path.basename(path)} ({len(data)}位元組)")
              #         await realtime_client.append_image(data)
              #         await asyncio.sleep(0.1)
              #     except Exception as e:
              #         print(f"發送圖片 {os.path.basename(path)} 失敗: {e}")
  
              # 3.3 提交並等待響應
              print("提交所有輸入，請求伺服器響應...")
              await realtime_client.commit_audio_buffer()
              await realtime_client.create_response()
  
              print("等待並播放伺服器響應音頻...")
              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等待逾時 ({max_wait_time}秒), 進入下一輪。")
                      break
                  await asyncio.sleep(0.2)
  
              print("\n本輪音頻播放完成！")
              turn_counter += 1
  
      except (asyncio.CancelledError, KeyboardInterrupt):
          print("\n程式被中斷。")
      except Exception as e:
          print(f"發生未處理的錯誤: {e}")
      finally:
          # ------------------- 4. 清理資源 -------------------
          print("\n正在關閉串連並清理資源...")
          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("串連已關閉。")
  
          audio_player.stop()
          print("程式退出。")
  
  
  if __name__ == "__main__":
      try:
          asyncio.run(interactive_test())
      except KeyboardInterrupt:
          print("\n程式被使用者強制退出。")

  運行manual_mode.py，按 Enter 鍵開始說話，再按一次擷取模型響應的音頻。

VAD 模式

將session.update事件的session.turn_detection 設為"server_vad"以啟用 VAD 模式。此模式下，服務端自動檢測語音起止並進行響應。適用於語音通話情境。

互動流程如下：

1. 服務端檢測到語音開始，發送input_audio_buffer.speech_started 事件。

2. 用戶端隨時發送 input_audio_buffer.append與input_image_buffer.append 事件追加音頻與圖片至緩衝區。

   發送 input_image_buffer.append 事件前，至少發送過一次 input_audio_buffer.append 事件。

3. 服務端檢測到語音結束，發送input_audio_buffer.speech_stopped 事件。

4. 服務端發送input_audio_buffer.committed 事件提交音頻緩衝區。

5. 服務端發送 conversation.item.created 事件，包含從緩衝區建立的使用者訊息項。

Manual 模式

將session.update事件的session.turn_detection 設為 null 以啟用 Manual 模式。此模式下，用戶端通過顯式發送input_audio_buffer.commit 和response.create事件請求伺服器響應。適用於按下即說情境，如聊天軟體中的發送語音。

互動流程如下：

1. 用戶端隨時發送 input_audio_buffer.append與input_image_buffer.append事件追加音頻與圖片至緩衝區。

   發送 input_image_buffer.append 事件前，至少發送過一次 input_audio_buffer.append 事件。

2. 用戶端發送input_audio_buffer.commit事件提交音頻緩衝區與映像緩衝區，告知服務端本輪的使用者輸入（音頻及圖片）已全部發送完畢。

3. 服務端響應 input_audio_buffer.committed事件。

4. 用戶端發送response.create事件，等待服務端返回模型的輸出。

5. 服務端響應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