diff --git a/dsLightRag/Routes/XueBanRoute.py b/dsLightRag/Routes/XueBanRoute.py
index b31759ef..23b41983 100644
--- a/dsLightRag/Routes/XueBanRoute.py
+++ b/dsLightRag/Routes/XueBanRoute.py
@@ -84,15 +84,6 @@ async def streaming_chat(websocket: WebSocket):
             logger.error(f"发送ASR结果失败: {str(e)}")
             return
 
-
-        # 获取学伴响应内容（包含题目信息）
-        logger.info("获取学伴响应内容...")
-        llm_chunks = []
-        async for chunk in get_xueban_response_async(asr_result['text'], stream=True):
-            llm_chunks.append(chunk)
-        full_llm_response = ''.join(llm_chunks)
-        logger.info(f"学伴响应内容: {full_llm_response}")
-        
         # 定义音频回调函数，将音频块发送给前端
         async def audio_callback(audio_chunk):
             logger.info(f"发送音频块，大小: {len(audio_chunk)}")
@@ -103,12 +94,14 @@ async def streaming_chat(websocket: WebSocket):
                 logger.error(f"发送音频块失败: {str(e)}")
                 raise
         
-        # 修改streaming_chat函数中的相关部分
         # 实时获取LLM流式输出并处理
         logger.info("开始LLM流式处理和TTS合成...")
         try:
-            # 直接使用stream_and_split_text获取LLM流式响应并断句
-            text_stream = stream_and_split_text(query_text=asr_result['text'])
+            # 获取LLM流式响应
+            llm_stream = get_xueban_response_async(asr_result['text'], stream=True)
+            
+            # 使用stream_and_split_text处理流式响应并断句
+            text_stream = stream_and_split_text(llm_stream=llm_stream)
             
             # 初始化TTS处理器
             tts = StreamingVolcanoTTS(max_concurrency=1)
diff --git a/dsLightRag/Routes/__pycache__/XueBanRoute.cpython-310.pyc b/dsLightRag/Routes/__pycache__/XueBanRoute.cpython-310.pyc
index d768879f..1d83a17f 100644
Binary files a/dsLightRag/Routes/__pycache__/XueBanRoute.cpython-310.pyc and b/dsLightRag/Routes/__pycache__/XueBanRoute.cpython-310.pyc differ
diff --git a/dsLightRag/Util/TTS_Pipeline.py b/dsLightRag/Util/TTS_Pipeline.py
deleted file mode 100644
index 06102ad5..00000000
--- a/dsLightRag/Util/TTS_Pipeline.py
+++ /dev/null
@@ -1,235 +0,0 @@
-import asyncio
-import json
-import os
-import re
-import uuid
-from queue import Queue
-
-import websockets
-
-from Config import Config
-from Util.TTS_Protocols import full_client_request, receive_message, MsgType, EventType
-# 添加必要的导入
-from Util.XueBanUtil import get_xueban_response_async
-
-
-async def stream_and_split_text(query_text=None, llm_stream=None):
-    """
-    流式获取LLM输出并按句子分割
-    @param query_text: 查询文本（如果直接提供查询文本）
-    @param llm_stream: LLM流式响应生成器（如果已有流式响应）
-    @return: 异步生成器，每次产生一个完整句子
-    """
-    buffer = ""
-    
-    if llm_stream is None and query_text is not None:
-        # 如果没有提供llm_stream但有query_text，则使用get_xueban_response_async获取流式响应
-        llm_stream = get_xueban_response_async(query_text, stream=True)
-    elif llm_stream is None:
-        raise ValueError("必须提供query_text或llm_stream参数")
-    
-    # 直接处理LLM流式输出
-    async for content in llm_stream:
-        buffer += content
-        
-        # 使用正则表达式检测句子结束
-        sentences = re.split(r'([。！？.!?])', buffer)
-        if len(sentences) > 1:
-            # 提取完整句子
-            for i in range(0, len(sentences)-1, 2):
-                if i+1 < len(sentences):
-                    sentence = sentences[i] + sentences[i+1]
-                    yield sentence
-            
-            # 保留不完整的部分
-            buffer = sentences[-1]
-    
-    # 处理最后剩余的部分
-    if buffer:
-        yield buffer
-
-# 修改streaming_tts_pipeline函数
-async def streaming_tts_pipeline(query_text, audio_callback):
-    """
-    流式TTS管道：获取LLM流式输出并断句，然后使用TTS合成语音
-    
-    Args:
-        query_text: 查询文本
-        audio_callback: 音频数据回调函数
-    """
-    # 1. 获取LLM流式输出并断句
-    text_stream = stream_and_split_text(query_text=query_text)
-    
-    # 2. 初始化TTS处理器
-    tts = StreamingVolcanoTTS()
-    
-    # 3. 流式处理文本并生成音频
-    await tts.synthesize_stream(text_stream, audio_callback)
-
-
-class StreamingVolcanoTTS:
-    def __init__(self, voice_type='zh_female_wanwanxiaohe_moon_bigtts', encoding='wav', max_concurrency=2):
-        self.voice_type = voice_type
-        self.encoding = encoding
-        self.app_key = Config.HS_APP_ID
-        self.access_token = Config.HS_ACCESS_TOKEN
-        self.endpoint = "wss://openspeech.bytedance.com/api/v3/tts/unidirectional/stream"
-        self.audio_queue = Queue()
-        self.max_concurrency = max_concurrency  # 最大并发数
-        self.semaphore = asyncio.Semaphore(max_concurrency)  # 并发控制信号量
-        
-    @staticmethod
-    def get_resource_id(voice: str) -> str:
-        if voice.startswith("S_"):
-            return "volc.megatts.default"
-        return "volc.service_type.10029"
-        
-    async def synthesize_stream(self, text_stream, audio_callback):
-        """
-        流式合成语音
-        
-        Args:
-            text_stream: 文本流生成器
-            audio_callback: 音频数据回调函数，接收音频片段
-        """
-        # 实时处理每个文本片段（删除任务列表和gather）
-        async for text in text_stream:
-            if text.strip():
-                await self._synthesize_single_with_semaphore(text, audio_callback)
-        
-    async def _synthesize_single_with_semaphore(self, text, audio_callback):
-        """使用信号量控制并发数的单个文本合成"""
-        async with self.semaphore:  # 获取信号量，限制并发数
-            await self._synthesize_single(text, audio_callback)
-            
-    async def _synthesize_single(self, text, audio_callback):
-        """合成单个文本片段"""
-        headers = {
-            "X-Api-App-Key": self.app_key,
-            "X-Api-Access-Key": self.access_token,
-            "X-Api-Resource-Id": self.get_resource_id(self.voice_type),
-            "X-Api-Connect-Id": str(uuid.uuid4()),
-        }
-
-        websocket = await websockets.connect(
-            self.endpoint, additional_headers=headers, max_size=10 * 1024 * 1024
-        )
-
-        try:
-            request = {
-                "user": {
-                    "uid": str(uuid.uuid4()),
-                },
-                "req_params": {
-                    "speaker": self.voice_type,
-                    "audio_params": {
-                        "format": self.encoding,
-                        "sample_rate": 24000,
-                        "enable_timestamp": True,
-                    },
-                    "text": text,
-                    "additions": json.dumps({"disable_markdown_filter": False}),
-                },
-            }
-
-            # 发送请求
-            await full_client_request(websocket, json.dumps(request).encode())
-
-            # 接收音频数据
-            audio_data = bytearray()
-            while True:
-                msg = await receive_message(websocket)
-
-                if msg.type == MsgType.FullServerResponse:
-                    if msg.event == EventType.SessionFinished:
-                        break
-                elif msg.type == MsgType.AudioOnlyServer:
-                    audio_data.extend(msg.payload)
-                else:
-                    raise RuntimeError(f"TTS conversion failed: {msg}")
-
-            # 通过回调函数返回音频数据
-            if audio_data:
-                await audio_callback(audio_data)
-
-        finally:
-            await websocket.close()
-
-
-async def streaming_tts_pipeline(prompt, audio_callback):
-    """
-    流式TTS管道：获取LLM流式输出并断句，然后使用TTS合成语音
-    
-    Args:
-        prompt: 提示文本
-        audio_callback: 音频数据回调函数
-    """
-    # 1. 获取LLM流式输出并断句
-    text_stream = stream_and_split_text(prompt)
-    
-    # 2. 初始化TTS处理器
-    tts = StreamingVolcanoTTS()
-    
-    # 3. 流式处理文本并生成音频
-    await tts.synthesize_stream(text_stream, audio_callback)
-
-
-def save_audio_callback(output_dir=None):
-    """
-    创建一个音频回调函数，用于保存音频数据到文件
-    
-    Args:
-        output_dir: 输出目录，默认为当前文件所在目录下的output文件夹
-    
-    Returns:
-        音频回调函数
-    """
-    if output_dir is None:
-        output_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "output")
-    
-    # 确保输出目录存在
-    os.makedirs(output_dir, exist_ok=True)
-    
-    def callback(audio_data):
-        # 生成文件名
-        filename = f"pipeline_tts_{uuid.uuid4().hex[:8]}.wav"
-        filepath = os.path.join(output_dir, filename)
-        
-        # 保存音频文件
-        with open(filepath, "wb") as f:
-            f.write(audio_data)
-        
-        print(f"音频片段已保存到: {filepath} ({len(audio_data)} 字节)")
-    
-    return callback
-
-
-async def test_pipeline():
-    """
-    测试流式TTS管道
-    """
-    # 创建音频回调函数
-    audio_handler = save_audio_callback()
-    
-    # 测试提示
-    prompt = "请详细解释一下量子力学的基本原理，包括波粒二象性、不确定性原理和薛定谔方程。"
-    
-    print("开始测试流式TTS管道...")
-    print(f"测试提示: {prompt}")
-    print("等待LLM生成文本并转换为语音...")
-    
-    # 运行管道
-    await streaming_tts_pipeline(prompt, audio_handler)
-    
-    print("流式TTS管道测试完成！")
-
-
-def main():
-    """
-    主函数，运行测试
-    """
-    asyncio.run(test_pipeline())
-
-
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff --git a/dsLightRag/Util/XueBanUtil.py b/dsLightRag/Util/XueBanUtil.py
index 6064f0a3..1e486a56 100644
--- a/dsLightRag/Util/XueBanUtil.py
+++ b/dsLightRag/Util/XueBanUtil.py
@@ -50,7 +50,7 @@ async def get_xueban_response_async(query_text: str, stream: bool = True):
     zhishiContent = f.read()
     zhishiContent = "选择作答的相应知识内容:" + zhishiContent + "\n"
     query_text = zhishiContent + "下面是用户提的问题：" + query_text
-    logger.info("query_text: " + query_text)
+    #logger.info("query_text: " + query_text)
 
     try:
         # 创建请求
diff --git a/dsLightRag/Util/__pycache__/TTS_Pipeline.cpython-310.pyc b/dsLightRag/Util/__pycache__/TTS_Pipeline.cpython-310.pyc
deleted file mode 100644
index 63e7b15b..00000000
Binary files a/dsLightRag/Util/__pycache__/TTS_Pipeline.cpython-310.pyc and /dev/null differ
diff --git a/dsLightRag/Util/__pycache__/XueBanUtil.cpython-310.pyc b/dsLightRag/Util/__pycache__/XueBanUtil.cpython-310.pyc
index 69af7d50..d14427b0 100644
Binary files a/dsLightRag/Util/__pycache__/XueBanUtil.cpython-310.pyc and b/dsLightRag/Util/__pycache__/XueBanUtil.cpython-310.pyc differ
diff --git a/dsLightRag/static/YunXiao/xueban.js b/dsLightRag/static/YunXiao/xueban.js
index 0207dcfa..f6218992 100644
--- a/dsLightRag/static/YunXiao/xueban.js
+++ b/dsLightRag/static/YunXiao/xueban.js
@@ -216,7 +216,7 @@ const WebSocketManager = {
             console.log('当前音频队列长度:', AudioState.playback.audioQueue.length);
             
             // 如果尚未开始流式播放，则开始播放
-            if (!AudioState.playback.isStreamPlaying && !AudioState.playback.isPlaying) {
+            if (!AudioState.playback.isStreamPlaying) {
                 console.log('开始流式播放音频');
                 AudioState.playback.isStreamPlaying = true;
                 AudioPlayer.processAudioQueue();
@@ -393,17 +393,16 @@ const AudioPlayer = {
     
     // 处理音频队列
     processAudioQueue() {
-        // 如果正在播放或队列为空，则返回
-        if (AudioState.playback.isStreamPlaying || AudioState.playback.audioQueue.length === 0) {
+        // 如果队列为空，则返回
+        if (AudioState.playback.audioQueue.length === 0) {
             AudioState.playback.isStreamPlaying = false;
+            console.log('音频队列为空，停止流式播放');
             return;
         }
         
-        // 设置播放状态
-        AudioState.playback.isStreamPlaying = true;
-        
         // 从队列中取出第一个音频块
         const audioBlob = AudioState.playback.audioQueue.shift();
+        console.log('从队列取出音频块，剩余队列长度:', AudioState.playback.audioQueue.length);
         
         // 创建音频URL
         const audioUrl = URL.createObjectURL(audioBlob);
@@ -417,7 +416,6 @@ const AudioPlayer = {
             .catch(error => {
                 console.error('播放音频块失败:', error);
                 // 播放失败，继续处理下一个
-                AudioState.playback.isStreamPlaying = false;
                 this.processAudioQueue();
             })
             .finally(() => {