'commit'

dsLightRag/Test/TTS/T1_LLM.py

@@ -1,47 +0,0 @@
-import re
-import sys
-import os
-sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
-from Util.LlmUtil import get_llm_response
-
-def stream_and_split_text(prompt):
-    """
-    流式获取LLM输出并按句子分割
-    @param prompt: 提示文本
-    @return: 生成器，每次产生一个完整句子
-    """
-    buffer = ""
-    
-    # 使用LlmUtil中的get_llm_response函数获取流式响应
-    for content in get_llm_response(prompt, stream=True):
-        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
-
-def main():
-    """
-    测试stream_and_split_text函数
-    """
-    test_prompt = "请简单介绍一下人工智能的发展历史。包括从图灵测试到深度学习的演进过程。"
-    print("测试文本：", test_prompt)
-    print("\n分割后的句子：")
-    
-    for i, sentence in enumerate(stream_and_split_text(test_prompt), 1):
-        print(f"句子 {i}: {sentence}")
-
-if __name__ == "__main__":
-    main()

dsLightRag/Test/TTS/T2_StreamingVolanoTTS.py

@@ -1,156 +0,0 @@
-import asyncio
-import json
-import uuid
-import websockets
-import os
-from queue import Queue
-from Config import Config
-from Util.TTS_Protocols import full_client_request, receive_message, MsgType, EventType
-
-
-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
-        """
-        # 为每个文本片段创建一个WebSocket连接，但限制并发数
-        tasks = []
-        for text in text_stream:
-            if text.strip():  # 忽略空文本
-                task = asyncio.create_task(self._synthesize_single_with_semaphore(text, audio_callback))
-                tasks.append(task)
-                
-        # 等待所有任务完成
-        await asyncio.gather(*tasks)
-        
-    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:
-                audio_callback(audio_data)
-
-        finally:
-            await websocket.close()
-
-
-def audio_callback(audio_data):
-    """
-    音频数据回调函数，将音频数据保存到文件
-    """
-    # 创建输出目录
-    output_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "output")
-    os.makedirs(output_dir, exist_ok=True)
-    
-    # 生成文件名
-    filename = f"streaming_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}")
-
-
-async def test_streaming_tts():
-    """
-    测试流式TTS功能
-    """
-    # 创建TTS实例
-    tts = StreamingVolcanoTTS()
-    
-    # 准备测试文本流
-    test_texts = [
-        "你好，我是火山引擎的语音合成服务。",
-        "这是一个流式语音合成的测试。",
-        "我们将文本分成多个片段进行合成。",
-        "这样可以减少等待时间，提高用户体验。"
-    ]
-    
-    print("开始测试流式TTS...")
-    print(f"测试文本: {test_texts}")
-    
-    # 调用流式合成
-    await tts.synthesize_stream(test_texts, audio_callback)
-    
-    print("流式TTS测试完成！")
-
-
-def main():
-    """
-    主函数，运行测试
-    """
-    asyncio.run(test_streaming_tts())
-
-
-if __name__ == "__main__":
-    main()

dsLightRag/Test/TTS/T3_TTS_Pipeline.py

@@ -1,12 +1,137 @@
 import asyncio
+import json
 import os
-import sys
+import re
 import uuid
+import websockets
+from queue import Queue
 
-# 添加路径以导入其他模块
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
-from T1_LLM import stream_and_split_text
-from T2_StreamingVolanoTTS import StreamingVolcanoTTS
+from Config import Config
+from Util.LlmUtil import get_llm_response
+from Util.TTS_Protocols import full_client_request, receive_message, MsgType, EventType
+
+
+def stream_and_split_text(prompt):
+    """
+    流式获取LLM输出并按句子分割
+    @param prompt: 提示文本
+    @return: 生成器，每次产生一个完整句子
+    """
+    buffer = ""
+    
+    # 使用LlmUtil中的get_llm_response函数获取流式响应
+    for content in get_llm_response(prompt, stream=True):
+        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
+
+
+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
+        """
+        # 为每个文本片段创建一个WebSocket连接，但限制并发数
+        tasks = []
+        for text in text_stream:
+            if text.strip():  # 忽略空文本
+                task = asyncio.create_task(self._synthesize_single_with_semaphore(text, audio_callback))
+                tasks.append(task)
+                
+        # 等待所有任务完成
+        await asyncio.gather(*tasks)
+        
+    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:
+                audio_callback(audio_data)
+
+        finally:
+            await websocket.close()
 
 
 async def streaming_tts_pipeline(prompt, audio_callback):