Merge pull request #9 from HonestQiao/main

LLM和TTS采用适配器模式进行解耦，方便扩展更多服务平台调用

README.md

@@ -39,7 +39,7 @@
 - `xiaozhi-esp32` 通信 WebSocket 协议
 - 支持唤醒对话、手动对话、实时打断对话
 - 支持国语、粤语、英语、日语、韩语 5 种语言识别（FunASR（默认））
-- 自由更换 LLM（支持ChatGLM（默认）、阿里百炼、Dify、DeepSeek）
+- 自由更换 LLM（openai接口支持ChatGLM（默认）、阿里百炼、DeepSeek等，dify接口支持Dify官方及私有化部署）
 - 自由更换 TTS（支持EdgeTTS（默认）、火山引擎豆包TTS）
 
 ## 正在实现
@@ -52,15 +52,17 @@
 
 | 类型  | 平台名称      | 使用方式 | 收费模式    | 备注                                                              |
 |:----|:----------|:----:|:--------|:----------------------------------------------------------------|
-| LLM | 阿里百炼      | 接口调用 | 消耗token | [点击申请密钥](https://bailian.console.aliyun.com/?apiKey=1#/api-key) |
-| LLM | 深度求索      | 接口调用 | 消耗token | [点击申请密钥](https://platform.deepseek.com/)                        |
-| LLM | Dify      | 接口调用 | 消耗token | 本地化部署                                                           |
-| LLM | 智谱        | 接口调用 | 免费      | [点击创建密钥](https://bigmodel.cn/usercenter/proj-mgmt/apikeys)      |
+| LLM | 阿里百炼      | openai接口调用 | 消耗token | [点击申请密钥](https://bailian.console.aliyun.com/?apiKey=1#/api-key) |
+| LLM | 深度求索      | openai接口调用 | 消耗token | [点击申请密钥](https://platform.deepseek.com/)                        |
+| LLM | 智谱        | openai接口调用 | 免费      | [点击创建密钥](https://bigmodel.cn/usercenter/proj-mgmt/apikeys)      |
+| LLM | Dify      | dify接口调用 | 消耗token | 本地化部署                                                           |
 | TTS | 火山引擎      | 接口调用 | 消耗token | [点击创建密钥](https://console.volcengine.com/speech/service/8)       |
 | TTS | EdgeTTS   | 接口调用 | 免费      |                                                                 |
 | VAD | SileroVAD | 本地使用 | 免费      |                                                                 |
 | ASR | FunASR    | 本地使用 | 免费      |                                                                 |
 
+实际上，任何支持openai接口调用的LLM，都可以接入使用。
+
 # 部署方式
 
 本项目支持docker快速部署、借助docker环境运行部署和本地源码运行三种方式。三种方式怎么选，每个人的情况不一样，以下是一些推荐。
@@ -110,17 +112,23 @@ selected_module:
   TTS: EdgeTTS
 ```
 
-比如修改`LLM`使用的组件，就看本项目支持哪些`LLM`，如下就是支持`DeepSeekLLM`、`ChatGLMLLM`。你们在`selected_module`修改成对应的LLM
+比如修改`LLM`使用的组件，就看本项目支持哪些`LLM` API接口，当前支持的是`openai`、`dify`。欢迎验证和支持更多LLM平台的接口。
+
+使用时，在`selected_module`修改成对应的如下LLM配置的名称：
 
 ```
 LLM:
   AliLLM:
+    type: openai
     ...
   DeepSeekLLM:
+    type: openai
     ...
   ChatGLMLLM:
+    type: openai
     ...
   DifyLLM:
+    type: dify
     ...
 ```
 
@@ -276,8 +284,7 @@ pip install -r requirements.txt
 目录下。下面两个下载路线任选一个。
 
 - 线路一：阿里魔塔下载[SenseVoiceSmall](https://modelscope.cn/models/iic/SenseVoiceSmall/resolve/master/model.pt)
-- 线路二：百度网盘下载[SenseVoiceSmall](https://pan.baidu.com/share/init?surl=QlgM58FHhYv1tFnUT_A8Sg&pwd=qvna) 提取码:
-  `qvna`
+- 线路二：百度网盘下载[SenseVoiceSmall](https://pan.baidu.com/share/init?surl=QlgM58FHhYv1tFnUT_A8Sg&pwd=qvna) 提取码:  `qvna`
 
 ### 4.配置项目
 
@@ -296,15 +303,19 @@ selected_module:
   TTS: EdgeTTS
 ```
 
-比如修改`LLM`使用的组件，就看本项目支持哪些`LLM`，如下就是支持`DeepSeekLLM`、`ChatGLMLLM`。你们在`selected_module`修改成对应的LLM
+比如修改`LLM`使用的组件，就看本项目支持哪些`LLM` API接口，当前支持的是`openai`、`dify`。欢迎验证和支持更多LLM平台的接口。
+使用时，在`selected_module`修改成对应的如下LLM配置的名称：
 
 ```
 LLM:
   DeepSeekLLM:
+    type: openai
     ...
   ChatGLMLLM:
+    type: openai
     ...
   DifyLLM:
+    type: dify
     ...
 ```
 

README_en.md

@@ -44,7 +44,7 @@ use it in the production environment.
 - `xiaozhi-esp32` WebSocket communication protocol
 - Supports wake-word initiated dialogue, manual dialogue, and real-time interruption of dialogue.
 - Support for 5 languages: Mandarin, Cantonese, English, Japanese, Korean (FunASR - default)
-- Flexible LLM switching (ChatGLM - default, Aliyun, Dify, DeepSeek)
+- Flexible LLM switching (openai:ChatGLM - default, Aliyun, DeepSeek; dify:Dify)
 - Flexible TTS switching (EdgeTTS - default, ByteDance Doubao TTS)
 
 ## In Progress
@@ -57,15 +57,17 @@ use it in the production environment.
 
 | Type | Service    |  Usage   | Pricing Model	 | Notes                                                                      |
 |:-----|:-----------|:--------:|:---------------|:---------------------------------------------------------------------------|
-| LLM  | Aliyun     | API call | Token-based    | [Apply for API Key](https://bailian.console.aliyun.com/?apiKey=1#/api-key) |
-| LLM  | DeepSeek   | API call | Token-based    | [Apply for API Key](https://platform.deepseek.com/)                        |
-| LLM  | Dify       | API call | Token-based    | Self-hosted                                                                |
-| LLM  | Bigmodel   | API call | Free           | [Create API Key](https://bigmodel.cn/usercenter/proj-mgmt/apikeys)         |
+| LLM  | Aliyun     | openai API call | Token-based    | [Apply for API Key](https://bailian.console.aliyun.com/?apiKey=1#/api-key) |
+| LLM  | DeepSeek   | openai API call | Token-based    | [Apply for API Key](https://platform.deepseek.com/)                        |
+| LLM  | Bigmodel   | openai API call | Free           | [Create API Key](https://bigmodel.cn/usercenter/proj-mgmt/apikeys)         |
+| LLM  | Dify       | dify API call | Token-based    | Self-hosted                                                                |
 | TTS  | HuoshanTTS | API call | Token-based    | [Create API Key](https://console.volcengine.com/speech/service/8)          |
 | TTS  | EdgeTTS    | API call | Free           |                                                                            |
 | VAD  | SileroVAD  |  Local   | Free           |                                                                            |
 | ASR  | FunASR     |  Local   | Free           |                                                                            |
 
+In fact, any LLM that supports OpenAI API calls can be integrated and used.
+
 # Deployment
 
 This project supports rapid deployment of docker and local source code operation. If you want to have a quick
@@ -117,18 +119,22 @@ selected_module:
   TTS: EdgeTTS
 ```
 
-For example, modify the components used by `llm`, depending on which` llm` supports this project, as follows, it
-supports `Deepseekllm` and` Chatglmllm`. You are modified to the corresponding LLM in `selectd_module`
+For example, to modify the components used by the `LLM`, it depends on which `LLM` API interfaces are supported by this project. Currently, the supported ones are `openai` and `dify`. We welcome validation and support for more LLM platforms' interfaces.
+When using it, change the `selected_module` to the corresponding name of the following LLM configurations:
 
 ```
 LLM:
   AliLLM:
+    type: openai
     ...
   DeepSeekLLM:
+    type: openai
     ...
   ChatGLMLLM:
+    type: openai
     ...
   DifyLLM:
+    type: openai
     ...
 ```
 
@@ -254,16 +260,22 @@ selected_module:
   TTS: EdgeTTS
 ```
 
-For example, modify the components used by `llm`, depending on which` llm` supports this project, as follows, it
-supports `Deepseekllm` and` Chatglmllm`. You are modified to the corresponding LLM in `selectd_module`
+For example, to modify the components used by the `LLM`, it depends on which `LLM` API interfaces are supported by this project. Currently, the supported ones are `openai` and `dify`. We welcome validation and support for more LLM platforms' interfaces.
+When using it, change the `selected_module` to the corresponding name of the following LLM configurations:
 
 ```
 LLM:
+  AliLLM:
+    type: openai
+    ...
   DeepSeekLLM:
+    type: openai
     ...
   ChatGLMLLM:
+    type: openai
     ...
   DifyLLM:
+    type: openai
     ...
 ```
 

config.yaml

@@ -27,7 +27,9 @@ delete_audio: true
 selected_module:
   ASR: FunASR
   VAD: SileroVAD
+  # 将根据配置名称对应的type调用实际的LLM适配器
   LLM: ChatGLMLLM
+  # TTS将根据配置名称对应的type调用实际的TTS适配器
   TTS: EdgeTTS
 
 ASR:
@@ -42,32 +44,46 @@ VAD:
     min_silence_duration_ms: 700  # 如果说话停顿比较长，可以把这个值设置大一些
 
 LLM:
+  # 当前支持的type为openai、dify，可自行适配
   AliLLM:
+    # 定义LLM API类型
+    type: openai
     # 可在这里找到你的 api_key https://bailian.console.aliyun.com/?apiKey=1#/api-key
     base_url: https://dashscope.aliyuncs.com/compatible-mode/v1
     model_name: qwen-turbo
     api_key: 你的阿里云dashscope api key
   DeepSeekLLM:
+    # 定义LLM API类型
+    type: openai
     # 可在这里找到你的api key https://platform.deepseek.com/
     model_name: deepseek-chat
     url: https://api.deepseek.com
     api_key: 你的deepseek api key
   ChatGLMLLM:
+    # 定义LLM API类型
+    type: openai
     # glm-4-flash 是免费的，但是还是需要注册填写api_key的
     # 可在这里找到你的api key https://bigmodel.cn/usercenter/proj-mgmt/apikeys
     model_name: glm-4-flash
     url: https://open.bigmodel.cn/api/paas/v4/
     api_key: 你的bigmodel api key
   DifyLLM:
+    # 定义LLM API类型
+    type: dify
     # 建议使用本地部署的dify接口，国内部分区域访问dify公有云接口可能会受限
     # 如果使用DifyLLM，配置文件里prompt(提示词)是无效的，需要在dify控制台设置提示词
     base_url: 你的私有化部署的dify接口地址
     api_key: 你的dify api key
 TTS:
+  # 当前支持的type为edge、doubao，可自行适配
   EdgeTTS:
+    # 定义TTS API类型
+    type: edge
     voice: zh-CN-XiaoxiaoNeural
     output_file: tmp/
   DoubaoTTS:
+    # 定义TTS API类型
+    type: doubao
     # 火山引擎语音合成服务，需要先在火山引擎控制台创建应用并获取appid和access_token
     # 山引擎语音一定要购买花钱，起步价30元，就有100并发了。如果用免费的只有2个并发，会经常报tts错误
     # 购买服务后，购买免费的音色后，可能要等半小时左右，才能使用。

core/providers/llm/base.py

@@ -0,0 +1,7 @@
+from abc import ABC, abstractmethod
+
+class LLMProviderBase(ABC):
+    @abstractmethod
+    def response(self, session_id, dialogue):
+        """LLM response generator"""
+        pass

core/providers/llm/dify/dify.py

@@ -0,0 +1,38 @@
+import json
+import logging
+import requests
+from core.providers.llm.base import LLMProviderBase
+
+logger = logging.getLogger(__name__)
+
+class LLMProvider(LLMProviderBase):
+    def __init__(self, config):
+        self.api_key = config["api_key"]
+        self.base_url = config.get("base_url", "https://api.dify.ai/v1").rstrip('/')
+
+    def response(self, session_id, dialogue):
+        try:
+            # 取最后一条用户消息
+            last_msg = next(m for m in reversed(dialogue) if m["role"] == "user")
+
+            # 发起流式请求
+            with requests.post(
+                    f"{self.base_url}/chat-messages",
+                    headers={"Authorization": f"Bearer {self.api_key}"},
+                    json={
+                        "query": last_msg["content"],
+                        "response_mode": "streaming",
+                        "user": session_id,
+                        "inputs": {}
+                    },
+                    stream=True
+            ) as r:
+                for line in r.iter_lines():
+                    if line.startswith(b'data: '):
+                        event = json.loads(line[6:])
+                        if event.get('answer'):
+                            yield event['answer']
+
+        except Exception as e:
+            logger.error(f"Error in response generation: {e}")
+            yield "【服务响应异常】"

core/providers/llm/openai/openai.py

@@ -0,0 +1,32 @@
+import logging
+import openai
+from core.providers.llm.base import LLMProviderBase
+
+logger = logging.getLogger(__name__)
+
+class LLMProvider(LLMProviderBase):
+    def __init__(self, config):
+        self.model_name = config.get("model_name")
+        self.api_key = config.get("api_key")
+        if 'base_url' in config:
+            self.base_url = config.get("base_url")
+        else:
+            self.base_url = config.get("url")
+        self.client = openai.OpenAI(api_key=self.api_key, base_url=self.base_url)
+
+    def response(self, session_id, dialogue):
+        try:
+            responses = self.client.chat.completions.create(
+                model=self.model_name,
+                messages=dialogue,
+                stream=True
+            )
+            for chunk in responses:
+                # 检查是否存在有效的choice且content不为空
+                if chunk.choices and len(chunk.choices) > 0:
+                    delta = chunk.choices[0].delta
+                    content = getattr(delta, 'content', '')
+                    if content:  # 仅在content非空时生成
+                        yield content
+        except Exception as e:
+            logger.error(f"Error in response generation: {e}")

core/providers/tts/base.py

@@ -0,0 +1,89 @@
+import asyncio
+import logging
+import os
+import json
+import uuid
+import base64
+# from datetime import datetime
+# import edge_tts
+import numpy as np
+import opuslib
+# import requests
+# from core.utils.util import read_config, get_project_dir
+from pydub import AudioSegment
+from abc import ABC, abstractmethod
+
+logger = logging.getLogger(__name__)
+
+class TTSProviderBase(ABC):
+    def __init__(self, config, delete_audio_file):
+        self.delete_audio_file = delete_audio_file
+        self.output_file = config.get("output_file")
+
+    @abstractmethod
+    def generate_filename(self):
+        pass
+
+    def to_tts(self, text):
+        tmp_file = self.generate_filename()
+        try:
+            max_repeat_time = 5
+            while not os.path.exists(tmp_file) and max_repeat_time > 0:
+                asyncio.run(self.text_to_speak(text, tmp_file))
+                if not os.path.exists(tmp_file):
+                    max_repeat_time = max_repeat_time - 1
+                    logger.error(f"语音生成失败: {text}:{tmp_file}，再试{max_repeat_time}次")
+
+            if max_repeat_time>0:
+                logger.info(f"语音生成成功: {text}:{tmp_file}，重试{5-max_repeat_time}次")
+
+            return tmp_file
+        except Exception as e:
+            logger.info(f"Failed to generate TTS file: {e}")
+            return None
+
+    @abstractmethod
+    async def text_to_speak(self, text, output_file):
+        pass
+
+    def wav_to_opus_data(self, wav_file_path):
+        # 使用pydub加载PCM文件
+        # 获取文件后缀名
+        file_type = os.path.splitext(wav_file_path)[1]
+        if file_type:
+            file_type = file_type.lstrip('.')
+        audio = AudioSegment.from_file(wav_file_path, format=file_type)
+
+        duration = len(audio) / 1000.0
+
+        # 转换为单声道和16kHz采样率（确保与编码器匹配）
+        audio = audio.set_channels(1).set_frame_rate(16000)
+
+        # 获取原始PCM数据（16位小端）
+        raw_data = audio.raw_data
+
+        # 初始化Opus编码器
+        encoder = opuslib.Encoder(16000, 1, opuslib.APPLICATION_AUDIO)
+
+        # 编码参数
+        frame_duration = 60  # 60ms per frame
+        frame_size = int(16000 * frame_duration / 1000)  # 960 samples/frame
+
+        opus_datas = []
+        # 按帧处理所有音频数据（包括最后一帧可能补零）
+        for i in range(0, len(raw_data), frame_size * 2):  # 16bit=2bytes/sample
+            # 获取当前帧的二进制数据
+            chunk = raw_data[i:i + frame_size * 2]
+
+            # 如果最后一帧不足，补零
+            if len(chunk) < frame_size * 2:
+                chunk += b'\x00' * (frame_size * 2 - len(chunk))
+
+            # 转换为numpy数组处理
+            np_frame = np.frombuffer(chunk, dtype=np.int16)
+
+            # 编码Opus数据
+            opus_data = encoder.encode(np_frame.tobytes(), frame_size)
+            opus_datas.append(opus_data)
+
+        return opus_datas, duration

core/providers/tts/doubao.py

@@ -0,0 +1,55 @@
+import os
+import uuid
+import json
+import base64
+import requests
+from datetime import datetime
+from core.providers.tts.base import TTSProviderBase
+
+class TTSProvider(TTSProviderBase):
+    def __init__(self, config, delete_audio_file):
+        super().__init__(config, delete_audio_file)
+        self.appid = config.get("appid")
+        self.access_token = config.get("access_token")
+        self.cluster = config.get("cluster")
+        self.voice = config.get("voice")
+
+        self.host = "openspeech.bytedance.com"
+        self.api_url = f"https://{self.host}/api/v1/tts"
+        self.header = {"Authorization": f"Bearer;{self.access_token}"}
+
+    def generate_filename(self, extension=".wav"):
+        return os.path.join(self.output_file, f"tts-{datetime.now().date()}@{uuid.uuid4().hex}{extension}")
+
+    async def text_to_speak(self, text, output_file):
+        request_json = {
+            "app": {
+                "appid": self.appid,
+                "token": "access_token",
+                "cluster": self.cluster
+            },
+            "user": {
+                "uid": "1"
+            },
+            "audio": {
+                "voice_type": self.voice,
+                "encoding": "wav",
+                "speed_ratio": 1.0,
+                "volume_ratio": 1.0,
+                "pitch_ratio": 1.0,
+            },
+            "request": {
+                "reqid": str(uuid.uuid4()),
+                "text": text,
+                "text_type": "plain",
+                "operation": "query",
+                "with_frontend": 1,
+                "frontend_type": "unitTson"
+            }
+        }
+
+        resp = requests.post(self.api_url, json.dumps(request_json), headers=self.header)
+        if "data" in resp.json():
+            data = resp.json()["data"]
+            file_to_save = open(output_file, "wb")
+            file_to_save.write(base64.b64decode(data))

core/providers/tts/edge.py

@@ -0,0 +1,17 @@
+import os
+import uuid
+import edge_tts
+from datetime import datetime
+from core.providers.tts.base import TTSProviderBase
+
+class TTSProvider(TTSProviderBase):
+    def __init__(self, config, delete_audio_file):
+        super().__init__(config, delete_audio_file)
+        self.voice = config.get("voice")
+
+    def generate_filename(self, extension=".mp3"):
+        return os.path.join(self.output_file, f"tts-{datetime.now().date()}@{uuid.uuid4().hex}{extension}")
+
+    async def text_to_speak(self, text, output_file):
+        communicate = edge_tts.Communicate(text, voice=self.voice)  # Use your preferred voice
+        await communicate.save(output_file)

core/server.py

@@ -25,11 +25,17 @@ class WebSocketServer:
                 self.config["delete_audio"]
             ),
             llm.create_instance(
-                self.config["selected_module"]["LLM"],
+                self.config["selected_module"]["LLM"]
+                if not 'type' in self.config["LLM"][self.config["selected_module"]["LLM"]]
+                else
+                self.config["LLM"][self.config["selected_module"]["LLM"]]['type'],
                 self.config["LLM"][self.config["selected_module"]["LLM"]],
             ),
             tts.create_instance(
-                self.config["selected_module"]["TTS"],
+                self.config["selected_module"]["TTS"]
+                if not 'type' in self.config["TTS"][self.config["selected_module"]["TTS"]]
+                else
+                self.config["TTS"][self.config["selected_module"]["TTS"]]["type"],
                 self.config["TTS"][self.config["selected_module"]["TTS"]],
                 self.config["delete_audio"]
             )

core/utils/llm.py

@@ -1,7 +1,10 @@
+import os
+import sys
 import json
 import logging
 import openai
 import requests
+import importlib
 from datetime import datetime
 from core.utils.util import is_segment
 from core.utils.util import get_string_no_punctuation_or_emoji
@@ -11,143 +14,15 @@ from abc import ABC, abstractmethod
 logger = logging.getLogger(__name__)
 
 
-class LLM(ABC):
-    def __init__(self, config):
-        api_key = config.get("api_key")
-        # 如果api_key包含中文“你”
-        if "你" in api_key:
-            logger.error("你还没配置LLM的密钥，请在配置文件中配置密钥，否则无法正常工作")
-        pass
-
-    @abstractmethod
-    def response(self, session_id, dialogue):
-        """LLM response generator"""
-        pass
-
-
-class DeepSeekLLM(LLM):
-    def __init__(self, config):
-        super().__init__(config)
-        self.model_name = config.get("model_name")
-        self.api_key = config.get("api_key")
-        self.base_url = config.get("url")
-        self.client = openai.OpenAI(api_key=self.api_key, base_url=self.base_url)
-
-    def response(self, session_id, dialogue):
-        logger.info(f"Generating response using {dialogue}")
-        try:
-            responses = self.client.chat.completions.create(
-                model=self.model_name,
-                messages=dialogue,
-                stream=True
-            )
-            for chunk in responses:
-                # 检查是否存在有效的choice且content不为空
-                if chunk.choices and len(chunk.choices) > 0:
-                    delta = chunk.choices[0].delta
-                    content = getattr(delta, 'content', '')
-                    if content:  # 仅在content非空时生成
-                        yield content
-        except Exception as e:
-            logger.error(f"Error in response generation: {e}")
-
-
-class ChatGLMLLM(LLM):
-    def __init__(self, config):
-        super().__init__(config)
-        self.model_name = config.get("model_name")
-        self.api_key = config.get("api_key")
-        self.base_url = config.get("url")
-        self.client = openai.OpenAI(api_key=self.api_key, base_url=self.base_url)
-
-    def response(self, session_id, dialogue):
-        try:
-            responses = self.client.chat.completions.create(
-                model=self.model_name,
-                messages=dialogue,
-                stream=True
-            )
-            for chunk in responses:
-                # 检查是否存在有效的choice且content不为空
-                if chunk.choices and len(chunk.choices) > 0:
-                    delta = chunk.choices[0].delta
-                    content = getattr(delta, 'content', '')
-                    if content:  # 仅在content非空时生成
-                        yield content
-        except Exception as e:
-            logger.error(f"Error in response generation: {e}")
-
-class AliLLM(LLM):
-    def __init__(self, config):
-        super().__init__(config)
-        self.model_name = config.get("model_name")
-        self.api_key = config.get("api_key")
-        self.base_url = config.get("base_url")
-        self.client = openai.OpenAI(api_key=self.api_key, base_url=self.base_url)
-
-    def response(self, session_id, dialogue):
-        try:
-            responses = self.client.chat.completions.create(
-                model=self.model_name,
-                messages=dialogue,
-                stream=True
-            )
-            for chunk in responses:
-                # 检查是否存在有效的choice且content不为空
-                if chunk.choices and len(chunk.choices) > 0:
-                    delta = chunk.choices[0].delta
-                    content = getattr(delta, 'content', '')
-                    if content:  # 仅在content非空时生成
-                        yield content
-        except Exception as e:
-            logger.error(f"Error in response generation: {e}")
-
-class DifyLLM(LLM):
-    def __init__(self, config):
-        super().__init__(config)
-        self.api_key = config["api_key"]
-        self.base_url = config.get("base_url", "https://api.dify.ai/v1").rstrip('/')
-
-    def response(self, session_id, dialogue):
-        try:
-            # 取最后一条用户消息
-            last_msg = next(m for m in reversed(dialogue) if m["role"] == "user")
-
-            # 发起流式请求
-            with requests.post(
-                    f"{self.base_url}/chat-messages",
-                    headers={"Authorization": f"Bearer {self.api_key}"},
-                    json={
-                        "query": last_msg["content"],
-                        "response_mode": "streaming",
-                        "user": session_id,
-                        "inputs": {}
-                    },
-                    stream=True
-            ) as r:
-                for line in r.iter_lines():
-                    if line.startswith(b'data: '):
-                        event = json.loads(line[6:])
-                        if event.get('answer'):
-                            yield event['answer']
-
-        except Exception:
-            yield "【服务响应异常】"
-
-
 def create_instance(class_name, *args, **kwargs):
-    # 获取类对象
-    cls_map = {
-        "DeepSeekLLM": DeepSeekLLM,
-        "ChatGLMLLM": ChatGLMLLM,
-        "DifyLLM": DifyLLM,
-        "AliLLM": AliLLM,
-        # 可扩展其他LLM实现
-    }
+    # 创建LLM实例
+    if os.path.exists(os.path.join('core', 'providers', 'llm', class_name, f'{class_name}.py')):
+        lib_name = f'core.providers.llm.{class_name}.{class_name}'
+        if lib_name not in sys.modules:
+            sys.modules[lib_name] = importlib.import_module(f'{lib_name}')
+            return sys.modules[lib_name].LLMProvider(*args, **kwargs)
 
-    if cls := cls_map.get(class_name):
-        return cls(*args, **kwargs)
-    raise ValueError(f"不支持的LLM类型: {class_name}")
+    raise ValueError(f"不支持的LLM类型: {class_name}，请检查该配置的type是否设置正确")
 
 
 if __name__ == "__main__":
@@ -156,7 +31,10 @@ if __name__ == "__main__":
     """
     config = read_config(get_project_dir() + "config.yaml")
     llm = create_instance(
-        config["selected_module"]["LLM"],
+        config["selected_module"]["LLM"]
+        if not "type" in config["LLM"][config["selected_module"]["LLM"]]
+        else
+        config["LLM"][config["selected_module"]["LLM"]]["type"],
         config["LLM"][config["selected_module"]["LLM"]]
     )
 

core/utils/tts.py

@@ -1,9 +1,11 @@
 import asyncio
 import logging
 import os
+import sys
 import json
 import uuid
 import base64
+import importlib
 from datetime import datetime
 import edge_tts
 import numpy as np
@@ -16,150 +18,15 @@ from abc import ABC, abstractmethod
 logger = logging.getLogger(__name__)
 
 
-class TTS(ABC):
-    def __init__(self, config, delete_audio_file):
-        self.delete_audio_file = delete_audio_file
-        self.output_file = config.get("output_file")
-
-    @abstractmethod
-    def generate_filename(self):
-        pass
-
-    def to_tts(self, text):
-        tmp_file = self.generate_filename()
-        try:
-            max_repeat_time = 5
-            while not os.path.exists(tmp_file) and max_repeat_time > 0:
-                asyncio.run(self.text_to_speak(text, tmp_file))
-                if not os.path.exists(tmp_file):
-                    max_repeat_time = max_repeat_time - 1
-                    logger.error(f"语音生成失败: {text}:{tmp_file}，再试{max_repeat_time}次")
-
-            return tmp_file
-        except Exception as e:
-            logger.info(f"Failed to generate TTS file: {e}")
-            return None
-
-    @abstractmethod
-    async def text_to_speak(self, text, output_file):
-        pass
-
-    def wav_to_opus_data(self, wav_file_path):
-        # 使用pydub加载PCM文件
-        # 获取文件后缀名
-        file_type = os.path.splitext(wav_file_path)[1]
-        if file_type:
-            file_type = file_type.lstrip('.')
-        audio = AudioSegment.from_file(wav_file_path, format=file_type)
-
-        duration = len(audio) / 1000.0
-
-        # 转换为单声道和16kHz采样率（确保与编码器匹配）
-        audio = audio.set_channels(1).set_frame_rate(16000)
-
-        # 获取原始PCM数据（16位小端）
-        raw_data = audio.raw_data
-
-        # 初始化Opus编码器
-        encoder = opuslib.Encoder(16000, 1, opuslib.APPLICATION_AUDIO)
-
-        # 编码参数
-        frame_duration = 60  # 60ms per frame
-        frame_size = int(16000 * frame_duration / 1000)  # 960 samples/frame
-
-        opus_datas = []
-        # 按帧处理所有音频数据（包括最后一帧可能补零）
-        for i in range(0, len(raw_data), frame_size * 2):  # 16bit=2bytes/sample
-            # 获取当前帧的二进制数据
-            chunk = raw_data[i:i + frame_size * 2]
-
-            # 如果最后一帧不足，补零
-            if len(chunk) < frame_size * 2:
-                chunk += b'\x00' * (frame_size * 2 - len(chunk))
-
-            # 转换为numpy数组处理
-            np_frame = np.frombuffer(chunk, dtype=np.int16)
-
-            # 编码Opus数据
-            opus_data = encoder.encode(np_frame.tobytes(), frame_size)
-            opus_datas.append(opus_data)
-
-        return opus_datas, duration
-
-
-class EdgeTTS(TTS):
-    def __init__(self, config, delete_audio_file):
-        super().__init__(config, delete_audio_file)
-        self.voice = config.get("voice")
-
-    def generate_filename(self, extension=".mp3"):
-        return os.path.join(self.output_file, f"tts-{datetime.now().date()}@{uuid.uuid4().hex}{extension}")
-
-    async def text_to_speak(self, text, output_file):
-        communicate = edge_tts.Communicate(text, voice=self.voice)  # Use your preferred voice
-        await communicate.save(output_file)
-
-
-class DoubaoTTS(TTS):
-    def __init__(self, config, delete_audio_file):
-        super().__init__(config, delete_audio_file)
-        self.appid = config.get("appid")
-        self.access_token = config.get("access_token")
-        self.cluster = config.get("cluster")
-        self.voice = config.get("voice")
-
-        self.host = "openspeech.bytedance.com"
-        self.api_url = f"https://{self.host}/api/v1/tts"
-        self.header = {"Authorization": f"Bearer;{self.access_token}"}
-
-    def generate_filename(self, extension=".wav"):
-        return os.path.join(self.output_file, f"tts-{datetime.now().date()}@{uuid.uuid4().hex}{extension}")
-
-    async def text_to_speak(self, text, output_file):
-        request_json = {
-            "app": {
-                "appid": self.appid,
-                "token": "access_token",
-                "cluster": self.cluster
-            },
-            "user": {
-                "uid": "1"
-            },
-            "audio": {
-                "voice_type": self.voice,
-                "encoding": "wav",
-                "speed_ratio": 1.0,
-                "volume_ratio": 1.0,
-                "pitch_ratio": 1.0,
-            },
-            "request": {
-                "reqid": str(uuid.uuid4()),
-                "text": text,
-                "text_type": "plain",
-                "operation": "query",
-                "with_frontend": 1,
-                "frontend_type": "unitTson"
-            }
-        }
-
-        resp = requests.post(self.api_url, json.dumps(request_json), headers=self.header)
-        if "data" in resp.json():
-            data = resp.json()["data"]
-            file_to_save = open(output_file, "wb")
-            file_to_save.write(base64.b64decode(data))
-
-
 def create_instance(class_name, *args, **kwargs):
-    # 获取类对象
-    cls_map = {
-        "DoubaoTTS": DoubaoTTS,
-        "EdgeTTS": EdgeTTS,
-        # 可扩展其他TTS实现
-    }
+    # 创建TTS实例
+    if os.path.exists(os.path.join('core', 'providers', 'tts', f'{class_name}.py')):
+        lib_name = f'core.providers.tts.{class_name}'
+        if lib_name not in sys.modules:
+            sys.modules[lib_name] = importlib.import_module(f'{lib_name}')
+            return sys.modules[lib_name].TTSProvider(*args, **kwargs)
 
-    if cls := cls_map.get(class_name):
-        return cls(*args, **kwargs)
-    raise ValueError(f"不支持的TTS类型: {class_name}")
+    raise ValueError(f"不支持的TTS类型: {class_name}，请检查该配置的type是否设置正确")
 
 
 if __name__ == "__main__":
@@ -168,7 +35,10 @@ if __name__ == "__main__":
     """
     config = read_config(get_project_dir() + "config.yaml")
     tts = create_instance(
-        config["selected_module"]["TTS"],
+        config["selected_module"]["TTS"]
+        if not 'type' in config["TTS"][config["selected_module"]["TTS"]]
+        else
+        config["TTS"][config["selected_module"]["TTS"]]["type"],
         config["TTS"][config["selected_module"]["TTS"]],
         config["delete_audio"]
     )