Tts302 (#170 )

* feat: add 302.ai TTS integration (#166) * update:将tts_302ai tts合并到doubao tts --------- Co-authored-by: Junsen <66542771+Huang-junsen@users.noreply.github.com> Co-authored-by: hrz <1710360675@qq.com>
音乐播放功能添加p3文件格式支持，添加文件目录(含子目录)扫描和文件列表刷新功能 (#157 )
2026-07-23 23:53:55 +08:00 · 2025-03-02 16:59:25 +08:00 · 2025-03-01 17:57:43 +08:00 · 2025-03-01 17:34:14 +08:00 · 2025-03-01 17:09:01 +08:00 · 2025-03-01 01:54:55 +08:00
33 changed files with 1413 additions and 915 deletions
@@ -1,4 +1,15 @@
-![图片](docs/images/banner.png)
+[![SVG Banners](https://svg-banners.vercel.app/api?type=origin&text1=你好😃，小智📟&text2=开源小智ESP-32后端服务&width=830&height=210)](https://github.com/xinnan-tech/xiaozhi-esp32-server)
 <p align="center">
  <a href="https://github.com/xinnan-tech/xiaozhi-esp32-server/graphs/contributors">
    <img alt="GitHub Contributors" src="https://img.shields.io/github/contributors/xinnan-tech/xiaozhi-esp32-server" />
  </a>
  <a href="https://github.com/xinnan-tech/xiaozhi-esp32-server/issues">
    <img alt="Issues" src="https://img.shields.io/github/issues/xinnan-tech/xiaozhi-esp32-server?color=0088ff" />
  </a>
  <a href="https://github.com/xinnan-tech/xiaozhi-esp32-server/pulls">
    <img alt="GitHub pull requests" src="https://img.shields.io/github/issues-pr/xinnan-tech/xiaozhi-esp32-server?color=0088ff" />
  </a>
 </p>
 # 小智 ESP-32 后端服务(xiaozhi-esp32-server)
@@ -58,37 +69,29 @@
    </tr>
 </table>
 ---
 ## 系统要求与部署前提 🖥️
 - **硬件**：一套兼容 `xiaozhi-esp32`
  的硬件设备（具体型号请参考 [此处](https://rcnv1t9vps13.feishu.cn/wiki/DdgIw4BUgivWDPkhMj1cGIYCnRf)）。
- **服务器**：至少 4 核 CPU、8G 内存的电脑或服务器。
+
- **固件编译**：请将后端服务的接口地址更新至 `xiaozhi-esp32` 项目中，再重新编译固件并烧录到设备上。
+- **电脑或服务器**：至少 4 核 CPU、8G 内存的电脑。
 - **固件编译**：请将本后端服务的接口地址更新至 `xiaozhi-esp32` 项目中，再重新编译`xiaozhi-esp32`固件并烧录到设备上。
 如果你没有esp32相关的硬件设备，但是非常想体验该项目，可以使用以下的项目让你的电脑、手机模拟成esp32设备。
 - [小智安卓端](https://github.com/TOM88812/xiaozhi-android-client)
 - [小智电脑端](https://github.com/Huang-junsen/py-xiaozhi)
 ---
 ## 警告 ⚠️
-本项目成立时间较短，还未通过网络安全测评，请勿在生产环境中使用。
+1、本项目为开源软件，本软件与对接的任何第三方API服务商（包括但不限于语音识别、大模型、语音合成等平台）均不存在商业合作关系，不为其服务质量及资金安全提供任何形式的担保。
 建议使用者优先选择持有相关业务牌照的服务商，并仔细阅读其服务协议及隐私政策。本软件不托管任何账户密钥、不参与资金流转、不承担充值资金损失风险。
-如果您在公网环境中部署学习本项目，请务必在配置文件 `config.yaml` 中开启防护：
+2、本项目成立时间较短，还未通过网络安全测评，请勿在生产环境中使用。 如果您在公网环境中部署学习本项目，请务必在配置文件 `config.yaml` 中开启防护：
 ```yaml
 server:
@@ -167,9 +170,10 @@ server:
 ### ASR
-| 类型  |  平台名称  | 使用方式 | 收费模式 | 备注 |
+| 类型  |   平台名称    | 使用方式 | 收费模式 | 备注 |
-|:---:|:------:|:----:|:----:|:--:|
+|:---:|:---------:|:----:|:----:|:--:|
-| ASR | FunASR | 本地使用 |  免费  |    |
+| ASR |  FunASR   | 本地使用 |  免费  |    |
 | ASR | DoubaoASR | 接口调用 |  收费  |    |
 ---
@@ -177,23 +181,25 @@ server:
 ### 一、[部署文档](./docs/Deployment.md)
-本项目支持以下三种部署方式，您可根据实际需求选择：
+本项目支持以下三种部署方式，您可根据实际需求选择。
-1. **[Docker 快速部署](./docs/Deployment.md)**  
+本项目的文档主要是`文字版本`的教程，如果你想要`视频版本`
-   适合快速体验，不需过多环境配置。缺点是，拉取镜像有点慢。
+的教程，您可以学习一下[这个大佬的手把手教程](https://www.bilibili.com/video/BV1gePuejEvT)。
 2.
    *
-*[借助 Docker 环境运行部署](./docs/Deployment.md#%E6%96%B9%E5%BC%8F%E4%BA%8C%E5%80%9F%E5%8A%A9docker%E7%8E%AF%E5%A2%83%E8%BF%90%E8%A1%8C%E9%83%A8%E7%BD%B2)
+如果你能把`文字版本的教程`和`视频版本的教程`结合起来一起看，可以让你更快上手。
 **  
 适用于已安装 Docker 且希望对代码进行自定义修改的用户。
-3.
+1. [Docker 快速部署](./docs/Deployment.md)
-    *
+
 适合快速体验的普通用户，不需过多环境配置。缺点是，拉取镜像有点慢。
 2. [借助 Docker 环境运行部署](./docs/Deployment.md#%E6%96%B9%E5%BC%8F%E4%BA%8C%E5%80%9F%E5%8A%A9docker%E7%8E%AF%E5%A2%83%E8%BF%90%E8%A1%8C%E9%83%A8%E7%BD%B2)
 适用于已安装 Docker 且希望对代码进行自定义修改的软件工程师。
 3. [本地源码运行](./docs/Deployment.md#%E6%96%B9%E5%BC%8F%E4%B8%89%E6%9C%AC%E5%9C%B0%E6%BA%90%E7%A0%81%E8%BF%90%E8%A1%8C)
 适合熟悉`Conda` 环境或希望从零搭建运行环境的用户。
 *[本地源码运行](./docs/Deployment.md#%E6%96%B9%E5%BC%8F%E4%B8%89%E6%9C%AC%E5%9C%B0%E6%BA%90%E7%A0%81%E8%BF%90%E8%A1%8C)
 **  
 适合熟悉 Conda 环境或希望从零搭建运行环境的用户。  
 对于对响应速度要求较高的场景，推荐使用本地源码运行方式以降低额外开销。
 ### 二、[固件编译](./docs/firmware-build.md)
@@ -303,14 +309,19 @@ TTS 性能排行:
 ### 7、更多问题，可联系我们反馈 💬
-![图片](docs/images/wechat.jpg)
+我们的联系方式放在[百度网盘中,点击前往](https://pan.baidu.com/s/1x6USjvP1nTRsZ45XlJu65Q)，提取码是`223y`。
 网盘里有“硬件烧录QQ群”、“开源服务端交流群”、“产品建议联系人” 三张图片，请根据需要选择加入。
 - 硬件烧录QQ群：适用于硬件烧录问题
 - 开源服务端交流群：适用于服务端问题
 - 产品建议联系人：适用于产品功能、产品设计等建议
 ---
 ## 鸣谢 🙏
 - 本项目受 [百聆语音对话机器人](https://github.com/wwbin2017/bailing) 启发，并在其基础上实现。
 - 感谢 [腾讯云](https://cloud.tencent.com/) 提供免费 Docker 镜像空间。
 - 感谢 [十方融海](https://www.tenclass.com/) 对小智通讯协议提供的详尽文档支持。
 <a href="https://star-history.com/#xinnan-tech/xiaozhi-esp32-server&Date">
@@ -1,427 +1,291 @@
 ![图片](docs/images/banner.png)
-# Xiaozhi ESP-32 Back-end Service (xiaozhi-esp32-server)
+[![SVG Banners](https://svg-banners.vercel.app/api?type=origin&text1=Hello😃,%20XiaoZhi📟&text2=Open%20Source%20XiaoZhi%20ESP-32%20Backend%20Service&width=830&height=210)](https://github.com/xinnan-tech/xiaozhi-esp32-server)
 <p align="center">
  <a href="https://github.com/xinnan-tech/xiaozhi-esp32-server/graphs/contributors">
    <img alt="GitHub Contributors" src="https://img.shields.io/github/contributors/xinnan-tech/xiaozhi-esp32-server" />
  </a>
  <a href="https://github.com/xinnan-tech/xiaozhi-esp32-server/issues">
    <img alt="Issues" src="https://img.shields.io/github/issues/xinnan-tech/xiaozhi-esp32-server?color=0088ff" />
  </a>
  <a href="https://github.com/xinnan-tech/xiaozhi-esp32-server/pulls">
    <img alt="GitHub pull requests" src="https://img.shields.io/github/issues-pr/xinnan-tech/xiaozhi-esp32-server?color=0088ff" />
  </a>
 </p>
-（[中文](README.md) | English）
+# XiaoZhi ESP-32 Backend Service (xiaozhi-esp32-server)
-This project provides backend services for the open-source smart hardware
+([中文](README.md) | English)
 project [xiaozhi-esp32](https://github.com/78/xiaozhi-esp32)。Implemented in Python following
 the[Xiaozhi Communication Protocol](https://ccnphfhqs21z.feishu.cn/wiki/M0XiwldO9iJwHikpXD5cEx71nKh)。
-## Target Audience
+This project provides the backend service for the open source smart hardware project [xiaozhi-esp32](https://github.com/78/xiaozhi-esp32). It is implemented in `Python` based on the [XiaoZhi Communication Protocol](https://ccnphfhqs21z.feishu.cn/wiki/M0XiwldO9iJwHikpXD5cEx71nKh).
-This project requires compatible esp32 hardware devices. If you have purchased esp32 hardware, successfully connected to
+---
 Xiage's deployed backend, and wish to independently set up the `xiaozhi-esp32` backend service, this project is for
 you.
-To see a demo, watch this video:
+## Target Audience 👥
-<a href="https://www.bilibili.com/video/BV1FMFyejExX">
+This project is designed to be used in conjunction with ESP32 hardware devices. If you have already purchased an ESP32 device, successfully connected to the backend service deployed by XieGe, and now wish to set up your own `xiaozhi-esp32` backend service, then this project is perfect for you.
 <picture>
   <img alt="小智esp32连接自己的后台模型" src="docs/images/demo.png" />
 </picture>
 </a>
-To fully experience this project, follow these steps:
+Want to see it in action? Check out the videos 🎥
- Prepare hardware compatible with the `xiaozhi-esp32` project. For supported
+<table>
-  models, [click here](https://rcnv1t9vps13.feishu.cn/wiki/DdgIw4BUgivWDPkhMj1cGIYCnRf).
+  <tr>
- Use a computer/server with at least 4-core CPU and 8GB RAM to run this project. After deployment, you'll see the
+    <td>
-  service endpoint address in the console.
+      <a href="https://www.bilibili.com/video/BV1FMFyejExX" target="_blank">
- Download the `xiaozhi-esp32` project, replace the default `endpoint address` with your own, compile, and flash the
+        <picture>
-  firmware to your device.
+          <img alt="XiaoZhi ESP32 connecting to a custom backend model" src="docs/images/demo1.png" />
- Start the device and check your server console logs to verify successful connection.
+        </picture>
      </a>
    </td>
    <td>
      <a href="https://www.bilibili.com/video/BV1CDKWemEU6" target="_blank">
        <picture>
          <img alt="Custom Voice" src="docs/images/demo2.png" />
        </picture>
      </a>
    </td>
    <td>
      <a href="https://www.bilibili.com/video/BV12yA2egEaC" target="_blank">
        <picture>
          <img alt="Conversing in Cantonese" src="docs/images/demo3.png" />
        </picture>
      </a>
    </td>
    <td>
      <a href="https://www.bilibili.com/video/av114036381327149" target="_blank">
        <picture>
          <img alt="Control Home Appliances" src="docs/images/demo5.png" />
        </picture>
      </a>
    </td>
    <td>
      <a href="https://www.bilibili.com/video/BV1kgA2eYEQ9" target="_blank">
        <picture>
          <img alt="Lowest Cost Configuration" src="docs/images/demo4.png" />
        </picture>
      </a>
    </td>
    <td>
    </td>
  </tr>
 </table>
-## Warning
+---
-This project has been established for a short time and has not passed the network security assessment, so please do not
+## System Requirements and Deployment Prerequisites 🖥️
 use it in the production environment.
-## Feature List
+- **Hardware**: A set of devices compatible with `xiaozhi-esp32` (for specific models, please refer to [this link](https://rcnv1t9vps13.feishu.cn/wiki/DdgIw4BUgivWDPkhMj1cGIYCnRf)).
 - **Server**: A computer with at least a 4-core CPU and 8GB of memory.
 - **Firmware Compilation**: Please update the backend service API endpoint in the `xiaozhi-esp32` project, then recompile the firmware and flash it to your device.
-## Implemented
+---
- `xiaozhi-esp32` WebSocket communication protocol
+## Warning ⚠️
 - 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 (openai:ChatGLM - default, Aliyun, DeepSeek; dify:Dify)
 - Flexible TTS switching (EdgeTTS - default, ByteDance Doubao TTS)
-## In Progress
+This project is relatively new and has not yet undergone network security evaluations. **Do not use it in a production environment.**
- Sleep mode after inactivity
+If you deploy this project on a public network for learning purposes, be sure to enable protection in the configuration file `config.yaml`:
 - Dialogue memory
 - Change the mood mode
-## Supported Services
+```yaml
-
+server:
-| Type | Service    |  Usage   | Pricing Model	 | Notes                                                                      |
+  auth:
-|:-----|:-----------|:--------:|:---------------|:---------------------------------------------------------------------------|
+    # Enable protection
-| LLM  | Aliyun     | openai API call | Token-based    | [Apply for API Key](https://bailian.console.aliyun.com/?apiKey=1#/api-key) |
+    enabled: true  
 | 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
 experience, it is recommended to use docker to deploy. If you want to have an in-depth understanding of this project, it
 is recommended to run the local source code.
 ## Method 1: Quick deployment of docker
 The docker image has supported the CPU of x86 architecture and arm64 architecture, and supports running on Chinese
 operating systems.
 1. Install docker
 If your computer has not installed docker, you can follow the tutorial here to install
 it:[Install docker](https://www.runoob.com/docker/ubuntu-docker-install.html)
 2. Create a directory
 After installation, you need to find a directory for the configuration file for this project. Let's call it the
 `project directory` for the time being. This directory is preferably a newly created empty directory.
 3. Download the configuration file
 Open with a browser[This link](https://github.com/xinnan-tech/xiaozhi-esp32-server/blob/main/config.yaml)。
 On the right side of the page, find the button named `RAW`, next to the `RAW` button, find the download icon, click the
 Download button, and download the `config.yaml` file. Download the file to your `project directory`.
 4. Configure Project
 Modify the `config.yaml` file to configure the various parameters required for this project. The default LLM uses
 `ChatGLMLLM`, you need to configure the key to start.
 The default TTS uses `EdgeTTS`. This does not require configuration. If you need to replace it with`Doubao TTS`, you
 need to
 configure the key.
 Configuration description: This is the default component of each function, such as LLM default to use the `ChatGLMLLM`
 model. If you need to switch the model, it is the corresponding name.
 The default configuration of this project is only the lowest operating cost configuration（`glm-4-flash`and`EdgeTTS`are
 free），If you need to be better and faster, you need to combine the use of the deployment environment to switch the use
 of each component。
 ```
 selected_module:
  ASR: FunASR
  VAD: SileroVAD
  LLM: ChatGLMLLM
  TTS: EdgeTTS
 ```
-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.
+Once protection is enabled, you will need to validate the machine's token or MAC address based on your actual situation. Please refer to the configuration documentation for details.
 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
    ...
 ```
-Some services, for example, if you use the TTS` of the `dify` and` bean bags, you need a key, remember to add the
+---
 configuration file!
-5. Execute the docker command
+## Feature List ✨
 Open the command line tool, `cd` enter your `project directory`, and execute the following command
-```
+### Implemented ✅
 #If you are Linux, execute
 ls
 #If you are Windows, execute
 dir
 ```
-If you can see the `config.yaml` file, you have indeed entered the `project directory`, and then execute the following
+- **Communication Protocol**  
-command:
+  Based on the `xiaozhi-esp32` protocol, data exchange is implemented via WebSocket.
-
+- **Dialogue Interaction**  
-```
+  Supports wake-up dialogues, manual conversations, and real-time interruptions. Automatically enters sleep mode after long periods of inactivity.
-docker run -d --name xiaozhi-esp32-server --restart always --security-opt seccomp:unconfined -p 8000:8000 -v $(pwd)/config.yaml:/opt/xiaozhi-esp32-server/config.yaml ccr.ccs.tencentyun.com/xinnan/xiaozhi-esp32-server:latest
+- **Multilingual Recognition**  
-```
+  Supports Mandarin, Cantonese, English, Japanese, and Korean (default using FunASR).
-
+- **LLM Module**  
-If executed for the first time, it may take several minutes, and you have to be patient to wait for it to complete the
+  Allows flexible switching of LLM modules. The default is ChatGLMLLM, with options to use AliLLM, DeepSeek, Ollama, and others.
-pull. After normal pulling is completed, you can execute the following command on the command line to see if the service
+- **TTS Module**  
-is started successfully.
+  Supports multiple TTS interfaces including EdgeTTS (default) and Volcano Engine Doubao TTS to meet speech synthesis requirements.
 ```
 docker ps
 ```
 If you can see `xiaozhi-server`, it means that the service starts successfully. Then you can further execute the
 following command to view the service log
 ```
 docker logs -f xiaozhi-esp32-server
 ```
 If you can see, similar to the following logs, it is a sign that the service of this project is successfully launched.
 ```
 2025-xx-xx xx:51:59,492 - core.server - INFO - Server is running at ws://xx.xx.xx.xxx:8000
 2025-xx-xx xx:51:59,516 - websockets.server - INFO - server listening on 0.0.0.0:8000
 ```
 Next, you can start `compiling esp32 firmware`. Please go down and turn to the relevant chapter on
 `compiling esp32 firmware`. So since you are deploying with docker, you have to check the IP of your native computer by
 yourself.
 Normally, assuming your ip is `192.168.1.25`, then your interface address is: `ws://192.168.1.25:8000`. This information
 is very useful, and it is required to `compile esp32 firmware` later.
 ## Method 2 : Local Source Code Deployment
 ### 1.Install Prerequisites
 This project uses 'conda' to manage dependencies, and after installation, start executing the following commands:
 ```
 conda remove -n xiaozhi-esp32-server --all -y
 conda create -n xiaozhi-esp32-server python=3.10 -y
 conda activate xiaozhi-esp32-server
 ```
 After executing the above command, if your computer is Windows or Mac, execute the following statement:
 ```
 conda activate xiaozhi-esp32-server
 conda install conda-forge::libopus
 conda install conda-forge::ffmpeg
 ```
 If your computer is ubuntu, execute the following statement:
 ```
 apt-get install libopus0 ffmpeg 
 ```
 ### 2.Install Dependencies
 ```
 # Clone the project
 cd xiaozhi-esp32-server
 conda activate xiaozhi-esp32-server
 pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/
 pip install -r requirements.txt
 ```
 ### 3.Download ASR Model
 Download [SenseVoiceSmall](https://modelscope.cn/models/iic/SenseVoiceSmall/resolve/master/model.pt) to
 `model/SenseVoiceSmall`.
 By default, the `SenseVoiceSmall` model is used to convert voice to text. Because the model is large, it needs to be
 downloaded independently. After downloading, place the `model.pt` file in the `model/SenseVoiceSmall` directory. Choose
 any of the following two download routes.
 - Line 1: Download Ali Magic
  Tower[SenseVoiceSmall](https://modelscope.cn/models/iic/SenseVoiceSmall/resolve/master/model.pt)
 - Line 2: Baidu Netdisk download[SenseVoiceSmall](https://pan.baidu.com/share/init?surl=QlgM58FHhYv1tFnUT_A8Sg&pwd=qvna)
  提取码: `qvna`
-### 4.Configure Project
+### In Development 🚧
 Modify the `config.yaml` file to configure the various parameters required for this project. The default LLM uses
 `ChatGLMLLM`, you need to configure the key to start.
 The default TTS uses `EdgeTTS`. This does not require configuration. If you need to replace it with`Doubao TTS`, you
 need to
 configure the key.
 Configuration description: This is the default component of each function, such as LLM default to use the `ChatGLMLLM`
 model. If you need to switch the model, it is the corresponding name.
 The default configuration of this project is only the lowest operating cost configuration（`glm-4-flash`and`EdgeTTS`are
 free），If you need to be better and faster, you need to combine the use of the deployment environment to switch the use
 of each component。
-```
+- Conversation Memory Feature
-selected_module:
+- Multiple Mood Modes
-  ASR: FunASR
+- Smart Control Panel Web UI
  VAD: SileroVAD
  LLM: ChatGLMLLM
  TTS: EdgeTTS
 ```
 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
    ...
 ```
 Some services, for example, if you use the TTS` of the `dify` and` bean bags, you need a key, remember to add the
 configuration file!
 ### 5.Run the Project
-Run the Project
+![Screenshot](docs/images/webui.png)
-```
+---
 # Make sure to execute in the root directory of this project
 conda activate xiaozhi-esp32-server
 python app.py
 ```
-You'll see the WebSocket endpoint in logs:
+## Supported Platforms/Components 📋
-```
+### LLM
 2025-xx-xx xx:51:59,492 - core.server - INFO - Server is running at ws://192.168.1.25:8000
 2025-xx-xx xx:51:59,516 - websockets.server - INFO - server listening on 0.0.0.0:8000
 ```
-Among them, the `ws://192.168.1.25:8000` is the interface address provided by this project. Of course, your own machine
+| Type |         Platform Name         |         Usage Method          |   Pricing Model   |                                 Remarks                                  |
-is different from mine. Remember to find your own address.
+|:----:|:-----------------------------:|:-----------------------------:|:-----------------:|:-------------------------------------------------------------------------:|
 | LLM  |   AliLLM (阿里百炼)           | OpenAI API call               | Token consumption | [Click to apply for API key](https://bailian.console.aliyun.com/?apiKey=1#/api-key) |
 | LLM  | DeepSeekLLM (深度求索)        | OpenAI API call               | Token consumption |          [Click to apply for API key](https://platform.deepseek.com/)         |
 | LLM  | ChatGLMLLM (智谱)             | OpenAI API call               | Free              | Although free, you still need to [click to apply for an API key](https://bigmodel.cn/usercenter/proj-mgmt/apikeys) |
 | LLM  | OllamaLLM                   | Ollama API call               | Free/Custom       | Requires pre-downloading the model (`ollama pull`); service URL: `http://localhost:11434` |
 | LLM  | DifyLLM                     | Dify API call                 | Token consumption | For local deployment. Note that prompt configuration must be set in the Dify console. |
 | LLM  | GeminiLLM                   | Gemini API call               | Free              | [Click to apply for API key](https://aistudio.google.com/apikey) |
 | LLM  | CozeLLM                     | Coze API call                 | Token consumption | Requires providing bot_id, user_id, and personal token. |
 | LLM  | Home Assistant              | Home Assistant voice assistant API call | Free       | Requires providing a Home Assistant token. |
-# Compile ESP32 Firmware
+In fact, any LLM that supports OpenAI API calls can be integrated.
-1. Download `xiaozhi-esp32` project, configure the project environment according to this
+---
   tutorial [" Windows builds ESP IDF 5.3.2 Development Environment and Compiles Xiaozhi "](https://icnynnzcwou8.feishu.cn/wiki/JEYDwTTALi5s2zkGlFGcDiRknXf)
   Cure
-2. Open the `xiaozhi-esp32/main/kconfig.projbuild` file, find the content of the` websocket_url` `default`, change the
+### TTS
   ` wss: // api.tenclass.net` to your own address, such as
-Before modification:
+| Type |             Platform Name              | Usage Method |   Pricing Model   |                                       Remarks                                        |
 |:----:|:--------------------------------------:|:------------:|:-----------------:|:--------------------------------------------------------------------------------------:|
 | TTS  |               EdgeTTS                  | API call     | Free              | Default TTS based on Microsoft's speech synthesis technology.                        |
 | TTS  | DoubaoTTS (火山引擎豆包 TTS)             | API call     | Token consumption | [Click to create an API key](https://console.volcengine.com/speech/service/8); it is recommended to use the paid version for higher concurrency. |
 | TTS  | CosyVoiceSiliconflow                   | API call     | Token consumption | Requires application for the Siliconflow API key; output format is WAV.                |
 | TTS  | CozeCnTTS                              | API call     | Token consumption | Requires providing a Coze API key; output format is WAV.                               |
 | TTS  | FishSpeech                             | API call     | Free/Custom       | Starts a local TTS service; see the configuration file for startup instructions.       |
 | TTS  | GPT_SOVITS_V2                          | API call     | Free/Custom       | Starts a local TTS service, suitable for personalized speech synthesis scenarios.      |
-```
+---
 config WEBSOCKET_URL
    depends on CONNECTION_TYPE_WEBSOCKET
    string "Websocket URL"
    default "wss://api.tenclass.net/xiaozhi/v1/"
    help
        Communication with the server through websocket after wake up.
 ```
-After modification (example):
+### VAD
-```
+| Type |    Platform Name    | Usage Method | Pricing Model | Remarks |
-config WEBSOCKET_URL
+|:----:|:-------------------:|:------------:|:-------------:|:-------:|
-    depends on CONNECTION_TYPE_WEBSOCKET
+| VAD  | SileroVAD           | Local        | Free          |         |
    string "Websocket URL"
    default "ws://192.168.1.25:8000/xiaozhi/v1/"
    help
        Communication with the server through websocket after wake up.
 ```
-3. Configure build settings:
+---
-```
+### ASR
 # The terminal command line enters the root directory of xiaozhi-esp32
 cd xiaozhi-esp32
 # For example, the board I use is ESP32S3, so the compile target is ESP32S3. If your board is other models, please replace it with the corresponding model
 idf.py set-target esp32s3
 # Enter the menu configuration
 idf.py menuconfig
 ```
-![图片](docs/images/build_setting01.png)
+| Type |    Platform Name    | Usage Method | Pricing Model | Remarks |
 |:----:|:-------------------:|:------------:|:-------------:|:-------:|
 | ASR  | FunASR              | Local        | Free          |         |
 | ASR  | DoubaoASR           | API call     | Paid          |         |
-After entering the menu configuration, then enter `xiaozhi assistant`, set the` connection_type` to `websocket`
+---
 Go back to the main menu, then enter `xiaozhi assistant`, set the `BOARD_TYPE` of your board
 Save exit and return to the terminal command line.
-![图片](docs/images/build_setting02.png)
+## Usage 🚀
-4. Build and package:
+### 1. [Deployment Documentation](./docs/Deployment.md)
-```
+This project supports three deployment methods. Choose the one that best fits your needs.
 idf.py build
 cd scripts
 python release.py
 ```
-After the compilation is successful, the firmware file `merged-binary.bin` is generated in the` build` directory in the
+The documentation provided here is a **written tutorial**. If you prefer a **video tutorial**, you can refer to [this expert's hands-on guide](https://www.bilibili.com/video/BV1gePuejEvT).
 project root directory.
 This `merged-binary.bin` is the firmware file that will be recorded on the hardware.
-6. Flash
+Combining both the written and video tutorials can help you get started more quickly.
   Connect the ESP32 device to the computer, use the Chrome browser, and open the following URL
-```
+1. [Docker Quick Deployment](./docs/Deployment.md)  
-https://espressif.github.io/esp-launchpad/
+   Suitable for general users who want a quick experience without extensive environment configuration. The only downside is that pulling the image can be a bit slow.
 ```
-Open this
+2. [Deployment Using Docker Environment](./docs/Deployment.md#%E6%96%B9%E5%BC%8F%E4%BA%8C%E5%80%9F%E5%8A%A9docker%E7%8E%AF%E5%A2%83%E8%BF%90%E8%A1%8C%E9%83%A8%E7%BD%B2)  
-tutorial, [Flash Tools/Web -side Burning Folding Step (No IDF Development Environment)](https://ccnphfhqs21z.feishu.cn/wiki/Zpz4wXBtdimBrLk25WdcXzxcnNS).
+   Ideal for software engineers who already have Docker installed and wish to customize the code.
 Turn to: `Method 2: ESP-LAUNCHPAD browser web-end burning`, start from
 `3. Burning firmware/download to the development board`, follow the tutorial operation.
-# FAQ
+3. [Running from Local Source Code](./docs/Deployment.md#%E6%96%B9%E5%BC%8F%E4%B8%89%E6%9C%AC%E5%9C%B0%E6%BA%90%E7%A0%81%E8%BF%90%E8%A1%8C)  
   Suitable for users familiar with the `Conda` environment or those who wish to build the runtime environment from scratch.
-## 1、TTS often fails, often overtime
+For scenarios requiring higher response speeds, running from the local source code is recommended to reduce additional overhead.
-Suggestion: If the `Edgetts` is slow or often fails, you can replace it with a bean bag TTS` with a volcanic engine. If
+### 2. [Firmware Compilation](./docs/firmware-build.md)
 both are slow, the network environment may need to be optimized.
-## 2、Big model reply is a bit slow
+Click [here](./docs/firmware-build.md) for a detailed guide on firmware compilation.
-Suggestions: Both big models and TTS are dependent interfaces. If the network environment is not good, you can consider
+After successful compilation and network connection, wake up XiaoZhi using the wake-up word and monitor the server console for output.
 changing the local model. Or try to switch different interface models.
-## 3、Why is my ChatGLMLLM replying to a bit? Obviously it is Xiaozhi, but treats me as Xiaozhi.
+---
-Suggestion: You can modify the prompts in the configuration file first. You can also replace the free `GLM-4-FLASH` to
+## Frequently Asked Questions ❓
 the model of other toll versions of `ChatGlm`.
-## 4、I want to control the operation of electric lights, air conditioners, remote switching and other operations through Xiaozhi.
+### 1. TTS often fails and times out ⏰
-Suggestion: In the configuration file, set the `LLM` to`DifyLLM`, and then arrange the smart application by the
+**Suggestion:**  
-`Dify`.
+If `EdgeTTS` frequently fails, please first check whether you are using a proxy (VPN). If so, try disabling the proxy and try again. If you are using Volcano Engine Doubao TTS and it often fails, it is recommended to use the paid version since the trial only supports 2 concurrent requests.
-## 5、I said very slowly, I paused, Xiaozhi always grabbed me, what to do.
+### 2. I want to control lights, air conditioners, remote power on/off, etc. with XiaoZhi 💡
-Suggestion: In the configuration file, find this section, change the `min_silence_duration_ms` value, such as change to
+**Suggestion:**  
-` 1000`.
+Set the `LLM` to `HomeAssistant` in the configuration file and use the `HomeAssistant` API to perform the relevant controls.
-```
+### 3. I speak slowly, and XiaoZhi always interrupts during pauses 🗣️
 **Suggestion:**  
 Locate the following section in the configuration file and increase the value of `min_silence_duration_ms` (for example, change it to `1000`):
 ```yaml
 VAD:
  SileroVAD:
    threshold: 0.5
    model_dir: models/snakers4_silero-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
+    min_silence_duration_ms: 700  # If your pauses are longer, increase this value
 ```
-## 6、For more questions, contact us to feedback
+### 4. Why does XiaoZhi recognize a lot of Korean, Japanese, and English in what I say? 🇰🇷
-![图片](docs/images/wechat.jpg)
+**Suggestion:**  
 Check whether the `model.pt` file exists in the `models/SenseVoiceSmall` directory. If it does not, please download it. See [Download ASR Model Files](docs/Deployment.md#模型文件) for details.
-# Acknowledgments
+### 5. Why does the error “TTS task error: file does not exist” occur? 📁
- This project is inspired by the [Bailin Voice Dialogue Robot](https://github.com/wwbin2017/bailing) project, and the
+**Suggestion:**  
-  basic idea of the project is completed。
+Verify that you have correctly installed the `libopus` and `ffmpeg` libraries using `conda`. If not, install them using:
- Thanks to [Tencent Cloud] (https://cloud.tencent.com/) for providing free docker space for this project。
+
- Thanks to [tenclass](https://www.tenclass.com/)Provide adequate documentation support on Xiaozhi Communication
+```
-  Protocol。
+conda install conda-forge::libopus
 conda install conda-forge::ffmpeg
 ```
 ### 6. How can I improve XiaoZhi's dialogue response speed? ⚡
 The default configuration of this project is designed to be cost-effective. It is recommended that beginners first use the default free models to ensure that the system runs smoothly, then optimize for faster response times.  
 To improve response speed, you can try replacing individual components. Below are the response time test results for each component (for reference only, not a guarantee):
 **LLM Performance Ranking:**
 | Module Name  | Average First Token Time | Average Total Response Time |
 |--------------|--------------------------|-----------------------------|
 | AliLLM       | 0.547s                   | 1.485s                      |
 | ChatGLMLLM   | 0.677s                   | 3.057s                      |
 | OllamaLLM    | 0.003s                   | 0.003s                      |
 **TTS Performance Ranking:**
 | Module Name                | Average Synthesis Time |
 |----------------------------|------------------------|
 | EdgeTTS                    | 1.019s                 |
 | DoubaoTTS                  | 0.503s                 |
 | CosyVoiceSiliconflow       | 3.732s                 |
 **Recommended Configuration Combination (Overall Response Speed):**
 | Combination Scheme                | Overall Score | LLM First Token | TTS Synthesis |
 |-----------------------------------|---------------|-----------------|---------------|
 | AliLLM + DoubaoTTS                | 0.539         | 0.547s          | 0.503s        |
 | AliLLM + EdgeTTS                  | 0.642         | 0.547s          | 1.019s        |
 | ChatGLMLLM + DoubaoTTS            | 0.642         | 0.677s          | 0.503s        |
 | ChatGLMLLM + EdgeTTS              | 0.745         | 0.677s          | 1.019s        |
 | AliLLM + CosyVoiceSiliconflow     | 1.184         | 0.547s          | 3.732s        |
 **Conclusion 🔍**
 _As of February 19, 2025, if my computer were located in Haizhu District, Guangzhou, Guangdong Province, and connected via China Unicom, I would prioritize using:_
 - **LLM:** `AliLLM`
 - **TTS:** `DoubaoTTS`
 ### 7. For more questions, feel free to contact us for feedback 💬
 Our contact information is in [Baidu Netdisk](https://pan.baidu.com/s/1x6USjvP1nTRsZ45XlJu65Q)，The extraction code is`223y`。
 ---
 ## Acknowledgements 🙏
 - This project was inspired by the [Bailing Voice Dialogue Robot](https://github.com/wwbin2017/bailing) and implemented based on it.
 - Many thanks to [Tenclass](https://www.tenclass.com/) for providing detailed documentation support for the XiaoZhi communication protocol.
 <a href="https://star-history.com/#xinnan-tech/xiaozhi-esp32-server&Date">
 <picture>
@@ -1,17 +1,20 @@
 import asyncio
 from config.logger import setup_logging
-from config.settings import load_config
+from config.settings import load_config, check_config_file
 from core.websocket_server import WebSocketServer
 from manager.http_server import WebUI
 from aiohttp import web
-from core.utils.util import get_local_ip
+from core.utils.util import get_local_ip, check_ffmpeg_installed
 TAG = __name__
 async def main():
    check_config_file()
    check_ffmpeg_installed()
    logger = setup_logging()
    config = load_config()
-    
+
    # 启动 WebSocket 服务器
    ws_server = WebSocketServer(config)
    ws_task = asyncio.create_task(ws_server.start())
@@ -33,7 +36,7 @@ async def main():
            logger.bind(tag=TAG).info(f"WebUI server is running at http://{local_ip}:{port}")
        except Exception as e:
            logger.bind(tag=TAG).error(f"Failed to start WebUI server: {e}")
-    
+
    try:
        # 等待 WebSocket 服务器运行
        await ws_task
@@ -42,5 +45,6 @@ async def main():
        if webui_runner:
            await webui_runner.cleanup()
 if __name__ == "__main__":
    asyncio.run(main())
@@ -22,13 +22,28 @@ server:
    # 可选:设备白名单，如果设置了白名单，那么白名单的机器无论是什么token都可以连接。
    #allowed_devices:
    #  - "24:0A:C4:1D:3B:F0"  # MAC地址列表
 log:
  # 设置控制台输出的日志格式，时间、日志级别、标签、消息
  log_format: "<green>{time:YY-MM-DD HH:mm:ss}</green>[<light-blue>{extra[tag]}</light-blue>] - <level>{level}</level> - <light-green>{message}</light-green>"
  # 设置日志文件输出的格式，时间、日志级别、标签、消息
  log_format_simple: "{time:YYYY-MM-DD HH:mm:ss} - {name} - {level} - {extra[tag]} - {message}"
  # 设置日志等级：INFO、DEBUG
  log_level: INFO
  # 设置日志路径
  log_dir: tmp
  # 设置日志文件
  log_file: "server.log"
  # 设置数据文件路径
  data_dir: data
 manager:
  # 是否启用管理后台
  # 目前这个模块还在开发中，建议：不要修改enabled选项
  enabled: false
  ip: 0.0.0.0
  port: 8002
-
+iot:
  Speaker:
    volume: 100
 xiaozhi:
  type: hello
  version: 1
@@ -59,7 +74,7 @@ CMD_exit:
 # 具体处理时选择的模块(The module selected for specific processing)
 selected_module:
-  ASR: DoubaoASR
+  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
@@ -134,6 +149,12 @@ LLM:
    user_id: 你的user_id
    base_url: "https://api.coze.cn/open_api/v2/chat" # 服务地址
    personal_access_token: 你的coze个人令牌
  LMStudioLLM:
    # 定义LLM API类型
    type: openai
    model_name: deepseek-r1-distill-llama-8b@q4_k_m # 使用的模型名称，需要预先在社区下载
    url: http://localhost:1234/v1 # LM Studio服务地址
    api_key: lm-studio # LM Studio服务的固定API Key
  HomeAssistant:
    # 定义LLM API类型
    type: homeassistant
@@ -154,8 +175,10 @@ TTS:
    # 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。
    # 地址：https://console.volcengine.com/speech/service/8
    api_url: https://openspeech.bytedance.com/api/v1/tts
    voice: BV001_streaming
    output_file: tmp/
    authorization: "Bearer;"
    appid: 你的火山引擎语音合成服务appid
    access_token: 你的火山引擎语音合成服务access_token
    cluster: volcano_tts
@@ -284,10 +307,42 @@ TTS:
    # volume: 50
    # speech_rate: 0
    # pitch_rate: 0
-
+    # 添加 302.ai TTS 配置
    # token申请地址：https://dash.302.ai/
  TTS302AI:
    # 302AI语音合成服务，需要先在302平台创建账户充值，并获取密钥信息
    # 获取api_keyn路径：https://dash.302.ai/apis/list
    # 价格，$35/百万字符。火山原版¥450元/万字符
    type: doubao
    api_url: https://api.302ai.cn/doubao/tts_hd
    authorization: "Bearer "
    voice: "zh_female_wanwanxiaohe_moon_bigtts"
    output_file: tmp/
    access_token: "你的302API密钥"
 # 模块测试配置
 module_test:
  test_sentences:  # 自定义测试语句
    - "你好，请介绍一下你自己"
    - "What's the weather like today?"
    - "请用100字概括量子计算的基本原理和应用前景"
 # 本地音乐播放配置
 music:
  music_commands:
    - "来一首歌"
    - "唱一首歌"
    - "播放音乐"
    - "来点音乐"
    - "背景音乐"
    - "放首歌"
    - "播放歌曲"
    - "来点背景音乐"
    - "我想听歌"
    - "我要听歌"
    - "放点音乐"
  music_dir: "./music"  # 音乐文件存放路径，将从该目录及子目录下搜索音乐文件
  music_ext: # 音乐文件类型，p3格式效率最高
    - ".mp3"
    - ".wav"
    - ".p3"
  refresh_time: 300 # 刷新音乐列表的时间间隔，单位为秒
@@ -1,27 +1,29 @@
 import os
 import sys
 from loguru import logger
 from config.settings import load_config
 def setup_logging():
    """从配置文件中读取日志配置，并设置日志输出格式和级别"""
    config = load_config()
    log_config = config["log"]
    log_format = log_config.get("log_format", "<green>{time:YY-MM-DD HH:mm:ss}</green>[<light-blue>{extra[tag]}</light-blue>] - <level>{level}</level> - <light-green>{message}</light-green>")
    log_format_simple = log_config.get("log_format_file", "{time:YYYY-MM-DD HH:mm:ss} - {name} - {level} - {extra[tag]} - {message}")
    log_level = log_config.get("log_level", "INFO")
    log_dir = log_config.get("log_dir", "tmp")
    log_file = log_config.get("log_file", "server.log")
    data_dir = log_config.get("data_dir", "data")
 def setup_logging(log_dir='tmp', data_dir='data'):
    """配置全局彩色日志（不同区块不同标签）"""
    os.makedirs(log_dir, exist_ok=True)
    os.makedirs(data_dir, exist_ok=True)
    # 设置日志格式，时间、日志级别、标签、消息
    log_format = (
        "<green>{time:YY-MM-DD HH:mm:ss}</green>"
        "[<light-blue>{extra[tag]}</light-blue>]"
        " - <level>{level}</level> - "
        "<light-green>{message}</light-green>"
    )
    # 配置日志输出
    logger.remove()
    # 输出到控制台
-    logger.add(sys.stdout, format=log_format, level="INFO")
+    logger.add(sys.stdout, format=log_format, level=log_level)
    # 输出到文件
-    logger.add(os.path.join(log_dir, "server.log"), format="{time:YYYY-MM-DD HH:mm:ss} - {name} - {level} - {extra[tag]} - {message}", level="INFO")
+    logger.add(os.path.join(log_dir, log_file), format=log_format_simple, level=log_level)
    return logger
@@ -1,22 +1,26 @@
 import os
 import argparse
 from ruamel.yaml import YAML
 from collections.abc import Mapping
 from core.utils.util import read_config, get_project_dir
 default_config_file = "config.yaml"
 def get_config_file():
-    default_config_file = "config.yaml"
+    global default_config_file
    # 判断是否存在私有的配置文件
    config_file = default_config_file
    if os.path.exists(get_project_dir() + "data/." + default_config_file):
-        default_config_file = "data/." + default_config_file
+        config_file = "data/." + default_config_file
-    return default_config_file
+    return config_file
 def load_config():
    """加载配置文件"""
    parser = argparse.ArgumentParser(description="Server configuration")
-    default_config_file = get_config_file()
+    config_file = get_config_file()
-    parser.add_argument("--config_path", type=str, default=default_config_file)
+    parser.add_argument("--config_path", type=str, default=config_file)
    args = parser.parse_args()
    return read_config(args.config_path)
@@ -27,3 +31,54 @@ def update_config(config):
    """将配置保存到YAML文件"""
    with open(get_config_file(), 'w') as f:
        yaml.dump(config, f)
 def find_missing_keys(new_config, old_config, parent_key=''):
    """
    递归查找缺失的配置项
    返回格式：[缺失配置路径]
    """
    missing_keys = []
    if not isinstance(new_config, Mapping):
        return missing_keys
    for key, value in new_config.items():
        # 构建当前配置路径
        full_path = f"{parent_key}.{key}" if parent_key else key
        # 检查键是否存在
        if key not in old_config:
            missing_keys.append(full_path)
            continue
        # 递归检查嵌套字典
        if isinstance(value, Mapping):
            sub_missing = find_missing_keys(
                value,
                old_config[key],
                parent_key=full_path
            )
            missing_keys.extend(sub_missing)
    return missing_keys
 def check_config_file():
    old_config_file = get_config_file()
    global default_config_file
    if not old_config_file.startswith('data'):
        return
    old_config = read_config(get_project_dir() + old_config_file)
    new_config = read_config(get_project_dir() + default_config_file)
    # 查找缺失的配置项
    missing_keys = find_missing_keys(new_config, old_config)
    if missing_keys:
        error_msg = "您的配置文件太旧了，缺少了：\n"
        error_msg += "\n".join(f"- {key}" for key in missing_keys)
        error_msg += "\n建议您：\n"
        error_msg += "1、备份data/.config.yaml文件\n"
        error_msg += "2、将根目录的config.yaml文件复制到data下，重命名为.config.yaml\n"
        error_msg += "3、将密钥逐个复制到新的配置文件中\n"
        raise ValueError(error_msg)
@@ -4,25 +4,26 @@ import uuid
 import time
 import queue
 import asyncio
 import traceback
 from config.logger import setup_logging
 import threading
 import websockets
 from typing import Dict, Any
 from collections import deque
 from core.utils.util import is_segment
 from core.utils.dialogue import Message, Dialogue
 from core.handle.textHandle import handleTextMessage
 from core.utils.util import get_string_no_punctuation_or_emoji
 from concurrent.futures import ThreadPoolExecutor, TimeoutError
-from core.handle.audioHandle import handleAudioMessage, sendAudioMessage
+from core.handle.sendAudioHandle import sendAudioMessage
 from core.handle.receiveAudioHandle import handleAudioMessage
 from config.private_config import PrivateConfig
 from core.auth import AuthMiddleware, AuthenticationError
-from core.utils.auth_code_gen import AuthCodeGenerator  # 添加导入
+from core.utils.auth_code_gen import AuthCodeGenerator
 TAG = __name__
 class ConnectionHandler:
-    def __init__(self, config: Dict[str, Any], _vad, _asr, _llm, _tts):
+    def __init__(self, config: Dict[str, Any], _vad, _asr, _llm, _tts, _music):
        self.config = config
        self.logger = setup_logging()
        self.auth = AuthMiddleware(config)
@@ -41,8 +42,8 @@ class ConnectionHandler:
        self.loop = asyncio.get_event_loop()
        self.stop_event = threading.Event()
        self.tts_queue = queue.Queue()
        self.audio_play_queue = queue.Queue()
        self.executor = ThreadPoolExecutor(max_workers=10)
        self.scheduled_tasks = deque()
        # 依赖的组件
        self.vad = _vad
@@ -72,28 +73,33 @@ class ConnectionHandler:
        self.tts_start_speak_time = None
        self.tts_duration = 0
        # iot相关变量
        self.iot_descriptors = {}
        self.cmd_exit = self.config["CMD_exit"]
        self.max_cmd_length = 0
        for cmd in self.cmd_exit:
            if len(cmd) > self.max_cmd_length:
                self.max_cmd_length = len(cmd)
-        
+
        self.private_config = None
        self.auth_code_gen = AuthCodeGenerator.get_instance()
        self.is_device_verified = False  # 添加设备验证状态标志
-
+        self.music_handler = _music
    async def handle_connection(self, ws):
        try:
            # 获取并验证headers
            self.headers = dict(ws.request.headers)
-            self.logger.bind(tag=TAG).info(f"New connection request - Headers: {self.headers}")
+            # 获取客户端ip地址
            client_ip = ws.remote_address[0]
            self.logger.bind(tag=TAG).info(f"{client_ip} conn - Headers: {self.headers}")
            # 进行认证
            await self.auth.authenticate(self.headers)
            device_id = self.headers.get("device-id", None)
-            
+
            # Load private configuration if device_id is provided
            bUsePrivateConfig = self.config.get("use_private_config", False)
            self.logger.bind(tag=TAG).info(f"bUsePrivateConfig: {bUsePrivateConfig}, device_id: {device_id}")
@@ -104,10 +110,10 @@ class ConnectionHandler:
                    # 判断是否已经绑定
                    owner = self.private_config.get_owner()
                    self.is_device_verified = owner is not None
-                    
+
                    if self.is_device_verified:
-                        await self.private_config.update_last_chat_time() 
+                        await self.private_config.update_last_chat_time()
-                    
+
                    llm, tts = self.private_config.create_private_instances()
                    if all([llm, tts]):
                        self.llm = llm
@@ -131,9 +137,14 @@ class ConnectionHandler:
            await self.loop.run_in_executor(None, self._initialize_components)
-            tts_priority = threading.Thread(target=self._priority_thread, daemon=True)
+            # tts 消化线程
            tts_priority = threading.Thread(target=self._tts_priority_thread, daemon=True)
            tts_priority.start()
            # 音频播放 消化线程
            audio_play_priority = threading.Thread(target=self._audio_play_priority_thread, daemon=True)
            audio_play_priority.start()
            try:
                async for message in self.websocket:
                    await self._route_message(message)
@@ -146,7 +157,8 @@ class ConnectionHandler:
            await ws.close()
            return
        except Exception as e:
-            self.logger.bind(tag=TAG).error(f"Connection error: {str(e)}")
+            stack_trace = traceback.format_exc()
            self.logger.bind(tag=TAG).error(f"Connection error: {str(e)}-{stack_trace}")
            await ws.close()
            return
@@ -166,7 +178,7 @@ class ConnectionHandler:
            date_time = time.strftime("%Y-%m-%d %H:%M", time.localtime())
            self.prompt = self.prompt.replace("{date_time}", date_time)
        self.dialogue.put(Message(role="system", content=self.prompt))
-          
+
    async def _check_and_broadcast_auth_code(self):
        """检查设备绑定状态并广播认证码"""
        if not self.private_config.get_owner():
@@ -186,12 +198,10 @@ class ConnectionHandler:
            # 如果不使用私有配置，就不需要验证
            return False
        return not self.is_device_verified
-    
+
    def chat(self, query):
        # 如果设备未验证，就发送验证码
        if self.isNeedAuth():
            self.llm_finish_task = True
            # 创建一个新的事件循环来运行异步函数
            loop = asyncio.new_event_loop()
            asyncio.set_event_loop(loop)
            try:
@@ -199,52 +209,64 @@ class ConnectionHandler:
            finally:
                loop.close()
            return True
-        
+
        self.dialogue.put(Message(role="user", content=query))
        response_message = []
-        start = 0
+        processed_chars = 0  # 跟踪已处理的字符位置
        # 提交 LLM 任务
        try:
-            start_time = time.time()  # 记录开始时间
+            start_time = time.time()
            llm_responses = self.llm.response(self.session_id, self.dialogue.get_llm_dialogue())
        except Exception as e:
            self.logger.bind(tag=TAG).error(f"LLM 处理出错 {query}: {e}")
            return None
-        # 提交 TTS 任务到线程池
+
        self.llm_finish_task = False
        for content in llm_responses:
            response_message.append(content)
            # 如果中途被打断，就停止生成
            if self.client_abort:
                start = len(response_message)
                break
-            end_time = time.time()  # 记录结束时间
+            end_time = time.time()
-            self.logger.bind(tag=TAG).debug(f"大模型返回时间时间: {end_time - start_time} 秒, 生成token={content}")
+            self.logger.bind(tag=TAG).debug(f"大模型返回时间: {end_time - start_time} 秒, 生成token={content}")
-            if is_segment(response_message):
+
-                segment_text = "".join(response_message[start:])
+            # 合并当前全部文本并处理未分割部分
-                segment_text = get_string_no_punctuation_or_emoji(segment_text)
+            full_text = "".join(response_message)
-                if len(segment_text) > 0:
+            current_text = full_text[processed_chars:]  # 从未处理的位置开始
            # 查找最后一个有效标点
            punctuations = ("。", "？", "！", "?", "!", ";", "；", ":", "：", "，")
            last_punct_pos = -1
            for punct in punctuations:
                pos = current_text.rfind(punct)
                if pos > last_punct_pos:
                    last_punct_pos = pos
            # 找到分割点则处理
            if last_punct_pos != -1:
                segment_text_raw = current_text[:last_punct_pos + 1]
                segment_text = get_string_no_punctuation_or_emoji(segment_text_raw)
                if segment_text:
                    self.recode_first_last_text(segment_text)
                    future = self.executor.submit(self.speak_and_play, segment_text)
                    self.tts_queue.put(future)
-                    start = len(response_message)
+                    processed_chars += len(segment_text_raw)  # 更新已处理字符位置
-        # 处理剩余的响应
+        # 处理最后剩余的文本
-        if start < len(response_message):
+        full_text = "".join(response_message)
-            segment_text = "".join(response_message[start:])
+        remaining_text = full_text[processed_chars:]
-            if len(segment_text) > 0:
+        if remaining_text:
            segment_text = get_string_no_punctuation_or_emoji(remaining_text)
            if segment_text:
                self.recode_first_last_text(segment_text)
                future = self.executor.submit(self.speak_and_play, segment_text)
                self.tts_queue.put(future)
        self.llm_finish_task = True
        # 更新对话
        self.dialogue.put(Message(role="assistant", content="".join(response_message)))
        self.logger.bind(tag=TAG).debug(json.dumps(self.dialogue.get_llm_dialogue(), indent=4, ensure_ascii=False))
        return True
-    def _priority_thread(self):
+    def _tts_priority_thread(self):
        while not self.stop_event.is_set():
            text = None
            try:
@@ -266,7 +288,6 @@ class ConnectionHandler:
                    else:
                        self.logger.bind(tag=TAG).error(f"TTS文件不存在: {tts_file}")
                        opus_datas = []
                        duration = 0
                except TimeoutError:
                    self.logger.bind(tag=TAG).error("TTS 任务超时")
                    continue
@@ -275,9 +296,7 @@ class ConnectionHandler:
                    continue
                if not self.client_abort:
                    # 如果没有中途打断就发送语音
-                    asyncio.run_coroutine_threadsafe(
+                    self.audio_play_queue.put((opus_datas, text))
                        sendAudioMessage(self, opus_datas, duration, text), self.loop
                    )
                if self.tts.delete_audio_file and os.path.exists(tts_file):
                    os.remove(tts_file)
            except Exception as e:
@@ -289,6 +308,16 @@ class ConnectionHandler:
                )
                self.logger.bind(tag=TAG).error(f"tts_priority priority_thread: {text}{e}")
    def _audio_play_priority_thread(self):
        while not self.stop_event.is_set():
            text = None
            try:
                opus_datas, text = self.audio_play_queue.get()
                future = asyncio.run_coroutine_threadsafe(sendAudioMessage(self, opus_datas, text), self.loop)
                future.result()
            except Exception as e:
                self.logger.bind(tag=TAG).error(f"audio_play_priority priority_thread: {text}{e}")
    def speak_and_play(self, text):
        if text is None or len(text) <= 0:
            self.logger.bind(tag=TAG).info(f"无需tts转换，query为空，{text}")
@@ -316,6 +345,8 @@ class ConnectionHandler:
    async def close(self):
        """资源清理方法"""
        # 清理其他资源
        self.stop_event.set()
        self.executor.shutdown(wait=False)
        if self.websocket:
@@ -328,9 +359,3 @@ class ConnectionHandler:
        self.client_have_voice_last_time = 0
        self.client_voice_stop = False
        self.logger.bind(tag=TAG).debug("VAD states reset.")
    def stop_all_tasks(self):
        while self.scheduled_tasks:
            task = self.scheduled_tasks.popleft()
            task.cancel()
        self.scheduled_tasks.clear()
@@ -1,4 +1,5 @@
 import json
 import queue
 from config.logger import setup_logging
 TAG = __name__
@@ -9,8 +10,6 @@ async def handleAbortMessage(conn):
    logger.bind(tag=TAG).info("Abort message received")
    # 设置成打断状态，会自动打断llm、tts任务
    conn.client_abort = True
    # 打断屏显任务
    conn.stop_all_tasks()
    # 打断客户端说话状态
    await conn.websocket.send(json.dumps({"type": "tts", "state": "stop", "session_id": conn.session_id}))
    conn.clearSpeakStatus()
@@ -1,167 +0,0 @@
 from config.logger import setup_logging
 import json
 import asyncio
 import time
 from core.utils.util import remove_punctuation_and_length, get_string_no_punctuation_or_emoji
 TAG = __name__
 logger = setup_logging()
 async def handleAudioMessage(conn, audio):
    if not conn.asr_server_receive:
        logger.bind(tag=TAG).debug(f"前期数据处理中，暂停接收")
        return
    if conn.client_listen_mode == "auto":
        have_voice = conn.vad.is_vad(conn, audio)
    else:
        have_voice = conn.client_have_voice
    # 如果本次没有声音，本段也没声音，就把声音丢弃了
    if have_voice == False and conn.client_have_voice == False:
        await no_voice_close_connect(conn)
        conn.asr_audio.clear()
        return
    conn.client_no_voice_last_time = 0.0
    conn.asr_audio.append(audio)
    # 如果本段有声音，且已经停止了
    if conn.client_voice_stop:
        conn.client_abort = False
        conn.asr_server_receive = False
        # 音频太短了，无法识别
        if len(conn.asr_audio) < 3:
            conn.asr_server_receive = True
        else:
            text, file_path = await conn.asr.speech_to_text(conn.asr_audio, conn.session_id)
            logger.bind(tag=TAG).info(f"识别文本: {text}")
            text_len, text_without_punctuation = remove_punctuation_and_length(text)
            if text_len <= conn.max_cmd_length and await handleCMDMessage(conn, text_without_punctuation):
                return
            if text_len > 0:
                await startToChat(conn, text)
            else:
                conn.asr_server_receive = True
        conn.asr_audio.clear()
        conn.reset_vad_states()
 async def handleCMDMessage(conn, text):
    cmd_exit = conn.cmd_exit
    for cmd in cmd_exit:
        if text == cmd:
            logger.bind(tag=TAG).info("识别到明确的退出命令".format(text))
            await finishToChat(conn)
            return True
    return False
 async def finishToChat(conn):
    await conn.close()
 async def isLLMWantToFinish(conn):
    first_text = conn.tts_first_text
    last_text = conn.tts_last_text
    _, last_text_without_punctuation = remove_punctuation_and_length(last_text)
    if "再见" in last_text_without_punctuation or "拜拜" in last_text_without_punctuation:
        return True
    _, first_text_without_punctuation = remove_punctuation_and_length(first_text)
    if "再见" in first_text_without_punctuation or "拜拜" in first_text_without_punctuation:
        return True
    return False
 async def startToChat(conn, text):
    # 异步发送 stt 信息
    stt_task = asyncio.create_task(
        schedule_with_interrupt(0, send_stt_message(conn, text))
    )
    conn.scheduled_tasks.append(stt_task)
    conn.executor.submit(conn.chat, text)
 async def sendAudioMessage(conn, audios, duration, text):
    base_delay = conn.tts_duration
    # 发送 tts.start
    if text == conn.tts_first_text:
        logger.bind(tag=TAG).info(f"发送第一段语音: {text}")
        conn.tts_start_speak_time = time.time()
    # 发送 sentence_start（每个音频文件之前发送一次）
    sentence_task = asyncio.create_task(
        schedule_with_interrupt(base_delay, send_tts_message(conn, "sentence_start", text))
    )
    conn.scheduled_tasks.append(sentence_task)
    conn.tts_duration += duration
    # 发送音频数据
    for idx, opus_packet in enumerate(audios):
        await conn.websocket.send(opus_packet)
    if conn.llm_finish_task and text == conn.tts_last_text:
        stop_duration = conn.tts_duration - (time.time() - conn.tts_start_speak_time)
        stop_task = asyncio.create_task(
            schedule_with_interrupt(stop_duration, send_tts_message(conn, 'stop'))
        )
        conn.scheduled_tasks.append(stop_task)
        if await isLLMWantToFinish(conn):
            finish_task = asyncio.create_task(
                schedule_with_interrupt(stop_duration, finishToChat(conn))
            )
            conn.scheduled_tasks.append(finish_task)
 async def send_tts_message(conn, state, text=None):
    """发送 TTS 状态消息"""
    message = {
        "type": "tts",
        "state": state,
        "session_id": conn.session_id
    }
    if text is not None:
        message["text"] = text
    await conn.websocket.send(json.dumps(message))
    if state == "stop":
        conn.clearSpeakStatus()
 async def send_stt_message(conn, text):
    """发送 STT 状态消息"""
    stt_text = get_string_no_punctuation_or_emoji(text)
    await conn.websocket.send(json.dumps({
        "type": "stt",
        "text": stt_text,
        "session_id": conn.session_id}
    ))
    await conn.websocket.send(
        json.dumps({
            "type": "llm",
            "text": "😊",
            "emotion": "happy",
            "session_id": conn.session_id}
        ))
    await send_tts_message(conn, "start")
 async def schedule_with_interrupt(delay, coro):
    """可中断的延迟调度"""
    try:
        await asyncio.sleep(delay)
        await coro
    except asyncio.CancelledError:
        pass
 async def no_voice_close_connect(conn):
    if conn.client_no_voice_last_time == 0.0:
        conn.client_no_voice_last_time = time.time() * 1000
    else:
        no_voice_time = time.time() * 1000 - conn.client_no_voice_last_time
        close_connection_no_voice_time = conn.config.get("close_connection_no_voice_time", 120)
        if no_voice_time > 1000 * close_connection_no_voice_time:
            conn.client_abort = False
            conn.asr_server_receive = False
            prompt = "时间过得真快，我都好久没说话了。请你用十个字左右话跟我告别，以“再见”或“拜拜拜”为结尾"
            await startToChat(conn, prompt)
@@ -0,0 +1,152 @@
 import json
 from config.logger import setup_logging
 TAG = __name__
 logger = setup_logging()
 class IotDescriptor:
    """
    A class to represent an IoT descriptor.
    Attributes:
    ----------
    name : str
        The name of the IoT descriptor.
    description : str
        A brief description of the IoT descriptor.
    properties : dict
        A dictionary containing properties of the IoT descriptor.
    methods : dict
        A dictionary containing methods of the IoT descriptor.
    -------
    """
    def __init__(self, name, description, properties, methods):
        self.name = name
        self.description = description
        self.properties = []
        self.methods = []
        # 根据描述创建属性
        for key, value in properties.items():
            # "volume":{"description":"当前音量 值","type":"number"}
            """
            等价于
            {
                'name': 名字,
                'description': 描述,
                'value': 0
            }
            """
            # setattr(self, key, {}) # 创建一个空字典, 名字是属性名
            property_item = globals()[key] = {}  # 创建一个空字典, 名字是属性名
            property_item['name'] = key
            property_item["description"] = value["description"]
            if value["type"] == "number":
                property_item["value"] = 0
            elif value["type"] == "boolean":
                property_item["value"] = False
            else:
                property_item["value"] = ""
            self.properties.append(property_item)
        # 根据描述创建方法
        for key, value in methods.items():
            # "SetVolume": {"description":"设置音量","parameters":{"volume":{"description":"0到100之间的整数","type":"number"}}}
            """
            等价于
            SetVolume = {
                `description`: 描述,
                `volume`: {
                    `description`: 描述,
                    `value`: 0
                }
            }
            """
            # setattr(self, key, {}) # 创建一个空字典, 名字是方法名
            method = globals()[key] = {}  # 创建一个空字典, 名字是方法名
            method["description"] = value["description"]
            method['name'] = key
            for k, v in value["parameters"].items():
                # 不同的参数解析
                method[k] = {}
                method[k]["description"] = v["description"]
                if v["type"] == "number":
                    method[k]["value"] = 0
                elif v["type"] == "boolean":
                    method[k]["value"] = False
                else:
                    method[k]["value"] = ""
            self.methods.append(method)
 async def handleIotDescriptors(conn, descriptors):
    """
    处理物联网描述
    示例: [{
        "name":"Speaker",
        "description":"当前 AI 机器人的扬声器",
        "properties":{
            "volume":{"description":"当前音量 值","type":"number"}  可以有boolean, number, string三种类型
        },
        "methods":{
            "SetVolume":{
                "description":"设置音量","parameters":{"volume":{"description":"0到100之间的整数","type":"number"}}
            }
        }
    }]
    descriptors: 描述列表
    """
    for descriptor in descriptors:
        iot_descriptor = IotDescriptor(descriptor["name"], descriptor["description"], descriptor["properties"],
                                       descriptor["methods"])
        conn.iot_descriptors[descriptor["name"]] = iot_descriptor
    # 暂时从配置文件中设置音量，后期通过意图识别控制音量
    default_iot_volume = 100
    if "iot" in conn.config:
        default_iot_volume = conn.config["iot"]["Speaker"]["volume"]
    logger.bind(tag=TAG).info(f"服务端设置音量为{default_iot_volume}")
    await send_iot_conn(conn, "Speaker", "SetVolume", {"volume": default_iot_volume})
 async def send_iot_conn(conn, name, method_name, parameters):
    """
    发送物联网指令
    name: 设备名称 "Speaker"
    method: 方法 "SetVolume"
    parameters: 参数, 是一个字典 {"volume": 100}
    发送示例:
    {
        "type": "iot",
        "commands": [
            {
                "name" :  "Speaker",
                "method": "SetVolume",
                "parameters": {
                    "volume": 100
                    }
            }
        ]
    }
    """
    for key, value in conn.iot_descriptors.items():
        if key == name:
            # 找到了设备
            for method in value.methods:
                # 找到了方法
                if method["name"] == method_name:
                    await conn.websocket.send(json.dumps({
                        "type": "iot",
                        "commands": [
                            {
                                "name": name,
                                "method": method_name,
                                "parameters": parameters
                            }
                        ]
                    }))
                    return
    logger.bind(tag=TAG).error(f"未找到方法{method_name}")
@@ -0,0 +1,153 @@
 from config.logger import setup_logging
 import os
 import random
 import difflib
 import re
 import traceback
 from pathlib import Path
 import time
 from core.handle.sendAudioHandle import send_stt_message
 from core.utils import p3
 TAG = __name__
 logger = setup_logging()
 def _extract_song_name(text):
    """从用户输入中提取歌名"""
    for keyword in ["听", "播放", "放", "唱"]:
        if keyword in text:
            parts = text.split(keyword)
            if len(parts) > 1:
                return parts[1].strip()
    return None
 def _find_best_match(potential_song, music_files):
    """查找最匹配的歌曲"""
    best_match = None
    highest_ratio = 0
    for music_file in music_files:
        song_name = os.path.splitext(music_file)[0]
        ratio = difflib.SequenceMatcher(None, potential_song, song_name).ratio()
        if ratio > highest_ratio and ratio > 0.4:
            highest_ratio = ratio
            best_match = music_file
    return best_match
 class MusicManager:
    def __init__(self, music_dir, music_ext):
        self.music_dir = Path(music_dir)
        self.music_ext = music_ext
    def get_music_files(self):
        music_files = []
        for file in self.music_dir.rglob("*"):
            # 判断是否是文件
            if file.is_file():
                # 获取文件扩展名
                ext = file.suffix.lower()
                # 判断扩展名是否在列表中
                if ext in self.music_ext:
                    # music_files.append(str(file.resolve()))  # 添加绝对路径
                    # 添加相对路径
                    music_files.append(str(file.relative_to(self.music_dir)))
        return music_files
 class MusicHandler:
    def __init__(self, config):
        self.config = config
        self.music_related_keywords = []
        if "music" in self.config:
            self.music_config = self.config["music"]
            self.music_dir = os.path.abspath(
                self.music_config.get("music_dir", "./music")  # 默认路径修改
            )
            self.music_related_keywords = self.music_config.get("music_commands", [])
            self.music_ext = self.music_config.get("music_ext", (".mp3", ".wav", ".p3"))
            self.refresh_time = self.music_config.get("refresh_time", 60)
        else:
            self.music_dir = os.path.abspath("./music")
            self.music_related_keywords = ["来一首歌", "唱一首歌", "播放音乐", "来点音乐", "背景音乐", "放首歌",
                                           "播放歌曲", "来点背景音乐", "我想听歌", "我要听歌", "放点音乐"]
            self.music_ext = (".mp3", ".wav", ".p3")
            self.refresh_time = 60
        # 获取音乐文件列表
        self.music_files = MusicManager(self.music_dir, self.music_ext).get_music_files()
        self.scan_time = time.time()
        logger.bind(tag=TAG).debug(f"找到的音乐文件: {self.music_files}")
    async def handle_music_command(self, conn, text):
        """处理音乐播放指令"""
        clean_text = re.sub(r'[^\w\s]', '', text).strip()
        logger.bind(tag=TAG).debug(f"检查是否是音乐命令: {clean_text}")
        # 尝试匹配具体歌名
        if os.path.exists(self.music_dir):
            if time.time() - self.scan_time > self.refresh_time:
                # 刷新音乐文件列表
                self.music_files = MusicManager(self.music_dir, self.music_ext).get_music_files()
                self.scan_time = time.time()
                logger.bind(tag=TAG).debug(f"刷新的音乐文件: {self.music_files}")
            potential_song = _extract_song_name(clean_text)
            if potential_song:
                best_match = _find_best_match(potential_song, self.music_files)
                if best_match:
                    logger.bind(tag=TAG).info(f"找到最匹配的歌曲: {best_match}")
                    await self.play_local_music(conn, specific_file=best_match)
                    return True
        # 检查是否是通用播放音乐命令
        if any(cmd in clean_text for cmd in self.music_related_keywords):
            await self.play_local_music(conn)
            return True
        return False
    async def play_local_music(self, conn, specific_file=None):
        """播放本地音乐文件"""
        try:
            if not os.path.exists(self.music_dir):
                logger.bind(tag=TAG).error(f"音乐目录不存在: {self.music_dir}")
                return
            # 确保路径正确性
            if specific_file:
                music_path = os.path.join(self.music_dir, specific_file)
                if not os.path.exists(music_path):
                    logger.bind(tag=TAG).error(f"指定的音乐文件不存在: {music_path}")
                    return
                selected_music = specific_file
            else:
                if time.time() - self.scan_time > self.refresh_time:
                    # 刷新音乐文件列表
                    self.music_files = MusicManager(self.music_dir, self.music_ext).get_music_files()
                    self.scan_time = time.time()
                    logger.bind(tag=TAG).debug(f"刷新的音乐文件列表: {self.music_files}")
                if not self.music_files:
                    logger.bind(tag=TAG).error("未找到MP3音乐文件")
                    return
                selected_music = random.choice(self.music_files)
                music_path = os.path.join(self.music_dir, selected_music)
                if not os.path.exists(music_path):
                    logger.bind(tag=TAG).error(f"选定的音乐文件不存在: {music_path}")
                    return
            text = f"正在播放{selected_music}"
            await send_stt_message(conn, text)
            conn.tts_first_text = selected_music
            conn.tts_last_text = selected_music
            conn.llm_finish_task = True
            if music_path.endswith(".p3"):
                opus_packets, duration = p3.decode_opus_from_file(music_path)
            else:
                opus_packets, duration = conn.tts.wav_to_opus_data(music_path)
            conn.audio_play_queue.put((opus_packets, selected_music))
        except Exception as e:
            logger.bind(tag=TAG).error(f"播放音乐失败: {str(e)}")
            logger.bind(tag=TAG).error(f"详细错误: {traceback.format_exc()}")
@@ -0,0 +1,77 @@
 from config.logger import setup_logging
 import time
 from core.utils.util import remove_punctuation_and_length
 from core.handle.sendAudioHandle import send_stt_message
 TAG = __name__
 logger = setup_logging()
 async def handleAudioMessage(conn, audio):
    if not conn.asr_server_receive:
        logger.bind(tag=TAG).debug(f"前期数据处理中，暂停接收")
        return
    if conn.client_listen_mode == "auto":
        have_voice = conn.vad.is_vad(conn, audio)
    else:
        have_voice = conn.client_have_voice
    # 如果本次没有声音，本段也没声音，就把声音丢弃了
    if have_voice == False and conn.client_have_voice == False:
        await no_voice_close_connect(conn)
        conn.asr_audio.clear()
        return
    conn.client_no_voice_last_time = 0.0
    conn.asr_audio.append(audio)
    # 如果本段有声音，且已经停止了
    if conn.client_voice_stop:
        conn.client_abort = False
        conn.asr_server_receive = False
        # 音频太短了，无法识别
        if len(conn.asr_audio) < 3:
            conn.asr_server_receive = True
        else:
            text, file_path = await conn.asr.speech_to_text(conn.asr_audio, conn.session_id)
            logger.bind(tag=TAG).info(f"识别文本: {text}")
            text_len, text_without_punctuation = remove_punctuation_and_length(text)
            if await conn.music_handler.handle_music_command(conn, text_without_punctuation):
                conn.asr_server_receive = True
                conn.asr_audio.clear()
                return
            if text_len <= conn.max_cmd_length and await handleCMDMessage(conn, text_without_punctuation):
                return
            if text_len > 0:
                await startToChat(conn, text)
            else:
                conn.asr_server_receive = True
        conn.asr_audio.clear()
        conn.reset_vad_states()
 async def handleCMDMessage(conn, text):
    cmd_exit = conn.cmd_exit
    for cmd in cmd_exit:
        if text == cmd:
            logger.bind(tag=TAG).info("识别到明确的退出命令".format(text))
            await conn.close()
            return True
    return False
 async def startToChat(conn, text):
    # 异步发送 stt 信息
    await send_stt_message(conn, text)
    conn.executor.submit(conn.chat, text)
 async def no_voice_close_connect(conn):
    if conn.client_no_voice_last_time == 0.0:
        conn.client_no_voice_last_time = time.time() * 1000
    else:
        no_voice_time = time.time() * 1000 - conn.client_no_voice_last_time
        close_connection_no_voice_time = conn.config.get("close_connection_no_voice_time", 120)
        if no_voice_time > 1000 * close_connection_no_voice_time:
            conn.client_abort = False
            conn.asr_server_receive = False
            prompt = "时间过得真快，我都好久没说话了。请你用十个字左右话跟我告别，以“再见”或“拜拜”为结尾"
            await startToChat(conn, prompt)
@@ -0,0 +1,84 @@
 from config.logger import setup_logging
 import json
 import asyncio
 import time
 from core.utils.util import remove_punctuation_and_length, get_string_no_punctuation_or_emoji
 TAG = __name__
 logger = setup_logging()
 async def isLLMWantToFinish(last_text):
    _, last_text_without_punctuation = remove_punctuation_and_length(last_text)
    if "再见" in last_text_without_punctuation or "拜拜" in last_text_without_punctuation:
        return True
    return False
 async def sendAudioMessage(conn, audios, text):
    # 发送句子开始消息
    if text == conn.tts_first_text:
        logger.bind(tag=TAG).info(f"发送第一段语音: {text}")
        conn.tts_start_speak_time = time.perf_counter()
    await send_tts_message(conn, "sentence_start", text)
    # 初始化流控参数
    frame_duration = 60  # 毫秒
    start_time = time.perf_counter()  # 使用高精度计时器
    play_position = 0  # 已播放的时长（毫秒）
    for opus_packet in audios:
        if conn.client_abort:
            return
        # 计算当前包的预期发送时间
        expected_time = start_time + (play_position / 1000)
        current_time = time.perf_counter()
        # 等待直到预期时间
        delay = expected_time - current_time
        if delay > 0:
            await asyncio.sleep(delay)
        # 发送音频包
        await conn.websocket.send(opus_packet)
        play_position += frame_duration  # 更新播放位置
    await send_tts_message(conn, "sentence_end", text)
    # 发送结束消息（如果是最后一个文本）
    if conn.llm_finish_task and text == conn.tts_last_text:
        await send_tts_message(conn, 'stop', None)
        if await isLLMWantToFinish(text):
            await conn.close()
 async def send_tts_message(conn, state, text=None):
    """发送 TTS 状态消息"""
    message = {
        "type": "tts",
        "state": state,
        "session_id": conn.session_id
    }
    if text is not None:
        message["text"] = text
    await conn.websocket.send(json.dumps(message))
    if state == "stop":
        conn.clearSpeakStatus()
 async def send_stt_message(conn, text):
    """发送 STT 状态消息"""
    stt_text = get_string_no_punctuation_or_emoji(text)
    await conn.websocket.send(json.dumps({
        "type": "stt",
        "text": stt_text,
        "session_id": conn.session_id}
    ))
    await conn.websocket.send(
        json.dumps({
            "type": "llm",
            "text": "😊",
            "emotion": "happy",
            "session_id": conn.session_id}
        ))
    await send_tts_message(conn, "start")
@@ -2,7 +2,8 @@ from config.logger import setup_logging
 import json
 from core.handle.abortHandle import handleAbortMessage
 from core.handle.helloHandle import handleHelloMessage
-from core.handle.audioHandle import startToChat
+from core.handle.receiveAudioHandle import startToChat
 from core.handle.iotHandle import handleIotDescriptors
 TAG = __name__
 logger = setup_logging()
@@ -36,5 +37,8 @@ async def handleTextMessage(conn, message):
                conn.asr_audio.clear()
                if "text" in msg_json:
                    await startToChat(conn, msg_json["text"])
        elif msg_json["type"] == "iot":
            if "descriptors" in msg_json:
                await handleIotDescriptors(conn, msg_json["descriptors"])
    except json.JSONDecodeError:
        await conn.websocket.send(message)
@@ -8,7 +8,7 @@ import websockets
 import json
 import gzip
-import opuslib
+import opuslib_next
 from core.providers.asr.base import ASRProviderBase
 from config.logger import setup_logging
@@ -103,14 +103,14 @@ class ASRProvider(ASRProviderBase):
        file_name = f"asr_{session_id}_{uuid.uuid4()}.wav"
        file_path = os.path.join(self.output_dir, file_name)
-        decoder = opuslib.Decoder(16000, 1)  # 16kHz, 单声道
+        decoder = opuslib_next.Decoder(16000, 1)  # 16kHz, 单声道
        pcm_data = []
        for opus_packet in opus_data:
            try:
                pcm_frame = decoder.decode(opus_packet, 960)  # 960 samples = 60ms
                pcm_data.append(pcm_frame)
-            except opuslib.OpusError as e:
+            except opuslib_next.OpusError as e:
                logger.bind(tag=TAG).error(f"Opus解码错误: {e}", exc_info=True)
        with wave.open(file_path, "wb") as wf:
@@ -216,14 +216,14 @@ class ASRProvider(ASRProviderBase):
    @staticmethod
    def decode_opus(opus_data: List[bytes], session_id: str) -> List[bytes]:
-        decoder = opuslib.Decoder(16000, 1)  # 16kHz, 单声道
+        decoder = opuslib_next.Decoder(16000, 1)  # 16kHz, 单声道
        pcm_data = []
        for opus_packet in opus_data:
            try:
                pcm_frame = decoder.decode(opus_packet, 960)  # 960 samples = 60ms
                pcm_data.append(pcm_frame)
-            except opuslib.OpusError as e:
+            except opuslib_next.OpusError as e:
                logger.bind(tag=TAG).error(f"Opus解码错误: {e}", exc_info=True)
        return pcm_data
@@ -25,12 +25,25 @@ class LLMProvider(LLMProviderBase):
                messages=dialogue,
                stream=True
            )
            is_active = True
            for chunk in responses:
-                # 检查是否存在有效的choice且content不为空
+                try:
-                if chunk.choices and len(chunk.choices) > 0:
+                    # 检查是否存在有效的choice且content不为空
-                    delta = chunk.choices[0].delta
+                    delta = chunk.choices[0].delta if getattr(chunk, 'choices', None) else None
-                    content = getattr(delta, 'content', '')
+                    content = delta.content if hasattr(delta, 'content') else ''
-                    if content:  # 仅在content非空时生成
+                except IndexError:
                    content = ''
                if content:
                    # 处理标签跨多个chunk的情况
                    if '<think>' in content:
                        is_active = False
                        content = content.split('<think>')[0]
                    if '</think>' in content:
                        is_active = True
                        content = content.split('</think>')[-1]
                    if is_active:
                        yield content
        except Exception as e:
            logger.bind(tag=TAG).error(f"Error in response generation: {e}")
@@ -14,10 +14,9 @@ class TTSProvider(TTSProviderBase):
        self.access_token = config.get("access_token")
        self.cluster = config.get("cluster")
        self.voice = config.get("voice")
-
+        self.api_url = config.get("api_url")
-        self.host = "openspeech.bytedance.com"
+        self.authorization = config.get("authorization")
-        self.api_url = f"https://{self.host}/api/v1/tts"
+        self.header = {"Authorization": f"{self.authorization}{self.access_token}"}
        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}")
@@ -1,11 +1,13 @@
 import os
 import sys
 # 添加项目根目录到Python路径
 current_dir = os.path.dirname(os.path.abspath(__file__))
 project_root = os.path.abspath(os.path.join(current_dir, "..", ".."))
 sys.path.insert(0, project_root)
 from config.logger import setup_logging
 import importlib
 from datetime import datetime
 from core.utils.util import is_segment
 from core.utils.util import get_string_no_punctuation_or_emoji
 from core.utils.util import read_config, get_project_dir
 logger = setup_logging()
@@ -19,41 +21,3 @@ def create_instance(class_name, *args, **kwargs):
        return sys.modules[lib_name].LLMProvider(*args, **kwargs)
    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__":
    """
      响应速度测试
    """
    config = read_config(get_project_dir() + "config.yaml")
    llm = create_instance(
        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"]]
    )
    start_time = datetime.now()
    dialogue = []
    dialogue.append({"role": "system", "content": config.get("prompt")})
    dialogue.append({"role": "user", "content": "你好小智"})
    llm_responses = llm.response("test", dialogue)
    response_message = []
    first_text = None
    start = 0
    for content in llm_responses:
        response_message.append(content)
        if is_segment(response_message):
            segment_text = "".join(response_message[start:])
            segment_text = get_string_no_punctuation_or_emoji(segment_text)
            if len(segment_text) > 0:
                if first_text is None:
                    first_text = segment_text
                    print("大模型首次返回耗时：" + str(datetime.now() - start_time))
                start = len(response_message)
    print("大模型返回总耗时：" + str(datetime.now() - start_time))
@@ -0,0 +1,33 @@
 import struct
 def decode_opus_from_file(input_file):
    """
    从p3文件中解码 Opus 数据，并返回一个 Opus 数据包的列表以及总时长。
    """
    opus_datas = []
    total_frames = 0
    sample_rate = 16000  # 文件采样率
    frame_duration_ms = 60  # 帧时长
    frame_size = int(sample_rate * frame_duration_ms / 1000)
    with open(input_file, 'rb') as f:
        while True:
            # 读取头部（4字节）：[1字节类型，1字节保留，2字节长度]
            header = f.read(4)
            if not header:
                break
            # 解包头部信息
            _, _, data_len = struct.unpack('>BBH', header)
            # 根据头部指定的长度读取 Opus 数据
            opus_data = f.read(data_len)
            if len(opus_data) != data_len:
                raise ValueError(f"Data length({len(opus_data)}) mismatch({data_len}) in the file.")
            opus_datas.append(opus_data)
            total_frames += 1
    # 计算总时长
    total_duration = (total_frames * frame_duration_ms) / 1000.0
    return opus_datas, total_duration
@@ -2,8 +2,6 @@ import os
 import sys
 from config.logger import setup_logging
 import importlib
 from datetime import datetime
 from core.utils.util import read_config, get_project_dir
 logger = setup_logging()
@@ -16,26 +14,4 @@ def create_instance(class_name, *args, **kwargs):
            sys.modules[lib_name] = importlib.import_module(f'{lib_name}')
        return sys.modules[lib_name].TTSProvider(*args, **kwargs)
-    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")
+    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__":
    """
      响应速度测试
    """
    config = read_config(get_project_dir() + "config.yaml")
    tts = create_instance(
        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"]
    )
    tts.output_file = get_project_dir() + tts.output_file
    start = datetime.now()
    file_path = tts.to_tts("你好，测试,我是人工智能小智")
    print("语音合成耗时：" + str(datetime.now() - start))
    start = datetime.now()
    tts.wav_to_opus_data(file_path)
    print("语音opus耗时：" + str(datetime.now() - start))
@@ -3,6 +3,7 @@ import re
 import json
 import yaml
 import socket
 import subprocess
 def get_project_dir():
@@ -34,13 +35,6 @@ def write_json_file(file_path, data):
        json.dump(data, file, ensure_ascii=False, indent=4)
 def is_segment(tokens):
    if tokens[-1] in (",", ".", "?", "，", "。", "？", "！", "!", ";", "；", ":", "："):
        return True
    else:
        return False
 def is_punctuation_or_emoji(char):
    """检查字符是否为空格、指定标点或表情符号"""
    # 定义需要去除的中英文标点（包括全角/半角）
@@ -90,7 +84,7 @@ def remove_punctuation_and_length(text):
                      char not in full_width_punctuations and char not in half_width_punctuations and char not in space and char not in full_width_space])
    if result == "Yeah":
-        return 0
+        return 0, ""
    return len(result), result
@@ -120,4 +114,30 @@ def check_password(password):
        return False
    # 如果满足所有条件，则返回True
-    return True
+    return True
 def check_ffmpeg_installed():
    ffmpeg_installed = False
    try:
        # 执行ffmpeg -version命令，并捕获输出
        result = subprocess.run(
            ['ffmpeg', '-version'],
            stdout=subprocess.PIPE,
            stderr=subprocess.PIPE,
            text=True,
            check=True  # 如果返回码非零则抛出异常
        )
        # 检查输出中是否包含版本信息（可选）
        output = result.stdout + result.stderr
        if 'ffmpeg version' in output.lower():
            ffmpeg_installed = True
        return False
    except (subprocess.CalledProcessError, FileNotFoundError):
        # 命令执行失败或未找到
        ffmpeg_installed = False
    if not ffmpeg_installed:
        error_msg = "您的电脑还没正确安装ffmpeg\n"
        error_msg += "\n建议您：\n"
        error_msg += "1、按照项目的安装文档，正确进入conda环境\n"
        error_msg += "2、查阅安装文档，如何在conda环境中安装ffmpeg\n"
        raise ValueError(error_msg)
@@ -2,6 +2,7 @@ import asyncio
 import websockets
 from config.logger import setup_logging
 from core.connection import ConnectionHandler
 from core.handle.musicHandler import MusicHandler
 from core.utils.util import get_local_ip
 from core.utils import asr, vad, llm, tts
@@ -12,7 +13,8 @@ class WebSocketServer:
    def __init__(self, config: dict):
        self.config = config
        self.logger = setup_logging()
-        self._vad, self._asr, self._llm, self._tts = self._create_processing_instances()
+        self._vad, self._asr, self._llm, self._tts, self._music = self._create_processing_instances()
        self.active_connections = set()  # 添加全局连接记录
    def _create_processing_instances(self):
        """创建处理模块实例"""
@@ -43,7 +45,8 @@ class WebSocketServer:
                self.config["TTS"][self.config["selected_module"]["TTS"]]["type"],
                self.config["TTS"][self.config["selected_module"]["TTS"]],
                self.config["delete_audio"]
-            )
+            ),
            MusicHandler(self.config)
        )
    async def start(self):
@@ -62,5 +65,10 @@ class WebSocketServer:
    async def _handle_connection(self, websocket):
        """处理新连接，每次创建独立的ConnectionHandler"""
-        handler = ConnectionHandler(self.config, self._vad, self._asr, self._llm, self._tts)
+        # 创建ConnectionHandler时传入当前server实例
-        await handler.handle_connection(websocket)
+        handler = ConnectionHandler(self.config, self._vad, self._asr, self._llm, self._tts, self._music)
        self.active_connections.add(handler)
        try:
            await handler.handle_connection(websocket)
        finally:
            self.active_connections.discard(handler)
@@ -1,3 +1,4 @@
 version: '3'
 services:
  xiaozhi-esp32-server:
    image: ghcr.nju.edu.cn/xinnan-tech/xiaozhi-esp32-server:latest
@@ -117,7 +117,7 @@ docker run -it --name xiaozhi-env --restart always --security-opt seccomp:unconf
  -p 8000:8000 \
  -p 8002:8002 \
  -v ./:/app \
-  ccr.ccs.tencentyun.com/kalicyh/poetry:v3.10_latest
+  kalicyh/poetry:v3.10_xiaozhi
 ```
 然后就和正常开发一样了
@@ -159,14 +159,31 @@ poetry run python app.py
 本项目使用`conda`管理依赖环境。如果不方便安装`conda`，需要根据实际的操作系统安装好`libopus`和`ffmpeg`。
 如果确定使用`conda`，则安装好后，开始执行以下命令。
 重要提示！windows 用户，可以通过安装`Anaconda`来管理环境。安装好`Anaconda`后，在`开始`那里搜索`anaconda`相关的关键词，
 找到`Anaconda Prpmpt`，使用管理员身份运行它。如下图。
 ![conda_prompt](./images/conda_env_1.png)
 运行之后，如果你能看到命令行窗口前面有一个(base)字样，说明你成功进入了`conda`环境。那么你就可以执行以下命令了。
 ![conda_env](./images/conda_env_2.png)
 ```
 conda remove -n xiaozhi-esp32-server --all -y
 conda create -n xiaozhi-esp32-server python=3.10 -y
 conda activate xiaozhi-esp32-server
-conda install conda-forge::libopus
+
-conda install conda-forge::ffmpeg
+# 添加清华源通道
 conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
 conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
 conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
 conda install libopus -y
 conda install ffmpeg -y
 ```
 请注意，以上命令，不是一股脑执行就成功的，你需要一步步执行，每一步执行完后，都检查一下输出的日志，查看是否成功。
 ## 2.安装本项目依赖
 你先要下载本项目源码，源码可以通过`git clone`命令下载，如果你不熟悉`git clone`命令。
@@ -179,7 +196,7 @@ conda install conda-forge::ffmpeg
 你需要把它重命名成`xiaozhi-esp32-server`，好了请记住这个目录，我们暂且称它为`项目目录`。
 ```
-# 使用dos或者终端，进入到你的项目目录，执行以下命令
+# 继续使用conda环境，进入到你的项目目录，执行以下命令
 conda activate xiaozhi-esp32-server
 pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/
 pip install -r requirements.txt
@@ -250,7 +267,7 @@ LLM:
 ## 模型文件
 本项目语音识别模型，默认使用`SenseVoiceSmall`模型，进行语音转文字。因为模型较大，需要独立下载，下载后把`model.pt`
-文件放在`model/SenseVoiceSmall`
+文件放在`models/SenseVoiceSmall`
 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)
@@ -1,27 +1,27 @@
 import time
 import aiohttp
 import asyncio
 from tabulate import tabulate
-from typing import Dict
+from typing import Dict, List
 from core.utils.llm import create_instance as create_llm_instance
 from core.utils.tts import create_instance as create_tts_instance
 from core.utils.util import read_config
 import statistics
 from config.settings import get_config_file
 from concurrent.futures import ThreadPoolExecutor
 import inspect
 import os
 import requests
 import logging
 # 设置全局日志级别为WARNING，抑制INFO级别日志
 logging.basicConfig(level=logging.WARNING)
-class PerformanceTester:
+
 class AsyncPerformanceTester:
    def __init__(self):
        self.config = read_config(get_config_file())
        # 从配置读取测试句子，如果不存在则使用默认
        self.test_sentences = self.config.get("module_test", {}).get(
-            "test_sentences", 
+            "test_sentences",
-            ["你好，请介绍一下你自己", "What's the weather like today?", 
+            ["你好，请介绍一下你自己", "What's the weather like today?",
             "请用100字概括量子计算的基本原理和应用前景"]
        )
        self.results = {
@@ -30,258 +30,407 @@ class PerformanceTester:
            "combinations": []
        }
-    def _test_llm(self, llm_name: str, config: Dict) -> Dict:
+    async def _check_ollama_service(self, base_url: str, model_name: str) -> bool:
-        """测试单个LLM性能"""
+        """异步检查Ollama服务状态"""
        async with aiohttp.ClientSession() as session:
            try:
                # 检查服务是否可用
                async with session.get(f"{base_url}/api/version") as response:
                    if response.status != 200:
                        print(f"🚫 Ollama服务未启动或无法访问: {base_url}")
                        return False
                # 检查模型是否存在
                async with session.get(f"{base_url}/api/tags") as response:
                    if response.status == 200:
                        data = await response.json()
                        models = data.get("models", [])
                        if not any(model["name"] == model_name for model in models):
                            print(f"🚫 Ollama模型 {model_name} 未找到，请先使用 ollama pull {model_name} 下载")
                            return False
                    else:
                        print(f"🚫 无法获取Ollama模型列表")
                        return False
                return True
            except Exception as e:
                print(f"🚫 无法连接到Ollama服务: {str(e)}")
                return False
    async def _test_tts(self, tts_name: str, config: Dict) -> Dict:
        """异步测试单个TTS性能"""
        try:
-            # 跳过未配置密钥的模块
+            logging.getLogger("core.providers.tts.base").setLevel(logging.WARNING)
-            if "api_key" in config and any(x in config["api_key"] for x in ["你的", "placeholder", "sk-xxx"]):
+
-                print(f"🚫 跳过未配置的LLM: {llm_name}")
+            token_fields = ["access_token", "api_key", "token"]
-                return {"errors": 1}
+            if any(field in config and any(x in config[field] for x in ["你的", "placeholder"]) for field in
-            
+                   token_fields):
                print(f"⏭️  TTS {tts_name} 未配置access_token/api_key，已跳过")
                return {"name": tts_name, "type": "tts", "errors": 1}
            module_type = config.get('type', tts_name)
            tts = create_tts_instance(
                module_type,
                config,
                delete_audio_file=True
            )
            print(f"🎵 测试 TTS: {tts_name}")
            tmp_file = tts.generate_filename()
            await tts.text_to_speak("连接测试", tmp_file)
            if not tmp_file or not os.path.exists(tmp_file):
                print(f"❌ {tts_name} 连接失败")
                return {"name": tts_name, "type": "tts", "errors": 1}
            total_time = 0
            test_count = len(self.test_sentences[:2])
            for i, sentence in enumerate(self.test_sentences[:2], 1):
                start = time.time()
                tmp_file = tts.generate_filename()
                await tts.text_to_speak(sentence, tmp_file)
                duration = time.time() - start
                total_time += duration
                if tmp_file and os.path.exists(tmp_file):
                    print(f"✓ {tts_name} [{i}/{test_count}]")
                else:
                    print(f"✗ {tts_name} [{i}/{test_count}]")
                    return {"name": tts_name, "type": "tts", "errors": 1}
            return {
                "name": tts_name,
                "type": "tts",
                "avg_time": total_time / test_count,
                "errors": 0
            }
        except Exception as e:
            print(f"⚠️ {tts_name} 测试失败: {str(e)}")
            return {"name": tts_name, "type": "tts", "errors": 1}
    async def _test_llm(self, llm_name: str, config: Dict) -> Dict:
        """异步测试单个LLM性能"""
        try:
            # 对于Ollama，跳过api_key检查并进行特殊处理
            if llm_name == "Ollama":
                base_url = config.get('base_url', 'http://localhost:11434')
                model_name = config.get('model_name')
                if not model_name:
                    print(f"🚫 Ollama未配置model_name")
                    return {"name": llm_name, "type": "llm", "errors": 1}
                if not await self._check_ollama_service(base_url, model_name):
                    return {"name": llm_name, "type": "llm", "errors": 1}
            else:
                if "api_key" in config and any(x in config["api_key"] for x in ["你的", "placeholder", "sk-xxx"]):
                    print(f"🚫 跳过未配置的LLM: {llm_name}")
                    return {"name": llm_name, "type": "llm", "errors": 1}
            # 获取实际类型（兼容旧配置）
            module_type = config.get('type', llm_name)
            llm = create_llm_instance(module_type, config)
-            
+
            # 统一使用UTF-8编码
            test_sentences = [s.encode('utf-8').decode('utf-8') for s in self.test_sentences]
-            
+
-            total_time = 0
+            # 创建所有句子的测试任务
-            first_token_times = []
+            sentence_tasks = []
            valid_times = []
            for sentence in test_sentences:
-                sentence_start = time.time()  # 记录整句开始时间
+                sentence_tasks.append(self._test_single_sentence(llm_name, llm, sentence))
-                first_token_received = False
+
-                
+            # 并发执行所有句子测试
-                # 遍历响应流
+            sentence_results = await asyncio.gather(*sentence_tasks)
-                for chunk in llm.response("perf_test", [{"role": "user", "content": sentence}]):
+
-                    if not first_token_received and chunk.strip() != '':
+            # 处理结果
-                        first_token_times.append(time.time() - sentence_start)
+            valid_results = [r for r in sentence_results if r is not None]
-                        first_token_received = True
+            if not valid_results:
                # 计算整句耗时
                sentence_duration = time.time() - sentence_start
                total_time += sentence_duration
                valid_times.append(sentence_duration)
            # 新增有效性检查
            if len(first_token_times) == 0 or len(valid_times) == 0:
                print(f"⚠️  {llm_name} 无有效数据，可能配置错误")
-                return {"errors": 1}
+                return {"name": llm_name, "type": "llm", "errors": 1}
-                
+
-            # 过滤异常数据（超过3倍标准差）
+            first_token_times = [r["first_token_time"] for r in valid_results]
-            mean = statistics.mean(valid_times)
+            response_times = [r["response_time"] for r in valid_results]
-            stdev = statistics.stdev(valid_times) if len(valid_times) > 1 else 0
+
-            filtered_times = [t for t in valid_times if t <= mean + 3*stdev]
+            # 过滤异常数据
-            
+            mean = statistics.mean(response_times)
-            # 当有效数据不足时标记错误
+            stdev = statistics.stdev(response_times) if len(response_times) > 1 else 0
            filtered_times = [t for t in response_times if t <= mean + 3 * stdev]
            if len(filtered_times) < len(test_sentences) * 0.5:
                print(f"⚠️  {llm_name} 有效数据不足，可能网络不稳定")
-                return {"errors": 1}
+                return {"name": llm_name, "type": "llm", "errors": 1}
            return {
-                "avg_response": total_time / len(test_sentences),
+                "name": llm_name,
-                "avg_first_token": sum(first_token_times)/len(first_token_times),
+                "type": "llm",
                "avg_response": sum(response_times) / len(response_times),
                "avg_first_token": sum(first_token_times) / len(first_token_times),
                "std_first_token": statistics.stdev(first_token_times) if len(first_token_times) > 1 else 0,
-                "std_response": statistics.stdev(valid_times) if len(valid_times) > 1 else 0,
+                "std_response": statistics.stdev(response_times) if len(response_times) > 1 else 0,
                "errors": 0
            }
        except Exception as e:
            print(f"LLM {llm_name} 测试失败: {str(e)}")
-            return {"errors": 1}
+            return {"name": llm_name, "type": "llm", "errors": 1}
-    def _test_tts(self, tts_name: str, config: Dict) -> Dict:
+    async def _test_single_sentence(self, llm_name: str, llm, sentence: str) -> Dict:
-        """测试单个TTS性能"""
+        """测试单个句子的性能"""
        try:
-            # 关闭详细日志
+            print(f"📝 {llm_name} 开始测试: {sentence[:20]}...")
-            logging.getLogger("core.providers.tts.base").setLevel(logging.WARNING)
+            sentence_start = time.time()
-            
+            first_token_received = False
-            # 跳过未配置密钥的模块
+            first_token_time = None
            token_fields = ["access_token", "api_key", "token"]
            if any(field in config and any(x in config[field] for x in ["你的", "placeholder"]) for field in token_fields):
                print(f"⏭️  TTS {tts_name} 未配置access_token/api_key，已跳过")
                return {"errors": 1}
            # 获取实际类型（兼容旧配置）
            module_type = config.get('type', tts_name)
            tts = create_tts_instance(
                module_type,
                config, 
                delete_audio_file=True  # 确保参数名称正确
            )
            # 简化后的输出
            print(f"\n🎵 正在测试 TTS: {tts_name}")
            print(f"🔊 测试 {tts_name}：", end="", flush=True)
            # 连接测试
            test_conn = tts.to_tts("连接测试")
            if not os.path.exists(test_conn):
                print("❌ 连接失败")
                return {"errors": 1}
            else:
                print("✅")
            total_time = 0
            test_count = len(self.test_sentences[:2])
            for i, sentence in enumerate(self.test_sentences[:2], 1):
                start = time.time()
                file_path = tts.to_tts(sentence)
                duration = time.time() - start
                total_time += duration
                # 显示简单的进度标识
                if os.path.exists(file_path):
                    print(f"✓[{i}/{test_count}]", end="", flush=True)
                else:
                    print(f"✗[{i}/{test_count}]", end="", flush=True)
            print()  # 换行
            return {"avg_time": total_time / test_count, "errors": 0}
        except requests.exceptions.ConnectionError:
            print(f"\n⛔ {tts_name} 无法连接服务端")
            return {"errors": 1}
        except Exception as e:
            print(f"\n⚠️ {tts_name} 测试失败: {str(e)}")
            return {"errors": 1}
-    def run(self):
+            async def process_response():
-        """执行全量测试并自动跳过未配置的模块"""
+                nonlocal first_token_received, first_token_time
-        print("🔍 开始自动检测已配置的模块...")
+                for chunk in llm.response("perf_test", [{"role": "user", "content": sentence}]):
-        
+                    if not first_token_received and chunk.strip() != '':
-        # 测试所有LLM
+                        first_token_time = time.time() - sentence_start
-        for llm_name, config in self.config.get("LLM", {}).items():
+                        first_token_received = True
-            # 特殊处理CozeLLM的配置检查
+                        print(f"✓ {llm_name} 首个Token: {first_token_time:.3f}s")
-            if llm_name == "CozeLLM":
+                    yield chunk
-                if any(x in config.get("bot_id", "") for x in ["你的"]) \
+
-                or any(x in config.get("user_id", "") for x in ["你的"]):
+            response_chunks = []
-                    print(f"⏭️  LLM {llm_name} 未配置bot_id/user_id，已跳过")
+            async for chunk in process_response():
-                    continue
+                response_chunks.append(chunk)
-            # 通用的api_key配置检查
+
-            if "api_key" in config and any(x in config["api_key"] for x in ["你的", "placeholder"]):
+            response_time = time.time() - sentence_start
-                print(f"⏭️  LLM {llm_name} 未配置api_key，已跳过")
+            print(f"✓ {llm_name} 完成响应: {response_time:.3f}s")
-                continue
+
-                
+            if first_token_time is None:
-            print(f"🚀 正在测试 LLM: {llm_name}")
+                first_token_time = response_time  # 如果没有检测到first token，使用总响应时间
-            self.results["llm"][llm_name] = self._test_llm(llm_name, config)
+
-        
+            return {
-        # 测试所有TTS
+                "name": llm_name,
-        for tts_name, config in self.config.get("TTS", {}).items():
+                "type": "llm",
-            # 根据不同服务的token字段检测
+                "first_token_time": first_token_time,
-            token_fields = ["access_token", "api_key", "token"]
+                "response_time": response_time
-            if any(field in config and any(x in config[field] for x in ["你的", "placeholder"]) for field in token_fields):
+            }
-                print(f"⏭️  TTS {tts_name} 未配置access_token/api_key，已跳过")
+        except Exception as e:
-                continue
+            print(f"⚠️ {llm_name} 句子测试失败: {str(e)}")
-                
+            return None
            print(f"🎵 正在测试 TTS: {tts_name}")
            self.results["tts"][tts_name] = self._test_tts(tts_name, config)
        # 生成组合建议
        self._generate_combinations()
        self._print_results()
    def _generate_combinations(self):
        """生成最佳组合建议"""
        # 调整过滤条件，例如设为 >= 0.05
        valid_llms = [
-            k for k, v in self.results["llm"].items() 
+            k for k, v in self.results["llm"].items()
            if v["errors"] == 0 and v["avg_first_token"] >= 0.05
        ]
        valid_tts = [k for k, v in self.results["tts"].items() if v["errors"] == 0]
        # 找出基准值
        min_first_token = min([self.results["llm"][llm]["avg_first_token"] for llm in valid_llms]) if valid_llms else 1
        min_tts_time = min([self.results["tts"][tts]["avg_time"] for tts in valid_tts]) if valid_tts else 1
        for llm in valid_llms:
            for tts in valid_tts:
-                llm_weight = 0.8 if self.results["llm"][llm]["avg_first_token"] < 1.0 else 0.6
+                # 计算相对性能分数（越小越好）
-                tts_weight = 1 - llm_weight
+                llm_score = self.results["llm"][llm]["avg_first_token"] / min_first_token
-                score = (
+                tts_score = self.results["tts"][tts]["avg_time"] / min_tts_time
-                    self.results["llm"][llm]["avg_first_token"] * llm_weight +
+
-                    self.results["tts"][tts]["avg_time"] * tts_weight
+                # 计算稳定性分数（标准差/平均值，越小越稳定）
-                )
+                llm_stability = self.results["llm"][llm]["std_first_token"] / self.results["llm"][llm][
                    "avg_first_token"]
                # 综合得分（考虑性能和稳定性）
                # 性能权重0.7，稳定性权重0.3
                llm_final_score = llm_score * 0.7 + llm_stability * 0.3
                # 总分 = LLM得分(70%) + TTS得分(30%)
                total_score = llm_final_score * 0.7 + tts_score * 0.3
                self.results["combinations"].append({
                    "llm": llm,
                    "tts": tts,
-                    "score": score,
+                    "score": total_score,
                    "details": {
                        "llm_first_token": self.results["llm"][llm]["avg_first_token"],
                        "llm_stability": llm_stability,
                        "tts_time": self.results["tts"][tts]["avg_time"]
                    }
                })
-        # 按综合得分排序
+        # 分数越小越好
        self.results["combinations"].sort(key=lambda x: x["score"])
    def _print_results(self):
-        """控制台输出结果"""
+        """打印测试结果"""
        # LLM结果表格
        llm_table = []
        for name, data in self.results["llm"].items():
            if data["errors"] == 0:
                stability = data["std_first_token"] / data["avg_first_token"]
                llm_table.append([
-                    name,
+                    name,  # 不需要固定宽度，让tabulate自己处理对齐
-                    f"{data['avg_first_token']:.3f}s",
+                    f"{data['avg_first_token']:.3f}秒",
-                    f"{data['avg_response']:.3f}s"
+                    f"{data['avg_response']:.3f}秒",
                    f"{stability:.3f}"
                ])
        if llm_table:
            print("\nLLM 性能排行:")
            print(tabulate(
                llm_table,
-                headers=["模块名称", "平均首Token时间", "平均总响应时间"],
+                headers=["模型名称", "首字耗时", "总耗时", "稳定性"],
-                tablefmt="github"
+                tablefmt="github",
                colalign=("left", "right", "right", "right"),
                disable_numparse=True
            ))
        else:
            print("\n⚠️ 没有可用的LLM模块进行测试。")
        # TTS结果表格
        tts_table = []
        for name, data in self.results["tts"].items():
            if data["errors"] == 0:
                tts_table.append([
-                    name,
+                    name,  # 不需要固定宽度
-                    f"{data['avg_time']:.3f}s"
+                    f"{data['avg_time']:.3f}秒"
                ])
        if tts_table:
            print("\nTTS 性能排行:")
            print(tabulate(
                tts_table,
-                headers=["模块名称", "平均合成时间"],
+                headers=["模型名称", "合成耗时"],
-                tablefmt="github"
+                tablefmt="github",
                colalign=("left", "right"),
                disable_numparse=True
            ))
        else:
            print("\n⚠️ 没有可用的TTS模块进行测试。")
        # 最佳组合建议
        if self.results["combinations"]:
-            print("\n推荐配置组合 (综合响应速度):")
+            print("\n推荐配置组合 (得分越小越好):")
            combo_table = []
-            for combo in self.results["combinations"][:5]:  # 显示前5名
+            for combo in self.results["combinations"][:5]:
                combo_table.append([
-                    f"{combo['llm']} + {combo['tts']}",
+                    f"{combo['llm']} + {combo['tts']}",  # 不需要固定宽度
                    f"{combo['score']:.3f}",
-                    f"{combo['details']['llm_first_token']:.3f}s",
+                    f"{combo['details']['llm_first_token']:.3f}秒",
-                    f"{combo['details']['tts_time']:.3f}s"
+                    f"{combo['details']['llm_stability']:.3f}",
                    f"{combo['details']['tts_time']:.3f}秒"
                ])
-            
+
            print(tabulate(
                combo_table,
-                headers=["组合方案", "综合得分", "LLM首Token", "TTS合成"],
+                headers=["组合方案", "综合得分", "LLM首字耗时", "稳定性", "TTS合成耗时"],
-                tablefmt="github"
+                tablefmt="github",
                colalign=("left", "right", "right", "right", "right"),
                disable_numparse=True
            ))
        else:
            print("\n⚠️ 没有可用的模块组合建议。")
-    def _execute_with_timeout(self, func, args=(), kwargs={}, timeout=None):
+    def _process_results(self, all_results):
-        with ThreadPoolExecutor(max_workers=1) as executor:
+        """处理测试结果"""
-            future = executor.submit(func, *args, **kwargs)
+        for result in all_results:
-            try:
+            if result["errors"] == 0:
-                result = future.result(timeout)
+                if result["type"] == "llm":
-                return list(result) if inspect.isgenerator(result) else result
+                    self.results["llm"][result["name"]] = result
-            except TimeoutError:
+                else:
-                raise Exception("操作超时")
+                    self.results["tts"][result["name"]] = result
    async def run(self):
        """执行全量异步测试"""
        print("🔍 开始筛选可用模块...")
        # 创建所有测试任务
        all_tasks = []
        # LLM测试任务
        for llm_name, config in self.config.get("LLM", {}).items():
            # 检查配置有效性
            if llm_name == "CozeLLM":
                if any(x in config.get("bot_id", "") for x in ["你的"]) \
                        or any(x in config.get("user_id", "") for x in ["你的"]):
                    print(f"⏭️  LLM {llm_name} 未配置bot_id/user_id，已跳过")
                    continue
            elif "api_key" in config and any(x in config["api_key"] for x in ["你的", "placeholder", "sk-xxx"]):
                print(f"⏭️  LLM {llm_name} 未配置api_key，已跳过")
                continue
            # 对于Ollama，先检查服务状态
            if llm_name == "Ollama":
                base_url = config.get('base_url', 'http://localhost:11434')
                model_name = config.get('model_name')
                if not model_name:
                    print(f"🚫 Ollama未配置model_name")
                    continue
                if not await self._check_ollama_service(base_url, model_name):
                    continue
            print(f"📋 添加LLM测试任务: {llm_name}")
            module_type = config.get('type', llm_name)
            llm = create_llm_instance(module_type, config)
            # 为每个句子创建独立任务
            for sentence in self.test_sentences:
                sentence = sentence.encode('utf-8').decode('utf-8')
                all_tasks.append(self._test_single_sentence(llm_name, llm, sentence))
        # TTS测试任务
        for tts_name, config in self.config.get("TTS", {}).items():
            token_fields = ["access_token", "api_key", "token"]
            if any(field in config and any(x in config[field] for x in ["你的", "placeholder"]) for field in
                   token_fields):
                print(f"⏭️  TTS {tts_name} 未配置access_token/api_key，已跳过")
                continue
            print(f"🎵 添加TTS测试任务: {tts_name}")
            all_tasks.append(self._test_tts(tts_name, config))
        print(
            f"\n✅ 找到 {len([t for t in all_tasks if 'test_single_sentence' in str(t)]) / len(self.test_sentences):.0f} 个可用LLM模块")
        print(f"✅ 找到 {len([t for t in all_tasks if '_test_tts' in str(t)])} 个可用TTS模块")
        print("\n⏳ 开始并发测试所有模块...\n")
        # 并发执行所有测试任务
        all_results = await asyncio.gather(*all_tasks, return_exceptions=True)
        # 处理LLM结果
        llm_results = {}
        for result in [r for r in all_results if r and isinstance(r, dict) and r.get("type") == "llm"]:
            llm_name = result["name"]
            if llm_name not in llm_results:
                llm_results[llm_name] = {
                    "name": llm_name,
                    "type": "llm",
                    "first_token_times": [],
                    "response_times": [],
                    "errors": 0
                }
            llm_results[llm_name]["first_token_times"].append(result["first_token_time"])
            llm_results[llm_name]["response_times"].append(result["response_time"])
        # 计算LLM平均值和标准差
        for llm_name, data in llm_results.items():
            if len(data["first_token_times"]) >= len(self.test_sentences) * 0.5:
                self.results["llm"][llm_name] = {
                    "name": llm_name,
                    "type": "llm",
                    "avg_response": sum(data["response_times"]) / len(data["response_times"]),
                    "avg_first_token": sum(data["first_token_times"]) / len(data["first_token_times"]),
                    "std_first_token": statistics.stdev(data["first_token_times"]) if len(
                        data["first_token_times"]) > 1 else 0,
                    "std_response": statistics.stdev(data["response_times"]) if len(data["response_times"]) > 1 else 0,
                    "errors": 0
                }
        # 处理TTS结果
        for result in [r for r in all_results if r and isinstance(r, dict) and r.get("type") == "tts"]:
            if result["errors"] == 0:
                self.results["tts"][result["name"]] = result
        # 生成组合建议并打印结果
        print("\n📊 生成测试报告...")
        self._generate_combinations()
        self._print_results()
 async def main():
    tester = AsyncPerformanceTester()
    await tester.run()
 if __name__ == "__main__":
-    tester = PerformanceTester()
+    asyncio.run(main())
    tester.run()
@@ -16,4 +16,4 @@ aiohttp_cors==0.7.0
 ormsgpack==1.7.0
 ruamel.yaml==0.18.10
 loguru==0.7.3
-requests>=2.0.0
+requests==2.32.3
Author	SHA1	Message	Date
欣南科技 GitHub Junsen hrz	effd79b465	Tts302 (#170 ) * feat: add 302.ai TTS integration (#166) * update:将tts_302ai tts合并到doubao tts --------- Co-authored-by: Junsen <66542771+Huang-junsen@users.noreply.github.com> Co-authored-by: hrz <1710360675@qq.com>	2025-03-02 16:59:25 +08:00
HonestQiao GitHub hrz	bc531652e9	音乐播放功能添加p3文件格式支持，添加文件目录(含子目录)扫描和文件列表刷新功能 (#157 ) Co-authored-by: hrz <1710360675@qq.com>	2025-03-01 17:57:43 +08:00
欣南科技 GitHub hrz	1c5581b765	Fix ffmpeg env (#163 ) * update:ffmpeg依赖检查 * update:微信群更换发布方式 --------- Co-authored-by: hrz <1710360675@qq.com>	2025-03-01 17:34:14 +08:00
欣南科技 GitHub hrz	b486599ab8	update:ffmpeg依赖检查 (#162 ) Co-authored-by: hrz <1710360675@qq.com>	2025-03-01 17:09:01 +08:00
欣南科技 GitHub hrz	b94842c312	Fix long audio bug (#158 ) * update:异步生成音频 * update:优化LLM断句 --------- Co-authored-by: hrz <1710360675@qq.com>	2025-03-01 01:54:55 +08:00
欣南科技 GitHub hrz	2c88a36f32	update:启动时检查配置文件 (#155 ) Co-authored-by: hrz <1710360675@qq.com>	2025-02-28 19:17:36 +08:00
欣南科技 GitHub hrz	242f4d87e7	Qq group (#146 ) * update:qq group * update:更新英文版说明 --------- Co-authored-by: hrz <1710360675@qq.com>	2025-02-26 16:02:59 +08:00
欣南科技 GitHub hrz	2255aacfd0	update:qq group (#145 ) Co-authored-by: hrz <1710360675@qq.com>	2025-02-26 15:10:12 +08:00
欣南科技 GitHub linqingping Chris hrz	87cb0b484d	Music play (#142 ) * Update util.py解决返回元组报错 (#103) 解决返回元组报错 * 增加播放本地音乐功能 (#105) * Please enter the commit message for your changes. Lines starting with '#' will be ignored, and an empty message aborts the commit. On branch Music-playback Your branch is up to date with 'origin/Music-playback'. Changes to be committed: modified: core/handle/audioHandle.py new file: "music/\346\234\210\344\272\256\344\273\243\350\241\250\346\210\221\347\232\204\345\277\203_\351\202\223\344\270\275\345\220\233.mp3" new file: "music/\350\270\217\345\261\261\346\262\263.mp3" * Please enter the commit message for your changes. Lines starting with '#' will be ignored, and an empty message aborts the commit. On branch Music-playback Your branch is up to date with 'origin/Music-playback'. Changes to be committed: modified: config.yaml modified: core/connection.py modified: core/handle/abortHandle.py modified: core/handle/audioHandle.py new file: core/handle/musicHandler.py modified: core/handle/textHandle.py modified: core/websocket_server.py new file: "music/\344\270\200\345\277\265\345\215\203\345\271\264_\345\233\275\351\243\216\347\211\210.mp3" new file: "music/\344\270\255\347\247\213\346\234\210.mp3" deleted: "music/\346\234\210\344\272\256\344\273\243\350\241\250\346\210\221\347\232\204\345\277\203_\351\202\223\344\270\275\345\220\233.mp3" deleted: "music/\350\270\217\345\261\261\346\262\263.mp3" * 增加播放本地音乐功能 * 增加播放本地音乐功能 * 让音乐配置变的优雅 On branch Music-playback Your branch is up to date with 'origin/Music-playback'. Changes to be committed: modified: config.yaml modified: core/handle/musicHandler.py renamed: "music/\344\270\200\345\277\265\345\215\203\345\271\264_\345\233\275\351\243\216\347\211\210.mp3" -> "music/mp3/\344\270\200\345\277\265\345\215\203\345\271\264_\345\233\275\351\243\216\347\211\210.mp3" renamed: "music/\344\270\255\347\247\213\346\234\210.mp3" -> "music/mp3/\344\270\255\347\247\213\346\234\210.mp3" renamed: "music/\345\273\211\346\263\242\350\200\201\347\237\243\357\274\214\345\260\232\350\203\275\351\245\255\345\220\246.mp3" -> "music/mp3/\345\273\211\346\263\242\350\200\201\347\237\243\357\274\214\345\260\232\350\203\275\351\245\255\345\220\246.mp3" new file: music/music_config.yaml --------- Co-authored-by: 欣南科技 <huangrongzhuang@xin-nan.com> * update:优化代码 * update:流控调试 * update:优化音乐播放 * update:优化音乐配置初始化 --------- Co-authored-by: linqingping <linqingping@users.noreply.github.com> Co-authored-by: Chris <119588753+Chris-websketch@users.noreply.github.com> Co-authored-by: hrz <1710360675@qq.com>	2025-02-26 01:33:05 +08:00
欣南科技 GitHub hrz	73803f49fc	update:wechat group (#141 ) Co-authored-by: hrz <1710360675@qq.com>	2025-02-25 23:51:57 +08:00
欣南科技 GitHub hrz	5fd87752d5	fixed：更新基础docker环境地址 (#139 ) Co-authored-by: hrz <1710360675@qq.com>	2025-02-25 21:11:06 +08:00
欣南科技 GitHub Jiao Haoyang hrz	94a90c4124	Iot (#129 ) * 2025-2-17-iot配置消息处理, 以及控制iot命令发送 (#40) * update:优化 * update:增加视频教程 * update:iot兼容性又问题，撤回 * fixed:iot boolean 参数接收bug --------- Co-authored-by: Jiao Haoyang <108573524+XuSenfeng@users.noreply.github.com> Co-authored-by: hrz <1710360675@qq.com>	2025-02-24 22:04:48 +08:00
欣南科技 GitHub Jiao Haoyang hrz	c3b0347155	Iot (#128 ) * 2025-2-17-iot配置消息处理, 以及控制iot命令发送 (#40) * update:优化 * update:增加视频教程 * update:iot兼容性又问题，撤回 --------- Co-authored-by: Jiao Haoyang <108573524+XuSenfeng@users.noreply.github.com> Co-authored-by: hrz <1710360675@qq.com>	2025-02-24 21:20:42 +08:00
欣南科技 GitHub Jiao Haoyang hrz	600f712750	Iot (#127 ) * 2025-2-17-iot配置消息处理, 以及控制iot命令发送 (#40) * update:优化 * update:增加视频教程 --------- Co-authored-by: Jiao Haoyang <108573524+XuSenfeng@users.noreply.github.com> Co-authored-by: hrz <1710360675@qq.com>	2025-02-24 18:18:08 +08:00
欣南科技 GitHub Jiao Haoyang hrz	9e76869c36	Iot (#126 ) * 2025-2-17-iot配置消息处理, 以及控制iot命令发送 (#40) * update:优化 --------- Co-authored-by: Jiao Haoyang <108573524+XuSenfeng@users.noreply.github.com> Co-authored-by: hrz <1710360675@qq.com>	2025-02-24 18:06:13 +08:00
kalicyh GitHub Sinyo hrz	cb540736ab	跳过使用Open ai 接口时，DeepSeek-R1 模型的深度思考内容 (#54 ) (#95 ) * 跳过使用Open ai 接口时，DeepSeek-R1 模型的深度思考内容 (#54) * 跳过 DeepSeek-R1 模型的深度思考内容 * 跳过 DeepSeek-R1 模型的深度思考内容 * 新增LM Studio本地大模型API接口 * 优化代码，遇到Bad Case安全处理 * update:优化 --------- Co-authored-by: Sinyo <38577585+SinyoWong@users.noreply.github.com> Co-authored-by: hrz <1710360675@qq.com>	2025-02-24 16:16:06 +08:00
欣南科技 GitHub hrz	66cd5bb4a5	fixed:修复低版本docker-compose模版参数 (#124 ) Co-authored-by: hrz <1710360675@qq.com>	2025-02-24 14:56:39 +08:00
欣南科技 GitHub hrz	1226e8be47	Fix opuslib (#122 ) * fixed:ModuleNotFoundError: No module named 'opuslib' * update:格式化文档 --------- Co-authored-by: hrz <1710360675@qq.com>	2025-02-23 20:14:46 +08:00
欣南科技 GitHub hrz	22ff7a92b8	fixed:ModuleNotFoundError: No module named 'opuslib' (#121 ) Co-authored-by: hrz <1710360675@qq.com>	2025-02-23 19:59:26 +08:00
欣南科技 GitHub Alexisxty hrz	2d01812f8d	Performance test (#118 ) * update: optimize performance test files * update:异步测试方法 * update:改回默认FunASR --------- Co-authored-by: Alexisxty <alexisty233@gmail.com> Co-authored-by: hrz <1710360675@qq.com>	2025-02-23 16:22:21 +08:00
Teery GitHub hrz	3bc0b821a1	update: 修改了服务组件的性能测试代码 (#111 ) * update: optimize performance test files * update:异步测试方法 --------- Co-authored-by: hrz <1710360675@qq.com>	2025-02-23 16:07:33 +08:00
HonestQiao GitHub 欣南科技 hrz	148578399f	使用配置文件设置日志参数 (#116 ) * 使用配置文件设置日志参数 * update：优化 --------- Co-authored-by: 欣南科技 <huangrongzhuang@xin-nan.com> Co-authored-by: hrz <1710360675@qq.com>	2025-02-23 15:18:59 +08:00