mirror of
https://github.com/xinnan-tech/xiaozhi-esp32-server.git
synced 2026-07-21 22:53:56 +08:00
Doubao asr (#117)
* 添加豆包在线 ASR (#82) --------- Co-authored-by: 胡垚 <qsct9501@163.com>
This commit is contained in:
欣南科技
胡垚
@@ -1,5 +1,6 @@
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# Byte-compiled / optimized / DLL files
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__pycache__/
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.idea/
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*.py[cod]
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*$py.class
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+3
-3
@@ -25,7 +25,7 @@ COPY requirements.txt .
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# 优化apt安装
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RUN pip install --no-cache-dir -r requirements.txt \
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-i https://pypi.tuna.tsinghua.edu.cn/simple
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-i https://mirrors.aliyun.com/pypi/simple/
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# 第三阶段：生产镜像
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FROM python:3.10-slim
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@@ -33,8 +33,8 @@ FROM python:3.10-slim
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WORKDIR /opt/xiaozhi-esp32-server
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# 优化apt安装
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RUN echo "deb https://mirrors.tuna.tsinghua.edu.cn/debian/ bookworm main contrib non-free non-free-firmware" > /etc/apt/sources.list && \
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echo "deb https://mirrors.tuna.tsinghua.edu.cn/debian/ bookworm-updates main contrib non-free non-free-firmware" >> /etc/apt/sources.list && \
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RUN echo "deb https://mirrors.aliyun.com/debian/ bookworm main contrib non-free non-free-firmware" > /etc/apt/sources.list && \
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echo "deb https://mirrors.aliyun.com/debian/ bookworm-updates main contrib non-free non-free-firmware" >> /etc/apt/sources.list && \
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apt-get update && \
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apt-get install -y --no-install-recommends libopus0 ffmpeg && \
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apt-get clean && \
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@@ -14,15 +14,64 @@
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本项目需要配合 ESP32 硬件设备使用。如果您已经购买了 ESP32 相关硬件，且成功对接过虾哥部署的后端服务，并希望独立搭建自己的
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`xiaozhi-esp32` 后端服务，那么本项目非常适合您。
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跳转到[使用方式](#使用方式-)
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想看使用效果？请猛戳视频 🎥
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<table>
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<tr>
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<td>
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<a href="https://www.bilibili.com/video/BV1FMFyejExX" target="_blank">
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<picture>
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<img alt="小智esp32连接自己的后台模型" src="docs/images/demo1.png" />
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</picture>
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</a>
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</td>
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<td>
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<a href="https://www.bilibili.com/video/BV1CDKWemEU6" target="_blank">
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<picture>
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<img alt="自定义音色" src="docs/images/demo2.png" />
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</picture>
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</a>
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</td>
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<td>
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<a href="https://www.bilibili.com/video/BV12yA2egEaC" target="_blank">
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<picture>
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<img alt="使用粤语交流" src="docs/images/demo3.png" />
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</picture>
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</a>
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</td>
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<td>
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<a href="https://www.bilibili.com/video/av114036381327149" target="_blank">
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<picture>
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<img alt="控制家电开关" src="docs/images/demo5.png" />
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</picture>
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</a>
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</td>
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<td>
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<a href="https://www.bilibili.com/video/BV1kgA2eYEQ9" target="_blank">
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<picture>
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<img alt="成本最低配置" src="docs/images/demo4.png" />
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</picture>
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</a>
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</td>
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<td>
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</td>
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</tr>
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</table>
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想看使用效果？请猛戳这个视频 🎥
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<a href="https://www.bilibili.com/video/BV1FMFyejExX">
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<picture>
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<img alt="小智esp32连接自己的后台模型" src="docs/images/demo.png" />
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</picture>
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</a>
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---
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@@ -128,8 +177,22 @@ server:
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### 一、[部署文档](./docs/Deployment.md)
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1.**[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)**
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本项目支持以下三种部署方式，您可根据实际需求选择：
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1. **[Docker 快速部署](./docs/Deployment.md)**
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适合快速体验，不需过多环境配置。缺点是，拉取镜像有点慢。
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2.
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*
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*[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)
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**
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适用于已安装 Docker 且希望对代码进行自定义修改的用户。
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3.
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*
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*[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)
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**
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适合熟悉 Conda 环境或希望从零搭建运行环境的用户。
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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。
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@@ -166,13 +229,15 @@ VAD:
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### 4、为什么我说的话，小智识别出来很多韩文、日文、英文？🇰🇷
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建议：检查一下`models/SenseVoiceSmall`是否已经有`model.pt`文件，如果没有就要下载，查看这里[下载语音识别模型文件](docs/Deployment.md#模型文件)
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建议：检查一下`models/SenseVoiceSmall`是否已经有`model.pt`
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文件，如果没有就要下载，查看这里[下载语音识别模型文件](docs/Deployment.md#模型文件)
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### 5、为什么会出现“TTS 任务出错 文件不存在”？📁
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建议：检查一下是否正确使用`conda` 安装了`libopus`和`ffmpeg`库。
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如果没有安装，就安装
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```
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conda install conda-forge::libopus
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conda install conda-forge::ffmpeg
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+8
-2
@@ -59,7 +59,7 @@ CMD_exit:
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# 具体处理时选择的模块(The module selected for specific processing)
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selected_module:
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ASR: FunASR
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ASR: DoubaoASR
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VAD: SileroVAD
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# 将根据配置名称对应的type调用实际的LLM适配器
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LLM: ChatGLMLLM
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@@ -68,9 +68,15 @@ selected_module:
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ASR:
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FunASR:
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type: fun_local
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model_dir: models/SenseVoiceSmall
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output_dir: tmp/
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DoubaoASR:
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type: doubao
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appid: 你的火山引擎语音合成服务appid
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access_token: 你的火山引擎语音合成服务access_token
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cluster: volcengine_input_common
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output_dir: tmp/
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VAD:
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SileroVAD:
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threshold: 0.5
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+1
-1
@@ -9,7 +9,7 @@ def setup_logging(log_dir='tmp', data_dir='data'):
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# 设置日志格式，时间、日志级别、标签、消息
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log_format = (
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# "<green>{time:YYYY-MM-DD HH:mm:ss}</green>"
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"<green>{time:YY-MM-DD HH:mm:ss}</green>"
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"[<light-blue>{extra[tag]}</light-blue>]"
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" - <level>{level}</level> - "
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"<light-green>{message}</light-green>"
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+13
-10
@@ -28,19 +28,22 @@ async def handleAudioMessage(conn, audio):
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if conn.client_voice_stop:
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conn.client_abort = False
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conn.asr_server_receive = False
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text, file_path = conn.asr.speech_to_text(conn.asr_audio, conn.session_id)
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logger.bind(tag=TAG).info(f"识别文本: {text}")
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text_len, text_without_punctuation = remove_punctuation_and_length(text)
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if text_len <= conn.max_cmd_length and await handleCMDMessage(conn, text_without_punctuation):
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return
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if text_len > 0:
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await startToChat(conn, text)
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else:
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# 音频太短了，无法识别
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if len(conn.asr_audio) < 3:
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conn.asr_server_receive = True
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else:
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text, file_path = await conn.asr.speech_to_text(conn.asr_audio, conn.session_id)
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logger.bind(tag=TAG).info(f"识别文本: {text}")
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text_len, text_without_punctuation = remove_punctuation_and_length(text)
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if text_len <= conn.max_cmd_length and await handleCMDMessage(conn, text_without_punctuation):
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return
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if text_len > 0:
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await startToChat(conn, text)
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else:
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conn.asr_server_receive = True
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conn.asr_audio.clear()
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conn.reset_vad_states()
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async def handleCMDMessage(conn, text):
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cmd_exit = conn.cmd_exit
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for cmd in cmd_exit:
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@@ -161,4 +164,4 @@ async def no_voice_close_connect(conn):
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conn.client_abort = False
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conn.asr_server_receive = False
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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"
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await startToChat(conn, prompt)
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await startToChat(conn, prompt)
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@@ -0,0 +1,19 @@
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from abc import ABC, abstractmethod
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from typing import Optional, Tuple, List
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from config.logger import setup_logging
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TAG = __name__
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logger = setup_logging()
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class ASRProviderBase(ABC):
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@abstractmethod
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def save_audio_to_file(self, opus_data: List[bytes], session_id: str) -> str:
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"""解码Opus数据并保存为WAV文件"""
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pass
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@abstractmethod
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async def speech_to_text(self, opus_data: List[bytes], session_id: str) -> Tuple[Optional[str], Optional[str]]:
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"""将语音数据转换为文本"""
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pass
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@@ -0,0 +1,286 @@
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import time
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import io
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import wave
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import os
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from typing import Optional, Tuple, List
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import uuid
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import websockets
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import json
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import gzip
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import opuslib
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from core.providers.asr.base import ASRProviderBase
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from config.logger import setup_logging
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TAG = __name__
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logger = setup_logging()
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CLIENT_FULL_REQUEST = 0b0001
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CLIENT_AUDIO_ONLY_REQUEST = 0b0010
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NO_SEQUENCE = 0b0000
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NEG_SEQUENCE = 0b0010
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SERVER_FULL_RESPONSE = 0b1001
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SERVER_ACK = 0b1011
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SERVER_ERROR_RESPONSE = 0b1111
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NO_SERIALIZATION = 0b0000
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JSON = 0b0001
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THRIFT = 0b0011
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CUSTOM_TYPE = 0b1111
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NO_COMPRESSION = 0b0000
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GZIP = 0b0001
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CUSTOM_COMPRESSION = 0b1111
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def parse_response(res):
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"""
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protocol_version(4 bits), header_size(4 bits),
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message_type(4 bits), message_type_specific_flags(4 bits)
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serialization_method(4 bits) message_compression(4 bits)
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reserved （8bits) 保留字段
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header_extensions 扩展头(大小等于 8 * 4 * (header_size - 1) )
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payload 类似与http 请求体
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"""
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protocol_version = res[0] >> 4
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header_size = res[0] & 0x0f
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message_type = res[1] >> 4
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message_type_specific_flags = res[1] & 0x0f
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serialization_method = res[2] >> 4
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message_compression = res[2] & 0x0f
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reserved = res[3]
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header_extensions = res[4:header_size * 4]
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payload = res[header_size * 4:]
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result = {}
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payload_msg = None
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payload_size = 0
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if message_type == SERVER_FULL_RESPONSE:
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payload_size = int.from_bytes(payload[:4], "big", signed=True)
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payload_msg = payload[4:]
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elif message_type == SERVER_ACK:
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seq = int.from_bytes(payload[:4], "big", signed=True)
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result['seq'] = seq
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if len(payload) >= 8:
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payload_size = int.from_bytes(payload[4:8], "big", signed=False)
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payload_msg = payload[8:]
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elif message_type == SERVER_ERROR_RESPONSE:
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code = int.from_bytes(payload[:4], "big", signed=False)
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result['code'] = code
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payload_size = int.from_bytes(payload[4:8], "big", signed=False)
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payload_msg = payload[8:]
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if payload_msg is None:
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return result
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if message_compression == GZIP:
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payload_msg = gzip.decompress(payload_msg)
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if serialization_method == JSON:
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payload_msg = json.loads(str(payload_msg, "utf-8"))
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elif serialization_method != NO_SERIALIZATION:
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payload_msg = str(payload_msg, "utf-8")
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result['payload_msg'] = payload_msg
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result['payload_size'] = payload_size
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return result
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class ASRProvider(ASRProviderBase):
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def __init__(self, config: dict, delete_audio_file: bool):
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self.appid = config.get("appid")
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self.cluster = config.get("cluster")
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self.access_token = config.get("access_token")
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self.output_dir = config.get("output_dir")
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self.host = "openspeech.bytedance.com"
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self.ws_url = f"wss://{self.host}/api/v2/asr"
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self.success_code = 1000
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self.seg_duration = 15000
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# 确保输出目录存在
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os.makedirs(self.output_dir, exist_ok=True)
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def save_audio_to_file(self, opus_data: List[bytes], session_id: str) -> str:
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"""将Opus音频数据解码并保存为WAV文件"""
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file_name = f"asr_{session_id}_{uuid.uuid4()}.wav"
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file_path = os.path.join(self.output_dir, file_name)
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decoder = opuslib.Decoder(16000, 1) # 16kHz, 单声道
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pcm_data = []
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for opus_packet in opus_data:
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try:
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pcm_frame = decoder.decode(opus_packet, 960) # 960 samples = 60ms
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pcm_data.append(pcm_frame)
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except opuslib.OpusError as e:
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logger.bind(tag=TAG).error(f"Opus解码错误: {e}", exc_info=True)
|
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|
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with wave.open(file_path, "wb") as wf:
|
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wf.setnchannels(1)
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wf.setsampwidth(2) # 2 bytes = 16-bit
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wf.setframerate(16000)
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wf.writeframes(b"".join(pcm_data))
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return file_path
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@staticmethod
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def _generate_header(message_type=CLIENT_FULL_REQUEST, message_type_specific_flags=NO_SEQUENCE) -> bytearray:
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"""Generate protocol header."""
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header = bytearray()
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header_size = 1
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header.append((0b0001 << 4) | header_size) # Protocol version
|
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header.append((message_type << 4) | message_type_specific_flags)
|
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header.append((0b0001 << 4) | 0b0001) # JSON serialization & GZIP compression
|
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header.append(0x00) # reserved
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return header
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def _construct_request(self, reqid) -> dict:
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"""Construct the request payload."""
|
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return {
|
||||
"app": {
|
||||
"appid": f"{self.appid}",
|
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"cluster": self.cluster,
|
||||
"token": self.access_token,
|
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},
|
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"user": {
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"uid": str(uuid.uuid4()),
|
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},
|
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"request": {
|
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"reqid": reqid,
|
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"show_utterances": False,
|
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"sequence": 1
|
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},
|
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"audio": {
|
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"format": "wav",
|
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"rate": 16000,
|
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"language": "zh-CN",
|
||||
"bits": 16,
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||||
"channel": 1,
|
||||
"codec": "raw",
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},
|
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}
|
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|
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async def _send_request(self, audio_data: List[bytes], segment_size: int) -> Optional[str]:
|
||||
"""Send request to Volcano ASR service."""
|
||||
try:
|
||||
auth_header = {'Authorization': 'Bearer; {}'.format(self.access_token)}
|
||||
async with websockets.connect(self.ws_url, additional_headers=auth_header) as websocket:
|
||||
# Prepare request data
|
||||
request_params = self._construct_request(str(uuid.uuid4()))
|
||||
print(request_params)
|
||||
payload_bytes = str.encode(json.dumps(request_params))
|
||||
payload_bytes = gzip.compress(payload_bytes)
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||||
full_client_request = self._generate_header()
|
||||
full_client_request.extend((len(payload_bytes)).to_bytes(4, 'big')) # payload size(4 bytes)
|
||||
full_client_request.extend(payload_bytes) # payload
|
||||
|
||||
# Send header and metadata
|
||||
# full_client_request
|
||||
await websocket.send(full_client_request)
|
||||
res = await websocket.recv()
|
||||
result = parse_response(res)
|
||||
if 'payload_msg' in result and result['payload_msg']['code'] != self.success_code:
|
||||
logger.bind(tag=TAG).error(f"ASR error: {result}")
|
||||
return None
|
||||
|
||||
for seq, (chunk, last) in enumerate(self.slice_data(audio_data, segment_size), 1):
|
||||
if last:
|
||||
audio_only_request = self._generate_header(
|
||||
message_type=CLIENT_AUDIO_ONLY_REQUEST,
|
||||
message_type_specific_flags=NEG_SEQUENCE
|
||||
)
|
||||
else:
|
||||
audio_only_request = self._generate_header(
|
||||
message_type=CLIENT_AUDIO_ONLY_REQUEST
|
||||
)
|
||||
payload_bytes = gzip.compress(chunk)
|
||||
audio_only_request.extend((len(payload_bytes)).to_bytes(4, 'big')) # payload size(4 bytes)
|
||||
audio_only_request.extend(payload_bytes) # payload
|
||||
# Send audio data
|
||||
await websocket.send(audio_only_request)
|
||||
|
||||
# Receive response
|
||||
response = await websocket.recv()
|
||||
result = parse_response(response)
|
||||
|
||||
if 'payload_msg' in result and result['payload_msg']['code'] == self.success_code:
|
||||
if len(result['payload_msg']['result']) > 0:
|
||||
return result['payload_msg']['result'][0]["text"]
|
||||
return None
|
||||
else:
|
||||
logger.bind(tag=TAG).error(f"ASR error: {result}")
|
||||
return None
|
||||
|
||||
except Exception as e:
|
||||
logger.bind(tag=TAG).error(f"ASR request failed: {e}", exc_info=True)
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
def decode_opus(opus_data: List[bytes], session_id: str) -> List[bytes]:
|
||||
|
||||
decoder = opuslib.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:
|
||||
logger.bind(tag=TAG).error(f"Opus解码错误: {e}", exc_info=True)
|
||||
|
||||
return pcm_data
|
||||
|
||||
@staticmethod
|
||||
def read_wav_info(data: io.BytesIO = None) -> (int, int, int, int, int):
|
||||
with io.BytesIO(data) as _f:
|
||||
wave_fp = wave.open(_f, 'rb')
|
||||
nchannels, sampwidth, framerate, nframes = wave_fp.getparams()[:4]
|
||||
wave_bytes = wave_fp.readframes(nframes)
|
||||
return nchannels, sampwidth, framerate, nframes, len(wave_bytes)
|
||||
|
||||
@staticmethod
|
||||
def slice_data(data: bytes, chunk_size: int) -> (list, bool):
|
||||
"""
|
||||
slice data
|
||||
:param data: wav data
|
||||
:param chunk_size: the segment size in one request
|
||||
:return: segment data, last flag
|
||||
"""
|
||||
data_len = len(data)
|
||||
offset = 0
|
||||
while offset + chunk_size < data_len:
|
||||
yield data[offset: offset + chunk_size], False
|
||||
offset += chunk_size
|
||||
else:
|
||||
yield data[offset: data_len], True
|
||||
|
||||
async def speech_to_text(self, opus_data: List[bytes], session_id: str) -> Tuple[Optional[str], Optional[str]]:
|
||||
"""将语音数据转换为文本"""
|
||||
try:
|
||||
# 合并所有opus数据包
|
||||
pcm_data = self.decode_opus(opus_data, session_id)
|
||||
combined_pcm_data = b''.join(pcm_data)
|
||||
|
||||
wav_buffer = io.BytesIO()
|
||||
|
||||
with wave.open(wav_buffer, "wb") as wav_file:
|
||||
wav_file.setnchannels(1) # 设置声道数
|
||||
wav_file.setsampwidth(2) # 设置采样宽度
|
||||
wav_file.setframerate(16000) # 设置采样率
|
||||
wav_file.writeframes(combined_pcm_data) # 写入 PCM 数据
|
||||
|
||||
# 获取封装后的 WAV 数据
|
||||
wav_data = wav_buffer.getvalue()
|
||||
nchannels, sampwidth, framerate, nframes, wav_len = self.read_wav_info(wav_data)
|
||||
size_per_sec = nchannels * sampwidth * framerate
|
||||
segment_size = int(size_per_sec * self.seg_duration / 1000)
|
||||
|
||||
# 语音识别
|
||||
start_time = time.time()
|
||||
text = await self._send_request(wav_data, segment_size)
|
||||
if text:
|
||||
logger.bind(tag=TAG).debug(f"语音识别耗时: {time.time() - start_time:.3f}s | 结果: {text}")
|
||||
return text, None
|
||||
return "", None
|
||||
|
||||
except Exception as e:
|
||||
logger.bind(tag=TAG).error(f"语音识别失败: {e}", exc_info=True)
|
||||
return "", None
|
||||
@@ -0,0 +1,110 @@
|
||||
import time
|
||||
import wave
|
||||
import os
|
||||
import sys
|
||||
import io
|
||||
from config.logger import setup_logging
|
||||
from typing import Optional, Tuple, List
|
||||
import uuid
|
||||
import opuslib_next
|
||||
from core.providers.asr.base import ASRProviderBase
|
||||
|
||||
from funasr import AutoModel
|
||||
from funasr.utils.postprocess_utils import rich_transcription_postprocess
|
||||
|
||||
TAG = __name__
|
||||
logger = setup_logging()
|
||||
|
||||
|
||||
# 捕获标准输出
|
||||
class CaptureOutput:
|
||||
def __enter__(self):
|
||||
self._output = io.StringIO()
|
||||
self._original_stdout = sys.stdout
|
||||
sys.stdout = self._output
|
||||
|
||||
def __exit__(self, exc_type, exc_value, traceback):
|
||||
sys.stdout = self._original_stdout
|
||||
self.output = self._output.getvalue()
|
||||
self._output.close()
|
||||
|
||||
# 将捕获到的内容通过 logger 输出
|
||||
if self.output:
|
||||
logger.bind(tag=TAG).info(self.output.strip())
|
||||
|
||||
|
||||
class ASRProvider(ASRProviderBase):
|
||||
def __init__(self, config: dict, delete_audio_file: bool):
|
||||
self.model_dir = config.get("model_dir")
|
||||
self.output_dir = config.get("output_dir") # 修正配置键名
|
||||
self.delete_audio_file = delete_audio_file
|
||||
|
||||
# 确保输出目录存在
|
||||
os.makedirs(self.output_dir, exist_ok=True)
|
||||
with CaptureOutput():
|
||||
self.model = AutoModel(
|
||||
model=self.model_dir,
|
||||
vad_kwargs={"max_single_segment_time": 30000},
|
||||
disable_update=True,
|
||||
hub="hf"
|
||||
# device="cuda:0", # 启用GPU加速
|
||||
)
|
||||
|
||||
def save_audio_to_file(self, opus_data: List[bytes], session_id: str) -> str:
|
||||
"""将Opus音频数据解码并保存为WAV文件"""
|
||||
file_name = f"asr_{session_id}_{uuid.uuid4()}.wav"
|
||||
file_path = os.path.join(self.output_dir, file_name)
|
||||
|
||||
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_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:
|
||||
wf.setnchannels(1)
|
||||
wf.setsampwidth(2) # 2 bytes = 16-bit
|
||||
wf.setframerate(16000)
|
||||
wf.writeframes(b"".join(pcm_data))
|
||||
|
||||
return file_path
|
||||
|
||||
async def speech_to_text(self, opus_data: List[bytes], session_id: str) -> Tuple[Optional[str], Optional[str]]:
|
||||
"""语音转文本主处理逻辑"""
|
||||
file_path = None
|
||||
try:
|
||||
# 保存音频文件
|
||||
start_time = time.time()
|
||||
file_path = self.save_audio_to_file(opus_data, session_id)
|
||||
logger.bind(tag=TAG).debug(f"音频文件保存耗时: {time.time() - start_time:.3f}s | 路径: {file_path}")
|
||||
|
||||
# 语音识别
|
||||
start_time = time.time()
|
||||
result = self.model.generate(
|
||||
input=file_path,
|
||||
cache={},
|
||||
language="auto",
|
||||
use_itn=True,
|
||||
batch_size_s=60,
|
||||
)
|
||||
text = rich_transcription_postprocess(result[0]["text"])
|
||||
logger.bind(tag=TAG).debug(f"语音识别耗时: {time.time() - start_time:.3f}s | 结果: {text}")
|
||||
|
||||
return text, file_path
|
||||
|
||||
except Exception as e:
|
||||
logger.bind(tag=TAG).error(f"语音识别失败: {e}", exc_info=True)
|
||||
return "", None
|
||||
|
||||
finally:
|
||||
# 文件清理逻辑
|
||||
if self.delete_audio_file and file_path and os.path.exists(file_path):
|
||||
try:
|
||||
os.remove(file_path)
|
||||
logger.bind(tag=TAG).debug(f"已删除临时音频文件: {file_path}")
|
||||
except Exception as e:
|
||||
logger.bind(tag=TAG).error(f"文件删除失败: {file_path} | 错误: {e}")
|
||||
@@ -25,7 +25,7 @@ class TTSProvider(TTSProviderBase):
|
||||
async def text_to_speak(self, text, output_file):
|
||||
request_json = {
|
||||
"app": {
|
||||
"appid": self.appid,
|
||||
"appid": f"{self.appid}",
|
||||
"token": "access_token",
|
||||
"cluster": self.cluster
|
||||
},
|
||||
@@ -49,8 +49,13 @@ class TTSProvider(TTSProviderBase):
|
||||
}
|
||||
}
|
||||
|
||||
resp = requests.post(self.api_url, json.dumps(request_json), headers=self.header)
|
||||
if "data" in resp.json():
|
||||
data = resp.json()["data"]
|
||||
file_to_save = open(output_file, "wb")
|
||||
file_to_save.write(base64.b64decode(data))
|
||||
try:
|
||||
resp = requests.post(self.api_url, json.dumps(request_json), headers=self.header)
|
||||
if "data" in resp.json():
|
||||
data = resp.json()["data"]
|
||||
file_to_save = open(output_file, "wb")
|
||||
file_to_save.write(base64.b64decode(data))
|
||||
else:
|
||||
raise Exception(f"{__name__} status_code: {resp.status_code} response: {resp.content}")
|
||||
except Exception as e:
|
||||
raise Exception(f"{__name__} error: {e}")
|
||||
|
||||
@@ -89,7 +89,7 @@ class TTSProvider(TTSProviderBase):
|
||||
self.channels = config.get("channels",1)
|
||||
self.rate = config.get("rate",44100)
|
||||
self.api_key = config.get("api_key","YOUR_API_KEY")
|
||||
if not self.api_key or "你" in self.api_key:
|
||||
if "你" in self.api_key:
|
||||
logger.bind(tag=TAG).error("你还没配置FishSpeech TTS的密钥，请在配置文件中配置密钥，否则无法正常工作")
|
||||
return
|
||||
self.normalize = config.get("normalize",True)
|
||||
|
||||
+15
-123
@@ -1,132 +1,24 @@
|
||||
import time
|
||||
import wave
|
||||
import importlib
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
import io
|
||||
from abc import ABC, abstractmethod
|
||||
from config.logger import setup_logging
|
||||
from typing import Optional, Tuple, List
|
||||
import time
|
||||
import wave
|
||||
import uuid
|
||||
|
||||
import opuslib_next
|
||||
from funasr import AutoModel
|
||||
from funasr.utils.postprocess_utils import rich_transcription_postprocess
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Optional, Tuple, List
|
||||
from core.providers.asr.base import ASRProviderBase
|
||||
from config.logger import setup_logging
|
||||
|
||||
TAG = __name__
|
||||
logger = setup_logging()
|
||||
|
||||
# 捕获标准输出
|
||||
class CaptureOutput:
|
||||
def __enter__(self):
|
||||
self._output = io.StringIO()
|
||||
self._original_stdout = sys.stdout
|
||||
sys.stdout = self._output
|
||||
|
||||
def __exit__(self, exc_type, exc_value, traceback):
|
||||
sys.stdout = self._original_stdout
|
||||
self.output = self._output.getvalue()
|
||||
self._output.close()
|
||||
|
||||
# 将捕获到的内容通过 logger 输出
|
||||
if self.output:
|
||||
logger.bind(tag=TAG).info(self.output.strip())
|
||||
|
||||
class ASR(ABC):
|
||||
@abstractmethod
|
||||
def save_audio_to_file(self, opus_data: List[bytes], session_id: str) -> str:
|
||||
"""解码Opus数据并保存为WAV文件"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def speech_to_text(self, opus_data: List[bytes], session_id: str) -> Tuple[Optional[str], Optional[str]]:
|
||||
"""将语音数据转换为文本"""
|
||||
pass
|
||||
|
||||
|
||||
class FunASR(ASR):
|
||||
def __init__(self, config: dict, delete_audio_file: bool):
|
||||
self.model_dir = config.get("model_dir")
|
||||
self.output_dir = config.get("output_dir") # 修正配置键名
|
||||
self.delete_audio_file = delete_audio_file
|
||||
|
||||
# 确保输出目录存在
|
||||
os.makedirs(self.output_dir, exist_ok=True)
|
||||
with CaptureOutput():
|
||||
self.model = AutoModel(
|
||||
model=self.model_dir,
|
||||
vad_kwargs={"max_single_segment_time": 30000},
|
||||
disable_update=True,
|
||||
hub="hf"
|
||||
# device="cuda:0", # 启用GPU加速
|
||||
)
|
||||
|
||||
def save_audio_to_file(self, opus_data: List[bytes], session_id: str) -> str:
|
||||
"""将Opus音频数据解码并保存为WAV文件"""
|
||||
file_name = f"asr_{session_id}_{uuid.uuid4()}.wav"
|
||||
file_path = os.path.join(self.output_dir, file_name)
|
||||
|
||||
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_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:
|
||||
wf.setnchannels(1)
|
||||
wf.setsampwidth(2) # 2 bytes = 16-bit
|
||||
wf.setframerate(16000)
|
||||
wf.writeframes(b"".join(pcm_data))
|
||||
|
||||
return file_path
|
||||
|
||||
def speech_to_text(self, opus_data: List[bytes], session_id: str) -> Tuple[Optional[str], Optional[str]]:
|
||||
"""语音转文本主处理逻辑"""
|
||||
file_path = None
|
||||
try:
|
||||
# 保存音频文件
|
||||
start_time = time.time()
|
||||
file_path = self.save_audio_to_file(opus_data, session_id)
|
||||
logger.bind(tag=TAG).debug(f"音频文件保存耗时: {time.time() - start_time:.3f}s | 路径: {file_path}")
|
||||
|
||||
# 语音识别
|
||||
start_time = time.time()
|
||||
result = self.model.generate(
|
||||
input=file_path,
|
||||
cache={},
|
||||
language="auto",
|
||||
use_itn=True,
|
||||
batch_size_s=60,
|
||||
)
|
||||
text = rich_transcription_postprocess(result[0]["text"])
|
||||
logger.bind(tag=TAG).debug(f"语音识别耗时: {time.time() - start_time:.3f}s | 结果: {text}")
|
||||
|
||||
return text, file_path
|
||||
|
||||
except Exception as e:
|
||||
logger.bind(tag=TAG).error(f"语音识别失败: {e}", exc_info=True)
|
||||
return None, None
|
||||
|
||||
finally:
|
||||
# 文件清理逻辑
|
||||
if self.delete_audio_file and file_path and os.path.exists(file_path):
|
||||
try:
|
||||
os.remove(file_path)
|
||||
logger.bind(tag=TAG).debug(f"已删除临时音频文件: {file_path}")
|
||||
except Exception as e:
|
||||
logger.bind(tag=TAG).error(f"文件删除失败: {file_path} | 错误: {e}")
|
||||
|
||||
|
||||
def create_instance(class_name: str, *args, **kwargs) -> ASR:
|
||||
def create_instance(class_name: str, *args, **kwargs) -> ASRProviderBase:
|
||||
"""工厂方法创建ASR实例"""
|
||||
cls_map = {
|
||||
"FunASR": FunASR,
|
||||
# 可扩展其他ASR实现
|
||||
}
|
||||
if os.path.exists(os.path.join('core', 'providers', 'asr', f'{class_name}.py')):
|
||||
lib_name = f'core.providers.asr.{class_name}'
|
||||
if lib_name not in sys.modules:
|
||||
sys.modules[lib_name] = importlib.import_module(f'{lib_name}')
|
||||
return sys.modules[lib_name].ASRProvider(*args, **kwargs)
|
||||
|
||||
if cls := cls_map.get(class_name):
|
||||
return cls(*args, **kwargs)
|
||||
raise ValueError(f"不支持的ASR类型: {class_name}")
|
||||
raise ValueError(f"不支持的ASR类型: {class_name}，请检查该配置的type是否设置正确")
|
||||
@@ -7,6 +7,7 @@ from core.utils import asr, vad, llm, tts
|
||||
|
||||
TAG = __name__
|
||||
|
||||
|
||||
class WebSocketServer:
|
||||
def __init__(self, config: dict):
|
||||
self.config = config
|
||||
@@ -21,7 +22,10 @@ class WebSocketServer:
|
||||
self.config["VAD"][self.config["selected_module"]["VAD"]]
|
||||
),
|
||||
asr.create_instance(
|
||||
self.config["selected_module"]["ASR"],
|
||||
self.config["selected_module"]["ASR"]
|
||||
if not 'type' in self.config["ASR"][self.config["selected_module"]["ASR"]]
|
||||
else
|
||||
self.config["ASR"][self.config["selected_module"]["ASR"]]["type"],
|
||||
self.config["ASR"][self.config["selected_module"]["ASR"]],
|
||||
self.config["delete_audio"]
|
||||
),
|
||||
|
||||
@@ -1,17 +0,0 @@
|
||||
services:
|
||||
xiaozhi-esp32-server:
|
||||
image: ccr.ccs.tencentyun.com/xinnan/xiaozhi-esp32-server:latest
|
||||
container_name: xiaozhi-esp32-server
|
||||
restart: always
|
||||
#security_opt:
|
||||
# - seccomp:unconfined
|
||||
ports:
|
||||
- "8000:8000" # ws服务端
|
||||
- "8002:8002" # 管理后台
|
||||
volumes:
|
||||
# 配置文件 记得创建文件夹
|
||||
- ./data:/opt/xiaozhi-esp32-server/data
|
||||
# 模型文件 //可以挂载本地下载好的模型
|
||||
#- ./models:/opt/xiaozhi-esp32-server/models
|
||||
# 临时文件 //可以看log和音频文件 记得创建文件夹
|
||||
#- ./tmp:/opt/xiaozhi-esp32-server/tmp
|
||||
+7
-3
@@ -1,13 +1,17 @@
|
||||
services:
|
||||
xiaozhi-esp32-server:
|
||||
image: ghcr.io/kalicyh/xiaozhi-esp32-server:latest
|
||||
image: ghcr.nju.edu.cn/xinnan-tech/xiaozhi-esp32-server:latest
|
||||
container_name: xiaozhi-esp32-server
|
||||
restart: always
|
||||
#security_opt:
|
||||
# - seccomp:unconfined
|
||||
security_opt:
|
||||
- seccomp:unconfined
|
||||
ports:
|
||||
# ws服务端
|
||||
- "8000:8000"
|
||||
# 管理后台
|
||||
- "8002:8002"
|
||||
volumes:
|
||||
# 配置文件目录
|
||||
- ./data:/app/data
|
||||
# 模型文件挂接，很重要
|
||||
- ./models/SenseVoiceSmall/model.pt:/app/models/SenseVoiceSmall/model.pt
|
||||
@@ -1,217 +0,0 @@
|
||||
# 部署指南
|
||||
|
||||
本项目支持 **x86** 和 **arm64** 架构，可运行于国产操作系统。
|
||||
|
||||
## 方式一：快速部署（推荐）
|
||||
|
||||
### 1. 安装 Docker
|
||||
|
||||
如果未安装 Docker，可参考 [Docker 安装教程](https://www.runoob.com/docker/ubuntu-docker-install.html)。
|
||||
|
||||
### 2. 创建项目目录
|
||||
|
||||
选择一个空目录作为 **项目目录**。
|
||||
|
||||
### 3. 下载配置文件
|
||||
|
||||
访问 [config.yaml](https://github.com/xinnan-tech/xiaozhi-esp32-server/blob/main/config.yaml)，点击 `RAW` 旁的下载按钮，保存到 **项目目录**。
|
||||
|
||||
### 4. 运行 Docker
|
||||
|
||||
打开终端，`cd` 进入 **项目目录**，执行：
|
||||
|
||||
```sh
|
||||
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
|
||||
```
|
||||
|
||||
### 5. 确认运行状态
|
||||
|
||||
```sh
|
||||
docker ps # 查看容器状态
|
||||
docker logs -f xiaozhi-esp32-server # 查看日志
|
||||
```
|
||||
|
||||
## 方式二：使用 Docker Compose 部署
|
||||
|
||||
### 1. 创建 `docker-compose.yml`
|
||||
在 `项目目录` 下创建 `docker-compose.yml` 文件，内容如下：
|
||||
|
||||
```yaml
|
||||
version: '3'
|
||||
services:
|
||||
xiaozhi-esp32-server:
|
||||
image: ccr.ccs.tencentyun.com/xinnan/xiaozhi-esp32-server:latest
|
||||
container_name: xiaozhi-esp32-server
|
||||
restart: always
|
||||
security_opt:
|
||||
- seccomp:unconfined
|
||||
ports:
|
||||
- "8000:8000"
|
||||
volumes:
|
||||
- ./config.yaml:/opt/xiaozhi-esp32-server/config.yaml
|
||||
```
|
||||
|
||||
### 2. 启动服务
|
||||
```sh
|
||||
docker-compose up -d
|
||||
```
|
||||
|
||||
### 3. 确认运行状态
|
||||
```sh
|
||||
docker-compose ps
|
||||
docker-compose logs -f
|
||||
```
|
||||
|
||||
### 4. 停止和删除容器
|
||||
```sh
|
||||
docker-compose down
|
||||
```
|
||||
|
||||
### 5. 版本升级
|
||||
|
||||
#### 备份配置文件
|
||||
```sh
|
||||
cp config.yaml config_backup.yaml
|
||||
```
|
||||
|
||||
#### 更新 Docker 镜像
|
||||
```sh
|
||||
docker-compose pull
|
||||
docker-compose down
|
||||
docker-compose up -d
|
||||
```
|
||||
|
||||
|
||||
## 方式三：源码部署（免环境部署/可修改代码）
|
||||
|
||||
### 1. 下载源码
|
||||
|
||||
```sh
|
||||
git clone https://github.com/xinnan-tech/xiaozhi-esp32-server.git
|
||||
cd xiaozhi-esp32-server
|
||||
```
|
||||
|
||||
或手动下载 [ZIP 包](https://github.com/xinnan-tech/xiaozhi-esp32-server/archive/refs/heads/main.zip)，解压后重命名为 `xiaozhi-esp32-server`。
|
||||
|
||||
## 2.运行docker
|
||||
|
||||
修改完配置后，打开命令行工具，`cd`进入到你的项目目录下，执行以下命令
|
||||
|
||||
```sh
|
||||
docker run -it --name xiaozhi-env --restart always --security-opt seccomp:unconfined \
|
||||
-p 8000:8000 \
|
||||
-p 8002:8002 \
|
||||
-v ./:/app \
|
||||
kalicyh/poetry:v3.10_latest
|
||||
```
|
||||
|
||||
然后就和正常开发一样了
|
||||
|
||||
## 3.安装依赖
|
||||
|
||||
在刚刚的打开的终端运行
|
||||
|
||||
```sh
|
||||
poetry install --no-root
|
||||
```
|
||||
|
||||
```sh
|
||||
apt-get update
|
||||
apt-get install -y --no-install-recommends libopus0 ffmpeg
|
||||
```
|
||||
|
||||
速度慢可以尝试使用清华镜像
|
||||
|
||||
```sh
|
||||
echo "deb https://mirrors.tuna.tsinghua.edu.cn/debian/ bookworm main contrib non-free non-free-firmware" > /etc/apt/sources.list
|
||||
echo "deb https://mirrors.tuna.tsinghua.edu.cn/debian/ bookworm-updates main contrib non-free non-free-firmware" >> /etc/apt/sources.list
|
||||
echo "deb https://mirrors.tuna.tsinghua.edu.cn/debian/ bookworm-backports main contrib non-free non-free-firmware" >> /etc/apt/sources.list
|
||||
echo "deb https://mirrors.tuna.tsinghua.edu.cn/debian-security bookworm-security main contrib non-free non-free-firmware" >> /etc/apt/sources.list
|
||||
apt-get update
|
||||
apt-get install -y --no-install-recommends libopus0 ffmpeg
|
||||
```
|
||||
|
||||
## 4.运行项目
|
||||
|
||||
```sh
|
||||
poetry run python app.py
|
||||
```
|
||||
|
||||
## 方式三：本地运行（适用于开发）
|
||||
|
||||
### 1. 安装环境
|
||||
|
||||
```sh
|
||||
conda create -n xiaozhi-esp32-server python=3.10 -y
|
||||
conda activate xiaozhi-esp32-server
|
||||
```
|
||||
|
||||
#### Mac/Windows:
|
||||
|
||||
```sh
|
||||
conda install conda-forge::libopus conda-forge::ffmpeg
|
||||
```
|
||||
|
||||
#### Ubuntu:
|
||||
|
||||
```sh
|
||||
apt-get install libopus0 ffmpeg
|
||||
```
|
||||
|
||||
### 2. 下载源码 & 安装依赖
|
||||
|
||||
```sh
|
||||
git clone https://github.com/xinnan-tech/xiaozhi-esp32-server.git
|
||||
cd xiaozhi-esp32-server
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
### 3. 运行项目
|
||||
|
||||
```sh
|
||||
python app.py
|
||||
```
|
||||
|
||||
## 版本升级
|
||||
|
||||
```sh
|
||||
docker stop xiaozhi-esp32-server
|
||||
docker rm xiaozhi-esp32-server
|
||||
docker rmi ccr.ccs.tencentyun.com/xinnan/xiaozhi-esp32-server:latest
|
||||
```
|
||||
|
||||
然后重新按照 **方式一** 或 **方式二** 运行 Docker。
|
||||
|
||||
## 重要配置
|
||||
|
||||
修改 `config.yaml` 以适配不同 LLM 和 TTS 组件。
|
||||
|
||||
示例：
|
||||
|
||||
```yaml
|
||||
selected_module:
|
||||
ASR: FunASR
|
||||
VAD: SileroVAD
|
||||
LLM: ChatGLMLLM
|
||||
TTS: EdgeTTS
|
||||
```
|
||||
|
||||
如需使用 `Dify` 或 `DeepSeekLLM`，修改 `LLM` 部分并填写密钥。
|
||||
|
||||
## 模型文件下载
|
||||
|
||||
语音转文字默认使用 `SenseVoiceSmall`，需下载 `model.pt` 并存放至 `model/SenseVoiceSmall` 目录。
|
||||
|
||||
- [阿里魔塔下载](https://modelscope.cn/models/iic/SenseVoiceSmall/resolve/master/model.pt)
|
||||
- [百度网盘下载](https://pan.baidu.com/share/init?surl=QlgM58FHhYv1tFnUT_A8Sg&pwd=qvna) 提取码：`qvna`
|
||||
|
||||
---
|
||||
|
||||
如遇问题，可参考 `docker logs -f xiaozhi-esp32-server` 进行排查。
|
||||
|
||||
**至此，部署完成！** 🚀
|
||||
|
||||
|
||||
+182
-43
@@ -1,30 +1,172 @@
|
||||
# 方式一：docker快速部署
|
||||
|
||||
# 本地源码运行
|
||||
docker镜像已支持x86架构、arm64架构的CPU，支持在国产操作系统上运行。
|
||||
|
||||
## 1. 安装docker
|
||||
|
||||
如果您的电脑还没安装docker，可以按照这里的教程安装：[docker安装](https://www.runoob.com/docker/ubuntu-docker-install.html)
|
||||
|
||||
## 2. 创建目录
|
||||
|
||||
安装完后，你需要为这个项目找一个安放配置文件的目录，我们暂且称它为`项目目录`，这个目录最好是一个新建的空的目录。
|
||||
|
||||
创建好目录后，你需要在`项目目录`下面创建`data`文件夹和`models`文件夹，`models`下面还要再创建`SenseVoiceSmall`文件夹。
|
||||
|
||||
最终目录结构如下所示：
|
||||
|
||||
```
|
||||
你的项目根目录
|
||||
├─ data
|
||||
├─ models
|
||||
├─ SenseVoiceSmall
|
||||
```
|
||||
|
||||
## 4. 下载语音识别模型文件
|
||||
|
||||
你需要下载语音识别的模型文件，因为本项目的默认语音识别用的是本地离线语音识别方案。可通过这个方式下载
|
||||
[跳转到下载语音识别模型文件](#模型文件)
|
||||
|
||||
下载完后，回到本教程。
|
||||
|
||||
## 3. 下载docker-compose.yaml
|
||||
|
||||
用浏览器打开[这个链接](https://github.com/xinnan-tech/xiaozhi-esp32-server/blob/main/docker-compose.yml)。
|
||||
|
||||
在页面的右侧找到名称为`RAW`按钮，在`RAW`按钮的旁边，找到下载的图标，点击下载按钮，下载`docker-compose.yml`文件。 把文件下载到你的
|
||||
`项目目录`中。
|
||||
|
||||
下载完后，回到本教程继续往下。
|
||||
|
||||
## 3. 下载配置文件
|
||||
|
||||
用浏览器打开[这个链接](https://github.com/xinnan-tech/xiaozhi-esp32-server/blob/main/config.yaml)。
|
||||
|
||||
在页面的右侧找到名称为`RAW`按钮，在`RAW`按钮的旁边，找到下载的图标，点击下载按钮，下载`config.yaml`文件。 把文件下载到你的
|
||||
`项目目录`下面的`data`文件夹中，然后把`config.yaml`文件重命名为`.config.yaml`。
|
||||
|
||||
下载完配置文件后，我们确认一下整个`项目目录`里面的文件如下所示：
|
||||
|
||||
```
|
||||
你的项目根目录
|
||||
├─ docker-compose.yml
|
||||
├─ data
|
||||
├─ .config.yaml
|
||||
├─ models
|
||||
├─ SenseVoiceSmall
|
||||
├─ model.pt
|
||||
```
|
||||
|
||||
如果你的文件目录结构也是上面的，就继续往下。如果不是，你就再仔细看看是不是漏操作了什么。
|
||||
|
||||
## 4. 配置项目文件
|
||||
|
||||
接下里，程序还不能直接运行，你需要配置一下，你到底使用的是什么模型。你可以看这个教程：
|
||||
[跳转到配置项目文件](#配置项目)
|
||||
|
||||
配置完项目文件后，回到本教程继续往下。
|
||||
|
||||
## 5. 执行docker命令
|
||||
|
||||
打开命令行工具，使用`终端`或`命令行`工具 进入到你的`项目目录`，执行以下命令
|
||||
|
||||
```
|
||||
docker-compose up -d
|
||||
```
|
||||
|
||||
执行完后，再执行以下命令，查看日志信息。
|
||||
|
||||
```
|
||||
docker logs -f xiaozhi-esp32-server
|
||||
```
|
||||
|
||||
这时，你就要留意日志信息，可以根据这个教程，判断是否成功了。[跳转到运行状态确认](#运行状态确认)
|
||||
|
||||
## 6.版本升级操作
|
||||
|
||||
如果后期想升级版本，可以这么操作
|
||||
|
||||
1、备份好`data`文件夹中的`.config.yaml`文件，一些关键的配置到时复制到新的`.config.yaml`文件里。
|
||||
请注意是对关键密钥逐个复制，不要直接覆盖。因为新的`.config.yaml`文件可能有一些新的配置项，旧的`.config.yaml`文件不一定有。
|
||||
|
||||
2、执行以下命令
|
||||
|
||||
```
|
||||
docker stop xiaozhi-esp32-server
|
||||
docker rm xiaozhi-esp32-server
|
||||
docker rmi ghcr.nju.edu.cn/xinnan-tech/xiaozhi-esp32-server:latest
|
||||
```
|
||||
|
||||
3、重新按docker方式部署
|
||||
|
||||
# 方式二：借助Docker环境运行部署
|
||||
|
||||
开发人员如果不想安装`conda`环境，可以使用这种方法管理好依赖。
|
||||
|
||||
## 1.克隆项目
|
||||
|
||||
## 2.[跳转到下载语音识别模型文件](#模型文件)
|
||||
|
||||
## 3.[跳转到配置项目文件](#配置项目)
|
||||
|
||||
## 4.运行docker
|
||||
|
||||
修改完配置后，打开命令行工具，`cd`进入到你的项目目录下，执行以下命令
|
||||
|
||||
```sh
|
||||
docker run -it --name xiaozhi-env --restart always --security-opt seccomp:unconfined \
|
||||
-p 8000:8000 \
|
||||
-p 8002:8002 \
|
||||
-v ./:/app \
|
||||
ccr.ccs.tencentyun.com/kalicyh/poetry:v3.10_latest
|
||||
```
|
||||
|
||||
然后就和正常开发一样了
|
||||
|
||||
## 5.安装依赖
|
||||
|
||||
在刚刚的打开的终端运行
|
||||
|
||||
```sh
|
||||
poetry install --no-root
|
||||
```
|
||||
|
||||
```sh
|
||||
apt-get update
|
||||
apt-get install -y --no-install-recommends libopus0 ffmpeg
|
||||
```
|
||||
|
||||
速度慢可以尝试使用清华镜像
|
||||
|
||||
```sh
|
||||
echo "deb https://mirrors.tuna.tsinghua.edu.cn/debian/ bookworm main contrib non-free non-free-firmware" > /etc/apt/sources.list
|
||||
echo "deb https://mirrors.tuna.tsinghua.edu.cn/debian/ bookworm-updates main contrib non-free non-free-firmware" >> /etc/apt/sources.list
|
||||
echo "deb https://mirrors.tuna.tsinghua.edu.cn/debian/ bookworm-backports main contrib non-free non-free-firmware" >> /etc/apt/sources.list
|
||||
echo "deb https://mirrors.tuna.tsinghua.edu.cn/debian-security bookworm-security main contrib non-free non-free-firmware" >> /etc/apt/sources.list
|
||||
apt-get update
|
||||
apt-get install -y --no-install-recommends libopus0 ffmpeg
|
||||
```
|
||||
|
||||
## 6.运行项目
|
||||
|
||||
```sh
|
||||
poetry run python app.py
|
||||
```
|
||||
|
||||
# 方式三：本地源码运行
|
||||
|
||||
## 1.安装基础环境
|
||||
|
||||
本项目使用`conda`管理依赖环境，安装好后，开始执行以下命令。
|
||||
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。
|
||||
|
||||
```
|
||||
conda remove -n xiaozhi-esp32-server --all -y
|
||||
conda create -n xiaozhi-esp32-server python=3.10 -y
|
||||
conda activate xiaozhi-esp32-server
|
||||
```
|
||||
|
||||
执行以上命令后， 如果你的电脑是Windows或Mac，执行下面的语句：
|
||||
|
||||
```
|
||||
conda activate xiaozhi-esp32-server
|
||||
conda install conda-forge::libopus
|
||||
conda install conda-forge::ffmpeg
|
||||
```
|
||||
|
||||
如果你的电脑是ubuntu，执行下面的语句：
|
||||
|
||||
```
|
||||
apt-get install libopus0 ffmpeg
|
||||
```
|
||||
|
||||
## 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。
|
||||
@@ -43,9 +185,17 @@ pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
## 3.[跳转到下载语音识别模型文件](#模型文件)
|
||||
## 3.下载语音识别模型文件
|
||||
|
||||
## 4.[跳转到配置项目文件](#配置项目)
|
||||
你需要下载语音识别的模型文件，因为本项目的默认语音识别用的是本地离线语音识别方案。可通过这个方式下载
|
||||
[跳转到下载语音识别模型文件](#模型文件)
|
||||
|
||||
下载完后，回到本教程。
|
||||
|
||||
## 4.配置项目文件
|
||||
|
||||
接下里，程序还不能直接运行，你需要配置一下，你到底使用的是什么模型。你可以看这个教程：
|
||||
[跳转到配置项目文件](#配置项目)
|
||||
|
||||
## 5.运行项目
|
||||
|
||||
@@ -55,25 +205,20 @@ conda activate xiaozhi-esp32-server
|
||||
python app.py
|
||||
```
|
||||
|
||||
## 6.[跳转到运行状态确认](#运行状态确认)
|
||||
这时，你就要留意日志信息，可以根据这个教程，判断是否成功了。[跳转到运行状态确认](#运行状态确认)
|
||||
|
||||
# 汇总
|
||||
|
||||
## 配置项目
|
||||
|
||||
修改`config.yaml`文件，配置本项目所需的各种参数。默认的LLM使用的是`ChatGLMLLM`
|
||||
如果你的`项目目录`目录没有`data`，你需要创建`data`目录。
|
||||
如果你的`data`下面没有`.config.yaml`文件，你可以把源码目录下的`config.yaml`文件复制一份，重命名为`.config.yaml`
|
||||
|
||||
修改`项目目录`下`data`目录下的`.config.yaml`文件，配置本项目所需的各种参数。默认的LLM使用的是`ChatGLMLLM`
|
||||
，你需要配置密钥，因为他们的模型，虽然有免费的，但是仍要去[官网](https://bigmodel.cn/usercenter/proj-mgmt/apikeys)注册密钥，才能启动。
|
||||
默认的TTS使用的是`EdgeTTS`，这个无需配置，如果你需要更换成`豆包TTS`，则需要配置密钥。
|
||||
|
||||
```
|
||||
# 如果您是一名开发者，建议阅读以下内容。如果不是开发者，可以忽略这部分内容。
|
||||
# 在开发中，在项目根目录创建data目录，将【config.yaml】复制一份，改成【.config.yaml】，放进data目录中
|
||||
# 系统会优先读取【data/.config.yaml】文件的配置。
|
||||
# 这样做，可以避免在提交代码的时候，错误地提交密钥信息，保护您的密钥安全。
|
||||
```
|
||||
|
||||
配置说明：这里是各个功能使用的默认组件，例如LLM默认使用`ChatGLMLLM`模型。如果需要切换模型，就是改对应的名称。
|
||||
|
||||
本项目的默认配置仅是成本最低配置（`glm-4-flash`和`EdgeTTS`都是免费的），如果需要更优的更快的搭配，需要自己结合部署环境切换各组件的使用。
|
||||
|
||||
```
|
||||
@@ -104,7 +249,7 @@ LLM:
|
||||
|
||||
## 模型文件
|
||||
|
||||
下载源码后，需要下载模型文件。 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`
|
||||
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`
|
||||
目录下。下面两个下载路线任选一个。
|
||||
|
||||
@@ -112,27 +257,21 @@ LLM:
|
||||
- 线路二：百度网盘下载[SenseVoiceSmall](https://pan.baidu.com/share/init?surl=QlgM58FHhYv1tFnUT_A8Sg&pwd=qvna) 提取码:
|
||||
`qvna`
|
||||
|
||||
huggingface地址：
|
||||
|
||||
```
|
||||
https://huggingface.co/jinaai/jina-embeddings-v2-base-zh?clone=true
|
||||
https://huggingface.co/FunAudioLLM/SenseVoiceSmall/tree/main?clone=true
|
||||
```
|
||||
|
||||
镜像地址`https://hf-mirror.com/`
|
||||
|
||||
|
||||
## 运行状态确认
|
||||
|
||||
如果你能看到，类似以下日志,则是本项目服务启动成功的标志。
|
||||
|
||||
```
|
||||
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
|
||||
25-02-23 12:01:09[core.websocket_server] - INFO - Server is running at ws://xxx.xx.xx.xx:8000
|
||||
25-02-23 12:01:09[core.websocket_server] - INFO - =======上面的地址是websocket协议地址，请勿用浏览器访问=======
|
||||
```
|
||||
|
||||
接下来，你就可以开始 `编译esp32固件`了，请往下翻，翻到编译`esp32固件`相关章节。
|
||||
正常来说，假设你的ip是`192.168.1.25`，那么你的接口地址就是：`ws://192.168.1.25:8000`。这个信息很有用的，后面`编译esp32固件`
|
||||
需要用到。
|
||||
正常来说，如果您是通过源码运行本项目，日志会有你的接口地址信息。
|
||||
但是如果你用docker部署，那么你的日志里给出的接口地址信息就不是真实的接口地址。
|
||||
|
||||
请注意，你的接口地址是`websocket`协议的地址，你可以使用`apifox`等工具调试。但是不能直接用浏览器打开访问，如果用浏览器打开，日志会显示错误，会让你怀疑是否部署成功了。
|
||||
最正确的方法，是根据电脑的局域网IP来确定你的接口地址。
|
||||
如果你的电脑的局域网IP比如是`192.168.1.25`，那么你的接口地址就是：`ws://192.168.1.25:8000`。
|
||||
|
||||
这个信息很有用的，后面`编译esp32固件`需要用到。
|
||||
|
||||
接下来，你就可以开始 [编译esp32固件](firmware-build.md)了。
|
||||
@@ -1,250 +0,0 @@
|
||||
# 方式一：docker快速部署
|
||||
|
||||
docker镜像已支持x86架构、arm64架构的CPU，支持在国产操作系统上运行。
|
||||
|
||||
## 1. 安装docker
|
||||
|
||||
如果您的电脑还没安装docker，可以按照这里的教程安装：[docker安装](https://www.runoob.com/docker/ubuntu-docker-install.html)
|
||||
|
||||
## 2. 创建目录
|
||||
|
||||
安装完后，你需要为这个项目找一个安放配置文件的目录，我们暂且称它为`项目目录`，这个目录最好是一个新建的空的目录。
|
||||
|
||||
## 3. 下载配置文件
|
||||
|
||||
用浏览器打开[这个链接](https://github.com/xinnan-tech/xiaozhi-esp32-server/blob/main/config.yaml)。
|
||||
|
||||
在页面的右侧找到名称为`RAW`按钮，在`RAW`按钮的旁边，找到下载的图标，点击下载按钮，下载`config.yaml`文件。 把文件下载到你的
|
||||
`项目目录`。
|
||||
|
||||
## 4.[跳转到配置项目文件](#配置项目)
|
||||
|
||||
## 5. 执行docker命令
|
||||
|
||||
打开命令行工具，`cd` 进入到你的`项目目录`，执行以下命令
|
||||
|
||||
```
|
||||
#如果你是linux，执行
|
||||
ls
|
||||
#如果你是windows，执行
|
||||
dir
|
||||
```
|
||||
|
||||
如果你能看到`config.yaml`文件，确确实实进入到了`项目目录`，接着执行以下命令：
|
||||
|
||||
```
|
||||
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
|
||||
```
|
||||
|
||||
## 6.[跳转到运行状态确认](#运行状态确认)
|
||||
|
||||
## [跳转到版本升级操作](#版本升级操作)
|
||||
|
||||
# 方式二：借助docker环境运行部署（仅限开发人员/小白勿用）
|
||||
|
||||
## 1.克隆项目
|
||||
|
||||
## 2.[跳转到下载语音识别模型文件](#模型文件)
|
||||
|
||||
## 3.[跳转到配置项目文件](#配置项目)
|
||||
|
||||
## 4.运行docker
|
||||
|
||||
修改完配置后，打开命令行工具，`cd`进入到你的项目目录下，执行以下命令
|
||||
|
||||
```sh
|
||||
docker run -it --name xiaozhi-env --restart always --security-opt seccomp:unconfined \
|
||||
-p 8000:8000 \
|
||||
-p 8002:8002 \
|
||||
-v ./:/app \
|
||||
ccr.ccs.tencentyun.com/kalicyh/poetry:v3.10_latest
|
||||
```
|
||||
|
||||
然后就和正常开发一样了
|
||||
|
||||
## 5.安装依赖
|
||||
|
||||
在刚刚的打开的终端运行
|
||||
|
||||
```sh
|
||||
poetry install --no-root
|
||||
```
|
||||
|
||||
```sh
|
||||
apt-get update
|
||||
apt-get install -y --no-install-recommends libopus0 ffmpeg
|
||||
```
|
||||
|
||||
速度慢可以尝试使用清华镜像
|
||||
|
||||
```sh
|
||||
echo "deb https://mirrors.tuna.tsinghua.edu.cn/debian/ bookworm main contrib non-free non-free-firmware" > /etc/apt/sources.list
|
||||
echo "deb https://mirrors.tuna.tsinghua.edu.cn/debian/ bookworm-updates main contrib non-free non-free-firmware" >> /etc/apt/sources.list
|
||||
echo "deb https://mirrors.tuna.tsinghua.edu.cn/debian/ bookworm-backports main contrib non-free non-free-firmware" >> /etc/apt/sources.list
|
||||
echo "deb https://mirrors.tuna.tsinghua.edu.cn/debian-security bookworm-security main contrib non-free non-free-firmware" >> /etc/apt/sources.list
|
||||
apt-get update
|
||||
apt-get install -y --no-install-recommends libopus0 ffmpeg
|
||||
```
|
||||
|
||||
## 6.运行项目
|
||||
|
||||
```sh
|
||||
poetry run python app.py
|
||||
```
|
||||
|
||||
# 方式三：本地源码运行
|
||||
|
||||
## 1.安装基础环境
|
||||
|
||||
本项目使用`conda`管理依赖环境，安装好后，开始执行以下命令。
|
||||
|
||||
```
|
||||
conda remove -n xiaozhi-esp32-server --all -y
|
||||
conda create -n xiaozhi-esp32-server python=3.10 -y
|
||||
conda activate xiaozhi-esp32-server
|
||||
```
|
||||
|
||||
执行以上命令后， 如果你的电脑是Windows或Mac，执行下面的语句：
|
||||
|
||||
```
|
||||
conda activate xiaozhi-esp32-server
|
||||
conda install conda-forge::libopus
|
||||
conda install conda-forge::ffmpeg
|
||||
```
|
||||
|
||||
如果你的电脑是ubuntu，执行下面的语句：
|
||||
|
||||
```
|
||||
apt-get install libopus0 ffmpeg
|
||||
```
|
||||
|
||||
## 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。
|
||||
|
||||
你可以用浏览器打开这个地址`https://github.com/xinnan-tech/xiaozhi-esp32-server.git`
|
||||
|
||||
打开完，找到页面中一个绿色的按钮，写着`Code`的按钮，点开它，然后你就看到`Download ZIP`的按钮。
|
||||
|
||||
点击它，下载本项目源码压缩包。下载到你电脑后，解压它，此时它的名字可能叫`xiaozhi-esp32-server-main`
|
||||
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
|
||||
conda activate xiaozhi-esp32-server
|
||||
pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
## 3.[跳转到下载语音识别模型文件](#模型文件)
|
||||
|
||||
## 4.[跳转到配置项目文件](#配置项目)
|
||||
|
||||
## 5.运行项目
|
||||
|
||||
```
|
||||
# 确保在本项目的根目录下执行
|
||||
conda activate xiaozhi-esp32-server
|
||||
python app.py
|
||||
```
|
||||
|
||||
## 6.[跳转到运行状态确认](#运行状态确认)
|
||||
|
||||
# 汇总
|
||||
|
||||
## 配置项目
|
||||
|
||||
修改`config.yaml`文件，配置本项目所需的各种参数。默认的LLM使用的是`ChatGLMLLM`
|
||||
，你需要配置密钥，因为他们的模型，虽然有免费的，但是仍要去[官网](https://bigmodel.cn/usercenter/proj-mgmt/apikeys)注册密钥，才能启动。
|
||||
默认的TTS使用的是`EdgeTTS`，这个无需配置，如果你需要更换成`豆包TTS`，则需要配置密钥。
|
||||
|
||||
```
|
||||
# 如果您是一名开发者，建议阅读以下内容。如果不是开发者，可以忽略这部分内容。
|
||||
# 在开发中，在项目根目录创建data目录，将【config.yaml】复制一份，改成【.config.yaml】，放进data目录中
|
||||
# 系统会优先读取【data/.config.yaml】文件的配置。
|
||||
# 这样做，可以避免在提交代码的时候，错误地提交密钥信息，保护您的密钥安全。
|
||||
```
|
||||
|
||||
配置说明：这里是各个功能使用的默认组件，例如LLM默认使用`ChatGLMLLM`模型。如果需要切换模型，就是改对应的名称。
|
||||
|
||||
本项目的默认配置仅是成本最低配置（`glm-4-flash`和`EdgeTTS`都是免费的），如果需要更优的更快的搭配，需要自己结合部署环境切换各组件的使用。
|
||||
|
||||
```
|
||||
selected_module:
|
||||
ASR: FunASR
|
||||
VAD: SileroVAD
|
||||
LLM: ChatGLMLLM
|
||||
TTS: EdgeTTS
|
||||
```
|
||||
|
||||
比如修改`LLM`使用的组件，就看本项目支持哪些`LLM` API接口，当前支持的是`openai`、`dify`。欢迎验证和支持更多LLM平台的接口。
|
||||
使用时，在`selected_module`修改成对应的如下LLM配置的名称：
|
||||
|
||||
```
|
||||
LLM:
|
||||
DeepSeekLLM:
|
||||
type: openai
|
||||
...
|
||||
ChatGLMLLM:
|
||||
type: openai
|
||||
...
|
||||
DifyLLM:
|
||||
type: dify
|
||||
...
|
||||
```
|
||||
|
||||
有些服务，比如如果你使用`Dify`、`豆包的TTS`，是需要密钥的，记得在配置文件加上哦！
|
||||
|
||||
## 模型文件
|
||||
|
||||
下载源码后，需要下载模型文件。 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`
|
||||
目录下。下面两个下载路线任选一个。
|
||||
|
||||
- 线路一：阿里魔塔下载[SenseVoiceSmall](https://modelscope.cn/models/iic/SenseVoiceSmall/resolve/master/model.pt)
|
||||
- 线路二：百度网盘下载[SenseVoiceSmall](https://pan.baidu.com/share/init?surl=QlgM58FHhYv1tFnUT_A8Sg&pwd=qvna) 提取码:
|
||||
`qvna`
|
||||
|
||||
|
||||
## 运行状态确认
|
||||
|
||||
如果首次执行，可能需要几分钟时间，你要耐心等待他完成拉取。正常拉取完成后，你可以在命令行执行以下命令查看服务是否启动成功
|
||||
|
||||
```
|
||||
docker ps
|
||||
```
|
||||
|
||||
如果你能看到`xiaozhi-server`，说明服务启动成功。那你还可以进一步执行以下命令，查看服务的日志
|
||||
|
||||
```
|
||||
docker logs -f xiaozhi-esp32-server
|
||||
```
|
||||
|
||||
如果你能看到，类似以下日志,则是本项目服务启动成功的标志。
|
||||
|
||||
```
|
||||
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
|
||||
```
|
||||
|
||||
接下来，你就可以开始 `编译esp32固件`了，请往下翻，翻到编译`esp32固件`相关章节。那么由于你是用docker部署，你要自己查看自己本机电脑的ip是多少。
|
||||
正常来说，假设你的ip是`192.168.1.25`，那么你的接口地址就是：`ws://192.168.1.25:8000`。这个信息很有用的，后面`编译esp32固件`
|
||||
需要用到。
|
||||
|
||||
请注意，你的接口地址是`websocket`协议的地址，你可以使用`apifox`等工具调试。但是不能直接用浏览器打开访问，如果用浏览器打开，日志会显示错误，会让你怀疑是否部署成功了。
|
||||
|
||||
## 版本升级操作
|
||||
|
||||
如果想升级版本，可以这么操作
|
||||
|
||||
1、备份好`config.yaml`文件，一些关键的配置到时复制到新的`config.yaml`文件里。
|
||||
|
||||
2、执行以下命令
|
||||
|
||||
```
|
||||
docker stop xiaozhi-esp32-server
|
||||
docker rm xiaozhi-esp32-server
|
||||
docker rmi ccr.ccs.tencentyun.com/xinnan/xiaozhi-esp32-server:latest
|
||||
```
|
||||
|
||||
3、重新开始安装
|
||||
@@ -1,4 +1,7 @@
|
||||
# 编译docker镜像
|
||||
# 本地编译docker镜像方法
|
||||
|
||||
现在本项目已经使用github自动编译docker功能，本文档是提供给有本地编译docker镜像需求的朋友准备的。
|
||||
|
||||
1、安装docker
|
||||
```
|
||||
sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin
|
||||
@@ -6,7 +9,7 @@ sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin
|
||||
2、编译docker镜像
|
||||
```
|
||||
# 普通编译
|
||||
docker build -t xiaozhi-esp32-server:local -f ./Dockerfile .
|
||||
docker build -t xiaozhi-esp32-server:local -f ./Dockerfile-pip .
|
||||
```
|
||||
3、测试本地镜像
|
||||
```
|
||||
|
||||
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Reference in New Issue
Block a user