256 lines
4.5 KiB
Markdown
256 lines
4.5 KiB
Markdown
# Whisper 语音识别服务 - 服务器部署指南
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## 一、环境要求
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- Python 3.8+
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- 2GB+ 内存
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- 2GB+ 硬盘空间（用于 Whisper 模型）
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- ffmpeg
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---
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## 二、安装步骤（内网服务器）
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### 1️⃣ 安装 ffmpeg
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#### CentOS/RHEL 服务器
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```bash
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sudo yum install -y epel-release
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sudo yum install -y ffmpeg
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```
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#### Ubuntu/Debian 服务器
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```bash
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sudo apt update
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sudo apt install -y ffmpeg
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```
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#### Windows 服务器
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```powershell
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# 下载 ffmpeg
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# https://www.gyan.dev/ffmpeg/builds/ffmpeg-release-essentials.zip
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# 解压到 C:\ffmpeg
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# 添加环境变量：C:\ffmpeg\bin
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```
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验证安装：
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```bash
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ffmpeg -version
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```
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---
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### 2️⃣ 安装 Python 依赖（使用清华镜像）
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#### 方式一：自动安装（推荐）
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**Linux 服务器：**
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```bash
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chmod +x install_whisper.sh
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./install_whisper.sh
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```
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**Windows 服务器：**
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```cmd
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install_whisper.bat
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```
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#### 方式二：手动安装
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```bash
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# 配置镜像源
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pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
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# 安装依赖
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pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
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```
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---
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### 3️⃣ 首次运行（下载模型）
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```bash
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python whisper_server.py
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```
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**首次运行时，会自动下载 Whisper base 模型（约 140MB）**
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下载位置：
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- Linux: `~/.cache/whisper/`
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- Windows: `C:\Users\用户名\.cache\whisper\`
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⚠️ **内网环境注意：**
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如果服务器无法联网下载模型，需要**离线下载**：
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```bash
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# 在有网络的机器上下载模型
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python -c "import whisper; whisper.load_model('base')"
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# 将模型文件复制到服务器
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# 模型文件：base.pt（约 140MB）
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# 位置：~/.cache/whisper/base.pt
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```
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---
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### 4️⃣ 启动服务
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#### 前台运行（测试用）
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```bash
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python whisper_server.py
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```
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#### 后台运行（生产环境）
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**Linux - 使用 nohup：**
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```bash
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nohup python whisper_server.py > whisper.log 2>&1 &
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```
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**Linux - 使用 systemd（推荐）：**
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创建服务文件 `/etc/systemd/system/whisper.service`：
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```ini
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[Unit]
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Description=Whisper Speech Recognition Service
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After=network.target
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[Service]
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Type=simple
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User=root
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WorkingDirectory=/path/to/project/Test/python
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ExecStart=/usr/bin/python3 whisper_server.py
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Restart=always
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RestartSec=10
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[Install]
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WantedBy=multi-user.target
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```
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启动服务：
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```bash
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sudo systemctl daemon-reload
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sudo systemctl start whisper
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sudo systemctl enable whisper # 开机自启
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sudo systemctl status whisper # 查看状态
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```
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**Windows - 使用 NSSM（推荐）：**
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```powershell
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# 下载 NSSM：https://nssm.cc/download
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nssm install Whisper "C:\Python\python.exe" "C:\path\to\whisper_server.py"
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nssm start Whisper
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```
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---
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## 三、验证服务
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### 1. 健康检查
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```bash
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curl http://服务器IP:5001/health
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```
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返回：
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```json
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{
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"status": "ok",
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"service": "Whisper语音识别服务",
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"model": "base"
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}
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```
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### 2. 测试识别
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```bash
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curl -X POST -F "file=@test.mp3" -F "language=zh" http://服务器IP:5001/recognize
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```
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---
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## 四、Java 后端配置
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修改 `LocalWhisperService.java` 中的服务地址：
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```java
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private static final String WHISPER_URL = "http://服务器IP:5001";
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```
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重启 Java 应用。
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---
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## 五、防火墙配置
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### CentOS/RHEL
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```bash
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sudo firewall-cmd --permanent --add-port=5001/tcp
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sudo firewall-cmd --reload
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```
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### Ubuntu/Debian
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```bash
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sudo ufw allow 5001/tcp
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```
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### Windows
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```powershell
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netsh advfirewall firewall add rule name="Whisper Service" dir=in action=allow protocol=TCP localport=5001
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```
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---
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## 六、性能优化建议
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### 1. 使用更小的模型（更快）
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编辑 `whisper_server.py` 第 48 行：
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```python
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whisper_model = whisper.load_model("tiny") # 最快，准确度降低
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```
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### 2. 使用更大的模型（更准确）
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```python
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whisper_model = whisper.load_model("small") # 准确度提升，速度降低
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```
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### 3. GPU 加速（如果有显卡）
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```python
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whisper_model = whisper.load_model("base", device="cuda")
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```
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---
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## 七、常见问题
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### Q1: 模型下载失败？
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**A:** 使用离线下载，手动复制模型文件到 `~/.cache/whisper/`
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### Q2: 识别速度慢？
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**A:** 换用 `tiny` 模型，或使用 GPU
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### Q3: 内存不足？
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**A:** 至少 2GB 内存，关闭其他服务
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### Q4: 无法访问服务？
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**A:** 检查防火墙、端口、IP 地址配置
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---
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## 八、卸载
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```bash
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# 停止服务
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sudo systemctl stop whisper
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# 卸载 Python 包
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pip uninstall openai-whisper flask flask-cors
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# 删除模型文件
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rm -rf ~/.cache/whisper/
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```
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---
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## 联系支持
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如有问题，请检查日志文件 `whisper.log`
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