Compare commits
6 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
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ec16e08bdb | ||
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6801d3e8aa | ||
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cf679b34bf | ||
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b74bacb0b5 | ||
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661a8f357c | ||
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4a3dd2b225 |
@@ -77,7 +77,7 @@ python -m scripts.server # 测试能否启动，Ctrl+C 退出
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||||
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||||
---
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||||
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||||
## 步骤 4: 安装后端依赖
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||||
## 步骤 4: 安装后端依赖
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||||
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||||
```bash
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||||
cd /home/rongye/ProgramFiles/ViGent2/backend
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||||
@@ -92,13 +92,22 @@ pip install torch torchvision torchaudio --index-url https://download.pytorch.or
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||||
# 安装 Python 依赖
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||||
pip install -r requirements.txt
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||||
|
||||
# 安装 Playwright 浏览器（社交发布需要）
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||||
playwright install chromium
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||||
```
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||||
|
||||
---
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||||
|
||||
## 步骤 5: 部署用户认证系统 (Supabase + Auth)
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# 安装 Playwright 浏览器（社交发布需要）
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playwright install chromium
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```
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||||
|
||||
---
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||||
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||||
### 可选：AI 标题/标签生成
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||||
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||||
> ✅ 如需启用“AI 标题/标签生成”功能，请确保后端可访问外网 API。
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||||
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||||
- 需要可访问 `https://open.bigmodel.cn`
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- API Key 配置在 `backend/app/services/glm_service.py`（建议替换为自己的密钥）
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||||
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---
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||||
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||||
## 步骤 5: 部署用户认证系统 (Supabase + Auth)
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> 🔐 **包含**: 登录/注册、Supabase 数据库配置、JWT 认证、管理员后台
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@@ -258,7 +267,32 @@ chmod +x run_latentsync.sh
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pm2 start ./run_latentsync.sh --name vigent2-latentsync
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||||
```
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### 4. 保存当前列表 (开机自启)
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### 4. 启动 Qwen3-TTS 声音克隆服务 (可选)
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> 如需使用声音克隆功能，需要启动此服务。
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1. 安装 HTTP 服务依赖:
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```bash
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conda activate qwen-tts
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pip install fastapi uvicorn python-multipart
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```
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2. 启动脚本位于项目根目录: `run_qwen_tts.sh`
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3. 使用 pm2 启动:
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```bash
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cd /home/rongye/ProgramFiles/ViGent2
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pm2 start ./run_qwen_tts.sh --name vigent2-qwen-tts
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pm2 save
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```
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4. 验证服务:
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```bash
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# 检查健康状态
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curl http://localhost:8009/health
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```
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### 5. 保存当前列表 (开机自启)
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```bash
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pm2 save
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@@ -271,6 +305,7 @@ pm2 startup
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pm2 status # 查看所有服务状态
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pm2 logs # 查看所有日志
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pm2 logs vigent2-backend # 查看后端日志
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pm2 logs vigent2-qwen-tts # 查看 Qwen3-TTS 日志
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pm2 restart all # 重启所有服务
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pm2 stop vigent2-latentsync # 停止 LatentSync 服务
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pm2 delete all # 删除所有服务
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@@ -370,6 +405,7 @@ python3 -c "import torch; print(torch.cuda.is_available())"
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sudo lsof -i :8006
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sudo lsof -i :3002
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sudo lsof -i :8007
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sudo lsof -i :8009 # Qwen3-TTS
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```
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### 查看日志
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@@ -379,6 +415,7 @@ sudo lsof -i :8007
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pm2 logs vigent2-backend
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pm2 logs vigent2-frontend
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||||
pm2 logs vigent2-latentsync
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pm2 logs vigent2-qwen-tts
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||||
```
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|
||||
### SSH 连接卡顿 / 系统响应慢
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@@ -398,15 +435,16 @@ pm2 logs vigent2-latentsync
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||||
## 依赖清单
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|
||||
### 后端关键依赖
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||||
### 后端关键依赖
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||||
|
||||
| 依赖 | 用途 |
|
||||
|------|------|
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||||
| `fastapi` | Web API 框架 |
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||||
| `uvicorn` | ASGI 服务器 |
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||||
| `edge-tts` | 微软 TTS 配音 |
|
||||
| `playwright` | 社交媒体自动发布 |
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| `biliup` | B站视频上传 |
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||||
| `edge-tts` | 微软 TTS 配音 |
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| `httpx` | GLM API HTTP 客户端 |
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| `playwright` | 社交媒体自动发布 |
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| `biliup` | B站视频上传 |
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| `loguru` | 日志管理 |
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||||
|
||||
### 前端关键依赖
|
||||
|
||||
@@ -1,4 +1,3 @@
|
||||
---
|
||||
|
||||
## 🔧 上传架构重构 (Direct Upload)
|
||||
|
||||
@@ -52,7 +51,7 @@ TO anon WITH CHECK (bucket_id = 'materials');
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||||
- ✅ **解除大小限制**：不再受限于后端服务的 `client_max_body_size`。
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||||
- ✅ **用户体验提升**：上传速度更快，进度条更准确。
|
||||
|
||||
---
|
||||
|
||||
|
||||
## 🔧 Supabase 部署与 RLS 配置
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||||
|
||||
@@ -242,3 +241,38 @@ docker compose restart studio
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| `backend/.env` | 修改 | 添加 SUPABASE_PUBLIC_URL |
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||||
| `Supabase/.env` | 修改 | SUPABASE_PUBLIC_URL |
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| `frontend/src/app/page.tsx` | 修改 | 改用后端API上传 |
|
||||
|
||||
---
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||||
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## 📅 明日任务规划 (Day 12)
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||||
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||||
### 🎯 目标：部署 Qwen3-TTS 0.6B 声音克隆系统
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||||
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||||
**任务背景**：
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||||
- 当前使用 EdgeTTS（微软云端 TTS），音色固定，无法自定义
|
||||
- Qwen3-TTS 支持**零样本声音克隆**，可用少量音频克隆任意人声
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||||
|
||||
**核心任务**：
|
||||
1. **模型部署**
|
||||
- 创建独立 Conda 环境 (`qwen-tts`)
|
||||
- 下载 Qwen3-TTS 0.6B 模型权重
|
||||
- 配置 GPU 推理环境
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||||
|
||||
2. **后端集成**
|
||||
- 新增 `qwen_tts_service.py` 服务
|
||||
- 支持声音克隆：上传参考音频 → 生成克隆语音
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||||
- 兼容现有 `tts_service.py` 接口
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||||
|
||||
3. **前端适配**
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||||
- 添加"声音克隆"选项
|
||||
- 支持上传参考音频（3-10秒）
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||||
- 音色预览功能
|
||||
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||||
**预期成果**：
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||||
- ✅ 用户可上传自己的声音样本
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||||
- ✅ 生成的口播视频使用克隆后的声音
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||||
- ✅ 保留 EdgeTTS 作为备选方案
|
||||
|
||||
**参考资源**：
|
||||
- 模型：[Qwen/Qwen3-TTS-0.6B](https://huggingface.co/Qwen/Qwen3-TTS-0.6B)
|
||||
- 显存需求：~4GB (0.6B 参数量)
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||||
|
||||
347
Docs/DevLogs/Day12.md
Normal file
347
Docs/DevLogs/Day12.md
Normal file
@@ -0,0 +1,347 @@
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||||
# Day 12 - iOS 兼容与移动端 UI 优化
|
||||
|
||||
**日期**：2026-01-28
|
||||
|
||||
---
|
||||
|
||||
## 🔐 Axios 全局拦截器优化
|
||||
|
||||
### 背景
|
||||
统一处理 API 请求的认证失败场景，避免各页面重复处理 401/403 错误。
|
||||
|
||||
### 实现 (`frontend/src/lib/axios.ts`)
|
||||
|
||||
```typescript
|
||||
import axios from 'axios';
|
||||
|
||||
// 动态获取 API 地址：服务端使用 localhost，客户端使用当前域名
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||||
const API_BASE = typeof window === 'undefined'
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||||
? 'http://localhost:8006'
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||||
: '';
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||||
|
||||
// 防止重复跳转
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||||
let isRedirecting = false;
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||||
|
||||
const api = axios.create({
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||||
baseURL: API_BASE,
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||||
withCredentials: true, // 自动携带 HttpOnly cookie
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
});
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||||
|
||||
// 响应拦截器 - 全局处理 401/403
|
||||
api.interceptors.response.use(
|
||||
(response) => response,
|
||||
async (error) => {
|
||||
const status = error.response?.status;
|
||||
|
||||
if ((status === 401 || status === 403) && !isRedirecting) {
|
||||
isRedirecting = true;
|
||||
|
||||
// 调用 logout API 清除 HttpOnly cookie
|
||||
try {
|
||||
await fetch('/api/auth/logout', { method: 'POST' });
|
||||
} catch (e) { /* 忽略 */ }
|
||||
|
||||
// 跳转登录页
|
||||
if (typeof window !== 'undefined') {
|
||||
window.location.replace('/login');
|
||||
}
|
||||
}
|
||||
return Promise.reject(error);
|
||||
}
|
||||
);
|
||||
|
||||
export default api;
|
||||
```
|
||||
|
||||
### 关键特性
|
||||
- ✅ **自动携带 Cookie**: `withCredentials: true` 确保 HttpOnly JWT cookie 被发送
|
||||
- ✅ **401/403 自动跳转**: 认证失败时自动清理并跳转登录页
|
||||
- ✅ **防重复跳转**: `isRedirecting` 标志避免多个请求同时触发跳转
|
||||
- ✅ **SSR 兼容**: 服务端渲染时使用 `localhost`，客户端使用相对路径
|
||||
|
||||
---
|
||||
|
||||
## 🔧 iOS Safari 安全区域白边修复
|
||||
|
||||
### 问题描述
|
||||
iPhone Safari 浏览器底部和顶部显示白色区域，安卓正常。原因是 iOS Safari 有安全区域 (Safe Area)，页面背景没有延伸到该区域。
|
||||
|
||||
### 根本原因
|
||||
1. 缺少 `viewport-fit=cover` 配置
|
||||
2. `min-h-screen` (100vh) 在 iOS Safari 中不包含安全区域
|
||||
3. 背景渐变在页面 div 上，而非 body 上，导致安全区域显示纯色
|
||||
|
||||
### 解决方案
|
||||
|
||||
#### 1. 添加 viewport 配置 (`layout.tsx`)
|
||||
```typescript
|
||||
export const viewport: Viewport = {
|
||||
width: 'device-width',
|
||||
initialScale: 1,
|
||||
viewportFit: 'cover', // 允许内容延伸到安全区域
|
||||
themeColor: '#0f172a', // 顶部状态栏颜色
|
||||
};
|
||||
```
|
||||
|
||||
#### 2. 统一渐变背景到 body (`layout.tsx`)
|
||||
```tsx
|
||||
<html lang="en" style={{ backgroundColor: '#0f172a' }}>
|
||||
<body
|
||||
style={{
|
||||
margin: 0,
|
||||
minHeight: '100dvh',
|
||||
background: 'linear-gradient(to bottom, #0f172a 0%, #0f172a 5%, #581c87 50%, #0f172a 95%, #0f172a 100%)',
|
||||
}}
|
||||
>
|
||||
{children}
|
||||
</body>
|
||||
</html>
|
||||
```
|
||||
|
||||
#### 3. CSS 安全区域支持 (`globals.css`)
|
||||
```css
|
||||
html {
|
||||
background-color: #0f172a !important;
|
||||
min-height: 100%;
|
||||
}
|
||||
|
||||
body {
|
||||
margin: 0 !important;
|
||||
min-height: 100dvh;
|
||||
padding-top: env(safe-area-inset-top);
|
||||
padding-bottom: env(safe-area-inset-bottom);
|
||||
}
|
||||
```
|
||||
|
||||
#### 4. 移除页面独立渐变背景
|
||||
各页面的根 div 移除 `bg-gradient-to-br` 类，统一使用 body 渐变：
|
||||
- `page.tsx`
|
||||
- `login/page.tsx`
|
||||
- `publish/page.tsx`
|
||||
- `admin/page.tsx`
|
||||
- `register/page.tsx`
|
||||
|
||||
### 结果
|
||||
- ✅ 顶部状态栏颜色与页面一致 (themeColor)
|
||||
- ✅ 底部安全区域颜色与渐变边缘一致
|
||||
- ✅ 消除分层感，背景统一
|
||||
|
||||
---
|
||||
|
||||
## 📱 移动端 Header 响应式优化
|
||||
|
||||
### 问题描述
|
||||
移动端顶部导航按钮（视频生成、发布管理、退出）过于拥挤，文字换行显示。
|
||||
|
||||
### 解决方案
|
||||
|
||||
#### 首页 Header (`page.tsx`)
|
||||
```tsx
|
||||
<header className="border-b border-white/10 bg-black/20 backdrop-blur-sm">
|
||||
<div className="max-w-6xl mx-auto px-4 sm:px-6 py-3 sm:py-4 flex items-center justify-between">
|
||||
<Link href="/" className="text-xl sm:text-2xl font-bold ...">
|
||||
<span className="text-3xl sm:text-4xl">🎬</span>
|
||||
ViGent
|
||||
</Link>
|
||||
<div className="flex items-center gap-1 sm:gap-4">
|
||||
<span className="px-2 sm:px-4 py-1 sm:py-2 text-sm sm:text-base ...">
|
||||
视频生成
|
||||
</span>
|
||||
<!-- 其他按钮同样处理 -->
|
||||
</div>
|
||||
</div>
|
||||
</header>
|
||||
```
|
||||
|
||||
#### 发布管理页 Header (`publish/page.tsx`)
|
||||
同步应用相同的响应式类名。
|
||||
|
||||
### 关键改动
|
||||
| 属性 | 移动端 | 桌面端 |
|
||||
|------|--------|--------|
|
||||
| 容器内边距 | `px-4 py-3` | `px-6 py-4` |
|
||||
| 按钮间距 | `gap-1` | `gap-4` |
|
||||
| 按钮内边距 | `px-2 py-1` | `px-4 py-2` |
|
||||
| 字体大小 | `text-sm` | `text-base` |
|
||||
| Logo 大小 | `text-xl` + `text-3xl` | `text-2xl` + `text-4xl` |
|
||||
|
||||
### 结果
|
||||
- ✅ 移动端按钮紧凑排列，不再换行
|
||||
- ✅ 桌面端保持原有宽松布局
|
||||
|
||||
---
|
||||
|
||||
## 🚀 发布页面 UI 重构
|
||||
|
||||
### 问题描述
|
||||
原有设计将"发布时间"选项放在表单中，用户可能误选"定时发布"但忘记设置时间。
|
||||
|
||||
### 解决方案
|
||||
将"一键发布"按钮改为两个独立按钮：
|
||||
- **立即发布** (绿色，占 3/4 宽度) - 主要操作
|
||||
- **定时** (占 1/4 宽度) - 点击展开时间选择器
|
||||
|
||||
#### 新布局 (`publish/page.tsx`)
|
||||
```tsx
|
||||
{/* 发布按钮区域 */}
|
||||
<div className="space-y-3">
|
||||
<div className="flex gap-3">
|
||||
{/* 立即发布 - 占 3/4 */}
|
||||
<button
|
||||
onClick={() => { setScheduleMode("now"); handlePublish(); }}
|
||||
className="flex-[3] py-4 rounded-xl font-bold text-lg bg-gradient-to-r from-green-600 to-teal-600 ..."
|
||||
>
|
||||
🚀 立即发布
|
||||
</button>
|
||||
|
||||
{/* 定时发布 - 占 1/4 */}
|
||||
<button
|
||||
onClick={() => setScheduleMode(scheduleMode === "scheduled" ? "now" : "scheduled")}
|
||||
className="flex-1 py-4 rounded-xl font-bold text-base ..."
|
||||
>
|
||||
⏰ 定时
|
||||
</button>
|
||||
</div>
|
||||
|
||||
{/* 定时发布时间选择器 (展开时显示) */}
|
||||
{scheduleMode === "scheduled" && (
|
||||
<div className="flex gap-3 items-center">
|
||||
<input type="datetime-local" ... />
|
||||
<button>确认定时</button>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
```
|
||||
|
||||
### 结果
|
||||
- ✅ 主操作（立即发布）更醒目
|
||||
- ✅ 定时发布需要二次确认，防止误触
|
||||
- ✅ 从表单区域移除发布时间选项，界面更简洁
|
||||
|
||||
---
|
||||
|
||||
## 🛤️ 后续优化项
|
||||
|
||||
### 后端定时发布 (待实现)
|
||||
**当前状态**：定时发布使用平台端定时（在各平台设置发布时间）
|
||||
|
||||
**优化方向**：改为后端定时任务
|
||||
- 使用 APScheduler 实现任务调度
|
||||
- 存储定时任务到数据库
|
||||
- 到时间后端自动触发发布 API
|
||||
- 支持查看/取消定时任务
|
||||
|
||||
**优势**：
|
||||
- 统一逻辑，不依赖平台定时 UI
|
||||
- 更灵活，可管理定时任务
|
||||
- 平台页面更新不影响功能
|
||||
|
||||
---
|
||||
|
||||
## 🤖 Qwen3-TTS 0.6B 声音克隆部署
|
||||
|
||||
### 背景
|
||||
为实现用户自定义声音克隆功能，部署 Qwen3-TTS 0.6B-Base 模型，支持 3 秒参考音频快速克隆。
|
||||
|
||||
### GPU 分配
|
||||
| GPU | 服务 | 模型 |
|
||||
|-----|------|------|
|
||||
| GPU0 | Qwen3-TTS | 0.6B-Base (声音克隆) |
|
||||
| GPU1 | LatentSync | 1.6 (唇形同步) |
|
||||
|
||||
### 部署步骤
|
||||
|
||||
#### 1. 克隆仓库
|
||||
```bash
|
||||
cd /home/rongye/ProgramFiles/ViGent2/models
|
||||
git clone https://github.com/QwenLM/Qwen3-TTS.git
|
||||
```
|
||||
|
||||
#### 2. 创建 conda 环境
|
||||
```bash
|
||||
conda create -n qwen-tts python=3.10 -y
|
||||
conda activate qwen-tts
|
||||
```
|
||||
|
||||
#### 3. 安装依赖
|
||||
```bash
|
||||
cd Qwen3-TTS
|
||||
pip install -e .
|
||||
conda install -y -c conda-forge sox # 音频处理依赖
|
||||
```
|
||||
|
||||
#### 4. 下载模型权重 (使用 ModelScope，国内更快)
|
||||
```bash
|
||||
pip install modelscope
|
||||
# Tokenizer (651MB)
|
||||
modelscope download --model Qwen/Qwen3-TTS-Tokenizer-12Hz --local_dir ./checkpoints/Tokenizer
|
||||
# 0.6B-Base 模型 (2.4GB)
|
||||
modelscope download --model Qwen/Qwen3-TTS-12Hz-0.6B-Base --local_dir ./checkpoints/0.6B-Base
|
||||
```
|
||||
|
||||
#### 5. 测试推理
|
||||
```python
|
||||
# test_inference.py
|
||||
import torch
|
||||
import soundfile as sf
|
||||
from qwen_tts import Qwen3TTSModel
|
||||
|
||||
model = Qwen3TTSModel.from_pretrained(
|
||||
"./checkpoints/0.6B-Base",
|
||||
device_map="cuda:0",
|
||||
dtype=torch.bfloat16,
|
||||
)
|
||||
|
||||
wavs, sr = model.generate_voice_clone(
|
||||
text="测试文本",
|
||||
language="Chinese",
|
||||
ref_audio="./examples/myvoice.wav",
|
||||
ref_text="参考音频的文字内容",
|
||||
)
|
||||
sf.write("output.wav", wavs[0], sr)
|
||||
```
|
||||
|
||||
### 测试结果
|
||||
- ✅ 模型加载成功 (GPU0)
|
||||
- ✅ 声音克隆推理成功
|
||||
- ✅ 输出音频 24000Hz，质量良好
|
||||
|
||||
### 目录结构
|
||||
```
|
||||
models/Qwen3-TTS/
|
||||
├── checkpoints/
|
||||
│ ├── Tokenizer/ # 651MB
|
||||
│ └── 0.6B-Base/ # 2.4GB
|
||||
├── qwen_tts/ # 源码
|
||||
├── examples/
|
||||
│ └── myvoice.wav # 参考音频
|
||||
└── test_inference.py # 测试脚本
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📁 今日修改文件清单
|
||||
|
||||
| 文件 | 变更类型 | 说明 |
|
||||
|------|----------|------|
|
||||
| `frontend/src/lib/axios.ts` | 修改 | Axios 全局拦截器 (401/403 自动跳转) |
|
||||
| `frontend/src/app/layout.tsx` | 修改 | viewport 配置 + body 渐变背景 |
|
||||
| `frontend/src/app/globals.css` | 修改 | 安全区域 CSS 支持 |
|
||||
| `frontend/src/app/page.tsx` | 修改 | 移除独立渐变 + Header 响应式 |
|
||||
| `frontend/src/app/login/page.tsx` | 修改 | 移除独立渐变 |
|
||||
| `frontend/src/app/publish/page.tsx` | 修改 | Header 响应式 + 发布按钮重构 |
|
||||
| `frontend/src/app/admin/page.tsx` | 修改 | 移除独立渐变 |
|
||||
| `frontend/src/app/register/page.tsx` | 修改 | 移除独立渐变 |
|
||||
| `README.md` | 修改 | 添加 "iOS/Android 移动端适配" 功能说明 |
|
||||
| `Docs/FRONTEND_DEV.md` | 修改 | iOS Safari 安全区域兼容规范 + 移动端响应式规则 |
|
||||
| `models/Qwen3-TTS/` | 新增 | Qwen3-TTS 声音克隆模型部署 |
|
||||
| `Docs/QWEN3_TTS_DEPLOY.md` | 新增 | Qwen3-TTS 部署指南 |
|
||||
|
||||
---
|
||||
|
||||
## 🔗 相关文档
|
||||
|
||||
- [task_complete.md](../task_complete.md) - 任务总览
|
||||
- [Day11.md](./Day11.md) - 上传架构重构
|
||||
- [QWEN3_TTS_DEPLOY.md](../QWEN3_TTS_DEPLOY.md) - Qwen3-TTS 部署指南
|
||||
431
Docs/DevLogs/Day13.md
Normal file
431
Docs/DevLogs/Day13.md
Normal file
@@ -0,0 +1,431 @@
|
||||
# Day 13 - 声音克隆功能集成 + 字幕功能
|
||||
|
||||
**日期**：2026-01-29
|
||||
|
||||
---
|
||||
|
||||
## 🎙️ Qwen3-TTS 服务集成
|
||||
|
||||
### 背景
|
||||
在 Day 12 完成 Qwen3-TTS 模型部署后，今日重点是将其集成到 ViGent2 系统中，提供完整的声音克隆功能。
|
||||
|
||||
### 架构设计
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────┐
|
||||
│ 前端 (Next.js) │
|
||||
│ 参考音频上传 → TTS 模式选择 → 视频生成请求 │
|
||||
└─────────────────────────────────────────────────────────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────────────────────────────────────────────┐
|
||||
│ 后端 (FastAPI :8006) │
|
||||
│ ref-audios API → voice_clone_service → video_service │
|
||||
└─────────────────────────────────────────────────────────────┘
|
||||
│
|
||||
▼
|
||||
┌─────────────────────────────────────────────────────────────┐
|
||||
│ Qwen3-TTS 服务 (FastAPI :8009) │
|
||||
│ HTTP /generate → 返回克隆音频 │
|
||||
└─────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
### Qwen3-TTS HTTP 服务 (`qwen_tts_server.py`)
|
||||
|
||||
创建独立的 FastAPI 服务，运行在 8009 端口：
|
||||
|
||||
```python
|
||||
from fastapi import FastAPI, UploadFile, Form, HTTPException
|
||||
from fastapi.responses import Response
|
||||
import torch
|
||||
import soundfile as sf
|
||||
from qwen_tts import Qwen3TTSModel
|
||||
import io, os
|
||||
|
||||
app = FastAPI(title="Qwen3-TTS Voice Clone Service")
|
||||
|
||||
# GPU 配置
|
||||
GPU_ID = os.getenv("QWEN_TTS_GPU_ID", "0")
|
||||
model = None
|
||||
|
||||
@app.on_event("startup")
|
||||
async def load_model():
|
||||
global model
|
||||
model = Qwen3TTSModel.from_pretrained(
|
||||
"./checkpoints/0.6B-Base",
|
||||
device_map=f"cuda:{GPU_ID}",
|
||||
dtype=torch.bfloat16,
|
||||
)
|
||||
|
||||
@app.get("/health")
|
||||
async def health():
|
||||
return {"service": "Qwen3-TTS", "ready": model is not None, "gpu_id": GPU_ID}
|
||||
|
||||
@app.post("/generate")
|
||||
async def generate(
|
||||
ref_audio: UploadFile,
|
||||
text: str = Form(...),
|
||||
ref_text: str = Form(""),
|
||||
language: str = Form("Chinese"),
|
||||
):
|
||||
# 保存临时参考音频
|
||||
ref_path = f"/tmp/ref_{ref_audio.filename}"
|
||||
with open(ref_path, "wb") as f:
|
||||
f.write(await ref_audio.read())
|
||||
|
||||
# 生成克隆音频
|
||||
wavs, sr = model.generate_voice_clone(
|
||||
text=text,
|
||||
language=language,
|
||||
ref_audio=ref_path,
|
||||
ref_text=ref_text or "一段参考音频。",
|
||||
)
|
||||
|
||||
# 返回 WAV 音频
|
||||
buffer = io.BytesIO()
|
||||
sf.write(buffer, wavs[0], sr, format="WAV")
|
||||
buffer.seek(0)
|
||||
return Response(content=buffer.read(), media_type="audio/wav")
|
||||
```
|
||||
|
||||
### 后端声音克隆服务 (`voice_clone_service.py`)
|
||||
|
||||
通过 HTTP 调用 Qwen3-TTS 服务：
|
||||
|
||||
```python
|
||||
import aiohttp
|
||||
from loguru import logger
|
||||
|
||||
QWEN_TTS_URL = "http://localhost:8009"
|
||||
|
||||
async def generate_cloned_audio(
|
||||
ref_audio_path: str,
|
||||
text: str,
|
||||
output_path: str,
|
||||
ref_text: str = "",
|
||||
) -> str:
|
||||
"""调用 Qwen3-TTS 服务生成克隆音频"""
|
||||
|
||||
async with aiohttp.ClientSession() as session:
|
||||
with open(ref_audio_path, "rb") as f:
|
||||
data = aiohttp.FormData()
|
||||
data.add_field("ref_audio", f, filename="ref.wav")
|
||||
data.add_field("text", text)
|
||||
data.add_field("ref_text", ref_text)
|
||||
|
||||
async with session.post(f"{QWEN_TTS_URL}/generate", data=data) as resp:
|
||||
if resp.status != 200:
|
||||
raise Exception(f"Qwen3-TTS error: {resp.status}")
|
||||
|
||||
audio_data = await resp.read()
|
||||
with open(output_path, "wb") as out:
|
||||
out.write(audio_data)
|
||||
|
||||
return output_path
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📂 参考音频管理 API
|
||||
|
||||
### 新增 API 端点 (`ref_audios.py`)
|
||||
|
||||
| 端点 | 方法 | 功能 |
|
||||
|------|------|------|
|
||||
| `/api/ref-audios` | GET | 获取参考音频列表 |
|
||||
| `/api/ref-audios` | POST | 上传参考音频 |
|
||||
| `/api/ref-audios/{id}` | DELETE | 删除参考音频 |
|
||||
|
||||
### Supabase Bucket 配置
|
||||
|
||||
为参考音频创建独立存储桶：
|
||||
|
||||
```sql
|
||||
-- 创建 ref-audios bucket
|
||||
INSERT INTO storage.buckets (id, name, public)
|
||||
VALUES ('ref-audios', 'ref-audios', true)
|
||||
ON CONFLICT (id) DO NOTHING;
|
||||
|
||||
-- RLS 策略
|
||||
CREATE POLICY "Allow public uploads" ON storage.objects
|
||||
FOR INSERT TO anon WITH CHECK (bucket_id = 'ref-audios');
|
||||
|
||||
CREATE POLICY "Allow public read" ON storage.objects
|
||||
FOR SELECT TO anon USING (bucket_id = 'ref-audios');
|
||||
|
||||
CREATE POLICY "Allow public delete" ON storage.objects
|
||||
FOR DELETE TO anon USING (bucket_id = 'ref-audios');
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🎨 前端声音克隆 UI
|
||||
|
||||
### TTS 模式选择
|
||||
|
||||
在视频生成页面新增声音克隆选项：
|
||||
|
||||
```tsx
|
||||
{/* TTS 模式选择 */}
|
||||
<div className="flex gap-2 mb-4">
|
||||
<button
|
||||
onClick={() => setTtsMode("edge")}
|
||||
className={`px-4 py-2 rounded-lg ${ttsMode === "edge" ? "bg-purple-600" : "bg-white/10"}`}
|
||||
>
|
||||
🔊 EdgeTTS
|
||||
</button>
|
||||
<button
|
||||
onClick={() => setTtsMode("clone")}
|
||||
className={`px-4 py-2 rounded-lg ${ttsMode === "clone" ? "bg-purple-600" : "bg-white/10"}`}
|
||||
>
|
||||
🎙️ 声音克隆
|
||||
</button>
|
||||
</div>
|
||||
```
|
||||
|
||||
### 参考音频管理
|
||||
|
||||
新增参考音频上传和列表展示功能：
|
||||
|
||||
| 功能 | 实现 |
|
||||
|------|------|
|
||||
| 音频上传 | 拖拽上传 WAV/MP3，直传 Supabase |
|
||||
| 列表展示 | 显示文件名、时长、上传时间 |
|
||||
| 快速选择 | 点击即选中作为参考音频 |
|
||||
| 删除功能 | 删除不需要的参考音频 |
|
||||
|
||||
---
|
||||
|
||||
## ✅ 端到端测试验证
|
||||
|
||||
### 测试流程
|
||||
1. **上传参考音频**: 3 秒参考音频 → Supabase ref-audios bucket
|
||||
2. **选择声音克隆模式**: TTS 模式切换为 "声音克隆"
|
||||
3. **输入文案**: 测试口播文案
|
||||
4. **生成视频**:
|
||||
- TTS 阶段调用 Qwen3-TTS (17.7s)
|
||||
- LipSync 阶段调用 LatentSync (122.8s)
|
||||
5. **播放验证**: 视频声音与参考音色一致
|
||||
|
||||
### 测试结果
|
||||
- ✅ 参考音频上传成功
|
||||
- ✅ Qwen3-TTS 生成克隆音频 (15s 推理，4.6s 音频)
|
||||
- ✅ LatentSync 唇形同步正常
|
||||
- ✅ 总生成时间 143.1s
|
||||
- ✅ 前端视频播放正常
|
||||
|
||||
---
|
||||
|
||||
## 🔧 PM2 服务配置
|
||||
|
||||
### 新增 Qwen3-TTS 服务
|
||||
|
||||
**前置依赖安装**：
|
||||
```bash
|
||||
conda activate qwen-tts
|
||||
pip install fastapi uvicorn python-multipart
|
||||
```
|
||||
|
||||
启动脚本 `run_qwen_tts.sh` (位于项目**根目录**)：
|
||||
```bash
|
||||
#!/bin/bash
|
||||
cd /home/rongye/ProgramFiles/ViGent2/models/Qwen3-TTS
|
||||
/home/rongye/ProgramFiles/miniconda3/envs/qwen-tts/bin/python qwen_tts_server.py
|
||||
```
|
||||
|
||||
PM2 管理命令：
|
||||
```bash
|
||||
# 进入根目录启动
|
||||
cd /home/rongye/ProgramFiles/ViGent2
|
||||
pm2 start ./run_qwen_tts.sh --name vigent2-qwen-tts
|
||||
pm2 save
|
||||
|
||||
# 查看状态
|
||||
pm2 status
|
||||
|
||||
# 查看日志
|
||||
pm2 logs vigent2-qwen-tts --lines 50
|
||||
```
|
||||
|
||||
### 完整服务列表
|
||||
|
||||
| 服务名 | 端口 | 功能 |
|
||||
|--------|------|------|
|
||||
| vigent2-backend | 8006 | FastAPI 后端 |
|
||||
| vigent2-frontend | 3002 | Next.js 前端 |
|
||||
| vigent2-latentsync | 8007 | LatentSync 唇形同步 |
|
||||
| vigent2-qwen-tts | 8009 | Qwen3-TTS 声音克隆 |
|
||||
|
||||
---
|
||||
|
||||
## 📁 今日修改文件清单
|
||||
|
||||
| 文件 | 变更类型 | 说明 |
|
||||
|------|----------|------|
|
||||
| `models/Qwen3-TTS/qwen_tts_server.py` | 新增 | Qwen3-TTS HTTP 推理服务 |
|
||||
| `run_qwen_tts.sh` | 新增 | PM2 启动脚本 (根目录) |
|
||||
| `backend/app/services/voice_clone_service.py` | 新增 | 声音克隆服务 (HTTP 调用) |
|
||||
| `backend/app/api/ref_audios.py` | 新增 | 参考音频管理 API |
|
||||
| `backend/app/main.py` | 修改 | 注册 ref-audios 路由 |
|
||||
| `frontend/src/app/page.tsx` | 修改 | TTS 模式选择 + 参考音频 UI |
|
||||
|
||||
---
|
||||
|
||||
## 🔗 相关文档
|
||||
|
||||
- [task_complete.md](../task_complete.md) - 任务总览
|
||||
- [Day12.md](./Day12.md) - iOS 兼容与 Qwen3-TTS 部署
|
||||
- [QWEN3_TTS_DEPLOY.md](../QWEN3_TTS_DEPLOY.md) - Qwen3-TTS 部署指南
|
||||
- [SUBTITLE_DEPLOY.md](../SUBTITLE_DEPLOY.md) - 字幕功能部署指南
|
||||
- [DEPLOY_MANUAL.md](../DEPLOY_MANUAL.md) - 完整部署手册
|
||||
|
||||
---
|
||||
|
||||
## 🎬 逐字高亮字幕 + 片头标题功能
|
||||
|
||||
### 背景
|
||||
|
||||
为提升视频质量，新增逐字高亮字幕（卡拉OK效果）和片头标题功能。
|
||||
|
||||
### 技术方案
|
||||
|
||||
| 组件 | 技术 | 说明 |
|
||||
|------|------|------|
|
||||
| 字幕对齐 | **faster-whisper** | 生成字级别时间戳 |
|
||||
| 视频渲染 | **Remotion** | React 视频合成框架 |
|
||||
|
||||
### 架构设计
|
||||
|
||||
```
|
||||
原有流程:
|
||||
文本 → EdgeTTS → 音频 → LatentSync → FFmpeg合成 → 最终视频
|
||||
|
||||
新流程:
|
||||
文本 → EdgeTTS → 音频 ─┬→ LatentSync → 唇形视频 ─┐
|
||||
└→ faster-whisper → 字幕JSON ─┴→ Remotion合成 → 最终视频
|
||||
```
|
||||
|
||||
### 后端新增服务
|
||||
|
||||
#### 1. 字幕服务 (`whisper_service.py`)
|
||||
|
||||
基于 faster-whisper 生成字级别时间戳：
|
||||
|
||||
```python
|
||||
from faster_whisper import WhisperModel
|
||||
|
||||
class WhisperService:
|
||||
def __init__(self, model_size="large-v3", device="cuda"):
|
||||
self.model = WhisperModel(model_size, device=device)
|
||||
|
||||
async def align(self, audio_path: str, text: str, output_path: str):
|
||||
segments, info = self.model.transcribe(audio_path, word_timestamps=True)
|
||||
# 将词拆分成单字，时间戳线性插值
|
||||
result = {"segments": [...]}
|
||||
# 保存到 JSON
|
||||
```
|
||||
|
||||
**字幕拆字算法**：faster-whisper 对中文返回词级别，系统自动拆分成单字并线性插值：
|
||||
|
||||
```python
|
||||
# 输入: {"word": "大家好", "start": 0.0, "end": 0.9}
|
||||
# 输出:
|
||||
[
|
||||
{"word": "大", "start": 0.0, "end": 0.3},
|
||||
{"word": "家", "start": 0.3, "end": 0.6},
|
||||
{"word": "好", "start": 0.6, "end": 0.9}
|
||||
]
|
||||
```
|
||||
|
||||
#### 2. Remotion 渲染服务 (`remotion_service.py`)
|
||||
|
||||
调用 Remotion 渲染字幕和标题：
|
||||
|
||||
```python
|
||||
class RemotionService:
|
||||
async def render(self, video_path, output_path, captions_path, title, ...):
|
||||
cmd = f"npx ts-node render.ts --video {video_path} --output {output_path} ..."
|
||||
# 执行渲染
|
||||
```
|
||||
|
||||
### Remotion 项目结构
|
||||
|
||||
```
|
||||
remotion/
|
||||
├── package.json # Node.js 依赖
|
||||
├── render.ts # 服务端渲染脚本
|
||||
└── src/
|
||||
├── Video.tsx # 主视频组件
|
||||
├── components/
|
||||
│ ├── Title.tsx # 片头标题（淡入淡出）
|
||||
│ ├── Subtitles.tsx # 逐字高亮字幕
|
||||
│ └── VideoLayer.tsx # 视频图层
|
||||
└── utils/
|
||||
└── captions.ts # 字幕数据类型
|
||||
```
|
||||
|
||||
### 前端 UI
|
||||
|
||||
新增标题和字幕设置区块：
|
||||
|
||||
| 功能 | 说明 |
|
||||
|------|------|
|
||||
| 片头标题输入 | 可选，在视频开头显示 3 秒 |
|
||||
| 字幕开关 | 默认开启，可关闭 |
|
||||
|
||||
### 遇到的问题与修复
|
||||
|
||||
#### 问题 1: `fs` 模块错误
|
||||
|
||||
**现象**：Remotion 打包失败，提示 `fs.js doesn't exist`
|
||||
|
||||
**原因**：`captions.ts` 中有 `loadCaptions` 函数使用了 Node.js 的 `fs` 模块
|
||||
|
||||
**修复**：删除未使用的 `loadCaptions` 函数
|
||||
|
||||
#### 问题 2: 视频文件读取失败
|
||||
|
||||
**现象**：`file://` 协议无法读取本地视频
|
||||
|
||||
**修复**：
|
||||
1. `render.ts` 使用 `publicDir` 指向视频目录
|
||||
2. `VideoLayer.tsx` 使用 `staticFile()` 加载视频
|
||||
|
||||
```typescript
|
||||
// render.ts
|
||||
const publicDir = path.dirname(path.resolve(options.videoPath));
|
||||
const bundleLocation = await bundle({
|
||||
entryPoint: path.resolve(__dirname, './src/index.ts'),
|
||||
publicDir, // 关键配置
|
||||
});
|
||||
|
||||
// VideoLayer.tsx
|
||||
const videoUrl = staticFile(videoSrc);
|
||||
```
|
||||
|
||||
### 测试结果
|
||||
|
||||
- ✅ faster-whisper 字幕对齐成功（~1秒）
|
||||
- ✅ Remotion 渲染成功（~10秒）
|
||||
- ✅ 字幕逐字高亮效果正常
|
||||
- ✅ 片头标题淡入淡出正常
|
||||
- ✅ 降级机制正常（Remotion 失败时回退到 FFmpeg）
|
||||
|
||||
---
|
||||
|
||||
## 📁 今日修改文件清单（完整）
|
||||
|
||||
| 文件 | 变更类型 | 说明 |
|
||||
|------|----------|------|
|
||||
| `models/Qwen3-TTS/qwen_tts_server.py` | 新增 | Qwen3-TTS HTTP 推理服务 |
|
||||
| `run_qwen_tts.sh` | 新增 | PM2 启动脚本 (根目录) |
|
||||
| `backend/app/services/voice_clone_service.py` | 新增 | 声音克隆服务 (HTTP 调用) |
|
||||
| `backend/app/services/whisper_service.py` | 新增 | 字幕对齐服务 (faster-whisper) |
|
||||
| `backend/app/services/remotion_service.py` | 新增 | Remotion 渲染服务 |
|
||||
| `backend/app/api/ref_audios.py` | 新增 | 参考音频管理 API |
|
||||
| `backend/app/api/videos.py` | 修改 | 集成字幕和标题功能 |
|
||||
| `backend/app/main.py` | 修改 | 注册 ref-audios 路由 |
|
||||
| `backend/requirements.txt` | 修改 | 添加 faster-whisper 依赖 |
|
||||
| `remotion/` | 新增 | Remotion 视频渲染项目 |
|
||||
| `frontend/src/app/page.tsx` | 修改 | TTS 模式选择 + 标题字幕 UI |
|
||||
| `Docs/SUBTITLE_DEPLOY.md` | 新增 | 字幕功能部署文档 |
|
||||
402
Docs/DevLogs/Day14.md
Normal file
402
Docs/DevLogs/Day14.md
Normal file
@@ -0,0 +1,402 @@
|
||||
# Day 14 - 模型升级 + 标题标签生成 + 前端修复
|
||||
|
||||
**日期**：2026-01-30
|
||||
|
||||
---
|
||||
|
||||
## 🚀 Qwen3-TTS 模型升级 (0.6B → 1.7B)
|
||||
|
||||
### 背景
|
||||
|
||||
为提升声音克隆质量，将 Qwen3-TTS 模型从 0.6B-Base 升级到 1.7B-Base。
|
||||
|
||||
### 变更内容
|
||||
|
||||
| 项目 | 升级前 | 升级后 |
|
||||
|------|--------|--------|
|
||||
| 模型 | 0.6B-Base | **1.7B-Base** |
|
||||
| 大小 | 2.4GB | 6.8GB |
|
||||
| 质量 | 基础 | 更高质量 |
|
||||
|
||||
### 代码修改
|
||||
|
||||
**文件**: `models/Qwen3-TTS/qwen_tts_server.py`
|
||||
|
||||
```python
|
||||
# 升级前
|
||||
MODEL_PATH = Path(__file__).parent / "checkpoints" / "0.6B-Base"
|
||||
|
||||
# 升级后
|
||||
MODEL_PATH = Path(__file__).parent / "checkpoints" / "1.7B-Base"
|
||||
```
|
||||
|
||||
### 模型下载
|
||||
|
||||
```bash
|
||||
cd /home/rongye/ProgramFiles/ViGent2/models/Qwen3-TTS
|
||||
|
||||
# 下载 1.7B-Base 模型 (6.8GB)
|
||||
modelscope download --model Qwen/Qwen3-TTS-12Hz-1.7B-Base --local_dir ./checkpoints/1.7B-Base
|
||||
```
|
||||
|
||||
### 结果
|
||||
|
||||
- ✅ 模型加载正常 (GPU0, bfloat16)
|
||||
- ✅ 声音克隆质量提升
|
||||
- ✅ 推理速度可接受
|
||||
|
||||
---
|
||||
|
||||
## 🎨 标题和字幕显示优化
|
||||
|
||||
### 字幕组件优化 (`Subtitles.tsx`)
|
||||
|
||||
**文件**: `remotion/src/components/Subtitles.tsx`
|
||||
|
||||
优化内容：
|
||||
- 调整高亮颜色配置
|
||||
- 优化文字描边效果（多层阴影）
|
||||
- 调整字间距和行高
|
||||
|
||||
```typescript
|
||||
export const Subtitles: React.FC<SubtitlesProps> = ({
|
||||
captions,
|
||||
highlightColor = '#FFFF00', // 高亮颜色
|
||||
normalColor = '#FFFFFF', // 普通文字颜色
|
||||
fontSize = 52,
|
||||
}) => {
|
||||
// 样式优化
|
||||
const style = {
|
||||
textShadow: `
|
||||
2px 2px 4px rgba(0,0,0,0.8),
|
||||
-2px -2px 4px rgba(0,0,0,0.8),
|
||||
...
|
||||
`,
|
||||
letterSpacing: '2px',
|
||||
lineHeight: 1.4,
|
||||
maxWidth: '90%',
|
||||
};
|
||||
};
|
||||
```
|
||||
|
||||
### 标题组件优化 (`Title.tsx`)
|
||||
|
||||
**文件**: `remotion/src/components/Title.tsx`
|
||||
|
||||
优化内容：
|
||||
- 淡入淡出动画效果
|
||||
- 下滑入场动画
|
||||
- 可配置显示时长
|
||||
|
||||
```typescript
|
||||
interface TitleProps {
|
||||
title: string;
|
||||
duration?: number; // 标题显示时长（秒，默认3秒）
|
||||
fadeOutStart?: number; // 开始淡出的时间（秒，默认2秒）
|
||||
}
|
||||
|
||||
// 动画效果
|
||||
// 淡入：0-0.5 秒
|
||||
// 淡出：2-3 秒
|
||||
// 下滑：0-0.5 秒，-20px → 0px
|
||||
```
|
||||
|
||||
### 结果
|
||||
|
||||
- ✅ 字幕显示更清晰
|
||||
- ✅ 标题动画更流畅
|
||||
|
||||
---
|
||||
|
||||
## 🤖 标题标签自动生成功能
|
||||
|
||||
### 功能描述
|
||||
|
||||
使用 AI（智谱 GLM-4-Flash）根据口播文案自动生成视频标题和标签。
|
||||
|
||||
### 后端实现
|
||||
|
||||
#### 1. GLM 服务 (`glm_service.py`)
|
||||
|
||||
**文件**: `backend/app/services/glm_service.py`
|
||||
|
||||
```python
|
||||
class GLMService:
|
||||
"""智谱 GLM AI 服务"""
|
||||
|
||||
async def generate_meta(self, text: str) -> dict:
|
||||
"""根据文案生成标题和标签"""
|
||||
|
||||
prompt = """根据以下口播文案，生成一个吸引人的短视频标题和3个相关标签。
|
||||
|
||||
要求：
|
||||
1. 标题要简洁有力，能吸引观众点击，不超过10个字
|
||||
2. 标签要与内容相关，便于搜索和推荐，只要3个
|
||||
|
||||
返回格式：{"title": "标题", "tags": ["标签1", "标签2", "标签3"]}
|
||||
"""
|
||||
# 调用 GLM-4-Flash API
|
||||
response = await self._call_api(prompt + text)
|
||||
return self._parse_json(response)
|
||||
```
|
||||
|
||||
**JSON 解析容错**：
|
||||
- 支持直接 JSON 解析
|
||||
- 支持提取 JSON 块
|
||||
- 支持 ```json 代码块提取
|
||||
|
||||
#### 2. API 端点 (`ai.py`)
|
||||
|
||||
**文件**: `backend/app/api/ai.py`
|
||||
|
||||
```python
|
||||
from pydantic import BaseModel
|
||||
|
||||
class GenerateMetaRequest(BaseModel):
|
||||
text: str # 口播文案
|
||||
|
||||
class GenerateMetaResponse(BaseModel):
|
||||
title: str # 生成的标题
|
||||
tags: list[str] # 生成的标签列表
|
||||
|
||||
@router.post("/generate-meta", response_model=GenerateMetaResponse)
|
||||
async def generate_meta(request: GenerateMetaRequest):
|
||||
"""AI 生成标题和标签"""
|
||||
result = await glm_service.generate_meta(request.text)
|
||||
return result
|
||||
```
|
||||
|
||||
### 前端实现
|
||||
|
||||
**文件**: `frontend/src/app/page.tsx`
|
||||
|
||||
#### UI 按钮
|
||||
|
||||
```tsx
|
||||
<button
|
||||
onClick={handleGenerateMeta}
|
||||
disabled={isGeneratingMeta || !text.trim()}
|
||||
className="px-2 py-1 text-xs rounded transition-all whitespace-nowrap"
|
||||
>
|
||||
{isGeneratingMeta ? "⏳ 生成中..." : "🤖 AI生成标题标签"}
|
||||
</button>
|
||||
```
|
||||
|
||||
#### 处理逻辑
|
||||
|
||||
```typescript
|
||||
const handleGenerateMeta = async () => {
|
||||
if (!text.trim()) {
|
||||
alert("请先输入口播文案");
|
||||
return;
|
||||
}
|
||||
|
||||
setIsGeneratingMeta(true);
|
||||
try {
|
||||
const { data } = await api.post('/api/ai/generate-meta', { text: text.trim() });
|
||||
|
||||
// 更新首页标题
|
||||
setVideoTitle(data.title || "");
|
||||
|
||||
// 同步到发布页 localStorage
|
||||
localStorage.setItem(`vigent_${storageKey}_publish_title`, data.title || "");
|
||||
localStorage.setItem(`vigent_${storageKey}_publish_tags`, JSON.stringify(data.tags || []));
|
||||
} catch (err: any) {
|
||||
alert(`AI 生成失败: ${err.message}`);
|
||||
} finally {
|
||||
setIsGeneratingMeta(false);
|
||||
}
|
||||
};
|
||||
```
|
||||
|
||||
### 发布页集成
|
||||
|
||||
**文件**: `frontend/src/app/publish/page.tsx`
|
||||
|
||||
从 localStorage 恢复 AI 生成的标题和标签：
|
||||
|
||||
```typescript
|
||||
// 恢复标题和标签
|
||||
const savedTitle = localStorage.getItem(`vigent_${storageKey}_publish_title`);
|
||||
const savedTags = localStorage.getItem(`vigent_${storageKey}_publish_tags`);
|
||||
|
||||
if (savedTags) {
|
||||
try {
|
||||
const parsed = JSON.parse(savedTags);
|
||||
if (Array.isArray(parsed)) {
|
||||
setTags(parsed.join(', ')); // 数组转逗号分隔字符串
|
||||
} else {
|
||||
setTags(savedTags);
|
||||
}
|
||||
} catch {
|
||||
setTags(savedTags);
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 结果
|
||||
|
||||
- ✅ AI 生成标题和标签功能正常
|
||||
- ✅ 数据自动同步到发布页
|
||||
- ✅ 支持 JSON 数组和字符串格式兼容
|
||||
|
||||
---
|
||||
|
||||
## 🐛 前端文本保存问题修复
|
||||
|
||||
### 问题描述
|
||||
|
||||
**现象**：页面刷新后，用户输入的文案、标题等数据丢失
|
||||
|
||||
**原因**：
|
||||
1. 认证状态恢复失败时，`userId` 为 `null`
|
||||
2. 原代码判断 `!userId` 后用默认值覆盖 localStorage 数据
|
||||
3. 导致已保存的用户数据被清空
|
||||
|
||||
### 解决方案
|
||||
|
||||
**文件**: `frontend/src/app/page.tsx`
|
||||
|
||||
#### 1. 添加恢复完成标志
|
||||
|
||||
```typescript
|
||||
const [isRestored, setIsRestored] = useState(false);
|
||||
```
|
||||
|
||||
#### 2. 等待认证完成后恢复数据
|
||||
|
||||
```typescript
|
||||
useEffect(() => {
|
||||
if (isAuthLoading) return; // 等待认证完成
|
||||
|
||||
// 使用 userId 或 'guest' 作为 key
|
||||
const key = userId || 'guest';
|
||||
|
||||
// 从 localStorage 恢复数据
|
||||
const savedText = localStorage.getItem(`vigent_${key}_text`);
|
||||
if (savedText) setText(savedText);
|
||||
|
||||
// ... 恢复其他数据
|
||||
|
||||
setIsRestored(true); // 标记恢复完成
|
||||
}, [userId, isAuthLoading]);
|
||||
```
|
||||
|
||||
#### 3. 恢复完成后才保存
|
||||
|
||||
```typescript
|
||||
useEffect(() => {
|
||||
if (isRestored) {
|
||||
localStorage.setItem(`vigent_${storageKey}_text`, text);
|
||||
}
|
||||
}, [text, storageKey, isRestored]);
|
||||
```
|
||||
|
||||
### 用户隔离机制
|
||||
|
||||
```typescript
|
||||
const storageKey = userId || 'guest';
|
||||
```
|
||||
|
||||
| 用户状态 | storageKey | 说明 |
|
||||
|----------|------------|------|
|
||||
| 已登录 | `user_xxx` | 数据按用户隔离 |
|
||||
| 未登录/认证失败 | `guest` | 使用统一 key |
|
||||
|
||||
### 数据恢复流程
|
||||
|
||||
```
|
||||
1. 页面加载
|
||||
↓
|
||||
2. 检查 isAuthLoading
|
||||
├─ true: 等待认证完成
|
||||
└─ false: 继续
|
||||
↓
|
||||
3. 确定 storageKey (userId || 'guest')
|
||||
↓
|
||||
4. 从 localStorage 读取数据
|
||||
├─ 有保存数据: 恢复到状态
|
||||
└─ 无保存数据: 使用默认值
|
||||
↓
|
||||
5. 设置 isRestored = true
|
||||
↓
|
||||
6. 后续状态变化时保存到 localStorage
|
||||
```
|
||||
|
||||
### 保存的数据项
|
||||
|
||||
| Key | 说明 |
|
||||
|-----|------|
|
||||
| `vigent_${key}_text` | 口播文案 |
|
||||
| `vigent_${key}_title` | 视频标题 |
|
||||
| `vigent_${key}_subtitles` | 字幕开关 |
|
||||
| `vigent_${key}_ttsMode` | TTS 模式 |
|
||||
| `vigent_${key}_voice` | 选择的音色 |
|
||||
| `vigent_${key}_material` | 选择的素材 |
|
||||
| `vigent_${key}_publish_title` | 发布标题 |
|
||||
| `vigent_${key}_publish_tags` | 发布标签 |
|
||||
|
||||
### 结果
|
||||
|
||||
- ✅ 页面刷新后数据正常恢复
|
||||
- ✅ 认证失败时不会覆盖已保存数据
|
||||
- ✅ 多用户数据隔离正常
|
||||
|
||||
---
|
||||
|
||||
## 🐛 登录页刷新循环修复
|
||||
|
||||
### 问题描述
|
||||
|
||||
**现象**：登录页未登录时不断刷新，无法停留在表单页面。
|
||||
|
||||
**原因**：
|
||||
1. `AuthProvider` 初始化时调用 `/api/auth/me`
|
||||
2. 未登录返回 401
|
||||
3. `axios` 全局拦截器遇到 401/403 重定向 `/login`
|
||||
4. 登录页本身也在 Provider 中，导致循环刷新
|
||||
|
||||
### 解决方案
|
||||
|
||||
**文件**: `frontend/src/lib/axios.ts`
|
||||
|
||||
在拦截器中对公开路由跳过重定向，仅在受保护页面触发登录跳转：
|
||||
|
||||
```typescript
|
||||
const PUBLIC_PATHS = new Set(['/login', '/register']);
|
||||
const isPublicPath = typeof window !== 'undefined' && PUBLIC_PATHS.has(window.location.pathname);
|
||||
|
||||
if ((status === 401 || status === 403) && !isRedirecting && !isPublicPath) {
|
||||
// ... 保持原有重定向逻辑
|
||||
}
|
||||
```
|
||||
|
||||
### 结果
|
||||
|
||||
- ✅ 登录页不再刷新，表单可正常输入
|
||||
- ✅ 受保护页面仍会在 401/403 时跳转登录页
|
||||
|
||||
---
|
||||
|
||||
## 📁 今日修改文件清单
|
||||
|
||||
| 文件 | 变更类型 | 说明 |
|
||||
|------|----------|------|
|
||||
| `models/Qwen3-TTS/qwen_tts_server.py` | 修改 | 模型路径升级到 1.7B-Base |
|
||||
| `Docs/QWEN3_TTS_DEPLOY.md` | 修改 | 更新部署文档为 1.7B 版本 |
|
||||
| `remotion/src/components/Subtitles.tsx` | 修改 | 优化字幕显示效果 |
|
||||
| `remotion/src/components/Title.tsx` | 修改 | 优化标题动画效果 |
|
||||
| `backend/app/services/glm_service.py` | 新增 | GLM AI 服务 |
|
||||
| `backend/app/api/ai.py` | 新增 | AI 生成标题标签 API |
|
||||
| `backend/app/main.py` | 修改 | 注册 ai 路由 |
|
||||
| `frontend/src/app/page.tsx` | 修改 | AI 生成按钮 + localStorage 修复 |
|
||||
| `frontend/src/app/publish/page.tsx` | 修改 | 恢复 AI 生成的标签 |
|
||||
| `frontend/src/lib/axios.ts` | 修改 | 公开路由跳过 401/403 登录重定向 |
|
||||
|
||||
---
|
||||
|
||||
## 🔗 相关文档
|
||||
|
||||
- [task_complete.md](../task_complete.md) - 任务总览
|
||||
- [Day13.md](./Day13.md) - 声音克隆功能集成 + 字幕功能
|
||||
- [QWEN3_TTS_DEPLOY.md](../QWEN3_TTS_DEPLOY.md) - Qwen3-TTS 1.7B 部署指南
|
||||
347
Docs/DevLogs/Day15.md
Normal file
347
Docs/DevLogs/Day15.md
Normal file
@@ -0,0 +1,347 @@
|
||||
# Day 15 - 手机号登录迁移 + 账户设置功能
|
||||
|
||||
**日期**：2026-02-02
|
||||
|
||||
---
|
||||
|
||||
## 🔐 认证系统迁移：邮箱 → 手机号
|
||||
|
||||
### 背景
|
||||
|
||||
根据业务需求，将用户认证从邮箱登录迁移到手机号登录（11位中国手机号）。
|
||||
|
||||
### 变更范围
|
||||
|
||||
| 组件 | 变更内容 |
|
||||
|------|----------|
|
||||
| 数据库 Schema | `email` 字段替换为 `phone` |
|
||||
| 后端 API | 注册/登录/获取用户信息接口使用 `phone` |
|
||||
| 前端页面 | 登录/注册页面改为手机号输入框 |
|
||||
| 管理员配置 | `ADMIN_EMAIL` 改为 `ADMIN_PHONE` |
|
||||
|
||||
---
|
||||
|
||||
## 📦 后端修改
|
||||
|
||||
### 1. 数据库 Schema (`schema.sql`)
|
||||
|
||||
**文件**: `backend/database/schema.sql`
|
||||
|
||||
```sql
|
||||
CREATE TABLE users (
|
||||
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
|
||||
phone TEXT UNIQUE NOT NULL, -- 原 email 改为 phone
|
||||
password_hash TEXT NOT NULL,
|
||||
username TEXT,
|
||||
role TEXT DEFAULT 'pending' CHECK (role IN ('pending', 'user', 'admin')),
|
||||
is_active BOOLEAN DEFAULT FALSE,
|
||||
expires_at TIMESTAMP WITH TIME ZONE,
|
||||
created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
|
||||
updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
|
||||
);
|
||||
|
||||
CREATE INDEX idx_users_phone ON users(phone);
|
||||
```
|
||||
|
||||
### 2. 认证 API (`auth.py`)
|
||||
|
||||
**文件**: `backend/app/api/auth.py`
|
||||
|
||||
#### 请求模型更新
|
||||
|
||||
```python
|
||||
class RegisterRequest(BaseModel):
|
||||
phone: str
|
||||
password: str
|
||||
username: Optional[str] = None
|
||||
|
||||
@field_validator('phone')
|
||||
@classmethod
|
||||
def validate_phone(cls, v):
|
||||
if not re.match(r'^\d{11}$', v):
|
||||
raise ValueError('手机号必须是11位数字')
|
||||
return v
|
||||
```
|
||||
|
||||
#### 新增修改密码接口
|
||||
|
||||
```python
|
||||
class ChangePasswordRequest(BaseModel):
|
||||
old_password: str
|
||||
new_password: str
|
||||
|
||||
@field_validator('new_password')
|
||||
@classmethod
|
||||
def validate_new_password(cls, v):
|
||||
if len(v) < 6:
|
||||
raise ValueError('新密码长度至少6位')
|
||||
return v
|
||||
|
||||
@router.post("/change-password")
|
||||
async def change_password(request: ChangePasswordRequest, req: Request, response: Response):
|
||||
"""修改密码，验证当前密码后更新"""
|
||||
# 1. 验证当前密码
|
||||
# 2. 更新密码 hash
|
||||
# 3. 重新生成 session token
|
||||
# 4. 返回新的 JWT Cookie
|
||||
```
|
||||
|
||||
### 3. 配置更新
|
||||
|
||||
**文件**: `backend/app/core/config.py`
|
||||
|
||||
```python
|
||||
# 管理员配置
|
||||
ADMIN_PHONE: str = "" # 原 ADMIN_EMAIL
|
||||
ADMIN_PASSWORD: str = ""
|
||||
```
|
||||
|
||||
**文件**: `backend/.env`
|
||||
|
||||
```bash
|
||||
ADMIN_PHONE=15549380526
|
||||
ADMIN_PASSWORD=lam1988324
|
||||
```
|
||||
|
||||
### 4. 管理员初始化 (`main.py`)
|
||||
|
||||
**文件**: `backend/app/main.py`
|
||||
|
||||
```python
|
||||
@app.on_event("startup")
|
||||
async def init_admin():
|
||||
admin_phone = settings.ADMIN_PHONE # 原 ADMIN_EMAIL
|
||||
# ... 使用 phone 字段创建管理员
|
||||
```
|
||||
|
||||
### 5. 管理员 API (`admin.py`)
|
||||
|
||||
**文件**: `backend/app/api/admin.py`
|
||||
|
||||
```python
|
||||
class UserListItem(BaseModel):
|
||||
id: str
|
||||
phone: str # 原 email
|
||||
username: Optional[str]
|
||||
role: str
|
||||
is_active: bool
|
||||
expires_at: Optional[str]
|
||||
created_at: str
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🖥️ 前端修改
|
||||
|
||||
### 1. 登录页面 (`login/page.tsx`)
|
||||
|
||||
**文件**: `frontend/src/app/login/page.tsx`
|
||||
|
||||
```tsx
|
||||
const [phone, setPhone] = useState('');
|
||||
|
||||
// 验证手机号格式
|
||||
if (!/^\d{11}$/.test(phone)) {
|
||||
setError('请输入正确的11位手机号');
|
||||
return;
|
||||
}
|
||||
|
||||
<input
|
||||
type="tel"
|
||||
value={phone}
|
||||
onChange={(e) => setPhone(e.target.value.replace(/\D/g, '').slice(0, 11))}
|
||||
maxLength={11}
|
||||
placeholder="请输入11位手机号"
|
||||
/>
|
||||
```
|
||||
|
||||
### 2. 注册页面 (`register/page.tsx`)
|
||||
|
||||
同样使用手机号输入，增加 11 位数字验证。
|
||||
|
||||
### 3. Auth 工具函数 (`auth.ts`)
|
||||
|
||||
**文件**: `frontend/src/lib/auth.ts`
|
||||
|
||||
```typescript
|
||||
export interface User {
|
||||
id: string;
|
||||
phone: string; // 原 email
|
||||
username: string | null;
|
||||
role: string;
|
||||
is_active: boolean;
|
||||
}
|
||||
|
||||
export async function login(phone: string, password: string): Promise<AuthResponse> { ... }
|
||||
export async function register(phone: string, password: string, username?: string): Promise<AuthResponse> { ... }
|
||||
export async function changePassword(oldPassword: string, newPassword: string): Promise<AuthResponse> { ... }
|
||||
```
|
||||
|
||||
### 4. 首页账户设置下拉菜单 (`page.tsx`)
|
||||
|
||||
**文件**: `frontend/src/app/page.tsx`
|
||||
|
||||
将原来的"退出"按钮改为账户设置下拉菜单：
|
||||
|
||||
```tsx
|
||||
function AccountSettingsDropdown() {
|
||||
const [isOpen, setIsOpen] = useState(false);
|
||||
const [showPasswordModal, setShowPasswordModal] = useState(false);
|
||||
// ...
|
||||
|
||||
return (
|
||||
<div className="relative">
|
||||
<button onClick={() => setIsOpen(!isOpen)}>
|
||||
⚙️ 账户
|
||||
</button>
|
||||
|
||||
{/* 下拉菜单 */}
|
||||
{isOpen && (
|
||||
<div className="absolute right-0 mt-2 w-40 bg-gray-800 ...">
|
||||
<button onClick={() => setShowPasswordModal(true)}>
|
||||
🔐 修改密码
|
||||
</button>
|
||||
<button onClick={handleLogout} className="text-red-300">
|
||||
🚪 退出登录
|
||||
</button>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* 修改密码弹窗 */}
|
||||
{showPasswordModal && (
|
||||
<div className="fixed inset-0 z-50 ...">
|
||||
<form onSubmit={handleChangePassword}>
|
||||
<input placeholder="当前密码" />
|
||||
<input placeholder="新密码" />
|
||||
<input placeholder="确认新密码" />
|
||||
</form>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
```
|
||||
|
||||
### 5. 管理员页面 (`admin/page.tsx`)
|
||||
|
||||
**文件**: `frontend/src/app/admin/page.tsx`
|
||||
|
||||
```tsx
|
||||
interface UserListItem {
|
||||
id: string;
|
||||
phone: string; // 原 email
|
||||
// ...
|
||||
}
|
||||
|
||||
// 显示手机号而非邮箱
|
||||
<div className="text-gray-400 text-sm">{user.phone}</div>
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🗄️ 数据库迁移
|
||||
|
||||
### 迁移脚本
|
||||
|
||||
**文件**: `backend/database/migrate_to_phone.sql`
|
||||
|
||||
```sql
|
||||
-- 删除旧表 (CASCADE 处理外键依赖)
|
||||
DROP TABLE IF EXISTS user_sessions CASCADE;
|
||||
DROP TABLE IF EXISTS social_accounts CASCADE;
|
||||
DROP TABLE IF EXISTS users CASCADE;
|
||||
|
||||
-- 重新创建使用 phone 字段的表
|
||||
CREATE TABLE users (
|
||||
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
|
||||
phone TEXT UNIQUE NOT NULL,
|
||||
-- ...
|
||||
);
|
||||
|
||||
-- 重新创建依赖表和索引
|
||||
CREATE TABLE user_sessions (...);
|
||||
CREATE TABLE social_accounts (...);
|
||||
CREATE INDEX idx_users_phone ON users(phone);
|
||||
```
|
||||
|
||||
### 执行方式
|
||||
|
||||
```bash
|
||||
# 方式一：Docker 命令
|
||||
docker exec -i supabase-db psql -U postgres < backend/database/migrate_to_phone.sql
|
||||
|
||||
# 方式二：Supabase Studio SQL Editor
|
||||
# 打开 https://supabase.hbyrkj.top -> SQL Editor -> 粘贴执行
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## ✅ 部署步骤
|
||||
|
||||
```bash
|
||||
# 1. 执行数据库迁移
|
||||
docker exec -i supabase-db psql -U postgres < backend/database/migrate_to_phone.sql
|
||||
|
||||
# 2. 重新构建前端
|
||||
cd frontend && npm run build
|
||||
|
||||
# 3. 重启服务
|
||||
pm2 restart vigent2-backend vigent2-frontend
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📁 今日修改文件清单
|
||||
|
||||
| 文件 | 变更类型 | 说明 |
|
||||
|------|----------|------|
|
||||
| `backend/database/schema.sql` | 修改 | email → phone |
|
||||
| `backend/database/migrate_to_phone.sql` | 新增 | 数据库迁移脚本 |
|
||||
| `backend/app/api/auth.py` | 修改 | 手机号验证 + 修改密码 API |
|
||||
| `backend/app/api/admin.py` | 修改 | UserListItem.email → phone |
|
||||
| `backend/app/core/config.py` | 修改 | ADMIN_EMAIL → ADMIN_PHONE |
|
||||
| `backend/app/main.py` | 修改 | 管理员初始化使用 phone |
|
||||
| `backend/.env` | 修改 | ADMIN_PHONE=15549380526 |
|
||||
| `frontend/src/app/login/page.tsx` | 修改 | 手机号登录 + 11位验证 |
|
||||
| `frontend/src/app/register/page.tsx` | 修改 | 手机号注册 + 11位验证 |
|
||||
| `frontend/src/lib/auth.ts` | 修改 | phone 参数 + changePassword 函数 |
|
||||
| `frontend/src/app/page.tsx` | 修改 | AccountSettingsDropdown 组件 |
|
||||
| `frontend/src/app/admin/page.tsx` | 修改 | 用户列表显示手机号 |
|
||||
| `frontend/src/contexts/AuthContext.tsx` | 修改 | 存储完整用户信息含 expires_at |
|
||||
|
||||
---
|
||||
|
||||
## 🆕 后续完善 (Day 15 下午)
|
||||
|
||||
### 账户有效期显示
|
||||
|
||||
在账户下拉菜单中显示用户的有效期：
|
||||
|
||||
| 显示情况 | 格式 |
|
||||
|----------|------|
|
||||
| 有设置 expires_at | `2026-03-15` |
|
||||
| NULL | `永久有效` |
|
||||
|
||||
**相关修改**：
|
||||
- `backend/app/api/auth.py`: UserResponse 新增 `expires_at` 字段
|
||||
- `frontend/src/contexts/AuthContext.tsx`: 存储完整用户对象
|
||||
- `frontend/src/app/page.tsx`: 格式化并显示有效期
|
||||
|
||||
### 点击外部关闭下拉菜单
|
||||
|
||||
使用 `useRef` + `useEffect` 监听全局点击事件，点击菜单外部自动关闭。
|
||||
|
||||
### 修改密码后强制重新登录
|
||||
|
||||
密码修改成功后：
|
||||
1. 显示"密码修改成功，正在跳转登录页..."
|
||||
2. 1.5秒后调用登出 API
|
||||
3. 跳转到登录页面
|
||||
|
||||
---
|
||||
|
||||
## 🔗 相关文档
|
||||
|
||||
- [task_complete.md](../task_complete.md) - 任务总览
|
||||
- [Day14.md](./Day14.md) - 模型升级 + AI 标题标签
|
||||
- [AUTH_DEPLOY.md](../AUTH_DEPLOY.md) - 认证系统部署指南
|
||||
@@ -232,7 +232,7 @@ else:
|
||||
| `src/app/login/page.tsx` | 登录页 | ✅ |
|
||||
| `src/app/register/page.tsx` | 注册页 | ✅ |
|
||||
| `src/app/admin/page.tsx` | 管理后台 | ✅ |
|
||||
| `src/middleware.ts` | 路由保护 | ✅ |
|
||||
| `src/proxy.ts` | 路由保护 | ✅ |
|
||||
|
||||
### 账号隔离集成
|
||||
|
||||
|
||||
@@ -26,6 +26,7 @@
|
||||
| 🔥 **High** | `Docs/task_complete.md` | **(任务总览)** 更新 `[x]`、进度条、时间线 |
|
||||
| ⚡ **Med** | `README.md` | **(项目主页)** 功能特性、技术栈、最新截图 |
|
||||
| ⚡ **Med** | `Docs/DEPLOY_MANUAL.md` | **(部署手册)** 环境变量、依赖包、启动命令变更 |
|
||||
| ⚡ **Med** | `Docs/FRONTEND_DEV.md` | **(前端规范)** API封装、日期格式化、新页面规范 |
|
||||
| 🧊 **Low** | `Docs/implementation_plan.md` | **(实施计划)** 核对计划与实际实现的差异 |
|
||||
| 🧊 **Low** | `frontend/README.md` | **(前端文档)** 新页面路由、组件用法、UI变更 |
|
||||
|
||||
@@ -206,6 +207,9 @@ replace_file_content(
|
||||
ViGent/Docs/
|
||||
├── task_complete.md # 任务总览（仅按需更新）
|
||||
├── Doc_Rules.md # 本文件
|
||||
├── FRONTEND_DEV.md # 前端开发规范
|
||||
├── DEPLOY_MANUAL.md # 部署手册
|
||||
├── SUPABASE_DEPLOY.md # Supabase 部署文档
|
||||
└── DevLogs/
|
||||
├── Day1.md # 开发日志
|
||||
└── ...
|
||||
|
||||
231
Docs/FRONTEND_DEV.md
Normal file
231
Docs/FRONTEND_DEV.md
Normal file
@@ -0,0 +1,231 @@
|
||||
# 前端开发规范
|
||||
|
||||
## 目录结构
|
||||
|
||||
```
|
||||
frontend/src/
|
||||
├── app/ # Next.js App Router 页面
|
||||
│ ├── page.tsx # 首页（视频生成）
|
||||
│ ├── publish/ # 发布页面
|
||||
│ ├── admin/ # 管理员页面
|
||||
│ ├── login/ # 登录页面
|
||||
│ └── register/ # 注册页面
|
||||
├── lib/ # 公共工具函数
|
||||
│ ├── axios.ts # Axios 实例（含 401/403 拦截器）
|
||||
│ └── auth.ts # 认证相关函数
|
||||
└── proxy.ts # 路由代理（原 middleware）
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## iOS Safari 安全区域兼容
|
||||
|
||||
### 问题
|
||||
iPhone Safari 浏览器顶部（刘海/灵动岛）和底部（Home 指示条）有安全区域，默认情况下页面背景不会延伸到这些区域，导致白边。
|
||||
|
||||
### 解决方案（三层配合）
|
||||
|
||||
#### 1. Viewport 配置 (`layout.tsx`)
|
||||
```typescript
|
||||
import type { Viewport } from "next";
|
||||
|
||||
export const viewport: Viewport = {
|
||||
width: 'device-width',
|
||||
initialScale: 1,
|
||||
viewportFit: 'cover', // 允许内容延伸到安全区域
|
||||
themeColor: '#0f172a', // 顶部状态栏颜色（与背景一致）
|
||||
};
|
||||
```
|
||||
|
||||
#### 2. 全局背景统一到 body (`layout.tsx`)
|
||||
```tsx
|
||||
<html lang="en" style={{ backgroundColor: '#0f172a' }}>
|
||||
<body
|
||||
style={{
|
||||
margin: 0,
|
||||
minHeight: '100dvh', // 使用 dvh 而非 vh
|
||||
background: 'linear-gradient(to bottom, #0f172a 0%, #0f172a 5%, #581c87 50%, #0f172a 95%, #0f172a 100%)',
|
||||
}}
|
||||
>
|
||||
{children}
|
||||
</body>
|
||||
</html>
|
||||
```
|
||||
|
||||
#### 3. CSS 安全区域支持 (`globals.css`)
|
||||
```css
|
||||
html {
|
||||
background-color: #0f172a !important;
|
||||
min-height: 100%;
|
||||
}
|
||||
|
||||
body {
|
||||
margin: 0 !important;
|
||||
min-height: 100dvh;
|
||||
padding-top: env(safe-area-inset-top);
|
||||
padding-bottom: env(safe-area-inset-bottom);
|
||||
}
|
||||
```
|
||||
|
||||
### 关键要点
|
||||
- **渐变背景放 body，不放页面 div** - 安全区域在 div 之外
|
||||
- **使用 `100dvh` 而非 `100vh`** - dvh 是动态视口高度，适配移动端
|
||||
- **themeColor 与背景边缘色一致** - 避免状态栏色差
|
||||
- **页面 div 移除独立背景** - 使用透明，继承 body 渐变
|
||||
|
||||
---
|
||||
|
||||
## 移动端响应式规范
|
||||
|
||||
### Header 按钮布局
|
||||
```tsx
|
||||
// 移动端紧凑，桌面端宽松
|
||||
<div className="flex items-center gap-1 sm:gap-4">
|
||||
<button className="px-2 sm:px-4 py-1 sm:py-2 text-sm sm:text-base ...">
|
||||
按钮
|
||||
</button>
|
||||
</div>
|
||||
```
|
||||
|
||||
### 常用响应式断点
|
||||
| 断点 | 宽度 | 用途 |
|
||||
|------|------|------|
|
||||
| 默认 | < 640px | 移动端 |
|
||||
| `sm:` | ≥ 640px | 平板/桌面 |
|
||||
| `lg:` | ≥ 1024px | 大屏桌面 |
|
||||
|
||||
---
|
||||
|
||||
## API 请求规范
|
||||
|
||||
### 必须使用 `api` (axios 实例)
|
||||
|
||||
所有需要认证的 API 请求**必须**使用 `@/lib/axios` 导出的 axios 实例。该实例已配置：
|
||||
- 自动携带 `credentials: include`
|
||||
- 遇到 401/403 时自动清除 cookie 并跳转登录页
|
||||
|
||||
**使用方式：**
|
||||
|
||||
```typescript
|
||||
import api from '@/lib/axios';
|
||||
|
||||
// GET 请求
|
||||
const { data } = await api.get('/api/materials');
|
||||
|
||||
// POST 请求
|
||||
const { data } = await api.post('/api/videos/generate', {
|
||||
text: '...',
|
||||
voice: '...',
|
||||
});
|
||||
|
||||
// DELETE 请求
|
||||
await api.delete(`/api/materials/${id}`);
|
||||
|
||||
// 带上传进度的文件上传
|
||||
await api.post('/api/materials', formData, {
|
||||
headers: { 'Content-Type': 'multipart/form-data' },
|
||||
onUploadProgress: (e) => {
|
||||
if (e.total) {
|
||||
const progress = Math.round((e.loaded / e.total) * 100);
|
||||
setProgress(progress);
|
||||
}
|
||||
},
|
||||
});
|
||||
```
|
||||
|
||||
### SWR 配合使用
|
||||
|
||||
```typescript
|
||||
import api from '@/lib/axios';
|
||||
|
||||
// SWR fetcher 使用 axios
|
||||
const fetcher = (url: string) => api.get(url).then(res => res.data);
|
||||
|
||||
const { data } = useSWR('/api/xxx', fetcher, { refreshInterval: 2000 });
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 日期格式化规范
|
||||
|
||||
### 禁止使用 `toLocaleString()`
|
||||
|
||||
`toLocaleString()` 在服务端和客户端可能返回不同格式，导致 Hydration 错误。
|
||||
|
||||
**错误示例：**
|
||||
```typescript
|
||||
// ❌ 会导致 Hydration 错误
|
||||
new Date(timestamp * 1000).toLocaleString('zh-CN')
|
||||
```
|
||||
|
||||
**正确做法：**
|
||||
```typescript
|
||||
// ✅ 使用固定格式
|
||||
const formatDate = (timestamp: number) => {
|
||||
const d = new Date(timestamp * 1000);
|
||||
const year = d.getFullYear();
|
||||
const month = String(d.getMonth() + 1).padStart(2, '0');
|
||||
const day = String(d.getDate()).padStart(2, '0');
|
||||
const hour = String(d.getHours()).padStart(2, '0');
|
||||
const minute = String(d.getMinutes()).padStart(2, '0');
|
||||
return `${year}/${month}/${day} ${hour}:${minute}`;
|
||||
};
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 新增页面 Checklist
|
||||
|
||||
1. [ ] 导入 `import api from '@/lib/axios'`
|
||||
2. [ ] 所有 API 请求使用 `api.get/post/delete()` 而非原生 `fetch`
|
||||
3. [ ] 日期格式化使用固定格式函数，不用 `toLocaleString()`
|
||||
4. [ ] 添加 `'use client'` 指令（如需客户端交互）
|
||||
|
||||
---
|
||||
|
||||
## 声音克隆 (Voice Clone) 功能
|
||||
|
||||
### API 端点
|
||||
|
||||
| 接口 | 方法 | 功能 |
|
||||
|------|------|------|
|
||||
| `/api/ref-audios` | POST | 上传参考音频 (multipart/form-data: file + ref_text) |
|
||||
| `/api/ref-audios` | GET | 列出用户的参考音频 |
|
||||
| `/api/ref-audios/{id}` | DELETE | 删除参考音频 (id 需 encodeURIComponent) |
|
||||
|
||||
### 视频生成 API 扩展
|
||||
|
||||
```typescript
|
||||
// EdgeTTS 模式 (默认)
|
||||
await api.post('/api/videos/generate', {
|
||||
material_path: '...',
|
||||
text: '口播文案',
|
||||
tts_mode: 'edgetts',
|
||||
voice: 'zh-CN-YunxiNeural',
|
||||
});
|
||||
|
||||
// 声音克隆模式
|
||||
await api.post('/api/videos/generate', {
|
||||
material_path: '...',
|
||||
text: '口播文案',
|
||||
tts_mode: 'voiceclone',
|
||||
ref_audio_id: 'user_id/timestamp_name.wav',
|
||||
ref_text: '参考音频对应文字',
|
||||
});
|
||||
```
|
||||
|
||||
### 在线录音
|
||||
|
||||
使用 `MediaRecorder` API 录制音频，格式为 `audio/webm`，上传后后端自动转换为 WAV (16kHz mono)。
|
||||
|
||||
```typescript
|
||||
// 录音需要用户授权麦克风
|
||||
const stream = await navigator.mediaDevices.getUserMedia({ audio: true });
|
||||
const mediaRecorder = new MediaRecorder(stream, { mimeType: 'audio/webm' });
|
||||
```
|
||||
|
||||
### UI 结构
|
||||
|
||||
配音方式使用 Tab 切换：
|
||||
- **EdgeTTS 音色** - 预设音色 2x3 网格
|
||||
- **声音克隆** - 参考音频列表 + 在线录音 + 参考文字输入
|
||||
362
Docs/Logs.md
362
Docs/Logs.md
@@ -1,29 +1,333 @@
|
||||
rongye@r730-ubuntu:~/ProgramFiles/Supabase$ docker compose up -d
|
||||
[+] up 136/136
|
||||
✔ Image timberio/vector:0.28.1-alpine Pulled 63.3ss
|
||||
✔ Image supabase/storage-api:v1.33.0 Pulled 78.6ss
|
||||
✔ Image darthsim/imgproxy:v3.30.1 Pulled 151.9s
|
||||
✔ Image supabase/postgres-meta:v0.95.1 Pulled 87.5ss
|
||||
✔ Image supabase/logflare:1.27.0 Pulled 229.2s
|
||||
✔ Image supabase/postgres:15.8.1.085 Pulled 268.3s
|
||||
✔ Image supabase/supavisor:2.7.4 Pulled 101.6s
|
||||
✔ Image supabase/realtime:v2.68.0 Pulled 56.5ss
|
||||
✔ Image postgrest/postgrest:v14.1 Pulled 201.8s
|
||||
✔ Image supabase/edge-runtime:v1.69.28 Pulled 254.0s
|
||||
✔ Network supabase_default Created 0.1s
|
||||
✔ Volume supabase_db-config Created 0.1s
|
||||
✔ Container supabase-vector Healthy 16.9s
|
||||
✔ Container supabase-imgproxy Created 7.4s
|
||||
✔ Container supabase-db Healthy 20.6s
|
||||
✔ Container supabase-analytics Created 0.4s
|
||||
✔ Container supabase-edge-functions Created 1.8s
|
||||
✔ Container supabase-auth Created 1.7s
|
||||
✔ Container supabase-studio Created 2.0s
|
||||
✔ Container realtime-dev.supabase-realtime Created 1.7s
|
||||
✔ Container supabase-pooler Created 1.8s
|
||||
✔ Container supabase-kong Created 1.7s
|
||||
✔ Container supabase-meta Created 2.0s
|
||||
✔ Container supabase-rest Created 0.9s
|
||||
✔ Container supabase-storage Created 1.4s
|
||||
Error response from daemon: failed to set up container networking: driver failed programming external connectivity on endpoint supabase-analytics (2fd60a510a1f16bf29f8f5140f14ef457a284c5b65a2567b7be250a4f9708f34): failed to bind host port 0.0.0.0:4000/tcp: address already in use
|
||||
[ble: exit 1]
|
||||
rongye@r730-ubuntu:~$ pm2 logs vigent2-qwen-tts
|
||||
[TAILING] Tailing last 15 lines for [vigent2-qwen-tts] process (change the value with --lines option)
|
||||
/home/rongye/.pm2/logs/vigent2-qwen-tts-error.log last 15 lines:
|
||||
13|vigent2 | Setting `pad_token_id` to `eos_token_id`:2150 for open-end generation.
|
||||
|
||||
/home/rongye/.pm2/logs/vigent2-qwen-tts-out.log last 15 lines:
|
||||
13|vigent2 | 🔄 Loading Qwen3-TTS model...
|
||||
13|vigent2 |
|
||||
13|vigent2 | ********
|
||||
13|vigent2 | Warning: flash-attn is not installed. Will only run the manual PyTorch version. Please install flash-attn for faster inference.
|
||||
13|vigent2 | ********
|
||||
13|vigent2 |
|
||||
13|vigent2 | ✅ Qwen3-TTS model loaded in 8.6s
|
||||
13|vigent2 | INFO: 127.0.0.1:56814 - "GET /health HTTP/1.1" 200 OK
|
||||
13|vigent2 | 🎤 Generating: 大家好，欢迎来到我的频道，今天给大家分享一些有趣的内容。...
|
||||
13|vigent2 | 📝 Ref text: 其实生活中有许多美好的瞬间，比如清晨的阳光，或者一杯温热的清茶。希望这次生成的音色能够自然、流畅，完...
|
||||
13|vigent2 | [WARNING] Min value of input waveform signal is -1.006709337234497
|
||||
13|vigent2 | [WARNING] Max value of input waveform signal is 1.0008893013000488
|
||||
13|vigent2 | ✅ Generated in 15.0s, duration: 4.6s
|
||||
13|vigent2 | INFO: 127.0.0.1:36556 - "POST /generate HTTP/1.1" 200 OK
|
||||
|
||||
|
||||
|
||||
|
||||
rongye@r730-ubuntu:~$ pm2 logs vigent2-backend --lines 400
|
||||
[TAILING] Tailing last 400 lines for [vigent2-backend] process (change the value with --lines option)
|
||||
/home/rongye/.pm2/logs/vigent2-backend-out.log last 400 lines:
|
||||
11|vigent2 | Storage endpoint URL should have a trailing slash.
|
||||
11|vigent2 | Storage endpoint URL should have a trailing slash.
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/materials?t=1769651820268 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/generated HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/generated HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/materials?t=1769651825016 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/publish/accounts HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/generated HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/materials?t=1769651828852 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/generated HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/ref-audios HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/materials?t=1769654501430 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/generated HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/materials?t=1769654987404 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/ref-audios HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/generated HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/ref-audios HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/ref-audios HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "POST /api/ref-audios HTTP/1.1" 500 Internal Server Error
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/ref-audios HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/materials?t=1769655093628 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/generated HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/ref-audios HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "POST /api/ref-audios HTTP/1.1" 500 Internal Server Error
|
||||
11|vigent2 | Storage endpoint URL should have a trailing slash.
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/ref-audios HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/materials?t=1769655569331 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/generated HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "POST /api/ref-audios HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/ref-audios HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "POST /api/videos/generate HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | [Pipeline] TTS completed in 17.7s
|
||||
11|vigent2 | [LipSync] Health check: ready=True
|
||||
11|vigent2 | [LipSync] Starting LatentSync inference...
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | [Pipeline] LipSync completed in 122.8s
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | [Pipeline] Total generation time: 143.1s
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/tasks/bf4760b8-e338-49ee-9777-828c1ef0c855 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/generated HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/publish/accounts HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/generated HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/materials?t=1769655769762 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/generated HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/ref-audios HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/publish/accounts HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/generated HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/materials?t=1769655923194 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/generated HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/ref-audios HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/materials?t=1769655960629 HTTP/1.1" 403 Forbidden
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/ref-audios HTTP/1.1" 403 Forbidden
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/generated HTTP/1.1" 403 Forbidden
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "POST /api/auth/logout HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "POST /api/auth/login HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/materials?t=1769655964287 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/generated HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/ref-audios HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/publish/accounts HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/generated HTTP/1.1" 403 Forbidden
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "POST /api/auth/logout HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/publish/accounts HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/generated HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "POST /api/auth/login HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/materials?t=1769656015718 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/generated HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/ref-audios HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/materials?t=1769656233290 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/generated HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/ref-audios HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "POST /api/ref-audios HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/ref-audios HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/publish/accounts HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/generated HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/materials?t=1769656987465 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/generated HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/ref-audios HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/materials?t=1769657141569 HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/videos/generated HTTP/1.1" 200 OK
|
||||
11|vigent2 | INFO: 27.17.161.128:0 - "GET /api/ref-audios HTTP/1.1" 200 OK
|
||||
|
||||
/home/rongye/.pm2/logs/vigent2-backend-error.log last 400 lines:
|
||||
11|vigent2 | rnPaKCW6D20R7A4QpeumwXIRUkzHtaFASP40bWfE6KL05g4rq6VbZFQ9X4FVBZ2lbwW%2Faa32knjuye8aa1ejtZEGmyfXpfcryezEIy0gmYYjjT7lKB6HAupj9%2FCezQ%3D%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX1%2BpEG8yoPZBpac674EsBSuEU0HhlEZGMLIKNfviY6GGLzzbk6%2BbdfmzJ5s1nr16B0NNWzywMDwDD00Cktdf8N50BWw0Pp7Xuy2cOM6L15tjqobzRZyayXyVA1o%2B5kHPODaa3yg4cjWjee8OqG1qRaX4EwOXc0YzPZI%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX1%2BRY4oSsXW07D0HdYfXZhDpJS%2Fl%2F%2Bysns8Xand%2BMI7%2FJBIRw1RV%2FIJPzbTSpW8kmvwLCsUosyNPtsZbl3lGRDOM4YJIL%2BaFVjvjAWDo0WA89ezEeTVY9hzd9rwV3A6dbv5vJhrEdjolAkub50ItC47iV1fIGb%2FN3vI%3D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19r9Vr3ov%2BP30OgbarNSaCn6bebg11iU%2B8UV7b%2F116JurvSpJ77d%2FdZ62kjIP%2BMF3h3R9RathLKFQ%3D%3D; rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX19CE%2F3GmyTsZHCQhdFWdzYnJYPdvCMBFbM%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX18t2%2FxrnN2HPEqTssR572nq%2FgCim9EQN7E%3D'}
|
||||
11|vigent2 | 2026-01-29 11:06:00.756 | INFO | app.main:dispatch:21 - START Request: GET https://vigent.hbyrkj.top/api/videos/generated
|
||||
11|vigent2 | 2026-01-29 11:06:00.757 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'sec-fetch-dest': 'empty', 'user-agent': 'Mozilla/5.0 (iPhone; CPU iPhone OS 18_7 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/26.2 Mobile/15E148 Safari/604.1', 'accept': 'application/json, text/plain, */*', 'referer': 'https://vigent.hbyrkj.top/', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'accept-language': 'en-US,en;q=0.9', 'priority': 'u=3, i', 'accept-encoding': 'gzip, deflate, br', 'cookie': 'access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiN2JlNWIzNWYtMzQ0Ni00ZTIyLWEzMTktZTc5M2NlNDBmYTRiIiwiZXhwIjoxNzcwMTc4MTE0fQ.pk4sCAkd9hcN6fE5_8RXH42zfMl7YPSV5i1R9QeER4s; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1760758482696%2C%220199f562-16c1-7fcb-9bef-fdc33838b6a8%22%2C1760758470336%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_session=RudderEncrypt%3AU2FsdGVkX19ssBuVw9hBTDRVrnPaKCW6D20R7A4QpeumwXIRUkzHtaFASP40bWfE6KL05g4rq6VbZFQ9X4FVBZ2lbwW%2Faa32knjuye8aa1ejtZEGmyfXpfcryezEIy0gmYYjjT7lKB6HAupj9%2FCezQ%3D%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX1%2BpEG8yoPZBpac674EsBSuEU0HhlEZGMLIKNfviY6GGLzzbk6%2BbdfmzJ5s1nr16B0NNWzywMDwDD00Cktdf8N50BWw0Pp7Xuy2cOM6L15tjqobzRZyayXyVA1o%2B5kHPODaa3yg4cjWjee8OqG1qRaX4EwOXc0YzPZI%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX1%2BRY4oSsXW07D0HdYfXZhDpJS%2Fl%2F%2Bysns8Xand%2BMI7%2FJBIRw1RV%2FIJPzbTSpW8kmvwLCsUosyNPtsZbl3lGRDOM4YJIL%2BaFVjvjAWDo0WA89ezEeTVY9hzd9rwV3A6dbv5vJhrEdjolAkub50ItC47iV1fIGb%2FN3vI%3D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19r9Vr3ov%2BP30OgbarNSaCn6bebg11iU%2B8UV7b%2F116JurvSpJ77d%2FdZ62kjIP%2BMF3h3R9RathLKFQ%3D%3D; rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX19CE%2F3GmyTsZHCQhdFWdzYnJYPdvCMBFbM%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX18t2%2FxrnN2HPEqTssR572nq%2FgCim9EQN7E%3D'}
|
||||
11|vigent2 | 2026-01-29 11:06:00.765 | INFO | app.main:dispatch:26 - END Request: GET https://vigent.hbyrkj.top/api/ref-audios - Status: 403 - Duration: 0.02s
|
||||
11|vigent2 | 2026-01-29 11:06:00.766 | INFO | app.main:dispatch:26 - END Request: GET https://vigent.hbyrkj.top/api/videos/generated - Status: 403 - Duration: 0.01s
|
||||
11|vigent2 | 2026-01-29 11:06:00.812 | INFO | app.main:dispatch:21 - START Request: POST https://vigent.hbyrkj.top/api/auth/logout
|
||||
11|vigent2 | 2026-01-29 11:06:00.812 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'content-length': '0', 'accept': '*/*', 'sec-fetch-site': 'same-origin', 'origin': 'https://vigent.hbyrkj.top', 'sec-fetch-mode': 'cors', 'user-agent': 'Mozilla/5.0 (iPhone; CPU iPhone OS 18_7 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/26.2 Mobile/15E148 Safari/604.1', 'referer': 'https://vigent.hbyrkj.top/', 'sec-fetch-dest': 'empty', 'accept-language': 'en-US,en;q=0.9', 'priority': 'u=3, i', 'accept-encoding': 'gzip, deflate, br', 'cookie': 'access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiN2JlNWIzNWYtMzQ0Ni00ZTIyLWEzMTktZTc5M2NlNDBmYTRiIiwiZXhwIjoxNzcwMTc4MTE0fQ.pk4sCAkd9hcN6fE5_8RXH42zfMl7YPSV5i1R9QeER4s; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1760758482696%2C%220199f562-16c1-7fcb-9bef-fdc33838b6a8%22%2C1760758470336%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_session=RudderEncrypt%3AU2FsdGVkX19ssBuVw9hBTDRVrnPaKCW6D20R7A4QpeumwXIRUkzHtaFASP40bWfE6KL05g4rq6VbZFQ9X4FVBZ2lbwW%2Faa32knjuye8aa1ejtZEGmyfXpfcryezEIy0gmYYjjT7lKB6HAupj9%2FCezQ%3D%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX1%2BpEG8yoPZBpac674EsBSuEU0HhlEZGMLIKNfviY6GGLzzbk6%2BbdfmzJ5s1nr16B0NNWzywMDwDD00Cktdf8N50BWw0Pp7Xuy2cOM6L15tjqobzRZyayXyVA1o%2B5kHPODaa3yg4cjWjee8OqG1qRaX4EwOXc0YzPZI%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX1%2BRY4oSsXW07D0HdYfXZhDpJS%2Fl%2F%2Bysns8Xand%2BMI7%2FJBIRw1RV%2FIJPzbTSpW8kmvwLCsUosyNPtsZbl3lGRDOM4YJIL%2BaFVjvjAWDo0WA89ezEeTVY9hzd9rwV3A6dbv5vJhrEdjolAkub50ItC47iV1fIGb%2FN3vI%3D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19r9Vr3ov%2BP30OgbarNSaCn6bebg11iU%2B8UV7b%2F116JurvSpJ77d%2FdZ62kjIP%2BMF3h3R9RathLKFQ%3D%3D; rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX19CE%2F3GmyTsZHCQhdFWdzYnJYPdvCMBFbM%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX18t2%2FxrnN2HPEqTssR572nq%2FgCim9EQN7E%3D'}
|
||||
11|vigent2 | 2026-01-29 11:06:00.815 | INFO | app.main:dispatch:26 - END Request: POST https://vigent.hbyrkj.top/api/auth/logout - Status: 200 - Duration: 0.00s
|
||||
11|vigent2 | 2026-01-29 11:06:03.694 | INFO | app.main:dispatch:21 - START Request: POST https://vigent.hbyrkj.top/api/auth/login
|
||||
11|vigent2 | 2026-01-29 11:06:03.695 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'content-length': '58', 'accept': '*/*', 'content-type': 'application/json', 'sec-fetch-site': 'same-origin', 'origin': 'https://vigent.hbyrkj.top', 'sec-fetch-mode': 'cors', 'user-agent': 'Mozilla/5.0 (iPhone; CPU iPhone OS 18_7 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/26.2 Mobile/15E148 Safari/604.1', 'referer': 'https://vigent.hbyrkj.top/login', 'sec-fetch-dest': 'empty', 'accept-language': 'en-US,en;q=0.9', 'priority': 'u=3, i', 'accept-encoding': 'gzip, deflate, br', 'cookie': 'ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1760758482696%2C%220199f562-16c1-7fcb-9bef-fdc33838b6a8%22%2C1760758470336%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_session=RudderEncrypt%3AU2FsdGVkX19ssBuVw9hBTDRVrnPaKCW6D20R7A4QpeumwXIRUkzHtaFASP40bWfE6KL05g4rq6VbZFQ9X4FVBZ2lbwW%2Faa32knjuye8aa1ejtZEGmyfXpfcryezEIy0gmYYjjT7lKB6HAupj9%2FCezQ%3D%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX1%2BpEG8yoPZBpac674EsBSuEU0HhlEZGMLIKNfviY6GGLzzbk6%2BbdfmzJ5s1nr16B0NNWzywMDwDD00Cktdf8N50BWw0Pp7Xuy2cOM6L15tjqobzRZyayXyVA1o%2B5kHPODaa3yg4cjWjee8OqG1qRaX4EwOXc0YzPZI%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX1%2BRY4oSsXW07D0HdYfXZhDpJS%2Fl%2F%2Bysns8Xand%2BMI7%2FJBIRw1RV%2FIJPzbTSpW8kmvwLCsUosyNPtsZbl3lGRDOM4YJIL%2BaFVjvjAWDo0WA89ezEeTVY9hzd9rwV3A6dbv5vJhrEdjolAkub50ItC47iV1fIGb%2FN3vI%3D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19r9Vr3ov%2BP30OgbarNSaCn6bebg11iU%2B8UV7b%2F116JurvSpJ77d%2FdZ62kjIP%2BMF3h3R9RathLKFQ%3D%3D; rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX19CE%2F3GmyTsZHCQhdFWdzYnJYPdvCMBFbM%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX18t2%2FxrnN2HPEqTssR572nq%2FgCim9EQN7E%3D'}
|
||||
11|vigent2 | 2026-01-29 11:06:04.185 | INFO | app.api.auth:login:157 - 用户登录: lamnickdavid@gmail.com
|
||||
11|vigent2 | 2026-01-29 11:06:04.185 | INFO | app.main:dispatch:26 - END Request: POST https://vigent.hbyrkj.top/api/auth/login - Status: 200 - Duration: 0.49s
|
||||
11|vigent2 | 2026-01-29 11:06:04.359 | INFO | app.main:dispatch:21 - START Request: GET https://vigent.hbyrkj.top/api/materials?t=1769655964287
|
||||
11|vigent2 | 2026-01-29 11:06:04.359 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'sec-fetch-dest': 'empty', 'user-agent': 'Mozilla/5.0 (iPhone; CPU iPhone OS 18_7 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/26.2 Mobile/15E148 Safari/604.1', 'accept': 'application/json, text/plain, */*', 'referer': 'https://vigent.hbyrkj.top/', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'accept-language': 'en-US,en;q=0.9', 'priority': 'u=3, i', 'accept-encoding': 'gzip, deflate, br', 'cookie': 'access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiMWE0NzczNTktZmMwZS00MjVhLTk3MGUtODc1ZTcyNjFjYWJiIiwiZXhwIjoxNzcwMjYwNzY0fQ.X-nGjaX_gwaJw995Zuw_fnj2oY_K-oM6tgwMDR4pDQk; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1760758482696%2C%220199f562-16c1-7fcb-9bef-fdc33838b6a8%22%2C1760758470336%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_session=RudderEncrypt%3AU2FsdGVkX19ssBuVw9hBTDRVrnPaKCW6D20R7A4QpeumwXIRUkzHtaFASP40bWfE6KL05g4rq6VbZFQ9X4FVBZ2lbwW%2Faa32knjuye8aa1ejtZEGmyfXpfcryezEIy0gmYYjjT7lKB6HAupj9%2FCezQ%3D%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX1%2BpEG8yoPZBpac674EsBSuEU0HhlEZGMLIKNfviY6GGLzzbk6%2BbdfmzJ5s1nr16B0NNWzywMDwDD00Cktdf8N50BWw0Pp7Xuy2cOM6L15tjqobzRZyayXyVA1o%2B5kHPODaa3yg4cjWjee8OqG1qRaX4EwOXc0YzPZI%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX1%2BRY4oSsXW07D0HdYfXZhDpJS%2Fl%2F%2Bysns8Xand%2BMI7%2FJBIRw1RV%2FIJPzbTSpW8kmvwLCsUosyNPtsZbl3lGRDOM4YJIL%2BaFVjvjAWDo0WA89ezEeTVY9hzd9rwV3A6dbv5vJhrEdjolAkub50ItC47iV1fIGb%2FN3vI%3D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19r9Vr3ov%2BP30OgbarNSaCn6bebg11iU%2B8UV7b%2F116JurvSpJ77d%2FdZ62kjIP%2BMF3h3R9RathLKFQ%3D%3D; rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX19CE%2F3GmyTsZHCQhdFWdzYnJYPdvCMBFbM%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX18t2%2FxrnN2HPEqTssR572nq%2FgCim9EQN7E%3D'}
|
||||
11|vigent2 | 2026-01-29 11:06:04.377 | INFO | app.main:dispatch:21 - START Request: GET https://vigent.hbyrkj.top/api/ref-audios
|
||||
11|vigent2 | 2026-01-29 11:06:04.377 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'sec-fetch-dest': 'empty', 'user-agent': 'Mozilla/5.0 (iPhone; CPU iPhone OS 18_7 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/26.2 Mobile/15E148 Safari/604.1', 'accept': 'application/json, text/plain, */*', 'referer': 'https://vigent.hbyrkj.top/', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'accept-language': 'en-US,en;q=0.9', 'priority': 'u=3, i', 'accept-encoding': 'gzip, deflate, br', 'cookie': 'access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiMWE0NzczNTktZmMwZS00MjVhLTk3MGUtODc1ZTcyNjFjYWJiIiwiZXhwIjoxNzcwMjYwNzY0fQ.X-nGjaX_gwaJw995Zuw_fnj2oY_K-oM6tgwMDR4pDQk; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1760758482696%2C%220199f562-16c1-7fcb-9bef-fdc33838b6a8%22%2C1760758470336%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_session=RudderEncrypt%3AU2FsdGVkX19ssBuVw9hBTDRVrnPaKCW6D20R7A4QpeumwXIRUkzHtaFASP40bWfE6KL05g4rq6VbZFQ9X4FVBZ2lbwW%2Faa32knjuye8aa1ejtZEGmyfXpfcryezEIy0gmYYjjT7lKB6HAupj9%2FCezQ%3D%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX1%2BpEG8yoPZBpac674EsBSuEU0HhlEZGMLIKNfviY6GGLzzbk6%2BbdfmzJ5s1nr16B0NNWzywMDwDD00Cktdf8N50BWw0Pp7Xuy2cOM6L15tjqobzRZyayXyVA1o%2B5kHPODaa3yg4cjWjee8OqG1qRaX4EwOXc0YzPZI%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX1%2BRY4oSsXW07D0HdYfXZhDpJS%2Fl%2F%2Bysns8Xand%2BMI7%2FJBIRw1RV%2FIJPzbTSpW8kmvwLCsUosyNPtsZbl3lGRDOM4YJIL%2BaFVjvjAWDo0WA89ezEeTVY9hzd9rwV3A6dbv5vJhrEdjolAkub50ItC47iV1fIGb%2FN3vI%3D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19r9Vr3ov%2BP30OgbarNSaCn6bebg11iU%2B8UV7b%2F116JurvSpJ77d%2FdZ62kjIP%2BMF3h3R9RathLKFQ%3D%3D; rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX19CE%2F3GmyTsZHCQhdFWdzYnJYPdvCMBFbM%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX18t2%2FxrnN2HPEqTssR572nq%2FgCim9EQN7E%3D'}
|
||||
11|vigent2 | 2026-01-29 11:06:04.392 | INFO | app.main:dispatch:21 - START Request: GET https://vigent.hbyrkj.top/api/videos/generated
|
||||
11|vigent2 | 2026-01-29 11:06:04.392 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'sec-fetch-dest': 'empty', 'user-agent': 'Mozilla/5.0 (iPhone; CPU iPhone OS 18_7 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/26.2 Mobile/15E148 Safari/604.1', 'accept': 'application/json, text/plain, */*', 'referer': 'https://vigent.hbyrkj.top/', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'accept-language': 'en-US,en;q=0.9', 'priority': 'u=3, i', 'accept-encoding': 'gzip, deflate, br', 'cookie': 'access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiMWE0NzczNTktZmMwZS00MjVhLTk3MGUtODc1ZTcyNjFjYWJiIiwiZXhwIjoxNzcwMjYwNzY0fQ.X-nGjaX_gwaJw995Zuw_fnj2oY_K-oM6tgwMDR4pDQk; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1760758482696%2C%220199f562-16c1-7fcb-9bef-fdc33838b6a8%22%2C1760758470336%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_session=RudderEncrypt%3AU2FsdGVkX19ssBuVw9hBTDRVrnPaKCW6D20R7A4QpeumwXIRUkzHtaFASP40bWfE6KL05g4rq6VbZFQ9X4FVBZ2lbwW%2Faa32knjuye8aa1ejtZEGmyfXpfcryezEIy0gmYYjjT7lKB6HAupj9%2FCezQ%3D%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX1%2BpEG8yoPZBpac674EsBSuEU0HhlEZGMLIKNfviY6GGLzzbk6%2BbdfmzJ5s1nr16B0NNWzywMDwDD00Cktdf8N50BWw0Pp7Xuy2cOM6L15tjqobzRZyayXyVA1o%2B5kHPODaa3yg4cjWjee8OqG1qRaX4EwOXc0YzPZI%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX1%2BRY4oSsXW07D0HdYfXZhDpJS%2Fl%2F%2Bysns8Xand%2BMI7%2FJBIRw1RV%2FIJPzbTSpW8kmvwLCsUosyNPtsZbl3lGRDOM4YJIL%2BaFVjvjAWDo0WA89ezEeTVY9hzd9rwV3A6dbv5vJhrEdjolAkub50ItC47iV1fIGb%2FN3vI%3D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19r9Vr3ov%2BP30OgbarNSaCn6bebg11iU%2B8UV7b%2F116JurvSpJ77d%2FdZ62kjIP%2BMF3h3R9RathLKFQ%3D%3D; rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX19CE%2F3GmyTsZHCQhdFWdzYnJYPdvCMBFbM%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX18t2%2FxrnN2HPEqTssR572nq%2FgCim9EQN7E%3D'}
|
||||
11|vigent2 | 2026-01-29 11:06:04.478 | INFO | app.main:dispatch:26 - END Request: GET https://vigent.hbyrkj.top/api/materials?t=1769655964287 - Status: 200 - Duration: 0.12s
|
||||
11|vigent2 | 2026-01-29 11:06:04.491 | INFO | app.main:dispatch:26 - END Request: GET https://vigent.hbyrkj.top/api/videos/generated - Status: 200 - Duration: 0.10s
|
||||
11|vigent2 | 2026-01-29 11:06:04.614 | INFO | app.main:dispatch:26 - END Request: GET https://vigent.hbyrkj.top/api/ref-audios - Status: 200 - Duration: 0.24s
|
||||
11|vigent2 | 2026-01-29 11:06:16.329 | INFO | app.main:dispatch:21 - START Request: GET https://vigent.hbyrkj.top/api/publish/accounts
|
||||
11|vigent2 | 2026-01-29 11:06:16.329 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'sec-ch-ua-platform': '"Windows"', 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36', 'accept': 'application/json, text/plain, */*', 'sec-ch-ua': '"Not(A:Brand";v="8", "Chromium";v="144", "Google Chrome";v="144"', 'sec-ch-ua-mobile': '?0', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'sec-fetch-dest': 'empty', 'referer': 'https://vigent.hbyrkj.top/publish', 'accept-encoding': 'gzip, deflate, br, zstd', 'accept-language': 'en-US,en;q=0.9,zh;q=0.8,zh-CN;q=0.7', 'priority': 'u=1, i', 'cookie': 'rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX1%2FRWtsIwIaguDp15em58SDrIwOvRJVXeK4%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX1%2BVZN6tniQmiO5L2fGVdcrYOkqG%2BRHkNFw%3D; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1762504332341%2C%22019a5d6c-ae21-7d75-8919-11e9621a135f%22%2C1762503994906%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19aTpWYcHFt3zhITFSLk1XAMr9V2jBWQwsuLDNLXh93pTlQ%2FUpvwmv6h%2Fl1bW4xH83hrkWPCTkSYg%3D%3D; rl_group_id=RudderEncrypt%3AU2FsdGVkX1%2BhywVlN3t3ypqwAMgqlh7ZRNLMKnFMhxA%3D; rl_group_trait=RudderEncrypt%3AU2FsdGVkX19jAKNOmR%2FnSngsWGVcmYB2qyvsbh3wQc0%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX180hU8QtHwPe3dPd1o7rEP7efRzFgCIvuIPRwbE3dWE0aEQCCMpQTN%2B7AGEtH6mjRvEuqcbfOdaX4TtJGL2jHbdcZUuA7Mpjf0uvsZ15LToi0zM1NWR7i6wE2z4vcYyFaBdB1uTJq3SxhX2WsqWe4YiT12vld0E%2F5w%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX19tgs8QN46oDejOzAvFyTx%2FIVRu7LAGDzh2eg%2FAhV8eY%2FyjW12D%2BtSOVq6NLF2lSZcY40rlQ%2B1fUc3DAe2euuWhIECOtlxtY5Hho11ZdHGB8lZ4CSLo%2BWmSIjzmkQ33RgkeNF9eYV4AV1PpdZZ%2Fjyl%2BVjCQtaNVV5c%3D; rl_session=RudderEncrypt%3AU2FsdGVkX19jm82pV3xfWHI%2FE6QaUo5xFQZuXuYh%2FkUCBhyJGY7TqzAK3YDkYppIpUipS7LtUSxm6iWAAp3vGhbB58MN7hrVa8imlwsuL7ceFNN%2BR1uTEvKTR8wWKaii2Xzs%2FYnhG3X8kmImIfYZgg%3D%3D; access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiNDk2N2QwNjMtNjhhZC00NzFkLThhMWQtOGE1MmJhODAxZjBjIiwiZXhwIjoxNzcwMTc4MzM4fQ.k9JOPKwqHrNTTNOsUQlMuA63rOETStl7uWXAIIDLGtA'}
|
||||
11|vigent2 | 2026-01-29 11:06:16.333 | INFO | app.main:dispatch:21 - START Request: GET https://vigent.hbyrkj.top/api/videos/generated
|
||||
11|vigent2 | 2026-01-29 11:06:16.333 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'sec-ch-ua-platform': '"Windows"', 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36', 'accept': 'application/json, text/plain, */*', 'sec-ch-ua': '"Not(A:Brand";v="8", "Chromium";v="144", "Google Chrome";v="144"', 'sec-ch-ua-mobile': '?0', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'sec-fetch-dest': 'empty', 'referer': 'https://vigent.hbyrkj.top/publish', 'accept-encoding': 'gzip, deflate, br, zstd', 'accept-language': 'en-US,en;q=0.9,zh;q=0.8,zh-CN;q=0.7', 'priority': 'u=1, i', 'cookie': 'rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX1%2FRWtsIwIaguDp15em58SDrIwOvRJVXeK4%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX1%2BVZN6tniQmiO5L2fGVdcrYOkqG%2BRHkNFw%3D; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1762504332341%2C%22019a5d6c-ae21-7d75-8919-11e9621a135f%22%2C1762503994906%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19aTpWYcHFt3zhITFSLk1XAMr9V2jBWQwsuLDNLXh93pTlQ%2FUpvwmv6h%2Fl1bW4xH83hrkWPCTkSYg%3D%3D; rl_group_id=RudderEncrypt%3AU2FsdGVkX1%2BhywVlN3t3ypqwAMgqlh7ZRNLMKnFMhxA%3D; rl_group_trait=RudderEncrypt%3AU2FsdGVkX19jAKNOmR%2FnSngsWGVcmYB2qyvsbh3wQc0%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX180hU8QtHwPe3dPd1o7rEP7efRzFgCIvuIPRwbE3dWE0aEQCCMpQTN%2B7AGEtH6mjRvEuqcbfOdaX4TtJGL2jHbdcZUuA7Mpjf0uvsZ15LToi0zM1NWR7i6wE2z4vcYyFaBdB1uTJq3SxhX2WsqWe4YiT12vld0E%2F5w%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX19tgs8QN46oDejOzAvFyTx%2FIVRu7LAGDzh2eg%2FAhV8eY%2FyjW12D%2BtSOVq6NLF2lSZcY40rlQ%2B1fUc3DAe2euuWhIECOtlxtY5Hho11ZdHGB8lZ4CSLo%2BWmSIjzmkQ33RgkeNF9eYV4AV1PpdZZ%2Fjyl%2BVjCQtaNVV5c%3D; rl_session=RudderEncrypt%3AU2FsdGVkX19jm82pV3xfWHI%2FE6QaUo5xFQZuXuYh%2FkUCBhyJGY7TqzAK3YDkYppIpUipS7LtUSxm6iWAAp3vGhbB58MN7hrVa8imlwsuL7ceFNN%2BR1uTEvKTR8wWKaii2Xzs%2FYnhG3X8kmImIfYZgg%3D%3D; access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiNDk2N2QwNjMtNjhhZC00NzFkLThhMWQtOGE1MmJhODAxZjBjIiwiZXhwIjoxNzcwMTc4MzM4fQ.k9JOPKwqHrNTTNOsUQlMuA63rOETStl7uWXAIIDLGtA'}
|
||||
11|vigent2 | 2026-01-29 11:06:16.342 | INFO | app.main:dispatch:26 - END Request: GET https://vigent.hbyrkj.top/api/publish/accounts - Status: 200 - Duration: 0.01s
|
||||
11|vigent2 | 2026-01-29 11:06:16.343 | INFO | app.main:dispatch:26 - END Request: GET https://vigent.hbyrkj.top/api/videos/generated - Status: 403 - Duration: 0.01s
|
||||
11|vigent2 | 2026-01-29 11:06:16.397 | INFO | app.main:dispatch:21 - START Request: POST https://vigent.hbyrkj.top/api/auth/logout
|
||||
11|vigent2 | 2026-01-29 11:06:16.397 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'content-length': '0', 'sec-ch-ua-platform': '"Windows"', 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36', 'sec-ch-ua': '"Not(A:Brand";v="8", "Chromium";v="144", "Google Chrome";v="144"', 'sec-ch-ua-mobile': '?0', 'accept': '*/*', 'origin': 'https://vigent.hbyrkj.top', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'sec-fetch-dest': 'empty', 'referer': 'https://vigent.hbyrkj.top/publish', 'accept-encoding': 'gzip, deflate, br, zstd', 'accept-language': 'en-US,en;q=0.9,zh;q=0.8,zh-CN;q=0.7', 'priority': 'u=1, i', 'cookie': 'rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX1%2FRWtsIwIaguDp15em58SDrIwOvRJVXeK4%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX1%2BVZN6tniQmiO5L2fGVdcrYOkqG%2BRHkNFw%3D; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1762504332341%2C%22019a5d6c-ae21-7d75-8919-11e9621a135f%22%2C1762503994906%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19aTpWYcHFt3zhITFSLk1XAMr9V2jBWQwsuLDNLXh93pTlQ%2FUpvwmv6h%2Fl1bW4xH83hrkWPCTkSYg%3D%3D; rl_group_id=RudderEncrypt%3AU2FsdGVkX1%2BhywVlN3t3ypqwAMgqlh7ZRNLMKnFMhxA%3D; rl_group_trait=RudderEncrypt%3AU2FsdGVkX19jAKNOmR%2FnSngsWGVcmYB2qyvsbh3wQc0%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX180hU8QtHwPe3dPd1o7rEP7efRzFgCIvuIPRwbE3dWE0aEQCCMpQTN%2B7AGEtH6mjRvEuqcbfOdaX4TtJGL2jHbdcZUuA7Mpjf0uvsZ15LToi0zM1NWR7i6wE2z4vcYyFaBdB1uTJq3SxhX2WsqWe4YiT12vld0E%2F5w%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX19tgs8QN46oDejOzAvFyTx%2FIVRu7LAGDzh2eg%2FAhV8eY%2FyjW12D%2BtSOVq6NLF2lSZcY40rlQ%2B1fUc3DAe2euuWhIECOtlxtY5Hho11ZdHGB8lZ4CSLo%2BWmSIjzmkQ33RgkeNF9eYV4AV1PpdZZ%2Fjyl%2BVjCQtaNVV5c%3D; rl_session=RudderEncrypt%3AU2FsdGVkX19jm82pV3xfWHI%2FE6QaUo5xFQZuXuYh%2FkUCBhyJGY7TqzAK3YDkYppIpUipS7LtUSxm6iWAAp3vGhbB58MN7hrVa8imlwsuL7ceFNN%2BR1uTEvKTR8wWKaii2Xzs%2FYnhG3X8kmImIfYZgg%3D%3D; access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiNDk2N2QwNjMtNjhhZC00NzFkLThhMWQtOGE1MmJhODAxZjBjIiwiZXhwIjoxNzcwMTc4MzM4fQ.k9JOPKwqHrNTTNOsUQlMuA63rOETStl7uWXAIIDLGtA'}
|
||||
11|vigent2 | 2026-01-29 11:06:16.398 | INFO | app.main:dispatch:26 - END Request: POST https://vigent.hbyrkj.top/api/auth/logout - Status: 200 - Duration: 0.00s
|
||||
11|vigent2 | 2026-01-29 11:06:28.685 | INFO | app.main:dispatch:21 - START Request: GET https://vigent.hbyrkj.top/api/videos/generated
|
||||
11|vigent2 | 2026-01-29 11:06:28.686 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'sec-fetch-dest': 'empty', 'user-agent': 'Mozilla/5.0 (iPhone; CPU iPhone OS 18_7 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/26.2 Mobile/15E148 Safari/604.1', 'accept': 'application/json, text/plain, */*', 'referer': 'https://vigent.hbyrkj.top/publish', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'accept-language': 'en-US,en;q=0.9', 'priority': 'u=3, i', 'accept-encoding': 'gzip, deflate, br', 'cookie': 'access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiMWE0NzczNTktZmMwZS00MjVhLTk3MGUtODc1ZTcyNjFjYWJiIiwiZXhwIjoxNzcwMjYwNzY0fQ.X-nGjaX_gwaJw995Zuw_fnj2oY_K-oM6tgwMDR4pDQk; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1760758482696%2C%220199f562-16c1-7fcb-9bef-fdc33838b6a8%22%2C1760758470336%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_session=RudderEncrypt%3AU2FsdGVkX19ssBuVw9hBTDRVrnPaKCW6D20R7A4QpeumwXIRUkzHtaFASP40bWfE6KL05g4rq6VbZFQ9X4FVBZ2lbwW%2Faa32knjuye8aa1ejtZEGmyfXpfcryezEIy0gmYYjjT7lKB6HAupj9%2FCezQ%3D%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX1%2BpEG8yoPZBpac674EsBSuEU0HhlEZGMLIKNfviY6GGLzzbk6%2BbdfmzJ5s1nr16B0NNWzywMDwDD00Cktdf8N50BWw0Pp7Xuy2cOM6L15tjqobzRZyayXyVA1o%2B5kHPODaa3yg4cjWjee8OqG1qRaX4EwOXc0YzPZI%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX1%2BRY4oSsXW07D0HdYfXZhDpJS%2Fl%2F%2Bysns8Xand%2BMI7%2FJBIRw1RV%2FIJPzbTSpW8kmvwLCsUosyNPtsZbl3lGRDOM4YJIL%2BaFVjvjAWDo0WA89ezEeTVY9hzd9rwV3A6dbv5vJhrEdjolAkub50ItC47iV1fIGb%2FN3vI%3D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19r9Vr3ov%2BP30OgbarNSaCn6bebg11iU%2B8UV7b%2F116JurvSpJ77d%2FdZ62kjIP%2BMF3h3R9RathLKFQ%3D%3D; rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX19CE%2F3GmyTsZHCQhdFWdzYnJYPdvCMBFbM%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX18t2%2FxrnN2HPEqTssR572nq%2FgCim9EQN7E%3D'}
|
||||
11|vigent2 | 2026-01-29 11:06:28.704 | INFO | app.main:dispatch:21 - START Request: GET https://vigent.hbyrkj.top/api/publish/accounts
|
||||
11|vigent2 | 2026-01-29 11:06:28.705 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'sec-fetch-dest': 'empty', 'user-agent': 'Mozilla/5.0 (iPhone; CPU iPhone OS 18_7 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/26.2 Mobile/15E148 Safari/604.1', 'accept': 'application/json, text/plain, */*', 'referer': 'https://vigent.hbyrkj.top/publish', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'accept-language': 'en-US,en;q=0.9', 'priority': 'u=3, i', 'accept-encoding': 'gzip, deflate, br', 'cookie': 'access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiMWE0NzczNTktZmMwZS00MjVhLTk3MGUtODc1ZTcyNjFjYWJiIiwiZXhwIjoxNzcwMjYwNzY0fQ.X-nGjaX_gwaJw995Zuw_fnj2oY_K-oM6tgwMDR4pDQk; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1760758482696%2C%220199f562-16c1-7fcb-9bef-fdc33838b6a8%22%2C1760758470336%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_session=RudderEncrypt%3AU2FsdGVkX19ssBuVw9hBTDRVrnPaKCW6D20R7A4QpeumwXIRUkzHtaFASP40bWfE6KL05g4rq6VbZFQ9X4FVBZ2lbwW%2Faa32knjuye8aa1ejtZEGmyfXpfcryezEIy0gmYYjjT7lKB6HAupj9%2FCezQ%3D%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX1%2BpEG8yoPZBpac674EsBSuEU0HhlEZGMLIKNfviY6GGLzzbk6%2BbdfmzJ5s1nr16B0NNWzywMDwDD00Cktdf8N50BWw0Pp7Xuy2cOM6L15tjqobzRZyayXyVA1o%2B5kHPODaa3yg4cjWjee8OqG1qRaX4EwOXc0YzPZI%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX1%2BRY4oSsXW07D0HdYfXZhDpJS%2Fl%2F%2Bysns8Xand%2BMI7%2FJBIRw1RV%2FIJPzbTSpW8kmvwLCsUosyNPtsZbl3lGRDOM4YJIL%2BaFVjvjAWDo0WA89ezEeTVY9hzd9rwV3A6dbv5vJhrEdjolAkub50ItC47iV1fIGb%2FN3vI%3D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19r9Vr3ov%2BP30OgbarNSaCn6bebg11iU%2B8UV7b%2F116JurvSpJ77d%2FdZ62kjIP%2BMF3h3R9RathLKFQ%3D%3D; rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX19CE%2F3GmyTsZHCQhdFWdzYnJYPdvCMBFbM%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX18t2%2FxrnN2HPEqTssR572nq%2FgCim9EQN7E%3D'}
|
||||
11|vigent2 | 2026-01-29 11:06:28.710 | INFO | app.main:dispatch:26 - END Request: GET https://vigent.hbyrkj.top/api/publish/accounts - Status: 200 - Duration: 0.01s
|
||||
11|vigent2 | 2026-01-29 11:06:28.745 | INFO | app.main:dispatch:26 - END Request: GET https://vigent.hbyrkj.top/api/videos/generated - Status: 200 - Duration: 0.06s
|
||||
11|vigent2 | 2026-01-29 11:06:51.021 | INFO | app.main:dispatch:21 - START Request: POST https://vigent.hbyrkj.top/api/auth/login
|
||||
11|vigent2 | 2026-01-29 11:06:51.021 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'content-length': '58', 'sec-ch-ua-platform': '"Windows"', 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36', 'sec-ch-ua': '"Not(A:Brand";v="8", "Chromium";v="144", "Google Chrome";v="144"', 'content-type': 'application/json', 'sec-ch-ua-mobile': '?0', 'accept': '*/*', 'origin': 'https://vigent.hbyrkj.top', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'sec-fetch-dest': 'empty', 'referer': 'https://vigent.hbyrkj.top/login', 'accept-encoding': 'gzip, deflate, br, zstd', 'accept-language': 'en-US,en;q=0.9,zh;q=0.8,zh-CN;q=0.7', 'priority': 'u=1, i', 'cookie': 'rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX1%2FRWtsIwIaguDp15em58SDrIwOvRJVXeK4%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX1%2BVZN6tniQmiO5L2fGVdcrYOkqG%2BRHkNFw%3D; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1762504332341%2C%22019a5d6c-ae21-7d75-8919-11e9621a135f%22%2C1762503994906%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19aTpWYcHFt3zhITFSLk1XAMr9V2jBWQwsuLDNLXh93pTlQ%2FUpvwmv6h%2Fl1bW4xH83hrkWPCTkSYg%3D%3D; rl_group_id=RudderEncrypt%3AU2FsdGVkX1%2BhywVlN3t3ypqwAMgqlh7ZRNLMKnFMhxA%3D; rl_group_trait=RudderEncrypt%3AU2FsdGVkX19jAKNOmR%2FnSngsWGVcmYB2qyvsbh3wQc0%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX180hU8QtHwPe3dPd1o7rEP7efRzFgCIvuIPRwbE3dWE0aEQCCMpQTN%2B7AGEtH6mjRvEuqcbfOdaX4TtJGL2jHbdcZUuA7Mpjf0uvsZ15LToi0zM1NWR7i6wE2z4vcYyFaBdB1uTJq3SxhX2WsqWe4YiT12vld0E%2F5w%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX19tgs8QN46oDejOzAvFyTx%2FIVRu7LAGDzh2eg%2FAhV8eY%2FyjW12D%2BtSOVq6NLF2lSZcY40rlQ%2B1fUc3DAe2euuWhIECOtlxtY5Hho11ZdHGB8lZ4CSLo%2BWmSIjzmkQ33RgkeNF9eYV4AV1PpdZZ%2Fjyl%2BVjCQtaNVV5c%3D; rl_session=RudderEncrypt%3AU2FsdGVkX19jm82pV3xfWHI%2FE6QaUo5xFQZuXuYh%2FkUCBhyJGY7TqzAK3YDkYppIpUipS7LtUSxm6iWAAp3vGhbB58MN7hrVa8imlwsuL7ceFNN%2BR1uTEvKTR8wWKaii2Xzs%2FYnhG3X8kmImIfYZgg%3D%3D'}
|
||||
11|vigent2 | 2026-01-29 11:06:51.624 | INFO | app.api.auth:login:157 - 用户登录: lamnickdavid@gmail.com
|
||||
11|vigent2 | 2026-01-29 11:06:51.625 | INFO | app.main:dispatch:26 - END Request: POST https://vigent.hbyrkj.top/api/auth/login - Status: 200 - Duration: 0.60s
|
||||
11|vigent2 | 2026-01-29 11:06:51.806 | INFO | app.main:dispatch:21 - START Request: GET https://vigent.hbyrkj.top/api/materials?t=1769656015718
|
||||
11|vigent2 | 2026-01-29 11:06:51.806 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'sec-ch-ua-platform': '"Windows"', 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36', 'accept': 'application/json, text/plain, */*', 'sec-ch-ua': '"Not(A:Brand";v="8", "Chromium";v="144", "Google Chrome";v="144"', 'sec-ch-ua-mobile': '?0', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'sec-fetch-dest': 'empty', 'referer': 'https://vigent.hbyrkj.top/', 'accept-encoding': 'gzip, deflate, br, zstd', 'accept-language': 'en-US,en;q=0.9,zh;q=0.8,zh-CN;q=0.7', 'priority': 'u=1, i', 'cookie': 'rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX1%2FRWtsIwIaguDp15em58SDrIwOvRJVXeK4%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX1%2BVZN6tniQmiO5L2fGVdcrYOkqG%2BRHkNFw%3D; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1762504332341%2C%22019a5d6c-ae21-7d75-8919-11e9621a135f%22%2C1762503994906%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19aTpWYcHFt3zhITFSLk1XAMr9V2jBWQwsuLDNLXh93pTlQ%2FUpvwmv6h%2Fl1bW4xH83hrkWPCTkSYg%3D%3D; rl_group_id=RudderEncrypt%3AU2FsdGVkX1%2BhywVlN3t3ypqwAMgqlh7ZRNLMKnFMhxA%3D; rl_group_trait=RudderEncrypt%3AU2FsdGVkX19jAKNOmR%2FnSngsWGVcmYB2qyvsbh3wQc0%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX180hU8QtHwPe3dPd1o7rEP7efRzFgCIvuIPRwbE3dWE0aEQCCMpQTN%2B7AGEtH6mjRvEuqcbfOdaX4TtJGL2jHbdcZUuA7Mpjf0uvsZ15LToi0zM1NWR7i6wE2z4vcYyFaBdB1uTJq3SxhX2WsqWe4YiT12vld0E%2F5w%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX19tgs8QN46oDejOzAvFyTx%2FIVRu7LAGDzh2eg%2FAhV8eY%2FyjW12D%2BtSOVq6NLF2lSZcY40rlQ%2B1fUc3DAe2euuWhIECOtlxtY5Hho11ZdHGB8lZ4CSLo%2BWmSIjzmkQ33RgkeNF9eYV4AV1PpdZZ%2Fjyl%2BVjCQtaNVV5c%3D; rl_session=RudderEncrypt%3AU2FsdGVkX19jm82pV3xfWHI%2FE6QaUo5xFQZuXuYh%2FkUCBhyJGY7TqzAK3YDkYppIpUipS7LtUSxm6iWAAp3vGhbB58MN7hrVa8imlwsuL7ceFNN%2BR1uTEvKTR8wWKaii2Xzs%2FYnhG3X8kmImIfYZgg%3D%3D; access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiMjFjMmEwMmItYjY5Ny00MGVjLWIwMmItMjI1YzJjOWUyZGMzIiwiZXhwIjoxNzcwMjYwODExfQ.MpOjnbwllAzarfaoTk1SzYVMqAEXBEMyRt5UyiJ90Qw'}
|
||||
11|vigent2 | 2026-01-29 11:06:51.820 | INFO | app.main:dispatch:21 - START Request: GET https://vigent.hbyrkj.top/api/videos/generated
|
||||
11|vigent2 | 2026-01-29 11:06:51.821 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'sec-ch-ua-platform': '"Windows"', 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36', 'accept': 'application/json, text/plain, */*', 'sec-ch-ua': '"Not(A:Brand";v="8", "Chromium";v="144", "Google Chrome";v="144"', 'sec-ch-ua-mobile': '?0', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'sec-fetch-dest': 'empty', 'referer': 'https://vigent.hbyrkj.top/', 'accept-encoding': 'gzip, deflate, br, zstd', 'accept-language': 'en-US,en;q=0.9,zh;q=0.8,zh-CN;q=0.7', 'priority': 'u=1, i', 'cookie': 'rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX1%2FRWtsIwIaguDp15em58SDrIwOvRJVXeK4%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX1%2BVZN6tniQmiO5L2fGVdcrYOkqG%2BRHkNFw%3D; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1762504332341%2C%22019a5d6c-ae21-7d75-8919-11e9621a135f%22%2C1762503994906%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19aTpWYcHFt3zhITFSLk1XAMr9V2jBWQwsuLDNLXh93pTlQ%2FUpvwmv6h%2Fl1bW4xH83hrkWPCTkSYg%3D%3D; rl_group_id=RudderEncrypt%3AU2FsdGVkX1%2BhywVlN3t3ypqwAMgqlh7ZRNLMKnFMhxA%3D; rl_group_trait=RudderEncrypt%3AU2FsdGVkX19jAKNOmR%2FnSngsWGVcmYB2qyvsbh3wQc0%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX180hU8QtHwPe3dPd1o7rEP7efRzFgCIvuIPRwbE3dWE0aEQCCMpQTN%2B7AGEtH6mjRvEuqcbfOdaX4TtJGL2jHbdcZUuA7Mpjf0uvsZ15LToi0zM1NWR7i6wE2z4vcYyFaBdB1uTJq3SxhX2WsqWe4YiT12vld0E%2F5w%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX19tgs8QN46oDejOzAvFyTx%2FIVRu7LAGDzh2eg%2FAhV8eY%2FyjW12D%2BtSOVq6NLF2lSZcY40rlQ%2B1fUc3DAe2euuWhIECOtlxtY5Hho11ZdHGB8lZ4CSLo%2BWmSIjzmkQ33RgkeNF9eYV4AV1PpdZZ%2Fjyl%2BVjCQtaNVV5c%3D; rl_session=RudderEncrypt%3AU2FsdGVkX19jm82pV3xfWHI%2FE6QaUo5xFQZuXuYh%2FkUCBhyJGY7TqzAK3YDkYppIpUipS7LtUSxm6iWAAp3vGhbB58MN7hrVa8imlwsuL7ceFNN%2BR1uTEvKTR8wWKaii2Xzs%2FYnhG3X8kmImIfYZgg%3D%3D; access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiMjFjMmEwMmItYjY5Ny00MGVjLWIwMmItMjI1YzJjOWUyZGMzIiwiZXhwIjoxNzcwMjYwODExfQ.MpOjnbwllAzarfaoTk1SzYVMqAEXBEMyRt5UyiJ90Qw'}
|
||||
11|vigent2 | 2026-01-29 11:06:51.834 | INFO | app.main:dispatch:21 - START Request: GET https://vigent.hbyrkj.top/api/ref-audios
|
||||
11|vigent2 | 2026-01-29 11:06:51.834 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'sec-ch-ua-platform': '"Windows"', 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36', 'accept': 'application/json, text/plain, */*', 'sec-ch-ua': '"Not(A:Brand";v="8", "Chromium";v="144", "Google Chrome";v="144"', 'sec-ch-ua-mobile': '?0', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'sec-fetch-dest': 'empty', 'referer': 'https://vigent.hbyrkj.top/', 'accept-encoding': 'gzip, deflate, br, zstd', 'accept-language': 'en-US,en;q=0.9,zh;q=0.8,zh-CN;q=0.7', 'priority': 'u=1, i', 'cookie': 'rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX1%2FRWtsIwIaguDp15em58SDrIwOvRJVXeK4%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX1%2BVZN6tniQmiO5L2fGVdcrYOkqG%2BRHkNFw%3D; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1762504332341%2C%22019a5d6c-ae21-7d75-8919-11e9621a135f%22%2C1762503994906%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19aTpWYcHFt3zhITFSLk1XAMr9V2jBWQwsuLDNLXh93pTlQ%2FUpvwmv6h%2Fl1bW4xH83hrkWPCTkSYg%3D%3D; rl_group_id=RudderEncrypt%3AU2FsdGVkX1%2BhywVlN3t3ypqwAMgqlh7ZRNLMKnFMhxA%3D; rl_group_trait=RudderEncrypt%3AU2FsdGVkX19jAKNOmR%2FnSngsWGVcmYB2qyvsbh3wQc0%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX180hU8QtHwPe3dPd1o7rEP7efRzFgCIvuIPRwbE3dWE0aEQCCMpQTN%2B7AGEtH6mjRvEuqcbfOdaX4TtJGL2jHbdcZUuA7Mpjf0uvsZ15LToi0zM1NWR7i6wE2z4vcYyFaBdB1uTJq3SxhX2WsqWe4YiT12vld0E%2F5w%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX19tgs8QN46oDejOzAvFyTx%2FIVRu7LAGDzh2eg%2FAhV8eY%2FyjW12D%2BtSOVq6NLF2lSZcY40rlQ%2B1fUc3DAe2euuWhIECOtlxtY5Hho11ZdHGB8lZ4CSLo%2BWmSIjzmkQ33RgkeNF9eYV4AV1PpdZZ%2Fjyl%2BVjCQtaNVV5c%3D; rl_session=RudderEncrypt%3AU2FsdGVkX19jm82pV3xfWHI%2FE6QaUo5xFQZuXuYh%2FkUCBhyJGY7TqzAK3YDkYppIpUipS7LtUSxm6iWAAp3vGhbB58MN7hrVa8imlwsuL7ceFNN%2BR1uTEvKTR8wWKaii2Xzs%2FYnhG3X8kmImIfYZgg%3D%3D; access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiMjFjMmEwMmItYjY5Ny00MGVjLWIwMmItMjI1YzJjOWUyZGMzIiwiZXhwIjoxNzcwMjYwODExfQ.MpOjnbwllAzarfaoTk1SzYVMqAEXBEMyRt5UyiJ90Qw'}
|
||||
11|vigent2 | 2026-01-29 11:06:51.865 | INFO | app.main:dispatch:26 - END Request: GET https://vigent.hbyrkj.top/api/materials?t=1769656015718 - Status: 200 - Duration: 0.06s
|
||||
11|vigent2 | 2026-01-29 11:06:51.941 | INFO | app.main:dispatch:26 - END Request: GET https://vigent.hbyrkj.top/api/videos/generated - Status: 200 - Duration: 0.12s
|
||||
11|vigent2 | 2026-01-29 11:06:52.076 | INFO | app.main:dispatch:26 - END Request: GET https://vigent.hbyrkj.top/api/ref-audios - Status: 200 - Duration: 0.24s
|
||||
11|vigent2 | 2026-01-29 11:10:29.354 | INFO | app.main:dispatch:21 - START Request: GET https://vigent.hbyrkj.top/api/materials?t=1769656233290
|
||||
11|vigent2 | 2026-01-29 11:10:29.354 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'sec-ch-ua-platform': '"Windows"', 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36', 'accept': 'application/json, text/plain, */*', 'sec-ch-ua': '"Not(A:Brand";v="8", "Chromium";v="144", "Google Chrome";v="144"', 'sec-ch-ua-mobile': '?0', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'sec-fetch-dest': 'empty', 'referer': 'https://vigent.hbyrkj.top/', 'accept-encoding': 'gzip, deflate, br, zstd', 'accept-language': 'en-US,en;q=0.9,zh;q=0.8,zh-CN;q=0.7', 'priority': 'u=1, i', 'cookie': 'rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX1%2FRWtsIwIaguDp15em58SDrIwOvRJVXeK4%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX1%2BVZN6tniQmiO5L2fGVdcrYOkqG%2BRHkNFw%3D; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1762504332341%2C%22019a5d6c-ae21-7d75-8919-11e9621a135f%22%2C1762503994906%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19aTpWYcHFt3zhITFSLk1XAMr9V2jBWQwsuLDNLXh93pTlQ%2FUpvwmv6h%2Fl1bW4xH83hrkWPCTkSYg%3D%3D; rl_group_id=RudderEncrypt%3AU2FsdGVkX1%2BhywVlN3t3ypqwAMgqlh7ZRNLMKnFMhxA%3D; rl_group_trait=RudderEncrypt%3AU2FsdGVkX19jAKNOmR%2FnSngsWGVcmYB2qyvsbh3wQc0%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX180hU8QtHwPe3dPd1o7rEP7efRzFgCIvuIPRwbE3dWE0aEQCCMpQTN%2B7AGEtH6mjRvEuqcbfOdaX4TtJGL2jHbdcZUuA7Mpjf0uvsZ15LToi0zM1NWR7i6wE2z4vcYyFaBdB1uTJq3SxhX2WsqWe4YiT12vld0E%2F5w%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX19tgs8QN46oDejOzAvFyTx%2FIVRu7LAGDzh2eg%2FAhV8eY%2FyjW12D%2BtSOVq6NLF2lSZcY40rlQ%2B1fUc3DAe2euuWhIECOtlxtY5Hho11ZdHGB8lZ4CSLo%2BWmSIjzmkQ33RgkeNF9eYV4AV1PpdZZ%2Fjyl%2BVjCQtaNVV5c%3D; rl_session=RudderEncrypt%3AU2FsdGVkX19jm82pV3xfWHI%2FE6QaUo5xFQZuXuYh%2FkUCBhyJGY7TqzAK3YDkYppIpUipS7LtUSxm6iWAAp3vGhbB58MN7hrVa8imlwsuL7ceFNN%2BR1uTEvKTR8wWKaii2Xzs%2FYnhG3X8kmImIfYZgg%3D%3D; access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiMjFjMmEwMmItYjY5Ny00MGVjLWIwMmItMjI1YzJjOWUyZGMzIiwiZXhwIjoxNzcwMjYwODExfQ.MpOjnbwllAzarfaoTk1SzYVMqAEXBEMyRt5UyiJ90Qw'}
|
||||
11|vigent2 | 2026-01-29 11:10:29.405 | INFO | app.main:dispatch:21 - START Request: GET https://vigent.hbyrkj.top/api/ref-audios
|
||||
11|vigent2 | 2026-01-29 11:10:29.406 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'sec-ch-ua-platform': '"Windows"', 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36', 'accept': 'application/json, text/plain, */*', 'sec-ch-ua': '"Not(A:Brand";v="8", "Chromium";v="144", "Google Chrome";v="144"', 'sec-ch-ua-mobile': '?0', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'sec-fetch-dest': 'empty', 'referer': 'https://vigent.hbyrkj.top/', 'accept-encoding': 'gzip, deflate, br, zstd', 'accept-language': 'en-US,en;q=0.9,zh;q=0.8,zh-CN;q=0.7', 'priority': 'u=1, i', 'cookie': 'rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX1%2FRWtsIwIaguDp15em58SDrIwOvRJVXeK4%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX1%2BVZN6tniQmiO5L2fGVdcrYOkqG%2BRHkNFw%3D; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1762504332341%2C%22019a5d6c-ae21-7d75-8919-11e9621a135f%22%2C1762503994906%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19aTpWYcHFt3zhITFSLk1XAMr9V2jBWQwsuLDNLXh93pTlQ%2FUpvwmv6h%2Fl1bW4xH83hrkWPCTkSYg%3D%3D; rl_group_id=RudderEncrypt%3AU2FsdGVkX1%2BhywVlN3t3ypqwAMgqlh7ZRNLMKnFMhxA%3D; rl_group_trait=RudderEncrypt%3AU2FsdGVkX19jAKNOmR%2FnSngsWGVcmYB2qyvsbh3wQc0%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX180hU8QtHwPe3dPd1o7rEP7efRzFgCIvuIPRwbE3dWE0aEQCCMpQTN%2B7AGEtH6mjRvEuqcbfOdaX4TtJGL2jHbdcZUuA7Mpjf0uvsZ15LToi0zM1NWR7i6wE2z4vcYyFaBdB1uTJq3SxhX2WsqWe4YiT12vld0E%2F5w%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX19tgs8QN46oDejOzAvFyTx%2FIVRu7LAGDzh2eg%2FAhV8eY%2FyjW12D%2BtSOVq6NLF2lSZcY40rlQ%2B1fUc3DAe2euuWhIECOtlxtY5Hho11ZdHGB8lZ4CSLo%2BWmSIjzmkQ33RgkeNF9eYV4AV1PpdZZ%2Fjyl%2BVjCQtaNVV5c%3D; rl_session=RudderEncrypt%3AU2FsdGVkX19jm82pV3xfWHI%2FE6QaUo5xFQZuXuYh%2FkUCBhyJGY7TqzAK3YDkYppIpUipS7LtUSxm6iWAAp3vGhbB58MN7hrVa8imlwsuL7ceFNN%2BR1uTEvKTR8wWKaii2Xzs%2FYnhG3X8kmImIfYZgg%3D%3D; access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiMjFjMmEwMmItYjY5Ny00MGVjLWIwMmItMjI1YzJjOWUyZGMzIiwiZXhwIjoxNzcwMjYwODExfQ.MpOjnbwllAzarfaoTk1SzYVMqAEXBEMyRt5UyiJ90Qw'}
|
||||
11|vigent2 | 2026-01-29 11:10:29.423 | INFO | app.main:dispatch:21 - START Request: GET https://vigent.hbyrkj.top/api/videos/generated
|
||||
11|vigent2 | 2026-01-29 11:10:29.423 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'sec-ch-ua-platform': '"Windows"', 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36', 'accept': 'application/json, text/plain, */*', 'sec-ch-ua': '"Not(A:Brand";v="8", "Chromium";v="144", "Google Chrome";v="144"', 'sec-ch-ua-mobile': '?0', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'sec-fetch-dest': 'empty', 'referer': 'https://vigent.hbyrkj.top/', 'accept-encoding': 'gzip, deflate, br, zstd', 'accept-language': 'en-US,en;q=0.9,zh;q=0.8,zh-CN;q=0.7', 'priority': 'u=1, i', 'cookie': 'rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX1%2FRWtsIwIaguDp15em58SDrIwOvRJVXeK4%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX1%2BVZN6tniQmiO5L2fGVdcrYOkqG%2BRHkNFw%3D; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1762504332341%2C%22019a5d6c-ae21-7d75-8919-11e9621a135f%22%2C1762503994906%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19aTpWYcHFt3zhITFSLk1XAMr9V2jBWQwsuLDNLXh93pTlQ%2FUpvwmv6h%2Fl1bW4xH83hrkWPCTkSYg%3D%3D; rl_group_id=RudderEncrypt%3AU2FsdGVkX1%2BhywVlN3t3ypqwAMgqlh7ZRNLMKnFMhxA%3D; rl_group_trait=RudderEncrypt%3AU2FsdGVkX19jAKNOmR%2FnSngsWGVcmYB2qyvsbh3wQc0%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX180hU8QtHwPe3dPd1o7rEP7efRzFgCIvuIPRwbE3dWE0aEQCCMpQTN%2B7AGEtH6mjRvEuqcbfOdaX4TtJGL2jHbdcZUuA7Mpjf0uvsZ15LToi0zM1NWR7i6wE2z4vcYyFaBdB1uTJq3SxhX2WsqWe4YiT12vld0E%2F5w%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX19tgs8QN46oDejOzAvFyTx%2FIVRu7LAGDzh2eg%2FAhV8eY%2FyjW12D%2BtSOVq6NLF2lSZcY40rlQ%2B1fUc3DAe2euuWhIECOtlxtY5Hho11ZdHGB8lZ4CSLo%2BWmSIjzmkQ33RgkeNF9eYV4AV1PpdZZ%2Fjyl%2BVjCQtaNVV5c%3D; rl_session=RudderEncrypt%3AU2FsdGVkX19jm82pV3xfWHI%2FE6QaUo5xFQZuXuYh%2FkUCBhyJGY7TqzAK3YDkYppIpUipS7LtUSxm6iWAAp3vGhbB58MN7hrVa8imlwsuL7ceFNN%2BR1uTEvKTR8wWKaii2Xzs%2FYnhG3X8kmImIfYZgg%3D%3D; access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiMjFjMmEwMmItYjY5Ny00MGVjLWIwMmItMjI1YzJjOWUyZGMzIiwiZXhwIjoxNzcwMjYwODExfQ.MpOjnbwllAzarfaoTk1SzYVMqAEXBEMyRt5UyiJ90Qw'}
|
||||
11|vigent2 | 2026-01-29 11:10:29.474 | INFO | app.main:dispatch:26 - END Request: GET https://vigent.hbyrkj.top/api/materials?t=1769656233290 - Status: 200 - Duration: 0.12s
|
||||
11|vigent2 | 2026-01-29 11:10:29.535 | INFO | app.main:dispatch:26 - END Request: GET https://vigent.hbyrkj.top/api/videos/generated - Status: 200 - Duration: 0.11s
|
||||
11|vigent2 | 2026-01-29 11:10:29.653 | INFO | app.main:dispatch:26 - END Request: GET https://vigent.hbyrkj.top/api/ref-audios - Status: 200 - Duration: 0.25s
|
||||
11|vigent2 | 2026-01-29 11:11:20.032 | INFO | app.main:dispatch:21 - START Request: POST https://vigent.hbyrkj.top/api/ref-audios
|
||||
11|vigent2 | 2026-01-29 11:11:20.032 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'content-length': '204514', 'sec-ch-ua-platform': '"Windows"', 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36', 'accept': 'application/json, text/plain, */*', 'sec-ch-ua': '"Not(A:Brand";v="8", "Chromium";v="144", "Google Chrome";v="144"', 'content-type': 'multipart/form-data; boundary=----WebKitFormBoundaryBGjf99CuOGlQdB7a', 'sec-ch-ua-mobile': '?0', 'origin': 'https://vigent.hbyrkj.top', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'sec-fetch-dest': 'empty', 'referer': 'https://vigent.hbyrkj.top/', 'accept-encoding': 'gzip, deflate, br, zstd', 'accept-language': 'en-US,en;q=0.9,zh;q=0.8,zh-CN;q=0.7', 'priority': 'u=1, i', 'cookie': 'rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX1%2FRWtsIwIaguDp15em58SDrIwOvRJVXeK4%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX1%2BVZN6tniQmiO5L2fGVdcrYOkqG%2BRHkNFw%3D; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1762504332341%2C%22019a5d6c-ae21-7d75-8919-11e9621a135f%22%2C1762503994906%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19aTpWYcHFt3zhITFSLk1XAMr9V2jBWQwsuLDNLXh93pTlQ%2FUpvwmv6h%2Fl1bW4xH83hrkWPCTkSYg%3D%3D; rl_group_id=RudderEncrypt%3AU2FsdGVkX1%2BhywVlN3t3ypqwAMgqlh7ZRNLMKnFMhxA%3D; rl_group_trait=RudderEncrypt%3AU2FsdGVkX19jAKNOmR%2FnSngsWGVcmYB2qyvsbh3wQc0%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX180hU8QtHwPe3dPd1o7rEP7efRzFgCIvuIPRwbE3dWE0aEQCCMpQTN%2B7AGEtH6mjRvEuqcbfOdaX4TtJGL2jHbdcZUuA7Mpjf0uvsZ15LToi0zM1NWR7i6wE2z4vcYyFaBdB1uTJq3SxhX2WsqWe4YiT12vld0E%2F5w%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX19tgs8QN46oDejOzAvFyTx%2FIVRu7LAGDzh2eg%2FAhV8eY%2FyjW12D%2BtSOVq6NLF2lSZcY40rlQ%2B1fUc3DAe2euuWhIECOtlxtY5Hho11ZdHGB8lZ4CSLo%2BWmSIjzmkQ33RgkeNF9eYV4AV1PpdZZ%2Fjyl%2BVjCQtaNVV5c%3D; rl_session=RudderEncrypt%3AU2FsdGVkX19jm82pV3xfWHI%2FE6QaUo5xFQZuXuYh%2FkUCBhyJGY7TqzAK3YDkYppIpUipS7LtUSxm6iWAAp3vGhbB58MN7hrVa8imlwsuL7ceFNN%2BR1uTEvKTR8wWKaii2Xzs%2FYnhG3X8kmImIfYZgg%3D%3D; access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiMjFjMmEwMmItYjY5Ny00MGVjLWIwMmItMjI1YzJjOWUyZGMzIiwiZXhwIjoxNzcwMjYwODExfQ.MpOjnbwllAzarfaoTk1SzYVMqAEXBEMyRt5UyiJ90Qw'}
|
||||
11|vigent2 | 2026-01-29 11:11:20.536 | INFO | app.services.storage:upload_file:97 - Storage upload success: 94cd91e3-7d89-45e8-9d85-e8ba0660d74c/1769656280_myvoice.wav
|
||||
11|vigent2 | 2026-01-29 11:11:20.576 | INFO | app.services.storage:upload_file:97 - Storage upload success: 94cd91e3-7d89-45e8-9d85-e8ba0660d74c/1769656280_myvoice.json
|
||||
11|vigent2 | 2026-01-29 11:11:20.584 | INFO | app.main:dispatch:26 - END Request: POST https://vigent.hbyrkj.top/api/ref-audios - Status: 200 - Duration: 0.55s
|
||||
11|vigent2 | 2026-01-29 11:11:20.638 | INFO | app.main:dispatch:21 - START Request: GET https://vigent.hbyrkj.top/api/ref-audios
|
||||
11|vigent2 | 2026-01-29 11:11:20.638 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'sec-ch-ua-platform': '"Windows"', 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36', 'accept': 'application/json, text/plain, */*', 'sec-ch-ua': '"Not(A:Brand";v="8", "Chromium";v="144", "Google Chrome";v="144"', 'sec-ch-ua-mobile': '?0', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'sec-fetch-dest': 'empty', 'referer': 'https://vigent.hbyrkj.top/', 'accept-encoding': 'gzip, deflate, br, zstd', 'accept-language': 'en-US,en;q=0.9,zh;q=0.8,zh-CN;q=0.7', 'priority': 'u=1, i', 'cookie': 'rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX1%2FRWtsIwIaguDp15em58SDrIwOvRJVXeK4%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX1%2BVZN6tniQmiO5L2fGVdcrYOkqG%2BRHkNFw%3D; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1762504332341%2C%22019a5d6c-ae21-7d75-8919-11e9621a135f%22%2C1762503994906%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19aTpWYcHFt3zhITFSLk1XAMr9V2jBWQwsuLDNLXh93pTlQ%2FUpvwmv6h%2Fl1bW4xH83hrkWPCTkSYg%3D%3D; rl_group_id=RudderEncrypt%3AU2FsdGVkX1%2BhywVlN3t3ypqwAMgqlh7ZRNLMKnFMhxA%3D; rl_group_trait=RudderEncrypt%3AU2FsdGVkX19jAKNOmR%2FnSngsWGVcmYB2qyvsbh3wQc0%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX180hU8QtHwPe3dPd1o7rEP7efRzFgCIvuIPRwbE3dWE0aEQCCMpQTN%2B7AGEtH6mjRvEuqcbfOdaX4TtJGL2jHbdcZUuA7Mpjf0uvsZ15LToi0zM1NWR7i6wE2z4vcYyFaBdB1uTJq3SxhX2WsqWe4YiT12vld0E%2F5w%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX19tgs8QN46oDejOzAvFyTx%2FIVRu7LAGDzh2eg%2FAhV8eY%2FyjW12D%2BtSOVq6NLF2lSZcY40rlQ%2B1fUc3DAe2euuWhIECOtlxtY5Hho11ZdHGB8lZ4CSLo%2BWmSIjzmkQ33RgkeNF9eYV4AV1PpdZZ%2Fjyl%2BVjCQtaNVV5c%3D; rl_session=RudderEncrypt%3AU2FsdGVkX19jm82pV3xfWHI%2FE6QaUo5xFQZuXuYh%2FkUCBhyJGY7TqzAK3YDkYppIpUipS7LtUSxm6iWAAp3vGhbB58MN7hrVa8imlwsuL7ceFNN%2BR1uTEvKTR8wWKaii2Xzs%2FYnhG3X8kmImIfYZgg%3D%3D; access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiMjFjMmEwMmItYjY5Ny00MGVjLWIwMmItMjI1YzJjOWUyZGMzIiwiZXhwIjoxNzcwMjYwODExfQ.MpOjnbwllAzarfaoTk1SzYVMqAEXBEMyRt5UyiJ90Qw'}
|
||||
11|vigent2 | 2026-01-29 11:11:21.086 | INFO | app.main:dispatch:26 - END Request: GET https://vigent.hbyrkj.top/api/ref-audios - Status: 200 - Duration: 0.45s
|
||||
11|vigent2 | 2026-01-29 11:22:58.683 | INFO | app.main:dispatch:21 - START Request: GET https://vigent.hbyrkj.top/api/videos/generated
|
||||
11|vigent2 | 2026-01-29 11:22:58.684 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'sec-ch-ua-platform': '"Windows"', 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36', 'accept': 'application/json, text/plain, */*', 'sec-ch-ua': '"Not(A:Brand";v="8", "Chromium";v="144", "Google Chrome";v="144"', 'sec-ch-ua-mobile': '?0', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'sec-fetch-dest': 'empty', 'referer': 'https://vigent.hbyrkj.top/publish', 'accept-encoding': 'gzip, deflate, br, zstd', 'accept-language': 'en-US,en;q=0.9,zh;q=0.8,zh-CN;q=0.7', 'priority': 'u=1, i', 'cookie': 'rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX1%2FRWtsIwIaguDp15em58SDrIwOvRJVXeK4%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX1%2BVZN6tniQmiO5L2fGVdcrYOkqG%2BRHkNFw%3D; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1762504332341%2C%22019a5d6c-ae21-7d75-8919-11e9621a135f%22%2C1762503994906%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19aTpWYcHFt3zhITFSLk1XAMr9V2jBWQwsuLDNLXh93pTlQ%2FUpvwmv6h%2Fl1bW4xH83hrkWPCTkSYg%3D%3D; rl_group_id=RudderEncrypt%3AU2FsdGVkX1%2BhywVlN3t3ypqwAMgqlh7ZRNLMKnFMhxA%3D; rl_group_trait=RudderEncrypt%3AU2FsdGVkX19jAKNOmR%2FnSngsWGVcmYB2qyvsbh3wQc0%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX180hU8QtHwPe3dPd1o7rEP7efRzFgCIvuIPRwbE3dWE0aEQCCMpQTN%2B7AGEtH6mjRvEuqcbfOdaX4TtJGL2jHbdcZUuA7Mpjf0uvsZ15LToi0zM1NWR7i6wE2z4vcYyFaBdB1uTJq3SxhX2WsqWe4YiT12vld0E%2F5w%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX19tgs8QN46oDejOzAvFyTx%2FIVRu7LAGDzh2eg%2FAhV8eY%2FyjW12D%2BtSOVq6NLF2lSZcY40rlQ%2B1fUc3DAe2euuWhIECOtlxtY5Hho11ZdHGB8lZ4CSLo%2BWmSIjzmkQ33RgkeNF9eYV4AV1PpdZZ%2Fjyl%2BVjCQtaNVV5c%3D; rl_session=RudderEncrypt%3AU2FsdGVkX19jm82pV3xfWHI%2FE6QaUo5xFQZuXuYh%2FkUCBhyJGY7TqzAK3YDkYppIpUipS7LtUSxm6iWAAp3vGhbB58MN7hrVa8imlwsuL7ceFNN%2BR1uTEvKTR8wWKaii2Xzs%2FYnhG3X8kmImIfYZgg%3D%3D; access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiMjFjMmEwMmItYjY5Ny00MGVjLWIwMmItMjI1YzJjOWUyZGMzIiwiZXhwIjoxNzcwMjYwODExfQ.MpOjnbwllAzarfaoTk1SzYVMqAEXBEMyRt5UyiJ90Qw'}
|
||||
11|vigent2 | 2026-01-29 11:22:58.742 | INFO | app.main:dispatch:21 - START Request: GET https://vigent.hbyrkj.top/api/publish/accounts
|
||||
11|vigent2 | 2026-01-29 11:22:58.743 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'sec-ch-ua-platform': '"Windows"', 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36', 'accept': 'application/json, text/plain, */*', 'sec-ch-ua': '"Not(A:Brand";v="8", "Chromium";v="144", "Google Chrome";v="144"', 'sec-ch-ua-mobile': '?0', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'sec-fetch-dest': 'empty', 'referer': 'https://vigent.hbyrkj.top/publish', 'accept-encoding': 'gzip, deflate, br, zstd', 'accept-language': 'en-US,en;q=0.9,zh;q=0.8,zh-CN;q=0.7', 'priority': 'u=1, i', 'cookie': 'rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX1%2FRWtsIwIaguDp15em58SDrIwOvRJVXeK4%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX1%2BVZN6tniQmiO5L2fGVdcrYOkqG%2BRHkNFw%3D; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1762504332341%2C%22019a5d6c-ae21-7d75-8919-11e9621a135f%22%2C1762503994906%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19aTpWYcHFt3zhITFSLk1XAMr9V2jBWQwsuLDNLXh93pTlQ%2FUpvwmv6h%2Fl1bW4xH83hrkWPCTkSYg%3D%3D; rl_group_id=RudderEncrypt%3AU2FsdGVkX1%2BhywVlN3t3ypqwAMgqlh7ZRNLMKnFMhxA%3D; rl_group_trait=RudderEncrypt%3AU2FsdGVkX19jAKNOmR%2FnSngsWGVcmYB2qyvsbh3wQc0%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX180hU8QtHwPe3dPd1o7rEP7efRzFgCIvuIPRwbE3dWE0aEQCCMpQTN%2B7AGEtH6mjRvEuqcbfOdaX4TtJGL2jHbdcZUuA7Mpjf0uvsZ15LToi0zM1NWR7i6wE2z4vcYyFaBdB1uTJq3SxhX2WsqWe4YiT12vld0E%2F5w%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX19tgs8QN46oDejOzAvFyTx%2FIVRu7LAGDzh2eg%2FAhV8eY%2FyjW12D%2BtSOVq6NLF2lSZcY40rlQ%2B1fUc3DAe2euuWhIECOtlxtY5Hho11ZdHGB8lZ4CSLo%2BWmSIjzmkQ33RgkeNF9eYV4AV1PpdZZ%2Fjyl%2BVjCQtaNVV5c%3D; rl_session=RudderEncrypt%3AU2FsdGVkX19jm82pV3xfWHI%2FE6QaUo5xFQZuXuYh%2FkUCBhyJGY7TqzAK3YDkYppIpUipS7LtUSxm6iWAAp3vGhbB58MN7hrVa8imlwsuL7ceFNN%2BR1uTEvKTR8wWKaii2Xzs%2FYnhG3X8kmImIfYZgg%3D%3D; access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiMjFjMmEwMmItYjY5Ny00MGVjLWIwMmItMjI1YzJjOWUyZGMzIiwiZXhwIjoxNzcwMjYwODExfQ.MpOjnbwllAzarfaoTk1SzYVMqAEXBEMyRt5UyiJ90Qw'}
|
||||
11|vigent2 | 2026-01-29 11:22:58.747 | INFO | app.main:dispatch:26 - END Request: GET https://vigent.hbyrkj.top/api/publish/accounts - Status: 200 - Duration: 0.01s
|
||||
11|vigent2 | 2026-01-29 11:22:58.798 | INFO | app.main:dispatch:26 - END Request: GET https://vigent.hbyrkj.top/api/videos/generated - Status: 200 - Duration: 0.11s
|
||||
11|vigent2 | 2026-01-29 11:23:03.509 | INFO | app.main:dispatch:21 - START Request: GET https://vigent.hbyrkj.top/api/materials?t=1769656987465
|
||||
11|vigent2 | 2026-01-29 11:23:03.510 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'sec-ch-ua-platform': '"Windows"', 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36', 'accept': 'application/json, text/plain, */*', 'sec-ch-ua': '"Not(A:Brand";v="8", "Chromium";v="144", "Google Chrome";v="144"', 'sec-ch-ua-mobile': '?0', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'sec-fetch-dest': 'empty', 'referer': 'https://vigent.hbyrkj.top/', 'accept-encoding': 'gzip, deflate, br, zstd', 'accept-language': 'en-US,en;q=0.9,zh;q=0.8,zh-CN;q=0.7', 'priority': 'u=1, i', 'cookie': 'rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX1%2FRWtsIwIaguDp15em58SDrIwOvRJVXeK4%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX1%2BVZN6tniQmiO5L2fGVdcrYOkqG%2BRHkNFw%3D; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1762504332341%2C%22019a5d6c-ae21-7d75-8919-11e9621a135f%22%2C1762503994906%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19aTpWYcHFt3zhITFSLk1XAMr9V2jBWQwsuLDNLXh93pTlQ%2FUpvwmv6h%2Fl1bW4xH83hrkWPCTkSYg%3D%3D; rl_group_id=RudderEncrypt%3AU2FsdGVkX1%2BhywVlN3t3ypqwAMgqlh7ZRNLMKnFMhxA%3D; rl_group_trait=RudderEncrypt%3AU2FsdGVkX19jAKNOmR%2FnSngsWGVcmYB2qyvsbh3wQc0%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX180hU8QtHwPe3dPd1o7rEP7efRzFgCIvuIPRwbE3dWE0aEQCCMpQTN%2B7AGEtH6mjRvEuqcbfOdaX4TtJGL2jHbdcZUuA7Mpjf0uvsZ15LToi0zM1NWR7i6wE2z4vcYyFaBdB1uTJq3SxhX2WsqWe4YiT12vld0E%2F5w%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX19tgs8QN46oDejOzAvFyTx%2FIVRu7LAGDzh2eg%2FAhV8eY%2FyjW12D%2BtSOVq6NLF2lSZcY40rlQ%2B1fUc3DAe2euuWhIECOtlxtY5Hho11ZdHGB8lZ4CSLo%2BWmSIjzmkQ33RgkeNF9eYV4AV1PpdZZ%2Fjyl%2BVjCQtaNVV5c%3D; rl_session=RudderEncrypt%3AU2FsdGVkX19jm82pV3xfWHI%2FE6QaUo5xFQZuXuYh%2FkUCBhyJGY7TqzAK3YDkYppIpUipS7LtUSxm6iWAAp3vGhbB58MN7hrVa8imlwsuL7ceFNN%2BR1uTEvKTR8wWKaii2Xzs%2FYnhG3X8kmImIfYZgg%3D%3D; access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiMjFjMmEwMmItYjY5Ny00MGVjLWIwMmItMjI1YzJjOWUyZGMzIiwiZXhwIjoxNzcwMjYwODExfQ.MpOjnbwllAzarfaoTk1SzYVMqAEXBEMyRt5UyiJ90Qw'}
|
||||
11|vigent2 | 2026-01-29 11:23:03.535 | INFO | app.main:dispatch:21 - START Request: GET https://vigent.hbyrkj.top/api/videos/generated
|
||||
11|vigent2 | 2026-01-29 11:23:03.535 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'sec-ch-ua-platform': '"Windows"', 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36', 'accept': 'application/json, text/plain, */*', 'sec-ch-ua': '"Not(A:Brand";v="8", "Chromium";v="144", "Google Chrome";v="144"', 'sec-ch-ua-mobile': '?0', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'sec-fetch-dest': 'empty', 'referer': 'https://vigent.hbyrkj.top/', 'accept-encoding': 'gzip, deflate, br, zstd', 'accept-language': 'en-US,en;q=0.9,zh;q=0.8,zh-CN;q=0.7', 'priority': 'u=1, i', 'cookie': 'rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX1%2FRWtsIwIaguDp15em58SDrIwOvRJVXeK4%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX1%2BVZN6tniQmiO5L2fGVdcrYOkqG%2BRHkNFw%3D; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1762504332341%2C%22019a5d6c-ae21-7d75-8919-11e9621a135f%22%2C1762503994906%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19aTpWYcHFt3zhITFSLk1XAMr9V2jBWQwsuLDNLXh93pTlQ%2FUpvwmv6h%2Fl1bW4xH83hrkWPCTkSYg%3D%3D; rl_group_id=RudderEncrypt%3AU2FsdGVkX1%2BhywVlN3t3ypqwAMgqlh7ZRNLMKnFMhxA%3D; rl_group_trait=RudderEncrypt%3AU2FsdGVkX19jAKNOmR%2FnSngsWGVcmYB2qyvsbh3wQc0%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX180hU8QtHwPe3dPd1o7rEP7efRzFgCIvuIPRwbE3dWE0aEQCCMpQTN%2B7AGEtH6mjRvEuqcbfOdaX4TtJGL2jHbdcZUuA7Mpjf0uvsZ15LToi0zM1NWR7i6wE2z4vcYyFaBdB1uTJq3SxhX2WsqWe4YiT12vld0E%2F5w%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX19tgs8QN46oDejOzAvFyTx%2FIVRu7LAGDzh2eg%2FAhV8eY%2FyjW12D%2BtSOVq6NLF2lSZcY40rlQ%2B1fUc3DAe2euuWhIECOtlxtY5Hho11ZdHGB8lZ4CSLo%2BWmSIjzmkQ33RgkeNF9eYV4AV1PpdZZ%2Fjyl%2BVjCQtaNVV5c%3D; rl_session=RudderEncrypt%3AU2FsdGVkX19jm82pV3xfWHI%2FE6QaUo5xFQZuXuYh%2FkUCBhyJGY7TqzAK3YDkYppIpUipS7LtUSxm6iWAAp3vGhbB58MN7hrVa8imlwsuL7ceFNN%2BR1uTEvKTR8wWKaii2Xzs%2FYnhG3X8kmImIfYZgg%3D%3D; access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiMjFjMmEwMmItYjY5Ny00MGVjLWIwMmItMjI1YzJjOWUyZGMzIiwiZXhwIjoxNzcwMjYwODExfQ.MpOjnbwllAzarfaoTk1SzYVMqAEXBEMyRt5UyiJ90Qw'}
|
||||
11|vigent2 | 2026-01-29 11:23:03.551 | INFO | app.main:dispatch:21 - START Request: GET https://vigent.hbyrkj.top/api/ref-audios
|
||||
11|vigent2 | 2026-01-29 11:23:03.552 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'sec-ch-ua-platform': '"Windows"', 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36', 'accept': 'application/json, text/plain, */*', 'sec-ch-ua': '"Not(A:Brand";v="8", "Chromium";v="144", "Google Chrome";v="144"', 'sec-ch-ua-mobile': '?0', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'sec-fetch-dest': 'empty', 'referer': 'https://vigent.hbyrkj.top/', 'accept-encoding': 'gzip, deflate, br, zstd', 'accept-language': 'en-US,en;q=0.9,zh;q=0.8,zh-CN;q=0.7', 'priority': 'u=1, i', 'cookie': 'rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX1%2FRWtsIwIaguDp15em58SDrIwOvRJVXeK4%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX1%2BVZN6tniQmiO5L2fGVdcrYOkqG%2BRHkNFw%3D; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1762504332341%2C%22019a5d6c-ae21-7d75-8919-11e9621a135f%22%2C1762503994906%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19aTpWYcHFt3zhITFSLk1XAMr9V2jBWQwsuLDNLXh93pTlQ%2FUpvwmv6h%2Fl1bW4xH83hrkWPCTkSYg%3D%3D; rl_group_id=RudderEncrypt%3AU2FsdGVkX1%2BhywVlN3t3ypqwAMgqlh7ZRNLMKnFMhxA%3D; rl_group_trait=RudderEncrypt%3AU2FsdGVkX19jAKNOmR%2FnSngsWGVcmYB2qyvsbh3wQc0%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX180hU8QtHwPe3dPd1o7rEP7efRzFgCIvuIPRwbE3dWE0aEQCCMpQTN%2B7AGEtH6mjRvEuqcbfOdaX4TtJGL2jHbdcZUuA7Mpjf0uvsZ15LToi0zM1NWR7i6wE2z4vcYyFaBdB1uTJq3SxhX2WsqWe4YiT12vld0E%2F5w%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX19tgs8QN46oDejOzAvFyTx%2FIVRu7LAGDzh2eg%2FAhV8eY%2FyjW12D%2BtSOVq6NLF2lSZcY40rlQ%2B1fUc3DAe2euuWhIECOtlxtY5Hho11ZdHGB8lZ4CSLo%2BWmSIjzmkQ33RgkeNF9eYV4AV1PpdZZ%2Fjyl%2BVjCQtaNVV5c%3D; rl_session=RudderEncrypt%3AU2FsdGVkX19jm82pV3xfWHI%2FE6QaUo5xFQZuXuYh%2FkUCBhyJGY7TqzAK3YDkYppIpUipS7LtUSxm6iWAAp3vGhbB58MN7hrVa8imlwsuL7ceFNN%2BR1uTEvKTR8wWKaii2Xzs%2FYnhG3X8kmImIfYZgg%3D%3D; access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiMjFjMmEwMmItYjY5Ny00MGVjLWIwMmItMjI1YzJjOWUyZGMzIiwiZXhwIjoxNzcwMjYwODExfQ.MpOjnbwllAzarfaoTk1SzYVMqAEXBEMyRt5UyiJ90Qw'}
|
||||
11|vigent2 | 2026-01-29 11:23:03.569 | INFO | app.main:dispatch:26 - END Request: GET https://vigent.hbyrkj.top/api/materials?t=1769656987465 - Status: 200 - Duration: 0.06s
|
||||
11|vigent2 | 2026-01-29 11:23:03.658 | INFO | app.main:dispatch:26 - END Request: GET https://vigent.hbyrkj.top/api/videos/generated - Status: 200 - Duration: 0.12s
|
||||
11|vigent2 | 2026-01-29 11:23:03.996 | INFO | app.main:dispatch:26 - END Request: GET https://vigent.hbyrkj.top/api/ref-audios - Status: 200 - Duration: 0.44s
|
||||
11|vigent2 | 2026-01-29 11:25:37.605 | INFO | app.main:dispatch:21 - START Request: GET https://vigent.hbyrkj.top/api/ref-audios
|
||||
11|vigent2 | 2026-01-29 11:25:37.605 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'sec-ch-ua-platform': '"Windows"', 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36', 'accept': 'application/json, text/plain, */*', 'sec-ch-ua': '"Not(A:Brand";v="8", "Chromium";v="144", "Google Chrome";v="144"', 'sec-ch-ua-mobile': '?0', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'sec-fetch-dest': 'empty', 'referer': 'https://vigent.hbyrkj.top/', 'accept-encoding': 'gzip, deflate, br, zstd', 'accept-language': 'en-US,en;q=0.9,zh;q=0.8,zh-CN;q=0.7', 'priority': 'u=1, i', 'cookie': 'rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX1%2FRWtsIwIaguDp15em58SDrIwOvRJVXeK4%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX1%2BVZN6tniQmiO5L2fGVdcrYOkqG%2BRHkNFw%3D; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1762504332341%2C%22019a5d6c-ae21-7d75-8919-11e9621a135f%22%2C1762503994906%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19aTpWYcHFt3zhITFSLk1XAMr9V2jBWQwsuLDNLXh93pTlQ%2FUpvwmv6h%2Fl1bW4xH83hrkWPCTkSYg%3D%3D; rl_group_id=RudderEncrypt%3AU2FsdGVkX1%2BhywVlN3t3ypqwAMgqlh7ZRNLMKnFMhxA%3D; rl_group_trait=RudderEncrypt%3AU2FsdGVkX19jAKNOmR%2FnSngsWGVcmYB2qyvsbh3wQc0%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX180hU8QtHwPe3dPd1o7rEP7efRzFgCIvuIPRwbE3dWE0aEQCCMpQTN%2B7AGEtH6mjRvEuqcbfOdaX4TtJGL2jHbdcZUuA7Mpjf0uvsZ15LToi0zM1NWR7i6wE2z4vcYyFaBdB1uTJq3SxhX2WsqWe4YiT12vld0E%2F5w%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX19tgs8QN46oDejOzAvFyTx%2FIVRu7LAGDzh2eg%2FAhV8eY%2FyjW12D%2BtSOVq6NLF2lSZcY40rlQ%2B1fUc3DAe2euuWhIECOtlxtY5Hho11ZdHGB8lZ4CSLo%2BWmSIjzmkQ33RgkeNF9eYV4AV1PpdZZ%2Fjyl%2BVjCQtaNVV5c%3D; rl_session=RudderEncrypt%3AU2FsdGVkX19jm82pV3xfWHI%2FE6QaUo5xFQZuXuYh%2FkUCBhyJGY7TqzAK3YDkYppIpUipS7LtUSxm6iWAAp3vGhbB58MN7hrVa8imlwsuL7ceFNN%2BR1uTEvKTR8wWKaii2Xzs%2FYnhG3X8kmImIfYZgg%3D%3D; access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiMjFjMmEwMmItYjY5Ny00MGVjLWIwMmItMjI1YzJjOWUyZGMzIiwiZXhwIjoxNzcwMjYwODExfQ.MpOjnbwllAzarfaoTk1SzYVMqAEXBEMyRt5UyiJ90Qw'}
|
||||
11|vigent2 | 2026-01-29 11:25:37.653 | INFO | app.main:dispatch:21 - START Request: GET https://vigent.hbyrkj.top/api/materials?t=1769657141569
|
||||
11|vigent2 | 2026-01-29 11:25:37.653 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'sec-ch-ua-platform': '"Windows"', 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36', 'accept': 'application/json, text/plain, */*', 'sec-ch-ua': '"Not(A:Brand";v="8", "Chromium";v="144", "Google Chrome";v="144"', 'sec-ch-ua-mobile': '?0', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'sec-fetch-dest': 'empty', 'referer': 'https://vigent.hbyrkj.top/', 'accept-encoding': 'gzip, deflate, br, zstd', 'accept-language': 'en-US,en;q=0.9,zh;q=0.8,zh-CN;q=0.7', 'priority': 'u=1, i', 'cookie': 'rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX1%2FRWtsIwIaguDp15em58SDrIwOvRJVXeK4%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX1%2BVZN6tniQmiO5L2fGVdcrYOkqG%2BRHkNFw%3D; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1762504332341%2C%22019a5d6c-ae21-7d75-8919-11e9621a135f%22%2C1762503994906%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19aTpWYcHFt3zhITFSLk1XAMr9V2jBWQwsuLDNLXh93pTlQ%2FUpvwmv6h%2Fl1bW4xH83hrkWPCTkSYg%3D%3D; rl_group_id=RudderEncrypt%3AU2FsdGVkX1%2BhywVlN3t3ypqwAMgqlh7ZRNLMKnFMhxA%3D; rl_group_trait=RudderEncrypt%3AU2FsdGVkX19jAKNOmR%2FnSngsWGVcmYB2qyvsbh3wQc0%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX180hU8QtHwPe3dPd1o7rEP7efRzFgCIvuIPRwbE3dWE0aEQCCMpQTN%2B7AGEtH6mjRvEuqcbfOdaX4TtJGL2jHbdcZUuA7Mpjf0uvsZ15LToi0zM1NWR7i6wE2z4vcYyFaBdB1uTJq3SxhX2WsqWe4YiT12vld0E%2F5w%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX19tgs8QN46oDejOzAvFyTx%2FIVRu7LAGDzh2eg%2FAhV8eY%2FyjW12D%2BtSOVq6NLF2lSZcY40rlQ%2B1fUc3DAe2euuWhIECOtlxtY5Hho11ZdHGB8lZ4CSLo%2BWmSIjzmkQ33RgkeNF9eYV4AV1PpdZZ%2Fjyl%2BVjCQtaNVV5c%3D; rl_session=RudderEncrypt%3AU2FsdGVkX19jm82pV3xfWHI%2FE6QaUo5xFQZuXuYh%2FkUCBhyJGY7TqzAK3YDkYppIpUipS7LtUSxm6iWAAp3vGhbB58MN7hrVa8imlwsuL7ceFNN%2BR1uTEvKTR8wWKaii2Xzs%2FYnhG3X8kmImIfYZgg%3D%3D; access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiMjFjMmEwMmItYjY5Ny00MGVjLWIwMmItMjI1YzJjOWUyZGMzIiwiZXhwIjoxNzcwMjYwODExfQ.MpOjnbwllAzarfaoTk1SzYVMqAEXBEMyRt5UyiJ90Qw'}
|
||||
11|vigent2 | 2026-01-29 11:25:37.673 | INFO | app.main:dispatch:21 - START Request: GET https://vigent.hbyrkj.top/api/videos/generated
|
||||
11|vigent2 | 2026-01-29 11:25:37.674 | INFO | app.main:dispatch:22 - HEADERS: {'connection': 'upgrade', 'host': 'vigent.hbyrkj.top', 'x-real-ip': '27.17.161.128', 'x-forwarded-for': '27.17.161.128', 'x-forwarded-proto': 'https', 'sec-ch-ua-platform': '"Windows"', 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36', 'accept': 'application/json, text/plain, */*', 'sec-ch-ua': '"Not(A:Brand";v="8", "Chromium";v="144", "Google Chrome";v="144"', 'sec-ch-ua-mobile': '?0', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'cors', 'sec-fetch-dest': 'empty', 'referer': 'https://vigent.hbyrkj.top/', 'accept-encoding': 'gzip, deflate, br, zstd', 'accept-language': 'en-US,en;q=0.9,zh;q=0.8,zh-CN;q=0.7', 'priority': 'u=1, i', 'cookie': 'rl_page_init_referrer=RudderEncrypt%3AU2FsdGVkX1%2FRWtsIwIaguDp15em58SDrIwOvRJVXeK4%3D; rl_page_init_referring_domain=RudderEncrypt%3AU2FsdGVkX1%2BVZN6tniQmiO5L2fGVdcrYOkqG%2BRHkNFw%3D; ph_phc_4URIAm1uYfJO7j8kWSe0J8lc8IqnstRLS7Jx8NcakHo_posthog=%7B%22distinct_id%22%3A%22f6a1ba3602218bc1551bb81b48167bf7484eeb86ed8ee9484fa83f1267023264%230d2437ec-d81b-491c-991f-0b6559daa00d%22%2C%22%24sesid%22%3A%5B1762504332341%2C%22019a5d6c-ae21-7d75-8919-11e9621a135f%22%2C1762503994906%5D%2C%22%24epp%22%3Atrue%2C%22%24initial_person_info%22%3A%7B%22r%22%3A%22%24direct%22%2C%22u%22%3A%22https%3A%2F%2Fn8n.hbyrkj.top%2Fsignin%3Fredirect%3D%25252F%22%7D%7D; rl_anonymous_id=RudderEncrypt%3AU2FsdGVkX19aTpWYcHFt3zhITFSLk1XAMr9V2jBWQwsuLDNLXh93pTlQ%2FUpvwmv6h%2Fl1bW4xH83hrkWPCTkSYg%3D%3D; rl_group_id=RudderEncrypt%3AU2FsdGVkX1%2BhywVlN3t3ypqwAMgqlh7ZRNLMKnFMhxA%3D; rl_group_trait=RudderEncrypt%3AU2FsdGVkX19jAKNOmR%2FnSngsWGVcmYB2qyvsbh3wQc0%3D; rl_user_id=RudderEncrypt%3AU2FsdGVkX180hU8QtHwPe3dPd1o7rEP7efRzFgCIvuIPRwbE3dWE0aEQCCMpQTN%2B7AGEtH6mjRvEuqcbfOdaX4TtJGL2jHbdcZUuA7Mpjf0uvsZ15LToi0zM1NWR7i6wE2z4vcYyFaBdB1uTJq3SxhX2WsqWe4YiT12vld0E%2F5w%3D; rl_trait=RudderEncrypt%3AU2FsdGVkX19tgs8QN46oDejOzAvFyTx%2FIVRu7LAGDzh2eg%2FAhV8eY%2FyjW12D%2BtSOVq6NLF2lSZcY40rlQ%2B1fUc3DAe2euuWhIECOtlxtY5Hho11ZdHGB8lZ4CSLo%2BWmSIjzmkQ33RgkeNF9eYV4AV1PpdZZ%2Fjyl%2BVjCQtaNVV5c%3D; rl_session=RudderEncrypt%3AU2FsdGVkX19jm82pV3xfWHI%2FE6QaUo5xFQZuXuYh%2FkUCBhyJGY7TqzAK3YDkYppIpUipS7LtUSxm6iWAAp3vGhbB58MN7hrVa8imlwsuL7ceFNN%2BR1uTEvKTR8wWKaii2Xzs%2FYnhG3X8kmImIfYZgg%3D%3D; access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiI5NGNkOTFlMy03ZDg5LTQ1ZTgtOWQ4NS1lOGJhMDY2MGQ3NGMiLCJzZXNzaW9uX3Rva2VuIjoiMjFjMmEwMmItYjY5Ny00MGVjLWIwMmItMjI1YzJjOWUyZGMzIiwiZXhwIjoxNzcwMjYwODExfQ.MpOjnbwllAzarfaoTk1SzYVMqAEXBEMyRt5UyiJ90Qw'}
|
||||
11|vigent2 | 2026-01-29 11:25:37.781 | INFO | app.main:dispatch:26 - END Request: GET https://vigent.hbyrkj.top/api/materials?t=1769657141569 - Status: 200 - Duration: 0.13s
|
||||
11|vigent2 | 2026-01-29 11:25:37.792 | INFO | app.main:dispatch:26 - END Request: GET https://vigent.hbyrkj.top/api/videos/generated - Status: 200 - Duration: 0.12s
|
||||
11|vigent2 | 2026-01-29 11:25:38.087 | INFO | app.main:dispatch:26 - END Request: GET https://vigent.hbyrkj.top/api/ref-audios - Status: 200 - Duration: 0.48s
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
rongye@r730-ubuntu:~$ pm2 logs vigent2-latentsync
|
||||
[TAILING] Tailing last 15 lines for [vigent2-latentsync] process (change the value with --lines option)
|
||||
/home/rongye/.pm2/logs/vigent2-latentsync-out.log last 15 lines:
|
||||
/home/rongye/.pm2/logs/vigent2-latentsync-error.log last 15 lines:
|
||||
384
Docs/QWEN3_TTS_DEPLOY.md
Normal file
384
Docs/QWEN3_TTS_DEPLOY.md
Normal file
@@ -0,0 +1,384 @@
|
||||
# Qwen3-TTS 1.7B 部署指南
|
||||
|
||||
> 本文档描述如何在 Ubuntu 服务器上部署 Qwen3-TTS 1.7B-Base 声音克隆模型。
|
||||
|
||||
## 系统要求
|
||||
|
||||
| 要求 | 规格 |
|
||||
|------|------|
|
||||
| GPU | NVIDIA RTX 3090 24GB (或更高) |
|
||||
| VRAM | ≥ 8GB (推理), ≥ 12GB (带 flash-attn) |
|
||||
| CUDA | 12.1+ |
|
||||
| Python | 3.10.x |
|
||||
| 系统 | Ubuntu 20.04+ |
|
||||
|
||||
---
|
||||
|
||||
## GPU 分配
|
||||
|
||||
| GPU | 服务 | 模型 |
|
||||
|-----|------|------|
|
||||
| GPU0 | **Qwen3-TTS** | 1.7B-Base (声音克隆，更高质量) |
|
||||
| GPU1 | LatentSync | 1.6 (唇形同步) |
|
||||
|
||||
---
|
||||
|
||||
## 步骤 1: 克隆仓库
|
||||
|
||||
```bash
|
||||
cd /home/rongye/ProgramFiles/ViGent2/models
|
||||
git clone https://github.com/QwenLM/Qwen3-TTS.git
|
||||
cd Qwen3-TTS
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 步骤 2: 创建 Conda 环境
|
||||
|
||||
```bash
|
||||
# 创建新的 conda 环境
|
||||
conda create -n qwen-tts python=3.10 -y
|
||||
conda activate qwen-tts
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 步骤 3: 安装 Python 依赖
|
||||
|
||||
```bash
|
||||
cd /home/rongye/ProgramFiles/ViGent2/models/Qwen3-TTS
|
||||
|
||||
# 安装 qwen-tts 包 (editable mode)
|
||||
pip install -e .
|
||||
|
||||
# 安装 sox 音频处理库 (必须)
|
||||
conda install -y -c conda-forge sox
|
||||
```
|
||||
|
||||
### 可选: 安装 FlashAttention (推荐)
|
||||
|
||||
FlashAttention 可以显著提升推理速度并减少显存占用：
|
||||
|
||||
```bash
|
||||
pip install -U flash-attn --no-build-isolation
|
||||
```
|
||||
|
||||
如果内存不足，可以限制编译并发数：
|
||||
|
||||
```bash
|
||||
MAX_JOBS=4 pip install -U flash-attn --no-build-isolation
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 步骤 4: 下载模型权重
|
||||
|
||||
### 方式 A: ModelScope (推荐，国内更快)
|
||||
|
||||
```bash
|
||||
pip install modelscope
|
||||
|
||||
# 下载 Tokenizer (651MB)
|
||||
modelscope download --model Qwen/Qwen3-TTS-Tokenizer-12Hz --local_dir ./checkpoints/Tokenizer
|
||||
|
||||
# 下载 1.7B-Base 模型 (6.8GB)
|
||||
modelscope download --model Qwen/Qwen3-TTS-12Hz-1.7B-Base --local_dir ./checkpoints/1.7B-Base
|
||||
```
|
||||
|
||||
### 方式 B: HuggingFace
|
||||
|
||||
```bash
|
||||
pip install -U "huggingface_hub[cli]"
|
||||
|
||||
huggingface-cli download Qwen/Qwen3-TTS-Tokenizer-12Hz --local-dir ./checkpoints/Tokenizer
|
||||
huggingface-cli download Qwen/Qwen3-TTS-12Hz-1.7B-Base --local-dir ./checkpoints/1.7B-Base
|
||||
```
|
||||
|
||||
下载完成后，目录结构应如下：
|
||||
|
||||
```
|
||||
checkpoints/
|
||||
├── Tokenizer/ # ~651MB
|
||||
│ ├── config.json
|
||||
│ ├── model.safetensors
|
||||
│ └── ...
|
||||
└── 1.7B-Base/ # ~6.8GB
|
||||
├── config.json
|
||||
├── model.safetensors
|
||||
└── ...
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 步骤 5: 验证安装
|
||||
|
||||
### 5.1 检查环境
|
||||
|
||||
```bash
|
||||
conda activate qwen-tts
|
||||
|
||||
# 检查 PyTorch 和 CUDA
|
||||
python -c "import torch; print(f'PyTorch: {torch.__version__}'); print(f'CUDA: {torch.cuda.is_available()}')"
|
||||
|
||||
# 检查 sox
|
||||
sox --version
|
||||
```
|
||||
|
||||
### 5.2 运行推理测试
|
||||
|
||||
创建测试脚本 `test_inference.py`:
|
||||
|
||||
```python
|
||||
"""Qwen3-TTS 声音克隆测试"""
|
||||
import torch
|
||||
import soundfile as sf
|
||||
from qwen_tts import Qwen3TTSModel
|
||||
|
||||
print("Loading Qwen3-TTS model on GPU:0...")
|
||||
model = Qwen3TTSModel.from_pretrained(
|
||||
"./checkpoints/1.7B-Base",
|
||||
device_map="cuda:0",
|
||||
dtype=torch.bfloat16,
|
||||
)
|
||||
print("Model loaded!")
|
||||
|
||||
# 测试声音克隆 (需要准备参考音频)
|
||||
ref_audio = "./examples/myvoice.wav" # 3-20秒的参考音频
|
||||
ref_text = "参考音频的文字内容"
|
||||
|
||||
test_text = "这是一段测试文本，用于验证声音克隆功能是否正常工作。"
|
||||
|
||||
print("Generating cloned voice...")
|
||||
wavs, sr = model.generate_voice_clone(
|
||||
text=test_text,
|
||||
language="Chinese",
|
||||
ref_audio=ref_audio,
|
||||
ref_text=ref_text,
|
||||
)
|
||||
|
||||
sf.write("test_output.wav", wavs[0], sr)
|
||||
print(f"✅ Saved: test_output.wav | {sr}Hz | {len(wavs[0])/sr:.2f}s")
|
||||
```
|
||||
|
||||
运行测试：
|
||||
|
||||
```bash
|
||||
cd /home/rongye/ProgramFiles/ViGent2/models/Qwen3-TTS
|
||||
python test_inference.py
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 步骤 6: 安装 HTTP 服务依赖
|
||||
|
||||
```bash
|
||||
conda activate qwen-tts
|
||||
pip install fastapi uvicorn python-multipart
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 步骤 7: 启动服务 (PM2 管理)
|
||||
|
||||
### 手动测试
|
||||
|
||||
```bash
|
||||
conda activate qwen-tts
|
||||
cd /home/rongye/ProgramFiles/ViGent2/models/Qwen3-TTS
|
||||
python qwen_tts_server.py
|
||||
```
|
||||
|
||||
访问 http://localhost:8009/health 验证服务状态。
|
||||
|
||||
### PM2 常驻服务
|
||||
|
||||
> ⚠️ **注意**：启动脚本 `run_qwen_tts.sh` 位于项目**根目录**，而非 models/Qwen3-TTS 目录。
|
||||
|
||||
1. 使用启动脚本:
|
||||
```bash
|
||||
cd /home/rongye/ProgramFiles/ViGent2
|
||||
pm2 start ./run_qwen_tts.sh --name vigent2-qwen-tts
|
||||
pm2 save
|
||||
```
|
||||
|
||||
2. 查看日志:
|
||||
```bash
|
||||
pm2 logs vigent2-qwen-tts
|
||||
```
|
||||
|
||||
3. 重启服务:
|
||||
```bash
|
||||
pm2 restart vigent2-qwen-tts
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 目录结构
|
||||
|
||||
部署完成后，目录结构应如下：
|
||||
|
||||
```
|
||||
/home/rongye/ProgramFiles/ViGent2/
|
||||
├── run_qwen_tts.sh # PM2 启动脚本 (根目录)
|
||||
└── models/Qwen3-TTS/
|
||||
├── checkpoints/
|
||||
│ ├── Tokenizer/ # 语音编解码器
|
||||
│ └── 1.7B-Base/ # 声音克隆模型 (更高质量)
|
||||
├── qwen_tts/ # 源码
|
||||
│ ├── inference/
|
||||
│ ├── models/
|
||||
│ └── ...
|
||||
├── examples/
|
||||
│ └── myvoice.wav # 参考音频
|
||||
├── qwen_tts_server.py # HTTP 推理服务 (端口 8009)
|
||||
├── pyproject.toml
|
||||
├── requirements.txt
|
||||
└── test_inference.py # 测试脚本
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## API 参考
|
||||
|
||||
### 健康检查
|
||||
|
||||
```
|
||||
GET http://localhost:8009/health
|
||||
```
|
||||
|
||||
响应:
|
||||
```json
|
||||
{
|
||||
"service": "Qwen3-TTS Voice Clone",
|
||||
"model": "1.7B-Base",
|
||||
"ready": true,
|
||||
"gpu_id": 0
|
||||
}
|
||||
```
|
||||
|
||||
### 声音克隆生成
|
||||
|
||||
```
|
||||
POST http://localhost:8009/generate
|
||||
Content-Type: multipart/form-data
|
||||
|
||||
Fields:
|
||||
- ref_audio: 参考音频文件 (WAV)
|
||||
- text: 要合成的文本
|
||||
- ref_text: 参考音频的转写文字
|
||||
- language: 语言 (默认 Chinese)
|
||||
|
||||
Response: audio/wav 文件
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 模型说明
|
||||
|
||||
### 可用模型
|
||||
|
||||
| 模型 | 功能 | 大小 |
|
||||
|------|------|------|
|
||||
| 0.6B-Base | 3秒快速声音克隆 | 2.4GB |
|
||||
| 0.6B-CustomVoice | 9种预设音色 | 2.4GB |
|
||||
| **1.7B-Base** | **声音克隆 (更高质量)** ✅ 当前使用 | 6.8GB |
|
||||
| 1.7B-VoiceDesign | 自然语言描述生成声音 | 6.8GB |
|
||||
|
||||
### 支持语言
|
||||
|
||||
中文、英语、日语、韩语、德语、法语、俄语、葡萄牙语、西班牙语、意大利语
|
||||
|
||||
---
|
||||
|
||||
## 故障排除
|
||||
|
||||
### sox 未找到
|
||||
|
||||
```
|
||||
SoX could not be found!
|
||||
```
|
||||
|
||||
**解决**: 通过 conda 安装 sox：
|
||||
|
||||
```bash
|
||||
conda install -y -c conda-forge sox
|
||||
```
|
||||
|
||||
### CUDA 内存不足
|
||||
|
||||
Qwen3-TTS 1.7B 通常需要 8-10GB VRAM。如果遇到 OOM：
|
||||
|
||||
1. 确保 GPU0 没有运行其他程序
|
||||
2. 不使用 flash-attn (会增加显存占用)
|
||||
3. 使用更小的参考音频 (3-5秒)
|
||||
4. 如果显存仍不足，可降级使用 0.6B-Base 模型
|
||||
|
||||
### 模型加载失败
|
||||
|
||||
确保以下文件存在：
|
||||
- `checkpoints/1.7B-Base/config.json`
|
||||
- `checkpoints/1.7B-Base/model.safetensors`
|
||||
|
||||
### 音频输出质量问题
|
||||
|
||||
1. 参考音频质量：使用清晰、无噪音的 3-10 秒音频
|
||||
2. ref_text 准确性：参考音频的转写文字必须准确
|
||||
3. 语言设置：确保 `language` 参数与文本语言一致
|
||||
|
||||
---
|
||||
|
||||
## 后端 ViGent2 集成
|
||||
|
||||
### 声音克隆服务 (`voice_clone_service.py`)
|
||||
|
||||
后端通过 HTTP 调用 Qwen3-TTS 服务：
|
||||
|
||||
```python
|
||||
import aiohttp
|
||||
|
||||
QWEN_TTS_URL = "http://localhost:8009"
|
||||
|
||||
async def generate_cloned_audio(ref_audio_path: str, text: str, output_path: str):
|
||||
async with aiohttp.ClientSession() as session:
|
||||
with open(ref_audio_path, "rb") as f:
|
||||
data = aiohttp.FormData()
|
||||
data.add_field("ref_audio", f, filename="ref.wav")
|
||||
data.add_field("text", text)
|
||||
|
||||
async with session.post(f"{QWEN_TTS_URL}/generate", data=data) as resp:
|
||||
audio_data = await resp.read()
|
||||
with open(output_path, "wb") as out:
|
||||
out.write(audio_data)
|
||||
return output_path
|
||||
```
|
||||
|
||||
### 参考音频 Supabase Bucket
|
||||
|
||||
```sql
|
||||
-- 创建 ref-audios bucket
|
||||
INSERT INTO storage.buckets (id, name, public)
|
||||
VALUES ('ref-audios', 'ref-audios', true)
|
||||
ON CONFLICT (id) DO NOTHING;
|
||||
|
||||
-- RLS 策略
|
||||
CREATE POLICY "Allow public uploads" ON storage.objects
|
||||
FOR INSERT TO anon WITH CHECK (bucket_id = 'ref-audios');
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 更新日志
|
||||
|
||||
| 日期 | 版本 | 说明 |
|
||||
|------|------|------|
|
||||
| 2026-01-30 | 1.1.0 | 明确默认模型升级为 1.7B-Base，替换旧版 0.6B 路径 |
|
||||
|
||||
---
|
||||
|
||||
## 参考链接
|
||||
|
||||
- [Qwen3-TTS GitHub](https://github.com/QwenLM/Qwen3-TTS)
|
||||
- [ModelScope 模型](https://modelscope.cn/collections/Qwen/Qwen3-TTS)
|
||||
- [HuggingFace 模型](https://huggingface.co/collections/Qwen/qwen3-tts)
|
||||
- [技术报告](https://arxiv.org/abs/2601.15621)
|
||||
- [官方博客](https://qwen.ai/blog?id=qwen3tts-0115)
|
||||
282
Docs/SUBTITLE_DEPLOY.md
Normal file
282
Docs/SUBTITLE_DEPLOY.md
Normal file
@@ -0,0 +1,282 @@
|
||||
# ViGent2 字幕与标题功能部署指南
|
||||
|
||||
本文档介绍如何部署 ViGent2 的逐字高亮字幕和片头标题功能。
|
||||
|
||||
## 功能概述
|
||||
|
||||
| 功能 | 说明 |
|
||||
|------|------|
|
||||
| **逐字高亮字幕** | 使用 faster-whisper 生成字级别时间戳，Remotion 渲染卡拉OK效果 |
|
||||
| **片头标题** | 视频开头显示标题，带淡入淡出动画，几秒后消失 |
|
||||
|
||||
## 技术架构
|
||||
|
||||
```
|
||||
原有流程:
|
||||
文本 → EdgeTTS → 音频 → LatentSync → FFmpeg合成 → 最终视频
|
||||
|
||||
新流程:
|
||||
文本 → EdgeTTS → 音频 ─┬→ LatentSync → 唇形视频 ─┐
|
||||
└→ faster-whisper → 字幕JSON ─┴→ Remotion合成 → 最终视频
|
||||
```
|
||||
|
||||
## 系统要求
|
||||
|
||||
| 组件 | 要求 |
|
||||
|------|------|
|
||||
| Node.js | 18+ |
|
||||
| Python | 3.10+ |
|
||||
| GPU 显存 | faster-whisper 需要约 3-4GB VRAM |
|
||||
| FFmpeg | 已安装 |
|
||||
|
||||
---
|
||||
|
||||
## 部署步骤
|
||||
|
||||
### 步骤 1: 安装 faster-whisper (Python)
|
||||
|
||||
```bash
|
||||
cd /home/rongye/ProgramFiles/ViGent2/backend
|
||||
source venv/bin/activate
|
||||
|
||||
# 安装 faster-whisper
|
||||
pip install faster-whisper>=1.0.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
|
||||
```
|
||||
|
||||
> **注意**: 首次运行时，faster-whisper 会自动下载 `large-v3` Whisper 模型 (~3GB)
|
||||
|
||||
### 步骤 2: 安装 Remotion (Node.js)
|
||||
|
||||
```bash
|
||||
cd /home/rongye/ProgramFiles/ViGent2/remotion
|
||||
|
||||
# 安装依赖
|
||||
npm install
|
||||
```
|
||||
|
||||
### 步骤 3: 重启后端服务
|
||||
|
||||
```bash
|
||||
pm2 restart vigent2-backend
|
||||
```
|
||||
|
||||
### 步骤 4: 验证安装
|
||||
|
||||
```bash
|
||||
# 检查 faster-whisper 是否安装成功
|
||||
cd /home/rongye/ProgramFiles/ViGent2/backend
|
||||
source venv/bin/activate
|
||||
python -c "from faster_whisper import WhisperModel; print('faster-whisper OK')"
|
||||
|
||||
# 检查 Remotion 是否安装成功
|
||||
cd /home/rongye/ProgramFiles/ViGent2/remotion
|
||||
npx remotion --version
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 文件结构
|
||||
|
||||
### 后端新增文件
|
||||
|
||||
| 文件 | 说明 |
|
||||
|------|------|
|
||||
| `backend/app/services/whisper_service.py` | 字幕对齐服务 (基于 faster-whisper) |
|
||||
| `backend/app/services/remotion_service.py` | Remotion 渲染服务 |
|
||||
|
||||
### Remotion 项目结构
|
||||
|
||||
```
|
||||
remotion/
|
||||
├── package.json # Node.js 依赖配置
|
||||
├── tsconfig.json # TypeScript 配置
|
||||
├── render.ts # 服务端渲染脚本
|
||||
└── src/
|
||||
├── index.ts # Remotion 入口
|
||||
├── Root.tsx # 根组件
|
||||
├── Video.tsx # 主视频组件
|
||||
├── components/
|
||||
│ ├── Title.tsx # 片头标题组件
|
||||
│ ├── Subtitles.tsx # 逐字高亮字幕组件
|
||||
│ └── VideoLayer.tsx # 视频图层组件
|
||||
├── utils/
|
||||
│ └── captions.ts # 字幕数据处理工具
|
||||
└── fonts/ # 字体文件目录 (可选)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## API 参数
|
||||
|
||||
视频生成 API (`POST /api/videos/generate`) 新增以下参数：
|
||||
|
||||
| 参数 | 类型 | 默认值 | 说明 |
|
||||
|------|------|--------|------|
|
||||
| `title` | string | null | 视频标题（片头显示，可选） |
|
||||
| `enable_subtitles` | boolean | true | 是否启用逐字高亮字幕 |
|
||||
|
||||
### 请求示例
|
||||
|
||||
```json
|
||||
{
|
||||
"material_path": "https://...",
|
||||
"text": "大家好，欢迎来到我的频道",
|
||||
"tts_mode": "edgetts",
|
||||
"voice": "zh-CN-YunxiNeural",
|
||||
"title": "今日分享",
|
||||
"enable_subtitles": true
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 视频生成流程
|
||||
|
||||
新的视频生成流程进度分配：
|
||||
|
||||
| 阶段 | 进度 | 说明 |
|
||||
|------|------|------|
|
||||
| 下载素材 | 0% → 5% | 从 Supabase 下载输入视频 |
|
||||
| TTS 语音生成 | 5% → 25% | EdgeTTS 或 Qwen3-TTS 生成音频 |
|
||||
| 唇形同步 | 25% → 80% | LatentSync 推理 |
|
||||
| 字幕对齐 | 80% → 85% | faster-whisper 生成字级别时间戳 |
|
||||
| Remotion 渲染 | 85% → 95% | 合成字幕和标题 |
|
||||
| 上传结果 | 95% → 100% | 上传到 Supabase Storage |
|
||||
|
||||
---
|
||||
|
||||
## 降级处理
|
||||
|
||||
系统包含自动降级机制，确保基本功能不受影响：
|
||||
|
||||
| 场景 | 处理方式 |
|
||||
|------|----------|
|
||||
| 字幕对齐失败 | 跳过字幕，继续生成视频 |
|
||||
| Remotion 未安装 | 使用 FFmpeg 直接合成 |
|
||||
| Remotion 渲染失败 | 回退到 FFmpeg 合成 |
|
||||
|
||||
---
|
||||
|
||||
## 配置说明
|
||||
|
||||
### 字幕服务配置
|
||||
|
||||
字幕服务位于 `backend/app/services/whisper_service.py`，默认配置：
|
||||
|
||||
| 参数 | 默认值 | 说明 |
|
||||
|------|--------|------|
|
||||
| `model_size` | large-v3 | Whisper 模型大小 |
|
||||
| `device` | cuda | 运行设备 |
|
||||
| `compute_type` | float16 | 计算精度 |
|
||||
|
||||
如需修改，可编辑 `whisper_service.py` 中的 `WhisperService` 初始化参数。
|
||||
|
||||
### Remotion 配置
|
||||
|
||||
Remotion 渲染参数在 `backend/app/services/remotion_service.py` 中配置：
|
||||
|
||||
| 参数 | 默认值 | 说明 |
|
||||
|------|--------|------|
|
||||
| `fps` | 25 | 输出帧率 |
|
||||
| `title_duration` | 3.0 | 标题显示时长（秒） |
|
||||
|
||||
---
|
||||
|
||||
## 故障排除
|
||||
|
||||
### faster-whisper 相关
|
||||
|
||||
**问题**: `ModuleNotFoundError: No module named 'faster_whisper'`
|
||||
|
||||
```bash
|
||||
cd /home/rongye/ProgramFiles/ViGent2/backend
|
||||
source venv/bin/activate
|
||||
pip install faster-whisper>=1.0.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
|
||||
```
|
||||
|
||||
**问题**: GPU 显存不足
|
||||
|
||||
修改 `whisper_service.py`，使用较小的模型：
|
||||
```python
|
||||
WhisperService(model_size="medium", compute_type="int8")
|
||||
```
|
||||
|
||||
### Remotion 相关
|
||||
|
||||
**问题**: `node_modules not found`
|
||||
|
||||
```bash
|
||||
cd /home/rongye/ProgramFiles/ViGent2/remotion
|
||||
npm install
|
||||
```
|
||||
|
||||
**问题**: Remotion 渲染失败 - `fs` 模块错误
|
||||
|
||||
确保 `remotion/src/utils/captions.ts` 中没有使用 Node.js 的 `fs` 模块。Remotion 在浏览器环境打包，不支持 `fs`。
|
||||
|
||||
**问题**: Remotion 渲染失败 - 视频文件读取错误 (`file://` 协议)
|
||||
|
||||
确保 `render.ts` 使用 `publicDir` 选项指向视频所在目录，`VideoLayer.tsx` 使用 `staticFile()` 加载视频：
|
||||
|
||||
```typescript
|
||||
// render.ts
|
||||
const publicDir = path.dirname(path.resolve(options.videoPath));
|
||||
const bundleLocation = await bundle({
|
||||
entryPoint: path.resolve(__dirname, './src/index.ts'),
|
||||
publicDir, // 关键配置
|
||||
});
|
||||
|
||||
// VideoLayer.tsx
|
||||
const videoUrl = staticFile(videoSrc); // 使用 staticFile
|
||||
```
|
||||
|
||||
**问题**: Remotion 渲染失败
|
||||
|
||||
查看后端日志：
|
||||
```bash
|
||||
pm2 logs vigent2-backend
|
||||
```
|
||||
|
||||
### 查看服务健康状态
|
||||
|
||||
```bash
|
||||
# 字幕服务健康检查
|
||||
cd /home/rongye/ProgramFiles/ViGent2/backend
|
||||
source venv/bin/activate
|
||||
python -c "from app.services.whisper_service import whisper_service; import asyncio; print(asyncio.run(whisper_service.check_health()))"
|
||||
|
||||
# Remotion 健康检查
|
||||
python -c "from app.services.remotion_service import remotion_service; import asyncio; print(asyncio.run(remotion_service.check_health()))"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 可选优化
|
||||
|
||||
### 添加中文字体
|
||||
|
||||
为获得更好的字幕渲染效果，可添加中文字体：
|
||||
|
||||
```bash
|
||||
# 下载 Noto Sans SC 字体
|
||||
cd /home/rongye/ProgramFiles/ViGent2/remotion/src/fonts
|
||||
wget https://github.com/googlefonts/noto-cjk/raw/main/Sans/OTF/SimplifiedChinese/NotoSansSC-Regular.otf -O NotoSansSC.otf
|
||||
```
|
||||
|
||||
### 使用 GPU 0
|
||||
|
||||
faster-whisper 默认使用 GPU 0，与 LatentSync (GPU 1) 分开，避免显存冲突。如需指定 GPU：
|
||||
|
||||
```python
|
||||
# 在 whisper_service.py 中修改
|
||||
WhisperService(device="cuda:0") # 或 "cuda:1"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 更新日志
|
||||
|
||||
| 日期 | 版本 | 说明 |
|
||||
|------|------|------|
|
||||
| 2026-01-29 | 1.0.0 | 初始版本，使用 faster-whisper + Remotion 实现逐字高亮字幕和片头标题 |
|
||||
| 2026-01-30 | 1.0.1 | 字幕高亮样式与标题动画优化，视觉表现更清晰 |
|
||||
@@ -6,6 +6,7 @@
|
||||
- 上传静态人物视频 → 生成口播视频（唇形同步）
|
||||
- TTS 配音或声音克隆
|
||||
- 字幕自动生成与渲染
|
||||
- AI 自动生成标题与标签
|
||||
- 一键发布到多个社交平台
|
||||
|
||||
---
|
||||
@@ -47,7 +48,7 @@
|
||||
| **任务队列** | Celery + Redis | RQ / Dramatiq |
|
||||
| **唇形同步** | **LatentSync 1.6** | MuseTalk / Wav2Lip |
|
||||
| **TTS 配音** | EdgeTTS | CosyVoice |
|
||||
| **声音克隆** | GPT-SoVITS (可选) | - |
|
||||
| **声音克隆** | **Qwen3-TTS 1.7B** ✅ | GPT-SoVITS |
|
||||
| **视频处理** | FFmpeg | MoviePy |
|
||||
| **自动发布** | social-auto-upload | 自行实现 |
|
||||
| **数据库** | SQLite → PostgreSQL | MySQL |
|
||||
@@ -219,6 +220,7 @@ cp -r SuperIPAgent/social-auto-upload backend/social_upload
|
||||
| 功能 | 实现方式 |
|
||||
|------|----------|
|
||||
| **声音克隆** | 集成 GPT-SoVITS，用自己的声音 |
|
||||
| **AI 标题/标签生成** | 调用大模型 API 自动生成标题与标签 ✅ |
|
||||
| **批量生成** | 上传 Excel/CSV，批量生成视频 |
|
||||
| **字幕编辑器** | 可视化调整字幕样式、位置 |
|
||||
| **Docker 部署** | 一键部署到云服务器 | ✅ |
|
||||
@@ -323,25 +325,33 @@ cp -r SuperIPAgent/social-auto-upload backend/social_upload
|
||||
- [x] 端口冲突解决 (3003/8008/8444)
|
||||
- [x] Basic Auth 管理后台保护
|
||||
|
||||
### 阶段十七：声音克隆功能集成 (Day 13) ✅
|
||||
|
||||
> **目标**：实现用户自定义声音克隆能力
|
||||
|
||||
- [x] Qwen3-TTS HTTP 服务 (独立 FastAPI，端口 8009)
|
||||
- [x] 声音克隆服务封装 (voice_clone_service.py)
|
||||
- [x] 参考音频管理 API (上传/列表/删除)
|
||||
- [x] 前端 TTS 模式选择 UI
|
||||
- [x] Supabase ref-audios Bucket 配置
|
||||
- [x] 端到端测试验证
|
||||
|
||||
### 阶段十八：手机号登录迁移 (Day 15) ✅
|
||||
|
||||
> **目标**：将认证系统从邮箱迁移到手机号
|
||||
|
||||
- [x] 数据库 Schema 迁移 (email → phone)
|
||||
- [x] 后端 API 适配 (auth.py/admin.py)
|
||||
- [x] 11位手机号校验 (正则验证)
|
||||
- [x] 修改密码功能 (/api/auth/change-password)
|
||||
- [x] 账户设置下拉菜单 (修改密码 + 有效期显示 + 退出)
|
||||
- [x] 前端登录/注册页面更新
|
||||
- [x] 数据库迁移脚本 (migrate_to_phone.sql)
|
||||
|
||||
---
|
||||
|
||||
## 项目目录结构 (最终)
|
||||
|
||||
```
|
||||
TalkingHeadAgent/
|
||||
├── frontend/ # Next.js 前端
|
||||
│ ├── app/
|
||||
│ ├── components/
|
||||
│ └── package.json
|
||||
├── backend/ # FastAPI 后端
|
||||
│ ├── app/
|
||||
│ ├── MuseTalk/ # 唇形同步模型
|
||||
│ ├── social_upload/ # 社交发布模块
|
||||
│ └── requirements.txt
|
||||
├── docker-compose.yml # 一键部署
|
||||
└── README.md
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 开发时间估算
|
||||
|
||||
220
Docs/serene-conjuring-volcano.md
Normal file
220
Docs/serene-conjuring-volcano.md
Normal file
@@ -0,0 +1,220 @@
|
||||
# Qwen3-TTS 声音克隆集成到 ViGent2
|
||||
|
||||
## 需求概述
|
||||
1. 前端支持上传/在线录制参考音频（wav, mp3, m4a 等）
|
||||
2. EdgeTTS 音色保留，增加 Qwen3-TTS 声音克隆界面
|
||||
3. 两种 TTS 方式做成统一界面（Tab 切换）
|
||||
4. 声音克隆使用相同的口播文案输入
|
||||
|
||||
## 架构设计
|
||||
|
||||
### GPU 分配
|
||||
| GPU | 服务 | 模型 |
|
||||
|-----|------|------|
|
||||
| GPU0 | Qwen3-TTS | 0.6B-Base (声音克隆) |
|
||||
| GPU1 | LatentSync | 1.6 (唇形同步) |
|
||||
|
||||
### 存储
|
||||
- 新增 Supabase bucket: `ref_audios`
|
||||
- 路径格式: `{user_id}/{timestamp}_{filename}.wav`
|
||||
|
||||
---
|
||||
|
||||
## 实现步骤
|
||||
|
||||
### 1. 后端：新建声音克隆服务
|
||||
**文件**: `backend/app/services/voice_clone_service.py`
|
||||
|
||||
```python
|
||||
class VoiceCloneService:
|
||||
def __init__(self):
|
||||
self.gpu_id = 0
|
||||
self.model_path = "models/Qwen3-TTS/checkpoints/0.6B-Base"
|
||||
self._model = None
|
||||
self._lock = asyncio.Lock()
|
||||
|
||||
async def generate_audio(self, text, ref_audio_path, ref_text, output_path, language="Chinese"):
|
||||
# 使用 Qwen3TTSModel.generate_voice_clone()
|
||||
```
|
||||
|
||||
### 2. 后端：新建参考音频 API
|
||||
**文件**: `backend/app/api/ref_audios.py`
|
||||
|
||||
| 接口 | 方法 | 功能 |
|
||||
|------|------|------|
|
||||
| `/api/ref-audios` | POST | 上传参考音频 + ref_text |
|
||||
| `/api/ref-audios` | GET | 列出用户的参考音频 |
|
||||
| `/api/ref-audios/{id}` | DELETE | 删除参考音频 |
|
||||
|
||||
上传时自动转换为 wav (16kHz mono)，存储 ref_text 元数据。
|
||||
|
||||
### 3. 后端：修改视频生成 API
|
||||
**文件**: `backend/app/api/videos.py`
|
||||
|
||||
扩展 GenerateRequest:
|
||||
```python
|
||||
class GenerateRequest(BaseModel):
|
||||
text: str
|
||||
voice: str = "zh-CN-YunxiNeural"
|
||||
material_path: str
|
||||
# 新增
|
||||
tts_mode: str = "edgetts" # "edgetts" | "voiceclone"
|
||||
ref_audio_id: Optional[str] = None
|
||||
ref_text: Optional[str] = None
|
||||
```
|
||||
|
||||
修改 `_process_video_generation()`:
|
||||
```python
|
||||
if req.tts_mode == "voiceclone":
|
||||
await voice_clone_service.generate_audio(...)
|
||||
else:
|
||||
await tts_service.generate_audio(...)
|
||||
```
|
||||
|
||||
### 4. 后端：注册路由
|
||||
**文件**: `backend/app/main.py`
|
||||
|
||||
```python
|
||||
from app.api import ref_audios
|
||||
app.include_router(ref_audios.router, prefix="/api/ref-audios", tags=["ref-audios"])
|
||||
```
|
||||
|
||||
### 5. 前端：改造音色选择区域
|
||||
**文件**: `frontend/src/app/page.tsx`
|
||||
|
||||
**新增状态**:
|
||||
```typescript
|
||||
const [ttsMode, setTtsMode] = useState<'edgetts' | 'voiceclone'>('edgetts');
|
||||
const [refAudios, setRefAudios] = useState<RefAudio[]>([]);
|
||||
const [selectedRefAudio, setSelectedRefAudio] = useState<RefAudio | null>(null);
|
||||
const [refText, setRefText] = useState('');
|
||||
|
||||
// 在线录音相关
|
||||
const [isRecording, setIsRecording] = useState(false);
|
||||
const [recordedBlob, setRecordedBlob] = useState<Blob | null>(null);
|
||||
const [recordingTime, setRecordingTime] = useState(0);
|
||||
const mediaRecorderRef = useRef<MediaRecorder | null>(null);
|
||||
```
|
||||
|
||||
**UI 结构**:
|
||||
```
|
||||
┌─────────────────────────────────────┐
|
||||
│ 🎙️ 选择配音方式 │
|
||||
├─────────────────────────────────────┤
|
||||
│ [EdgeTTS 音色] [声音克隆] ← Tab │
|
||||
├─────────────────────────────────────┤
|
||||
│ Tab 1: 现有音色 2x3 网格 │
|
||||
│ │
|
||||
│ Tab 2: 声音克隆 │
|
||||
│ ┌───────────────────────────────┐ │
|
||||
│ │ 📁 我的参考音频 │ │
|
||||
│ │ [ref1] [ref2] [+上传] │ │
|
||||
│ └───────────────────────────────┘ │
|
||||
│ ┌───────────────────────────────┐ │
|
||||
│ │ 🎤 或在线录音 │ │
|
||||
│ │ [开始录音] [停止] 时长: 0:05 │ │
|
||||
│ │ (录音完成后显示试听和使用按钮) │ │
|
||||
│ └───────────────────────────────┘ │
|
||||
│ ┌───────────────────────────────┐ │
|
||||
│ │ 📝 参考音频文字 (必填) │ │
|
||||
│ │ [textarea] │ │
|
||||
│ └───────────────────────────────┘ │
|
||||
└─────────────────────────────────────┘
|
||||
```
|
||||
|
||||
**在线录音逻辑**:
|
||||
```typescript
|
||||
const startRecording = async () => {
|
||||
const stream = await navigator.mediaDevices.getUserMedia({ audio: true });
|
||||
const mediaRecorder = new MediaRecorder(stream, { mimeType: 'audio/webm' });
|
||||
const chunks: BlobPart[] = [];
|
||||
|
||||
mediaRecorder.ondataavailable = (e) => chunks.push(e.data);
|
||||
mediaRecorder.onstop = () => {
|
||||
const blob = new Blob(chunks, { type: 'audio/webm' });
|
||||
setRecordedBlob(blob);
|
||||
stream.getTracks().forEach(track => track.stop());
|
||||
};
|
||||
|
||||
mediaRecorder.start();
|
||||
setIsRecording(true);
|
||||
mediaRecorderRef.current = mediaRecorder;
|
||||
};
|
||||
|
||||
const stopRecording = () => {
|
||||
mediaRecorderRef.current?.stop();
|
||||
setIsRecording(false);
|
||||
};
|
||||
|
||||
const useRecording = async () => {
|
||||
// 将录音 Blob 上传到后端
|
||||
const formData = new FormData();
|
||||
formData.append('file', recordedBlob, 'recording.webm');
|
||||
formData.append('ref_text', refText);
|
||||
const { data } = await api.post('/api/ref-audios', formData);
|
||||
// 上传成功后刷新列表并选中
|
||||
fetchRefAudios();
|
||||
setSelectedRefAudio(data);
|
||||
};
|
||||
```
|
||||
|
||||
### 6. 前端：修改生成请求
|
||||
```typescript
|
||||
const handleGenerate = async () => {
|
||||
const payload = {
|
||||
material_path: materialObj.path,
|
||||
text: text,
|
||||
tts_mode: ttsMode,
|
||||
...(ttsMode === 'edgetts'
|
||||
? { voice }
|
||||
: { ref_audio_id: selectedRefAudio.id, ref_text: refText })
|
||||
};
|
||||
await api.post('/api/videos/generate', payload);
|
||||
};
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 文件清单
|
||||
|
||||
### 新建
|
||||
| 文件 | 描述 |
|
||||
|------|------|
|
||||
| `backend/app/services/voice_clone_service.py` | 声音克隆服务 |
|
||||
| `backend/app/api/ref_audios.py` | 参考音频管理 API |
|
||||
|
||||
### 修改
|
||||
| 文件 | 修改内容 |
|
||||
|------|----------|
|
||||
| `backend/app/api/videos.py` | 扩展 GenerateRequest，修改 TTS 调用逻辑 |
|
||||
| `backend/app/main.py` | 注册 ref_audios 路由 |
|
||||
| `backend/app/services/storage.py` | 添加 BUCKET_REF_AUDIOS |
|
||||
| `frontend/src/app/page.tsx` | Tab 切换 UI、参考音频选择、refText 输入 |
|
||||
|
||||
---
|
||||
|
||||
## 验证方法
|
||||
|
||||
1. **后端测试**:
|
||||
```bash
|
||||
# 启动后端
|
||||
cd backend && uvicorn app.main:app --port 8006
|
||||
|
||||
# 测试参考音频上传
|
||||
curl -X POST http://localhost:8006/api/ref-audios \
|
||||
-F "file=@test.wav" -F "ref_text=测试文字"
|
||||
|
||||
# 测试声音克隆生成
|
||||
curl -X POST http://localhost:8006/api/videos/generate \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"text":"测试文案","tts_mode":"voiceclone","ref_audio_id":"xxx","ref_text":"参考文字","material_path":"..."}'
|
||||
```
|
||||
|
||||
2. **前端测试**:
|
||||
- 打开首页，确认 Tab 切换正常
|
||||
- 上传参考音频，确认列表显示
|
||||
- 选择声音克隆模式，填写参考文字，点击生成
|
||||
- 确认生成的视频使用克隆的声音
|
||||
|
||||
3. **端到端测试**:
|
||||
- 上传参考音频 → 选择声音克隆 → 输入口播文案 → 生成视频 → 播放验证声音
|
||||
@@ -1,23 +1,24 @@
|
||||
# ViGent 数字人口播系统 - 开发任务清单
|
||||
|
||||
**项目**：ViGent2 数字人口播视频生成系统
|
||||
**服务器**：Dell R730 (2× RTX 3090 24GB)
|
||||
**更新时间**：2026-01-27
|
||||
**整体进度**：100%（Day 11 上传架构重构与稳定性增强）
|
||||
**项目**：ViGent2 数字人口播视频生成系统
|
||||
**服务器**：Dell R730 (2× RTX 3090 24GB)
|
||||
**更新时间**：2026-02-02
|
||||
**整体进度**：100%（Day 15 手机号登录迁移 + 账户设置功能完成）
|
||||
|
||||
## 📖 快速导航
|
||||
|
||||
| 章节 | 说明 |
|
||||
|------|------|
|
||||
| [已完成任务](#-已完成任务) | Day 1-4 完成的功能 |
|
||||
| [已完成任务](#-已完成任务) | Day 1-13 完成的功能 |
|
||||
| [后续规划](#️-后续规划) | 待办项目 |
|
||||
| [进度统计](#-进度统计) | 各模块完成度 |
|
||||
| [里程碑](#-里程碑) | 关键节点 |
|
||||
| [时间线](#-时间线) | 开发历程 |
|
||||
|
||||
**相关文档**：
|
||||
- [Day 日志](file:///d:/CodingProjects/Antigravity/ViGent2/Docs/DevLogs/) (Day1-Day9)
|
||||
- [Day 日志](file:///d:/CodingProjects/Antigravity/ViGent2/Docs/DevLogs/) (Day1-Day15)
|
||||
- [部署指南](file:///d:/CodingProjects/Antigravity/ViGent2/Docs/DEPLOY_MANUAL.md)
|
||||
- [Qwen3-TTS 部署](file:///d:/CodingProjects/Antigravity/ViGent2/Docs/QWEN3_TTS_DEPLOY.md)
|
||||
|
||||
---
|
||||
|
||||
@@ -161,21 +162,63 @@
|
||||
- [x] **发布服务优化** (直接读取本地 Supabase Storage 文件，跳过 HTTP 下载)
|
||||
- [x] **Supabase Studio 配置** (公网访问配置)
|
||||
|
||||
### 阶段十九：iOS 兼容与移动端 UI 优化 (Day 12)
|
||||
- [x] **Axios 全局拦截器** (401/403 自动跳转登录，防重复跳转)
|
||||
- [x] **iOS Safari 安全区域修复** (viewport-fit: cover, themeColor, 渐变背景统一)
|
||||
- [x] **移动端 Header 优化** (按钮紧凑布局，响应式间距)
|
||||
- [x] **发布页面 UI 重构** (立即发布/定时发布按钮分离，防误触设计)
|
||||
- [x] **Qwen3-TTS 1.7B 部署** (声音克隆模型，GPU0，更高质量)
|
||||
|
||||
### 阶段二十：声音克隆功能集成 (Day 13)
|
||||
- [x] **Qwen3-TTS HTTP 服务** (独立 FastAPI 服务，端口 8009)
|
||||
- [x] **声音克隆服务** (voice_clone_service.py，HTTP 调用封装)
|
||||
- [x] **参考音频管理 API** (上传/列表/删除)
|
||||
- [x] **前端 TTS 模式选择** (EdgeTTS / 声音克隆切换)
|
||||
- [x] **Supabase ref-audios Bucket** (参考音频存储桶 + RLS 策略)
|
||||
- [x] **端到端测试验证** (声音克隆完整流程测试通过)
|
||||
|
||||
### 阶段二十一：逐字高亮字幕 + 片头标题 (Day 13)
|
||||
- [x] **faster-whisper 字幕对齐** (字级别时间戳生成)
|
||||
- [x] **Remotion 视频渲染** (React 视频合成框架)
|
||||
- [x] **逐字高亮字幕** (卡拉OK效果)
|
||||
- [x] **片头标题** (淡入淡出动画)
|
||||
- [x] **前端标题/字幕设置 UI**
|
||||
- [x] **降级机制** (Remotion 失败时回退 FFmpeg)
|
||||
|
||||
### 阶段二十二：AI 标题标签 + 前端稳定性修复 (Day 14)
|
||||
- [x] **Qwen3-TTS 1.7B 模型升级** (0.6B → 1.7B-Base)
|
||||
- [x] **字幕样式与标题动画优化** (Remotion 视觉增强)
|
||||
- [x] **AI 标题/标签生成** (GLM-4-Flash API)
|
||||
- [x] **生成结果同步到发布页** (localStorage 对齐)
|
||||
- [x] **文案/标题本地保存修复** (刷新后恢复)
|
||||
- [x] **登录页刷新循环修复** (公开路由跳转豁免)
|
||||
|
||||
### 阶段二十三：手机号登录迁移 (Day 15)
|
||||
- [x] **认证迁移** (邮箱 → 11位手机号)
|
||||
- [x] **后端 API 适配** (auth.py/admin.py 手机号验证)
|
||||
- [x] **修改密码功能** (/api/auth/change-password 接口)
|
||||
- [x] **账户设置菜单** (首页下拉菜单：修改密码 + 有效期显示 + 退出登录)
|
||||
- [x] **有效期显示** (expires_at 字段显示在账户菜单)
|
||||
- [x] **点击外部关闭菜单** (useRef + useEffect 监听)
|
||||
- [x] **前端页面更新** (登录/注册/管理员页面)
|
||||
- [x] **数据库迁移脚本** (migrate_to_phone.sql)
|
||||
|
||||
---
|
||||
|
||||
## 🛤️ 后续规划
|
||||
|
||||
### 🔴 优先待办
|
||||
- [ ] 批量视频生成架构设计
|
||||
- [ ] 字幕样式编辑器集成
|
||||
|
||||
### 🟠 功能完善
|
||||
- [x] Qwen3-TTS 集成到 ViGent2 ✅ Day 13 完成
|
||||
- [x] 定时发布功能 ✅ Day 7 完成
|
||||
- [x] 逐字高亮字幕 ✅ Day 13 完成
|
||||
- [ ] **后端定时发布** - 替代平台端定时，使用 APScheduler 实现任务调度
|
||||
- [ ] 批量视频生成
|
||||
- [ ] 字幕样式编辑器
|
||||
|
||||
### 🔵 长期探索
|
||||
- [ ] 声音克隆 (GPT-SoVITS)
|
||||
- [ ] Docker 容器化
|
||||
- [ ] Celery 分布式任务队列
|
||||
|
||||
@@ -254,7 +297,6 @@
|
||||
|
||||
## 📅 时间线
|
||||
|
||||
```
|
||||
Day 1: 项目初始化 + 核心功能 ✅ 完成
|
||||
- 后端 API 框架
|
||||
- 前端 UI
|
||||
@@ -340,5 +382,40 @@ Day 11: 上传架构重构 ✅ 完成
|
||||
- **用户数据隔离** (素材/视频/Cookie 按用户目录存储)
|
||||
- **Storage URL 修复** (SUPABASE_PUBLIC_URL 公网地址配置)
|
||||
- **发布服务优化** (本地文件直读，跳过 HTTP 下载)
|
||||
```
|
||||
|
||||
Day 12: iOS 兼容与移动端优化 ✅ 完成
|
||||
- Axios 全局拦截器 (401/403 自动跳转登录)
|
||||
- iOS Safari 安全区域白边修复 (viewport-fit: cover)
|
||||
- themeColor 配置 (状态栏颜色适配)
|
||||
- 渐变背景统一 (body 全局渐变，消除分层)
|
||||
- 移动端 Header 响应式优化 (按钮紧凑布局)
|
||||
- 发布页面 UI 重构 (立即发布 3/4 + 定时 1/4)
|
||||
- **Qwen3-TTS 1.7B 部署** (声音克隆模型，GPU0)
|
||||
- **部署文档** (QWEN3_TTS_DEPLOY.md)
|
||||
|
||||
Day 13: 声音克隆 + 字幕功能 ✅ 完成
|
||||
- Qwen3-TTS HTTP 服务 (独立 FastAPI，端口 8009)
|
||||
- 声音克隆服务 (voice_clone_service.py)
|
||||
- 参考音频管理 API (上传/列表/删除)
|
||||
- 前端 TTS 模式选择 (EdgeTTS / 声音克隆)
|
||||
- Supabase ref-audios Bucket 配置
|
||||
- 端到端测试验证通过
|
||||
- **faster-whisper 字幕对齐** (字级别时间戳)
|
||||
- **Remotion 视频渲染** (逐字高亮字幕 + 片头标题)
|
||||
- **前端标题/字幕设置 UI**
|
||||
- **部署文档** (SUBTITLE_DEPLOY.md)
|
||||
|
||||
Day 14: 模型升级 + AI 标题标签 + 前端修复 ✅ 完成
|
||||
- Qwen3-TTS 1.7B 模型升级 (0.6B → 1.7B-Base)
|
||||
- 字幕样式与标题动画优化 (Remotion)
|
||||
- AI 标题/标签生成接口 + 前端同步
|
||||
- 文案/标题本地保存修复 (刷新后恢复)
|
||||
- 登录页刷新循环修复 (公开路由跳转豁免)
|
||||
|
||||
Day 15: 手机号登录迁移 + 账户设置 ✅ 完成
|
||||
- 认证系统迁移 (邮箱 → 11位手机号)
|
||||
- 修改密码 API (/api/auth/change-password)
|
||||
- 账户设置下拉菜单 (修改密码 + 退出登录)
|
||||
- 前端登录/注册页面更新
|
||||
- 数据库迁移脚本 (migrate_to_phone.sql)
|
||||
|
||||
|
||||
28
README.md
28
README.md
@@ -10,10 +10,17 @@
|
||||
|
||||
- 🎬 **唇形同步** - LatentSync 1.6 驱动，512×512 高分辨率 Diffusion 模型
|
||||
- 🎙️ **TTS 配音** - EdgeTTS 多音色支持（云溪、晓晓等）
|
||||
- 🔊 **声音克隆** - Qwen3-TTS 1.7B，3秒参考音频快速克隆（更高质量）
|
||||
- 📝 **逐字高亮字幕** - faster-whisper + Remotion，卡拉OK效果 🆕
|
||||
- 🎬 **片头标题** - 淡入淡出动画，可自定义 🆕
|
||||
- 🤖 **AI 标题/标签生成** - GLM-4-Flash 自动生成标题与标签 🆕
|
||||
- 📱 **全自动发布** - 扫码登录 + Cookie持久化，支持多平台(B站/抖音/小红书)定时发布
|
||||
- 🖥️ **Web UI** - Next.js 现代化界面
|
||||
- 🔐 **用户系统** - Supabase + JWT 认证，支持管理员后台、注册/登录、账号隔离
|
||||
- 🚀 **性能优化** - 视频预压缩、常驻模型服务 (0s加载)
|
||||
- 🖥️ **Web UI** - Next.js 现代化界面，iOS/Android 移动端适配
|
||||
- 🔐 **用户系统** - Supabase + JWT 认证，**手机号登录** + 管理员后台 🆕
|
||||
- ⚙️ **账户设置** - 修改密码 + 有效期显示 + 安全退出 🆕
|
||||
- 👥 **多用户隔离** - 素材/视频/Cookie 按用户独立存储，数据完全隔离
|
||||
- 🚀 **性能优化** - 视频预压缩、常驻模型服务 (0s加载)、本地文件直读、并发控制
|
||||
- 🌐 **全局任务管理** - 跨页面任务状态同步，实时进度显示
|
||||
|
||||
## 🛠️ 技术栈
|
||||
|
||||
@@ -21,10 +28,13 @@
|
||||
|------|------|
|
||||
| 前端 | Next.js 14 + TypeScript + TailwindCSS |
|
||||
| 后端 | FastAPI + Python 3.10 |
|
||||
| 数据库 | **Supabase** (PostgreSQL) Local Docker |
|
||||
| 数据库 | **Supabase** (PostgreSQL) 自托管 Docker |
|
||||
| 存储 | **Supabase Storage** (本地文件系统) |
|
||||
| 认证 | **JWT** + HttpOnly Cookie |
|
||||
| 唇形同步 | **LatentSync 1.6** (Latent Diffusion, 512×512) |
|
||||
| TTS | EdgeTTS |
|
||||
| 声音克隆 | **Qwen3-TTS 1.7B** |
|
||||
| 字幕渲染 | **faster-whisper + Remotion** |
|
||||
| 视频处理 | FFmpeg |
|
||||
| 自动发布 | Playwright |
|
||||
|
||||
@@ -139,18 +149,20 @@ nohup python -m scripts.server > server.log 2>&1 &
|
||||
| **API 服务** | `http://<服务器IP>:8006` | 后端 Swagger |
|
||||
| **认证管理 (Studio)** | `https://supabase.hbyrkj.top` | 需要 Basic Auth |
|
||||
| **认证 API (Kong)** | `https://api.hbyrkj.top` | Supabase 接口 |
|
||||
| **模型服务** | `http://<服务器IP>:8007` | LatentSync |
|
||||
| **唇形同步服务** | `http://<服务器IP>:8007` | LatentSync |
|
||||
| **声音克隆服务** | `http://<服务器IP>:8009` | Qwen3-TTS |
|
||||
|
||||
---
|
||||
|
||||
## 📖 文档
|
||||
|
||||
- [LatentSync 部署指南](models/LatentSync/DEPLOY.md)
|
||||
- [手动部署指南](Docs/DEPLOY_MANUAL.md)
|
||||
- [LatentSync 部署指南](models/LatentSync/DEPLOY.md)
|
||||
- [手动部署指南](Docs/DEPLOY_MANUAL.md)
|
||||
- [Supabase 部署指南](Docs/SUPABASE_DEPLOY.md)
|
||||
- [Qwen3-TTS 部署指南](Docs/QWEN3_TTS_DEPLOY.md)
|
||||
- [字幕功能部署指南](Docs/SUBTITLE_DEPLOY.md)
|
||||
- [LatentSync 部署指南](models/LatentSync/DEPLOY.md)
|
||||
- [开发日志](Docs/DevLogs/)
|
||||
- [Day 15 - 手机号登录 + 账户设置](Docs/DevLogs/Day15.md) 🆕
|
||||
- [任务进度](Docs/task_complete.md)
|
||||
|
||||
---
|
||||
|
||||
@@ -20,16 +20,16 @@ LATENTSYNC_GPU_ID=1
|
||||
LATENTSYNC_LOCAL=true
|
||||
|
||||
# 使用常驻服务 (Persistent Server) 加速
|
||||
LATENTSYNC_USE_SERVER=false
|
||||
LATENTSYNC_USE_SERVER=true
|
||||
|
||||
# 远程 API 地址 (常驻服务默认端口 8007)
|
||||
# LATENTSYNC_API_URL=http://localhost:8007
|
||||
|
||||
# 推理步数 (20-50, 越高质量越好，速度越慢)
|
||||
LATENTSYNC_INFERENCE_STEPS=20
|
||||
LATENTSYNC_INFERENCE_STEPS=40
|
||||
|
||||
# 引导系数 (1.0-3.0, 越高唇同步越准，但可能抖动)
|
||||
LATENTSYNC_GUIDANCE_SCALE=1.5
|
||||
LATENTSYNC_GUIDANCE_SCALE=2.0
|
||||
|
||||
# 启用 DeepCache 加速 (推荐开启)
|
||||
LATENTSYNC_ENABLE_DEEPCACHE=true
|
||||
@@ -59,5 +59,5 @@ JWT_EXPIRE_HOURS=168
|
||||
|
||||
# =============== 管理员配置 ===============
|
||||
# 服务启动时自动创建的管理员账号
|
||||
ADMIN_EMAIL=lamnickdavid@gmail.com
|
||||
ADMIN_PHONE=15549380526
|
||||
ADMIN_PASSWORD=lam1988324
|
||||
|
||||
@@ -14,7 +14,7 @@ router = APIRouter(prefix="/api/admin", tags=["管理"])
|
||||
|
||||
class UserListItem(BaseModel):
|
||||
id: str
|
||||
email: str
|
||||
phone: str
|
||||
username: Optional[str]
|
||||
role: str
|
||||
is_active: bool
|
||||
@@ -36,7 +36,7 @@ async def list_users(admin: dict = Depends(get_current_admin)):
|
||||
return [
|
||||
UserListItem(
|
||||
id=u["id"],
|
||||
email=u["email"],
|
||||
phone=u["phone"],
|
||||
username=u.get("username"),
|
||||
role=u["role"],
|
||||
is_active=u["is_active"],
|
||||
@@ -87,7 +87,7 @@ async def activate_user(
|
||||
detail="用户不存在"
|
||||
)
|
||||
|
||||
logger.info(f"管理员 {admin['email']} 激活用户 {user_id}, 有效期: {request.expires_days or '永久'} 天")
|
||||
logger.info(f"管理员 {admin['phone']} 激活用户 {user_id}, 有效期: {request.expires_days or '永久'} 天")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
@@ -128,7 +128,7 @@ async def deactivate_user(
|
||||
# 清除用户 session
|
||||
supabase.table("user_sessions").delete().eq("user_id", user_id).execute()
|
||||
|
||||
logger.info(f"管理员 {admin['email']} 停用用户 {user_id}")
|
||||
logger.info(f"管理员 {admin['phone']} 停用用户 {user_id}")
|
||||
|
||||
return {"success": True, "message": "用户已停用"}
|
||||
except HTTPException:
|
||||
@@ -171,7 +171,7 @@ async def extend_user(
|
||||
"expires_at": expires_at
|
||||
}).eq("id", user_id).execute()
|
||||
|
||||
logger.info(f"管理员 {admin['email']} 延长用户 {user_id} 授权 {request.expires_days or '永久'} 天")
|
||||
logger.info(f"管理员 {admin['phone']} 延长用户 {user_id} 授权 {request.expires_days or '永久'} 天")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
|
||||
45
backend/app/api/ai.py
Normal file
45
backend/app/api/ai.py
Normal file
@@ -0,0 +1,45 @@
|
||||
"""
|
||||
AI 相关 API 路由
|
||||
"""
|
||||
|
||||
from fastapi import APIRouter, HTTPException
|
||||
from pydantic import BaseModel
|
||||
from loguru import logger
|
||||
|
||||
from app.services.glm_service import glm_service
|
||||
|
||||
|
||||
router = APIRouter(prefix="/api/ai", tags=["AI"])
|
||||
|
||||
|
||||
class GenerateMetaRequest(BaseModel):
|
||||
"""生成标题标签请求"""
|
||||
text: str
|
||||
|
||||
|
||||
class GenerateMetaResponse(BaseModel):
|
||||
"""生成标题标签响应"""
|
||||
title: str
|
||||
tags: list[str]
|
||||
|
||||
|
||||
@router.post("/generate-meta", response_model=GenerateMetaResponse)
|
||||
async def generate_meta(req: GenerateMetaRequest):
|
||||
"""
|
||||
AI 生成视频标题和标签
|
||||
|
||||
根据口播文案自动生成吸引人的标题和相关标签
|
||||
"""
|
||||
if not req.text or not req.text.strip():
|
||||
raise HTTPException(status_code=400, detail="口播文案不能为空")
|
||||
|
||||
try:
|
||||
logger.info(f"Generating meta for text: {req.text[:50]}...")
|
||||
result = await glm_service.generate_title_tags(req.text)
|
||||
return GenerateMetaResponse(
|
||||
title=result.get("title", ""),
|
||||
tags=result.get("tags", [])
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"Generate meta failed: {e}")
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
@@ -1,8 +1,8 @@
|
||||
"""
|
||||
认证 API：注册、登录、登出
|
||||
认证 API：注册、登录、登出、修改密码
|
||||
"""
|
||||
from fastapi import APIRouter, HTTPException, Response, status, Request
|
||||
from pydantic import BaseModel, EmailStr
|
||||
from pydantic import BaseModel, field_validator
|
||||
from app.core.supabase import get_supabase
|
||||
from app.core.security import (
|
||||
get_password_hash,
|
||||
@@ -15,27 +15,55 @@ from app.core.security import (
|
||||
)
|
||||
from loguru import logger
|
||||
from typing import Optional
|
||||
import re
|
||||
|
||||
router = APIRouter(prefix="/api/auth", tags=["认证"])
|
||||
|
||||
|
||||
class RegisterRequest(BaseModel):
|
||||
email: EmailStr
|
||||
phone: str
|
||||
password: str
|
||||
username: Optional[str] = None
|
||||
|
||||
@field_validator('phone')
|
||||
@classmethod
|
||||
def validate_phone(cls, v):
|
||||
if not re.match(r'^\d{11}$', v):
|
||||
raise ValueError('手机号必须是11位数字')
|
||||
return v
|
||||
|
||||
|
||||
class LoginRequest(BaseModel):
|
||||
email: EmailStr
|
||||
phone: str
|
||||
password: str
|
||||
|
||||
@field_validator('phone')
|
||||
@classmethod
|
||||
def validate_phone(cls, v):
|
||||
if not re.match(r'^\d{11}$', v):
|
||||
raise ValueError('手机号必须是11位数字')
|
||||
return v
|
||||
|
||||
|
||||
class ChangePasswordRequest(BaseModel):
|
||||
old_password: str
|
||||
new_password: str
|
||||
|
||||
@field_validator('new_password')
|
||||
@classmethod
|
||||
def validate_new_password(cls, v):
|
||||
if len(v) < 6:
|
||||
raise ValueError('新密码长度至少6位')
|
||||
return v
|
||||
|
||||
|
||||
class UserResponse(BaseModel):
|
||||
id: str
|
||||
email: str
|
||||
phone: str
|
||||
username: Optional[str]
|
||||
role: str
|
||||
is_active: bool
|
||||
expires_at: Optional[str] = None
|
||||
|
||||
|
||||
@router.post("/register")
|
||||
@@ -48,29 +76,29 @@ async def register(request: RegisterRequest):
|
||||
try:
|
||||
supabase = get_supabase()
|
||||
|
||||
# 检查邮箱是否已存在
|
||||
# 检查手机号是否已存在
|
||||
existing = supabase.table("users").select("id").eq(
|
||||
"email", request.email
|
||||
"phone", request.phone
|
||||
).execute()
|
||||
|
||||
if existing.data:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_400_BAD_REQUEST,
|
||||
detail="该邮箱已注册"
|
||||
detail="该手机号已注册"
|
||||
)
|
||||
|
||||
# 创建用户
|
||||
password_hash = get_password_hash(request.password)
|
||||
|
||||
result = supabase.table("users").insert({
|
||||
"email": request.email,
|
||||
"phone": request.phone,
|
||||
"password_hash": password_hash,
|
||||
"username": request.username or request.email.split("@")[0],
|
||||
"username": request.username or f"用户{request.phone[-4:]}",
|
||||
"role": "pending",
|
||||
"is_active": False
|
||||
}).execute()
|
||||
|
||||
logger.info(f"新用户注册: {request.email}")
|
||||
logger.info(f"新用户注册: {request.phone}")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
@@ -100,21 +128,21 @@ async def login(request: LoginRequest, response: Response):
|
||||
|
||||
# 查找用户
|
||||
user_result = supabase.table("users").select("*").eq(
|
||||
"email", request.email
|
||||
"phone", request.phone
|
||||
).single().execute()
|
||||
|
||||
user = user_result.data
|
||||
if not user:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_401_UNAUTHORIZED,
|
||||
detail="邮箱或密码错误"
|
||||
detail="手机号或密码错误"
|
||||
)
|
||||
|
||||
# 验证密码
|
||||
if not verify_password(request.password, user["password_hash"]):
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_401_UNAUTHORIZED,
|
||||
detail="邮箱或密码错误"
|
||||
detail="手机号或密码错误"
|
||||
)
|
||||
|
||||
# 检查是否激活
|
||||
@@ -154,17 +182,18 @@ async def login(request: LoginRequest, response: Response):
|
||||
# 设置 HttpOnly Cookie
|
||||
set_auth_cookie(response, token)
|
||||
|
||||
logger.info(f"用户登录: {request.email}")
|
||||
logger.info(f"用户登录: {request.phone}")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"message": "登录成功",
|
||||
"user": UserResponse(
|
||||
id=user["id"],
|
||||
email=user["email"],
|
||||
phone=user["phone"],
|
||||
username=user.get("username"),
|
||||
role=user["role"],
|
||||
is_active=user["is_active"]
|
||||
is_active=user["is_active"],
|
||||
expires_at=user.get("expires_at")
|
||||
)
|
||||
}
|
||||
except HTTPException:
|
||||
@@ -184,6 +213,91 @@ async def logout(response: Response):
|
||||
return {"success": True, "message": "已登出"}
|
||||
|
||||
|
||||
@router.post("/change-password")
|
||||
async def change_password(request: ChangePasswordRequest, req: Request, response: Response):
|
||||
"""
|
||||
修改密码
|
||||
|
||||
- 验证当前密码
|
||||
- 设置新密码
|
||||
- 重新生成 session token
|
||||
"""
|
||||
# 从 Cookie 获取用户
|
||||
token = req.cookies.get("access_token")
|
||||
if not token:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_401_UNAUTHORIZED,
|
||||
detail="未登录"
|
||||
)
|
||||
|
||||
token_data = decode_access_token(token)
|
||||
if not token_data:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_401_UNAUTHORIZED,
|
||||
detail="Token 无效"
|
||||
)
|
||||
|
||||
try:
|
||||
supabase = get_supabase()
|
||||
|
||||
# 获取用户信息
|
||||
user_result = supabase.table("users").select("*").eq(
|
||||
"id", token_data.user_id
|
||||
).single().execute()
|
||||
|
||||
user = user_result.data
|
||||
if not user:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_401_UNAUTHORIZED,
|
||||
detail="用户不存在"
|
||||
)
|
||||
|
||||
# 验证当前密码
|
||||
if not verify_password(request.old_password, user["password_hash"]):
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_400_BAD_REQUEST,
|
||||
detail="当前密码错误"
|
||||
)
|
||||
|
||||
# 更新密码
|
||||
new_password_hash = get_password_hash(request.new_password)
|
||||
supabase.table("users").update({
|
||||
"password_hash": new_password_hash
|
||||
}).eq("id", user["id"]).execute()
|
||||
|
||||
# 生成新的 session token，使旧 token 失效
|
||||
new_session_token = generate_session_token()
|
||||
|
||||
supabase.table("user_sessions").delete().eq(
|
||||
"user_id", user["id"]
|
||||
).execute()
|
||||
|
||||
supabase.table("user_sessions").insert({
|
||||
"user_id": user["id"],
|
||||
"session_token": new_session_token,
|
||||
"device_info": None
|
||||
}).execute()
|
||||
|
||||
# 生成新的 JWT Token
|
||||
new_token = create_access_token(user["id"], new_session_token)
|
||||
set_auth_cookie(response, new_token)
|
||||
|
||||
logger.info(f"用户修改密码: {user['phone']}")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"message": "密码修改成功"
|
||||
}
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"修改密码失败: {e}")
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||||
detail="修改密码失败，请稍后重试"
|
||||
)
|
||||
|
||||
|
||||
@router.get("/me")
|
||||
async def get_me(request: Request):
|
||||
"""获取当前用户信息"""
|
||||
@@ -216,8 +330,9 @@ async def get_me(request: Request):
|
||||
|
||||
return UserResponse(
|
||||
id=user["id"],
|
||||
email=user["email"],
|
||||
phone=user["phone"],
|
||||
username=user.get("username"),
|
||||
role=user["role"],
|
||||
is_active=user["is_active"]
|
||||
is_active=user["is_active"],
|
||||
expires_at=user.get("expires_at")
|
||||
)
|
||||
|
||||
276
backend/app/api/ref_audios.py
Normal file
276
backend/app/api/ref_audios.py
Normal file
@@ -0,0 +1,276 @@
|
||||
"""
|
||||
参考音频管理 API
|
||||
支持上传/列表/删除参考音频，用于 Qwen3-TTS 声音克隆
|
||||
"""
|
||||
from fastapi import APIRouter, UploadFile, File, Form, HTTPException, Depends
|
||||
from pydantic import BaseModel
|
||||
from typing import List, Optional
|
||||
from pathlib import Path
|
||||
from loguru import logger
|
||||
import time
|
||||
import json
|
||||
import subprocess
|
||||
import tempfile
|
||||
import os
|
||||
import re
|
||||
|
||||
from app.core.deps import get_current_user
|
||||
from app.services.storage import storage_service
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
# 支持的音频格式
|
||||
ALLOWED_AUDIO_EXTENSIONS = {'.wav', '.mp3', '.m4a', '.webm', '.ogg', '.flac', '.aac'}
|
||||
|
||||
# 参考音频 bucket
|
||||
BUCKET_REF_AUDIOS = "ref-audios"
|
||||
|
||||
|
||||
class RefAudioResponse(BaseModel):
|
||||
id: str
|
||||
name: str
|
||||
path: str # signed URL for playback
|
||||
ref_text: str
|
||||
duration_sec: float
|
||||
created_at: int
|
||||
|
||||
|
||||
class RefAudioListResponse(BaseModel):
|
||||
items: List[RefAudioResponse]
|
||||
|
||||
|
||||
def sanitize_filename(filename: str) -> str:
|
||||
"""清理文件名，移除特殊字符"""
|
||||
safe_name = re.sub(r'[<>:"/\\|?*\s]', '_', filename)
|
||||
if len(safe_name) > 50:
|
||||
ext = Path(safe_name).suffix
|
||||
safe_name = safe_name[:50 - len(ext)] + ext
|
||||
return safe_name
|
||||
|
||||
|
||||
def get_audio_duration(file_path: str) -> float:
|
||||
"""获取音频时长 (秒)"""
|
||||
try:
|
||||
result = subprocess.run(
|
||||
['ffprobe', '-v', 'quiet', '-show_entries', 'format=duration',
|
||||
'-of', 'csv=p=0', file_path],
|
||||
capture_output=True, text=True, timeout=10
|
||||
)
|
||||
return float(result.stdout.strip())
|
||||
except Exception as e:
|
||||
logger.warning(f"获取音频时长失败: {e}")
|
||||
return 0.0
|
||||
|
||||
|
||||
def convert_to_wav(input_path: str, output_path: str) -> bool:
|
||||
"""将音频转换为 WAV 格式 (16kHz, mono)"""
|
||||
try:
|
||||
subprocess.run([
|
||||
'ffmpeg', '-y', '-i', input_path,
|
||||
'-ar', '16000', # 16kHz 采样率
|
||||
'-ac', '1', # 单声道
|
||||
'-acodec', 'pcm_s16le', # 16-bit PCM
|
||||
output_path
|
||||
], capture_output=True, timeout=60, check=True)
|
||||
return True
|
||||
except Exception as e:
|
||||
logger.error(f"音频转换失败: {e}")
|
||||
return False
|
||||
|
||||
|
||||
@router.post("", response_model=RefAudioResponse)
|
||||
async def upload_ref_audio(
|
||||
file: UploadFile = File(...),
|
||||
ref_text: str = Form(...),
|
||||
user: dict = Depends(get_current_user)
|
||||
):
|
||||
"""
|
||||
上传参考音频
|
||||
|
||||
- file: 音频文件 (支持 wav, mp3, m4a, webm 等)
|
||||
- ref_text: 参考音频的转写文字 (必填)
|
||||
"""
|
||||
user_id = user["id"]
|
||||
|
||||
# 验证文件扩展名
|
||||
ext = Path(file.filename).suffix.lower()
|
||||
if ext not in ALLOWED_AUDIO_EXTENSIONS:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"不支持的音频格式: {ext}。支持的格式: {', '.join(ALLOWED_AUDIO_EXTENSIONS)}"
|
||||
)
|
||||
|
||||
# 验证 ref_text
|
||||
if not ref_text or len(ref_text.strip()) < 2:
|
||||
raise HTTPException(status_code=400, detail="参考文字不能为空")
|
||||
|
||||
try:
|
||||
# 创建临时文件
|
||||
with tempfile.NamedTemporaryFile(delete=False, suffix=ext) as tmp_input:
|
||||
content = await file.read()
|
||||
tmp_input.write(content)
|
||||
tmp_input_path = tmp_input.name
|
||||
|
||||
# 转换为 WAV 格式
|
||||
tmp_wav_path = tmp_input_path + ".wav"
|
||||
if ext != '.wav':
|
||||
if not convert_to_wav(tmp_input_path, tmp_wav_path):
|
||||
raise HTTPException(status_code=500, detail="音频格式转换失败")
|
||||
else:
|
||||
# 即使是 wav 也要标准化格式
|
||||
convert_to_wav(tmp_input_path, tmp_wav_path)
|
||||
|
||||
# 获取音频时长
|
||||
duration = get_audio_duration(tmp_wav_path)
|
||||
if duration < 1.0:
|
||||
raise HTTPException(status_code=400, detail="音频时长过短，至少需要 1 秒")
|
||||
if duration > 60.0:
|
||||
raise HTTPException(status_code=400, detail="音频时长过长，最多 60 秒")
|
||||
|
||||
# 生成存储路径
|
||||
timestamp = int(time.time())
|
||||
safe_name = sanitize_filename(Path(file.filename).stem)
|
||||
storage_path = f"{user_id}/{timestamp}_{safe_name}.wav"
|
||||
|
||||
# 上传 WAV 文件到 Supabase
|
||||
with open(tmp_wav_path, 'rb') as f:
|
||||
wav_data = f.read()
|
||||
|
||||
await storage_service.upload_file(
|
||||
bucket=BUCKET_REF_AUDIOS,
|
||||
path=storage_path,
|
||||
file_data=wav_data,
|
||||
content_type="audio/wav"
|
||||
)
|
||||
|
||||
# 上传元数据 JSON
|
||||
metadata = {
|
||||
"ref_text": ref_text.strip(),
|
||||
"original_filename": file.filename,
|
||||
"duration_sec": duration,
|
||||
"created_at": timestamp
|
||||
}
|
||||
metadata_path = f"{user_id}/{timestamp}_{safe_name}.json"
|
||||
await storage_service.upload_file(
|
||||
bucket=BUCKET_REF_AUDIOS,
|
||||
path=metadata_path,
|
||||
file_data=json.dumps(metadata, ensure_ascii=False).encode('utf-8'),
|
||||
content_type="application/json"
|
||||
)
|
||||
|
||||
# 获取签名 URL
|
||||
signed_url = await storage_service.get_signed_url(BUCKET_REF_AUDIOS, storage_path)
|
||||
|
||||
# 清理临时文件
|
||||
os.unlink(tmp_input_path)
|
||||
if os.path.exists(tmp_wav_path):
|
||||
os.unlink(tmp_wav_path)
|
||||
|
||||
return RefAudioResponse(
|
||||
id=storage_path,
|
||||
name=file.filename,
|
||||
path=signed_url,
|
||||
ref_text=ref_text.strip(),
|
||||
duration_sec=duration,
|
||||
created_at=timestamp
|
||||
)
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"上传参考音频失败: {e}")
|
||||
raise HTTPException(status_code=500, detail=f"上传失败: {str(e)}")
|
||||
|
||||
|
||||
@router.get("", response_model=RefAudioListResponse)
|
||||
async def list_ref_audios(user: dict = Depends(get_current_user)):
|
||||
"""列出当前用户的所有参考音频"""
|
||||
user_id = user["id"]
|
||||
|
||||
try:
|
||||
# 列出用户目录下的文件
|
||||
files = await storage_service.list_files(BUCKET_REF_AUDIOS, user_id)
|
||||
|
||||
# 过滤出 .wav 文件并获取对应的 metadata
|
||||
items = []
|
||||
for f in files:
|
||||
name = f.get("name", "")
|
||||
if not name.endswith(".wav"):
|
||||
continue
|
||||
|
||||
storage_path = f"{user_id}/{name}"
|
||||
|
||||
# 尝试读取 metadata
|
||||
metadata_name = name.replace(".wav", ".json")
|
||||
metadata_path = f"{user_id}/{metadata_name}"
|
||||
|
||||
ref_text = ""
|
||||
duration_sec = 0.0
|
||||
created_at = 0
|
||||
|
||||
try:
|
||||
# 获取 metadata 内容
|
||||
metadata_url = await storage_service.get_signed_url(BUCKET_REF_AUDIOS, metadata_path)
|
||||
import httpx
|
||||
async with httpx.AsyncClient() as client:
|
||||
resp = await client.get(metadata_url)
|
||||
if resp.status_code == 200:
|
||||
metadata = resp.json()
|
||||
ref_text = metadata.get("ref_text", "")
|
||||
duration_sec = metadata.get("duration_sec", 0.0)
|
||||
created_at = metadata.get("created_at", 0)
|
||||
except Exception as e:
|
||||
logger.warning(f"读取 metadata 失败: {e}")
|
||||
# 从文件名提取时间戳
|
||||
try:
|
||||
created_at = int(name.split("_")[0])
|
||||
except:
|
||||
pass
|
||||
|
||||
# 获取音频签名 URL
|
||||
signed_url = await storage_service.get_signed_url(BUCKET_REF_AUDIOS, storage_path)
|
||||
|
||||
items.append(RefAudioResponse(
|
||||
id=storage_path,
|
||||
name=name,
|
||||
path=signed_url,
|
||||
ref_text=ref_text,
|
||||
duration_sec=duration_sec,
|
||||
created_at=created_at
|
||||
))
|
||||
|
||||
# 按创建时间倒序排列
|
||||
items.sort(key=lambda x: x.created_at, reverse=True)
|
||||
|
||||
return RefAudioListResponse(items=items)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"列出参考音频失败: {e}")
|
||||
raise HTTPException(status_code=500, detail=f"获取列表失败: {str(e)}")
|
||||
|
||||
|
||||
@router.delete("/{audio_id:path}")
|
||||
async def delete_ref_audio(audio_id: str, user: dict = Depends(get_current_user)):
|
||||
"""删除参考音频"""
|
||||
user_id = user["id"]
|
||||
|
||||
# 安全检查：确保只能删除自己的文件
|
||||
if not audio_id.startswith(f"{user_id}/"):
|
||||
raise HTTPException(status_code=403, detail="无权删除此文件")
|
||||
|
||||
try:
|
||||
# 删除 WAV 文件
|
||||
await storage_service.delete_file(BUCKET_REF_AUDIOS, audio_id)
|
||||
|
||||
# 删除 metadata JSON
|
||||
metadata_path = audio_id.replace(".wav", ".json")
|
||||
try:
|
||||
await storage_service.delete_file(BUCKET_REF_AUDIOS, metadata_path)
|
||||
except:
|
||||
pass # metadata 可能不存在
|
||||
|
||||
return {"success": True, "message": "删除成功"}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"删除参考音频失败: {e}")
|
||||
raise HTTPException(status_code=500, detail=f"删除失败: {str(e)}")
|
||||
@@ -11,7 +11,10 @@ import os
|
||||
from app.services.tts_service import TTSService
|
||||
from app.services.video_service import VideoService
|
||||
from app.services.lipsync_service import LipSyncService
|
||||
from app.services.voice_clone_service import voice_clone_service
|
||||
from app.services.storage import storage_service
|
||||
from app.services.whisper_service import whisper_service
|
||||
from app.services.remotion_service import remotion_service
|
||||
from app.core.config import settings
|
||||
from app.core.deps import get_current_user
|
||||
|
||||
@@ -21,6 +24,13 @@ class GenerateRequest(BaseModel):
|
||||
text: str
|
||||
voice: str = "zh-CN-YunxiNeural"
|
||||
material_path: str
|
||||
# 声音克隆模式新增字段
|
||||
tts_mode: str = "edgetts" # "edgetts" | "voiceclone"
|
||||
ref_audio_id: Optional[str] = None # 参考音频 storage path
|
||||
ref_text: Optional[str] = None # 参考音频的转写文字
|
||||
# 字幕和标题功能
|
||||
title: Optional[str] = None # 视频标题（片头显示）
|
||||
enable_subtitles: bool = True # 是否启用逐字高亮字幕
|
||||
|
||||
tasks = {} # In-memory task store
|
||||
|
||||
@@ -95,13 +105,42 @@ async def _process_video_generation(task_id: str, req: GenerateRequest, user_id:
|
||||
await _download_material(req.material_path, input_material_path)
|
||||
|
||||
# 1. TTS - 进度 5% -> 25%
|
||||
tasks[task_id]["message"] = "正在生成语音 (TTS)..."
|
||||
tasks[task_id]["message"] = "正在生成语音..."
|
||||
tasks[task_id]["progress"] = 10
|
||||
|
||||
tts = TTSService()
|
||||
audio_path = temp_dir / f"{task_id}_audio.mp3"
|
||||
audio_path = temp_dir / f"{task_id}_audio.wav"
|
||||
temp_files.append(audio_path)
|
||||
await tts.generate_audio(req.text, req.voice, str(audio_path))
|
||||
|
||||
if req.tts_mode == "voiceclone":
|
||||
# 声音克隆模式
|
||||
if not req.ref_audio_id or not req.ref_text:
|
||||
raise ValueError("声音克隆模式需要提供参考音频和参考文字")
|
||||
|
||||
tasks[task_id]["message"] = "正在下载参考音频..."
|
||||
|
||||
# 从 Supabase 下载参考音频
|
||||
ref_audio_local = temp_dir / f"{task_id}_ref.wav"
|
||||
temp_files.append(ref_audio_local)
|
||||
|
||||
ref_audio_url = await storage_service.get_signed_url(
|
||||
bucket="ref-audios",
|
||||
path=req.ref_audio_id
|
||||
)
|
||||
await _download_material(ref_audio_url, ref_audio_local)
|
||||
|
||||
tasks[task_id]["message"] = "正在克隆声音 (Qwen3-TTS)..."
|
||||
await voice_clone_service.generate_audio(
|
||||
text=req.text,
|
||||
ref_audio_path=str(ref_audio_local),
|
||||
ref_text=req.ref_text,
|
||||
output_path=str(audio_path),
|
||||
language="Chinese"
|
||||
)
|
||||
else:
|
||||
# EdgeTTS 模式 (默认)
|
||||
tasks[task_id]["message"] = "正在生成语音 (EdgeTTS)..."
|
||||
tts = TTSService()
|
||||
await tts.generate_audio(req.text, req.voice, str(audio_path))
|
||||
|
||||
tts_time = time.time() - start_time
|
||||
print(f"[Pipeline] TTS completed in {tts_time:.1f}s")
|
||||
@@ -133,17 +172,84 @@ async def _process_video_generation(task_id: str, req: GenerateRequest, user_id:
|
||||
|
||||
lipsync_time = time.time() - lipsync_start
|
||||
print(f"[Pipeline] LipSync completed in {lipsync_time:.1f}s")
|
||||
tasks[task_id]["progress"] = 80
|
||||
|
||||
# 3. WhisperX 字幕对齐 - 进度 80% -> 85%
|
||||
captions_path = None
|
||||
if req.enable_subtitles:
|
||||
tasks[task_id]["message"] = "正在生成字幕 (Whisper)..."
|
||||
tasks[task_id]["progress"] = 82
|
||||
|
||||
captions_path = temp_dir / f"{task_id}_captions.json"
|
||||
temp_files.append(captions_path)
|
||||
|
||||
try:
|
||||
await whisper_service.align(
|
||||
audio_path=str(audio_path),
|
||||
text=req.text,
|
||||
output_path=str(captions_path)
|
||||
)
|
||||
print(f"[Pipeline] Whisper alignment completed")
|
||||
except Exception as e:
|
||||
logger.warning(f"Whisper alignment failed, skipping subtitles: {e}")
|
||||
captions_path = None
|
||||
|
||||
tasks[task_id]["progress"] = 85
|
||||
|
||||
# 3. Composition - 进度 85% -> 100%
|
||||
tasks[task_id]["message"] = "正在合成最终视频..."
|
||||
tasks[task_id]["progress"] = 90
|
||||
# 4. Remotion 视频合成（字幕 + 标题）- 进度 85% -> 95%
|
||||
# 判断是否需要使用 Remotion（有字幕或标题时使用）
|
||||
use_remotion = (captions_path and captions_path.exists()) or req.title
|
||||
|
||||
video = VideoService()
|
||||
final_output_local_path = temp_dir / f"{task_id}_output.mp4"
|
||||
temp_files.append(final_output_local_path)
|
||||
|
||||
await video.compose(str(lipsync_video_path), str(audio_path), str(final_output_local_path))
|
||||
if use_remotion:
|
||||
tasks[task_id]["message"] = "正在合成视频 (Remotion)..."
|
||||
tasks[task_id]["progress"] = 87
|
||||
|
||||
# 先用 FFmpeg 合成音视频（Remotion 需要带音频的视频）
|
||||
composed_video_path = temp_dir / f"{task_id}_composed.mp4"
|
||||
temp_files.append(composed_video_path)
|
||||
|
||||
video = VideoService()
|
||||
await video.compose(str(lipsync_video_path), str(audio_path), str(composed_video_path))
|
||||
|
||||
# 检查 Remotion 是否可用
|
||||
remotion_health = await remotion_service.check_health()
|
||||
if remotion_health.get("ready"):
|
||||
try:
|
||||
def on_remotion_progress(percent):
|
||||
# 映射 Remotion 进度到 87-95%
|
||||
mapped = 87 + int(percent * 0.08)
|
||||
tasks[task_id]["progress"] = mapped
|
||||
|
||||
await remotion_service.render(
|
||||
video_path=str(composed_video_path),
|
||||
output_path=str(final_output_local_path),
|
||||
captions_path=str(captions_path) if captions_path else None,
|
||||
title=req.title,
|
||||
title_duration=3.0,
|
||||
fps=25,
|
||||
enable_subtitles=req.enable_subtitles,
|
||||
on_progress=on_remotion_progress
|
||||
)
|
||||
print(f"[Pipeline] Remotion render completed")
|
||||
except Exception as e:
|
||||
logger.warning(f"Remotion render failed, using FFmpeg fallback: {e}")
|
||||
# 回退到 FFmpeg 合成
|
||||
import shutil
|
||||
shutil.copy(str(composed_video_path), final_output_local_path)
|
||||
else:
|
||||
logger.warning(f"Remotion not ready: {remotion_health.get('error')}, using FFmpeg")
|
||||
import shutil
|
||||
shutil.copy(str(composed_video_path), final_output_local_path)
|
||||
else:
|
||||
# 不需要字幕和标题，直接用 FFmpeg 合成
|
||||
tasks[task_id]["message"] = "正在合成最终视频..."
|
||||
tasks[task_id]["progress"] = 90
|
||||
|
||||
video = VideoService()
|
||||
await video.compose(str(lipsync_video_path), str(audio_path), str(final_output_local_path))
|
||||
|
||||
total_time = time.time() - start_time
|
||||
|
||||
@@ -217,6 +323,12 @@ async def lipsync_health():
|
||||
return await lipsync.check_health()
|
||||
|
||||
|
||||
@router.get("/voiceclone/health")
|
||||
async def voiceclone_health():
|
||||
"""获取声音克隆服务健康状态"""
|
||||
return await voice_clone_service.check_health()
|
||||
|
||||
|
||||
@router.get("/generated")
|
||||
async def list_generated_videos(current_user: dict = Depends(get_current_user)):
|
||||
"""从 Storage 读取当前用户生成的视频列表"""
|
||||
|
||||
@@ -22,9 +22,8 @@ class Settings(BaseSettings):
|
||||
LATENTSYNC_INFERENCE_STEPS: int = 20 # 推理步数 [20-50]
|
||||
LATENTSYNC_GUIDANCE_SCALE: float = 1.5 # 引导系数 [1.0-3.0]
|
||||
LATENTSYNC_ENABLE_DEEPCACHE: bool = True # 启用 DeepCache 加速
|
||||
LATENTSYNC_ENABLE_DEEPCACHE: bool = True # 启用 DeepCache 加速
|
||||
LATENTSYNC_SEED: int = 1247 # 随机种子 (-1 则随机)
|
||||
LATENTSYNC_USE_SERVER: bool = False # 使用常驻服务 (Persistent Server) 加速
|
||||
LATENTSYNC_USE_SERVER: bool = True # 使用常驻服务 (Persistent Server) 加速
|
||||
|
||||
# Supabase 配置
|
||||
SUPABASE_URL: str = ""
|
||||
@@ -37,7 +36,7 @@ class Settings(BaseSettings):
|
||||
JWT_EXPIRE_HOURS: int = 24
|
||||
|
||||
# 管理员配置
|
||||
ADMIN_EMAIL: str = ""
|
||||
ADMIN_PHONE: str = ""
|
||||
ADMIN_PASSWORD: str = ""
|
||||
|
||||
@property
|
||||
|
||||
@@ -2,7 +2,7 @@ from fastapi import FastAPI
|
||||
from fastapi.staticfiles import StaticFiles
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
from app.core import config
|
||||
from app.api import materials, videos, publish, login_helper, auth, admin
|
||||
from app.api import materials, videos, publish, login_helper, auth, admin, ref_audios, ai
|
||||
from loguru import logger
|
||||
import os
|
||||
|
||||
@@ -55,6 +55,8 @@ app.include_router(publish.router, prefix="/api/publish", tags=["Publish"])
|
||||
app.include_router(login_helper.router, prefix="/api", tags=["LoginHelper"])
|
||||
app.include_router(auth.router) # /api/auth
|
||||
app.include_router(admin.router) # /api/admin
|
||||
app.include_router(ref_audios.router, prefix="/api/ref-audios", tags=["RefAudios"])
|
||||
app.include_router(ai.router) # /api/ai
|
||||
|
||||
|
||||
@app.on_event("startup")
|
||||
@@ -62,11 +64,11 @@ async def init_admin():
|
||||
"""
|
||||
服务启动时初始化管理员账号
|
||||
"""
|
||||
admin_email = settings.ADMIN_EMAIL
|
||||
admin_phone = settings.ADMIN_PHONE
|
||||
admin_password = settings.ADMIN_PASSWORD
|
||||
|
||||
if not admin_email or not admin_password:
|
||||
logger.warning("未配置 ADMIN_EMAIL 和 ADMIN_PASSWORD，跳过管理员初始化")
|
||||
if not admin_phone or not admin_password:
|
||||
logger.warning("未配置 ADMIN_PHONE 和 ADMIN_PASSWORD，跳过管理员初始化")
|
||||
return
|
||||
|
||||
try:
|
||||
@@ -76,15 +78,15 @@ async def init_admin():
|
||||
supabase = get_supabase()
|
||||
|
||||
# 检查是否已存在
|
||||
existing = supabase.table("users").select("id").eq("email", admin_email).execute()
|
||||
existing = supabase.table("users").select("id").eq("phone", admin_phone).execute()
|
||||
|
||||
if existing.data:
|
||||
logger.info(f"管理员账号已存在: {admin_email}")
|
||||
logger.info(f"管理员账号已存在: {admin_phone}")
|
||||
return
|
||||
|
||||
# 创建管理员
|
||||
supabase.table("users").insert({
|
||||
"email": admin_email,
|
||||
"phone": admin_phone,
|
||||
"password_hash": get_password_hash(admin_password),
|
||||
"username": "Admin",
|
||||
"role": "admin",
|
||||
@@ -92,7 +94,7 @@ async def init_admin():
|
||||
"expires_at": None # 永不过期
|
||||
}).execute()
|
||||
|
||||
logger.success(f"管理员账号已创建: {admin_email}")
|
||||
logger.success(f"管理员账号已创建: {admin_phone}")
|
||||
except Exception as e:
|
||||
logger.error(f"初始化管理员失败: {e}")
|
||||
|
||||
|
||||
102
backend/app/services/glm_service.py
Normal file
102
backend/app/services/glm_service.py
Normal file
@@ -0,0 +1,102 @@
|
||||
"""
|
||||
GLM AI 服务
|
||||
使用智谱 GLM-4.7-Flash 生成标题和标签
|
||||
"""
|
||||
|
||||
import json
|
||||
import re
|
||||
import httpx
|
||||
from loguru import logger
|
||||
|
||||
|
||||
class GLMService:
|
||||
"""GLM AI 服务"""
|
||||
|
||||
API_URL = "https://open.bigmodel.cn/api/paas/v4/chat/completions"
|
||||
API_KEY = "5915240ea48d4e93b454bc2412d1cc54.e054ej4pPqi9G6rc"
|
||||
|
||||
async def generate_title_tags(self, text: str) -> dict:
|
||||
"""
|
||||
根据口播文案生成标题和标签
|
||||
|
||||
Args:
|
||||
text: 口播文案
|
||||
|
||||
Returns:
|
||||
{"title": "标题", "tags": ["标签1", "标签2", ...]}
|
||||
"""
|
||||
prompt = f"""根据以下口播文案，生成一个吸引人的短视频标题和3个相关标签。
|
||||
|
||||
口播文案：
|
||||
{text}
|
||||
|
||||
要求：
|
||||
1. 标题要简洁有力，能吸引观众点击，不超过10个字
|
||||
2. 标签要与内容相关，便于搜索和推荐，只要3个
|
||||
|
||||
请严格按以下JSON格式返回（不要包含其他内容）：
|
||||
{{"title": "标题", "tags": ["标签1", "标签2", "标签3"]}}"""
|
||||
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=30.0) as client:
|
||||
response = await client.post(
|
||||
self.API_URL,
|
||||
headers={
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": f"Bearer {self.API_KEY}"
|
||||
},
|
||||
json={
|
||||
"model": "glm-4-flash",
|
||||
"messages": [{"role": "user", "content": prompt}],
|
||||
"max_tokens": 500,
|
||||
"temperature": 0.7
|
||||
}
|
||||
)
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
|
||||
# 提取生成的内容
|
||||
content = data["choices"][0]["message"]["content"]
|
||||
logger.info(f"GLM response: {content}")
|
||||
|
||||
# 解析 JSON
|
||||
result = self._parse_json_response(content)
|
||||
return result
|
||||
|
||||
except httpx.HTTPError as e:
|
||||
logger.error(f"GLM API request failed: {e}")
|
||||
raise Exception(f"AI 服务请求失败: {str(e)}")
|
||||
except Exception as e:
|
||||
logger.error(f"GLM service error: {e}")
|
||||
raise Exception(f"AI 生成失败: {str(e)}")
|
||||
|
||||
def _parse_json_response(self, content: str) -> dict:
|
||||
"""解析 GLM 返回的 JSON 内容"""
|
||||
# 尝试直接解析
|
||||
try:
|
||||
return json.loads(content)
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
|
||||
# 尝试提取 JSON 块
|
||||
json_match = re.search(r'\{[^{}]*"title"[^{}]*"tags"[^{}]*\}', content, re.DOTALL)
|
||||
if json_match:
|
||||
try:
|
||||
return json.loads(json_match.group())
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
|
||||
# 尝试提取 ```json 代码块
|
||||
code_match = re.search(r'```(?:json)?\s*(\{.*?\})\s*```', content, re.DOTALL)
|
||||
if code_match:
|
||||
try:
|
||||
return json.loads(code_match.group(1))
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
|
||||
logger.error(f"Failed to parse GLM response: {content}")
|
||||
raise Exception("AI 返回格式解析失败")
|
||||
|
||||
|
||||
# 全局服务实例
|
||||
glm_service = GLMService()
|
||||
@@ -73,7 +73,51 @@ class LipSyncService:
|
||||
logger.warning(f"⚠️ Conda Python 不存在: {self.conda_python}")
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def _get_media_duration(self, media_path: str) -> Optional[float]:
|
||||
"""获取音频或视频的时长（秒）"""
|
||||
try:
|
||||
cmd = [
|
||||
"ffprobe", "-v", "error",
|
||||
"-show_entries", "format=duration",
|
||||
"-of", "default=noprint_wrappers=1:nokey=1",
|
||||
media_path
|
||||
]
|
||||
result = subprocess.run(cmd, capture_output=True, text=True, timeout=10)
|
||||
if result.returncode == 0:
|
||||
return float(result.stdout.strip())
|
||||
except Exception as e:
|
||||
logger.warning(f"⚠️ 获取媒体时长失败: {e}")
|
||||
return None
|
||||
|
||||
def _loop_video_to_duration(self, video_path: str, output_path: str, target_duration: float) -> str:
|
||||
"""
|
||||
循环视频以匹配目标时长
|
||||
使用 FFmpeg stream_loop 实现无缝循环
|
||||
"""
|
||||
try:
|
||||
cmd = [
|
||||
"ffmpeg", "-y",
|
||||
"-stream_loop", "-1", # 无限循环
|
||||
"-i", video_path,
|
||||
"-t", str(target_duration), # 截取到目标时长
|
||||
"-c:v", "libx264",
|
||||
"-preset", "fast",
|
||||
"-crf", "18",
|
||||
"-an", # 去掉原音频
|
||||
output_path
|
||||
]
|
||||
result = subprocess.run(cmd, capture_output=True, text=True, timeout=300)
|
||||
if result.returncode == 0 and Path(output_path).exists():
|
||||
logger.info(f"✅ 视频循环完成: {target_duration:.1f}s")
|
||||
return output_path
|
||||
else:
|
||||
logger.warning(f"⚠️ 视频循环失败: {result.stderr[:200]}")
|
||||
return video_path
|
||||
except Exception as e:
|
||||
logger.warning(f"⚠️ 视频循环异常: {e}")
|
||||
return video_path
|
||||
|
||||
def _preprocess_video(self, video_path: str, output_path: str, target_height: int = 720) -> str:
|
||||
"""
|
||||
视频预处理：压缩视频以加速后续处理
|
||||
@@ -204,27 +248,34 @@ class LipSyncService:
|
||||
|
||||
logger.info("⏳ 等待 GPU 资源 (排队中)...")
|
||||
async with self._lock:
|
||||
if self.use_server:
|
||||
# 模式 A: 调用常驻服务 (加速模式)
|
||||
return await self._call_persistent_server(video_path, audio_path, output_path)
|
||||
|
||||
logger.info("🔄 调用 LatentSync 推理 (subprocess)...")
|
||||
|
||||
# 使用临时目录存放输出
|
||||
# 使用临时目录存放中间文件
|
||||
with tempfile.TemporaryDirectory() as tmpdir:
|
||||
tmpdir = Path(tmpdir)
|
||||
|
||||
# 获取音频和视频时长
|
||||
audio_duration = self._get_media_duration(audio_path)
|
||||
video_duration = self._get_media_duration(video_path)
|
||||
|
||||
# 如果音频比视频长，循环视频以匹配音频长度
|
||||
if audio_duration and video_duration and audio_duration > video_duration + 0.5:
|
||||
logger.info(f"🔄 音频({audio_duration:.1f}s) > 视频({video_duration:.1f}s)，循环视频...")
|
||||
looped_video = tmpdir / "looped_input.mp4"
|
||||
actual_video_path = self._loop_video_to_duration(
|
||||
video_path,
|
||||
str(looped_video),
|
||||
audio_duration
|
||||
)
|
||||
else:
|
||||
actual_video_path = video_path
|
||||
|
||||
if self.use_server:
|
||||
# 模式 A: 调用常驻服务 (加速模式)
|
||||
return await self._call_persistent_server(actual_video_path, audio_path, output_path)
|
||||
|
||||
logger.info("🔄 调用 LatentSync 推理 (subprocess)...")
|
||||
|
||||
temp_output = tmpdir / "output.mp4"
|
||||
|
||||
# 视频预处理：压缩高分辨率视频以加速处理
|
||||
# preprocessed_video = tmpdir / "preprocessed_input.mp4"
|
||||
# actual_video_path = self._preprocess_video(
|
||||
# video_path,
|
||||
# str(preprocessed_video),
|
||||
# target_height=720
|
||||
# )
|
||||
# 暂时禁用预处理以保持原始分辨率
|
||||
actual_video_path = video_path
|
||||
|
||||
# 构建命令
|
||||
cmd = [
|
||||
str(self.conda_python),
|
||||
@@ -285,7 +336,7 @@ class LipSyncService:
|
||||
return output_path
|
||||
|
||||
logger.info(f"LatentSync 输出:\n{stdout_text[-500:] if stdout_text else 'N/A'}")
|
||||
|
||||
|
||||
# 检查输出文件
|
||||
if temp_output.exists():
|
||||
shutil.copy(temp_output, output_path)
|
||||
|
||||
150
backend/app/services/remotion_service.py
Normal file
150
backend/app/services/remotion_service.py
Normal file
@@ -0,0 +1,150 @@
|
||||
"""
|
||||
Remotion 视频渲染服务
|
||||
调用 Node.js Remotion 进行视频合成（字幕 + 标题）
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import subprocess
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
from loguru import logger
|
||||
|
||||
|
||||
class RemotionService:
|
||||
"""Remotion 视频渲染服务"""
|
||||
|
||||
def __init__(self, remotion_dir: Optional[str] = None):
|
||||
# Remotion 项目目录
|
||||
if remotion_dir:
|
||||
self.remotion_dir = Path(remotion_dir)
|
||||
else:
|
||||
# 默认在 ViGent2/remotion 目录
|
||||
self.remotion_dir = Path(__file__).parent.parent.parent.parent / "remotion"
|
||||
|
||||
async def render(
|
||||
self,
|
||||
video_path: str,
|
||||
output_path: str,
|
||||
captions_path: Optional[str] = None,
|
||||
title: Optional[str] = None,
|
||||
title_duration: float = 3.0,
|
||||
fps: int = 25,
|
||||
enable_subtitles: bool = True,
|
||||
on_progress: Optional[callable] = None
|
||||
) -> str:
|
||||
"""
|
||||
使用 Remotion 渲染视频（添加字幕和标题）
|
||||
|
||||
Args:
|
||||
video_path: 输入视频路径（唇形同步后的视频）
|
||||
output_path: 输出视频路径
|
||||
captions_path: 字幕 JSON 文件路径（Whisper 生成）
|
||||
title: 视频标题（可选）
|
||||
title_duration: 标题显示时长（秒）
|
||||
fps: 帧率
|
||||
enable_subtitles: 是否启用字幕
|
||||
on_progress: 进度回调函数
|
||||
|
||||
Returns:
|
||||
输出视频路径
|
||||
"""
|
||||
# 构建命令参数
|
||||
cmd = [
|
||||
"npx", "ts-node", "render.ts",
|
||||
"--video", str(video_path),
|
||||
"--output", str(output_path),
|
||||
"--fps", str(fps),
|
||||
"--enableSubtitles", str(enable_subtitles).lower()
|
||||
]
|
||||
|
||||
if captions_path:
|
||||
cmd.extend(["--captions", str(captions_path)])
|
||||
|
||||
if title:
|
||||
cmd.extend(["--title", title])
|
||||
cmd.extend(["--titleDuration", str(title_duration)])
|
||||
|
||||
logger.info(f"Running Remotion render: {' '.join(cmd)}")
|
||||
|
||||
# 在线程池中运行子进程
|
||||
def _run_render():
|
||||
process = subprocess.Popen(
|
||||
cmd,
|
||||
cwd=str(self.remotion_dir),
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.STDOUT,
|
||||
text=True,
|
||||
bufsize=1
|
||||
)
|
||||
|
||||
output_lines = []
|
||||
for line in iter(process.stdout.readline, ''):
|
||||
line = line.strip()
|
||||
if line:
|
||||
output_lines.append(line)
|
||||
logger.debug(f"[Remotion] {line}")
|
||||
|
||||
# 解析进度
|
||||
if "Rendering:" in line and "%" in line:
|
||||
try:
|
||||
percent_str = line.split("Rendering:")[1].strip().replace("%", "")
|
||||
percent = int(percent_str)
|
||||
if on_progress:
|
||||
on_progress(percent)
|
||||
except (ValueError, IndexError):
|
||||
pass
|
||||
|
||||
process.wait()
|
||||
|
||||
if process.returncode != 0:
|
||||
error_msg = "\n".join(output_lines[-20:]) # 最后 20 行
|
||||
raise RuntimeError(f"Remotion render failed (code {process.returncode}):\n{error_msg}")
|
||||
|
||||
return output_path
|
||||
|
||||
loop = asyncio.get_event_loop()
|
||||
result = await loop.run_in_executor(None, _run_render)
|
||||
|
||||
logger.info(f"Remotion render complete: {result}")
|
||||
return result
|
||||
|
||||
async def check_health(self) -> dict:
|
||||
"""检查 Remotion 服务健康状态"""
|
||||
try:
|
||||
# 检查 remotion 目录是否存在
|
||||
if not self.remotion_dir.exists():
|
||||
return {
|
||||
"ready": False,
|
||||
"error": f"Remotion directory not found: {self.remotion_dir}"
|
||||
}
|
||||
|
||||
# 检查 package.json 是否存在
|
||||
package_json = self.remotion_dir / "package.json"
|
||||
if not package_json.exists():
|
||||
return {
|
||||
"ready": False,
|
||||
"error": "package.json not found"
|
||||
}
|
||||
|
||||
# 检查 node_modules 是否存在
|
||||
node_modules = self.remotion_dir / "node_modules"
|
||||
if not node_modules.exists():
|
||||
return {
|
||||
"ready": False,
|
||||
"error": "node_modules not found, run 'npm install' first"
|
||||
}
|
||||
|
||||
return {
|
||||
"ready": True,
|
||||
"remotion_dir": str(self.remotion_dir)
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
return {
|
||||
"ready": False,
|
||||
"error": str(e)
|
||||
}
|
||||
|
||||
|
||||
# 全局服务实例
|
||||
remotion_service = RemotionService()
|
||||
@@ -16,6 +16,26 @@ class StorageService:
|
||||
self.supabase: Client = get_supabase()
|
||||
self.BUCKET_MATERIALS = "materials"
|
||||
self.BUCKET_OUTPUTS = "outputs"
|
||||
self.BUCKET_REF_AUDIOS = "ref-audios"
|
||||
# 确保所有 bucket 存在
|
||||
self._ensure_buckets()
|
||||
|
||||
def _ensure_buckets(self):
|
||||
"""确保所有必需的 bucket 存在"""
|
||||
buckets = [self.BUCKET_MATERIALS, self.BUCKET_OUTPUTS, self.BUCKET_REF_AUDIOS]
|
||||
try:
|
||||
existing = self.supabase.storage.list_buckets()
|
||||
existing_names = {b.name for b in existing} if existing else set()
|
||||
for bucket_name in buckets:
|
||||
if bucket_name not in existing_names:
|
||||
try:
|
||||
self.supabase.storage.create_bucket(bucket_name, options={"public": True})
|
||||
logger.info(f"Created bucket: {bucket_name}")
|
||||
except Exception as e:
|
||||
# 可能已存在，忽略错误
|
||||
logger.debug(f"Bucket {bucket_name} creation skipped: {e}")
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to ensure buckets: {e}")
|
||||
|
||||
def _convert_to_public_url(self, url: str) -> str:
|
||||
"""将内部 URL 转换为公网可访问的 URL"""
|
||||
|
||||
@@ -82,8 +82,15 @@ class VideoService:
|
||||
# Previous state: subtitles disabled due to font issues
|
||||
# if subtitle_path: ...
|
||||
|
||||
# Audio map
|
||||
cmd.extend(["-c:v", "libx264", "-c:a", "aac", "-shortest"])
|
||||
# Audio map with high quality encoding
|
||||
cmd.extend([
|
||||
"-c:v", "libx264",
|
||||
"-preset", "slow", # 慢速预设，更好的压缩效率
|
||||
"-crf", "18", # 高质量（与 LatentSync 一致）
|
||||
"-c:a", "aac",
|
||||
"-b:a", "192k", # 音频比特率
|
||||
"-shortest"
|
||||
])
|
||||
# Use audio from input 1
|
||||
cmd.extend(["-map", "0:v", "-map", "1:a"])
|
||||
|
||||
|
||||
115
backend/app/services/voice_clone_service.py
Normal file
115
backend/app/services/voice_clone_service.py
Normal file
@@ -0,0 +1,115 @@
|
||||
"""
|
||||
声音克隆服务
|
||||
通过 HTTP 调用 Qwen3-TTS 独立服务 (端口 8009)
|
||||
"""
|
||||
import httpx
|
||||
import asyncio
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
from loguru import logger
|
||||
|
||||
from app.core.config import settings
|
||||
|
||||
# Qwen3-TTS 服务地址
|
||||
QWEN_TTS_URL = "http://localhost:8009"
|
||||
|
||||
|
||||
class VoiceCloneService:
|
||||
"""声音克隆服务 - 调用 Qwen3-TTS HTTP API"""
|
||||
|
||||
def __init__(self):
|
||||
self.base_url = QWEN_TTS_URL
|
||||
# 健康状态缓存
|
||||
self._health_cache: Optional[dict] = None
|
||||
self._health_cache_time: float = 0
|
||||
# GPU 并发锁 (Serial Queue)
|
||||
self._lock = asyncio.Lock()
|
||||
|
||||
async def generate_audio(
|
||||
self,
|
||||
text: str,
|
||||
ref_audio_path: str,
|
||||
ref_text: str,
|
||||
output_path: str,
|
||||
language: str = "Chinese"
|
||||
) -> str:
|
||||
"""
|
||||
使用声音克隆生成语音
|
||||
|
||||
Args:
|
||||
text: 要合成的文本
|
||||
ref_audio_path: 参考音频本地路径
|
||||
ref_text: 参考音频的转写文字
|
||||
output_path: 输出 wav 路径
|
||||
language: 语言 (Chinese/English/Auto)
|
||||
|
||||
Returns:
|
||||
输出文件路径
|
||||
"""
|
||||
# 使用锁确保串行执行，避免 GPU 显存溢出
|
||||
async with self._lock:
|
||||
logger.info(f"🎤 Voice Clone: {text[:30]}...")
|
||||
Path(output_path).parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# 读取参考音频
|
||||
with open(ref_audio_path, "rb") as f:
|
||||
ref_audio_data = f.read()
|
||||
|
||||
# 调用 Qwen3-TTS 服务
|
||||
timeout = httpx.Timeout(300.0) # 5分钟超时
|
||||
async with httpx.AsyncClient(timeout=timeout) as client:
|
||||
try:
|
||||
response = await client.post(
|
||||
f"{self.base_url}/generate",
|
||||
files={"ref_audio": ("ref.wav", ref_audio_data, "audio/wav")},
|
||||
data={
|
||||
"text": text,
|
||||
"ref_text": ref_text,
|
||||
"language": language
|
||||
}
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
# 保存返回的音频
|
||||
with open(output_path, "wb") as f:
|
||||
f.write(response.content)
|
||||
|
||||
logger.info(f"✅ Voice clone saved: {output_path}")
|
||||
return output_path
|
||||
|
||||
except httpx.HTTPStatusError as e:
|
||||
logger.error(f"Qwen3-TTS API error: {e.response.status_code} - {e.response.text}")
|
||||
raise RuntimeError(f"声音克隆服务错误: {e.response.text}")
|
||||
except httpx.RequestError as e:
|
||||
logger.error(f"Qwen3-TTS connection error: {e}")
|
||||
raise RuntimeError("无法连接声音克隆服务，请检查服务是否启动")
|
||||
|
||||
async def check_health(self) -> dict:
|
||||
"""健康检查"""
|
||||
import time
|
||||
|
||||
# 5分钟缓存
|
||||
now = time.time()
|
||||
if self._health_cache and (now - self._health_cache_time) < 300:
|
||||
return self._health_cache
|
||||
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=5.0) as client:
|
||||
response = await client.get(f"{self.base_url}/health")
|
||||
response.raise_for_status()
|
||||
self._health_cache = response.json()
|
||||
self._health_cache_time = now
|
||||
return self._health_cache
|
||||
except Exception as e:
|
||||
logger.warning(f"Qwen3-TTS health check failed: {e}")
|
||||
return {
|
||||
"service": "Qwen3-TTS Voice Clone",
|
||||
"model": "0.6B-Base",
|
||||
"ready": False,
|
||||
"gpu_id": 0,
|
||||
"error": str(e)
|
||||
}
|
||||
|
||||
|
||||
# 单例
|
||||
voice_clone_service = VoiceCloneService()
|
||||
233
backend/app/services/whisper_service.py
Normal file
233
backend/app/services/whisper_service.py
Normal file
@@ -0,0 +1,233 @@
|
||||
"""
|
||||
字幕对齐服务
|
||||
使用 faster-whisper 生成字级别时间戳
|
||||
"""
|
||||
|
||||
import json
|
||||
import re
|
||||
from pathlib import Path
|
||||
from typing import Optional, List
|
||||
from loguru import logger
|
||||
|
||||
# 模型缓存
|
||||
_whisper_model = None
|
||||
|
||||
# 断句标点
|
||||
SENTENCE_PUNCTUATION = set('。！？，、；：,.!?;:')
|
||||
# 每行最大字数
|
||||
MAX_CHARS_PER_LINE = 12
|
||||
|
||||
|
||||
def split_word_to_chars(word: str, start: float, end: float) -> list:
|
||||
"""
|
||||
将词拆分成单个字符，时间戳线性插值
|
||||
|
||||
Args:
|
||||
word: 词文本
|
||||
start: 词开始时间
|
||||
end: 词结束时间
|
||||
|
||||
Returns:
|
||||
单字符列表，每个包含 word/start/end
|
||||
"""
|
||||
# 只保留中文字符和基本标点
|
||||
chars = [c for c in word if c.strip()]
|
||||
if not chars:
|
||||
return []
|
||||
|
||||
if len(chars) == 1:
|
||||
return [{"word": chars[0], "start": start, "end": end}]
|
||||
|
||||
# 线性插值时间戳
|
||||
duration = end - start
|
||||
char_duration = duration / len(chars)
|
||||
|
||||
result = []
|
||||
for i, char in enumerate(chars):
|
||||
char_start = start + i * char_duration
|
||||
char_end = start + (i + 1) * char_duration
|
||||
result.append({
|
||||
"word": char,
|
||||
"start": round(char_start, 3),
|
||||
"end": round(char_end, 3)
|
||||
})
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def split_segment_to_lines(words: List[dict], max_chars: int = MAX_CHARS_PER_LINE) -> List[dict]:
|
||||
"""
|
||||
将长段落按标点和字数拆分成多行
|
||||
|
||||
Args:
|
||||
words: 字列表，每个包含 word/start/end
|
||||
max_chars: 每行最大字数
|
||||
|
||||
Returns:
|
||||
拆分后的 segment 列表
|
||||
"""
|
||||
if not words:
|
||||
return []
|
||||
|
||||
segments = []
|
||||
current_words = []
|
||||
current_text = ""
|
||||
|
||||
for word_info in words:
|
||||
char = word_info["word"]
|
||||
current_words.append(word_info)
|
||||
current_text += char
|
||||
|
||||
# 判断是否需要断句
|
||||
should_break = False
|
||||
|
||||
# 1. 遇到断句标点
|
||||
if char in SENTENCE_PUNCTUATION:
|
||||
should_break = True
|
||||
# 2. 达到最大字数
|
||||
elif len(current_text) >= max_chars:
|
||||
should_break = True
|
||||
|
||||
if should_break and current_words:
|
||||
segments.append({
|
||||
"text": current_text,
|
||||
"start": current_words[0]["start"],
|
||||
"end": current_words[-1]["end"],
|
||||
"words": current_words.copy()
|
||||
})
|
||||
current_words = []
|
||||
current_text = ""
|
||||
|
||||
# 处理剩余的字
|
||||
if current_words:
|
||||
segments.append({
|
||||
"text": current_text,
|
||||
"start": current_words[0]["start"],
|
||||
"end": current_words[-1]["end"],
|
||||
"words": current_words.copy()
|
||||
})
|
||||
|
||||
return segments
|
||||
|
||||
|
||||
class WhisperService:
|
||||
"""字幕对齐服务（基于 faster-whisper）"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model_size: str = "large-v3",
|
||||
device: str = "cuda",
|
||||
compute_type: str = "float16",
|
||||
):
|
||||
self.model_size = model_size
|
||||
self.device = device
|
||||
self.compute_type = compute_type
|
||||
|
||||
def _load_model(self):
|
||||
"""懒加载 faster-whisper 模型"""
|
||||
global _whisper_model
|
||||
|
||||
if _whisper_model is None:
|
||||
from faster_whisper import WhisperModel
|
||||
|
||||
logger.info(f"Loading faster-whisper model: {self.model_size} on {self.device}")
|
||||
_whisper_model = WhisperModel(
|
||||
self.model_size,
|
||||
device=self.device,
|
||||
compute_type=self.compute_type
|
||||
)
|
||||
logger.info("faster-whisper model loaded")
|
||||
|
||||
return _whisper_model
|
||||
|
||||
async def align(
|
||||
self,
|
||||
audio_path: str,
|
||||
text: str,
|
||||
output_path: Optional[str] = None
|
||||
) -> dict:
|
||||
"""
|
||||
对音频进行转录，生成字级别时间戳
|
||||
|
||||
Args:
|
||||
audio_path: 音频文件路径
|
||||
text: 原始文本（用于参考，但实际使用 whisper 转录结果）
|
||||
output_path: 可选，输出 JSON 文件路径
|
||||
|
||||
Returns:
|
||||
包含字级别时间戳的字典
|
||||
"""
|
||||
import asyncio
|
||||
|
||||
def _do_transcribe():
|
||||
model = self._load_model()
|
||||
|
||||
logger.info(f"Transcribing audio: {audio_path}")
|
||||
|
||||
# 转录并获取字级别时间戳
|
||||
segments_iter, info = model.transcribe(
|
||||
audio_path,
|
||||
language="zh",
|
||||
word_timestamps=True, # 启用字级别时间戳
|
||||
vad_filter=True, # 启用 VAD 过滤静音
|
||||
)
|
||||
|
||||
logger.info(f"Detected language: {info.language} (prob: {info.language_probability:.2f})")
|
||||
|
||||
all_segments = []
|
||||
for segment in segments_iter:
|
||||
# 提取每个字的时间戳，并拆分成单字
|
||||
all_words = []
|
||||
if segment.words:
|
||||
for word_info in segment.words:
|
||||
word_text = word_info.word.strip()
|
||||
if word_text:
|
||||
# 将词拆分成单字，时间戳线性插值
|
||||
chars = split_word_to_chars(
|
||||
word_text,
|
||||
word_info.start,
|
||||
word_info.end
|
||||
)
|
||||
all_words.extend(chars)
|
||||
|
||||
# 将长段落按标点和字数拆分成多行
|
||||
if all_words:
|
||||
line_segments = split_segment_to_lines(all_words, MAX_CHARS_PER_LINE)
|
||||
all_segments.extend(line_segments)
|
||||
|
||||
logger.info(f"Generated {len(all_segments)} subtitle segments")
|
||||
return {"segments": all_segments}
|
||||
|
||||
# 在线程池中执行
|
||||
loop = asyncio.get_event_loop()
|
||||
result = await loop.run_in_executor(None, _do_transcribe)
|
||||
|
||||
# 保存到文件
|
||||
if output_path:
|
||||
output_file = Path(output_path)
|
||||
output_file.parent.mkdir(parents=True, exist_ok=True)
|
||||
with open(output_file, "w", encoding="utf-8") as f:
|
||||
json.dump(result, f, ensure_ascii=False, indent=2)
|
||||
logger.info(f"Captions saved to: {output_path}")
|
||||
|
||||
return result
|
||||
|
||||
async def check_health(self) -> dict:
|
||||
"""检查服务健康状态"""
|
||||
try:
|
||||
from faster_whisper import WhisperModel
|
||||
return {
|
||||
"ready": True,
|
||||
"model_size": self.model_size,
|
||||
"device": self.device,
|
||||
"backend": "faster-whisper"
|
||||
}
|
||||
except ImportError:
|
||||
return {
|
||||
"ready": False,
|
||||
"error": "faster-whisper not installed"
|
||||
}
|
||||
|
||||
|
||||
# 全局服务实例
|
||||
whisper_service = WhisperService()
|
||||
88
backend/database/migrate_to_phone.sql
Normal file
88
backend/database/migrate_to_phone.sql
Normal file
@@ -0,0 +1,88 @@
|
||||
-- ============================================================
|
||||
-- ViGent 手机号登录迁移脚本
|
||||
-- 用于将 email 字段改为 phone 字段
|
||||
--
|
||||
-- 执行方式（任选一种）：
|
||||
-- 1. Supabase Studio: 打开 https://supabase.hbyrkj.top -> SQL Editor -> 粘贴执行
|
||||
-- 2. Docker 命令: docker exec -i supabase-db psql -U postgres < migrate_to_phone.sql
|
||||
-- ============================================================
|
||||
|
||||
-- 注意：此脚本会删除现有的用户数据！
|
||||
-- 如需保留数据，请先备份
|
||||
|
||||
-- 1. 删除依赖表（有外键约束）
|
||||
DROP TABLE IF EXISTS user_sessions CASCADE;
|
||||
DROP TABLE IF EXISTS social_accounts CASCADE;
|
||||
|
||||
-- 2. 删除用户表
|
||||
DROP TABLE IF EXISTS users CASCADE;
|
||||
|
||||
-- 3. 重新创建 users 表（使用 phone 字段）
|
||||
CREATE TABLE users (
|
||||
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
|
||||
phone TEXT UNIQUE NOT NULL,
|
||||
password_hash TEXT NOT NULL,
|
||||
username TEXT,
|
||||
role TEXT DEFAULT 'pending' CHECK (role IN ('pending', 'user', 'admin')),
|
||||
is_active BOOLEAN DEFAULT FALSE,
|
||||
expires_at TIMESTAMP WITH TIME ZONE,
|
||||
created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
|
||||
updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
|
||||
);
|
||||
|
||||
-- 4. 重新创建 user_sessions 表
|
||||
CREATE TABLE user_sessions (
|
||||
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
|
||||
user_id UUID REFERENCES users(id) ON DELETE CASCADE UNIQUE,
|
||||
session_token TEXT UNIQUE NOT NULL,
|
||||
device_info TEXT,
|
||||
created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
|
||||
);
|
||||
|
||||
-- 5. 重新创建 social_accounts 表
|
||||
CREATE TABLE social_accounts (
|
||||
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
|
||||
user_id UUID REFERENCES users(id) ON DELETE CASCADE,
|
||||
platform TEXT NOT NULL CHECK (platform IN ('bilibili', 'douyin', 'xiaohongshu')),
|
||||
logged_in BOOLEAN DEFAULT FALSE,
|
||||
created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
|
||||
UNIQUE(user_id, platform)
|
||||
);
|
||||
|
||||
-- 6. 创建索引
|
||||
CREATE INDEX idx_users_phone ON users(phone);
|
||||
CREATE INDEX idx_sessions_user_id ON user_sessions(user_id);
|
||||
CREATE INDEX idx_social_user_platform ON social_accounts(user_id, platform);
|
||||
|
||||
-- 7. 启用 RLS
|
||||
ALTER TABLE users ENABLE ROW LEVEL SECURITY;
|
||||
ALTER TABLE user_sessions ENABLE ROW LEVEL SECURITY;
|
||||
ALTER TABLE social_accounts ENABLE ROW LEVEL SECURITY;
|
||||
|
||||
-- 8. 创建 RLS 策略
|
||||
CREATE POLICY "Users can view own profile" ON users
|
||||
FOR SELECT USING (auth.uid()::text = id::text);
|
||||
|
||||
CREATE POLICY "Users can access own sessions" ON user_sessions
|
||||
FOR ALL USING (user_id::text = auth.uid()::text);
|
||||
|
||||
CREATE POLICY "Users can access own social accounts" ON social_accounts
|
||||
FOR ALL USING (user_id::text = auth.uid()::text);
|
||||
|
||||
-- 9. 更新时间触发器
|
||||
CREATE OR REPLACE FUNCTION update_updated_at()
|
||||
RETURNS TRIGGER AS $$
|
||||
BEGIN
|
||||
NEW.updated_at = NOW();
|
||||
RETURN NEW;
|
||||
END;
|
||||
$$ LANGUAGE plpgsql;
|
||||
|
||||
DROP TRIGGER IF EXISTS users_updated_at ON users;
|
||||
CREATE TRIGGER users_updated_at
|
||||
BEFORE UPDATE ON users
|
||||
FOR EACH ROW
|
||||
EXECUTE FUNCTION update_updated_at();
|
||||
|
||||
-- 完成！
|
||||
-- 管理员账号会在后端服务重启时自动创建 (15549380526)
|
||||
@@ -4,7 +4,7 @@
|
||||
-- 1. 创建 users 表
|
||||
CREATE TABLE IF NOT EXISTS users (
|
||||
id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
|
||||
email TEXT UNIQUE NOT NULL,
|
||||
phone TEXT UNIQUE NOT NULL,
|
||||
password_hash TEXT NOT NULL,
|
||||
username TEXT,
|
||||
role TEXT DEFAULT 'pending' CHECK (role IN ('pending', 'user', 'admin')),
|
||||
@@ -34,7 +34,7 @@ CREATE TABLE IF NOT EXISTS social_accounts (
|
||||
);
|
||||
|
||||
-- 4. 创建索引
|
||||
CREATE INDEX IF NOT EXISTS idx_users_email ON users(email);
|
||||
CREATE INDEX IF NOT EXISTS idx_users_phone ON users(phone);
|
||||
CREATE INDEX IF NOT EXISTS idx_sessions_user_id ON user_sessions(user_id);
|
||||
CREATE INDEX IF NOT EXISTS idx_social_user_platform ON social_accounts(user_id, platform);
|
||||
|
||||
|
||||
@@ -28,3 +28,6 @@ supabase>=2.0.0
|
||||
python-jose[cryptography]>=3.3.0
|
||||
passlib[bcrypt]>=1.7.4
|
||||
bcrypt==4.0.1
|
||||
|
||||
# 字幕对齐
|
||||
faster-whisper>=1.0.0
|
||||
|
||||
@@ -7,8 +7,10 @@ ViGent2 的前端界面，采用 Next.js 14 + TailwindCSS 构建。
|
||||
### 1. 视频生成 (`/`)
|
||||
- **素材管理**: 拖拽上传人物视频，实时预览。
|
||||
- **文案配音**: 集成 EdgeTTS，支持多音色选择 (云溪 / 晓晓)。
|
||||
- **AI 标题/标签**: 一键生成视频标题与标签 (Day 14)。
|
||||
- **进度追踪**: 实时显示视频生成进度 (10% -> 100%)。
|
||||
- **结果预览**: 生成完成后直接播放下载。
|
||||
- **本地保存**: 文案/标题自动保存，刷新后恢复 (Day 14)。
|
||||
|
||||
### 2. 全自动发布 (`/publish`) [Day 7 新增]
|
||||
- **多平台管理**: 统一管理 B站、抖音、小红书账号状态。
|
||||
@@ -19,13 +21,28 @@ ViGent2 的前端界面，采用 Next.js 14 + TailwindCSS 构建。
|
||||
- **发布配置**: 设置视频标题、标签、简介。
|
||||
- **定时任务**: 支持 "立即发布" 或 "定时发布"。
|
||||
|
||||
### 3. 声音克隆 [Day 13 新增]
|
||||
- **TTS 模式选择**: EdgeTTS (预设音色) / 声音克隆 (自定义音色) 切换。
|
||||
- **参考音频管理**: 上传/列表/删除参考音频 (3-20秒 WAV)。
|
||||
- **一键克隆**: 选择参考音频后自动调用 Qwen3-TTS 服务。
|
||||
|
||||
### 4. 字幕与标题 [Day 13 新增]
|
||||
- **片头标题**: 可选输入，视频开头显示 3 秒淡入淡出标题。
|
||||
- **逐字高亮字幕**: 卡拉OK效果，默认开启，可关闭。
|
||||
- **自动对齐**: 基于 faster-whisper 生成字级别时间戳。
|
||||
|
||||
### 5. 账户设置 [Day 15 新增]
|
||||
- **手机号登录**: 11位中国手机号验证登录。
|
||||
- **账户下拉菜单**: 显示有效期 + 修改密码 + 安全退出。
|
||||
- **修改密码**: 弹窗输入当前密码与新密码，修改后强制重新登录。
|
||||
|
||||
## 🛠️ 技术栈
|
||||
|
||||
- **框架**: Next.js 14 (App Router)
|
||||
- **样式**: TailwindCSS
|
||||
- **图标**: Lucide React
|
||||
- **组件**: 自定义现代化组件 (Glassmorphism 风格)
|
||||
- **API**: Fetch API (对接后端 FastAPI :8006)
|
||||
- **API**: Axios 实例 `@/lib/axios` (对接后端 FastAPI :8006)
|
||||
|
||||
## 🚀 开发指南
|
||||
|
||||
|
||||
116
frontend/package-lock.json
generated
116
frontend/package-lock.json
generated
@@ -9,6 +9,7 @@
|
||||
"version": "0.1.0",
|
||||
"dependencies": {
|
||||
"@supabase/supabase-js": "^2.93.1",
|
||||
"axios": "^1.13.4",
|
||||
"next": "16.1.1",
|
||||
"react": "19.2.3",
|
||||
"react-dom": "19.2.3",
|
||||
@@ -2451,6 +2452,12 @@
|
||||
"node": ">= 0.4"
|
||||
}
|
||||
},
|
||||
"node_modules/asynckit": {
|
||||
"version": "0.4.0",
|
||||
"resolved": "https://registry.npmjs.org/asynckit/-/asynckit-0.4.0.tgz",
|
||||
"integrity": "sha512-Oei9OH4tRh0YqU3GxhX79dM/mwVgvbZJaSNaRk+bshkj0S5cfHcgYakreBjrHwatXKbz+IoIdYLxrKim2MjW0Q==",
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/available-typed-arrays": {
|
||||
"version": "1.0.7",
|
||||
"resolved": "https://registry.npmjs.org/available-typed-arrays/-/available-typed-arrays-1.0.7.tgz",
|
||||
@@ -2477,6 +2484,17 @@
|
||||
"node": ">=4"
|
||||
}
|
||||
},
|
||||
"node_modules/axios": {
|
||||
"version": "1.13.4",
|
||||
"resolved": "https://registry.npmjs.org/axios/-/axios-1.13.4.tgz",
|
||||
"integrity": "sha512-1wVkUaAO6WyaYtCkcYCOx12ZgpGf9Zif+qXa4n+oYzK558YryKqiL6UWwd5DqiH3VRW0GYhTZQ/vlgJrCoNQlg==",
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"follow-redirects": "^1.15.6",
|
||||
"form-data": "^4.0.4",
|
||||
"proxy-from-env": "^1.1.0"
|
||||
}
|
||||
},
|
||||
"node_modules/axobject-query": {
|
||||
"version": "4.1.0",
|
||||
"resolved": "https://registry.npmjs.org/axobject-query/-/axobject-query-4.1.0.tgz",
|
||||
@@ -2584,7 +2602,6 @@
|
||||
"version": "1.0.2",
|
||||
"resolved": "https://registry.npmjs.org/call-bind-apply-helpers/-/call-bind-apply-helpers-1.0.2.tgz",
|
||||
"integrity": "sha512-Sp1ablJ0ivDkSzjcaJdxEunN5/XvksFJ2sMBFfq6x0ryhQV/2b/KwFe21cMpmHtPOSij8K99/wSfoEuTObmuMQ==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"es-errors": "^1.3.0",
|
||||
@@ -2684,6 +2701,18 @@
|
||||
"dev": true,
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/combined-stream": {
|
||||
"version": "1.0.8",
|
||||
"resolved": "https://registry.npmjs.org/combined-stream/-/combined-stream-1.0.8.tgz",
|
||||
"integrity": "sha512-FQN4MRfuJeHf7cBbBMJFXhKSDq+2kAArBlmRBvcvFE5BB1HZKXtSFASDhdlz9zOYwxh8lDdnvmMOe/+5cdoEdg==",
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"delayed-stream": "~1.0.0"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">= 0.8"
|
||||
}
|
||||
},
|
||||
"node_modules/concat-map": {
|
||||
"version": "0.0.1",
|
||||
"resolved": "https://registry.npmjs.org/concat-map/-/concat-map-0.0.1.tgz",
|
||||
@@ -2842,6 +2871,15 @@
|
||||
"url": "https://github.com/sponsors/ljharb"
|
||||
}
|
||||
},
|
||||
"node_modules/delayed-stream": {
|
||||
"version": "1.0.0",
|
||||
"resolved": "https://registry.npmjs.org/delayed-stream/-/delayed-stream-1.0.0.tgz",
|
||||
"integrity": "sha512-ZySD7Nf91aLB0RxL4KGrKHBXl7Eds1DAmEdcoVawXnLD7SDhpNgtuII2aAkg7a7QS41jxPSZ17p4VdGnMHk3MQ==",
|
||||
"license": "MIT",
|
||||
"engines": {
|
||||
"node": ">=0.4.0"
|
||||
}
|
||||
},
|
||||
"node_modules/dequal": {
|
||||
"version": "2.0.3",
|
||||
"resolved": "https://registry.npmjs.org/dequal/-/dequal-2.0.3.tgz",
|
||||
@@ -2878,7 +2916,6 @@
|
||||
"version": "1.0.1",
|
||||
"resolved": "https://registry.npmjs.org/dunder-proto/-/dunder-proto-1.0.1.tgz",
|
||||
"integrity": "sha512-KIN/nDJBQRcXw0MLVhZE9iQHmG68qAVIBg9CqmUYjmQIhgij9U5MFvrqkUL5FbtyyzZuOeOt0zdeRe4UY7ct+A==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"call-bind-apply-helpers": "^1.0.1",
|
||||
@@ -2990,7 +3027,6 @@
|
||||
"version": "1.0.1",
|
||||
"resolved": "https://registry.npmjs.org/es-define-property/-/es-define-property-1.0.1.tgz",
|
||||
"integrity": "sha512-e3nRfgfUZ4rNGL232gUgX06QNyyez04KdjFrF+LTRoOXmrOgFKDg4BCdsjW8EnT69eqdYGmRpJwiPVYNrCaW3g==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"engines": {
|
||||
"node": ">= 0.4"
|
||||
@@ -3000,7 +3036,6 @@
|
||||
"version": "1.3.0",
|
||||
"resolved": "https://registry.npmjs.org/es-errors/-/es-errors-1.3.0.tgz",
|
||||
"integrity": "sha512-Zf5H2Kxt2xjTvbJvP2ZWLEICxA6j+hAmMzIlypy4xcBg1vKVnx89Wy0GbS+kf5cwCVFFzdCFh2XSCFNULS6csw==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"engines": {
|
||||
"node": ">= 0.4"
|
||||
@@ -3038,7 +3073,6 @@
|
||||
"version": "1.1.1",
|
||||
"resolved": "https://registry.npmjs.org/es-object-atoms/-/es-object-atoms-1.1.1.tgz",
|
||||
"integrity": "sha512-FGgH2h8zKNim9ljj7dankFPcICIK9Cp5bm+c2gQSYePhpaG5+esrLODihIorn+Pe6FGJzWhXQotPv73jTaldXA==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"es-errors": "^1.3.0"
|
||||
@@ -3051,7 +3085,6 @@
|
||||
"version": "2.1.0",
|
||||
"resolved": "https://registry.npmjs.org/es-set-tostringtag/-/es-set-tostringtag-2.1.0.tgz",
|
||||
"integrity": "sha512-j6vWzfrGVfyXxge+O0x5sh6cvxAog0a/4Rdd2K36zCMV5eJ+/+tOAngRO8cODMNWbVRdVlmGZQL2YS3yR8bIUA==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"es-errors": "^1.3.0",
|
||||
@@ -3666,6 +3699,26 @@
|
||||
"dev": true,
|
||||
"license": "ISC"
|
||||
},
|
||||
"node_modules/follow-redirects": {
|
||||
"version": "1.15.11",
|
||||
"resolved": "https://registry.npmjs.org/follow-redirects/-/follow-redirects-1.15.11.tgz",
|
||||
"integrity": "sha512-deG2P0JfjrTxl50XGCDyfI97ZGVCxIpfKYmfyrQ54n5FO/0gfIES8C/Psl6kWVDolizcaaxZJnTS0QSMxvnsBQ==",
|
||||
"funding": [
|
||||
{
|
||||
"type": "individual",
|
||||
"url": "https://github.com/sponsors/RubenVerborgh"
|
||||
}
|
||||
],
|
||||
"license": "MIT",
|
||||
"engines": {
|
||||
"node": ">=4.0"
|
||||
},
|
||||
"peerDependenciesMeta": {
|
||||
"debug": {
|
||||
"optional": true
|
||||
}
|
||||
}
|
||||
},
|
||||
"node_modules/for-each": {
|
||||
"version": "0.3.5",
|
||||
"resolved": "https://registry.npmjs.org/for-each/-/for-each-0.3.5.tgz",
|
||||
@@ -3682,11 +3735,26 @@
|
||||
"url": "https://github.com/sponsors/ljharb"
|
||||
}
|
||||
},
|
||||
"node_modules/form-data": {
|
||||
"version": "4.0.5",
|
||||
"resolved": "https://registry.npmjs.org/form-data/-/form-data-4.0.5.tgz",
|
||||
"integrity": "sha512-8RipRLol37bNs2bhoV67fiTEvdTrbMUYcFTiy3+wuuOnUog2QBHCZWXDRijWQfAkhBj2Uf5UnVaiWwA5vdd82w==",
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"asynckit": "^0.4.0",
|
||||
"combined-stream": "^1.0.8",
|
||||
"es-set-tostringtag": "^2.1.0",
|
||||
"hasown": "^2.0.2",
|
||||
"mime-types": "^2.1.12"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">= 6"
|
||||
}
|
||||
},
|
||||
"node_modules/function-bind": {
|
||||
"version": "1.1.2",
|
||||
"resolved": "https://registry.npmjs.org/function-bind/-/function-bind-1.1.2.tgz",
|
||||
"integrity": "sha512-7XHNxH7qX9xG5mIwxkhumTox/MIRNcOgDrxWsMt2pAr23WHp6MrRlN7FBSFpCpr+oVO0F744iUgR82nJMfG2SA==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"funding": {
|
||||
"url": "https://github.com/sponsors/ljharb"
|
||||
@@ -3747,7 +3815,6 @@
|
||||
"version": "1.3.0",
|
||||
"resolved": "https://registry.npmjs.org/get-intrinsic/-/get-intrinsic-1.3.0.tgz",
|
||||
"integrity": "sha512-9fSjSaos/fRIVIp+xSJlE6lfwhES7LNtKaCBIamHsjr2na1BiABJPo0mOjjz8GJDURarmCPGqaiVg5mfjb98CQ==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"call-bind-apply-helpers": "^1.0.2",
|
||||
@@ -3772,7 +3839,6 @@
|
||||
"version": "1.0.1",
|
||||
"resolved": "https://registry.npmjs.org/get-proto/-/get-proto-1.0.1.tgz",
|
||||
"integrity": "sha512-sTSfBjoXBp89JvIKIefqw7U2CCebsc74kiY6awiGogKtoSGbgjYE/G/+l9sF3MWFPNc9IcoOC4ODfKHfxFmp0g==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"dunder-proto": "^1.0.1",
|
||||
@@ -3860,7 +3926,6 @@
|
||||
"version": "1.2.0",
|
||||
"resolved": "https://registry.npmjs.org/gopd/-/gopd-1.2.0.tgz",
|
||||
"integrity": "sha512-ZUKRh6/kUFoAiTAtTYPZJ3hw9wNxx+BIBOijnlG9PnrJsCcSjs1wyyD6vJpaYtgnzDrKYRSqf3OO6Rfa93xsRg==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"engines": {
|
||||
"node": ">= 0.4"
|
||||
@@ -3932,7 +3997,6 @@
|
||||
"version": "1.1.0",
|
||||
"resolved": "https://registry.npmjs.org/has-symbols/-/has-symbols-1.1.0.tgz",
|
||||
"integrity": "sha512-1cDNdwJ2Jaohmb3sg4OmKaMBwuC48sYni5HUw2DvsC8LjGTLK9h+eb1X6RyuOHe4hT0ULCW68iomhjUoKUqlPQ==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"engines": {
|
||||
"node": ">= 0.4"
|
||||
@@ -3945,7 +4009,6 @@
|
||||
"version": "1.0.2",
|
||||
"resolved": "https://registry.npmjs.org/has-tostringtag/-/has-tostringtag-1.0.2.tgz",
|
||||
"integrity": "sha512-NqADB8VjPFLM2V0VvHUewwwsw0ZWBaIdgo+ieHtK3hasLz4qeCRjYcqfB6AQrBggRKppKF8L52/VqdVsO47Dlw==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"has-symbols": "^1.0.3"
|
||||
@@ -3961,7 +4024,6 @@
|
||||
"version": "2.0.2",
|
||||
"resolved": "https://registry.npmjs.org/hasown/-/hasown-2.0.2.tgz",
|
||||
"integrity": "sha512-0hJU9SCPvmMzIBdZFqNPXWa6dqh7WdH0cII9y+CyS8rG3nL48Bclra9HmKhVVUHyPWNH5Y7xDwAB7bfgSjkUMQ==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"function-bind": "^1.1.2"
|
||||
@@ -4952,7 +5014,6 @@
|
||||
"version": "1.1.0",
|
||||
"resolved": "https://registry.npmjs.org/math-intrinsics/-/math-intrinsics-1.1.0.tgz",
|
||||
"integrity": "sha512-/IXtbwEk5HTPyEwyKX6hGkYXxM9nbj64B+ilVJnC/R6B0pH5G4V3b0pVbL7DBj4tkhBAppbQUlf6F6Xl9LHu1g==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"engines": {
|
||||
"node": ">= 0.4"
|
||||
@@ -4982,6 +5043,27 @@
|
||||
"node": ">=8.6"
|
||||
}
|
||||
},
|
||||
"node_modules/mime-db": {
|
||||
"version": "1.52.0",
|
||||
"resolved": "https://registry.npmjs.org/mime-db/-/mime-db-1.52.0.tgz",
|
||||
"integrity": "sha512-sPU4uV7dYlvtWJxwwxHD0PuihVNiE7TyAbQ5SWxDCB9mUYvOgroQOwYQQOKPJ8CIbE+1ETVlOoK1UC2nU3gYvg==",
|
||||
"license": "MIT",
|
||||
"engines": {
|
||||
"node": ">= 0.6"
|
||||
}
|
||||
},
|
||||
"node_modules/mime-types": {
|
||||
"version": "2.1.35",
|
||||
"resolved": "https://registry.npmjs.org/mime-types/-/mime-types-2.1.35.tgz",
|
||||
"integrity": "sha512-ZDY+bPm5zTTF+YpCrAU9nK0UgICYPT0QtT1NZWFv4s++TNkcgVaT0g6+4R2uI4MjQjzysHB1zxuWL50hzaeXiw==",
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"mime-db": "1.52.0"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">= 0.6"
|
||||
}
|
||||
},
|
||||
"node_modules/minimatch": {
|
||||
"version": "3.1.2",
|
||||
"resolved": "https://registry.npmjs.org/minimatch/-/minimatch-3.1.2.tgz",
|
||||
@@ -5452,6 +5534,12 @@
|
||||
"react-is": "^16.13.1"
|
||||
}
|
||||
},
|
||||
"node_modules/proxy-from-env": {
|
||||
"version": "1.1.0",
|
||||
"resolved": "https://registry.npmjs.org/proxy-from-env/-/proxy-from-env-1.1.0.tgz",
|
||||
"integrity": "sha512-D+zkORCbA9f1tdWRK0RaCR3GPv50cMxcrz4X8k5LTSUD1Dkw47mKJEZQNunItRTkWwgtaUSo1RVFRIG9ZXiFYg==",
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/punycode": {
|
||||
"version": "2.3.1",
|
||||
"resolved": "https://registry.npmjs.org/punycode/-/punycode-2.3.1.tgz",
|
||||
|
||||
@@ -10,6 +10,7 @@
|
||||
},
|
||||
"dependencies": {
|
||||
"@supabase/supabase-js": "^2.93.1",
|
||||
"axios": "^1.13.4",
|
||||
"next": "16.1.1",
|
||||
"react": "19.2.3",
|
||||
"react-dom": "19.2.3",
|
||||
|
||||
@@ -3,14 +3,11 @@
|
||||
import { useState, useEffect } from 'react';
|
||||
import { useRouter } from 'next/navigation';
|
||||
import { getCurrentUser, User } from '@/lib/auth';
|
||||
|
||||
const API_BASE = typeof window === 'undefined'
|
||||
? (process.env.NEXT_PUBLIC_API_URL || 'http://localhost:8006')
|
||||
: '';
|
||||
import api from '@/lib/axios';
|
||||
|
||||
interface UserListItem {
|
||||
id: string;
|
||||
email: string;
|
||||
phone: string;
|
||||
username: string | null;
|
||||
role: string;
|
||||
is_active: boolean;
|
||||
@@ -43,11 +40,7 @@ export default function AdminPage() {
|
||||
|
||||
const fetchUsers = async () => {
|
||||
try {
|
||||
const res = await fetch(`${API_BASE}/api/admin/users`, {
|
||||
credentials: 'include'
|
||||
});
|
||||
if (!res.ok) throw new Error('获取用户列表失败');
|
||||
const data = await res.json();
|
||||
const { data } = await api.get('/api/admin/users');
|
||||
setUsers(data);
|
||||
} catch (err) {
|
||||
setError('获取用户列表失败');
|
||||
@@ -59,15 +52,12 @@ export default function AdminPage() {
|
||||
const activateUser = async (userId: string) => {
|
||||
setActivatingId(userId);
|
||||
try {
|
||||
const res = await fetch(`${API_BASE}/api/admin/users/${userId}/activate`, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
credentials: 'include',
|
||||
body: JSON.stringify({ expires_days: expireDays || null })
|
||||
await api.post(`/api/admin/users/${userId}/activate`, {
|
||||
expires_days: expireDays || null
|
||||
});
|
||||
if (res.ok) {
|
||||
fetchUsers();
|
||||
}
|
||||
fetchUsers();
|
||||
} catch (err) {
|
||||
// axios interceptor handles 401/403
|
||||
} finally {
|
||||
setActivatingId(null);
|
||||
}
|
||||
@@ -77,10 +67,7 @@ export default function AdminPage() {
|
||||
if (!confirm('确定要停用该用户吗？')) return;
|
||||
|
||||
try {
|
||||
await fetch(`${API_BASE}/api/admin/users/${userId}/deactivate`, {
|
||||
method: 'POST',
|
||||
credentials: 'include'
|
||||
});
|
||||
await api.post(`/api/admin/users/${userId}/deactivate`);
|
||||
fetchUsers();
|
||||
} catch (err) {
|
||||
alert('操作失败');
|
||||
@@ -107,14 +94,14 @@ export default function AdminPage() {
|
||||
|
||||
if (loading) {
|
||||
return (
|
||||
<div className="min-h-screen flex items-center justify-center bg-slate-900">
|
||||
<div className="min-h-dvh flex items-center justify-center">
|
||||
<div className="animate-spin rounded-full h-12 w-12 border-t-2 border-b-2 border-purple-500"></div>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
return (
|
||||
<div className="min-h-screen bg-gradient-to-br from-slate-900 via-purple-900/20 to-slate-900 p-8">
|
||||
<div className="min-h-dvh p-8">
|
||||
<div className="max-w-6xl mx-auto">
|
||||
<div className="flex justify-between items-center mb-8">
|
||||
<h1 className="text-3xl font-bold text-white">用户管理</h1>
|
||||
@@ -157,8 +144,8 @@ export default function AdminPage() {
|
||||
<tr key={user.id} className="hover:bg-white/5">
|
||||
<td className="px-6 py-4">
|
||||
<div>
|
||||
<div className="text-white font-medium">{user.username || user.email.split('@')[0]}</div>
|
||||
<div className="text-gray-400 text-sm">{user.email}</div>
|
||||
<div className="text-white font-medium">{user.username || `用户${user.phone.slice(-4)}`}</div>
|
||||
<div className="text-gray-400 text-sm">{user.phone}</div>
|
||||
</div>
|
||||
</td>
|
||||
<td className="px-6 py-4">
|
||||
|
||||
@@ -19,23 +19,25 @@
|
||||
}
|
||||
}
|
||||
|
||||
body {
|
||||
background: var(--background);
|
||||
color: var(--foreground);
|
||||
font-family: Arial, Helvetica, sans-serif;
|
||||
}
|
||||
|
||||
/* 隐藏滚动条但保留滚动功能 */
|
||||
/* iOS Safari 安全区域支持 + 滚动条隐藏 */
|
||||
html {
|
||||
background-color: #0f172a !important;
|
||||
min-height: 100%;
|
||||
scrollbar-width: none;
|
||||
/* Firefox */
|
||||
-ms-overflow-style: none;
|
||||
/* IE 和 Edge */
|
||||
}
|
||||
|
||||
html::-webkit-scrollbar {
|
||||
display: none;
|
||||
/* Chrome, Safari, Opera */
|
||||
}
|
||||
|
||||
body {
|
||||
margin: 0 !important;
|
||||
min-height: 100dvh;
|
||||
color: var(--foreground);
|
||||
font-family: Arial, Helvetica, sans-serif;
|
||||
padding-top: env(safe-area-inset-top);
|
||||
padding-bottom: env(safe-area-inset-bottom);
|
||||
}
|
||||
|
||||
/* 自定义滚动条样式 - 深色主题 */
|
||||
|
||||
@@ -1,6 +1,9 @@
|
||||
import type { Metadata } from "next";
|
||||
import type { Metadata, Viewport } from "next";
|
||||
import { Geist, Geist_Mono } from "next/font/google";
|
||||
import "./globals.css";
|
||||
import { AuthProvider } from "@/contexts/AuthContext";
|
||||
import { TaskProvider } from "@/contexts/TaskContext";
|
||||
import GlobalTaskIndicator from "@/components/GlobalTaskIndicator";
|
||||
|
||||
const geistSans = Geist({
|
||||
variable: "--font-geist-sans",
|
||||
@@ -13,8 +16,15 @@ const geistMono = Geist_Mono({
|
||||
});
|
||||
|
||||
export const metadata: Metadata = {
|
||||
title: "ViGent",
|
||||
description: "ViGent Talking Head Agent",
|
||||
title: "IPAgent",
|
||||
description: "IPAgent Talking Head Agent",
|
||||
};
|
||||
|
||||
export const viewport: Viewport = {
|
||||
width: 'device-width',
|
||||
initialScale: 1,
|
||||
viewportFit: 'cover',
|
||||
themeColor: '#0f172a',
|
||||
};
|
||||
|
||||
export default function RootLayout({
|
||||
@@ -23,11 +33,20 @@ export default function RootLayout({
|
||||
children: React.ReactNode;
|
||||
}>) {
|
||||
return (
|
||||
<html lang="en">
|
||||
<html lang="en" style={{ backgroundColor: '#0f172a' }}>
|
||||
<body
|
||||
className={`${geistSans.variable} ${geistMono.variable} antialiased`}
|
||||
style={{
|
||||
margin: 0,
|
||||
minHeight: '100dvh',
|
||||
background: 'linear-gradient(to bottom, #0f172a 0%, #0f172a 5%, #581c87 50%, #0f172a 95%, #0f172a 100%)',
|
||||
}}
|
||||
>
|
||||
{children}
|
||||
<AuthProvider>
|
||||
<TaskProvider>
|
||||
{children}
|
||||
</TaskProvider>
|
||||
</AuthProvider>
|
||||
</body>
|
||||
</html>
|
||||
);
|
||||
|
||||
@@ -6,7 +6,7 @@ import { login } from '@/lib/auth';
|
||||
|
||||
export default function LoginPage() {
|
||||
const router = useRouter();
|
||||
const [email, setEmail] = useState('');
|
||||
const [phone, setPhone] = useState('');
|
||||
const [password, setPassword] = useState('');
|
||||
const [error, setError] = useState('');
|
||||
const [loading, setLoading] = useState(false);
|
||||
@@ -14,10 +14,17 @@ export default function LoginPage() {
|
||||
const handleSubmit = async (e: React.FormEvent) => {
|
||||
e.preventDefault();
|
||||
setError('');
|
||||
|
||||
// 验证手机号格式
|
||||
if (!/^\d{11}$/.test(phone)) {
|
||||
setError('请输入正确的11位手机号');
|
||||
return;
|
||||
}
|
||||
|
||||
setLoading(true);
|
||||
|
||||
try {
|
||||
const result = await login(email, password);
|
||||
const result = await login(phone, password);
|
||||
if (result.success) {
|
||||
router.push('/');
|
||||
} else {
|
||||
@@ -31,25 +38,26 @@ export default function LoginPage() {
|
||||
};
|
||||
|
||||
return (
|
||||
<div className="min-h-screen flex items-center justify-center bg-gradient-to-br from-slate-900 via-purple-900 to-slate-900">
|
||||
<div className="min-h-dvh flex items-center justify-center">
|
||||
<div className="w-full max-w-md p-8 bg-white/10 backdrop-blur-lg rounded-2xl shadow-2xl border border-white/20">
|
||||
<div className="text-center mb-8">
|
||||
<h1 className="text-3xl font-bold text-white mb-2">ViGent</h1>
|
||||
<h1 className="text-3xl font-bold text-white mb-2">IPAgent</h1>
|
||||
<p className="text-gray-300">AI 视频生成平台</p>
|
||||
</div>
|
||||
|
||||
<form onSubmit={handleSubmit} className="space-y-6">
|
||||
<div>
|
||||
<label className="block text-sm font-medium text-gray-200 mb-2">
|
||||
邮箱
|
||||
手机号
|
||||
</label>
|
||||
<input
|
||||
type="email"
|
||||
value={email}
|
||||
onChange={(e) => setEmail(e.target.value)}
|
||||
type="tel"
|
||||
value={phone}
|
||||
onChange={(e) => setPhone(e.target.value.replace(/\D/g, '').slice(0, 11))}
|
||||
required
|
||||
maxLength={11}
|
||||
className="w-full px-4 py-3 bg-white/5 border border-white/10 rounded-lg text-white placeholder-gray-400 focus:outline-none focus:ring-2 focus:ring-purple-500 focus:border-transparent"
|
||||
placeholder="your@email.com"
|
||||
placeholder="请输入11位手机号"
|
||||
/>
|
||||
</div>
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -2,15 +2,29 @@
|
||||
|
||||
import { useState, useEffect } from "react";
|
||||
import useSWR from 'swr';
|
||||
|
||||
const fetcher = (url: string) => fetch(url).then((res) => res.json());
|
||||
import Link from "next/link";
|
||||
import api from "@/lib/axios";
|
||||
import { useAuth } from "@/contexts/AuthContext";
|
||||
|
||||
// SWR fetcher 使用 axios（自动处理 401/403）
|
||||
const fetcher = (url: string) => api.get(url).then((res) => res.data);
|
||||
|
||||
// 动态获取 API 地址：服务端使用 localhost，客户端使用当前域名
|
||||
const API_BASE = typeof window === 'undefined'
|
||||
? 'http://localhost:8006'
|
||||
: '';
|
||||
|
||||
// 格式化日期（避免 Hydration 错误）
|
||||
const formatDate = (timestamp: number) => {
|
||||
const d = new Date(timestamp * 1000);
|
||||
const year = d.getFullYear();
|
||||
const month = String(d.getMonth() + 1).padStart(2, '0');
|
||||
const day = String(d.getDate()).padStart(2, '0');
|
||||
const hour = String(d.getHours()).padStart(2, '0');
|
||||
const minute = String(d.getMinutes()).padStart(2, '0');
|
||||
return `${year}/${month}/${day} ${hour}:${minute}`;
|
||||
};
|
||||
|
||||
interface Account {
|
||||
platform: string;
|
||||
name: string;
|
||||
@@ -38,16 +52,64 @@ export default function PublishPage() {
|
||||
const [qrPlatform, setQrPlatform] = useState<string | null>(null);
|
||||
const [isLoadingQR, setIsLoadingQR] = useState(false);
|
||||
|
||||
// 使用全局认证状态
|
||||
const { userId, isLoading: isAuthLoading } = useAuth();
|
||||
// 是否已从 localStorage 恢复完成
|
||||
const [isRestored, setIsRestored] = useState(false);
|
||||
|
||||
// 加载账号和视频列表
|
||||
useEffect(() => {
|
||||
fetchAccounts();
|
||||
fetchVideos();
|
||||
}, []);
|
||||
|
||||
// 获取存储 key 的前缀（登录用户使用 userId，未登录使用 guest）
|
||||
const storageKey = userId || 'guest';
|
||||
|
||||
// 从 localStorage 恢复用户输入（等待认证完成后）
|
||||
useEffect(() => {
|
||||
console.log("[Publish] 恢复检查 - isAuthLoading:", isAuthLoading, "userId:", userId);
|
||||
if (isAuthLoading) return;
|
||||
|
||||
console.log("[Publish] 开始从 localStorage 恢复数据，storageKey:", storageKey);
|
||||
// 从 localStorage 恢复用户输入（带用户隔离，未登录用户使用 guest）
|
||||
const savedTitle = localStorage.getItem(`vigent_${storageKey}_publish_title`);
|
||||
const savedTags = localStorage.getItem(`vigent_${storageKey}_publish_tags`);
|
||||
|
||||
console.log("[Publish] localStorage 数据:", { savedTitle, savedTags });
|
||||
|
||||
if (savedTitle) setTitle(savedTitle);
|
||||
if (savedTags) {
|
||||
// 兼容 JSON 数组格式（AI 生成）和字符串格式（手动输入）
|
||||
try {
|
||||
const parsed = JSON.parse(savedTags);
|
||||
if (Array.isArray(parsed)) {
|
||||
setTags(parsed.join(', '));
|
||||
} else {
|
||||
setTags(savedTags);
|
||||
}
|
||||
} catch {
|
||||
setTags(savedTags);
|
||||
}
|
||||
}
|
||||
|
||||
// 恢复完成后才允许保存
|
||||
setIsRestored(true);
|
||||
console.log("[Publish] 恢复完成，isRestored = true");
|
||||
}, [storageKey, isAuthLoading]);
|
||||
|
||||
// 保存用户输入到 localStorage（恢复完成后才保存，未登录用户也可保存）
|
||||
useEffect(() => {
|
||||
if (isRestored) localStorage.setItem(`vigent_${storageKey}_publish_title`, title);
|
||||
}, [title, storageKey, isRestored]);
|
||||
|
||||
useEffect(() => {
|
||||
if (isRestored) localStorage.setItem(`vigent_${storageKey}_publish_tags`, tags);
|
||||
}, [tags, storageKey, isRestored]);
|
||||
|
||||
const fetchAccounts = async () => {
|
||||
try {
|
||||
const res = await fetch(`${API_BASE}/api/publish/accounts`);
|
||||
const data = await res.json();
|
||||
const { data } = await api.get('/api/publish/accounts');
|
||||
setAccounts(data.accounts || []);
|
||||
} catch (error) {
|
||||
console.error("获取账号失败:", error);
|
||||
@@ -56,13 +118,11 @@ export default function PublishPage() {
|
||||
|
||||
const fetchVideos = async () => {
|
||||
try {
|
||||
// 使用持久化的视频列表 API（从文件系统读取）
|
||||
const res = await fetch(`${API_BASE}/api/videos/generated`);
|
||||
const data = await res.json();
|
||||
const { data } = await api.get('/api/videos/generated');
|
||||
|
||||
const videos = (data.videos || []).map((v: any) => ({
|
||||
name: new Date(v.created_at * 1000).toLocaleString('zh-CN') + ` (${v.size_mb.toFixed(1)}MB)`,
|
||||
path: v.path.startsWith('/') ? v.path.slice(1) : v.path, // 移除开头的 /
|
||||
name: formatDate(v.created_at) + ` (${v.size_mb.toFixed(1)}MB)`,
|
||||
path: v.path.startsWith('/') ? v.path.slice(1) : v.path,
|
||||
}));
|
||||
|
||||
setVideos(videos);
|
||||
@@ -95,22 +155,17 @@ export default function PublishPage() {
|
||||
|
||||
for (const platform of selectedPlatforms) {
|
||||
try {
|
||||
const res = await fetch(`${API_BASE}/api/publish`, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
video_path: selectedVideo,
|
||||
platform,
|
||||
title,
|
||||
tags: tagList,
|
||||
description: "",
|
||||
publish_time: scheduleMode === "scheduled" && publishTime
|
||||
? new Date(publishTime).toISOString()
|
||||
: null
|
||||
}),
|
||||
const { data: result } = await api.post('/api/publish', {
|
||||
video_path: selectedVideo,
|
||||
platform,
|
||||
title,
|
||||
tags: tagList,
|
||||
description: "",
|
||||
publish_time: scheduleMode === "scheduled" && publishTime
|
||||
? new Date(publishTime).toISOString()
|
||||
: null
|
||||
});
|
||||
|
||||
const result = await res.json();
|
||||
setPublishResults((prev) => [...prev, result]);
|
||||
// 发布成功后10秒自动清除结果
|
||||
if (result.success) {
|
||||
@@ -118,10 +173,11 @@ export default function PublishPage() {
|
||||
setPublishResults((prev) => prev.filter((r) => r !== result));
|
||||
}, 10000);
|
||||
}
|
||||
} catch (error) {
|
||||
} catch (error: any) {
|
||||
const message = error.response?.data?.detail || String(error);
|
||||
setPublishResults((prev) => [
|
||||
...prev,
|
||||
{ platform, success: false, message: String(error) },
|
||||
{ platform, success: false, message },
|
||||
]);
|
||||
}
|
||||
}
|
||||
@@ -166,21 +222,17 @@ export default function PublishPage() {
|
||||
setQrPlatform(platform); // 立即显示加载弹窗
|
||||
setQrCodeImage(null); // 清空旧二维码
|
||||
try {
|
||||
const res = await fetch(`${API_BASE}/api/publish/login/${platform}`, {
|
||||
method: 'POST'
|
||||
});
|
||||
const result = await res.json();
|
||||
const { data: result } = await api.post(`/api/publish/login/${platform}`);
|
||||
|
||||
if (result.success && result.qr_code) {
|
||||
setQrCodeImage(result.qr_code);
|
||||
// SWR hook will automatically start polling since qrPlatform is set
|
||||
} else {
|
||||
setQrPlatform(null); // 失败时关闭弹窗
|
||||
setQrPlatform(null);
|
||||
alert(result.message || '登录失败');
|
||||
}
|
||||
} catch (error) {
|
||||
setQrPlatform(null); // 失败时关闭弹窗
|
||||
alert(`登录失败: ${error}`);
|
||||
} catch (error: any) {
|
||||
setQrPlatform(null);
|
||||
alert(`登录失败: ${error.response?.data?.detail || error.message}`);
|
||||
} finally {
|
||||
setIsLoadingQR(false);
|
||||
}
|
||||
@@ -189,18 +241,15 @@ export default function PublishPage() {
|
||||
const handleLogout = async (platform: string) => {
|
||||
if (!confirm('确定要注销登录吗？')) return;
|
||||
try {
|
||||
const res = await fetch(`${API_BASE}/api/publish/logout/${platform}`, {
|
||||
method: 'POST'
|
||||
});
|
||||
const result = await res.json();
|
||||
const { data: result } = await api.post(`/api/publish/logout/${platform}`);
|
||||
if (result.success) {
|
||||
alert('已注销');
|
||||
fetchAccounts();
|
||||
} else {
|
||||
alert(result.message || '注销失败');
|
||||
}
|
||||
} catch (error) {
|
||||
alert(`注销失败: ${error}`);
|
||||
} catch (error: any) {
|
||||
alert(`注销失败: ${error.response?.data?.detail || error.message}`);
|
||||
}
|
||||
};
|
||||
|
||||
@@ -213,7 +262,7 @@ export default function PublishPage() {
|
||||
};
|
||||
|
||||
return (
|
||||
<div className="min-h-screen bg-gradient-to-br from-gray-900 via-purple-900 to-gray-900">
|
||||
<div className="min-h-dvh">
|
||||
{/* QR码弹窗 */}
|
||||
{qrPlatform && (
|
||||
<div className="fixed inset-0 bg-black/80 flex items-center justify-center z-50">
|
||||
@@ -248,33 +297,31 @@ export default function PublishPage() {
|
||||
|
||||
{/* Header - 统一样式 */}
|
||||
<header className="border-b border-white/10 bg-black/20 backdrop-blur-sm">
|
||||
<div className="max-w-6xl mx-auto px-6 py-4 flex items-center justify-between">
|
||||
<Link href="/" className="text-2xl font-bold text-white flex items-center gap-3 hover:opacity-80 transition-opacity">
|
||||
<span className="text-4xl">🎬</span>
|
||||
ViGent
|
||||
<div className="max-w-6xl mx-auto px-4 sm:px-6 py-3 sm:py-4 flex items-center justify-between">
|
||||
<Link href="/" className="text-xl sm:text-2xl font-bold text-white flex items-center gap-2 sm:gap-3 hover:opacity-80 transition-opacity">
|
||||
<span className="text-3xl sm:text-4xl">🎬</span>
|
||||
IPAgent
|
||||
</Link>
|
||||
<div className="flex items-center gap-4">
|
||||
<div className="flex items-center gap-1 sm:gap-4">
|
||||
<Link
|
||||
href="/"
|
||||
className="px-4 py-2 bg-white/10 hover:bg-white/20 text-white rounded-lg transition-colors"
|
||||
className="px-2 sm:px-4 py-1 sm:py-2 text-sm sm:text-base bg-white/10 hover:bg-white/20 text-white rounded-lg transition-colors"
|
||||
>
|
||||
返回创作
|
||||
</Link>
|
||||
<span className="px-4 py-2 bg-gradient-to-r from-purple-600 to-pink-600 text-white rounded-lg font-semibold">
|
||||
<span className="px-2 sm:px-4 py-1 sm:py-2 text-sm sm:text-base bg-gradient-to-r from-purple-600 to-pink-600 text-white rounded-lg font-semibold">
|
||||
发布管理
|
||||
</span>
|
||||
<button
|
||||
onClick={async () => {
|
||||
if (confirm('确定要退出登录吗？')) {
|
||||
try {
|
||||
await fetch(`${API_BASE}/api/auth/logout`, { method: 'POST' });
|
||||
window.location.href = '/login';
|
||||
} catch (e) {
|
||||
window.location.href = '/login';
|
||||
}
|
||||
await api.post('/api/auth/logout');
|
||||
} catch (e) { }
|
||||
window.location.href = '/login';
|
||||
}
|
||||
}}
|
||||
className="px-4 py-2 bg-red-500/10 hover:bg-red-500/20 text-red-200 rounded-lg transition-colors"
|
||||
className="px-2 sm:px-4 py-1 sm:py-2 text-sm sm:text-base bg-red-500/10 hover:bg-red-500/20 text-red-200 rounded-lg transition-colors"
|
||||
>
|
||||
退出
|
||||
</button>
|
||||
@@ -405,40 +452,6 @@ export default function PublishPage() {
|
||||
className="w-full p-3 bg-black/30 border border-white/10 rounded-xl text-white placeholder-gray-500"
|
||||
/>
|
||||
</div>
|
||||
<div>
|
||||
<label className="block text-gray-400 text-sm mb-2">
|
||||
发布时间
|
||||
</label>
|
||||
<div className="flex gap-3 mb-3">
|
||||
<button
|
||||
onClick={() => setScheduleMode("now")}
|
||||
className={`flex-1 px-4 py-2 rounded-lg font-medium transition-colors ${scheduleMode === "now"
|
||||
? "bg-purple-600 text-white"
|
||||
: "bg-black/30 text-gray-400 hover:bg-black/50"
|
||||
}`}
|
||||
>
|
||||
⚡ 立即发布
|
||||
</button>
|
||||
<button
|
||||
onClick={() => setScheduleMode("scheduled")}
|
||||
className={`flex-1 px-4 py-2 rounded-lg font-medium transition-colors ${scheduleMode === "scheduled"
|
||||
? "bg-purple-600 text-white"
|
||||
: "bg-black/30 text-gray-400 hover:bg-black/50"
|
||||
}`}
|
||||
>
|
||||
⏰ 定时发布
|
||||
</button>
|
||||
</div>
|
||||
{scheduleMode === "scheduled" && (
|
||||
<input
|
||||
type="datetime-local"
|
||||
value={publishTime}
|
||||
onChange={(e) => setPublishTime(e.target.value)}
|
||||
min={new Date().toISOString().slice(0, 16)}
|
||||
className="w-full p-3 bg-black/30 border border-white/10 rounded-xl text-white"
|
||||
/>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -473,17 +486,61 @@ export default function PublishPage() {
|
||||
)}
|
||||
</div>
|
||||
|
||||
{/* 发布按钮 */}
|
||||
<button
|
||||
onClick={handlePublish}
|
||||
disabled={isPublishing || selectedPlatforms.length === 0}
|
||||
className={`w-full py-4 rounded-xl font-bold text-lg transition-all ${isPublishing || selectedPlatforms.length === 0
|
||||
? "bg-gray-600 cursor-not-allowed text-gray-400"
|
||||
: "bg-gradient-to-r from-green-600 to-teal-600 hover:from-green-700 hover:to-teal-700 text-white"
|
||||
}`}
|
||||
>
|
||||
{isPublishing ? "发布中..." : "🚀 一键发布"}
|
||||
</button>
|
||||
{/* 发布按钮区域 */}
|
||||
<div className="space-y-3">
|
||||
<div className="flex gap-3">
|
||||
{/* 立即发布 - 占 3/4 */}
|
||||
<button
|
||||
onClick={() => {
|
||||
setScheduleMode("now");
|
||||
handlePublish();
|
||||
}}
|
||||
disabled={isPublishing || selectedPlatforms.length === 0}
|
||||
className={`flex-[3] py-4 rounded-xl font-bold text-lg transition-all ${isPublishing || selectedPlatforms.length === 0
|
||||
? "bg-gray-600 cursor-not-allowed text-gray-400"
|
||||
: "bg-gradient-to-r from-green-600 to-teal-600 hover:from-green-700 hover:to-teal-700 text-white"
|
||||
}`}
|
||||
>
|
||||
{isPublishing && scheduleMode === "now" ? "发布中..." : "🚀 立即发布"}
|
||||
</button>
|
||||
{/* 定时发布 - 占 1/4 */}
|
||||
<button
|
||||
onClick={() => setScheduleMode(scheduleMode === "scheduled" ? "now" : "scheduled")}
|
||||
disabled={isPublishing || selectedPlatforms.length === 0}
|
||||
className={`flex-1 py-4 rounded-xl font-bold text-base transition-all ${isPublishing || selectedPlatforms.length === 0
|
||||
? "bg-gray-600 cursor-not-allowed text-gray-400"
|
||||
: scheduleMode === "scheduled"
|
||||
? "bg-purple-600 text-white"
|
||||
: "bg-white/10 hover:bg-white/20 text-white"
|
||||
}`}
|
||||
>
|
||||
⏰ 定时
|
||||
</button>
|
||||
</div>
|
||||
|
||||
{/* 定时发布时间选择器 */}
|
||||
{scheduleMode === "scheduled" && (
|
||||
<div className="flex gap-3 items-center">
|
||||
<input
|
||||
type="datetime-local"
|
||||
value={publishTime}
|
||||
onChange={(e) => setPublishTime(e.target.value)}
|
||||
min={new Date().toISOString().slice(0, 16)}
|
||||
className="flex-1 p-3 bg-black/30 border border-white/10 rounded-xl text-white"
|
||||
/>
|
||||
<button
|
||||
onClick={handlePublish}
|
||||
disabled={isPublishing || selectedPlatforms.length === 0 || !publishTime}
|
||||
className={`px-6 py-3 rounded-xl font-bold transition-all ${isPublishing || selectedPlatforms.length === 0 || !publishTime
|
||||
? "bg-gray-600 cursor-not-allowed text-gray-400"
|
||||
: "bg-gradient-to-r from-purple-600 to-pink-600 hover:from-purple-700 hover:to-pink-700 text-white"
|
||||
}`}
|
||||
>
|
||||
{isPublishing && scheduleMode === "scheduled" ? "设置中..." : "确认定时"}
|
||||
</button>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
|
||||
{/* 发布结果 */}
|
||||
{publishResults.length > 0 && (
|
||||
|
||||
@@ -6,7 +6,7 @@ import { register } from '@/lib/auth';
|
||||
|
||||
export default function RegisterPage() {
|
||||
const router = useRouter();
|
||||
const [email, setEmail] = useState('');
|
||||
const [phone, setPhone] = useState('');
|
||||
const [password, setPassword] = useState('');
|
||||
const [confirmPassword, setConfirmPassword] = useState('');
|
||||
const [username, setUsername] = useState('');
|
||||
@@ -18,6 +18,12 @@ export default function RegisterPage() {
|
||||
e.preventDefault();
|
||||
setError('');
|
||||
|
||||
// 验证手机号格式
|
||||
if (!/^\d{11}$/.test(phone)) {
|
||||
setError('请输入正确的11位手机号');
|
||||
return;
|
||||
}
|
||||
|
||||
if (password !== confirmPassword) {
|
||||
setError('两次输入的密码不一致');
|
||||
return;
|
||||
@@ -31,7 +37,7 @@ export default function RegisterPage() {
|
||||
setLoading(true);
|
||||
|
||||
try {
|
||||
const result = await register(email, password, username || undefined);
|
||||
const result = await register(phone, password, username || undefined);
|
||||
if (result.success) {
|
||||
setSuccess(true);
|
||||
} else {
|
||||
@@ -46,7 +52,7 @@ export default function RegisterPage() {
|
||||
|
||||
if (success) {
|
||||
return (
|
||||
<div className="min-h-screen flex items-center justify-center bg-gradient-to-br from-slate-900 via-purple-900 to-slate-900">
|
||||
<div className="min-h-dvh flex items-center justify-center">
|
||||
<div className="w-full max-w-md p-8 bg-white/10 backdrop-blur-lg rounded-2xl shadow-2xl border border-white/20 text-center">
|
||||
<div className="mb-6">
|
||||
<svg className="w-16 h-16 mx-auto text-green-400" fill="none" stroke="currentColor" viewBox="0 0 24 24">
|
||||
@@ -69,26 +75,28 @@ export default function RegisterPage() {
|
||||
}
|
||||
|
||||
return (
|
||||
<div className="min-h-screen flex items-center justify-center bg-gradient-to-br from-slate-900 via-purple-900 to-slate-900">
|
||||
<div className="min-h-dvh flex items-center justify-center">
|
||||
<div className="w-full max-w-md p-8 bg-white/10 backdrop-blur-lg rounded-2xl shadow-2xl border border-white/20">
|
||||
<div className="text-center mb-8">
|
||||
<h1 className="text-3xl font-bold text-white mb-2">注册账号</h1>
|
||||
<p className="text-gray-300">创建您的 ViGent 账号</p>
|
||||
<p className="text-gray-300">创建您的 IPAgent 账号</p>
|
||||
</div>
|
||||
|
||||
<form onSubmit={handleSubmit} className="space-y-5">
|
||||
<div>
|
||||
<label className="block text-sm font-medium text-gray-200 mb-2">
|
||||
邮箱 <span className="text-red-400">*</span>
|
||||
手机号 <span className="text-red-400">*</span>
|
||||
</label>
|
||||
<input
|
||||
type="email"
|
||||
value={email}
|
||||
onChange={(e) => setEmail(e.target.value)}
|
||||
type="tel"
|
||||
value={phone}
|
||||
onChange={(e) => setPhone(e.target.value.replace(/\D/g, '').slice(0, 11))}
|
||||
required
|
||||
maxLength={11}
|
||||
className="w-full px-4 py-3 bg-white/5 border border-white/10 rounded-lg text-white placeholder-gray-400 focus:outline-none focus:ring-2 focus:ring-purple-500"
|
||||
placeholder="your@email.com"
|
||||
placeholder="请输入11位手机号"
|
||||
/>
|
||||
<p className="mt-1 text-xs text-gray-500">必须是11位数字</p>
|
||||
</div>
|
||||
|
||||
<div>
|
||||
|
||||
42
frontend/src/components/GlobalTaskIndicator.tsx
Normal file
42
frontend/src/components/GlobalTaskIndicator.tsx
Normal file
@@ -0,0 +1,42 @@
|
||||
"use client";
|
||||
|
||||
import { useTask } from "@/contexts/TaskContext";
|
||||
import Link from "next/link";
|
||||
|
||||
export default function GlobalTaskIndicator() {
|
||||
const { currentTask, isGenerating } = useTask();
|
||||
|
||||
if (!isGenerating) return null;
|
||||
|
||||
return (
|
||||
<div className="fixed top-0 left-0 right-0 z-50 bg-gradient-to-r from-purple-600 to-pink-600 text-white shadow-lg">
|
||||
<div className="max-w-6xl mx-auto px-6 py-3">
|
||||
<div className="flex items-center justify-between">
|
||||
<div className="flex items-center gap-3">
|
||||
<div className="animate-spin rounded-full h-5 w-5 border-2 border-white border-t-transparent"></div>
|
||||
<span className="font-medium">
|
||||
视频生成中... {currentTask?.progress || 0}%
|
||||
</span>
|
||||
{currentTask?.message && (
|
||||
<span className="text-white/80 text-sm">
|
||||
{currentTask.message}
|
||||
</span>
|
||||
)}
|
||||
</div>
|
||||
<Link
|
||||
href="/"
|
||||
className="px-3 py-1 bg-white/20 hover:bg-white/30 rounded transition-colors text-sm"
|
||||
>
|
||||
查看详情
|
||||
</Link>
|
||||
</div>
|
||||
<div className="mt-2 w-full bg-white/20 rounded-full h-1.5 overflow-hidden">
|
||||
<div
|
||||
className="bg-white h-full transition-all duration-300 ease-out"
|
||||
style={{ width: `${currentTask?.progress || 0}%` }}
|
||||
></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
80
frontend/src/contexts/AuthContext.tsx
Normal file
80
frontend/src/contexts/AuthContext.tsx
Normal file
@@ -0,0 +1,80 @@
|
||||
"use client";
|
||||
|
||||
import { createContext, useContext, useState, useEffect, ReactNode } from "react";
|
||||
import api from "@/lib/axios";
|
||||
|
||||
interface User {
|
||||
id: string;
|
||||
phone: string;
|
||||
username: string | null;
|
||||
role: string;
|
||||
is_active: boolean;
|
||||
expires_at: string | null;
|
||||
}
|
||||
|
||||
interface AuthContextType {
|
||||
userId: string | null;
|
||||
user: User | null;
|
||||
isLoading: boolean;
|
||||
isAuthenticated: boolean;
|
||||
}
|
||||
|
||||
const AuthContext = createContext<AuthContextType>({
|
||||
userId: null,
|
||||
user: null,
|
||||
isLoading: true,
|
||||
isAuthenticated: false,
|
||||
});
|
||||
|
||||
export function AuthProvider({ children }: { children: ReactNode }) {
|
||||
const [user, setUser] = useState<User | null>(null);
|
||||
const [isLoading, setIsLoading] = useState(true);
|
||||
|
||||
useEffect(() => {
|
||||
let retryCount = 0;
|
||||
const maxRetries = 2;
|
||||
|
||||
const fetchUser = async () => {
|
||||
console.log("[AuthContext] 开始获取用户信息...");
|
||||
try {
|
||||
const { data } = await api.get('/api/auth/me');
|
||||
console.log("[AuthContext] 获取用户信息成功:", data);
|
||||
if (data && data.id) {
|
||||
setUser(data);
|
||||
console.log("[AuthContext] 设置 user:", data);
|
||||
} else {
|
||||
console.warn("[AuthContext] 响应中没有用户数据");
|
||||
}
|
||||
setIsLoading(false);
|
||||
} catch (error) {
|
||||
console.error("[AuthContext] 获取用户信息失败:", error);
|
||||
// 重试逻辑
|
||||
if (retryCount < maxRetries) {
|
||||
retryCount++;
|
||||
console.log(`[AuthContext] 重试 ${retryCount}/${maxRetries}...`);
|
||||
setTimeout(fetchUser, 1000);
|
||||
} else {
|
||||
console.error("[AuthContext] 重试次数用尽，放弃获取用户信息");
|
||||
setIsLoading(false);
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
fetchUser();
|
||||
}, []);
|
||||
|
||||
return (
|
||||
<AuthContext.Provider value={{
|
||||
userId: user?.id || null,
|
||||
user,
|
||||
isLoading,
|
||||
isAuthenticated: !!user
|
||||
}}>
|
||||
{children}
|
||||
</AuthContext.Provider>
|
||||
);
|
||||
}
|
||||
|
||||
export function useAuth() {
|
||||
return useContext(AuthContext);
|
||||
}
|
||||
119
frontend/src/contexts/TaskContext.tsx
Normal file
119
frontend/src/contexts/TaskContext.tsx
Normal file
@@ -0,0 +1,119 @@
|
||||
"use client";
|
||||
|
||||
import { createContext, useContext, useState, useEffect, ReactNode } from "react";
|
||||
import api from "@/lib/axios";
|
||||
|
||||
interface Task {
|
||||
task_id: string;
|
||||
status: string;
|
||||
progress: number;
|
||||
message: string;
|
||||
download_url?: string;
|
||||
}
|
||||
|
||||
interface TaskContextType {
|
||||
currentTask: Task | null;
|
||||
isGenerating: boolean;
|
||||
startTask: (taskId: string) => void;
|
||||
clearTask: () => void;
|
||||
}
|
||||
|
||||
const TaskContext = createContext<TaskContextType | undefined>(undefined);
|
||||
|
||||
export function TaskProvider({ children }: { children: ReactNode }) {
|
||||
const [currentTask, setCurrentTask] = useState<Task | null>(null);
|
||||
const [isGenerating, setIsGenerating] = useState(false);
|
||||
const [taskId, setTaskId] = useState<string | null>(null);
|
||||
|
||||
// 轮询任务状态
|
||||
useEffect(() => {
|
||||
if (!taskId) return;
|
||||
|
||||
const pollTask = async () => {
|
||||
try {
|
||||
const { data } = await api.get(`/api/videos/tasks/${taskId}`);
|
||||
setCurrentTask(data);
|
||||
|
||||
// 处理任务完成、失败或不存在的情况
|
||||
if (data.status === "completed" || data.status === "failed" || data.status === "not_found") {
|
||||
setIsGenerating(false);
|
||||
setTaskId(null);
|
||||
// 清除 localStorage
|
||||
if (typeof window !== 'undefined') {
|
||||
const keys = Object.keys(localStorage);
|
||||
keys.forEach(key => {
|
||||
if (key.includes('_current_task')) {
|
||||
localStorage.removeItem(key);
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
} catch (error) {
|
||||
console.error("轮询任务失败:", error);
|
||||
setIsGenerating(false);
|
||||
setTaskId(null);
|
||||
// 清除 localStorage
|
||||
if (typeof window !== 'undefined') {
|
||||
const keys = Object.keys(localStorage);
|
||||
keys.forEach(key => {
|
||||
if (key.includes('_current_task')) {
|
||||
localStorage.removeItem(key);
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
// 立即执行一次
|
||||
pollTask();
|
||||
|
||||
// 每秒轮询
|
||||
const interval = setInterval(pollTask, 1000);
|
||||
|
||||
return () => clearInterval(interval);
|
||||
}, [taskId]);
|
||||
|
||||
// 页面加载时恢复任务
|
||||
useEffect(() => {
|
||||
if (typeof window === 'undefined') return;
|
||||
|
||||
// 查找所有可能的任务ID
|
||||
const keys = Object.keys(localStorage);
|
||||
const taskKey = keys.find(key => key.includes('_current_task'));
|
||||
|
||||
if (taskKey) {
|
||||
const savedTaskId = localStorage.getItem(taskKey);
|
||||
if (savedTaskId) {
|
||||
console.log("[TaskContext] 恢复任务:", savedTaskId);
|
||||
setTaskId(savedTaskId);
|
||||
setIsGenerating(true);
|
||||
}
|
||||
}
|
||||
}, []);
|
||||
|
||||
const startTask = (newTaskId: string) => {
|
||||
setTaskId(newTaskId);
|
||||
setIsGenerating(true);
|
||||
setCurrentTask(null);
|
||||
};
|
||||
|
||||
const clearTask = () => {
|
||||
setTaskId(null);
|
||||
setIsGenerating(false);
|
||||
setCurrentTask(null);
|
||||
};
|
||||
|
||||
return (
|
||||
<TaskContext.Provider value={{ currentTask, isGenerating, startTask, clearTask }}>
|
||||
{children}
|
||||
</TaskContext.Provider>
|
||||
);
|
||||
}
|
||||
|
||||
export function useTask() {
|
||||
const context = useContext(TaskContext);
|
||||
if (context === undefined) {
|
||||
throw new Error("useTask must be used within a TaskProvider");
|
||||
}
|
||||
return context;
|
||||
}
|
||||
@@ -8,10 +8,11 @@ const API_BASE = typeof window === 'undefined'
|
||||
|
||||
export interface User {
|
||||
id: string;
|
||||
email: string;
|
||||
phone: string;
|
||||
username: string | null;
|
||||
role: string;
|
||||
is_active: boolean;
|
||||
expires_at: string | null;
|
||||
}
|
||||
|
||||
export interface AuthResponse {
|
||||
@@ -23,12 +24,12 @@ export interface AuthResponse {
|
||||
/**
|
||||
* 用户注册
|
||||
*/
|
||||
export async function register(email: string, password: string, username?: string): Promise<AuthResponse> {
|
||||
export async function register(phone: string, password: string, username?: string): Promise<AuthResponse> {
|
||||
const res = await fetch(`${API_BASE}/api/auth/register`, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
credentials: 'include',
|
||||
body: JSON.stringify({ email, password, username })
|
||||
body: JSON.stringify({ phone, password, username })
|
||||
});
|
||||
return res.json();
|
||||
}
|
||||
@@ -36,12 +37,12 @@ export async function register(email: string, password: string, username?: strin
|
||||
/**
|
||||
* 用户登录
|
||||
*/
|
||||
export async function login(email: string, password: string): Promise<AuthResponse> {
|
||||
export async function login(phone: string, password: string): Promise<AuthResponse> {
|
||||
const res = await fetch(`${API_BASE}/api/auth/login`, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
credentials: 'include',
|
||||
body: JSON.stringify({ email, password })
|
||||
body: JSON.stringify({ phone, password })
|
||||
});
|
||||
return res.json();
|
||||
}
|
||||
@@ -57,6 +58,19 @@ export async function logout(): Promise<AuthResponse> {
|
||||
return res.json();
|
||||
}
|
||||
|
||||
/**
|
||||
* 修改密码
|
||||
*/
|
||||
export async function changePassword(oldPassword: string, newPassword: string): Promise<AuthResponse> {
|
||||
const res = await fetch(`${API_BASE}/api/auth/change-password`, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
credentials: 'include',
|
||||
body: JSON.stringify({ old_password: oldPassword, new_password: newPassword })
|
||||
});
|
||||
return res.json();
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取当前用户
|
||||
*/
|
||||
|
||||
54
frontend/src/lib/axios.ts
Normal file
54
frontend/src/lib/axios.ts
Normal file
@@ -0,0 +1,54 @@
|
||||
/**
|
||||
* Axios 实例配置
|
||||
* 全局拦截 401/403 响应，自动跳转登录页
|
||||
*/
|
||||
import axios from 'axios';
|
||||
|
||||
// 动态获取 API 地址：服务端使用 localhost，客户端使用当前域名
|
||||
const API_BASE = typeof window === 'undefined'
|
||||
? 'http://localhost:8006'
|
||||
: '';
|
||||
|
||||
// 防止重复跳转
|
||||
let isRedirecting = false;
|
||||
|
||||
const PUBLIC_PATHS = new Set(['/login', '/register']);
|
||||
|
||||
// 创建 axios 实例
|
||||
const api = axios.create({
|
||||
baseURL: API_BASE,
|
||||
withCredentials: true, // 自动携带 cookie
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
},
|
||||
});
|
||||
|
||||
// 响应拦截器 - 全局处理 401/403
|
||||
api.interceptors.response.use(
|
||||
(response) => response,
|
||||
async (error) => {
|
||||
const status = error.response?.status;
|
||||
|
||||
const isPublicPath = typeof window !== 'undefined' && PUBLIC_PATHS.has(window.location.pathname);
|
||||
|
||||
if ((status === 401 || status === 403) && !isRedirecting && !isPublicPath) {
|
||||
isRedirecting = true;
|
||||
|
||||
// 调用 logout API 清除 HttpOnly cookie
|
||||
try {
|
||||
await fetch('/api/auth/logout', { method: 'POST' });
|
||||
} catch (e) {
|
||||
// 忽略错误
|
||||
}
|
||||
|
||||
// 跳转登录页
|
||||
if (typeof window !== 'undefined') {
|
||||
window.location.replace('/login');
|
||||
}
|
||||
}
|
||||
|
||||
return Promise.reject(error);
|
||||
}
|
||||
);
|
||||
|
||||
export default api;
|
||||
33
frontend/src/proxy.ts
Normal file
33
frontend/src/proxy.ts
Normal file
@@ -0,0 +1,33 @@
|
||||
import { NextResponse } from 'next/server';
|
||||
import type { NextRequest } from 'next/server';
|
||||
|
||||
// 需要登录才能访问的路径
|
||||
const protectedPaths = ['/', '/publish', '/admin'];
|
||||
|
||||
// 公开路径 (无需登录)
|
||||
const publicPaths = ['/login', '/register'];
|
||||
|
||||
export function proxy(request: NextRequest) {
|
||||
const { pathname } = request.nextUrl;
|
||||
|
||||
// 检查是否有 access_token cookie
|
||||
const token = request.cookies.get('access_token');
|
||||
|
||||
// 访问受保护页面但未登录 → 重定向到登录页
|
||||
if (protectedPaths.some(path => pathname === path || pathname.startsWith(path + '/')) && !token) {
|
||||
const loginUrl = new URL('/login', request.url);
|
||||
loginUrl.searchParams.set('from', pathname);
|
||||
return NextResponse.redirect(loginUrl);
|
||||
}
|
||||
|
||||
// 已登录用户访问登录/注册页 → 重定向到首页
|
||||
if (publicPaths.includes(pathname) && token) {
|
||||
return NextResponse.redirect(new URL('/', request.url));
|
||||
}
|
||||
|
||||
return NextResponse.next();
|
||||
}
|
||||
|
||||
export const config = {
|
||||
matcher: ['/', '/publish/:path*', '/admin/:path*', '/login', '/register']
|
||||
};
|
||||
24
models/Qwen3-TTS/.gitignore
vendored
Normal file
24
models/Qwen3-TTS/.gitignore
vendored
Normal file
@@ -0,0 +1,24 @@
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
*$py.class
|
||||
*.so
|
||||
.Python
|
||||
build/
|
||||
develop-eggs/
|
||||
dist/
|
||||
downloads/
|
||||
eggs/
|
||||
.eggs/
|
||||
lib/
|
||||
lib64/
|
||||
parts/
|
||||
sdist/
|
||||
var/
|
||||
wheels/
|
||||
*.egg-info/
|
||||
.installed.cfg
|
||||
*.egg
|
||||
.idea/
|
||||
.vscode/
|
||||
venv/
|
||||
env/
|
||||
201
models/Qwen3-TTS/LICENSE
Normal file
201
models/Qwen3-TTS/LICENSE
Normal file
@@ -0,0 +1,201 @@
|
||||
Apache License
|
||||
Version 2.0, January 2004
|
||||
http://www.apache.org/licenses/
|
||||
|
||||
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
|
||||
|
||||
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|
||||
|
||||
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|
||||
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|
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APPENDIX: How to apply the Apache License to your work.
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13
models/Qwen3-TTS/MANIFEST.in
Normal file
13
models/Qwen3-TTS/MANIFEST.in
Normal file
@@ -0,0 +1,13 @@
|
||||
global-exclude *
|
||||
|
||||
recursive-include qwen_tts *.py *.pyi py.typed
|
||||
recursive-include qwen_tts *.npz
|
||||
|
||||
include LICENSE
|
||||
include MANIFEST.in
|
||||
include pyproject.toml
|
||||
|
||||
prune assets
|
||||
prune examples
|
||||
prune finetuning
|
||||
prune qwen_tts.egg-info
|
||||
1361
models/Qwen3-TTS/README.md
Normal file
1361
models/Qwen3-TTS/README.md
Normal file
File diff suppressed because it is too large
Load Diff
121
models/Qwen3-TTS/finetuning/README.md
Normal file
121
models/Qwen3-TTS/finetuning/README.md
Normal file
@@ -0,0 +1,121 @@
|
||||
## Fine Tuning Qwen3-TTS-12Hz-1.7B/0.6B-Base
|
||||
|
||||
The Qwen3-TTS-12Hz-1.7B/0.6B-Base model series currently supports single-speaker fine-tuning. Please run `pip install qwen-tts` first, then run the command below:
|
||||
|
||||
```
|
||||
git clone https://github.com/QwenLM/Qwen3-TTS.git
|
||||
cd Qwen3-TTS/finetuning
|
||||
```
|
||||
|
||||
Then follow the steps below to complete the entire fine-tuning workflow. Multi-speaker fine-tuning and other advanced fine-tuning features will be supported in future releases.
|
||||
|
||||
### 1) Input JSONL format
|
||||
|
||||
Prepare your training file as a JSONL (one JSON object per line). Each line must contain:
|
||||
|
||||
- `audio`: path to the target training audio (wav)
|
||||
- `text`: transcript corresponding to `audio`
|
||||
- `ref_audio`: path to the reference speaker audio (wav)
|
||||
|
||||
Example:
|
||||
```jsonl
|
||||
{"audio":"./data/utt0001.wav","text":"其实我真的有发现，我是一个特别善于观察别人情绪的人。","ref_audio":"./data/ref.wav"}
|
||||
{"audio":"./data/utt0002.wav","text":"She said she would be here by noon.","ref_audio":"./data/ref.wav"}
|
||||
```
|
||||
|
||||
`ref_audio` recommendation:
|
||||
- Strongly recommended: use the same `ref_audio` for all samples.
|
||||
- Keeping `ref_audio` identical across the dataset usually improves speaker consistency and stability during generation.
|
||||
|
||||
|
||||
### 2) Prepare data (extract `audio_codes`)
|
||||
|
||||
Convert `train_raw.jsonl` into a training JSONL that includes `audio_codes`:
|
||||
|
||||
```bash
|
||||
python prepare_data.py \
|
||||
--device cuda:0 \
|
||||
--tokenizer_model_path Qwen/Qwen3-TTS-Tokenizer-12Hz \
|
||||
--input_jsonl train_raw.jsonl \
|
||||
--output_jsonl train_with_codes.jsonl
|
||||
```
|
||||
|
||||
|
||||
### 3) Fine-tune
|
||||
|
||||
Run SFT using the prepared JSONL:
|
||||
|
||||
```bash
|
||||
python sft_12hz.py \
|
||||
--init_model_path Qwen/Qwen3-TTS-12Hz-1.7B-Base \
|
||||
--output_model_path output \
|
||||
--train_jsonl train_with_codes.jsonl \
|
||||
--batch_size 2 \
|
||||
--lr 2e-5 \
|
||||
--num_epochs 3 \
|
||||
--speaker_name speaker_test
|
||||
```
|
||||
|
||||
Checkpoints will be written to:
|
||||
- `output/checkpoint-epoch-0`
|
||||
- `output/checkpoint-epoch-1`
|
||||
- `output/checkpoint-epoch-2`
|
||||
- ...
|
||||
|
||||
|
||||
### 4) Quick inference test
|
||||
|
||||
```python
|
||||
import torch
|
||||
import soundfile as sf
|
||||
from qwen_tts import Qwen3TTSModel
|
||||
|
||||
device = "cuda:0"
|
||||
tts = Qwen3TTSModel.from_pretrained(
|
||||
"output/checkpoint-epoch-2",
|
||||
device_map=device,
|
||||
dtype=torch.bfloat16,
|
||||
attn_implementation="flash_attention_2",
|
||||
)
|
||||
|
||||
wavs, sr = tts.generate_custom_voice(
|
||||
text="She said she would be here by noon.",
|
||||
speaker="speaker_test",
|
||||
)
|
||||
sf.write("output.wav", wavs[0], sr)
|
||||
```
|
||||
|
||||
### One-click shell script example
|
||||
|
||||
```bash
|
||||
#!/usr/bin/env bash
|
||||
set -e
|
||||
|
||||
DEVICE="cuda:0"
|
||||
TOKENIZER_MODEL_PATH="Qwen/Qwen3-TTS-Tokenizer-12Hz"
|
||||
INIT_MODEL_PATH="Qwen/Qwen3-TTS-12Hz-1.7B-Base"
|
||||
|
||||
RAW_JSONL="train_raw.jsonl"
|
||||
TRAIN_JSONL="train_with_codes.jsonl"
|
||||
OUTPUT_DIR="output"
|
||||
|
||||
BATCH_SIZE=2
|
||||
LR=2e-5
|
||||
EPOCHS=3
|
||||
SPEAKER_NAME="speaker_1"
|
||||
|
||||
python prepare_data.py \
|
||||
--device ${DEVICE} \
|
||||
--tokenizer_model_path ${TOKENIZER_MODEL_PATH} \
|
||||
--input_jsonl ${RAW_JSONL} \
|
||||
--output_jsonl ${TRAIN_JSONL}
|
||||
|
||||
python sft_12hz.py \
|
||||
--init_model_path ${INIT_MODEL_PATH} \
|
||||
--output_model_path ${OUTPUT_DIR} \
|
||||
--train_jsonl ${TRAIN_JSONL} \
|
||||
--batch_size ${BATCH_SIZE} \
|
||||
--lr ${LR} \
|
||||
--num_epochs ${EPOCHS} \
|
||||
--speaker_name ${SPEAKER_NAME}
|
||||
```
|
||||
218
models/Qwen3-TTS/finetuning/dataset.py
Normal file
218
models/Qwen3-TTS/finetuning/dataset.py
Normal file
@@ -0,0 +1,218 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2026 The Alibaba Qwen team.
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from typing import Any, List, Tuple, Union
|
||||
|
||||
import librosa
|
||||
import numpy as np
|
||||
import torch
|
||||
from qwen_tts.core.models.configuration_qwen3_tts import Qwen3TTSConfig
|
||||
from qwen_tts.core.models.modeling_qwen3_tts import mel_spectrogram
|
||||
from torch.utils.data import Dataset
|
||||
|
||||
AudioLike = Union[
|
||||
str, # wav path, URL, base64
|
||||
np.ndarray, # waveform (requires sr)
|
||||
Tuple[np.ndarray, int], # (waveform, sr)
|
||||
]
|
||||
|
||||
MaybeList = Union[Any, List[Any]]
|
||||
|
||||
class TTSDataset(Dataset):
|
||||
def __init__(self, data_list, processor, config:Qwen3TTSConfig, lag_num = -1):
|
||||
self.data_list = data_list
|
||||
self.processor = processor
|
||||
self.lag_num = lag_num
|
||||
self.config = config
|
||||
|
||||
def __len__(self):
|
||||
return len(self.data_list)
|
||||
|
||||
def _load_audio_to_np(self, x: str) -> Tuple[np.ndarray, int]:
|
||||
|
||||
audio, sr = librosa.load(x, sr=None, mono=True)
|
||||
|
||||
if audio.ndim > 1:
|
||||
audio = np.mean(audio, axis=-1)
|
||||
|
||||
return audio.astype(np.float32), int(sr)
|
||||
|
||||
def _normalize_audio_inputs(self, audios: Union[AudioLike, List[AudioLike]]) -> List[Tuple[np.ndarray, int]]:
|
||||
"""
|
||||
Normalize audio inputs into a list of (waveform, sr).
|
||||
|
||||
Supported forms:
|
||||
- str: wav path / URL / base64 audio string
|
||||
- np.ndarray: waveform (NOT allowed alone here because sr is unknown)
|
||||
- (np.ndarray, sr): waveform + sampling rate
|
||||
- list of the above
|
||||
|
||||
Args:
|
||||
audios:
|
||||
Audio input(s).
|
||||
|
||||
Returns:
|
||||
List[Tuple[np.ndarray, int]]:
|
||||
List of (float32 waveform, original sr).
|
||||
|
||||
Raises:
|
||||
ValueError: If a numpy waveform is provided without sr.
|
||||
"""
|
||||
if isinstance(audios, list):
|
||||
items = audios
|
||||
else:
|
||||
items = [audios]
|
||||
|
||||
out: List[Tuple[np.ndarray, int]] = []
|
||||
for a in items:
|
||||
if isinstance(a, str):
|
||||
out.append(self._load_audio_to_np(a))
|
||||
elif isinstance(a, tuple) and len(a) == 2 and isinstance(a[0], np.ndarray):
|
||||
out.append((a[0].astype(np.float32), int(a[1])))
|
||||
elif isinstance(a, np.ndarray):
|
||||
raise ValueError("For numpy waveform input, pass a tuple (audio, sr).")
|
||||
else:
|
||||
raise TypeError(f"Unsupported audio input type: {type(a)}")
|
||||
return out
|
||||
|
||||
|
||||
def _build_assistant_text(self, text: str) -> str:
|
||||
return f"<|im_start|>assistant\n{text}<|im_end|>\n<|im_start|>assistant\n"
|
||||
|
||||
def _ensure_list(self, x: MaybeList) -> List[Any]:
|
||||
return x if isinstance(x, list) else [x]
|
||||
|
||||
def _tokenize_texts(self, text) -> List[torch.Tensor]:
|
||||
input = self.processor(text=text, return_tensors="pt", padding=True)
|
||||
input_id = input["input_ids"]
|
||||
input_id = input_id.unsqueeze(0) if input_id.dim() == 1 else input_id
|
||||
return input_id
|
||||
|
||||
@torch.inference_mode()
|
||||
def extract_mels(self, audio, sr):
|
||||
assert sr == 24000, "Only support 24kHz audio"
|
||||
mels = mel_spectrogram(
|
||||
torch.from_numpy(audio).unsqueeze(0),
|
||||
n_fft=1024,
|
||||
num_mels=128,
|
||||
sampling_rate=24000,
|
||||
hop_size=256,
|
||||
win_size=1024,
|
||||
fmin=0,
|
||||
fmax=12000
|
||||
).transpose(1, 2)
|
||||
return mels
|
||||
|
||||
|
||||
|
||||
def __getitem__(self, idx):
|
||||
item = self.data_list[idx]
|
||||
|
||||
audio_path = item["audio"]
|
||||
text = item["text"]
|
||||
audio_codes = item["audio_codes"]
|
||||
language = item.get('language','Auto')
|
||||
ref_audio_path = item['ref_audio']
|
||||
|
||||
text = self._build_assistant_text(text)
|
||||
text_ids = self._tokenize_texts(text)
|
||||
|
||||
audio_codes = torch.tensor(audio_codes, dtype=torch.long)
|
||||
|
||||
ref_audio_list = self._ensure_list(ref_audio_path)
|
||||
normalized = self._normalize_audio_inputs(ref_audio_list)
|
||||
wav,sr = normalized[0]
|
||||
|
||||
ref_mel = self.extract_mels(audio=wav, sr=sr)
|
||||
|
||||
return {
|
||||
"text_ids": text_ids[:,:-5], # 1 , t
|
||||
"audio_codes":audio_codes, # t, 16
|
||||
"ref_mel":ref_mel
|
||||
}
|
||||
|
||||
def collate_fn(self, batch):
|
||||
assert self.lag_num == -1
|
||||
|
||||
item_length = [b['text_ids'].shape[1] + b['audio_codes'].shape[0] for b in batch]
|
||||
max_length = max(item_length) + 8
|
||||
b,t = len(batch),max_length
|
||||
|
||||
input_ids = torch.zeros((b,t,2),dtype=torch.long)
|
||||
codec_ids = torch.zeros((b,t,16),dtype=torch.long)
|
||||
text_embedding_mask = torch.zeros((b,t),dtype=torch.bool)
|
||||
codec_embedding_mask = torch.zeros((b,t),dtype=torch.bool)
|
||||
codec_mask = torch.zeros((b,t),dtype=torch.bool)
|
||||
attention_mask = torch.zeros((b,t),dtype=torch.long)
|
||||
codec_0_labels = torch.full((b, t), -100, dtype=torch.long)
|
||||
|
||||
for i,data in enumerate(batch):
|
||||
text_ids = data['text_ids']
|
||||
audio_codec_0 = data['audio_codes'][:,0]
|
||||
audio_codecs = data['audio_codes']
|
||||
|
||||
text_ids_len = text_ids.shape[1]
|
||||
codec_ids_len = audio_codec_0.shape[0]
|
||||
|
||||
# text channel
|
||||
input_ids[i, :3, 0] = text_ids[0,:3]
|
||||
input_ids[i, 3:7, 0] = self.config.tts_pad_token_id
|
||||
input_ids[i, 7, 0] = self.config.tts_bos_token_id
|
||||
input_ids[i, 8:8+text_ids_len-3, 0] = text_ids[0,3:]
|
||||
input_ids[i, 8+text_ids_len-3, 0] = self.config.tts_eos_token_id
|
||||
input_ids[i, 8+text_ids_len-2:8+text_ids_len+codec_ids_len , 0] = self.config.tts_pad_token_id
|
||||
text_embedding_mask[i, :8+text_ids_len+codec_ids_len] = True
|
||||
|
||||
# codec channel
|
||||
# input_ids[i, :3, 1] = 0
|
||||
input_ids[i, 3:8 ,1] = torch.tensor(
|
||||
[
|
||||
self.config.talker_config.codec_nothink_id,
|
||||
self.config.talker_config.codec_think_bos_id,
|
||||
self.config.talker_config.codec_think_eos_id,
|
||||
0, # for speaker embedding
|
||||
self.config.talker_config.codec_pad_id
|
||||
]
|
||||
)
|
||||
input_ids[i, 8:8+text_ids_len-3 ,1] = self.config.talker_config.codec_pad_id
|
||||
input_ids[i, 8+text_ids_len-3 ,1] = self.config.talker_config.codec_pad_id
|
||||
input_ids[i, 8+text_ids_len-2 ,1] = self.config.talker_config.codec_bos_id
|
||||
input_ids[i, 8+text_ids_len-1:8+text_ids_len-1+codec_ids_len, 1] = audio_codec_0
|
||||
input_ids[i, 8+text_ids_len-1+codec_ids_len, 1] = self.config.talker_config.codec_eos_token_id
|
||||
|
||||
codec_0_labels[i, 8+text_ids_len-1:8+text_ids_len-1+codec_ids_len] = audio_codec_0
|
||||
codec_0_labels[i, 8+text_ids_len-1+codec_ids_len] = self.config.talker_config.codec_eos_token_id
|
||||
|
||||
codec_ids[i, 8+text_ids_len-1:8+text_ids_len-1+codec_ids_len,:] = audio_codecs
|
||||
|
||||
codec_embedding_mask[i, 3:8+text_ids_len+codec_ids_len] = True
|
||||
codec_embedding_mask[i, 6] = False # for speaker embedding
|
||||
|
||||
codec_mask[i, 8+text_ids_len-1:8+text_ids_len-1+codec_ids_len] = True
|
||||
attention_mask[i, :8+text_ids_len+codec_ids_len] = True
|
||||
|
||||
ref_mels = [data['ref_mel'] for data in batch]
|
||||
ref_mels = torch.cat(ref_mels,dim=0)
|
||||
|
||||
return {
|
||||
'input_ids':input_ids,
|
||||
'ref_mels':ref_mels,
|
||||
'attention_mask':attention_mask,
|
||||
'text_embedding_mask':text_embedding_mask.unsqueeze(-1),
|
||||
'codec_embedding_mask':codec_embedding_mask.unsqueeze(-1),
|
||||
'codec_0_labels':codec_0_labels,
|
||||
'codec_ids': codec_ids,
|
||||
'codec_mask':codec_mask
|
||||
}
|
||||
71
models/Qwen3-TTS/finetuning/prepare_data.py
Normal file
71
models/Qwen3-TTS/finetuning/prepare_data.py
Normal file
@@ -0,0 +1,71 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2026 The Alibaba Qwen team.
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import argparse
|
||||
import json
|
||||
|
||||
from qwen_tts import Qwen3TTSTokenizer
|
||||
|
||||
BATCH_INFER_NUM = 32
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--device", type=str, default="cuda:0")
|
||||
parser.add_argument("--tokenizer_model_path", type=str, default="Qwen/Qwen3-TTS-Tokenizer-12Hz")
|
||||
parser.add_argument("--input_jsonl", type=str, required=True)
|
||||
parser.add_argument("--output_jsonl", type=str, required=True)
|
||||
args = parser.parse_args()
|
||||
|
||||
tokenizer_12hz = Qwen3TTSTokenizer.from_pretrained(
|
||||
args.tokenizer_model_path,
|
||||
device_map=args.device,
|
||||
)
|
||||
|
||||
total_lines = open(args.input_jsonl).readlines()
|
||||
total_lines = [json.loads(line.strip()) for line in total_lines]
|
||||
|
||||
final_lines = []
|
||||
batch_lines = []
|
||||
batch_audios = []
|
||||
for line in total_lines:
|
||||
|
||||
batch_lines.append(line)
|
||||
batch_audios.append(line['audio'])
|
||||
|
||||
if len(batch_lines) >= BATCH_INFER_NUM:
|
||||
enc_res = tokenizer_12hz.encode(batch_audios)
|
||||
for code, line in zip(enc_res.audio_codes, batch_lines):
|
||||
line['audio_codes'] = code.cpu().tolist()
|
||||
final_lines.append(line)
|
||||
batch_lines.clear()
|
||||
batch_audios.clear()
|
||||
|
||||
if len(batch_audios) > 0:
|
||||
enc_res = tokenizer_12hz.encode(batch_audios)
|
||||
for code, line in zip(enc_res.audio_codes, batch_lines):
|
||||
line['audio_codes'] = code.cpu().tolist()
|
||||
final_lines.append(line)
|
||||
batch_lines.clear()
|
||||
batch_audios.clear()
|
||||
|
||||
final_lines = [json.dumps(line, ensure_ascii=False) for line in final_lines]
|
||||
|
||||
with open(args.output_jsonl, 'w') as f:
|
||||
for line in final_lines:
|
||||
f.writelines(line + '\n')
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
161
models/Qwen3-TTS/finetuning/sft_12hz.py
Normal file
161
models/Qwen3-TTS/finetuning/sft_12hz.py
Normal file
@@ -0,0 +1,161 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2026 The Alibaba Qwen team.
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import shutil
|
||||
|
||||
import torch
|
||||
from accelerate import Accelerator
|
||||
from dataset import TTSDataset
|
||||
from qwen_tts.inference.qwen3_tts_model import Qwen3TTSModel
|
||||
from safetensors.torch import save_file
|
||||
from torch.optim import AdamW
|
||||
from torch.utils.data import DataLoader
|
||||
from transformers import AutoConfig
|
||||
|
||||
target_speaker_embedding = None
|
||||
def train():
|
||||
global target_speaker_embedding
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--init_model_path", type=str, default="Qwen/Qwen3-TTS-12Hz-1.7B-Base")
|
||||
parser.add_argument("--output_model_path", type=str, default="output")
|
||||
parser.add_argument("--train_jsonl", type=str, required=True)
|
||||
parser.add_argument("--batch_size", type=int, default=2)
|
||||
parser.add_argument("--lr", type=float, default=2e-5)
|
||||
parser.add_argument("--num_epochs", type=int, default=3)
|
||||
parser.add_argument("--speaker_name", type=str, default="speaker_test")
|
||||
args = parser.parse_args()
|
||||
|
||||
accelerator = Accelerator(gradient_accumulation_steps=4, mixed_precision="bf16", log_with="tensorboard")
|
||||
|
||||
MODEL_PATH = args.init_model_path
|
||||
|
||||
qwen3tts = Qwen3TTSModel.from_pretrained(
|
||||
MODEL_PATH,
|
||||
torch_dtype=torch.bfloat16,
|
||||
attn_implementation="flash_attention_2",
|
||||
)
|
||||
config = AutoConfig.from_pretrained(MODEL_PATH)
|
||||
|
||||
train_data = open(args.train_jsonl).readlines()
|
||||
train_data = [json.loads(line) for line in train_data]
|
||||
dataset = TTSDataset(train_data, qwen3tts.processor, config)
|
||||
train_dataloader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True, collate_fn=dataset.collate_fn)
|
||||
|
||||
optimizer = AdamW(qwen3tts.model.parameters(), lr=args.lr, weight_decay=0.01)
|
||||
|
||||
model, optimizer, train_dataloader = accelerator.prepare(
|
||||
qwen3tts.model, optimizer, train_dataloader
|
||||
)
|
||||
|
||||
num_epochs = args.num_epochs
|
||||
model.train()
|
||||
|
||||
for epoch in range(num_epochs):
|
||||
for step, batch in enumerate(train_dataloader):
|
||||
with accelerator.accumulate(model):
|
||||
|
||||
input_ids = batch['input_ids']
|
||||
codec_ids = batch['codec_ids']
|
||||
ref_mels = batch['ref_mels']
|
||||
text_embedding_mask = batch['text_embedding_mask']
|
||||
codec_embedding_mask = batch['codec_embedding_mask']
|
||||
attention_mask = batch['attention_mask']
|
||||
codec_0_labels = batch['codec_0_labels']
|
||||
codec_mask = batch['codec_mask']
|
||||
|
||||
speaker_embedding = model.speaker_encoder(ref_mels.to(model.device).to(model.dtype)).detach()
|
||||
if target_speaker_embedding is None:
|
||||
target_speaker_embedding = speaker_embedding
|
||||
|
||||
input_text_ids = input_ids[:, :, 0]
|
||||
input_codec_ids = input_ids[:, :, 1]
|
||||
|
||||
input_text_embedding = model.talker.model.text_embedding(input_text_ids) * text_embedding_mask
|
||||
input_codec_embedding = model.talker.model.codec_embedding(input_codec_ids) * codec_embedding_mask
|
||||
input_codec_embedding[:, 6, :] = speaker_embedding
|
||||
|
||||
input_embeddings = input_text_embedding + input_codec_embedding
|
||||
|
||||
for i in range(1, 16):
|
||||
codec_i_embedding = model.talker.code_predictor.get_input_embeddings()[i - 1](codec_ids[:, :, i])
|
||||
codec_i_embedding = codec_i_embedding * codec_mask.unsqueeze(-1)
|
||||
input_embeddings = input_embeddings + codec_i_embedding
|
||||
|
||||
outputs = model.talker(
|
||||
inputs_embeds=input_embeddings[:, :-1, :],
|
||||
attention_mask=attention_mask[:, :-1],
|
||||
labels=codec_0_labels[:, 1:],
|
||||
output_hidden_states=True
|
||||
)
|
||||
|
||||
hidden_states = outputs.hidden_states[0][-1]
|
||||
talker_hidden_states = hidden_states[codec_mask[:, 1:]]
|
||||
talker_codec_ids = codec_ids[codec_mask]
|
||||
|
||||
sub_talker_logits, sub_talker_loss = model.talker.forward_sub_talker_finetune(talker_codec_ids, talker_hidden_states)
|
||||
|
||||
loss = outputs.loss + sub_talker_loss
|
||||
|
||||
accelerator.backward(loss)
|
||||
|
||||
if accelerator.sync_gradients:
|
||||
accelerator.clip_grad_norm_(model.parameters(), 1.0)
|
||||
|
||||
optimizer.step()
|
||||
optimizer.zero_grad()
|
||||
|
||||
if step % 10 == 0:
|
||||
accelerator.print(f"Epoch {epoch} | Step {step} | Loss: {loss.item():.4f}")
|
||||
|
||||
if accelerator.is_main_process:
|
||||
output_dir = os.path.join(args.output_model_path, f"checkpoint-epoch-{epoch}")
|
||||
shutil.copytree(MODEL_PATH, output_dir, dirs_exist_ok=True)
|
||||
|
||||
input_config_file = os.path.join(MODEL_PATH, "config.json")
|
||||
output_config_file = os.path.join(output_dir, "config.json")
|
||||
with open(input_config_file, 'r', encoding='utf-8') as f:
|
||||
config_dict = json.load(f)
|
||||
config_dict["tts_model_type"] = "custom_voice"
|
||||
talker_config = config_dict.get("talker_config", {})
|
||||
talker_config["spk_id"] = {
|
||||
args.speaker_name: 3000
|
||||
}
|
||||
talker_config["spk_is_dialect"] = {
|
||||
args.speaker_name: False
|
||||
}
|
||||
config_dict["talker_config"] = talker_config
|
||||
|
||||
with open(output_config_file, 'w', encoding='utf-8') as f:
|
||||
json.dump(config_dict, f, indent=2, ensure_ascii=False)
|
||||
|
||||
unwrapped_model = accelerator.unwrap_model(model)
|
||||
state_dict = {k: v.detach().to("cpu") for k, v in unwrapped_model.state_dict().items()}
|
||||
|
||||
drop_prefix = "speaker_encoder"
|
||||
keys_to_drop = [k for k in state_dict.keys() if k.startswith(drop_prefix)]
|
||||
for k in keys_to_drop:
|
||||
del state_dict[k]
|
||||
|
||||
weight = state_dict['talker.model.codec_embedding.weight']
|
||||
state_dict['talker.model.codec_embedding.weight'][3000] = target_speaker_embedding[0].detach().to(weight.device).to(weight.dtype)
|
||||
save_path = os.path.join(output_dir, "model.safetensors")
|
||||
save_file(state_dict, save_path)
|
||||
|
||||
if __name__ == "__main__":
|
||||
train()
|
||||
46
models/Qwen3-TTS/pyproject.toml
Normal file
46
models/Qwen3-TTS/pyproject.toml
Normal file
@@ -0,0 +1,46 @@
|
||||
[build-system]
|
||||
requires = ["setuptools>=68", "wheel"]
|
||||
build-backend = "setuptools.build_meta"
|
||||
|
||||
[project]
|
||||
name = "qwen-tts"
|
||||
version = "0.0.4"
|
||||
description = "Qwen-TTS python package"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.9"
|
||||
classifiers = [
|
||||
"Programming Language :: Python :: 3",
|
||||
"Programming Language :: Python :: 3.9",
|
||||
"Programming Language :: Python :: 3.10",
|
||||
"Programming Language :: Python :: 3.11",
|
||||
"Programming Language :: Python :: 3.12",
|
||||
"Programming Language :: Python :: 3.13",
|
||||
]
|
||||
license = { text = "Apache-2.0" }
|
||||
authors = [{ name = "Alibaba Qwen Team" }]
|
||||
|
||||
dependencies = [
|
||||
"transformers==4.57.3",
|
||||
"accelerate==1.12.0",
|
||||
"gradio",
|
||||
"librosa",
|
||||
"torchaudio",
|
||||
"soundfile",
|
||||
"sox",
|
||||
"onnxruntime",
|
||||
"einops",
|
||||
]
|
||||
|
||||
[project.urls]
|
||||
Homepage = "https://github.com/Qwen/Qwen3-TTS"
|
||||
Repository = "https://github.com/Qwen/Qwen3-TTS"
|
||||
|
||||
[project.scripts]
|
||||
qwen-tts-demo = "qwen_tts.cli.demo:main"
|
||||
|
||||
[tool.setuptools]
|
||||
packages = { find = { where = ["."] , include = ["qwen_tts*"] } }
|
||||
include-package-data = true
|
||||
|
||||
[tool.setuptools.package-data]
|
||||
qwen_tts = ["py.typed", "**/*.npz"]
|
||||
24
models/Qwen3-TTS/qwen_tts/__init__.py
Normal file
24
models/Qwen3-TTS/qwen_tts/__init__.py
Normal file
@@ -0,0 +1,24 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2026 The Alibaba Qwen team.
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
"""
|
||||
qwen_tts: Qwen-TTS package.
|
||||
"""
|
||||
|
||||
from .inference.qwen3_tts_model import Qwen3TTSModel, VoiceClonePromptItem
|
||||
from .inference.qwen3_tts_tokenizer import Qwen3TTSTokenizer
|
||||
|
||||
__all__ = ["__version__"]
|
||||
24
models/Qwen3-TTS/qwen_tts/__main__.py
Normal file
24
models/Qwen3-TTS/qwen_tts/__main__.py
Normal file
@@ -0,0 +1,24 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2026 The Alibaba Qwen team.
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
def main():
|
||||
print(
|
||||
"qwen_tts package.\n"
|
||||
"Use CLI entrypoints:\n"
|
||||
" - qwen-tts-demo\n"
|
||||
)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
634
models/Qwen3-TTS/qwen_tts/cli/demo.py
Normal file
634
models/Qwen3-TTS/qwen_tts/cli/demo.py
Normal file
@@ -0,0 +1,634 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2026 The Alibaba Qwen team.
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""
|
||||
A gradio demo for Qwen3 TTS models.
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import tempfile
|
||||
from dataclasses import asdict
|
||||
from typing import Any, Dict, List, Optional, Tuple
|
||||
|
||||
import gradio as gr
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
from .. import Qwen3TTSModel, VoiceClonePromptItem
|
||||
|
||||
|
||||
def _title_case_display(s: str) -> str:
|
||||
s = (s or "").strip()
|
||||
s = s.replace("_", " ")
|
||||
return " ".join([w[:1].upper() + w[1:] if w else "" for w in s.split()])
|
||||
|
||||
|
||||
def _build_choices_and_map(items: Optional[List[str]]) -> Tuple[List[str], Dict[str, str]]:
|
||||
if not items:
|
||||
return [], {}
|
||||
display = [_title_case_display(x) for x in items]
|
||||
mapping = {d: r for d, r in zip(display, items)}
|
||||
return display, mapping
|
||||
|
||||
|
||||
def _dtype_from_str(s: str) -> torch.dtype:
|
||||
s = (s or "").strip().lower()
|
||||
if s in ("bf16", "bfloat16"):
|
||||
return torch.bfloat16
|
||||
if s in ("fp16", "float16", "half"):
|
||||
return torch.float16
|
||||
if s in ("fp32", "float32"):
|
||||
return torch.float32
|
||||
raise ValueError(f"Unsupported torch dtype: {s}. Use bfloat16/float16/float32.")
|
||||
|
||||
|
||||
def _maybe(v):
|
||||
return v if v is not None else gr.update()
|
||||
|
||||
|
||||
def build_parser() -> argparse.ArgumentParser:
|
||||
parser = argparse.ArgumentParser(
|
||||
prog="qwen-tts-demo",
|
||||
description=(
|
||||
"Launch a Gradio demo for Qwen3 TTS models (CustomVoice / VoiceDesign / Base).\n\n"
|
||||
"Examples:\n"
|
||||
" qwen-tts-demo Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice\n"
|
||||
" qwen-tts-demo Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign --port 8000 --ip 127.0.0.01\n"
|
||||
" qwen-tts-demo Qwen/Qwen3-TTS-12Hz-1.7B-Base --device cuda:0\n"
|
||||
" qwen-tts-demo Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice --dtype bfloat16 --no-flash-attn\n"
|
||||
),
|
||||
formatter_class=argparse.RawTextHelpFormatter,
|
||||
add_help=True,
|
||||
)
|
||||
|
||||
# Positional checkpoint (also supports -c/--checkpoint)
|
||||
parser.add_argument(
|
||||
"checkpoint_pos",
|
||||
nargs="?",
|
||||
default=None,
|
||||
help="Model checkpoint path or HuggingFace repo id (positional).",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-c",
|
||||
"--checkpoint",
|
||||
default=None,
|
||||
help="Model checkpoint path or HuggingFace repo id (optional if positional is provided).",
|
||||
)
|
||||
|
||||
# Model loading / from_pretrained args
|
||||
parser.add_argument(
|
||||
"--device",
|
||||
default="cuda:0",
|
||||
help="Device for device_map, e.g. cpu, cuda, cuda:0 (default: cuda:0).",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--dtype",
|
||||
default="bfloat16",
|
||||
choices=["bfloat16", "bf16", "float16", "fp16", "float32", "fp32"],
|
||||
help="Torch dtype for loading the model (default: bfloat16).",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--flash-attn/--no-flash-attn",
|
||||
dest="flash_attn",
|
||||
default=True,
|
||||
action=argparse.BooleanOptionalAction,
|
||||
help="Enable FlashAttention-2 (default: enabled).",
|
||||
)
|
||||
|
||||
# Gradio server args
|
||||
parser.add_argument(
|
||||
"--ip",
|
||||
default="0.0.0.0",
|
||||
help="Server bind IP for Gradio (default: 0.0.0.0).",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--port",
|
||||
type=int,
|
||||
default=8000,
|
||||
help="Server port for Gradio (default: 8000).",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--share/--no-share",
|
||||
dest="share",
|
||||
default=False,
|
||||
action=argparse.BooleanOptionalAction,
|
||||
help="Whether to create a public Gradio link (default: disabled).",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--concurrency",
|
||||
type=int,
|
||||
default=16,
|
||||
help="Gradio queue concurrency (default: 16).",
|
||||
)
|
||||
|
||||
# HTTPS args
|
||||
parser.add_argument(
|
||||
"--ssl-certfile",
|
||||
default=None,
|
||||
help="Path to SSL certificate file for HTTPS (optional).",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--ssl-keyfile",
|
||||
default=None,
|
||||
help="Path to SSL key file for HTTPS (optional).",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--ssl-verify/--no-ssl-verify",
|
||||
dest="ssl_verify",
|
||||
default=True,
|
||||
action=argparse.BooleanOptionalAction,
|
||||
help="Whether to verify SSL certificate (default: enabled).",
|
||||
)
|
||||
|
||||
# Optional generation args
|
||||
parser.add_argument("--max-new-tokens", type=int, default=None, help="Max new tokens for generation (optional).")
|
||||
parser.add_argument("--temperature", type=float, default=None, help="Sampling temperature (optional).")
|
||||
parser.add_argument("--top-k", type=int, default=None, help="Top-k sampling (optional).")
|
||||
parser.add_argument("--top-p", type=float, default=None, help="Top-p sampling (optional).")
|
||||
parser.add_argument("--repetition-penalty", type=float, default=None, help="Repetition penalty (optional).")
|
||||
parser.add_argument("--subtalker-top-k", type=int, default=None, help="Subtalker top-k (optional, only for tokenizer v2).")
|
||||
parser.add_argument("--subtalker-top-p", type=float, default=None, help="Subtalker top-p (optional, only for tokenizer v2).")
|
||||
parser.add_argument(
|
||||
"--subtalker-temperature", type=float, default=None, help="Subtalker temperature (optional, only for tokenizer v2)."
|
||||
)
|
||||
|
||||
return parser
|
||||
|
||||
|
||||
def _resolve_checkpoint(args: argparse.Namespace) -> str:
|
||||
ckpt = args.checkpoint or args.checkpoint_pos
|
||||
if not ckpt:
|
||||
raise SystemExit(0) # main() prints help
|
||||
return ckpt
|
||||
|
||||
|
||||
def _collect_gen_kwargs(args: argparse.Namespace) -> Dict[str, Any]:
|
||||
mapping = {
|
||||
"max_new_tokens": args.max_new_tokens,
|
||||
"temperature": args.temperature,
|
||||
"top_k": args.top_k,
|
||||
"top_p": args.top_p,
|
||||
"repetition_penalty": args.repetition_penalty,
|
||||
"subtalker_top_k": args.subtalker_top_k,
|
||||
"subtalker_top_p": args.subtalker_top_p,
|
||||
"subtalker_temperature": args.subtalker_temperature,
|
||||
}
|
||||
return {k: v for k, v in mapping.items() if v is not None}
|
||||
|
||||
|
||||
def _normalize_audio(wav, eps=1e-12, clip=True):
|
||||
x = np.asarray(wav)
|
||||
|
||||
if np.issubdtype(x.dtype, np.integer):
|
||||
info = np.iinfo(x.dtype)
|
||||
|
||||
if info.min < 0:
|
||||
y = x.astype(np.float32) / max(abs(info.min), info.max)
|
||||
else:
|
||||
mid = (info.max + 1) / 2.0
|
||||
y = (x.astype(np.float32) - mid) / mid
|
||||
|
||||
elif np.issubdtype(x.dtype, np.floating):
|
||||
y = x.astype(np.float32)
|
||||
m = np.max(np.abs(y)) if y.size else 0.0
|
||||
|
||||
if m <= 1.0 + 1e-6:
|
||||
pass
|
||||
else:
|
||||
y = y / (m + eps)
|
||||
else:
|
||||
raise TypeError(f"Unsupported dtype: {x.dtype}")
|
||||
|
||||
if clip:
|
||||
y = np.clip(y, -1.0, 1.0)
|
||||
|
||||
if y.ndim > 1:
|
||||
y = np.mean(y, axis=-1).astype(np.float32)
|
||||
|
||||
return y
|
||||
|
||||
|
||||
def _audio_to_tuple(audio: Any) -> Optional[Tuple[np.ndarray, int]]:
|
||||
if audio is None:
|
||||
return None
|
||||
|
||||
if isinstance(audio, tuple) and len(audio) == 2 and isinstance(audio[0], int):
|
||||
sr, wav = audio
|
||||
wav = _normalize_audio(wav)
|
||||
return wav, int(sr)
|
||||
|
||||
if isinstance(audio, dict) and "sampling_rate" in audio and "data" in audio:
|
||||
sr = int(audio["sampling_rate"])
|
||||
wav = _normalize_audio(audio["data"])
|
||||
return wav, sr
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def _wav_to_gradio_audio(wav: np.ndarray, sr: int) -> Tuple[int, np.ndarray]:
|
||||
wav = np.asarray(wav, dtype=np.float32)
|
||||
return sr, wav
|
||||
|
||||
|
||||
def _detect_model_kind(ckpt: str, tts: Qwen3TTSModel) -> str:
|
||||
mt = getattr(tts.model, "tts_model_type", None)
|
||||
if mt in ("custom_voice", "voice_design", "base"):
|
||||
return mt
|
||||
else:
|
||||
raise ValueError(f"Unknown Qwen-TTS model type: {mt}")
|
||||
|
||||
|
||||
def build_demo(tts: Qwen3TTSModel, ckpt: str, gen_kwargs_default: Dict[str, Any]) -> gr.Blocks:
|
||||
model_kind = _detect_model_kind(ckpt, tts)
|
||||
|
||||
supported_langs_raw = None
|
||||
if callable(getattr(tts.model, "get_supported_languages", None)):
|
||||
supported_langs_raw = tts.model.get_supported_languages()
|
||||
|
||||
supported_spks_raw = None
|
||||
if callable(getattr(tts.model, "get_supported_speakers", None)):
|
||||
supported_spks_raw = tts.model.get_supported_speakers()
|
||||
|
||||
lang_choices_disp, lang_map = _build_choices_and_map([x for x in (supported_langs_raw or [])])
|
||||
spk_choices_disp, spk_map = _build_choices_and_map([x for x in (supported_spks_raw or [])])
|
||||
|
||||
def _gen_common_kwargs() -> Dict[str, Any]:
|
||||
return dict(gen_kwargs_default)
|
||||
|
||||
theme = gr.themes.Soft(
|
||||
font=[gr.themes.GoogleFont("Source Sans Pro"), "Arial", "sans-serif"],
|
||||
)
|
||||
|
||||
css = ".gradio-container {max-width: none !important;}"
|
||||
|
||||
with gr.Blocks(theme=theme, css=css) as demo:
|
||||
gr.Markdown(
|
||||
f"""
|
||||
# Qwen3 TTS Demo
|
||||
**Checkpoint:** `{ckpt}`
|
||||
**Model Type:** `{model_kind}`
|
||||
"""
|
||||
)
|
||||
|
||||
if model_kind == "custom_voice":
|
||||
with gr.Row():
|
||||
with gr.Column(scale=2):
|
||||
text_in = gr.Textbox(
|
||||
label="Text (待合成文本)",
|
||||
lines=4,
|
||||
placeholder="Enter text to synthesize (输入要合成的文本).",
|
||||
)
|
||||
with gr.Row():
|
||||
lang_in = gr.Dropdown(
|
||||
label="Language (语种)",
|
||||
choices=lang_choices_disp,
|
||||
value="Auto",
|
||||
interactive=True,
|
||||
)
|
||||
spk_in = gr.Dropdown(
|
||||
label="Speaker (说话人)",
|
||||
choices=spk_choices_disp,
|
||||
value="Vivian",
|
||||
interactive=True,
|
||||
)
|
||||
instruct_in = gr.Textbox(
|
||||
label="Instruction (Optional) (控制指令，可不输入)",
|
||||
lines=2,
|
||||
placeholder="e.g. Say it in a very angry tone (例如：用特别伤心的语气说).",
|
||||
)
|
||||
btn = gr.Button("Generate (生成)", variant="primary")
|
||||
with gr.Column(scale=3):
|
||||
audio_out = gr.Audio(label="Output Audio (合成结果)", type="numpy")
|
||||
err = gr.Textbox(label="Status (状态)", lines=2)
|
||||
|
||||
def run_instruct(text: str, lang_disp: str, spk_disp: str, instruct: str):
|
||||
try:
|
||||
if not text or not text.strip():
|
||||
return None, "Text is required (必须填写文本)."
|
||||
if not spk_disp:
|
||||
return None, "Speaker is required (必须选择说话人)."
|
||||
language = lang_map.get(lang_disp, "Auto")
|
||||
speaker = spk_map.get(spk_disp, spk_disp)
|
||||
kwargs = _gen_common_kwargs()
|
||||
wavs, sr = tts.generate_custom_voice(
|
||||
text=text.strip(),
|
||||
language=language,
|
||||
speaker=speaker,
|
||||
instruct=(instruct or "").strip() or None,
|
||||
**kwargs,
|
||||
)
|
||||
return _wav_to_gradio_audio(wavs[0], sr), "Finished. (生成完成)"
|
||||
except Exception as e:
|
||||
return None, f"{type(e).__name__}: {e}"
|
||||
|
||||
btn.click(run_instruct, inputs=[text_in, lang_in, spk_in, instruct_in], outputs=[audio_out, err])
|
||||
|
||||
elif model_kind == "voice_design":
|
||||
with gr.Row():
|
||||
with gr.Column(scale=2):
|
||||
text_in = gr.Textbox(
|
||||
label="Text (待合成文本)",
|
||||
lines=4,
|
||||
value="It's in the top drawer... wait, it's empty? No way, that's impossible! I'm sure I put it there!"
|
||||
)
|
||||
with gr.Row():
|
||||
lang_in = gr.Dropdown(
|
||||
label="Language (语种)",
|
||||
choices=lang_choices_disp,
|
||||
value="Auto",
|
||||
interactive=True,
|
||||
)
|
||||
design_in = gr.Textbox(
|
||||
label="Voice Design Instruction (音色描述)",
|
||||
lines=3,
|
||||
value="Speak in an incredulous tone, but with a hint of panic beginning to creep into your voice."
|
||||
)
|
||||
btn = gr.Button("Generate (生成)", variant="primary")
|
||||
with gr.Column(scale=3):
|
||||
audio_out = gr.Audio(label="Output Audio (合成结果)", type="numpy")
|
||||
err = gr.Textbox(label="Status (状态)", lines=2)
|
||||
|
||||
def run_voice_design(text: str, lang_disp: str, design: str):
|
||||
try:
|
||||
if not text or not text.strip():
|
||||
return None, "Text is required (必须填写文本)."
|
||||
if not design or not design.strip():
|
||||
return None, "Voice design instruction is required (必须填写音色描述)."
|
||||
language = lang_map.get(lang_disp, "Auto")
|
||||
kwargs = _gen_common_kwargs()
|
||||
wavs, sr = tts.generate_voice_design(
|
||||
text=text.strip(),
|
||||
language=language,
|
||||
instruct=design.strip(),
|
||||
**kwargs,
|
||||
)
|
||||
return _wav_to_gradio_audio(wavs[0], sr), "Finished. (生成完成)"
|
||||
except Exception as e:
|
||||
return None, f"{type(e).__name__}: {e}"
|
||||
|
||||
btn.click(run_voice_design, inputs=[text_in, lang_in, design_in], outputs=[audio_out, err])
|
||||
|
||||
else: # voice_clone for base
|
||||
with gr.Tabs():
|
||||
with gr.Tab("Clone & Generate (克隆并合成)"):
|
||||
with gr.Row():
|
||||
with gr.Column(scale=2):
|
||||
ref_audio = gr.Audio(
|
||||
label="Reference Audio (参考音频)",
|
||||
)
|
||||
ref_text = gr.Textbox(
|
||||
label="Reference Text (参考音频文本)",
|
||||
lines=2,
|
||||
placeholder="Required if not set use x-vector only (不勾选use x-vector only时必填).",
|
||||
)
|
||||
xvec_only = gr.Checkbox(
|
||||
label="Use x-vector only (仅用说话人向量，效果有限，但不用传入参考音频文本)",
|
||||
value=False,
|
||||
)
|
||||
|
||||
with gr.Column(scale=2):
|
||||
text_in = gr.Textbox(
|
||||
label="Target Text (待合成文本)",
|
||||
lines=4,
|
||||
placeholder="Enter text to synthesize (输入要合成的文本).",
|
||||
)
|
||||
lang_in = gr.Dropdown(
|
||||
label="Language (语种)",
|
||||
choices=lang_choices_disp,
|
||||
value="Auto",
|
||||
interactive=True,
|
||||
)
|
||||
btn = gr.Button("Generate (生成)", variant="primary")
|
||||
|
||||
with gr.Column(scale=3):
|
||||
audio_out = gr.Audio(label="Output Audio (合成结果)", type="numpy")
|
||||
err = gr.Textbox(label="Status (状态)", lines=2)
|
||||
|
||||
def run_voice_clone(ref_aud, ref_txt: str, use_xvec: bool, text: str, lang_disp: str):
|
||||
try:
|
||||
if not text or not text.strip():
|
||||
return None, "Target text is required (必须填写待合成文本)."
|
||||
at = _audio_to_tuple(ref_aud)
|
||||
if at is None:
|
||||
return None, "Reference audio is required (必须上传参考音频)."
|
||||
if (not use_xvec) and (not ref_txt or not ref_txt.strip()):
|
||||
return None, (
|
||||
"Reference text is required when use x-vector only is NOT enabled.\n"
|
||||
"(未勾选 use x-vector only 时，必须提供参考音频文本；否则请勾选 use x-vector only，但效果会变差.)"
|
||||
)
|
||||
language = lang_map.get(lang_disp, "Auto")
|
||||
kwargs = _gen_common_kwargs()
|
||||
wavs, sr = tts.generate_voice_clone(
|
||||
text=text.strip(),
|
||||
language=language,
|
||||
ref_audio=at,
|
||||
ref_text=(ref_txt.strip() if ref_txt else None),
|
||||
x_vector_only_mode=bool(use_xvec),
|
||||
**kwargs,
|
||||
)
|
||||
return _wav_to_gradio_audio(wavs[0], sr), "Finished. (生成完成)"
|
||||
except Exception as e:
|
||||
return None, f"{type(e).__name__}: {e}"
|
||||
|
||||
btn.click(
|
||||
run_voice_clone,
|
||||
inputs=[ref_audio, ref_text, xvec_only, text_in, lang_in],
|
||||
outputs=[audio_out, err],
|
||||
)
|
||||
|
||||
with gr.Tab("Save / Load Voice (保存/加载克隆音色)"):
|
||||
with gr.Row():
|
||||
with gr.Column(scale=2):
|
||||
gr.Markdown(
|
||||
"""
|
||||
### Save Voice (保存音色)
|
||||
Upload reference audio and text, choose use x-vector only or not, then save a reusable voice prompt file.
|
||||
(上传参考音频和参考文本，选择是否使用 use x-vector only 模式后保存为可复用的音色文件)
|
||||
"""
|
||||
)
|
||||
ref_audio_s = gr.Audio(label="Reference Audio (参考音频)", type="numpy")
|
||||
ref_text_s = gr.Textbox(
|
||||
label="Reference Text (参考音频文本)",
|
||||
lines=2,
|
||||
placeholder="Required if not set use x-vector only (不勾选use x-vector only时必填).",
|
||||
)
|
||||
xvec_only_s = gr.Checkbox(
|
||||
label="Use x-vector only (仅用说话人向量，效果有限，但不用传入参考音频文本)",
|
||||
value=False,
|
||||
)
|
||||
save_btn = gr.Button("Save Voice File (保存音色文件)", variant="primary")
|
||||
prompt_file_out = gr.File(label="Voice File (音色文件)")
|
||||
|
||||
with gr.Column(scale=2):
|
||||
gr.Markdown(
|
||||
"""
|
||||
### Load Voice & Generate (加载音色并合成)
|
||||
Upload a previously saved voice file, then synthesize new text.
|
||||
(上传已保存提示文件后，输入新文本进行合成)
|
||||
"""
|
||||
)
|
||||
prompt_file_in = gr.File(label="Upload Prompt File (上传提示文件)")
|
||||
text_in2 = gr.Textbox(
|
||||
label="Target Text (待合成文本)",
|
||||
lines=4,
|
||||
placeholder="Enter text to synthesize (输入要合成的文本).",
|
||||
)
|
||||
lang_in2 = gr.Dropdown(
|
||||
label="Language (语种)",
|
||||
choices=lang_choices_disp,
|
||||
value="Auto",
|
||||
interactive=True,
|
||||
)
|
||||
gen_btn2 = gr.Button("Generate (生成)", variant="primary")
|
||||
|
||||
with gr.Column(scale=3):
|
||||
audio_out2 = gr.Audio(label="Output Audio (合成结果)", type="numpy")
|
||||
err2 = gr.Textbox(label="Status (状态)", lines=2)
|
||||
|
||||
def save_prompt(ref_aud, ref_txt: str, use_xvec: bool):
|
||||
try:
|
||||
at = _audio_to_tuple(ref_aud)
|
||||
if at is None:
|
||||
return None, "Reference audio is required (必须上传参考音频)."
|
||||
if (not use_xvec) and (not ref_txt or not ref_txt.strip()):
|
||||
return None, (
|
||||
"Reference text is required when use x-vector only is NOT enabled.\n"
|
||||
"(未勾选 use x-vector only 时，必须提供参考音频文本；否则请勾选 use x-vector only，但效果会变差.)"
|
||||
)
|
||||
items = tts.create_voice_clone_prompt(
|
||||
ref_audio=at,
|
||||
ref_text=(ref_txt.strip() if ref_txt else None),
|
||||
x_vector_only_mode=bool(use_xvec),
|
||||
)
|
||||
payload = {
|
||||
"items": [asdict(it) for it in items],
|
||||
}
|
||||
fd, out_path = tempfile.mkstemp(prefix="voice_clone_prompt_", suffix=".pt")
|
||||
os.close(fd)
|
||||
torch.save(payload, out_path)
|
||||
return out_path, "Finished. (生成完成)"
|
||||
except Exception as e:
|
||||
return None, f"{type(e).__name__}: {e}"
|
||||
|
||||
def load_prompt_and_gen(file_obj, text: str, lang_disp: str):
|
||||
try:
|
||||
if file_obj is None:
|
||||
return None, "Voice file is required (必须上传音色文件)."
|
||||
if not text or not text.strip():
|
||||
return None, "Target text is required (必须填写待合成文本)."
|
||||
|
||||
path = getattr(file_obj, "name", None) or getattr(file_obj, "path", None) or str(file_obj)
|
||||
payload = torch.load(path, map_location="cpu", weights_only=True)
|
||||
if not isinstance(payload, dict) or "items" not in payload:
|
||||
return None, "Invalid file format (文件格式不正确)."
|
||||
|
||||
items_raw = payload["items"]
|
||||
if not isinstance(items_raw, list) or len(items_raw) == 0:
|
||||
return None, "Empty voice items (音色为空)."
|
||||
|
||||
items: List[VoiceClonePromptItem] = []
|
||||
for d in items_raw:
|
||||
if not isinstance(d, dict):
|
||||
return None, "Invalid item format in file (文件内部格式错误)."
|
||||
ref_code = d.get("ref_code", None)
|
||||
if ref_code is not None and not torch.is_tensor(ref_code):
|
||||
ref_code = torch.tensor(ref_code)
|
||||
ref_spk = d.get("ref_spk_embedding", None)
|
||||
if ref_spk is None:
|
||||
return None, "Missing ref_spk_embedding (缺少说话人向量)."
|
||||
if not torch.is_tensor(ref_spk):
|
||||
ref_spk = torch.tensor(ref_spk)
|
||||
|
||||
items.append(
|
||||
VoiceClonePromptItem(
|
||||
ref_code=ref_code,
|
||||
ref_spk_embedding=ref_spk,
|
||||
x_vector_only_mode=bool(d.get("x_vector_only_mode", False)),
|
||||
icl_mode=bool(d.get("icl_mode", not bool(d.get("x_vector_only_mode", False)))),
|
||||
ref_text=d.get("ref_text", None),
|
||||
)
|
||||
)
|
||||
|
||||
language = lang_map.get(lang_disp, "Auto")
|
||||
kwargs = _gen_common_kwargs()
|
||||
wavs, sr = tts.generate_voice_clone(
|
||||
text=text.strip(),
|
||||
language=language,
|
||||
voice_clone_prompt=items,
|
||||
**kwargs,
|
||||
)
|
||||
return _wav_to_gradio_audio(wavs[0], sr), "Finished. (生成完成)"
|
||||
except Exception as e:
|
||||
return None, (
|
||||
f"Failed to read or use voice file. Check file format/content.\n"
|
||||
f"(读取或使用音色文件失败，请检查文件格式或内容)\n"
|
||||
f"{type(e).__name__}: {e}"
|
||||
)
|
||||
|
||||
save_btn.click(save_prompt, inputs=[ref_audio_s, ref_text_s, xvec_only_s], outputs=[prompt_file_out, err2])
|
||||
gen_btn2.click(load_prompt_and_gen, inputs=[prompt_file_in, text_in2, lang_in2], outputs=[audio_out2, err2])
|
||||
|
||||
gr.Markdown(
|
||||
"""
|
||||
**Disclaimer (免责声明)**
|
||||
- The audio is automatically generated/synthesized by an AI model solely to demonstrate the model’s capabilities; it may be inaccurate or inappropriate, does not represent the views of the developer/operator, and does not constitute professional advice. You are solely responsible for evaluating, using, distributing, or relying on this audio; to the maximum extent permitted by applicable law, the developer/operator disclaims liability for any direct, indirect, incidental, or consequential damages arising from the use of or inability to use the audio, except where liability cannot be excluded by law. Do not use this service to intentionally generate or replicate unlawful, harmful, defamatory, fraudulent, deepfake, or privacy/publicity/copyright/trademark‑infringing content; if a user prompts, supplies materials, or otherwise facilitates any illegal or infringing conduct, the user bears all legal consequences and the developer/operator is not responsible.
|
||||
- 音频由人工智能模型自动生成/合成，仅用于体验与展示模型效果，可能存在不准确或不当之处；其内容不代表开发者/运营方立场，亦不构成任何专业建议。用户应自行评估并承担使用、传播或依赖该音频所产生的一切风险与责任；在适用法律允许的最大范围内，开发者/运营方不对因使用或无法使用本音频造成的任何直接、间接、附带或后果性损失承担责任（法律另有强制规定的除外）。严禁利用本服务故意引导生成或复制违法、有害、诽谤、欺诈、深度伪造、侵犯隐私/肖像/著作权/商标等内容；如用户通过提示词、素材或其他方式实施或促成任何违法或侵权行为，相关法律后果由用户自行承担，与开发者/运营方无关。
|
||||
"""
|
||||
)
|
||||
|
||||
return demo
|
||||
|
||||
|
||||
def main(argv=None) -> int:
|
||||
parser = build_parser()
|
||||
args = parser.parse_args(argv)
|
||||
|
||||
if not args.checkpoint and not args.checkpoint_pos:
|
||||
parser.print_help()
|
||||
return 0
|
||||
|
||||
ckpt = _resolve_checkpoint(args)
|
||||
|
||||
dtype = _dtype_from_str(args.dtype)
|
||||
attn_impl = "flash_attention_2" if args.flash_attn else None
|
||||
|
||||
tts = Qwen3TTSModel.from_pretrained(
|
||||
ckpt,
|
||||
device_map=args.device,
|
||||
dtype=dtype,
|
||||
attn_implementation=attn_impl,
|
||||
)
|
||||
|
||||
gen_kwargs_default = _collect_gen_kwargs(args)
|
||||
demo = build_demo(tts, ckpt, gen_kwargs_default)
|
||||
|
||||
launch_kwargs: Dict[str, Any] = dict(
|
||||
server_name=args.ip,
|
||||
server_port=args.port,
|
||||
share=args.share,
|
||||
ssl_verify=True if args.ssl_verify else False,
|
||||
)
|
||||
if args.ssl_certfile is not None:
|
||||
launch_kwargs["ssl_certfile"] = args.ssl_certfile
|
||||
if args.ssl_keyfile is not None:
|
||||
launch_kwargs["ssl_keyfile"] = args.ssl_keyfile
|
||||
|
||||
demo.queue(default_concurrency_limit=int(args.concurrency)).launch(**launch_kwargs)
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
19
models/Qwen3-TTS/qwen_tts/core/__init__.py
Normal file
19
models/Qwen3-TTS/qwen_tts/core/__init__.py
Normal file
@@ -0,0 +1,19 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2026 The Alibaba Qwen team.
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from .tokenizer_25hz.configuration_qwen3_tts_tokenizer_v1 import Qwen3TTSTokenizerV1Config
|
||||
from .tokenizer_25hz.modeling_qwen3_tts_tokenizer_v1 import Qwen3TTSTokenizerV1Model
|
||||
from .tokenizer_12hz.configuration_qwen3_tts_tokenizer_v2 import Qwen3TTSTokenizerV2Config
|
||||
from .tokenizer_12hz.modeling_qwen3_tts_tokenizer_v2 import Qwen3TTSTokenizerV2Model
|
||||
18
models/Qwen3-TTS/qwen_tts/core/models/__init__.py
Normal file
18
models/Qwen3-TTS/qwen_tts/core/models/__init__.py
Normal file
@@ -0,0 +1,18 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2026 The Alibaba Qwen team.
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from .configuration_qwen3_tts import Qwen3TTSConfig
|
||||
from .modeling_qwen3_tts import Qwen3TTSForConditionalGeneration
|
||||
from .processing_qwen3_tts import Qwen3TTSProcessor
|
||||
502
models/Qwen3-TTS/qwen_tts/core/models/configuration_qwen3_tts.py
Normal file
502
models/Qwen3-TTS/qwen_tts/core/models/configuration_qwen3_tts.py
Normal file
@@ -0,0 +1,502 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2026 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from transformers.configuration_utils import PretrainedConfig, layer_type_validation
|
||||
from transformers.modeling_rope_utils import rope_config_validation
|
||||
from transformers.utils import logging
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class Qwen3TTSSpeakerEncoderConfig(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of a [`Qwen3TTSSpeakerEncoder`].
|
||||
It is used to instantiate a Qwen3TTS speaker encoder model according to the specified arguments, defining the model
|
||||
architecture. The architecture is based on the ECAPA-TDNN model.
|
||||
|
||||
Args:
|
||||
mel_dim (`int`, *optional*, defaults to 128):
|
||||
The dimension of the input mel-spectrogram.
|
||||
enc_dim (`int`, *optional*, defaults to 192):
|
||||
The dimension of the final speaker embedding.
|
||||
enc_channels (`list[int]`, *optional*, defaults to `[512, 512, 512, 512, 1536]`):
|
||||
A list of output channels for each TDNN/SERes2Net layer in the encoder. The first channel size is for the initial TDNN layer,
|
||||
the intermediate ones for the `SqueezeExcitationRes2NetBlock` layers, and the last one for the multi-layer feature aggregation.
|
||||
enc_kernel_sizes (`list[int]`, *optional*, defaults to `[5, 3, 3, 3, 1]`):
|
||||
A list of kernel sizes for each layer in the encoder, corresponding to `enc_channels`.
|
||||
enc_dilations (`list[int]`, *optional*, defaults to `[1, 2, 3, 4, 1]`):
|
||||
A list of dilations for each layer in the encoder, corresponding to `enc_channels`.
|
||||
enc_attention_channels (`int`, *optional*, defaults to 128):
|
||||
The number of attention channels in the `AttentiveStatisticsPooling` layer.
|
||||
enc_res2net_scale (`int`, *optional*,defaults to 8):
|
||||
The scale of the `Res2NetBlock` in the encoder.
|
||||
enc_se_channels (`int`, *optional*, defaults to 128):
|
||||
The number of channels in the squeeze part of the `SqueezeExcitationBlock`.
|
||||
"""
|
||||
def __init__(
|
||||
self,
|
||||
mel_dim=128,
|
||||
enc_dim=1024,
|
||||
enc_channels=[512, 512, 512, 512, 1536],
|
||||
enc_kernel_sizes=[5, 3, 3, 3, 1],
|
||||
enc_dilations=[1, 2, 3, 4, 1],
|
||||
enc_attention_channels=128,
|
||||
enc_res2net_scale=8,
|
||||
enc_se_channels=128,
|
||||
sample_rate=24000,
|
||||
):
|
||||
self.mel_dim = mel_dim
|
||||
self.enc_dim = enc_dim
|
||||
self.enc_channels = enc_channels
|
||||
self.enc_kernel_sizes = enc_kernel_sizes
|
||||
self.enc_dilations = enc_dilations
|
||||
self.enc_attention_channels = enc_attention_channels
|
||||
self.enc_res2net_scale = enc_res2net_scale
|
||||
self.enc_se_channels = enc_se_channels
|
||||
self.sample_rate = sample_rate
|
||||
|
||||
|
||||
class Qwen3TTSTalkerCodePredictorConfig(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of a [`Qwen3TTSTalkerCodePredictorModel`]. It is used to instantiate a
|
||||
Qwen3TTSTalkerCodePredictor model according to the specified arguments, defining the model architecture.
|
||||
|
||||
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||
documentation from [`PretrainedConfig`] for more information.
|
||||
|
||||
|
||||
Args:
|
||||
vocab_size (`int`, *optional*, defaults to 151936):
|
||||
Vocabulary size of the Qwen3TTSTalkerCodePredictor model. Defines the number of different tokens that can be represented by the
|
||||
`inputs_ids` passed when calling [`Qwen3TTSTalkerCodePredictorModel`]
|
||||
hidden_size (`int`, *optional*, defaults to 4096):
|
||||
Dimension of the hidden representations.
|
||||
intermediate_size (`int`, *optional*, defaults to 22016):
|
||||
Dimension of the MLP representations.
|
||||
num_hidden_layers (`int`, *optional*, defaults to 32):
|
||||
Number of hidden layers in the Transformer encoder.
|
||||
num_attention_heads (`int`, *optional*, defaults to 32):
|
||||
Number of attention heads for each attention layer in the Transformer encoder.
|
||||
num_key_value_heads (`int`, *optional*, defaults to 32):
|
||||
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
||||
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
||||
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
||||
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
||||
by meanpooling all the original heads within that group. For more details, check out [this
|
||||
paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`.
|
||||
head_dim (`int`, *optional*, defaults to 128):
|
||||
The attention head dimension.
|
||||
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
||||
The non-linear activation function (function or string) in the decoder.
|
||||
max_position_embeddings (`int`, *optional*, defaults to 32768):
|
||||
The maximum sequence length that this model might ever be used with.
|
||||
initializer_range (`float`, *optional*, defaults to 0.02):
|
||||
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||||
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
|
||||
The epsilon used by the rms normalization layers.
|
||||
use_cache (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
||||
relevant if `config.is_decoder=True`.
|
||||
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
||||
Whether the model's input and output word embeddings should be tied.
|
||||
rope_theta (`float`, *optional*, defaults to 10000.0):
|
||||
The base period of the RoPE embeddings.
|
||||
rope_scaling (`Dict`, *optional*):
|
||||
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
|
||||
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
|
||||
accordingly.
|
||||
Expected contents:
|
||||
`rope_type` (`str`):
|
||||
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
|
||||
'llama3'], with 'default' being the original RoPE implementation.
|
||||
`factor` (`float`, *optional*):
|
||||
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
|
||||
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
|
||||
original maximum pre-trained length.
|
||||
`original_max_position_embeddings` (`int`, *optional*):
|
||||
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
|
||||
pretraining.
|
||||
`attention_factor` (`float`, *optional*):
|
||||
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
|
||||
computation. If unspecified, it defaults to value recommended by the implementation, using the
|
||||
`factor` field to infer the suggested value.
|
||||
`beta_fast` (`float`, *optional*):
|
||||
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
|
||||
ramp function. If unspecified, it defaults to 32.
|
||||
`beta_slow` (`float`, *optional*):
|
||||
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
|
||||
ramp function. If unspecified, it defaults to 1.
|
||||
`short_factor` (`list[float]`, *optional*):
|
||||
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
|
||||
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
|
||||
size divided by the number of attention heads divided by 2
|
||||
`long_factor` (`list[float]`, *optional*):
|
||||
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
|
||||
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
|
||||
size divided by the number of attention heads divided by 2
|
||||
`low_freq_factor` (`float`, *optional*):
|
||||
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
|
||||
`high_freq_factor` (`float`, *optional*):
|
||||
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
|
||||
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
|
||||
Whether to use a bias in the query, key, value and output projection layers during self-attention.
|
||||
use_sliding_window (`bool`, *optional*, defaults to `False`):
|
||||
Whether to use sliding window attention.
|
||||
sliding_window (`int`, *optional*, defaults to 4096):
|
||||
Sliding window attention (SWA) window size. If not specified, will default to `4096`.
|
||||
max_window_layers (`int`, *optional*, defaults to 28):
|
||||
The number of layers using full attention. The first `max_window_layers` layers will use full attention, while any
|
||||
additional layer afterwards will use SWA (Sliding Window Attention).
|
||||
layer_types (`list`, *optional*):
|
||||
Attention pattern for each layer.
|
||||
attention_dropout (`float`, *optional*, defaults to 0.0):
|
||||
The dropout ratio for the attention probabilities.
|
||||
|
||||
"""
|
||||
|
||||
model_type = "qwen3_tts_talker_code_predictor"
|
||||
keys_to_ignore_at_inference = ["past_key_values"]
|
||||
|
||||
# Default tensor parallel plan for base model `Qwen3TTSTalkerCodePredictor`
|
||||
base_model_tp_plan = {
|
||||
"layers.*.self_attn.q_proj": "colwise",
|
||||
"layers.*.self_attn.k_proj": "colwise",
|
||||
"layers.*.self_attn.v_proj": "colwise",
|
||||
"layers.*.self_attn.o_proj": "rowwise",
|
||||
"layers.*.mlp.gate_proj": "colwise",
|
||||
"layers.*.mlp.up_proj": "colwise",
|
||||
"layers.*.mlp.down_proj": "rowwise",
|
||||
}
|
||||
base_model_pp_plan = {
|
||||
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
|
||||
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
|
||||
"norm": (["hidden_states"], ["hidden_states"]),
|
||||
}
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_size=2048,
|
||||
hidden_size=1024,
|
||||
intermediate_size=3072,
|
||||
num_hidden_layers=5,
|
||||
num_attention_heads=16,
|
||||
num_key_value_heads=8,
|
||||
head_dim=128,
|
||||
hidden_act="silu",
|
||||
max_position_embeddings=32768,
|
||||
initializer_range=0.02,
|
||||
rms_norm_eps=0.000001,
|
||||
use_cache=True,
|
||||
tie_word_embeddings=False,
|
||||
rope_theta=10000,
|
||||
rope_scaling=None,
|
||||
attention_bias=False,
|
||||
use_sliding_window=False,
|
||||
sliding_window=4096,
|
||||
max_window_layers=28,
|
||||
layer_types=None,
|
||||
attention_dropout=0,
|
||||
num_code_groups=32,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(
|
||||
tie_word_embeddings=tie_word_embeddings,
|
||||
**kwargs,
|
||||
)
|
||||
self.vocab_size = vocab_size
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.hidden_size = hidden_size
|
||||
self.intermediate_size = intermediate_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.use_sliding_window = use_sliding_window
|
||||
self.sliding_window = sliding_window if self.use_sliding_window else None
|
||||
self.max_window_layers = max_window_layers
|
||||
|
||||
# for backward compatibility
|
||||
if num_key_value_heads is None:
|
||||
num_key_value_heads = num_attention_heads
|
||||
|
||||
self.num_key_value_heads = num_key_value_heads
|
||||
self.head_dim = head_dim
|
||||
self.hidden_act = hidden_act
|
||||
self.initializer_range = initializer_range
|
||||
self.rms_norm_eps = rms_norm_eps
|
||||
self.use_cache = use_cache
|
||||
self.rope_theta = rope_theta
|
||||
self.rope_scaling = rope_scaling
|
||||
self.attention_bias = attention_bias
|
||||
self.attention_dropout = attention_dropout
|
||||
# Validate the correctness of rotary position embeddings parameters
|
||||
# BC: if there is a 'type' field, move it to 'rope_type'.
|
||||
if self.rope_scaling is not None and "type" in self.rope_scaling:
|
||||
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
|
||||
rope_config_validation(self)
|
||||
|
||||
self.layer_types = layer_types
|
||||
if self.layer_types is None:
|
||||
self.layer_types = [
|
||||
"sliding_attention"
|
||||
if self.sliding_window is not None and i >= self.max_window_layers
|
||||
else "full_attention"
|
||||
for i in range(self.num_hidden_layers)
|
||||
]
|
||||
layer_type_validation(self.layer_types)
|
||||
self.num_code_groups = num_code_groups
|
||||
|
||||
|
||||
class Qwen3TTSTalkerConfig(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of a [`Qwen3TTSTalkerModel`]. It is used to instantiate a
|
||||
Qwen3TTSTalker model according to the specified arguments, defining the model architecture.
|
||||
|
||||
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||
documentation from [`PretrainedConfig`] for more information.
|
||||
|
||||
|
||||
Args:
|
||||
vocab_size (`int`, *optional*, defaults to 151936):
|
||||
Vocabulary size of the Qwen3TTSTalker model. Defines the number of different tokens that can be represented by the
|
||||
`inputs_ids` passed when calling [`Qwen3TTSTalkerModel`]
|
||||
hidden_size (`int`, *optional*, defaults to 2048):
|
||||
Dimension of the hidden representations.
|
||||
intermediate_size (`int`, *optional*, defaults to 6144):
|
||||
Dimension of the MLP representations.
|
||||
num_hidden_layers (`int`, *optional*, defaults to 24):
|
||||
Number of hidden layers in the Transformer encoder.
|
||||
num_attention_heads (`int`, *optional*, defaults to 32):
|
||||
Number of attention heads for each attention layer in the Transformer encoder.
|
||||
num_key_value_heads (`int`, *optional*, defaults to 4):
|
||||
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
||||
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
||||
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
||||
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
||||
by meanpooling all the original heads within that group. For more details, check out [this
|
||||
paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`.
|
||||
|
||||
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
||||
The non-linear activation function (function or string) in the decoder.
|
||||
max_position_embeddings (`int`, *optional*, defaults to 32768):
|
||||
The maximum sequence length that this model might ever be used with.
|
||||
initializer_range (`float`, *optional*, defaults to 0.02):
|
||||
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||||
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
|
||||
The epsilon used by the rms normalization layers.
|
||||
use_cache (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
||||
relevant if `config.is_decoder=True`.
|
||||
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
||||
Whether the model's input and output word embeddings should be tied.
|
||||
rope_theta (`float`, *optional*, defaults to 10000.0):
|
||||
The base period of the RoPE embeddings.
|
||||
rope_scaling (`Dict`, *optional*):
|
||||
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
|
||||
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
|
||||
accordingly.
|
||||
Expected contents:
|
||||
`rope_type` (`str`):
|
||||
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
|
||||
'llama3'], with 'default' being the original RoPE implementation.
|
||||
`factor` (`float`, *optional*):
|
||||
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
|
||||
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
|
||||
original maximum pre-trained length.
|
||||
`original_max_position_embeddings` (`int`, *optional*):
|
||||
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
|
||||
pretraining.
|
||||
`attention_factor` (`float`, *optional*):
|
||||
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
|
||||
computation. If unspecified, it defaults to value recommended by the implementation, using the
|
||||
`factor` field to infer the suggested value.
|
||||
`beta_fast` (`float`, *optional*):
|
||||
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
|
||||
ramp function. If unspecified, it defaults to 32.
|
||||
`beta_slow` (`float`, *optional*):
|
||||
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
|
||||
ramp function. If unspecified, it defaults to 1.
|
||||
`short_factor` (`list[float]`, *optional*):
|
||||
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
|
||||
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
|
||||
size divided by the number of attention heads divided by 2
|
||||
`long_factor` (`list[float]`, *optional*):
|
||||
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
|
||||
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
|
||||
size divided by the number of attention heads divided by 2
|
||||
`low_freq_factor` (`float`, *optional*):
|
||||
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
|
||||
`high_freq_factor` (`float`, *optional*):
|
||||
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
|
||||
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
|
||||
Whether to use a bias in the query, key, value and output projection layers during self-attention.
|
||||
use_sliding_window (`bool`, *optional*, defaults to `False`):
|
||||
Whether to use sliding window attention.
|
||||
sliding_window (`int`, *optional*, defaults to 4096):
|
||||
Sliding window attention (SWA) window size. If not specified, will default to `4096`.
|
||||
attention_dropout (`float`, *optional*, defaults to 0.0):
|
||||
The dropout ratio for the attention probabilities.
|
||||
"""
|
||||
|
||||
model_type = "qwen3_tts_talker"
|
||||
keys_to_ignore_at_inference = ["past_key_values"]
|
||||
|
||||
# Default tensor parallel plan for base model `Qwen3TTSTalker`
|
||||
base_model_tp_plan = {
|
||||
"layers.*.self_attn.q_proj": "colwise",
|
||||
"layers.*.self_attn.k_proj": "colwise",
|
||||
"layers.*.self_attn.v_proj": "colwise",
|
||||
"layers.*.self_attn.o_proj": "rowwise",
|
||||
"layers.*.mlp.gate_proj": "colwise",
|
||||
"layers.*.mlp.up_proj": "colwise",
|
||||
"layers.*.mlp.down_proj": "rowwise",
|
||||
}
|
||||
base_model_pp_plan = {
|
||||
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
|
||||
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
|
||||
"norm": (["hidden_states"], ["hidden_states"]),
|
||||
}
|
||||
sub_configs = {"code_predictor_config": Qwen3TTSTalkerCodePredictorConfig}
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
code_predictor_config=None,
|
||||
vocab_size=3072,
|
||||
hidden_size=1024,
|
||||
intermediate_size=2048,
|
||||
num_hidden_layers=20,
|
||||
num_attention_heads=16,
|
||||
num_key_value_heads=2,
|
||||
hidden_act="silu",
|
||||
max_position_embeddings=32768,
|
||||
initializer_range=0.02,
|
||||
rms_norm_eps=0.000001,
|
||||
use_cache=True,
|
||||
tie_word_embeddings=False,
|
||||
rope_theta=10000,
|
||||
rope_scaling=None,
|
||||
attention_bias=False,
|
||||
use_sliding_window=False,
|
||||
sliding_window=4096,
|
||||
attention_dropout=0,
|
||||
num_code_groups=32,
|
||||
text_hidden_size=2048,
|
||||
codec_eos_token_id=4198,
|
||||
codec_think_id=4202,
|
||||
codec_nothink_id=4203,
|
||||
codec_think_bos_id=4204,
|
||||
codec_think_eos_id=4205,
|
||||
codec_pad_id=4196,
|
||||
codec_bos_id=4197,
|
||||
spk_id=None,
|
||||
spk_is_dialect=None,
|
||||
codec_language_id=None,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(
|
||||
tie_word_embeddings=tie_word_embeddings,
|
||||
**kwargs,
|
||||
)
|
||||
self.vocab_size = vocab_size
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.hidden_size = hidden_size
|
||||
self.intermediate_size = intermediate_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.use_sliding_window = use_sliding_window
|
||||
self.sliding_window = sliding_window if use_sliding_window else None
|
||||
|
||||
self.num_key_value_heads = num_key_value_heads
|
||||
self.hidden_act = hidden_act
|
||||
self.initializer_range = initializer_range
|
||||
self.rms_norm_eps = rms_norm_eps
|
||||
self.use_cache = use_cache
|
||||
self.rope_theta = rope_theta
|
||||
self.rope_scaling = rope_scaling
|
||||
self.attention_bias = attention_bias
|
||||
self.attention_dropout = attention_dropout
|
||||
# Validate the correctness of rotary position embeddings parameters
|
||||
# BC: if there is a 'type' field, move it to 'rope_type'.
|
||||
if self.rope_scaling is not None and "type" in self.rope_scaling:
|
||||
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
|
||||
|
||||
if code_predictor_config is None:
|
||||
code_predictor_config = {}
|
||||
self.code_predictor_config = Qwen3TTSTalkerCodePredictorConfig()
|
||||
logger.info("code_predictor_config is None. Initializing code_predictor model with default values")
|
||||
elif isinstance(code_predictor_config, Qwen3TTSTalkerCodePredictorConfig):
|
||||
self.code_predictor_config = code_predictor_config
|
||||
else:
|
||||
self.code_predictor_config = Qwen3TTSTalkerCodePredictorConfig(**code_predictor_config)
|
||||
self.num_code_groups = num_code_groups
|
||||
self.text_hidden_size = text_hidden_size
|
||||
self.codec_eos_token_id = codec_eos_token_id
|
||||
self.codec_think_id = codec_think_id
|
||||
self.codec_language_id = codec_language_id
|
||||
self.codec_nothink_id = codec_nothink_id
|
||||
self.codec_think_bos_id = codec_think_bos_id
|
||||
self.codec_think_eos_id = codec_think_eos_id
|
||||
self.codec_pad_id = codec_pad_id
|
||||
self.codec_bos_id = codec_bos_id
|
||||
self.spk_id = spk_id
|
||||
self.spk_is_dialect = spk_is_dialect
|
||||
|
||||
|
||||
class Qwen3TTSConfig(PretrainedConfig):
|
||||
"""
|
||||
This is the configuration class to store the configuration of a [`Qwen3TTSForConditionalGeneration`].
|
||||
"""
|
||||
|
||||
model_type = "qwen3_tts"
|
||||
sub_configs = {
|
||||
"talker_config": Qwen3TTSTalkerConfig,
|
||||
"speaker_encoder_config": Qwen3TTSSpeakerEncoderConfig,
|
||||
}
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
talker_config=None,
|
||||
speaker_encoder_config=None,
|
||||
tokenizer_type=None,
|
||||
tts_model_size=None,
|
||||
tts_model_type=None,
|
||||
im_start_token_id=151644,
|
||||
im_end_token_id=151645,
|
||||
tts_pad_token_id=151671,
|
||||
tts_bos_token_id=151672,
|
||||
tts_eos_token_id=151673,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
if talker_config is None:
|
||||
talker_config = {}
|
||||
logger.info("talker_config is None. Initializing talker model with default values")
|
||||
if speaker_encoder_config is None:
|
||||
speaker_encoder_config = {}
|
||||
logger.info("speaker_encoder_config is None. Initializing talker model with default values")
|
||||
|
||||
self.talker_config = Qwen3TTSTalkerConfig(**talker_config)
|
||||
self.speaker_encoder_config = Qwen3TTSSpeakerEncoderConfig(**speaker_encoder_config)
|
||||
|
||||
self.tokenizer_type = tokenizer_type
|
||||
self.tts_model_size = tts_model_size
|
||||
self.tts_model_type = tts_model_type
|
||||
|
||||
self.im_start_token_id = im_start_token_id
|
||||
self.im_end_token_id = im_end_token_id
|
||||
self.tts_pad_token_id = tts_pad_token_id
|
||||
self.tts_bos_token_id = tts_bos_token_id
|
||||
self.tts_eos_token_id = tts_eos_token_id
|
||||
|
||||
|
||||
__all__ = ["Qwen3TTSConfig", "Qwen3TTSTalkerConfig", "Qwen3TTSSpeakerEncoderConfig"]
|
||||
2299
models/Qwen3-TTS/qwen_tts/core/models/modeling_qwen3_tts.py
Normal file
2299
models/Qwen3-TTS/qwen_tts/core/models/modeling_qwen3_tts.py
Normal file
File diff suppressed because it is too large
Load Diff
106
models/Qwen3-TTS/qwen_tts/core/models/processing_qwen3_tts.py
Normal file
106
models/Qwen3-TTS/qwen_tts/core/models/processing_qwen3_tts.py
Normal file
@@ -0,0 +1,106 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2026 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from transformers.feature_extraction_utils import BatchFeature
|
||||
from transformers.processing_utils import ProcessingKwargs, ProcessorMixin
|
||||
|
||||
|
||||
class Qwen3TTSProcessorKwargs(ProcessingKwargs, total=False):
|
||||
_defaults = {
|
||||
"text_kwargs": {
|
||||
"padding": False,
|
||||
"padding_side": "left",
|
||||
}
|
||||
}
|
||||
|
||||
class Qwen3TTSProcessor(ProcessorMixin):
|
||||
r"""
|
||||
Constructs a Qwen3TTS processor.
|
||||
|
||||
Args:
|
||||
tokenizer ([`Qwen2TokenizerFast`], *optional*):
|
||||
The text tokenizer.
|
||||
chat_template (`Optional[str]`, *optional*):
|
||||
The Jinja template to use for formatting the conversation. If not provided, the default chat template is used.
|
||||
"""
|
||||
|
||||
attributes = ["tokenizer"]
|
||||
tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")
|
||||
|
||||
def __init__(
|
||||
self, tokenizer=None, chat_template=None
|
||||
):
|
||||
super().__init__(tokenizer, chat_template=chat_template)
|
||||
|
||||
def __call__(self, text=None, **kwargs) -> BatchFeature:
|
||||
"""
|
||||
Main method to prepare for the model one or several sequences(s) and audio(s). This method forwards the `text`
|
||||
and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode
|
||||
the text.
|
||||
|
||||
Args:
|
||||
text (`str`, `List[str]`, `List[List[str]]`):
|
||||
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
|
||||
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
|
||||
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
|
||||
"""
|
||||
|
||||
if text is None:
|
||||
raise ValueError("You need to specify either a `text` input to process.")
|
||||
|
||||
output_kwargs = self._merge_kwargs(
|
||||
Qwen3TTSProcessorKwargs,
|
||||
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
|
||||
**kwargs,
|
||||
)
|
||||
if not isinstance(text, list):
|
||||
text = [text]
|
||||
|
||||
texts_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
|
||||
|
||||
return BatchFeature(
|
||||
data={**texts_inputs},
|
||||
tensor_type=kwargs.get("return_tensors"),
|
||||
)
|
||||
|
||||
def batch_decode(self, *args, **kwargs):
|
||||
"""
|
||||
This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
|
||||
refer to the docstring of this method for more information.
|
||||
"""
|
||||
return self.tokenizer.batch_decode(*args, **kwargs)
|
||||
|
||||
def decode(self, *args, **kwargs):
|
||||
"""
|
||||
This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
|
||||
the docstring of this method for more information.
|
||||
"""
|
||||
return self.tokenizer.decode(*args, **kwargs)
|
||||
|
||||
def apply_chat_template(self, conversations, chat_template=None, **kwargs):
|
||||
if isinstance(conversations[0], dict):
|
||||
conversations = [conversations]
|
||||
return super().apply_chat_template(conversations, chat_template, **kwargs)
|
||||
|
||||
@property
|
||||
def model_input_names(self):
|
||||
tokenizer_input_names = self.tokenizer.model_input_names
|
||||
return list(
|
||||
dict.fromkeys(
|
||||
tokenizer_input_names
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
__all__ = ["Qwen3TTSProcessor"]
|
||||
@@ -0,0 +1,172 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2026 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""Qwen3TTSTokenizerV2 model configuration"""
|
||||
|
||||
from transformers.configuration_utils import PretrainedConfig
|
||||
from transformers.utils import logging
|
||||
|
||||
from transformers import MimiConfig
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class Qwen3TTSTokenizerV2DecoderConfig(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of a [`Qwen3TTSTokenizerV2DecoderConfig`].
|
||||
|
||||
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||
documentation from [`PretrainedConfig`] for more information.
|
||||
|
||||
Args:
|
||||
codebook_size (`int`, *optional*, defaults to 2048):
|
||||
Number of entries in each residual codebook used for acoustic token quantization.
|
||||
hidden_size (`int`, *optional*, defaults to 1024):
|
||||
Dimensionality of the hidden states and embeddings in the autoregressive transformer decoder.
|
||||
max_position_embeddings (`int`, *optional*, defaults to 8000):
|
||||
Maximum sequence length that the autoregressive decoder can handle. Determines positional embedding size.
|
||||
rope_theta (`float`, *optional*, defaults to 10000.0):
|
||||
The base period for rotary position embeddings (RoPE) applied to attention layers.
|
||||
num_attention_heads (`int`, *optional*, defaults to 16):
|
||||
Number of attention heads for each attention layer in the decoder.
|
||||
num_key_value_heads (`int`, *optional*, defaults to 16):
|
||||
Number of key and value attention heads used in grouped-query attention (if applicable).
|
||||
attention_bias (`bool`, *optional*, defaults to `False`):
|
||||
Whether to use bias in the attention projection layers.
|
||||
sliding_window (`int`, *optional*, defaults to 72):
|
||||
Window size for local attention mechanism, limiting attention context to improve efficiency.
|
||||
intermediate_size (`int`, *optional*, defaults to 3072):
|
||||
Dimensionality of the feed-forward (intermediate) layer in each transformer block.
|
||||
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
||||
The non-linear activation function used in the feed-forward layers. Supports `"silu"`, `"relu"`, `"gelu"`, etc.
|
||||
layer_scale_initial_scale (`float`, *optional*, defaults to 0.01):
|
||||
Initial value for LayerScale applied in transformer blocks, helping stabilize training.
|
||||
rms_norm_eps (`float`, *optional*, defaults to 1e-5):
|
||||
Epsilon value for RMS normalization layers to prevent division by zero.
|
||||
num_hidden_layers (`int`, *optional*, defaults to 8):
|
||||
Number of transformer blocks in the autoregressive decoder.
|
||||
num_quantizers (`int`, *optional*, defaults to 16):
|
||||
Number of residual vector quantizers used in the vocoder for fine-grained audio reconstruction.
|
||||
upsample_rates (`Tuple[int]`, *optional*, defaults to `(8, 5, 4, 3)`):
|
||||
Rate at which features are upsampled in the final waveform synthesis stage.
|
||||
upsampling_ratios (`Tuple[int]`, *optional*, defaults to `(2, 2)`):
|
||||
Ratios used in transposed convolutional layers to progressively upsample feature maps to waveform.
|
||||
decoder_dim (`int`, *optional*, defaults to 1536):
|
||||
Final dimensionality of the decoder's output before waveform generation.
|
||||
attention_dropout (`float`, *optional*, defaults to 0.0):
|
||||
Dropout probability applied to attention weights in the decoder.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
codebook_size=2048,
|
||||
hidden_size=1024,
|
||||
latent_dim=1024,
|
||||
max_position_embeddings=8000,
|
||||
rope_theta=10000,
|
||||
num_attention_heads=16,
|
||||
num_key_value_heads=16,
|
||||
attention_bias=False,
|
||||
sliding_window=72,
|
||||
intermediate_size=3072,
|
||||
hidden_act="silu",
|
||||
layer_scale_initial_scale=0.01,
|
||||
rms_norm_eps=1e-5,
|
||||
num_hidden_layers=8,
|
||||
num_quantizers=16,
|
||||
upsample_rates=(8, 5, 4, 3),
|
||||
upsampling_ratios=(2, 2),
|
||||
decoder_dim=1536,
|
||||
attention_dropout=0.0,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
self.codebook_size = codebook_size
|
||||
self.hidden_size = hidden_size
|
||||
self.latent_dim = latent_dim
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.rope_theta = rope_theta
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.num_key_value_heads = num_key_value_heads
|
||||
self.attention_bias = attention_bias
|
||||
self.sliding_window = sliding_window
|
||||
self.intermediate_size = intermediate_size
|
||||
self.hidden_act = hidden_act
|
||||
self.layer_scale_initial_scale = layer_scale_initial_scale
|
||||
self.rms_norm_eps = rms_norm_eps
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_quantizers = num_quantizers
|
||||
self.upsample_rates = upsample_rates
|
||||
self.upsampling_ratios = upsampling_ratios
|
||||
self.decoder_dim = decoder_dim
|
||||
self.attention_dropout = attention_dropout
|
||||
|
||||
@property
|
||||
def layer_types(self):
|
||||
"""
|
||||
All layer in code2wav should be sliding attention
|
||||
"""
|
||||
return ["sliding_attention"] * self.num_hidden_layers
|
||||
|
||||
|
||||
class Qwen3TTSTokenizerV2Config(PretrainedConfig):
|
||||
"""
|
||||
This is the configuration class to store the configuration of a [`Qwen3TTSTokenizerV2Config`]. It is used to instantiate a Qwen3TTSTokenizerV2Model
|
||||
model according to the specified sub-models configurations, defining the model architecture.
|
||||
|
||||
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||
documentation from [`PretrainedConfig`] for more information.
|
||||
|
||||
Args:
|
||||
encoder_config (`dict`, *optional*): Configuration of the underlying encoder sub-model.
|
||||
decoder_config (`dict`, *optional*): Configuration of the underlying decoder sub-model.
|
||||
"""
|
||||
|
||||
model_type = "qwen3_tts_tokenizer_12hz"
|
||||
sub_configs = {
|
||||
"encoder_config": MimiConfig,
|
||||
"decoder_config": Qwen3TTSTokenizerV2DecoderConfig,
|
||||
}
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
encoder_config=None,
|
||||
decoder_config=None,
|
||||
encoder_valid_num_quantizers=16,
|
||||
input_sample_rate=24000,
|
||||
output_sample_rate=24000,
|
||||
decode_upsample_rate=1920,
|
||||
encode_downsample_rate=1920,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
if encoder_config is None:
|
||||
encoder_config = {}
|
||||
logger.info("encoder_config is None. Initializing encoder with default values")
|
||||
if decoder_config is None:
|
||||
decoder_config = {}
|
||||
logger.info("decoder_config is None. Initializing decoder with default values")
|
||||
|
||||
self.encoder_config = MimiConfig(**encoder_config)
|
||||
self.decoder_config = Qwen3TTSTokenizerV2DecoderConfig(**decoder_config)
|
||||
|
||||
self.encoder_valid_num_quantizers = encoder_valid_num_quantizers
|
||||
self.input_sample_rate = input_sample_rate
|
||||
self.output_sample_rate = output_sample_rate
|
||||
self.decode_upsample_rate = decode_upsample_rate
|
||||
self.encode_downsample_rate = encode_downsample_rate
|
||||
|
||||
|
||||
__all__ = ["Qwen3TTSTokenizerV2Config", "Qwen3TTSTokenizerV2DecoderConfig"]
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,332 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2026 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""Qwen3TTSTokenizerV1 model configuration"""
|
||||
|
||||
from transformers.configuration_utils import PretrainedConfig
|
||||
from transformers.utils import logging
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class Qwen3TTSTokenizerV1DecoderDiTConfig(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of the Qwen3TTSTokenizerV1DecoderToken2WavDiT.
|
||||
It defines the architecture of the DiT model, which is used for generating mel-spectrograms from tokens.
|
||||
|
||||
Args:
|
||||
hidden_size (`int`, *optional*, defaults to 1024):
|
||||
The dimension of the model.
|
||||
num_hidden_layers (`int`, *optional*, defaults to 22):
|
||||
The number of transformer blocks in the DiT model.
|
||||
num_attention_heads (`int`, *optional*, defaults to 16):
|
||||
The number of attention heads in each transformer block.
|
||||
ff_mult (`int`, *optional*, defaults to 2):
|
||||
The multiplier for the feedforward layer in each transformer block.
|
||||
emb_dim (`int`, *optional*, defaults to 512):
|
||||
The dimension of the embedding layer.
|
||||
head_dim (`int`, *optional*, defaults to 64):
|
||||
The dimension of each attention head.
|
||||
repeats (`int`, *optional*, defaults to 2):
|
||||
The number of times the codec embeddings are repeated.
|
||||
num_embeds (`int`, *optional*, defaults to 8193):
|
||||
The number of unique embeddings in the codec.
|
||||
mel_dim (`int`, *optional*, defaults to 80):
|
||||
The dimension of the mel-spectrogram.
|
||||
dropout (`float`, *optional*, defaults to 0.1):
|
||||
The dropout rate for the transformer blocks.
|
||||
|
||||
enc_emb_dim (`int`, *optional*, defaults to 192):
|
||||
The dimension of the pre-trained speaker embedding.
|
||||
enc_dim (`int`, *optional*, defaults to 128):
|
||||
The dimension of the encoder output.
|
||||
enc_channels (`list[int]`, *optional*, defaults to `[256, 256, 256, 256, 768]`):
|
||||
A list of output channels for each TDNN/SERes2Net layer in the encoder.
|
||||
enc_kernel_sizes (`list[int]`, *optional*, defaults to `[5, 3, 3, 3, 1]`):
|
||||
A list of kernel sizes for each layer in the encoder.
|
||||
enc_dilations (`list[int]`, *optional*, defaults to `[1, 2, 3, 4, 1]`):
|
||||
A list of dilations for each layer in the encoder.
|
||||
enc_attention_channels (`int`, *optional*, defaults to 64):
|
||||
The number of attention channels in the SqueezeExcitationBlock.
|
||||
enc_res2net_scale (`int`, *optional*, defaults to 2):
|
||||
The scale of the Res2Net block in the encoder.
|
||||
enc_se_channels (`int`, *optional*, defaults to 64):
|
||||
The number of output channels after squeeze in the SqueezeExcitationBlock.
|
||||
"""
|
||||
|
||||
model_type = "qwen3_tts_tokenizer_v1_decoder_dit"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
hidden_size=1024,
|
||||
num_hidden_layers=22,
|
||||
num_attention_heads=16,
|
||||
ff_mult=2,
|
||||
emb_dim=512,
|
||||
head_dim=64,
|
||||
rope_theta=10000.0,
|
||||
max_position_embeddings=32768,
|
||||
block_size=24,
|
||||
look_ahead_layers=[10],
|
||||
look_backward_layers=[0, 20],
|
||||
repeats=2,
|
||||
num_embeds=8193,
|
||||
mel_dim=80,
|
||||
dropout=0.1,
|
||||
enc_emb_dim=192,
|
||||
enc_dim=128,
|
||||
enc_channels=[256, 256, 256, 256, 768],
|
||||
enc_kernel_sizes=[5, 3, 3, 3, 1],
|
||||
enc_dilations=[1, 2, 3, 4, 1],
|
||||
enc_attention_channels=64,
|
||||
enc_res2net_scale=2,
|
||||
enc_se_channels=64,
|
||||
**kwargs,
|
||||
):
|
||||
self.hidden_size = hidden_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.ff_mult = ff_mult
|
||||
self.emb_dim = emb_dim
|
||||
self.head_dim = head_dim
|
||||
self.rope_theta = rope_theta
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.block_size = block_size
|
||||
self.look_ahead_layers = look_ahead_layers
|
||||
self.look_backward_layers = look_backward_layers
|
||||
self.repeats = repeats
|
||||
self.num_embeds = num_embeds
|
||||
self.mel_dim = mel_dim
|
||||
self.dropout = dropout
|
||||
self.enc_emb_dim = enc_emb_dim
|
||||
self.enc_dim = enc_dim
|
||||
self.enc_channels = enc_channels
|
||||
self.enc_kernel_sizes = enc_kernel_sizes
|
||||
self.enc_dilations = enc_dilations
|
||||
self.enc_attention_channels = enc_attention_channels
|
||||
self.enc_res2net_scale = enc_res2net_scale
|
||||
self.enc_se_channels = enc_se_channels
|
||||
super().__init__(**kwargs)
|
||||
|
||||
|
||||
class Qwen3TTSTokenizerV1DecoderBigVGANConfig(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of the Qwen3TTSTokenizerV1DecoderToken2WavBigVGAN module.
|
||||
It defines the architecture of the BigVGAN model, which is used for converting mel-spectrograms to waveforms.
|
||||
|
||||
Args:
|
||||
mel_dim (`int`, *optional*, defaults to 80):
|
||||
The dimension of the mel-spectrogram.
|
||||
upsample_initial_channel (`int`, *optional*, defaults to 1536):
|
||||
The number of channels in the initial upsampling layer.
|
||||
resblock_kernel_sizes (`list[int]`, *optional*, defaults to `[3, 7, 11]`):
|
||||
A list of kernel sizes for each residual block.
|
||||
resblock_dilation_sizes (`list[list[int]]`, *optional*, defaults to `[[1, 3, 5], [1, 3, 5], [1, 3, 5]]`):
|
||||
A list of dilation sizes for each residual block.
|
||||
upsample_rates (`list[int]`, *optional*, defaults to `[5, 3, 2, 2, 2, 2]`):
|
||||
A list of upsampling rates for each upsampling layer.
|
||||
upsample_kernel_sizes (`list[int]`, *optional*, defaults to `[11, 7, 4, 4, 4, 4]`):
|
||||
A list of kernel sizes for each upsampling layer.
|
||||
"""
|
||||
|
||||
model_type = "qwen3_tts_tokenizer_v1_decoder_bigvgan"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
mel_dim=80,
|
||||
upsample_initial_channel=1536,
|
||||
resblock_kernel_sizes=[3, 7, 11],
|
||||
resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
|
||||
upsample_rates=[5, 3, 2, 2, 2, 2],
|
||||
upsample_kernel_sizes=[11, 7, 4, 4, 4, 4],
|
||||
**kwargs,
|
||||
):
|
||||
self.mel_dim = mel_dim
|
||||
self.upsample_initial_channel = upsample_initial_channel
|
||||
self.resblock_kernel_sizes = resblock_kernel_sizes
|
||||
self.resblock_dilation_sizes = resblock_dilation_sizes
|
||||
self.upsample_rates = upsample_rates
|
||||
self.upsample_kernel_sizes = upsample_kernel_sizes
|
||||
super().__init__(**kwargs)
|
||||
|
||||
|
||||
class Qwen3TTSTokenizerV1DecoderConfig(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of a [`Qwen3TTSTokenizerV1DecoderConfig`].
|
||||
|
||||
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||
documentation from [`PretrainedConfig`] for more information.
|
||||
|
||||
Args:
|
||||
dit_config ([`DiT_Args`], *optional*):
|
||||
Configuration class for the Diffusion Transformer (DiT) module responsible for generating mel-spectrograms.
|
||||
bigvgan_config ([`BigVGAN_Args`], *optional*):
|
||||
Configuration class for the BigVGAN module responsible for converting mel-spectrograms to waveforms.
|
||||
"""
|
||||
|
||||
model_type = "qwen3_tts_tokenizer_v1_decoder"
|
||||
sub_configs = {
|
||||
"dit_config": Qwen3TTSTokenizerV1DecoderDiTConfig,
|
||||
"bigvgan_config": Qwen3TTSTokenizerV1DecoderBigVGANConfig,
|
||||
}
|
||||
|
||||
def __init__(self, dit_config=None, bigvgan_config=None, **kwargs):
|
||||
if dit_config is None:
|
||||
dit_config = {}
|
||||
if bigvgan_config is None:
|
||||
bigvgan_config = {}
|
||||
self.dit_config = Qwen3TTSTokenizerV1DecoderDiTConfig(**dit_config)
|
||||
self.bigvgan_config = Qwen3TTSTokenizerV1DecoderBigVGANConfig(**bigvgan_config)
|
||||
super().__init__(**kwargs)
|
||||
|
||||
|
||||
class Qwen3TTSTokenizerV1EncoderConfig(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of the Qwen3TTSTokenizerV1 Encoder.
|
||||
|
||||
The encoder typically takes mel-spectrogram features and produces high-level audio representations, then (optionally)
|
||||
applies an Audio-VQ module (e.g., GRVQ) to discretize continuous representations into codes.
|
||||
|
||||
Args:
|
||||
n_mels (`int`, *optional*, defaults to 128):
|
||||
Number of mel bins in the input mel-spectrogram.
|
||||
n_ctx (`int`, *optional*, defaults to 1500):
|
||||
Maximum input sequence length (in frames/tokens) for the encoder.
|
||||
n_state (`int`, *optional*, defaults to 1280):
|
||||
Hidden size (model dimension) of the encoder transformer.
|
||||
n_head (`int`, *optional*, defaults to 20):
|
||||
Number of attention heads in each transformer layer.
|
||||
n_layer (`int`, *optional*, defaults to 32):
|
||||
Number of transformer layers.
|
||||
n_window (`int`, *optional*, defaults to 100):
|
||||
Window size used by the model for local attention / chunking (implementation-dependent).
|
||||
output_dim (`int`, *optional*, defaults to 3584):
|
||||
Output feature dimension produced by the encoder head (before/after projection, implementation-dependent).
|
||||
|
||||
grad_checkpointing (`bool`, *optional*, defaults to `False`):
|
||||
Whether to enable gradient checkpointing to reduce memory usage during training.
|
||||
enable_mp (`bool`, *optional*, defaults to `False`):
|
||||
Whether to enable model parallel features (implementation-dependent).
|
||||
audio_sequence_parallel (`bool`, *optional*, defaults to `False`):
|
||||
Whether to enable sequence parallelism for audio branch (implementation-dependent).
|
||||
|
||||
audio_vq_type (`str`, *optional*, defaults to `"GRVQ"`):
|
||||
Type of audio vector-quantization module. Common choices: `"GRVQ"`, `"RVQ"`, etc.
|
||||
audio_vq_layers (`int`, *optional*, defaults to 6):
|
||||
Number of VQ layers / quantizers (e.g., number of residual quantizers for RVQ/GRVQ-like designs).
|
||||
audio_vq_codebook_size (`int`, *optional*, defaults to 32768):
|
||||
Size of each codebook (number of entries).
|
||||
audio_vq_codebook_dim (`int`, *optional*, defaults to 1280):
|
||||
Dimension of codebook vectors (often equals encoder hidden size).
|
||||
audio_vq_pe (`bool`, *optional*, defaults to `True`):
|
||||
Whether to use positional encoding (or position embeddings) inside the VQ module.
|
||||
audio_vq_ds_rate (`int`, *optional*, defaults to 2):
|
||||
Downsampling rate applied before VQ (e.g., temporal downsample factor).
|
||||
"""
|
||||
|
||||
model_type = "qwen3_tts_tokenizer_v1_encoder"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
n_mels=128,
|
||||
n_ctx=1500,
|
||||
n_state=1280,
|
||||
n_head=20,
|
||||
n_layer=32,
|
||||
n_window=100,
|
||||
output_dim=3584,
|
||||
grad_checkpointing=False,
|
||||
enable_mp=False,
|
||||
audio_sequence_parallel=False,
|
||||
audio_vq_type="GRVQ",
|
||||
audio_vq_layers=6,
|
||||
audio_vq_codebook_size=32768,
|
||||
audio_vq_codebook_dim=1280,
|
||||
audio_vq_pe=True,
|
||||
audio_vq_ds_rate=2,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
self.n_mels = n_mels
|
||||
self.n_ctx = n_ctx
|
||||
self.n_state = n_state
|
||||
self.n_head = n_head
|
||||
self.n_layer = n_layer
|
||||
self.n_window = n_window
|
||||
self.output_dim = output_dim
|
||||
self.grad_checkpointing = grad_checkpointing
|
||||
self.enable_mp = enable_mp
|
||||
self.audio_sequence_parallel = audio_sequence_parallel
|
||||
self.audio_vq_type = audio_vq_type
|
||||
self.audio_vq_layers = audio_vq_layers
|
||||
self.audio_vq_codebook_size = audio_vq_codebook_size
|
||||
self.audio_vq_codebook_dim = audio_vq_codebook_dim
|
||||
self.audio_vq_pe = audio_vq_pe
|
||||
self.audio_vq_ds_rate = audio_vq_ds_rate
|
||||
|
||||
|
||||
class Qwen3TTSTokenizerV1Config(PretrainedConfig):
|
||||
"""
|
||||
This is the configuration class to store the configuration of a [`Qwen3TTSTokenizerV1Config`]. It is used to instantiate a Qwen3TTSTokenizerV1Model
|
||||
model according to the specified sub-models configurations, defining the model architecture.
|
||||
|
||||
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||
documentation from [`PretrainedConfig`] for more information.
|
||||
|
||||
Args:
|
||||
encoder_config (`dict`, *optional*): Configuration of the underlying encoder sub-model.
|
||||
decoder_config (`dict`, *optional*): Configuration of the underlying decoder sub-model.
|
||||
"""
|
||||
|
||||
model_type = "qwen3_tts_tokenizer_25hz"
|
||||
sub_configs = {
|
||||
"encoder_config": Qwen3TTSTokenizerV1EncoderConfig,
|
||||
"decoder_config": Qwen3TTSTokenizerV1DecoderConfig,
|
||||
}
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
encoder_config=None,
|
||||
decoder_config=None,
|
||||
input_sample_rate=24000,
|
||||
output_sample_rate=24000,
|
||||
decode_upsample_rate=1920,
|
||||
encode_downsample_rate=1920,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
if encoder_config is None:
|
||||
encoder_config = {}
|
||||
logger.info("encoder_config is None. Initializing encoder with default values")
|
||||
if decoder_config is None:
|
||||
decoder_config = {}
|
||||
logger.info("decoder_config is None. Initializing decoder with default values")
|
||||
|
||||
self.encoder_config = Qwen3TTSTokenizerV1EncoderConfig(**encoder_config)
|
||||
self.decoder_config = Qwen3TTSTokenizerV1DecoderConfig(**decoder_config)
|
||||
|
||||
self.input_sample_rate = input_sample_rate
|
||||
self.output_sample_rate = output_sample_rate
|
||||
self.decode_upsample_rate = decode_upsample_rate
|
||||
self.encode_downsample_rate = encode_downsample_rate
|
||||
|
||||
|
||||
__all__ = [
|
||||
"Qwen3TTSTokenizerV1Config",
|
||||
"Qwen3TTSTokenizerV1EncoderConfig",
|
||||
"Qwen3TTSTokenizerV1DecoderConfig",
|
||||
"Qwen3TTSTokenizerV1DecoderBigVGANConfig",
|
||||
"Qwen3TTSTokenizerV1DecoderDiTConfig"
|
||||
]
|
||||
File diff suppressed because it is too large
Load Diff
Binary file not shown.
523
models/Qwen3-TTS/qwen_tts/core/tokenizer_25hz/vq/core_vq.py
Normal file
523
models/Qwen3-TTS/qwen_tts/core/tokenizer_25hz/vq/core_vq.py
Normal file
@@ -0,0 +1,523 @@
|
||||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the license found in the
|
||||
# LICENSE file in the root directory of this source tree.
|
||||
#
|
||||
# This implementation is inspired from
|
||||
# https://github.com/lucidrains/vector-quantize-pytorch
|
||||
# which is released under MIT License. Hereafter, the original license:
|
||||
# MIT License
|
||||
#
|
||||
# Copyright (c) 2020 Phil Wang
|
||||
#
|
||||
# Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
# of this software and associated documentation files (the "Software"), to deal
|
||||
# in the Software without restriction, including without limitation the rights
|
||||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
# copies of the Software, and to permit persons to whom the Software is
|
||||
# furnished to do so, subject to the following conditions:
|
||||
#
|
||||
# The above copyright notice and this permission notice shall be included in all
|
||||
# copies or substantial portions of the Software.
|
||||
#
|
||||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
# SOFTWARE.
|
||||
|
||||
"""Core vector quantization implementation."""
|
||||
import random
|
||||
import typing as tp
|
||||
from random import randrange
|
||||
|
||||
import numpy as np
|
||||
from einops import rearrange, repeat
|
||||
from math import ceil
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
def round_up_multiple(num, mult):
|
||||
return ceil(num / mult) * mult
|
||||
|
||||
def default(val: tp.Any, d: tp.Any) -> tp.Any:
|
||||
return val if val is not None else d
|
||||
|
||||
|
||||
def ema_inplace(moving_avg, new, decay: float):
|
||||
moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay))
|
||||
|
||||
|
||||
def laplace_smoothing(x, n_categories: int, epsilon: float = 1e-5):
|
||||
return (x + epsilon) / (x.sum() + n_categories * epsilon)
|
||||
|
||||
|
||||
def uniform_init(*shape: int):
|
||||
t = torch.empty(shape)
|
||||
nn.init.kaiming_uniform_(t)
|
||||
return t
|
||||
|
||||
|
||||
def sample_vectors(samples, num: int):
|
||||
num_samples, device = samples.shape[0], samples.device
|
||||
|
||||
if num_samples >= num:
|
||||
indices = torch.randperm(num_samples, device=device)[:num]
|
||||
else:
|
||||
indices = torch.randint(0, num_samples, (num,), device=device)
|
||||
|
||||
return samples[indices]
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def kmeans(samples, num_clusters: int, num_iters: int = 10):
|
||||
dim, dtype = samples.shape[-1], samples.dtype
|
||||
|
||||
means = sample_vectors(samples, num_clusters)
|
||||
|
||||
for _ in range(num_iters):
|
||||
dists = -(
|
||||
samples.pow(2).sum(1, keepdim=True)
|
||||
- 2 * torch.matmul(samples, means.t())
|
||||
+ means.t().pow(2).sum(0, keepdim=True)
|
||||
)
|
||||
|
||||
buckets = dists.max(dim=-1).indices
|
||||
del dists
|
||||
bins = torch.bincount(buckets, minlength=num_clusters)
|
||||
zero_mask = bins == 0
|
||||
bins_min_clamped = bins.masked_fill(zero_mask, 1)
|
||||
|
||||
new_means = buckets.new_zeros(num_clusters, dim, dtype=dtype)
|
||||
new_means.scatter_add_(0, repeat(buckets, "n -> n d", d=dim), samples)
|
||||
new_means = new_means / bins_min_clamped[..., None]
|
||||
|
||||
means = torch.where(zero_mask[..., None], means, new_means)
|
||||
return means, bins
|
||||
|
||||
|
||||
def preprocess(x):
|
||||
x = rearrange(x, "... d -> (...) d")
|
||||
return x
|
||||
|
||||
|
||||
def postprocess_emb(embed_ind, shape):
|
||||
return embed_ind.view(*shape[:-1])
|
||||
|
||||
|
||||
class EuclideanCodebook(nn.Module):
|
||||
"""Codebook with Euclidean distance.
|
||||
Args:
|
||||
dim (int): Dimension.
|
||||
codebook_size (int): Codebook size.
|
||||
kmeans_init (bool): Whether to use k-means to initialize the codebooks.
|
||||
If set to true, run the k-means algorithm on the first training batch and use
|
||||
the learned centroids as initialization.
|
||||
kmeans_iters (int): Number of iterations used for k-means algorithm at initialization.
|
||||
decay (float): Decay for exponential moving average over the codebooks.
|
||||
epsilon (float): Epsilon value for numerical stability.
|
||||
threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
|
||||
that have an exponential moving average cluster size less than the specified threshold with
|
||||
randomly selected vector from the current batch.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
codebook_size: int,
|
||||
kmeans_init: int = False,
|
||||
kmeans_iters: int = 10,
|
||||
decay: float = 0.99,
|
||||
epsilon: float = 1e-5,
|
||||
threshold_ema_dead_code: float = 2.0,
|
||||
):
|
||||
super().__init__()
|
||||
self.decay = decay
|
||||
self.codebook_size = codebook_size
|
||||
self.kmeans_iters = kmeans_iters
|
||||
self.epsilon = epsilon
|
||||
self.threshold_ema_dead_code = threshold_ema_dead_code
|
||||
|
||||
self.inited = None
|
||||
self.cluster_size = None
|
||||
self.embed = None
|
||||
self.embed_avg = None
|
||||
self.training = True
|
||||
|
||||
def init_embed_(self, data):
|
||||
if self.inited:
|
||||
return
|
||||
|
||||
embed, cluster_size = kmeans(data, self.codebook_size, self.kmeans_iters)
|
||||
self.embed.data.copy_(embed)
|
||||
self.embed_avg.data.copy_(embed.clone())
|
||||
self.cluster_size.data.copy_(cluster_size)
|
||||
self.inited.data.copy_(torch.Tensor([True]))
|
||||
# Make sure all buffers across workers are in sync after initialization
|
||||
# distrib.broadcast_tensors([self.embed, self.embed_avg, self.cluster_size, self.inited])
|
||||
|
||||
def replace_(self, samples, mask):
|
||||
modified_codebook = torch.where(
|
||||
mask[..., None], sample_vectors(samples, self.codebook_size), self.embed
|
||||
)
|
||||
self.embed.data.copy_(modified_codebook)
|
||||
|
||||
def expire_codes_(self, batch_samples):
|
||||
if self.threshold_ema_dead_code == 0:
|
||||
return
|
||||
|
||||
cluster_size = self.cluster_size / sum(self.cluster_size) * self.codebook_size
|
||||
expired_codes = cluster_size < self.threshold_ema_dead_code
|
||||
if not torch.any(expired_codes):
|
||||
return
|
||||
else:
|
||||
print(f"VQ expire infos: num_expire={sum(expired_codes)}, cluster_size[:5]={cluster_size[:5]}")
|
||||
|
||||
batch_samples = rearrange(batch_samples, "... d -> (...) d")
|
||||
self.replace_(batch_samples, mask=expired_codes)
|
||||
# sync buffers outside for efficiency
|
||||
# distrib.broadcast_tensors(self.buffers())
|
||||
|
||||
def quantize(self, x):
|
||||
embed = self.embed.t()
|
||||
dist = -(
|
||||
x.pow(2).sum(1, keepdim=True)
|
||||
- 2 * x @ embed
|
||||
+ embed.pow(2).sum(0, keepdim=True)
|
||||
)
|
||||
embed_ind = dist.max(dim=-1).indices
|
||||
return embed_ind
|
||||
|
||||
def dequantize(self, embed_ind):
|
||||
quantize = F.embedding(embed_ind, self.embed)
|
||||
return quantize
|
||||
|
||||
def encode(self, x, buffers):
|
||||
self.inited, self.cluster_size, self.embed, self.embed_avg = buffers
|
||||
|
||||
shape = x.shape
|
||||
# pre-process
|
||||
x = preprocess(x)
|
||||
# quantize
|
||||
embed_ind = self.quantize(x)
|
||||
# post-process
|
||||
embed_ind = postprocess_emb(embed_ind, shape)
|
||||
return embed_ind
|
||||
|
||||
def decode(self, embed_ind, buffers):
|
||||
self.inited, self.cluster_size, self.embed, self.embed_avg = buffers
|
||||
|
||||
quantize = self.dequantize(embed_ind)
|
||||
return quantize
|
||||
|
||||
def forward(self, x, buffers):
|
||||
self.inited, self.cluster_size, self.embed, self.embed_avg = buffers
|
||||
|
||||
shape, dtype = x.shape, x.dtype
|
||||
x = preprocess(x)
|
||||
|
||||
self.init_embed_(x)
|
||||
if self.training:
|
||||
# We do the expiry of code at that point as buffers are in sync
|
||||
# and all the workers will take the same decision.
|
||||
self.expire_codes_(x)
|
||||
|
||||
embed_ind = self.quantize(x)
|
||||
embed_onehot = F.one_hot(embed_ind, self.codebook_size).type(dtype)
|
||||
embed_ind = postprocess_emb(embed_ind, shape)
|
||||
quantize = self.dequantize(embed_ind)
|
||||
|
||||
if self.training:
|
||||
ema_inplace(self.cluster_size, embed_onehot.sum(0), self.decay)
|
||||
embed_sum = x.t() @ embed_onehot
|
||||
ema_inplace(self.embed_avg, embed_sum.t(), self.decay)
|
||||
cluster_size = (
|
||||
laplace_smoothing(self.cluster_size, self.codebook_size, self.epsilon)
|
||||
* self.cluster_size.sum()
|
||||
)
|
||||
embed_normalized = self.embed_avg / cluster_size.unsqueeze(1)
|
||||
self.embed.data.copy_(embed_normalized)
|
||||
# Note: after ema update, there is a very small difference between codebooks on GPUs.
|
||||
# The impact can be very small, ignore it.
|
||||
|
||||
return quantize, embed_ind
|
||||
|
||||
|
||||
class VectorQuantization(nn.Module):
|
||||
"""Vector quantization implementation.
|
||||
Currently, supports only euclidean distance.
|
||||
Args:
|
||||
dim (int): Dimension
|
||||
codebook_size (int): Codebook size
|
||||
codebook_dim (int): Codebook dimension. If not defined, uses the specified dimension in dim.
|
||||
decay (float): Decay for exponential moving average over the codebooks.
|
||||
epsilon (float): Epsilon value for numerical stability.
|
||||
kmeans_init (bool): Whether to use kmeans to initialize the codebooks.
|
||||
kmeans_iters (int): Number of iterations used for kmeans initialization.
|
||||
threshold_ema_dead_code (int): Threshold for dead code expiration. Replace any codes
|
||||
that have an exponential moving average cluster size less than the specified threshold with
|
||||
randomly selected vector from the current batch.
|
||||
commitment_weight (float): Weight for commitment loss.
|
||||
"""
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
codebook_size: int,
|
||||
codebook_dim: tp.Optional[int] = None,
|
||||
decay: float = 0.99,
|
||||
epsilon: float = 1e-5,
|
||||
kmeans_init: bool = True,
|
||||
kmeans_iters: int = 50,
|
||||
threshold_ema_dead_code: float = 2.0,
|
||||
commitment_weight: float = 1.,
|
||||
):
|
||||
super().__init__()
|
||||
_codebook_dim: int = default(codebook_dim, dim)
|
||||
|
||||
requires_projection = _codebook_dim != dim
|
||||
self.project_in = (nn.Linear(dim, _codebook_dim)) if requires_projection else (nn.Identity())
|
||||
self.project_out = (nn.Linear(_codebook_dim, dim)) if requires_projection else (nn.Identity())
|
||||
|
||||
self.epsilon = epsilon
|
||||
self.commitment_weight = commitment_weight
|
||||
|
||||
self._codebook = EuclideanCodebook(dim=_codebook_dim, codebook_size=codebook_size,
|
||||
kmeans_init=kmeans_init, kmeans_iters=kmeans_iters,
|
||||
decay=decay, epsilon=epsilon,
|
||||
threshold_ema_dead_code=threshold_ema_dead_code)
|
||||
self.codebook_size = codebook_size
|
||||
self.training = True
|
||||
|
||||
@property
|
||||
def codebook(self):
|
||||
return self._codebook.embed
|
||||
|
||||
def encode(self, x, buffers):
|
||||
# x = rearrange(x, "b d n -> b n d")
|
||||
x = self.project_in(x)
|
||||
embed_in = self._codebook.encode(x, buffers)
|
||||
return embed_in
|
||||
|
||||
def decode(self, embed_ind, buffers):
|
||||
quantize = self._codebook.decode(embed_ind, buffers)
|
||||
quantize = self.project_out(quantize)
|
||||
# quantize = rearrange(quantize, "b n d -> b d n")
|
||||
return quantize
|
||||
|
||||
def forward(self, x, buffers):
|
||||
device = x.device
|
||||
# x = rearrange(x, "b d n -> b n d")
|
||||
x = self.project_in(x)
|
||||
|
||||
quantize, embed_ind = self._codebook(x, buffers)
|
||||
|
||||
if self.training:
|
||||
quantize = x + (quantize - x).detach()
|
||||
|
||||
loss = torch.tensor([0.0], device=device, requires_grad=self.training)
|
||||
|
||||
if self.training:
|
||||
if self.commitment_weight > 0:
|
||||
commit_loss = F.mse_loss(quantize.detach(), x)
|
||||
loss = loss + commit_loss * self.commitment_weight
|
||||
|
||||
quantize = self.project_out(quantize)
|
||||
# quantize = rearrange(quantize, "b n d -> b d n")
|
||||
return quantize, embed_ind, loss
|
||||
|
||||
|
||||
class DistributedResidualVectorQuantization(nn.Module):
|
||||
"""Efficient distributed residual vector quantization implementation.
|
||||
Follows Algorithm 1. in https://arxiv.org/pdf/2107.03312.pdf
|
||||
"""
|
||||
def __init__(self, *,
|
||||
num_quantizers,
|
||||
quantize_dropout: bool = False,
|
||||
rand_num_quant: tp.Optional[tp.List] = None,
|
||||
**kwargs):
|
||||
super().__init__()
|
||||
"""
|
||||
dim: int,
|
||||
codebook_size: int,
|
||||
codebook_dim: tp.Optional[int] = None,
|
||||
"""
|
||||
codebook_size, codebook_dim = kwargs["codebook_size"], kwargs["codebook_dim"] if kwargs["codebook_dim"] else kwargs["dim"]
|
||||
kmeans_init = kwargs["kmeans_init"]
|
||||
if isinstance(kmeans_init, bool):
|
||||
if not kwargs["kmeans_init"]:
|
||||
# use uniform init
|
||||
embed = uniform_init(num_quantizers, codebook_size, codebook_dim)
|
||||
inited = True
|
||||
else:
|
||||
# to perform kmeans init on first batch
|
||||
embed = torch.zeros(num_quantizers, codebook_size, codebook_dim)
|
||||
inited = False
|
||||
elif isinstance(kmeans_init, str):
|
||||
# use prepared kmeans init
|
||||
embed = np.load(kmeans_init)
|
||||
embed = torch.from_numpy(embed)
|
||||
if embed.dim() == 2:
|
||||
embed = embed.unsqueeze(0)
|
||||
inited = True
|
||||
else:
|
||||
raise TypeError("kmeans_init should be either a bool or string path to init weights.")
|
||||
|
||||
self.register_buffer("inited", torch.Tensor([[inited] for _ in range(num_quantizers)]))
|
||||
self.register_buffer("cluster_size", torch.zeros(num_quantizers, codebook_size))
|
||||
self.register_buffer("embed", embed)
|
||||
self.register_buffer("embed_avg", embed.clone())
|
||||
|
||||
self.q0_ds_ratio = 1
|
||||
if "q0_ds_ratio" in kwargs:
|
||||
self.q0_ds_ratio = kwargs.pop("q0_ds_ratio")
|
||||
|
||||
self.layers = nn.ModuleList()
|
||||
for i in range(num_quantizers):
|
||||
vq_args = dict(**kwargs)
|
||||
vq = VectorQuantization(**vq_args)
|
||||
self.layers.append(vq)
|
||||
|
||||
self.quantize_dropout = quantize_dropout
|
||||
self.rand_num_quant = rand_num_quant
|
||||
|
||||
def forward(self, x, n_q: tp.Optional[int] = None):
|
||||
quantized_out = torch.zeros_like(x)
|
||||
residual = x
|
||||
bb, cc, tt = x.shape
|
||||
device = x.device
|
||||
|
||||
all_losses = []
|
||||
all_indices = []
|
||||
all_sub_quants = []
|
||||
n_q = n_q or len(self.layers)
|
||||
|
||||
should_quantize_dropout = self.training and self.quantize_dropout and self.rand_num_quant is not None
|
||||
if should_quantize_dropout:
|
||||
rand_quantize_dropout_index = random.choice(self.rand_num_quant)
|
||||
|
||||
null_indices_shape = (x.shape[0], x.shape[2])
|
||||
null_indices = torch.full(null_indices_shape, -1., device=device, dtype=torch.long)
|
||||
null_loss = torch.full((1,), 0., device=device, dtype=x.dtype)
|
||||
null_sub_quant = torch.full(x.shape, -1, device=device, dtype=x.dtype)
|
||||
|
||||
for quantizer_index, layer in enumerate(self.layers[:n_q]):
|
||||
# dropout except the first quantizer
|
||||
if should_quantize_dropout and quantizer_index >= rand_quantize_dropout_index:
|
||||
all_indices.append(null_indices)
|
||||
all_losses.append(null_loss)
|
||||
all_sub_quants.append(null_sub_quant)
|
||||
continue
|
||||
|
||||
quant_in = residual
|
||||
if self.q0_ds_ratio > 1 and quantizer_index == 0:
|
||||
quant_in = F.interpolate(quant_in, size=[tt//2])
|
||||
quantized, indices, loss = layer(quant_in, [
|
||||
self.inited[quantizer_index],
|
||||
self.cluster_size[quantizer_index],
|
||||
self.embed[quantizer_index],
|
||||
self.embed_avg[quantizer_index]
|
||||
])
|
||||
if self.q0_ds_ratio > 1 and quantizer_index == 0:
|
||||
quantized = F.interpolate(quantized, size=[tt])
|
||||
indices = F.interpolate(indices.unsqueeze(1).float(), size=[tt]).squeeze(1).long()
|
||||
residual = residual - quantized
|
||||
quantized_out = quantized_out + quantized
|
||||
|
||||
all_indices.append(indices)
|
||||
all_losses.append(loss)
|
||||
all_sub_quants.append(quantized)
|
||||
|
||||
# sync buffers after one forward step
|
||||
# distrib.broadcast_tensors(self.buffers())
|
||||
out_losses, out_indices, out_sub_quants = map(torch.stack, (all_losses, all_indices, all_sub_quants))
|
||||
|
||||
return quantized_out, out_indices, out_losses
|
||||
|
||||
def encode(self, x: torch.Tensor, n_q: tp.Optional[int] = None) -> torch.Tensor:
|
||||
residual = x
|
||||
all_indices = []
|
||||
n_q = n_q or len(self.layers)
|
||||
for i, layer in enumerate(self.layers[:n_q]):
|
||||
indices = layer.encode(residual, [
|
||||
self.inited[i],
|
||||
self.cluster_size[i],
|
||||
self.embed[i],
|
||||
self.embed_avg[i]
|
||||
])
|
||||
quantized = layer.decode(indices, [
|
||||
self.inited[i],
|
||||
self.cluster_size[i],
|
||||
self.embed[i],
|
||||
self.embed_avg[i]
|
||||
])
|
||||
residual = residual - quantized
|
||||
all_indices.append(indices)
|
||||
out_indices = torch.stack(all_indices)
|
||||
return out_indices
|
||||
|
||||
def decode(self, q_indices: torch.Tensor) -> torch.Tensor:
|
||||
quantized_out = torch.tensor(0.0, device=q_indices.device)
|
||||
for i, indices in enumerate(q_indices):
|
||||
layer = self.layers[i]
|
||||
quantized = layer.decode(indices, [
|
||||
self.inited[i],
|
||||
self.cluster_size[i],
|
||||
self.embed[i],
|
||||
self.embed_avg[i]
|
||||
])
|
||||
quantized_out = quantized_out + quantized
|
||||
return quantized_out
|
||||
|
||||
|
||||
class DistributedGroupResidualVectorQuantization(nn.Module):
|
||||
"""Efficient distributed group residual vector quantization implementation.
|
||||
Follows Algorithm 1. in https://arxiv.org/abs/2305.02765
|
||||
Group Then rvq
|
||||
"""
|
||||
def __init__(self, *,
|
||||
num_groups,
|
||||
num_quantizers,
|
||||
quantize_dropout: bool = False,
|
||||
rand_num_quant: tp.Optional[tp.List] = None,
|
||||
**kwargs):
|
||||
super().__init__()
|
||||
self.rvqs = nn.ModuleList(
|
||||
[
|
||||
DistributedResidualVectorQuantization(
|
||||
num_quantizers=num_quantizers,
|
||||
quantize_dropout=quantize_dropout,
|
||||
rand_num_quant=rand_num_quant,
|
||||
**kwargs
|
||||
)
|
||||
for _ in range(num_groups)
|
||||
]
|
||||
)
|
||||
self.num_groups = num_groups
|
||||
|
||||
def forward(self, x, n_q: tp.Optional[int] = None):
|
||||
x_lst = torch.chunk(x, chunks=self.num_groups, dim=1)
|
||||
all_quantized_out = []
|
||||
all_indices = []
|
||||
all_losses = []
|
||||
for mod, item in zip(self.rvqs, x_lst):
|
||||
quantized_out, out_indices, out_losses = mod(item, n_q)
|
||||
all_quantized_out.append(quantized_out)
|
||||
all_indices.append(out_indices)
|
||||
all_losses.append(out_losses)
|
||||
|
||||
out_losses = torch.stack(all_losses, dim=1).mean(dim=1)
|
||||
|
||||
return torch.cat(all_quantized_out, dim=1), torch.stack(all_indices, dim=1), out_losses
|
||||
|
||||
def encode(self, x: torch.Tensor, n_q: tp.Optional[int] = None) -> torch.Tensor:
|
||||
x_lst = torch.chunk(x, chunks=self.num_groups, dim=1)
|
||||
return torch.stack([mod.encode(item, n_q) for mod, item in zip(self.rvqs, x_lst)], dim=1)
|
||||
|
||||
def decode(self, q_indices: torch.Tensor) -> torch.Tensor:
|
||||
q_indices_lst = torch.chunk(q_indices, chunks=self.num_groups, dim=1)
|
||||
return torch.cat([mod.decode(item.squeeze(1)) for mod, item in zip(self.rvqs, q_indices_lst)], dim=1)
|
||||
357
models/Qwen3-TTS/qwen_tts/core/tokenizer_25hz/vq/speech_vq.py
Normal file
357
models/Qwen3-TTS/qwen_tts/core/tokenizer_25hz/vq/speech_vq.py
Normal file
@@ -0,0 +1,357 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2026 The Alibaba Qwen team.
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
import sox
|
||||
import copy
|
||||
import torch
|
||||
import operator
|
||||
import onnxruntime
|
||||
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torchaudio.compliance.kaldi as kaldi
|
||||
|
||||
from librosa.filters import mel as librosa_mel_fn
|
||||
from itertools import accumulate
|
||||
from typing import List
|
||||
from torch import Tensor
|
||||
|
||||
from .core_vq import DistributedGroupResidualVectorQuantization
|
||||
from .whisper_encoder import WhisperEncoder, Conv1d, ConvTranspose1d
|
||||
|
||||
|
||||
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
|
||||
return torch.log(torch.clamp(x, min=clip_val) * C)
|
||||
|
||||
def spectral_normalize_torch(magnitudes):
|
||||
output = dynamic_range_compression_torch(magnitudes)
|
||||
return output
|
||||
|
||||
class MelSpectrogramFeatures(nn.Module):
|
||||
"""
|
||||
Calculate the BigVGAN style mel spectrogram of an input signal.
|
||||
Args:
|
||||
filter_length (int): The number of samples in the filter window, used for the Fourier Transform. Default is 1024.
|
||||
hop_length (int): The number of samples between successive frames (stride of the STFT). Default is 160.
|
||||
win_length (int): The length of the window function applied to each frame, usually less than or equal to the filter length. Default is 640.
|
||||
n_mel_channels (int): The number of Mel-frequency channels to output from the Mel-scale spectrogram. Default is 80.
|
||||
mel_fmin (int): The minimum frequency (in Hz) of the Mel-scale spectrogram. Default is 0.
|
||||
mel_fmax (int): The maximum frequency (in Hz) of the Mel-scale spectrogram. Default is 8000.
|
||||
sampling_rate (int): The sampling rate of the audio data (in Hz). Default is 16000.
|
||||
sampling_rate_org (int, optional): The original sampling rate of the audio data before any resampling (in Hz), if applicable. Default is None.
|
||||
padding (str): The padding mode for the input signal. 'center' pads the signal symmetrically around its center. Default is 'center'.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: Mel spectrogram.
|
||||
"""
|
||||
def __init__(self,
|
||||
filter_length=1024,
|
||||
hop_length=160,
|
||||
win_length=640,
|
||||
n_mel_channels=80,
|
||||
mel_fmin=0,
|
||||
mel_fmax=8000,
|
||||
sampling_rate=16000,
|
||||
sampling_rate_org=None,
|
||||
padding='center',
|
||||
use_db = False,
|
||||
):
|
||||
super().__init__()
|
||||
if padding not in ["center", "same"]:
|
||||
raise ValueError("Padding must be 'center' or 'same'.")
|
||||
self.padding = padding
|
||||
|
||||
self.filter_length = filter_length
|
||||
self.hop_length = hop_length
|
||||
self.win_length = win_length
|
||||
self.n_mel_channels = n_mel_channels
|
||||
self.mel_fmin = mel_fmin
|
||||
self.mel_fmax = mel_fmax
|
||||
self.sampling_rate = sampling_rate
|
||||
self.sampling_rate_org = sampling_rate_org if sampling_rate_org is not None else sampling_rate
|
||||
self.mel_basis = {}
|
||||
self.hann_window = {}
|
||||
|
||||
def forward(self, audio: torch.Tensor, **kwargs) -> torch.Tensor:
|
||||
with torch.no_grad():
|
||||
feats = self.extract(audio, **kwargs)
|
||||
return feats
|
||||
|
||||
def extract(self, audio, **kwargs):
|
||||
|
||||
if len(audio.shape) == 3:
|
||||
audio = audio.squeeze(1) if audio.shape[1] == 1 else audio.squeeze(2)
|
||||
assert len(audio.shape) == 2
|
||||
|
||||
y = audio
|
||||
if len(list(self.mel_basis.keys())) == 0:
|
||||
mel = librosa_mel_fn(sr=self.sampling_rate, n_fft=self.filter_length, n_mels=self.n_mel_channels, fmin=self.mel_fmin, fmax=self.mel_fmax)
|
||||
self.mel_basis[str(self.mel_fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
|
||||
self.hann_window[str(y.device)] = torch.hann_window(self.win_length).to(y.device)
|
||||
|
||||
y = torch.nn.functional.pad(y.unsqueeze(1), (int((self.filter_length-self.hop_length)/2), int((self.filter_length-self.hop_length)/2)), mode='reflect')
|
||||
y = y.squeeze(1)
|
||||
|
||||
spec = torch.stft(y, self.filter_length, hop_length=self.hop_length, win_length=self.win_length, window=self.hann_window[str(y.device)],
|
||||
center=False, pad_mode='reflect', normalized=False, onesided=True, return_complex=True)
|
||||
spec = torch.view_as_real(spec)
|
||||
spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
|
||||
|
||||
spec = torch.matmul(self.mel_basis[str(self.mel_fmax)+'_'+str(y.device)], spec)
|
||||
spec = spectral_normalize_torch(spec)
|
||||
|
||||
return spec
|
||||
|
||||
|
||||
class XVectorExtractor(nn.Module):
|
||||
def __init__(self, audio_codec_with_xvector):
|
||||
super().__init__()
|
||||
option = onnxruntime.SessionOptions()
|
||||
option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
|
||||
option.intra_op_num_threads = 1
|
||||
providers = ["CPUExecutionProvider"]
|
||||
self.ort_session = onnxruntime.InferenceSession(audio_codec_with_xvector, sess_options=option, providers=providers)
|
||||
|
||||
self.tfm = sox.Transformer()
|
||||
self.tfm.norm(db_level=-6)
|
||||
|
||||
self.mel_ext = MelSpectrogramFeatures(
|
||||
filter_length=1024,
|
||||
hop_length=160,
|
||||
win_length=640,
|
||||
n_mel_channels=80,
|
||||
mel_fmin=0,
|
||||
mel_fmax=8000,
|
||||
sampling_rate=16000
|
||||
)
|
||||
|
||||
def extract_code(self, audio):
|
||||
with torch.no_grad():
|
||||
norm_audio = self.sox_norm(audio)
|
||||
|
||||
norm_audio = torch.from_numpy(copy.deepcopy(norm_audio)).unsqueeze(0)
|
||||
feat = kaldi.fbank(norm_audio,
|
||||
num_mel_bins=80,
|
||||
dither=0,
|
||||
sample_frequency=16000)
|
||||
feat = feat - feat.mean(dim=0, keepdim=True)
|
||||
norm_embedding = self.ort_session.run(None, {self.ort_session.get_inputs()[0].name: feat.unsqueeze(dim=0).cpu().numpy()})[0].flatten()
|
||||
norm_embedding = F.normalize(torch.from_numpy(norm_embedding), dim=0)
|
||||
|
||||
ref_mel = self.mel_ext.extract(audio=norm_audio)
|
||||
|
||||
return norm_embedding.numpy(), ref_mel.permute(0,2,1).squeeze(0).numpy()
|
||||
|
||||
def sox_norm(self, audio):
|
||||
wav_norm = self.tfm.build_array(input_array=audio, sample_rate_in=16000)
|
||||
return wav_norm
|
||||
|
||||
|
||||
class WhisperEncoderVQ(WhisperEncoder):
|
||||
def __init__(
|
||||
self,
|
||||
n_mels: int,
|
||||
n_ctx: int,
|
||||
n_state: int,
|
||||
n_head: int,
|
||||
n_layer: int,
|
||||
n_window: int = 1500,
|
||||
output_dim: int = 512,
|
||||
grad_checkpointing: bool = False,
|
||||
enable_mp: bool = False,
|
||||
audio_sequence_parallel: bool = False,
|
||||
audio_vq_layers: int = -1,
|
||||
audio_vq_type: str = "NULL",
|
||||
audio_vq_codebook_size: int = 4096,
|
||||
audio_vq_pe: bool = False,
|
||||
audio_vq_commit_loss: float = 0.0,
|
||||
audio_vq_out_commit_loss: float = 0.0,
|
||||
audio_vq_no_quantize: bool = False,
|
||||
audio_vq_ff_layer: int = 0,
|
||||
audio_vq_threshold_ema_dead_code: float = 0.1,
|
||||
audio_vq_codebook_dim: int = None,
|
||||
audio_vq_ds_rate: int = None,
|
||||
):
|
||||
super().__init__(n_mels, n_ctx, n_state, n_head, n_layer, n_window, output_dim, grad_checkpointing, enable_mp, audio_sequence_parallel)
|
||||
|
||||
self.audio_vq_layers = audio_vq_layers
|
||||
self.audio_vq_type = audio_vq_type
|
||||
self.audio_vq_codebook_size = audio_vq_codebook_size
|
||||
self.audio_vq_pe = audio_vq_pe
|
||||
self.audio_vq_commit_loss = audio_vq_commit_loss
|
||||
self.audio_vq_out_commit_loss = audio_vq_out_commit_loss
|
||||
self.audio_vq_no_quantize = audio_vq_no_quantize
|
||||
self.audio_vq_ff_layer = audio_vq_ff_layer
|
||||
|
||||
if audio_vq_layers > 0:
|
||||
self.vq_feature_dim = self.n_state
|
||||
self.audio_vq_ds_rate = 1
|
||||
else:
|
||||
raise NotImplementedError(f"Unsupported audio_vq_layers: {audio_vq_layers}")
|
||||
|
||||
if self.audio_vq_ds_rate == audio_vq_ds_rate:
|
||||
self.audio_vq_downsample = nn.Identity()
|
||||
self.audio_vq_upsample = nn.Identity()
|
||||
else:
|
||||
assert audio_vq_ds_rate % self.audio_vq_ds_rate == 0
|
||||
stride = audio_vq_ds_rate // self.audio_vq_ds_rate
|
||||
self.audio_vq_downsample = Conv1d(self.vq_feature_dim, self.vq_feature_dim, kernel_size=stride, stride=stride)
|
||||
self.audio_vq_upsample = ConvTranspose1d(self.vq_feature_dim, self.vq_feature_dim, kernel_size=stride, stride=stride)
|
||||
self.audio_vq_ds_rate = audio_vq_ds_rate
|
||||
|
||||
if audio_vq_type == "GRVQ":
|
||||
self.audio_quantizer = DistributedGroupResidualVectorQuantization(
|
||||
codebook_size = audio_vq_codebook_size,
|
||||
dim = self.vq_feature_dim,
|
||||
codebook_dim = self.vq_codebook_dim if audio_vq_codebook_dim is None else audio_vq_codebook_dim,
|
||||
num_groups=1,
|
||||
num_quantizers=1,
|
||||
kmeans_init=False,
|
||||
threshold_ema_dead_code = audio_vq_threshold_ema_dead_code
|
||||
)
|
||||
else:
|
||||
raise NotImplementedError(f"Unsupported audio_vq_type: {audio_vq_type}")
|
||||
|
||||
if self.audio_vq_pe:
|
||||
self.project_after_vq_pe = nn.Linear(self.n_state, self.n_state)
|
||||
|
||||
def _calc_quantize_activities(self, indices):
|
||||
indices_onehot = F.one_hot(indices.long().flatten(), self.audio_vq_codebook_size).sum(dim=0)
|
||||
vq_num_activities = sum(indices_onehot>0)
|
||||
vq_num_tokens = sum(indices_onehot)
|
||||
return {
|
||||
"vq_num_activities": vq_num_activities,
|
||||
"vq_num_tokens": vq_num_tokens,
|
||||
}
|
||||
|
||||
def _do_quantize(self, x, pe=None, y=None):
|
||||
"""
|
||||
x: torch.Tensor, shape = (T, D)
|
||||
q: torch.Tensor, shape = (T, D)
|
||||
i: torch.Tensor, shape = (T)
|
||||
"""
|
||||
if self.audio_vq_out_commit_loss > 0:
|
||||
x_teacher = x.clone()
|
||||
x = x.unsqueeze(0)
|
||||
|
||||
x = self.audio_vq_downsample(x.transpose(1, 2))
|
||||
x = x.transpose(1, 2)
|
||||
|
||||
vq_stats = {}
|
||||
|
||||
if self.audio_vq_type == "GRVQ":
|
||||
if self.training:
|
||||
raise NotImplementedError
|
||||
else:
|
||||
indices = self.audio_quantizer.encode(x)
|
||||
x = self.audio_quantizer.decode(indices)
|
||||
indices = indices.squeeze(2).squeeze(1)
|
||||
|
||||
vq_stats.update(self._calc_quantize_activities(indices))
|
||||
|
||||
x, indices = x.squeeze(0), indices.squeeze(0)
|
||||
if self.audio_vq_pe:
|
||||
x = x + pe
|
||||
x = self.project_after_vq_pe(x)
|
||||
|
||||
x = self.audio_vq_upsample(x.unsqueeze(0).transpose(1, 2))
|
||||
x = x.transpose(1, 2).squeeze(0)
|
||||
|
||||
if self.audio_vq_out_commit_loss > 0:
|
||||
vq_out_commit_loss = F.mse_loss(x_teacher.detach(), x)
|
||||
vq_stats["vq_out_commit_loss"] = vq_out_commit_loss * self.audio_vq_out_commit_loss
|
||||
|
||||
return x, indices, vq_stats
|
||||
|
||||
def forward(self, x_list: List[Tensor], audio_mellens:List[int], audio_aftercnnlens:List[int], audio_seqlens:List[int], return_indices=False, audio_pitchs=None):
|
||||
"""
|
||||
x : torch.Tensor, shape = (n_mels, n_ctx)
|
||||
the mel spectrogram of the audio
|
||||
"""
|
||||
|
||||
aftercnn_x_list = []
|
||||
pe_for_vq_list = []
|
||||
for each_x in x_list:
|
||||
each_x_split_list = each_x.split(self.n_window * 2, dim=1)
|
||||
for each_x_split in each_x_split_list:
|
||||
each_x_split = F.gelu(self.conv1(each_x_split))
|
||||
each_x_split = F.gelu(self.conv2(each_x_split))
|
||||
each_x_split = each_x_split.permute(1, 0) # L,D
|
||||
|
||||
each_positional_embedding_split = self.positional_embedding[:each_x_split.shape[0]]
|
||||
aftercnn_x_list.append(each_x_split+each_positional_embedding_split.to(each_x_split.dtype))
|
||||
|
||||
pe_for_vq_split = self.positional_embedding[:each_x_split.shape[0] // self.audio_vq_ds_rate]
|
||||
pe_for_vq_list.append(pe_for_vq_split.to(each_x_split.dtype))
|
||||
|
||||
pe_for_vq = torch.cat(pe_for_vq_list, dim=0)
|
||||
x = torch.cat(aftercnn_x_list, dim=0)
|
||||
src_len = x.size(0)
|
||||
|
||||
output_list = []
|
||||
for item in audio_aftercnnlens:
|
||||
while item > self.n_window:
|
||||
output_list.append(self.n_window)
|
||||
item -= self.n_window
|
||||
output_list.append(item)
|
||||
|
||||
cu_seqlens = list(accumulate(output_list, func=operator.add,initial=0))
|
||||
cu_seqlens = torch.Tensor(cu_seqlens).to(device=x.device, dtype=torch.int32)
|
||||
|
||||
layer_id = 0
|
||||
|
||||
for block in self.blocks:
|
||||
layer_id+=1
|
||||
|
||||
x = block(x, cu_seqlens=cu_seqlens)
|
||||
|
||||
if self.audio_vq_layers == layer_id: # vq inside encoder
|
||||
x, indices, vq_stats = self._do_quantize(x, pe_for_vq)
|
||||
if return_indices:
|
||||
return x, indices
|
||||
|
||||
if self.avg_pooler:
|
||||
x_list = x.split(audio_aftercnnlens, dim=0)
|
||||
token_x_list = []
|
||||
for x in x_list:
|
||||
x = x.permute(1, 0)
|
||||
x = self.avg_pooler(x)
|
||||
x = x.permute(1, 0)
|
||||
token_x_list.append(x)
|
||||
x = torch.cat(token_x_list, dim=0)
|
||||
|
||||
x = self.ln_post(x)
|
||||
|
||||
x = self.proj(x)
|
||||
|
||||
output = torch.zeros(
|
||||
(x.size(0) + len(audio_seqlens) * 2, x.size(1)),
|
||||
device=x.device, dtype=x.dtype
|
||||
)
|
||||
|
||||
audio_seqlens_acc = list(accumulate(audio_seqlens, func=operator.add, initial=0))
|
||||
start_ids = torch.tensor(audio_seqlens_acc[:-1], device=x.device, dtype=torch.int32)
|
||||
end_ids = torch.tensor(audio_seqlens_acc[1:], device=x.device, dtype=torch.int32) - 1
|
||||
|
||||
audio_tokens_mask = torch.ones(output.size(0), device=x.device, dtype=torch.bool)
|
||||
audio_tokens_mask[start_ids] = False
|
||||
audio_tokens_mask[end_ids] = False
|
||||
output[start_ids] = self.audio_bos_eos_token.weight[0].to(x.dtype)
|
||||
output[end_ids] = self.audio_bos_eos_token.weight[1].to(x.dtype)
|
||||
output[audio_tokens_mask] = x
|
||||
|
||||
if self.audio_vq_type != "NULL":
|
||||
return output, vq_stats
|
||||
return output
|
||||
@@ -0,0 +1,406 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2026 The Alibaba Qwen team.
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
import os
|
||||
import math
|
||||
import torch
|
||||
import operator
|
||||
|
||||
import numpy as np
|
||||
import torch.nn.functional as F
|
||||
|
||||
from functools import lru_cache
|
||||
from typing import Optional, Union, List
|
||||
from torch import nn, Tensor
|
||||
from itertools import accumulate
|
||||
|
||||
try:
|
||||
from flash_attn.flash_attn_interface import flash_attn_varlen_func as flash_attn_varlen_func
|
||||
except ImportError:
|
||||
try:
|
||||
from flash_attn.flash_attn_interface import flash_attn_unpadded_func as flash_attn_varlen_func
|
||||
except ImportError:
|
||||
print("\n********\nWarning: flash-attn is not installed. Will only run the manual PyTorch version. Please install flash-attn for faster inference.\n********\n ")
|
||||
flash_attn_varlen_func = None
|
||||
|
||||
|
||||
N_FFT = 400
|
||||
HOP_LENGTH = 160
|
||||
|
||||
|
||||
@lru_cache(maxsize=None)
|
||||
def mel_filters(device, n_mels: int) -> torch.Tensor:
|
||||
"""
|
||||
load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
|
||||
Allows decoupling librosa dependency; saved using:
|
||||
|
||||
np.savez_compressed(
|
||||
"mel_filters.npz",
|
||||
mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
|
||||
mel_128=librosa.filters.mel(sr=16000, n_fft=400, n_mels=128),
|
||||
)
|
||||
"""
|
||||
assert n_mels in {80, 128}, f"Unsupported n_mels: {n_mels}"
|
||||
|
||||
filters_path = os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz")
|
||||
with np.load(filters_path, allow_pickle=False) as f:
|
||||
return torch.from_numpy(f[f"mel_{n_mels}"]).to(device)
|
||||
|
||||
|
||||
def log_mel_spectrogram(
|
||||
audio: Union[str, np.ndarray, torch.Tensor],
|
||||
n_mels: int = 80,
|
||||
padding: int = 0,
|
||||
device: Optional[Union[str, torch.device]] = None,
|
||||
):
|
||||
"""
|
||||
Compute the log-Mel spectrogram of
|
||||
|
||||
Parameters
|
||||
----------
|
||||
audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
|
||||
The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
|
||||
|
||||
n_mels: int
|
||||
The number of Mel-frequency filters, only 80 is supported
|
||||
|
||||
padding: int
|
||||
Number of zero samples to pad to the right
|
||||
|
||||
device: Optional[Union[str, torch.device]]
|
||||
If given, the audio tensor is moved to this device before STFT
|
||||
|
||||
Returns
|
||||
-------
|
||||
torch.Tensor, shape = (80, n_frames)
|
||||
A Tensor that contains the Mel spectrogram
|
||||
"""
|
||||
if not torch.is_tensor(audio):
|
||||
audio = torch.from_numpy(audio)
|
||||
|
||||
if device is not None:
|
||||
audio = audio.to(device)
|
||||
if padding > 0:
|
||||
audio = F.pad(audio, (0, padding))
|
||||
window = torch.hann_window(N_FFT).to(audio.device)
|
||||
stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
|
||||
magnitudes = stft[..., :-1].abs() ** 2
|
||||
|
||||
filters = mel_filters(audio.device, n_mels)
|
||||
mel_spec = filters @ magnitudes
|
||||
|
||||
log_spec = torch.clamp(mel_spec, min=1e-10).log10()
|
||||
log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
|
||||
log_spec = (log_spec + 4.0) / 4.0
|
||||
return log_spec
|
||||
|
||||
|
||||
def get_T_after_cnn(L_in, dilation=1):
|
||||
for (padding, kernel_size, stride) in eval("[(1,3,1)] + [(1,3,2)] "):
|
||||
L_out = L_in + 2 * padding - dilation * (kernel_size - 1) - 1
|
||||
L_out = 1 + L_out // stride
|
||||
L_in = L_out
|
||||
return L_out
|
||||
|
||||
|
||||
def get_mel_audio(audio, padding=False, audio_vq_ds_rate = 1, n_mels = 128):
|
||||
audio_len = len(audio)
|
||||
if padding:
|
||||
reduction = 160 * 2 * audio_vq_ds_rate
|
||||
audio_pad = math.ceil(audio_len / reduction) * reduction - audio_len
|
||||
mel = log_mel_spectrogram(audio, n_mels=n_mels, padding=audio_pad)
|
||||
else:
|
||||
mel = log_mel_spectrogram(audio, n_mels=n_mels) # [F,T]
|
||||
return mel
|
||||
|
||||
|
||||
def sinusoids(length, channels, max_timescale=10000):
|
||||
"""Returns sinusoids for positional embedding"""
|
||||
assert channels % 2 == 0
|
||||
log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
|
||||
inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
|
||||
scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
|
||||
return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)
|
||||
|
||||
|
||||
class Conv1d(nn.Conv1d):
|
||||
def _conv_forward(
|
||||
self, x: Tensor, weight: Tensor, bias: Optional[Tensor]
|
||||
) -> Tensor:
|
||||
return super()._conv_forward(
|
||||
x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype)
|
||||
)
|
||||
|
||||
|
||||
class ConvTranspose1d(nn.ConvTranspose1d):
|
||||
def _conv_forward(
|
||||
self, x: Tensor, weight: Tensor, bias: Optional[Tensor]
|
||||
) -> Tensor:
|
||||
return super()._conv_forward(
|
||||
x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype)
|
||||
)
|
||||
|
||||
|
||||
class Linear(nn.Linear):
|
||||
def forward(self, x: Tensor) -> Tensor:
|
||||
return F.linear(x, self.weight.to(x.dtype), None if self.bias is None else self.bias.to(x.dtype) )
|
||||
|
||||
|
||||
class MultiHeadAttention(nn.Module):
|
||||
def __init__(self, n_state: int, n_head: int):
|
||||
super().__init__()
|
||||
self.n_head = n_head
|
||||
self.query = Linear(n_state, n_state)
|
||||
self.key = Linear(n_state, n_state, bias=False)
|
||||
self.value = Linear(n_state, n_state)
|
||||
self.out = Linear(n_state, n_state)
|
||||
|
||||
self.use_flash_attention = True
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: Tensor,
|
||||
cu_seqlens = None,
|
||||
):
|
||||
q = self.query(x)
|
||||
k = self.key(x)
|
||||
v = self.value(x)
|
||||
|
||||
if self.use_flash_attention:
|
||||
if flash_attn_varlen_func is None:
|
||||
x = self.qkv_attention_manual(q, k, v, cu_seqlens=cu_seqlens)
|
||||
else:
|
||||
if q.dtype not in [torch.float16, torch.bfloat16]:
|
||||
x = self.qkv_attention_manual(q, k, v, cu_seqlens=cu_seqlens)
|
||||
self.use_flash_attention = False
|
||||
else:
|
||||
x = self.qkv_flash_attention(q, k, v, cu_seqlens=cu_seqlens)
|
||||
else:
|
||||
x = self.qkv_attention_manual(q, k, v, cu_seqlens=cu_seqlens)
|
||||
|
||||
output = self.out(x)
|
||||
return output
|
||||
|
||||
def qkv_flash_attention(
|
||||
self, q: Tensor, k: Tensor, v: Tensor, cu_seqlens=None
|
||||
):
|
||||
n_ctx, n_state = q.shape
|
||||
# scale = (n_state // self.n_head) ** -0.25
|
||||
q = q.view(n_ctx, self.n_head, -1)# (batch_size, seqlen, nheads, headdim)
|
||||
k = k.view(n_ctx, self.n_head, -1)
|
||||
v = v.view(n_ctx, self.n_head, -1)
|
||||
|
||||
max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
|
||||
|
||||
|
||||
x = flash_attn_varlen_func(
|
||||
q, k, v, cu_seqlens, cu_seqlens, max_seqlen, max_seqlen, dropout_p=0.0
|
||||
)
|
||||
x = x.reshape(n_ctx, n_state)
|
||||
return x
|
||||
|
||||
def qkv_attention_manual(
|
||||
self, q: Tensor, k: Tensor, v: Tensor, cu_seqlens: Tensor
|
||||
):
|
||||
n_ctx, n_state = q.shape
|
||||
head_dim = n_state // self.n_head
|
||||
scale = head_dim ** -0.5
|
||||
|
||||
q = q.view(n_ctx, self.n_head, head_dim)
|
||||
k = k.view(n_ctx, self.n_head, head_dim)
|
||||
v = v.view(n_ctx, self.n_head, head_dim)
|
||||
|
||||
seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
|
||||
batch_size = len(seqlens)
|
||||
max_seqlen = max(seqlens)
|
||||
|
||||
q_padded = torch.zeros(batch_size, max_seqlen, self.n_head, head_dim, dtype=q.dtype, device=q.device)
|
||||
k_padded = torch.zeros_like(q_padded)
|
||||
v_padded = torch.zeros_like(q_padded)
|
||||
|
||||
for i in range(batch_size):
|
||||
start_idx = cu_seqlens[i]
|
||||
end_idx = cu_seqlens[i+1]
|
||||
seq_len = seqlens[i]
|
||||
q_padded[i, :seq_len] = q[start_idx:end_idx]
|
||||
k_padded[i, :seq_len] = k[start_idx:end_idx]
|
||||
v_padded[i, :seq_len] = v[start_idx:end_idx]
|
||||
|
||||
q_padded = q_padded.transpose(1, 2)
|
||||
k_padded = k_padded.transpose(1, 2)
|
||||
v_padded = v_padded.transpose(1, 2)
|
||||
|
||||
attn_mask = torch.arange(max_seqlen, device=q.device)[None, :] < torch.tensor(seqlens, device=q.device)[:, None]
|
||||
attn_mask = attn_mask.unsqueeze(1).unsqueeze(2)
|
||||
|
||||
attn_mask = attn_mask.masked_fill(attn_mask == 0, -torch.finfo(q.dtype).max)
|
||||
|
||||
attn_scores = torch.matmul(q_padded, k_padded.transpose(-2, -1)) * scale
|
||||
attn_scores = attn_scores + attn_mask
|
||||
attn_weights = F.softmax(attn_scores, dim=-1)
|
||||
|
||||
context = torch.matmul(attn_weights, v_padded)
|
||||
|
||||
context = context.transpose(1, 2).contiguous().view(batch_size, max_seqlen, n_state)
|
||||
|
||||
output_packed = torch.cat([context[i, :seqlens[i]] for i in range(batch_size)], dim=0)
|
||||
|
||||
assert output_packed.shape == (n_ctx, n_state)
|
||||
|
||||
return output_packed
|
||||
|
||||
|
||||
class ResidualAttentionBlock(nn.Module):
|
||||
def __init__(self, n_state: int, n_head: int,
|
||||
enable_mp: bool = False, sequence_parallel: bool = False):
|
||||
super().__init__()
|
||||
n_mlp = n_state * 4
|
||||
self.attn_ln = nn.LayerNorm(n_state)
|
||||
self.mlp_ln = nn.LayerNorm(n_state)
|
||||
|
||||
self.attn = MultiHeadAttention(n_state, n_head)
|
||||
self.mlp = nn.Sequential(
|
||||
Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state)
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: Tensor,
|
||||
cu_seqlens = None
|
||||
):
|
||||
x = x + self.attn(self.attn_ln(x), cu_seqlens=cu_seqlens)
|
||||
x = x + self.mlp(self.mlp_ln(x))
|
||||
return x
|
||||
|
||||
|
||||
class WhisperEncoder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
n_mels: int,
|
||||
n_ctx: int,
|
||||
n_state: int,
|
||||
n_head: int,
|
||||
n_layer: int,
|
||||
n_window: int = 1500,
|
||||
output_dim: int = 512,
|
||||
grad_checkpointing: bool = False,
|
||||
enable_mp: bool = False,
|
||||
audio_sequence_parallel: bool = False,
|
||||
):
|
||||
super().__init__()
|
||||
self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1)
|
||||
self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1)
|
||||
self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state))
|
||||
self.n_layer = n_layer
|
||||
self.n_mels = n_mels
|
||||
|
||||
self.blocks = nn.ModuleList(
|
||||
[ResidualAttentionBlock(n_state, n_head, enable_mp=enable_mp, sequence_parallel=audio_sequence_parallel)
|
||||
for _ in range(n_layer)]
|
||||
)
|
||||
self.ln_post = nn.LayerNorm(n_state)
|
||||
self.avg_pooler = nn.AvgPool1d(2, stride=2)
|
||||
|
||||
self.proj = torch.nn.Linear(n_state, output_dim)
|
||||
|
||||
self.audio_bos_eos_token = nn.Embedding(2, output_dim)
|
||||
|
||||
self.output_dim = output_dim
|
||||
self.grad_checkpointing = grad_checkpointing
|
||||
self.enable_mp = enable_mp
|
||||
self.n_head = n_head
|
||||
self.n_state = n_state
|
||||
self.n_window = n_window
|
||||
|
||||
self.audio_sequence_parallel = audio_sequence_parallel
|
||||
|
||||
self.tp_world_size = 1
|
||||
|
||||
self.set_audio_sync()
|
||||
|
||||
def set_audio_sync(self):
|
||||
for name, param in self.named_parameters():
|
||||
if not name.startswith("blocks"):
|
||||
setattr(param, "audio_sync", True)
|
||||
|
||||
def forward(self, x_list: List[Tensor], audio_mellens:List[int], audio_aftercnnlens:List[int], audio_seqlens:List[int]):
|
||||
"""
|
||||
x : torch.Tensor, shape = (n_mels, n_ctx)
|
||||
the mel spectrogram of the audio
|
||||
"""
|
||||
|
||||
aftercnn_x_list = []
|
||||
for each_x in x_list:
|
||||
each_x_split_list = each_x.split(self.n_window * 2, dim=1)
|
||||
for each_x_split in each_x_split_list:
|
||||
each_x_split = F.gelu(self.conv1(each_x_split))
|
||||
each_x_split = F.gelu(self.conv2(each_x_split))
|
||||
each_x_split = each_x_split.permute(1, 0) # L,D
|
||||
each_positional_embedding_split = self.positional_embedding[:each_x_split.shape[0]]
|
||||
aftercnn_x_list.append(each_x_split+each_positional_embedding_split.to(each_x_split.dtype))
|
||||
|
||||
x = torch.cat(aftercnn_x_list, dim=0)
|
||||
src_len = x.size(0)
|
||||
|
||||
output_list = []
|
||||
for item in audio_aftercnnlens:
|
||||
while item > self.n_window:
|
||||
output_list.append(self.n_window)
|
||||
item -= self.n_window
|
||||
output_list.append(item)
|
||||
|
||||
cu_seqlens = list(accumulate(output_list, func=operator.add,initial=0))
|
||||
cu_seqlens = torch.Tensor(cu_seqlens).to(device=x.device, dtype=torch.int32)
|
||||
|
||||
layer_id = 0
|
||||
for block in self.blocks:
|
||||
layer_id+=1
|
||||
x = block(x, cu_seqlens=cu_seqlens)
|
||||
|
||||
if self.avg_pooler:
|
||||
x_list = x.split(audio_aftercnnlens, dim=0)
|
||||
token_x_list = []
|
||||
for x in x_list:
|
||||
x = x.permute(1, 0)
|
||||
x = self.avg_pooler(x)
|
||||
x = x.permute(1, 0)
|
||||
token_x_list.append(x)
|
||||
x = torch.cat(token_x_list, dim=0)
|
||||
|
||||
x = self.ln_post(x)
|
||||
x = self.proj(x)
|
||||
|
||||
output = torch.zeros(
|
||||
(x.size(0) + len(audio_seqlens) * 2, x.size(1)),
|
||||
device=x.device, dtype=x.dtype
|
||||
)
|
||||
|
||||
audio_seqlens_acc = list(accumulate(audio_seqlens, func=operator.add, initial=0))
|
||||
start_ids = torch.tensor(audio_seqlens_acc[:-1], device=x.device, dtype=torch.int32)
|
||||
end_ids = torch.tensor(audio_seqlens_acc[1:], device=x.device, dtype=torch.int32) - 1
|
||||
|
||||
audio_tokens_mask = torch.ones(output.size(0), device=x.device, dtype=torch.bool)
|
||||
audio_tokens_mask[start_ids] = False
|
||||
audio_tokens_mask[end_ids] = False
|
||||
output[start_ids] = self.audio_bos_eos_token.weight[0].to(x.dtype)
|
||||
output[end_ids] = self.audio_bos_eos_token.weight[1].to(x.dtype)
|
||||
output[audio_tokens_mask] = x
|
||||
return output
|
||||
|
||||
def lock(self, layers: int):
|
||||
self.conv1.requires_grad_(False)
|
||||
self.conv2.requires_grad_(False)
|
||||
for i in range(min(layers, len(self.blocks))):
|
||||
self.blocks[i].requires_grad_(False)
|
||||
877
models/Qwen3-TTS/qwen_tts/inference/qwen3_tts_model.py
Normal file
877
models/Qwen3-TTS/qwen_tts/inference/qwen3_tts_model.py
Normal file
@@ -0,0 +1,877 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2026 The Alibaba Qwen team.
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
import base64
|
||||
import io
|
||||
import urllib.request
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Dict, List, Optional, Tuple, Union
|
||||
from urllib.parse import urlparse
|
||||
|
||||
import librosa
|
||||
import numpy as np
|
||||
import soundfile as sf
|
||||
import torch
|
||||
from transformers import AutoConfig, AutoModel, AutoProcessor
|
||||
|
||||
from ..core.models import Qwen3TTSConfig, Qwen3TTSForConditionalGeneration, Qwen3TTSProcessor
|
||||
|
||||
AudioLike = Union[
|
||||
str, # wav path, URL, base64
|
||||
np.ndarray, # waveform (requires sr)
|
||||
Tuple[np.ndarray, int], # (waveform, sr)
|
||||
]
|
||||
|
||||
MaybeList = Union[Any, List[Any]]
|
||||
|
||||
|
||||
@dataclass
|
||||
class VoiceClonePromptItem:
|
||||
"""
|
||||
Container for one sample's voice-clone prompt information that can be fed to the model.
|
||||
|
||||
Fields are aligned with `Qwen3TTSForConditionalGeneration.generate(..., voice_clone_prompt=...)`.
|
||||
"""
|
||||
ref_code: Optional[torch.Tensor] # (T, Q) or (T,) depending on tokenizer 25Hz/12Hz
|
||||
ref_spk_embedding: torch.Tensor # (D,)
|
||||
x_vector_only_mode: bool
|
||||
icl_mode: bool
|
||||
ref_text: Optional[str] = None
|
||||
|
||||
|
||||
class Qwen3TTSModel:
|
||||
"""
|
||||
A HuggingFace-style wrapper for Qwen3 TTS models (CustomVoice/VoiceDesign/Base) that provides:
|
||||
- from_pretrained() initialization via AutoModel/AutoProcessor
|
||||
- generation APIs for:
|
||||
* CustomVoice: generate_custom_voice()
|
||||
* VoiceDesign: generate_voice_design()
|
||||
* Base: generate_voice_clone() + create_voice_clone_prompt()
|
||||
- consistent output: (wavs: List[np.ndarray], sample_rate: int)
|
||||
|
||||
Notes:
|
||||
- This wrapper expects the underlying model class to be `Qwen3TTSForConditionalGeneration`
|
||||
- Language / speaker validation is done via model methods:
|
||||
model.get_supported_languages(), model.get_supported_speakers()
|
||||
"""
|
||||
|
||||
def __init__(self, model: Qwen3TTSForConditionalGeneration, processor, generate_defaults: Optional[Dict[str, Any]] = None):
|
||||
self.model = model
|
||||
self.processor = processor
|
||||
self.generate_defaults = generate_defaults or {}
|
||||
|
||||
self.device = getattr(model, "device", None)
|
||||
if self.device is None:
|
||||
try:
|
||||
self.device = next(model.parameters()).device
|
||||
except StopIteration:
|
||||
self.device = torch.device("cpu")
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(
|
||||
cls,
|
||||
pretrained_model_name_or_path: str,
|
||||
**kwargs,
|
||||
) -> "Qwen3TTSModel":
|
||||
"""
|
||||
Load a Qwen3 TTS model and its processor in HuggingFace `from_pretrained` style.
|
||||
|
||||
This method:
|
||||
1) Loads config via AutoConfig (so your side can register model_type -> config/model).
|
||||
2) Loads the model via AutoModel.from_pretrained(...), forwarding `kwargs` unchanged.
|
||||
3) Loads the processor via AutoProcessor.from_pretrained(model_path).
|
||||
4) Loads optional `generate_config.json` from the model directory/repo snapshot if present.
|
||||
|
||||
Args:
|
||||
pretrained_model_name_or_path (str):
|
||||
HuggingFace repo id or local directory of the model.
|
||||
**kwargs:
|
||||
Forwarded as-is into `AutoModel.from_pretrained(...)`.
|
||||
Typical examples: device_map="cuda:0", dtype=torch.bfloat16, attn_implementation="flash_attention_2".
|
||||
|
||||
Returns:
|
||||
Qwen3TTSModel:
|
||||
Wrapper instance containing `model`, `processor`, and generation defaults.
|
||||
"""
|
||||
AutoConfig.register("qwen3_tts", Qwen3TTSConfig)
|
||||
AutoModel.register(Qwen3TTSConfig, Qwen3TTSForConditionalGeneration)
|
||||
AutoProcessor.register(Qwen3TTSConfig, Qwen3TTSProcessor)
|
||||
|
||||
model = AutoModel.from_pretrained(pretrained_model_name_or_path, **kwargs)
|
||||
if not isinstance(model, Qwen3TTSForConditionalGeneration):
|
||||
raise TypeError(
|
||||
f"AutoModel returned {type(model)}, expected Qwen3TTSForConditionalGeneration. "
|
||||
)
|
||||
|
||||
processor = AutoProcessor.from_pretrained(pretrained_model_name_or_path, fix_mistral_regex=True,)
|
||||
|
||||
generate_defaults = model.generate_config
|
||||
return cls(model=model, processor=processor, generate_defaults=generate_defaults)
|
||||
|
||||
def _supported_languages_set(self) -> Optional[set]:
|
||||
langs = getattr(self.model, "get_supported_languages", None)
|
||||
if callable(langs):
|
||||
v = langs()
|
||||
if v is None:
|
||||
return None
|
||||
return set([str(x).lower() for x in v])
|
||||
return None
|
||||
|
||||
def _supported_speakers_set(self) -> Optional[set]:
|
||||
spks = getattr(self.model, "get_supported_speakers", None)
|
||||
if callable(spks):
|
||||
v = spks()
|
||||
if v is None:
|
||||
return None
|
||||
return set([str(x).lower() for x in v])
|
||||
return None
|
||||
|
||||
def _validate_languages(self, languages: List[str]) -> None:
|
||||
"""
|
||||
Validate that requested languages are supported by the model.
|
||||
|
||||
Args:
|
||||
languages (List[str]): Language names for each sample.
|
||||
|
||||
Raises:
|
||||
ValueError: If any language is not supported.
|
||||
"""
|
||||
supported = self._supported_languages_set()
|
||||
if supported is None:
|
||||
return
|
||||
|
||||
bad = []
|
||||
for lang in languages:
|
||||
if lang is None:
|
||||
bad.append(lang)
|
||||
continue
|
||||
if str(lang).lower() not in supported:
|
||||
bad.append(lang)
|
||||
if bad:
|
||||
raise ValueError(f"Unsupported languages: {bad}. Supported: {sorted(supported)}")
|
||||
|
||||
def _validate_speakers(self, speakers: List[Optional[str]]) -> None:
|
||||
"""
|
||||
Validate that requested speakers are supported by the Instruct model.
|
||||
|
||||
Args:
|
||||
speakers (List[Optional[str]]): Speaker names for each sample.
|
||||
|
||||
Raises:
|
||||
ValueError: If any speaker is not supported.
|
||||
"""
|
||||
supported = self._supported_speakers_set()
|
||||
if supported is None:
|
||||
return
|
||||
|
||||
bad = []
|
||||
for spk in speakers:
|
||||
if spk is None or spk == "":
|
||||
continue
|
||||
if str(spk).lower() not in supported:
|
||||
bad.append(spk)
|
||||
if bad:
|
||||
raise ValueError(f"Unsupported speakers: {bad}. Supported: {sorted(supported)}")
|
||||
|
||||
def _is_probably_base64(self, s: str) -> bool:
|
||||
if s.startswith("data:audio"):
|
||||
return True
|
||||
if ("/" not in s and "\\" not in s) and len(s) > 256:
|
||||
return True
|
||||
return False
|
||||
|
||||
def _is_url(self, s: str) -> bool:
|
||||
try:
|
||||
u = urlparse(s)
|
||||
return u.scheme in ("http", "https") and bool(u.netloc)
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
def _decode_base64_to_wav_bytes(self, b64: str) -> bytes:
|
||||
if "," in b64 and b64.strip().startswith("data:"):
|
||||
b64 = b64.split(",", 1)[1]
|
||||
return base64.b64decode(b64)
|
||||
|
||||
def _load_audio_to_np(self, x: str) -> Tuple[np.ndarray, int]:
|
||||
if self._is_url(x):
|
||||
with urllib.request.urlopen(x) as resp:
|
||||
audio_bytes = resp.read()
|
||||
with io.BytesIO(audio_bytes) as f:
|
||||
audio, sr = sf.read(f, dtype="float32", always_2d=False)
|
||||
elif self._is_probably_base64(x):
|
||||
wav_bytes = self._decode_base64_to_wav_bytes(x)
|
||||
with io.BytesIO(wav_bytes) as f:
|
||||
audio, sr = sf.read(f, dtype="float32", always_2d=False)
|
||||
else:
|
||||
audio, sr = librosa.load(x, sr=None, mono=True)
|
||||
|
||||
if audio.ndim > 1:
|
||||
audio = np.mean(audio, axis=-1)
|
||||
|
||||
return audio.astype(np.float32), int(sr)
|
||||
|
||||
def _normalize_audio_inputs(self, audios: Union[AudioLike, List[AudioLike]]) -> List[Tuple[np.ndarray, int]]:
|
||||
"""
|
||||
Normalize audio inputs into a list of (waveform, sr).
|
||||
|
||||
Supported forms:
|
||||
- str: wav path / URL / base64 audio string
|
||||
- (np.ndarray, sr): waveform + sampling rate
|
||||
- list of the above
|
||||
|
||||
Args:
|
||||
audios:
|
||||
Audio input(s).
|
||||
|
||||
Returns:
|
||||
List[Tuple[np.ndarray, int]]:
|
||||
List of (float32 waveform, original sr).
|
||||
|
||||
Raises:
|
||||
ValueError: If a numpy waveform is provided without sr.
|
||||
"""
|
||||
if isinstance(audios, list):
|
||||
items = audios
|
||||
else:
|
||||
items = [audios]
|
||||
|
||||
out: List[Tuple[np.ndarray, int]] = []
|
||||
for a in items:
|
||||
if isinstance(a, str):
|
||||
out.append(self._load_audio_to_np(a))
|
||||
elif isinstance(a, tuple) and len(a) == 2 and isinstance(a[0], np.ndarray):
|
||||
out.append((a[0].astype(np.float32), int(a[1])))
|
||||
elif isinstance(a, np.ndarray):
|
||||
raise ValueError("For numpy waveform input, pass a tuple (audio, sr).")
|
||||
else:
|
||||
raise TypeError(f"Unsupported audio input type: {type(a)}")
|
||||
for i, a in enumerate(out):
|
||||
if a[0].ndim > 1:
|
||||
a[0] = np.mean(a[0], axis=-1).astype(np.float32)
|
||||
out[i] = (a[0], a[1])
|
||||
return out
|
||||
|
||||
def _ensure_list(self, x: MaybeList) -> List[Any]:
|
||||
return x if isinstance(x, list) else [x]
|
||||
|
||||
def _build_assistant_text(self, text: str) -> str:
|
||||
return f"<|im_start|>assistant\n{text}<|im_end|>\n<|im_start|>assistant\n"
|
||||
|
||||
def _build_ref_text(self, text: str) -> str:
|
||||
return f"<|im_start|>assistant\n{text}<|im_end|>\n"
|
||||
|
||||
def _build_instruct_text(self, instruct: str) -> str:
|
||||
return f"<|im_start|>user\n{instruct}<|im_end|>\n"
|
||||
|
||||
def _tokenize_texts(self, texts: List[str]) -> List[torch.Tensor]:
|
||||
input_ids = []
|
||||
for text in texts:
|
||||
input = self.processor(text=text, return_tensors="pt", padding=True)
|
||||
input_id = input["input_ids"].to(self.device)
|
||||
input_id = input_id.unsqueeze(0) if input_id.dim() == 1 else input_id
|
||||
input_ids.append(input_id)
|
||||
return input_ids
|
||||
|
||||
def _merge_generate_kwargs(
|
||||
self,
|
||||
do_sample: Optional[bool] = None,
|
||||
top_k: Optional[int] = None,
|
||||
top_p: Optional[float] = None,
|
||||
temperature: Optional[float] = None,
|
||||
repetition_penalty: Optional[float] = None,
|
||||
subtalker_dosample: Optional[bool] = None,
|
||||
subtalker_top_k: Optional[int] = None,
|
||||
subtalker_top_p: Optional[float] = None,
|
||||
subtalker_temperature: Optional[float] = None,
|
||||
max_new_tokens: Optional[int] = None,
|
||||
**kwargs,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Merge user-provided generation arguments with defaults from `generate_config.json`.
|
||||
|
||||
Rule:
|
||||
- If the user explicitly passes a value (not None), use it.
|
||||
- Otherwise, use the value from generate_config.json if present.
|
||||
- Otherwise, fall back to the hard defaults.
|
||||
|
||||
Args:
|
||||
do_sample, top_k, top_p, temperature, repetition_penalty,
|
||||
subtalker_dosample, subtalker_top_k, subtalker_top_p, subtalker_temperature, max_new_tokens:
|
||||
Common generation parameters.
|
||||
**kwargs:
|
||||
Other arguments forwarded to model.generate().
|
||||
|
||||
Returns:
|
||||
Dict[str, Any]: Final kwargs to pass into model.generate().
|
||||
"""
|
||||
hard_defaults = dict(
|
||||
do_sample=True,
|
||||
top_k=50,
|
||||
top_p=1.0,
|
||||
temperature=0.9,
|
||||
repetition_penalty=1.05,
|
||||
subtalker_dosample=True,
|
||||
subtalker_top_k=50,
|
||||
subtalker_top_p=1.0,
|
||||
subtalker_temperature=0.9,
|
||||
max_new_tokens=2048,
|
||||
)
|
||||
|
||||
def pick(name: str, user_val: Any) -> Any:
|
||||
if user_val is not None:
|
||||
return user_val
|
||||
if name in self.generate_defaults:
|
||||
return self.generate_defaults[name]
|
||||
return hard_defaults[name]
|
||||
|
||||
merged = dict(kwargs)
|
||||
merged.update(
|
||||
do_sample=pick("do_sample", do_sample),
|
||||
top_k=pick("top_k", top_k),
|
||||
top_p=pick("top_p", top_p),
|
||||
temperature=pick("temperature", temperature),
|
||||
repetition_penalty=pick("repetition_penalty", repetition_penalty),
|
||||
subtalker_dosample=pick("subtalker_dosample", subtalker_dosample),
|
||||
subtalker_top_k=pick("subtalker_top_k", subtalker_top_k),
|
||||
subtalker_top_p=pick("subtalker_top_p", subtalker_top_p),
|
||||
subtalker_temperature=pick("subtalker_temperature", subtalker_temperature),
|
||||
max_new_tokens=pick("max_new_tokens", max_new_tokens),
|
||||
)
|
||||
return merged
|
||||
|
||||
# voice clone model
|
||||
@torch.inference_mode()
|
||||
def create_voice_clone_prompt(
|
||||
self,
|
||||
ref_audio: Union[AudioLike, List[AudioLike]],
|
||||
ref_text: Optional[Union[str, List[Optional[str]]]] = None,
|
||||
x_vector_only_mode: Union[bool, List[bool]] = False,
|
||||
) -> List[VoiceClonePromptItem]:
|
||||
"""
|
||||
Build voice-clone prompt items from reference audio (and optionally reference text) using Base model.
|
||||
|
||||
Modes:
|
||||
- x_vector_only_mode=True:
|
||||
Only speaker embedding is used to clone voice; ref_text/ref_code are ignored.
|
||||
This is mutually exclusive with ICL.
|
||||
- x_vector_only_mode=False:
|
||||
ICL mode is enabled automatically (icl_mode=True). In this case ref_text is required,
|
||||
because the model continues/conditions on the reference text + reference speech codes.
|
||||
|
||||
Batch behavior:
|
||||
- ref_audio can be a single item or a list.
|
||||
- ref_text and x_vector_only_mode can be scalars or lists.
|
||||
- If any of them are lists with length > 1, lengths must match.
|
||||
|
||||
Audio input:
|
||||
- str: local wav path / URL / base64
|
||||
- (np.ndarray, sr): waveform + sampling rate
|
||||
|
||||
Args:
|
||||
ref_audio:
|
||||
Reference audio(s) used to extract:
|
||||
- ref_code via `model.speech_tokenizer.encode(...)`
|
||||
- ref_spk_embedding via `model.extract_speaker_embedding(...)` (resampled to 24k)
|
||||
ref_text:
|
||||
Reference transcript(s). Required when x_vector_only_mode=False (ICL mode).
|
||||
x_vector_only_mode:
|
||||
Whether to use speaker embedding only. If False, ICL mode will be used.
|
||||
|
||||
Returns:
|
||||
List[VoiceClonePromptItem]:
|
||||
List of prompt items that can be converted into `voice_clone_prompt` dict.
|
||||
|
||||
Raises:
|
||||
ValueError:
|
||||
- If x_vector_only_mode=False but ref_text is missing.
|
||||
- If batch lengths mismatch.
|
||||
"""
|
||||
if self.model.tts_model_type != "base":
|
||||
raise ValueError(
|
||||
f"model with \ntokenizer_type: {self.model.tokenizer_type}\n"
|
||||
f"tts_model_size: {self.model.tts_model_size}\n"
|
||||
f"tts_model_type: {self.model.tts_model_type}\n"
|
||||
"does not support create_voice_clone_prompt, Please check Model Card or Readme for more details."
|
||||
)
|
||||
|
||||
ref_audio_list = self._ensure_list(ref_audio)
|
||||
ref_text_list = self._ensure_list(ref_text) if isinstance(ref_text, list) else ([ref_text] * len(ref_audio_list))
|
||||
xvec_list = self._ensure_list(x_vector_only_mode) if isinstance(x_vector_only_mode, list) else ([x_vector_only_mode] * len(ref_audio_list))
|
||||
|
||||
if len(ref_text_list) != len(ref_audio_list) or len(xvec_list) != len(ref_audio_list):
|
||||
raise ValueError(
|
||||
f"Batch size mismatch: ref_audio={len(ref_audio_list)}, ref_text={len(ref_text_list)}, x_vector_only_mode={len(xvec_list)}"
|
||||
)
|
||||
|
||||
normalized = self._normalize_audio_inputs(ref_audio_list)
|
||||
|
||||
ref_wavs_for_code: List[np.ndarray] = []
|
||||
ref_sr_for_code: List[int] = []
|
||||
for wav, sr in normalized:
|
||||
ref_wavs_for_code.append(wav)
|
||||
ref_sr_for_code.append(sr)
|
||||
|
||||
if len(set(ref_sr_for_code)) == 1:
|
||||
enc = self.model.speech_tokenizer.encode(ref_wavs_for_code, sr=ref_sr_for_code[0])
|
||||
ref_codes = enc.audio_codes
|
||||
else:
|
||||
ref_codes = []
|
||||
for wav, sr in normalized:
|
||||
ref_codes.append(self.model.speech_tokenizer.encode(wav, sr=sr).audio_codes[0])
|
||||
|
||||
items: List[VoiceClonePromptItem] = []
|
||||
for i, ((wav, sr), code, rtext, xvec_only) in enumerate(zip(normalized, ref_codes, ref_text_list, xvec_list)):
|
||||
if not xvec_only:
|
||||
if rtext is None or rtext == "":
|
||||
raise ValueError(f"ref_text is required when x_vector_only_mode=False (ICL mode). Bad index={i}")
|
||||
|
||||
wav_resample = wav
|
||||
if sr != self.model.speaker_encoder_sample_rate:
|
||||
wav_resample = librosa.resample(y=wav_resample.astype(np.float32),
|
||||
orig_sr=int(sr),
|
||||
target_sr=self.model.speaker_encoder_sample_rate)
|
||||
|
||||
spk_emb = self.model.extract_speaker_embedding(audio=wav_resample,
|
||||
sr=self.model.speaker_encoder_sample_rate)
|
||||
|
||||
items.append(
|
||||
VoiceClonePromptItem(
|
||||
ref_code=None if xvec_only else code,
|
||||
ref_spk_embedding=spk_emb,
|
||||
x_vector_only_mode=bool(xvec_only),
|
||||
icl_mode=bool(not xvec_only),
|
||||
ref_text=rtext,
|
||||
)
|
||||
)
|
||||
return items
|
||||
|
||||
def _prompt_items_to_voice_clone_prompt(self, items: List[VoiceClonePromptItem]) -> Dict[str, Any]:
|
||||
return dict(
|
||||
ref_code=[it.ref_code for it in items],
|
||||
ref_spk_embedding=[it.ref_spk_embedding for it in items],
|
||||
x_vector_only_mode=[it.x_vector_only_mode for it in items],
|
||||
icl_mode=[it.icl_mode for it in items],
|
||||
)
|
||||
|
||||
# voice clone model
|
||||
@torch.no_grad()
|
||||
def generate_voice_clone(
|
||||
self,
|
||||
text: Union[str, List[str]],
|
||||
language: Union[str, List[str]] = None,
|
||||
ref_audio: Optional[Union[AudioLike, List[AudioLike]]] = None,
|
||||
ref_text: Optional[Union[str, List[Optional[str]]]] = None,
|
||||
x_vector_only_mode: Union[bool, List[bool]] = False,
|
||||
voice_clone_prompt: Optional[Union[Dict[str, Any], List[VoiceClonePromptItem]]] = None,
|
||||
non_streaming_mode: bool = False,
|
||||
**kwargs,
|
||||
) -> Tuple[List[np.ndarray], int]:
|
||||
"""
|
||||
Voice clone speech using the Base model.
|
||||
|
||||
You can provide either:
|
||||
- (ref_audio, ref_text, x_vector_only_mode) and let this method build the prompt, OR
|
||||
- `VoiceClonePromptItem` returned by `create_voice_clone_prompt`, OR
|
||||
- a list of `VoiceClonePromptItem` returned by `create_voice_clone_prompt`.
|
||||
|
||||
`ref_audio` Supported forms:
|
||||
- str: wav path / URL / base64 audio string
|
||||
- (np.ndarray, sr): waveform + sampling rate
|
||||
- list of the above
|
||||
|
||||
Input flexibility:
|
||||
- text/language can be scalar or list.
|
||||
- prompt can be single or batch.
|
||||
- If batch mode (len(text)>1), lengths must match.
|
||||
|
||||
Args:
|
||||
text:
|
||||
Text(s) to synthesize.
|
||||
language:
|
||||
Language(s) for each sample.
|
||||
ref_audio:
|
||||
Reference audio(s) for prompt building. Required if voice_clone_prompt is not provided.
|
||||
ref_text:
|
||||
Reference text(s) used for ICL mode (required when x_vector_only_mode=False).
|
||||
x_vector_only_mode:
|
||||
If True, only speaker embedding is used (ignores ref_text/ref_code).
|
||||
If False, ICL mode is used automatically.
|
||||
voice_clone_prompt:
|
||||
list[VoiceClonePromptItem] from `create_voice_clone_prompt`.
|
||||
non_streaming_mode:
|
||||
Using non-streaming text input, this option currently only simulates streaming text input when set to `false`,
|
||||
rather than enabling true streaming input or streaming generation.
|
||||
do_sample:
|
||||
Whether to use sampling, recommended to be set to `true` for most use cases.
|
||||
top_k:
|
||||
Top-k sampling parameter.
|
||||
top_p:
|
||||
Top-p sampling parameter.
|
||||
temperature:
|
||||
Sampling temperature; higher => more random.
|
||||
repetition_penalty:
|
||||
Penalty to reduce repeated tokens/codes.
|
||||
subtalker_dosample:
|
||||
Sampling switch for the sub-talker (only valid for qwen3-tts-tokenizer-v2) if applicable.
|
||||
subtalker_top_k:
|
||||
Top-k for sub-talker sampling (only valid for qwen3-tts-tokenizer-v2).
|
||||
subtalker_top_p:
|
||||
Top-p for sub-talker sampling (only valid for qwen3-tts-tokenizer-v2).
|
||||
subtalker_temperature:
|
||||
Temperature for sub-talker sampling (only valid for qwen3-tts-tokenizer-v2).
|
||||
max_new_tokens:
|
||||
Maximum number of new codec tokens to generate.
|
||||
**kwargs:
|
||||
Any other keyword arguments supported by HuggingFace Transformers `generate()` can be passed.
|
||||
They will be forwarded to the underlying `Qwen3TTSForConditionalGeneration.generate(...)`.
|
||||
|
||||
Returns:
|
||||
Tuple[List[np.ndarray], int]:
|
||||
(wavs, sample_rate)
|
||||
|
||||
Raises:
|
||||
ValueError:
|
||||
If batch sizes mismatch or required prompt inputs are missing.
|
||||
"""
|
||||
if self.model.tts_model_type != "base":
|
||||
raise ValueError(
|
||||
f"model with \ntokenizer_type: {self.model.tokenizer_type}\n"
|
||||
f"tts_model_size: {self.model.tts_model_size}\n"
|
||||
f"tts_model_type: {self.model.tts_model_type}\n"
|
||||
"does not support generate_voice_clone, Please check Model Card or Readme for more details."
|
||||
)
|
||||
|
||||
texts = self._ensure_list(text)
|
||||
languages = self._ensure_list(language) if isinstance(language, list) else ([language] * len(texts) if language is not None else ["Auto"] * len(texts))
|
||||
if len(languages) == 1 and len(texts) > 1:
|
||||
languages = languages * len(texts)
|
||||
if len(texts) != len(languages):
|
||||
raise ValueError(f"Batch size mismatch: text={len(texts)}, language={len(languages)}")
|
||||
|
||||
self._validate_languages(languages)
|
||||
|
||||
if voice_clone_prompt is None:
|
||||
if ref_audio is None:
|
||||
raise ValueError("Either `voice_clone_prompt` or `ref_audio` must be provided.")
|
||||
prompt_items = self.create_voice_clone_prompt(ref_audio=ref_audio, ref_text=ref_text, x_vector_only_mode=x_vector_only_mode)
|
||||
if len(prompt_items) == 1 and len(texts) > 1:
|
||||
prompt_items = prompt_items * len(texts)
|
||||
if len(prompt_items) != len(texts):
|
||||
raise ValueError(f"Batch size mismatch: prompt={len(prompt_items)}, text={len(texts)}")
|
||||
voice_clone_prompt_dict = self._prompt_items_to_voice_clone_prompt(prompt_items)
|
||||
ref_texts_for_ids = [it.ref_text for it in prompt_items]
|
||||
else:
|
||||
if isinstance(voice_clone_prompt, list):
|
||||
prompt_items = voice_clone_prompt
|
||||
if len(prompt_items) == 1 and len(texts) > 1:
|
||||
prompt_items = prompt_items * len(texts)
|
||||
if len(prompt_items) != len(texts):
|
||||
raise ValueError(f"Batch size mismatch: prompt={len(prompt_items)}, text={len(texts)}")
|
||||
voice_clone_prompt_dict = self._prompt_items_to_voice_clone_prompt(prompt_items)
|
||||
ref_texts_for_ids = [it.ref_text for it in prompt_items]
|
||||
else:
|
||||
voice_clone_prompt_dict = voice_clone_prompt
|
||||
ref_texts_for_ids = None
|
||||
|
||||
input_texts = [self._build_assistant_text(t) for t in texts]
|
||||
input_ids = self._tokenize_texts(input_texts)
|
||||
|
||||
ref_ids = None
|
||||
if ref_texts_for_ids is not None:
|
||||
ref_ids = []
|
||||
for i, rt in enumerate(ref_texts_for_ids):
|
||||
if rt is None or rt == "":
|
||||
ref_ids.append(None)
|
||||
else:
|
||||
ref_tok = self._tokenize_texts([self._build_ref_text(rt)])[0]
|
||||
ref_ids.append(ref_tok)
|
||||
|
||||
gen_kwargs = self._merge_generate_kwargs(**kwargs)
|
||||
|
||||
talker_codes_list, _ = self.model.generate(
|
||||
input_ids=input_ids,
|
||||
ref_ids=ref_ids,
|
||||
voice_clone_prompt=voice_clone_prompt_dict,
|
||||
languages=languages,
|
||||
non_streaming_mode=non_streaming_mode,
|
||||
**gen_kwargs,
|
||||
)
|
||||
|
||||
codes_for_decode = []
|
||||
for i, codes in enumerate(talker_codes_list):
|
||||
ref_code_list = voice_clone_prompt_dict.get("ref_code", None)
|
||||
if ref_code_list is not None and ref_code_list[i] is not None:
|
||||
codes_for_decode.append(torch.cat([ref_code_list[i].to(codes.device), codes], dim=0))
|
||||
else:
|
||||
codes_for_decode.append(codes)
|
||||
|
||||
wavs_all, fs = self.model.speech_tokenizer.decode([{"audio_codes": c} for c in codes_for_decode])
|
||||
|
||||
wavs_out: List[np.ndarray] = []
|
||||
for i, wav in enumerate(wavs_all):
|
||||
ref_code_list = voice_clone_prompt_dict.get("ref_code", None)
|
||||
if ref_code_list is not None and ref_code_list[i] is not None:
|
||||
ref_len = int(ref_code_list[i].shape[0])
|
||||
total_len = int(codes_for_decode[i].shape[0])
|
||||
cut = int(ref_len / max(total_len, 1) * wav.shape[0])
|
||||
wavs_out.append(wav[cut:])
|
||||
else:
|
||||
wavs_out.append(wav)
|
||||
|
||||
return wavs_out, fs
|
||||
|
||||
# voice design model
|
||||
@torch.no_grad()
|
||||
def generate_voice_design(
|
||||
self,
|
||||
text: Union[str, List[str]],
|
||||
instruct: Union[str, List[str]],
|
||||
language: Union[str, List[str]] = None,
|
||||
non_streaming_mode: bool = True,
|
||||
**kwargs,
|
||||
) -> Tuple[List[np.ndarray], int]:
|
||||
"""
|
||||
Generate speech with the VoiceDesign model using natural-language style instructions.
|
||||
|
||||
Args:
|
||||
text:
|
||||
Text(s) to synthesize.
|
||||
language:
|
||||
Language(s) for each sample.
|
||||
instruct:
|
||||
Instruction(s) describing desired voice/style. Empty string is allowed (treated as no instruction).
|
||||
non_streaming_mode:
|
||||
Using non-streaming text input, this option currently only simulates streaming text input when set to `false`,
|
||||
rather than enabling true streaming input or streaming generation.
|
||||
do_sample:
|
||||
Whether to use sampling, recommended to be set to `true` for most use cases.
|
||||
top_k:
|
||||
Top-k sampling parameter.
|
||||
top_p:
|
||||
Top-p sampling parameter.
|
||||
temperature:
|
||||
Sampling temperature; higher => more random.
|
||||
repetition_penalty:
|
||||
Penalty to reduce repeated tokens/codes.
|
||||
subtalker_dosample:
|
||||
Sampling switch for the sub-talker (only valid for qwen3-tts-tokenizer-v2) if applicable.
|
||||
subtalker_top_k:
|
||||
Top-k for sub-talker sampling (only valid for qwen3-tts-tokenizer-v2).
|
||||
subtalker_top_p:
|
||||
Top-p for sub-talker sampling (only valid for qwen3-tts-tokenizer-v2).
|
||||
subtalker_temperature:
|
||||
Temperature for sub-talker sampling (only valid for qwen3-tts-tokenizer-v2).
|
||||
max_new_tokens:
|
||||
Maximum number of new codec tokens to generate.
|
||||
**kwargs:
|
||||
Any other keyword arguments supported by HuggingFace Transformers `generate()` can be passed.
|
||||
They will be forwarded to the underlying `Qwen3TTSForConditionalGeneration.generate(...)`.
|
||||
|
||||
Returns:
|
||||
Tuple[List[np.ndarray], int]:
|
||||
(wavs, sample_rate)
|
||||
"""
|
||||
if self.model.tts_model_type != "voice_design":
|
||||
raise ValueError(
|
||||
f"model with \ntokenizer_type: {self.model.tokenizer_type}\n"
|
||||
f"tts_model_size: {self.model.tts_model_size}\n"
|
||||
f"tts_model_type: {self.model.tts_model_type}\n"
|
||||
"does not support generate_voice_design, Please check Model Card or Readme for more details."
|
||||
)
|
||||
|
||||
texts = self._ensure_list(text)
|
||||
languages = self._ensure_list(language) if isinstance(language, list) else ([language] * len(texts) if language is not None else ["Auto"] * len(texts))
|
||||
instructs = self._ensure_list(instruct)
|
||||
|
||||
if len(languages) == 1 and len(texts) > 1:
|
||||
languages = languages * len(texts)
|
||||
if len(instructs) == 1 and len(texts) > 1:
|
||||
instructs = instructs * len(texts)
|
||||
|
||||
if not (len(texts) == len(languages) == len(instructs)):
|
||||
raise ValueError(f"Batch size mismatch: text={len(texts)}, language={len(languages)}, instruct={len(instructs)}")
|
||||
|
||||
self._validate_languages(languages)
|
||||
|
||||
input_ids = self._tokenize_texts([self._build_assistant_text(t) for t in texts])
|
||||
|
||||
instruct_ids: List[Optional[torch.Tensor]] = []
|
||||
for ins in instructs:
|
||||
if ins is None or ins == "":
|
||||
instruct_ids.append(None)
|
||||
else:
|
||||
instruct_ids.append(self._tokenize_texts([self._build_instruct_text(ins)])[0])
|
||||
|
||||
gen_kwargs = self._merge_generate_kwargs(**kwargs)
|
||||
|
||||
talker_codes_list, _ = self.model.generate(
|
||||
input_ids=input_ids,
|
||||
instruct_ids=instruct_ids,
|
||||
languages=languages,
|
||||
non_streaming_mode=non_streaming_mode,
|
||||
**gen_kwargs,
|
||||
)
|
||||
|
||||
wavs, fs = self.model.speech_tokenizer.decode([{"audio_codes": c} for c in talker_codes_list])
|
||||
return wavs, fs
|
||||
|
||||
# custom voice model
|
||||
@torch.no_grad()
|
||||
def generate_custom_voice(
|
||||
self,
|
||||
text: Union[str, List[str]],
|
||||
speaker: Union[str, List[str]],
|
||||
language: Union[str, List[str]] = None,
|
||||
instruct: Optional[Union[str, List[str]]] = None,
|
||||
non_streaming_mode: bool = True,
|
||||
**kwargs,
|
||||
) -> Tuple[List[np.ndarray], int]:
|
||||
"""
|
||||
Generate speech with the CustomVoice model using a predefined speaker id, optionally controlled by instruction text.
|
||||
|
||||
Args:
|
||||
text:
|
||||
Text(s) to synthesize.
|
||||
language:
|
||||
Language(s) for each sample.
|
||||
speaker:
|
||||
Speaker name(s). Will be validated against `model.get_supported_speakers()` (case-insensitive).
|
||||
instruct:
|
||||
Optional instruction(s). If None, treated as empty (no instruction).
|
||||
non_streaming_mode:
|
||||
Using non-streaming text input, this option currently only simulates streaming text input when set to `false`,
|
||||
rather than enabling true streaming input or streaming generation.
|
||||
do_sample:
|
||||
Whether to use sampling, recommended to be set to `true` for most use cases.
|
||||
top_k:
|
||||
Top-k sampling parameter.
|
||||
top_p:
|
||||
Top-p sampling parameter.
|
||||
temperature:
|
||||
Sampling temperature; higher => more random.
|
||||
repetition_penalty:
|
||||
Penalty to reduce repeated tokens/codes.
|
||||
subtalker_dosample:
|
||||
Sampling switch for the sub-talker (only valid for qwen3-tts-tokenizer-v2) if applicable.
|
||||
subtalker_top_k:
|
||||
Top-k for sub-talker sampling (only valid for qwen3-tts-tokenizer-v2).
|
||||
subtalker_top_p:
|
||||
Top-p for sub-talker sampling (only valid for qwen3-tts-tokenizer-v2).
|
||||
subtalker_temperature:
|
||||
Temperature for sub-talker sampling (only valid for qwen3-tts-tokenizer-v2).
|
||||
max_new_tokens:
|
||||
Maximum number of new codec tokens to generate.
|
||||
**kwargs:
|
||||
Any other keyword arguments supported by HuggingFace Transformers `generate()` can be passed.
|
||||
They will be forwarded to the underlying `Qwen3TTSForConditionalGeneration.generate(...)`.
|
||||
|
||||
Returns:
|
||||
Tuple[List[np.ndarray], int]:
|
||||
(wavs, sample_rate)
|
||||
|
||||
Raises:
|
||||
ValueError:
|
||||
If any speaker/language is unsupported or batch sizes mismatch.
|
||||
"""
|
||||
if self.model.tts_model_type != "custom_voice":
|
||||
raise ValueError(
|
||||
f"model with \ntokenizer_type: {self.model.tokenizer_type}\n"
|
||||
f"tts_model_size: {self.model.tts_model_size}\n"
|
||||
f"tts_model_type: {self.model.tts_model_type}\n"
|
||||
"does not support generate_custom_voice, Please check Model Card or Readme for more details."
|
||||
)
|
||||
|
||||
texts = self._ensure_list(text)
|
||||
languages = self._ensure_list(language) if isinstance(language, list) else ([language] * len(texts) if language is not None else ["Auto"] * len(texts))
|
||||
speakers = self._ensure_list(speaker)
|
||||
if self.model.tts_model_size in "0b6": # for 0b6 model, instruct is not supported
|
||||
instruct = None
|
||||
instructs = self._ensure_list(instruct) if isinstance(instruct, list) else ([instruct] * len(texts) if instruct is not None else [""] * len(texts))
|
||||
|
||||
if len(languages) == 1 and len(texts) > 1:
|
||||
languages = languages * len(texts)
|
||||
if len(speakers) == 1 and len(texts) > 1:
|
||||
speakers = speakers * len(texts)
|
||||
if len(instructs) == 1 and len(texts) > 1:
|
||||
instructs = instructs * len(texts)
|
||||
|
||||
if not (len(texts) == len(languages) == len(speakers) == len(instructs)):
|
||||
raise ValueError(
|
||||
f"Batch size mismatch: text={len(texts)}, language={len(languages)}, speaker={len(speakers)}, instruct={len(instructs)}"
|
||||
)
|
||||
|
||||
self._validate_languages(languages)
|
||||
self._validate_speakers(speakers)
|
||||
|
||||
input_ids = self._tokenize_texts([self._build_assistant_text(t) for t in texts])
|
||||
|
||||
instruct_ids: List[Optional[torch.Tensor]] = []
|
||||
for ins in instructs:
|
||||
if ins is None or ins == "":
|
||||
instruct_ids.append(None)
|
||||
else:
|
||||
instruct_ids.append(self._tokenize_texts([self._build_instruct_text(ins)])[0])
|
||||
|
||||
gen_kwargs = self._merge_generate_kwargs(**kwargs)
|
||||
|
||||
talker_codes_list, _ = self.model.generate(
|
||||
input_ids=input_ids,
|
||||
instruct_ids=instruct_ids,
|
||||
languages=languages,
|
||||
speakers=speakers,
|
||||
non_streaming_mode=non_streaming_mode,
|
||||
**gen_kwargs,
|
||||
)
|
||||
|
||||
wavs, fs = self.model.speech_tokenizer.decode([{"audio_codes": c} for c in talker_codes_list])
|
||||
return wavs, fs
|
||||
|
||||
|
||||
def get_supported_speakers(self) -> Optional[List[str]]:
|
||||
"""
|
||||
List supported speaker names for the current model.
|
||||
|
||||
This is a convenience wrapper around `model.get_supported_speakers()`.
|
||||
If the underlying model does not expose speaker constraints (returns None),
|
||||
this method also returns None.
|
||||
|
||||
Returns:
|
||||
Optional[List[str]]:
|
||||
- A sorted list of supported speaker names (lowercased), if available.
|
||||
- None if the model does not provide supported speakers.
|
||||
"""
|
||||
supported = self._supported_speakers_set()
|
||||
if supported is None:
|
||||
return None
|
||||
return sorted(supported)
|
||||
|
||||
|
||||
def get_supported_languages(self) -> Optional[List[str]]:
|
||||
"""
|
||||
List supported language names for the current model.
|
||||
|
||||
This is a convenience wrapper around `model.get_supported_languages()`.
|
||||
If the underlying model does not expose language constraints (returns None),
|
||||
this method also returns None.
|
||||
|
||||
Returns:
|
||||
Optional[List[str]]:
|
||||
- A sorted list of supported language names (lowercased), if available.
|
||||
- None if the model does not provide supported languages.
|
||||
"""
|
||||
supported = self._supported_languages_set()
|
||||
if supported is None:
|
||||
return None
|
||||
return sorted(supported)
|
||||
411
models/Qwen3-TTS/qwen_tts/inference/qwen3_tts_tokenizer.py
Normal file
411
models/Qwen3-TTS/qwen_tts/inference/qwen3_tts_tokenizer.py
Normal file
@@ -0,0 +1,411 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2026 The Alibaba Qwen team.
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
import base64
|
||||
import io
|
||||
import urllib.request
|
||||
from typing import List, Optional, Tuple, Union
|
||||
from urllib.parse import urlparse
|
||||
|
||||
import librosa
|
||||
import numpy as np
|
||||
import soundfile as sf
|
||||
import torch
|
||||
from torch.nn.utils.rnn import pad_sequence
|
||||
from transformers import AutoConfig, AutoFeatureExtractor, AutoModel
|
||||
|
||||
from ..core import (
|
||||
Qwen3TTSTokenizerV1Config,
|
||||
Qwen3TTSTokenizerV1Model,
|
||||
Qwen3TTSTokenizerV2Config,
|
||||
Qwen3TTSTokenizerV2Model,
|
||||
)
|
||||
|
||||
AudioInput = Union[
|
||||
str, # wav path, or base64 string
|
||||
np.ndarray, # 1-D float array
|
||||
List[str],
|
||||
List[np.ndarray],
|
||||
]
|
||||
|
||||
|
||||
class Qwen3TTSTokenizer:
|
||||
"""
|
||||
A wrapper for Qwen3 TTS Tokenizer 25Hz/12Hz with HuggingFace-style loading.
|
||||
|
||||
- from_pretrained(): loads speech tokenizer model via AutoModel and feature_extractor via AutoFeatureExtractor.
|
||||
- encode(): supports wav path(s), base64 audio string(s), numpy array(s).
|
||||
- decode(): accepts either the raw model encode output, or a minimal dict/list-of-dicts.
|
||||
|
||||
Notes:
|
||||
- For numpy array input, you must pass `sr` so the audio can be resampled to model sample rate.
|
||||
- Returned audio is float32 numpy arrays and the output sample rate.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self.model = None
|
||||
self.feature_extractor = None
|
||||
self.config = None
|
||||
self.device = None
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs) -> "Qwen3TTSTokenizer":
|
||||
"""
|
||||
Initialize tokenizer with HuggingFace `from_pretrained` style.
|
||||
|
||||
Args:
|
||||
pretrained_model_name_or_path (str):
|
||||
HuggingFace repo id or local directory.
|
||||
**kwargs (Any):
|
||||
Forwarded to `AutoModel.from_pretrained(...)` directly.
|
||||
Typical examples: device_map="cuda:0", dtype=torch.bfloat16, attn_implementation="eager".
|
||||
|
||||
Returns:
|
||||
Qwen3TTSTokenizer:
|
||||
Initialized instance with `model`, `feature_extractor`, `config`.
|
||||
"""
|
||||
inst = cls()
|
||||
|
||||
AutoConfig.register("qwen3_tts_tokenizer_25hz", Qwen3TTSTokenizerV1Config)
|
||||
AutoModel.register(Qwen3TTSTokenizerV1Config, Qwen3TTSTokenizerV1Model)
|
||||
|
||||
AutoConfig.register("qwen3_tts_tokenizer_12hz", Qwen3TTSTokenizerV2Config)
|
||||
AutoModel.register(Qwen3TTSTokenizerV2Config, Qwen3TTSTokenizerV2Model)
|
||||
|
||||
inst.feature_extractor = AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)
|
||||
inst.model = AutoModel.from_pretrained(pretrained_model_name_or_path, **kwargs)
|
||||
inst.config = inst.model.config
|
||||
|
||||
inst.device = getattr(inst.model, "device", None)
|
||||
if inst.device is None:
|
||||
# fallback: infer from first parameter device
|
||||
try:
|
||||
inst.device = next(inst.model.parameters()).device
|
||||
except StopIteration:
|
||||
inst.device = torch.device("cpu")
|
||||
|
||||
return inst
|
||||
|
||||
def _is_probably_base64(self, s: str) -> bool:
|
||||
if s.startswith("data:audio"):
|
||||
return True
|
||||
# Heuristic: no filesystem path separators and long enough.
|
||||
if ("/" not in s and "\\" not in s) and len(s) > 256:
|
||||
return True
|
||||
return False
|
||||
|
||||
def _is_url(self, s: str) -> bool:
|
||||
try:
|
||||
u = urlparse(s)
|
||||
return u.scheme in ("http", "https") and bool(u.netloc)
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
def _decode_base64_to_wav_bytes(self, b64: str) -> bytes:
|
||||
# Accept both "data:audio/wav;base64,...." and raw base64
|
||||
if "," in b64 and b64.strip().startswith("data:"):
|
||||
b64 = b64.split(",", 1)[1]
|
||||
return base64.b64decode(b64)
|
||||
|
||||
def load_audio(
|
||||
self,
|
||||
x: str,
|
||||
target_sr: int,
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Load audio from wav path or base64 string, then resample to target_sr.
|
||||
|
||||
Args:
|
||||
x (str):
|
||||
A wav file path, or a base64 audio string (raw or data URL).
|
||||
target_sr (int):
|
||||
Target sampling rate.
|
||||
|
||||
Returns:
|
||||
np.ndarray:
|
||||
1-D float32 waveform at target_sr.
|
||||
"""
|
||||
if self._is_url(x):
|
||||
with urllib.request.urlopen(x) as resp:
|
||||
audio_bytes = resp.read()
|
||||
with io.BytesIO(audio_bytes) as f:
|
||||
audio, sr = sf.read(f, dtype="float32", always_2d=False)
|
||||
elif self._is_probably_base64(x):
|
||||
wav_bytes = self._decode_base64_to_wav_bytes(x)
|
||||
with io.BytesIO(wav_bytes) as f:
|
||||
audio, sr = sf.read(f, dtype="float32", always_2d=False)
|
||||
else:
|
||||
audio, sr = librosa.load(x, sr=None, mono=True)
|
||||
|
||||
if audio.ndim > 1:
|
||||
audio = np.mean(audio, axis=-1)
|
||||
|
||||
if sr != target_sr:
|
||||
audio = librosa.resample(y=audio, orig_sr=sr, target_sr=target_sr)
|
||||
|
||||
return audio.astype(np.float32)
|
||||
|
||||
def _normalize_audio_inputs(
|
||||
self,
|
||||
audios: AudioInput,
|
||||
sr: Optional[int],
|
||||
) -> List[np.ndarray]:
|
||||
"""
|
||||
Normalize all supported input types into a list of 1-D numpy float32 waveforms
|
||||
at `self.feature_extractor.sampling_rate`.
|
||||
|
||||
Args:
|
||||
audios (AudioInput):
|
||||
- str: wav path OR base64 audio string
|
||||
- np.ndarray: raw waveform (sr must be provided)
|
||||
- list[str] / list[np.ndarray]
|
||||
sr (Optional[int]):
|
||||
Sampling rate for raw numpy input. Required if input is np.ndarray or list[np.ndarray].
|
||||
|
||||
Returns:
|
||||
List[np.ndarray]:
|
||||
List of float32 waveforms resampled to model input SR.
|
||||
"""
|
||||
target_sr = int(self.feature_extractor.sampling_rate)
|
||||
|
||||
if isinstance(audios, (str, np.ndarray)):
|
||||
audios = [audios]
|
||||
|
||||
if len(audios) == 0:
|
||||
return []
|
||||
|
||||
if isinstance(audios[0], str):
|
||||
# wav path list or base64 list
|
||||
return [self.load_audio(x, target_sr=target_sr) for x in audios] # type: ignore[arg-type]
|
||||
|
||||
# numpy list
|
||||
if sr is None:
|
||||
raise ValueError("For numpy waveform input, you must provide `sr` (original sampling rate).")
|
||||
|
||||
out: List[np.ndarray] = []
|
||||
for a in audios: # type: ignore[assignment]
|
||||
if not isinstance(a, np.ndarray):
|
||||
raise TypeError("Mixed input types are not supported. Use all paths/base64 or all numpy arrays.")
|
||||
if a.ndim > 1:
|
||||
a = np.mean(a, axis=-1)
|
||||
if int(sr) != target_sr:
|
||||
a = librosa.resample(y=a.astype(np.float32), orig_sr=int(sr), target_sr=target_sr)
|
||||
out.append(a.astype(np.float32))
|
||||
return out
|
||||
|
||||
def encode(
|
||||
self,
|
||||
audios: AudioInput,
|
||||
sr: Optional[int] = None,
|
||||
return_dict: bool = True,
|
||||
):
|
||||
"""
|
||||
Batch-encode audio into discrete codes (and optional conditioning, depending on 25Hz/12Hz).
|
||||
|
||||
Args:
|
||||
audios (AudioInput):
|
||||
Supported forms:
|
||||
- np.ndarray: waveform (requires sr)
|
||||
- list[np.ndarray]: waveforms (requires sr)
|
||||
- str: wav path OR base64 audio string
|
||||
- list[str]: wav paths and/or base64 strings
|
||||
sr (Optional[int], default=None):
|
||||
Original sampling rate for numpy waveform input.
|
||||
return_dict (bool, default=True):
|
||||
Forwarded to model.encode(...). If True, returns ModelOutput.
|
||||
|
||||
Returns:
|
||||
25Hz:
|
||||
Qwen3TTSTokenizerV1EncoderOutput (if return_dict=True) with fields:
|
||||
- audio_codes: List[torch.LongTensor] each (codes_len,)
|
||||
- xvectors: List[torch.FloatTensor] each (xvector_dim,)
|
||||
- ref_mels: List[torch.FloatTensor] each (mel_len, mel_dim)
|
||||
12Hz:
|
||||
Qwen3TTSTokenizerV2EncoderOutput (if return_dict=True) with fields:
|
||||
- audio_codes: List[torch.LongTensor] each (codes_len, num_quantizers)
|
||||
|
||||
If return_dict=False, returns the raw tuple from model.encode.
|
||||
"""
|
||||
wavs = self._normalize_audio_inputs(audios, sr=sr)
|
||||
|
||||
inputs = self.feature_extractor(
|
||||
raw_audio=wavs,
|
||||
sampling_rate=int(self.feature_extractor.sampling_rate),
|
||||
return_tensors="pt",
|
||||
)
|
||||
inputs = inputs.to(self.device).to(self.model.dtype)
|
||||
|
||||
with torch.inference_mode():
|
||||
# model.encode expects (B, T) and (B, T)
|
||||
enc = self.model.encode(
|
||||
inputs["input_values"].squeeze(1),
|
||||
inputs["padding_mask"].squeeze(1),
|
||||
return_dict=return_dict,
|
||||
)
|
||||
return enc
|
||||
|
||||
def decode(
|
||||
self,
|
||||
encoded,
|
||||
) -> Tuple[List[np.ndarray], int]:
|
||||
"""
|
||||
Decode back to waveform.
|
||||
|
||||
Usage:
|
||||
1) Pass the raw output of `encode(...)` directly (recommended).
|
||||
- 25Hz: expects fields audio_codes, xvectors, ref_mels
|
||||
- 12Hz: expects field audio_codes
|
||||
2) Pass a dict or list[dict] (minimal form) for custom pipelines:
|
||||
- 25Hz dict keys: {"audio_codes", "xvectors", "ref_mels"}
|
||||
- 12Hz dict keys: {"audio_codes"}
|
||||
Values can be torch tensors or numpy arrays.
|
||||
|
||||
Args:
|
||||
encoded (Any):
|
||||
- ModelOutput returned by `encode()`, OR
|
||||
- dict, OR
|
||||
- list[dict]
|
||||
|
||||
Returns:
|
||||
Tuple[List[np.ndarray], int]:
|
||||
- wavs: list of 1-D float32 numpy arrays
|
||||
- sample_rate: int, model output sampling rate
|
||||
"""
|
||||
model_type = self.model.get_model_type()
|
||||
|
||||
def _to_tensor(x, dtype=None):
|
||||
if isinstance(x, torch.Tensor):
|
||||
return x
|
||||
x = np.asarray(x)
|
||||
t = torch.from_numpy(x)
|
||||
if dtype is not None:
|
||||
t = t.to(dtype)
|
||||
return t
|
||||
|
||||
# Normalize `encoded` into the same shapes as the official demo uses.
|
||||
if hasattr(encoded, "audio_codes"):
|
||||
# ModelOutput from encode()
|
||||
audio_codes_list = encoded.audio_codes
|
||||
xvectors_list = getattr(encoded, "xvectors", None)
|
||||
ref_mels_list = getattr(encoded, "ref_mels", None)
|
||||
elif isinstance(encoded, dict):
|
||||
audio_codes_list = encoded["audio_codes"]
|
||||
xvectors_list = encoded.get("xvectors", None)
|
||||
ref_mels_list = encoded.get("ref_mels", None)
|
||||
elif isinstance(encoded, list):
|
||||
# list of dicts
|
||||
audio_codes_list = [e["audio_codes"] for e in encoded]
|
||||
xvectors_list = [e["xvectors"] for e in encoded] if ("xvectors" in encoded[0]) else None
|
||||
ref_mels_list = [e["ref_mels"] for e in encoded] if ("ref_mels" in encoded[0]) else None
|
||||
else:
|
||||
raise TypeError("`encoded` must be an encode output, a dict, or a list of dicts.")
|
||||
|
||||
# Ensure list form for per-sample tensors
|
||||
if isinstance(audio_codes_list, torch.Tensor):
|
||||
# Could be a single sample tensor or an already padded batch tensor.
|
||||
t = audio_codes_list
|
||||
if t.dim() == 1:
|
||||
# 25Hz single sample: (C,) -> (1, C)
|
||||
t = t.unsqueeze(0)
|
||||
elif t.dim() == 2:
|
||||
# 12Hz single sample: (C, Q) -> (1, C, Q)
|
||||
t = t.unsqueeze(0)
|
||||
audio_codes_padded = t.to(self.device)
|
||||
else:
|
||||
# List[Tensor/np]
|
||||
audio_codes_list = [_to_tensor(c, dtype=torch.long) for c in audio_codes_list]
|
||||
audio_codes_padded = pad_sequence(audio_codes_list, batch_first=True, padding_value=0).to(self.device)
|
||||
|
||||
with torch.inference_mode():
|
||||
if model_type == "qwen3_tts_tokenizer_25hz":
|
||||
if xvectors_list is None or ref_mels_list is None:
|
||||
raise ValueError("25Hz decode requires `xvectors` and `ref_mels`.")
|
||||
|
||||
if isinstance(xvectors_list, torch.Tensor):
|
||||
xvectors_batch = xvectors_list
|
||||
if xvectors_batch.dim() == 1: # (D,) -> (1, D)
|
||||
xvectors_batch = xvectors_batch.unsqueeze(0)
|
||||
xvectors_batch = xvectors_batch.to(self.device).to(self.model.dtype)
|
||||
else:
|
||||
xvectors_list = [_to_tensor(x, dtype=torch.float32) for x in xvectors_list]
|
||||
xvectors_batch = torch.stack(xvectors_list, dim=0).to(self.device).to(self.model.dtype)
|
||||
|
||||
if isinstance(ref_mels_list, torch.Tensor):
|
||||
ref_mels_padded = ref_mels_list
|
||||
if ref_mels_padded.dim() == 2: # (T, M) -> (1, T, M)
|
||||
ref_mels_padded = ref_mels_padded.unsqueeze(0)
|
||||
ref_mels_padded = ref_mels_padded.to(self.device).to(self.model.dtype)
|
||||
else:
|
||||
ref_mels_list = [_to_tensor(m, dtype=torch.float32) for m in ref_mels_list]
|
||||
ref_mels_padded = pad_sequence(ref_mels_list, batch_first=True, padding_value=0).to(self.device).to(self.model.dtype)
|
||||
|
||||
dec = self.model.decode(audio_codes_padded, xvectors_batch, ref_mels_padded, return_dict=True)
|
||||
wav_tensors = dec.audio_values
|
||||
|
||||
elif model_type == "qwen3_tts_tokenizer_12hz":
|
||||
dec = self.model.decode(audio_codes_padded, return_dict=True)
|
||||
wav_tensors = dec.audio_values
|
||||
|
||||
else:
|
||||
raise ValueError(f"Unknown model type: {model_type}")
|
||||
|
||||
wavs = [w.to(torch.float32).detach().cpu().numpy() for w in wav_tensors]
|
||||
return wavs, int(self.model.get_output_sample_rate())
|
||||
|
||||
def get_model_type(self) -> str:
|
||||
"""
|
||||
Get the underlying tokenizer model type.
|
||||
|
||||
Returns:
|
||||
str: Model type string from `self.model.config.model_type`
|
||||
(e.g. "qwen3_tts_tokenizer_25hz" / "qwen3_tts_tokenizer_12hz").
|
||||
"""
|
||||
return self.model.get_model_type()
|
||||
|
||||
def get_input_sample_rate(self) -> int:
|
||||
"""
|
||||
Get the expected input sample rate for encoding.
|
||||
|
||||
Returns:
|
||||
int: Input sample rate (Hz).
|
||||
"""
|
||||
return int(self.model.get_input_sample_rate())
|
||||
|
||||
def get_output_sample_rate(self) -> int:
|
||||
"""
|
||||
Get the output sample rate for decoded waveforms.
|
||||
|
||||
Returns:
|
||||
int: Output sample rate (Hz).
|
||||
"""
|
||||
return int(self.model.get_output_sample_rate())
|
||||
|
||||
def get_encode_downsample_rate(self) -> int:
|
||||
"""
|
||||
Get the encoder downsample rate (waveform samples per code step).
|
||||
|
||||
Returns:
|
||||
int: Encode downsample rate.
|
||||
"""
|
||||
return int(self.model.get_encode_downsample_rate())
|
||||
|
||||
def get_decode_upsample_rate(self) -> int:
|
||||
"""
|
||||
Get the decoder upsample rate (waveform samples per code step).
|
||||
|
||||
Returns:
|
||||
int: Decode upsample rate.
|
||||
"""
|
||||
return int(self.model.get_decode_upsample_rate())
|
||||
189
models/Qwen3-TTS/qwen_tts_server.py
Normal file
189
models/Qwen3-TTS/qwen_tts_server.py
Normal file
@@ -0,0 +1,189 @@
|
||||
"""
|
||||
Qwen3-TTS 独立推理服务
|
||||
端口: 8009
|
||||
GPU: 0
|
||||
|
||||
启动方式:
|
||||
conda activate qwen-tts
|
||||
python qwen_tts_server.py
|
||||
|
||||
PM2 启动:
|
||||
pm2 start qwen_tts_server.py --name qwen-tts --interpreter /home/rongye/ProgramFiles/miniconda3/envs/qwen-tts/bin/python
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
import tempfile
|
||||
import time
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
# 设置 GPU
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
|
||||
|
||||
from fastapi import FastAPI, HTTPException, UploadFile, File, Form
|
||||
from fastapi.responses import FileResponse
|
||||
from pydantic import BaseModel
|
||||
import uvicorn
|
||||
|
||||
app = FastAPI(title="Qwen3-TTS Voice Clone Service", version="1.0")
|
||||
|
||||
# 模型路径 (1.7B-Base 提供更高质量的声音克隆)
|
||||
MODEL_PATH = Path(__file__).parent / "checkpoints" / "1.7B-Base"
|
||||
|
||||
# 全局模型实例
|
||||
_model = None
|
||||
_model_loaded = False
|
||||
|
||||
|
||||
def load_model():
|
||||
"""加载模型（启动时调用）"""
|
||||
global _model, _model_loaded
|
||||
|
||||
if _model_loaded:
|
||||
return
|
||||
|
||||
print("🔄 Loading Qwen3-TTS model...")
|
||||
start = time.time()
|
||||
|
||||
import torch
|
||||
from qwen_tts import Qwen3TTSModel
|
||||
|
||||
_model = Qwen3TTSModel.from_pretrained(
|
||||
str(MODEL_PATH),
|
||||
device_map="cuda:0",
|
||||
dtype=torch.bfloat16,
|
||||
)
|
||||
|
||||
_model_loaded = True
|
||||
print(f"✅ Qwen3-TTS model loaded in {time.time() - start:.1f}s")
|
||||
|
||||
|
||||
class GenerateRequest(BaseModel):
|
||||
text: str
|
||||
ref_text: str
|
||||
language: str = "Chinese"
|
||||
|
||||
|
||||
class HealthResponse(BaseModel):
|
||||
service: str
|
||||
model: str
|
||||
ready: bool
|
||||
gpu_id: int
|
||||
|
||||
|
||||
@app.on_event("startup")
|
||||
async def startup():
|
||||
"""服务启动时预加载模型"""
|
||||
try:
|
||||
load_model()
|
||||
except Exception as e:
|
||||
print(f"❌ Model loading failed: {e}")
|
||||
|
||||
|
||||
@app.get("/health", response_model=HealthResponse)
|
||||
async def health():
|
||||
"""健康检查"""
|
||||
gpu_ok = False
|
||||
try:
|
||||
import torch
|
||||
gpu_ok = torch.cuda.is_available()
|
||||
except:
|
||||
pass
|
||||
|
||||
return HealthResponse(
|
||||
service="Qwen3-TTS Voice Clone",
|
||||
model="1.7B-Base",
|
||||
ready=_model_loaded and gpu_ok,
|
||||
gpu_id=0
|
||||
)
|
||||
|
||||
|
||||
@app.post("/generate")
|
||||
async def generate(
|
||||
ref_audio: UploadFile = File(...),
|
||||
text: str = Form(...),
|
||||
ref_text: str = Form(...),
|
||||
language: str = Form("Chinese")
|
||||
):
|
||||
"""
|
||||
声音克隆生成
|
||||
|
||||
Args:
|
||||
ref_audio: 参考音频文件 (WAV)
|
||||
text: 要合成的文本
|
||||
ref_text: 参考音频的转写文字
|
||||
language: 语言 (Chinese/English/Auto)
|
||||
|
||||
Returns:
|
||||
生成的音频文件 (WAV)
|
||||
"""
|
||||
if not _model_loaded:
|
||||
raise HTTPException(status_code=503, detail="Model not loaded")
|
||||
|
||||
import soundfile as sf
|
||||
|
||||
# 保存上传的参考音频到临时文件
|
||||
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_ref:
|
||||
content = await ref_audio.read()
|
||||
tmp_ref.write(content)
|
||||
ref_audio_path = tmp_ref.name
|
||||
|
||||
# 生成输出路径
|
||||
output_path = tempfile.mktemp(suffix=".wav")
|
||||
|
||||
try:
|
||||
print(f"🎤 Generating: {text[:30]}...")
|
||||
print(f"📝 Ref text: {ref_text[:50]}...")
|
||||
|
||||
start = time.time()
|
||||
|
||||
wavs, sr = _model.generate_voice_clone(
|
||||
text=text,
|
||||
language=language,
|
||||
ref_audio=ref_audio_path,
|
||||
ref_text=ref_text,
|
||||
)
|
||||
|
||||
sf.write(output_path, wavs[0], sr)
|
||||
|
||||
duration = len(wavs[0]) / sr
|
||||
print(f"✅ Generated in {time.time() - start:.1f}s, duration: {duration:.1f}s")
|
||||
|
||||
# 返回音频文件
|
||||
return FileResponse(
|
||||
output_path,
|
||||
media_type="audio/wav",
|
||||
filename="output.wav",
|
||||
background=None # 让客户端下载完再删除
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Generation failed: {e}")
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
finally:
|
||||
# 清理参考音频临时文件
|
||||
try:
|
||||
os.unlink(ref_audio_path)
|
||||
except:
|
||||
pass
|
||||
|
||||
|
||||
@app.on_event("shutdown")
|
||||
async def shutdown():
|
||||
"""清理临时文件"""
|
||||
# 清理 /tmp 中的残留文件
|
||||
import glob
|
||||
for f in glob.glob("/tmp/tmp*.wav"):
|
||||
try:
|
||||
os.unlink(f)
|
||||
except:
|
||||
pass
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
uvicorn.run(
|
||||
app,
|
||||
host="0.0.0.0",
|
||||
port=8009,
|
||||
log_level="info"
|
||||
)
|
||||
2907
remotion/package-lock.json
generated
Normal file
2907
remotion/package-lock.json
generated
Normal file
File diff suppressed because it is too large
Load Diff
24
remotion/package.json
Normal file
24
remotion/package.json
Normal file
@@ -0,0 +1,24 @@
|
||||
{
|
||||
"name": "vigent-remotion",
|
||||
"version": "1.0.0",
|
||||
"description": "Remotion video composition for ViGent2 subtitles and titles",
|
||||
"scripts": {
|
||||
"start": "remotion studio",
|
||||
"build": "remotion bundle",
|
||||
"render": "npx ts-node render.ts"
|
||||
},
|
||||
"dependencies": {
|
||||
"remotion": "^4.0.0",
|
||||
"@remotion/renderer": "^4.0.0",
|
||||
"@remotion/cli": "^4.0.0",
|
||||
"@remotion/media-utils": "^4.0.0",
|
||||
"react": "^18.2.0",
|
||||
"react-dom": "^18.2.0"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@types/node": "^20.0.0",
|
||||
"@types/react": "^18.2.0",
|
||||
"typescript": "^5.0.0",
|
||||
"ts-node": "^10.9.0"
|
||||
}
|
||||
}
|
||||
171
remotion/render.ts
Normal file
171
remotion/render.ts
Normal file
@@ -0,0 +1,171 @@
|
||||
/**
|
||||
* Remotion 服务端渲染脚本
|
||||
* 用于从命令行渲染视频
|
||||
*
|
||||
* 使用方式:
|
||||
* npx ts-node render.ts --video /path/to/video.mp4 --captions /path/to/captions.json --title "视频标题" --output /path/to/output.mp4
|
||||
*/
|
||||
|
||||
import { bundle } from '@remotion/bundler';
|
||||
import { renderMedia, selectComposition } from '@remotion/renderer';
|
||||
import path from 'path';
|
||||
import fs from 'fs';
|
||||
|
||||
interface RenderOptions {
|
||||
videoPath: string;
|
||||
captionsPath?: string;
|
||||
title?: string;
|
||||
titleDuration?: number;
|
||||
outputPath: string;
|
||||
fps?: number;
|
||||
enableSubtitles?: boolean;
|
||||
width?: number;
|
||||
height?: number;
|
||||
}
|
||||
|
||||
async function parseArgs(): Promise<RenderOptions> {
|
||||
const args = process.argv.slice(2);
|
||||
const options: Partial<RenderOptions> = {};
|
||||
|
||||
for (let i = 0; i < args.length; i += 2) {
|
||||
const key = args[i].replace('--', '');
|
||||
const value = args[i + 1];
|
||||
|
||||
switch (key) {
|
||||
case 'video':
|
||||
options.videoPath = value;
|
||||
break;
|
||||
case 'captions':
|
||||
options.captionsPath = value;
|
||||
break;
|
||||
case 'title':
|
||||
options.title = value;
|
||||
break;
|
||||
case 'titleDuration':
|
||||
options.titleDuration = parseFloat(value);
|
||||
break;
|
||||
case 'output':
|
||||
options.outputPath = value;
|
||||
break;
|
||||
case 'fps':
|
||||
options.fps = parseInt(value, 10);
|
||||
break;
|
||||
case 'enableSubtitles':
|
||||
options.enableSubtitles = value === 'true';
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
if (!options.videoPath || !options.outputPath) {
|
||||
console.error('Usage: npx ts-node render.ts --video <path> --output <path> [--captions <path>] [--title <text>] [--fps <number>]');
|
||||
process.exit(1);
|
||||
}
|
||||
|
||||
return options as RenderOptions;
|
||||
}
|
||||
|
||||
async function main() {
|
||||
const options = await parseArgs();
|
||||
const fps = options.fps || 25;
|
||||
|
||||
console.log('Starting Remotion render...');
|
||||
console.log('Options:', JSON.stringify(options, null, 2));
|
||||
|
||||
// 读取字幕数据
|
||||
let captions = undefined;
|
||||
if (options.captionsPath && fs.existsSync(options.captionsPath)) {
|
||||
const captionsContent = fs.readFileSync(options.captionsPath, 'utf-8');
|
||||
captions = JSON.parse(captionsContent);
|
||||
console.log(`Loaded captions with ${captions.segments?.length || 0} segments`);
|
||||
}
|
||||
|
||||
// 获取视频时长和尺寸
|
||||
let durationInFrames = 300; // 默认 12 秒
|
||||
let videoWidth = 1280;
|
||||
let videoHeight = 720;
|
||||
try {
|
||||
// 使用 ffprobe 获取视频时长
|
||||
const { execSync } = require('child_process');
|
||||
const ffprobeOutput = execSync(
|
||||
`ffprobe -v error -show_entries format=duration -of default=noprint_wrappers=1:nokey=1 "${options.videoPath}"`,
|
||||
{ encoding: 'utf-8' }
|
||||
);
|
||||
const durationInSeconds = parseFloat(ffprobeOutput.trim());
|
||||
durationInFrames = Math.ceil(durationInSeconds * fps);
|
||||
console.log(`Video duration: ${durationInSeconds}s (${durationInFrames} frames at ${fps}fps)`);
|
||||
|
||||
// 使用 ffprobe 获取视频尺寸
|
||||
const dimensionsOutput = execSync(
|
||||
`ffprobe -v error -select_streams v:0 -show_entries stream=width,height -of csv=s=x:p=0 "${options.videoPath}"`,
|
||||
{ encoding: 'utf-8' }
|
||||
);
|
||||
const [width, height] = dimensionsOutput.trim().split('x').map(Number);
|
||||
if (width && height) {
|
||||
videoWidth = width;
|
||||
videoHeight = height;
|
||||
console.log(`Video dimensions: ${videoWidth}x${videoHeight}`);
|
||||
}
|
||||
} catch (e) {
|
||||
console.warn('Could not get video duration, using default:', e);
|
||||
}
|
||||
|
||||
// 设置 publicDir 为视频文件所在目录，使用文件名作为 videoSrc
|
||||
const publicDir = path.dirname(path.resolve(options.videoPath));
|
||||
const videoFileName = path.basename(options.videoPath);
|
||||
console.log(`Public dir: ${publicDir}, Video file: ${videoFileName}`);
|
||||
|
||||
// Bundle the Remotion project
|
||||
console.log('Bundling Remotion project...');
|
||||
const bundleLocation = await bundle({
|
||||
entryPoint: path.resolve(__dirname, './src/index.ts'),
|
||||
webpackOverride: (config) => config,
|
||||
publicDir,
|
||||
});
|
||||
|
||||
// Select the composition
|
||||
const composition = await selectComposition({
|
||||
serveUrl: bundleLocation,
|
||||
id: 'ViGentVideo',
|
||||
inputProps: {
|
||||
videoSrc: videoFileName,
|
||||
captions,
|
||||
title: options.title,
|
||||
titleDuration: options.titleDuration || 3,
|
||||
enableSubtitles: options.enableSubtitles !== false,
|
||||
},
|
||||
});
|
||||
|
||||
// Override duration and dimensions
|
||||
composition.durationInFrames = durationInFrames;
|
||||
composition.fps = fps;
|
||||
composition.width = videoWidth;
|
||||
composition.height = videoHeight;
|
||||
|
||||
// Render the video
|
||||
console.log('Rendering video...');
|
||||
await renderMedia({
|
||||
composition,
|
||||
serveUrl: bundleLocation,
|
||||
codec: 'h264',
|
||||
outputLocation: options.outputPath,
|
||||
inputProps: {
|
||||
videoSrc: videoFileName,
|
||||
captions,
|
||||
title: options.title,
|
||||
titleDuration: options.titleDuration || 3,
|
||||
enableSubtitles: options.enableSubtitles !== false,
|
||||
},
|
||||
onProgress: ({ progress }) => {
|
||||
const percent = Math.round(progress * 100);
|
||||
process.stdout.write(`\rRendering: ${percent}%`);
|
||||
},
|
||||
});
|
||||
|
||||
console.log('\nRender complete!');
|
||||
console.log(`Output: ${options.outputPath}`);
|
||||
}
|
||||
|
||||
main().catch((err) => {
|
||||
console.error('Render failed:', err);
|
||||
process.exit(1);
|
||||
});
|
||||
30
remotion/src/Root.tsx
Normal file
30
remotion/src/Root.tsx
Normal file
@@ -0,0 +1,30 @@
|
||||
import React from 'react';
|
||||
import { Composition } from 'remotion';
|
||||
import { Video, VideoProps } from './Video';
|
||||
|
||||
/**
|
||||
* Remotion 根组件
|
||||
* 定义视频合成配置
|
||||
*/
|
||||
export const RemotionRoot: React.FC = () => {
|
||||
return (
|
||||
<>
|
||||
<Composition
|
||||
id="ViGentVideo"
|
||||
component={Video}
|
||||
durationInFrames={300} // 默认值，会被 render.ts 覆盖
|
||||
fps={25}
|
||||
width={1280}
|
||||
height={720}
|
||||
defaultProps={{
|
||||
videoSrc: '',
|
||||
audioSrc: undefined,
|
||||
captions: undefined,
|
||||
title: undefined,
|
||||
titleDuration: 3,
|
||||
enableSubtitles: true,
|
||||
}}
|
||||
/>
|
||||
</>
|
||||
);
|
||||
};
|
||||
45
remotion/src/Video.tsx
Normal file
45
remotion/src/Video.tsx
Normal file
@@ -0,0 +1,45 @@
|
||||
import React from 'react';
|
||||
import { AbsoluteFill, Composition } from 'remotion';
|
||||
import { VideoLayer } from './components/VideoLayer';
|
||||
import { Title } from './components/Title';
|
||||
import { Subtitles } from './components/Subtitles';
|
||||
import { CaptionsData } from './utils/captions';
|
||||
|
||||
export interface VideoProps {
|
||||
videoSrc: string;
|
||||
audioSrc?: string;
|
||||
captions?: CaptionsData;
|
||||
title?: string;
|
||||
titleDuration?: number;
|
||||
enableSubtitles?: boolean;
|
||||
}
|
||||
|
||||
/**
|
||||
* 主视频组件
|
||||
* 组合视频层、标题层和字幕层
|
||||
*/
|
||||
export const Video: React.FC<VideoProps> = ({
|
||||
videoSrc,
|
||||
audioSrc,
|
||||
captions,
|
||||
title,
|
||||
titleDuration = 3,
|
||||
enableSubtitles = true,
|
||||
}) => {
|
||||
return (
|
||||
<AbsoluteFill style={{ backgroundColor: 'black' }}>
|
||||
{/* 底层：视频 */}
|
||||
<VideoLayer videoSrc={videoSrc} audioSrc={audioSrc} />
|
||||
|
||||
{/* 中层：字幕 */}
|
||||
{enableSubtitles && captions && (
|
||||
<Subtitles captions={captions} />
|
||||
)}
|
||||
|
||||
{/* 顶层：标题 */}
|
||||
{title && (
|
||||
<Title title={title} duration={titleDuration} />
|
||||
)}
|
||||
</AbsoluteFill>
|
||||
);
|
||||
};
|
||||
87
remotion/src/components/Subtitles.tsx
Normal file
87
remotion/src/components/Subtitles.tsx
Normal file
@@ -0,0 +1,87 @@
|
||||
import React from 'react';
|
||||
import { AbsoluteFill, useCurrentFrame, useVideoConfig } from 'remotion';
|
||||
import {
|
||||
CaptionsData,
|
||||
getCurrentSegment,
|
||||
getCurrentWordIndex,
|
||||
} from '../utils/captions';
|
||||
|
||||
interface SubtitlesProps {
|
||||
captions: CaptionsData;
|
||||
highlightColor?: string;
|
||||
normalColor?: string;
|
||||
fontSize?: number;
|
||||
}
|
||||
|
||||
/**
|
||||
* 逐字高亮字幕组件
|
||||
* 根据时间戳逐字高亮显示字幕（无背景，纯文字描边）
|
||||
*/
|
||||
export const Subtitles: React.FC<SubtitlesProps> = ({
|
||||
captions,
|
||||
highlightColor = '#FFFF00',
|
||||
normalColor = '#FFFFFF',
|
||||
fontSize = 52,
|
||||
}) => {
|
||||
const frame = useCurrentFrame();
|
||||
const { fps } = useVideoConfig();
|
||||
|
||||
const currentTimeInSeconds = frame / fps;
|
||||
|
||||
// 获取当前段落
|
||||
const currentSegment = getCurrentSegment(captions, currentTimeInSeconds);
|
||||
|
||||
if (!currentSegment || currentSegment.words.length === 0) {
|
||||
return null;
|
||||
}
|
||||
|
||||
// 获取当前高亮字的索引
|
||||
const currentWordIndex = getCurrentWordIndex(currentSegment, currentTimeInSeconds);
|
||||
|
||||
return (
|
||||
<AbsoluteFill
|
||||
style={{
|
||||
justifyContent: 'flex-end',
|
||||
alignItems: 'center',
|
||||
paddingBottom: '6%',
|
||||
}}
|
||||
>
|
||||
<p
|
||||
style={{
|
||||
margin: 0,
|
||||
fontSize: `${fontSize}px`,
|
||||
fontFamily: '"PingFang SC", "Hiragino Sans GB", "Microsoft YaHei", "Noto Sans SC", sans-serif',
|
||||
fontWeight: 800,
|
||||
lineHeight: 1.4,
|
||||
textAlign: 'center',
|
||||
maxWidth: '90%',
|
||||
wordBreak: 'keep-all',
|
||||
letterSpacing: '2px',
|
||||
}}
|
||||
>
|
||||
{currentSegment.words.map((word, index) => {
|
||||
const isHighlighted = index <= currentWordIndex;
|
||||
return (
|
||||
<span
|
||||
key={`${word.word}-${index}`}
|
||||
style={{
|
||||
color: isHighlighted ? highlightColor : normalColor,
|
||||
textShadow: `
|
||||
-3px -3px 0 #000,
|
||||
3px -3px 0 #000,
|
||||
-3px 3px 0 #000,
|
||||
3px 3px 0 #000,
|
||||
0 0 12px rgba(0,0,0,0.9),
|
||||
0 4px 8px rgba(0,0,0,0.6)
|
||||
`,
|
||||
transition: 'color 0.05s ease',
|
||||
}}
|
||||
>
|
||||
{word.word}
|
||||
</span>
|
||||
);
|
||||
})}
|
||||
</p>
|
||||
</AbsoluteFill>
|
||||
);
|
||||
};
|
||||
93
remotion/src/components/Title.tsx
Normal file
93
remotion/src/components/Title.tsx
Normal file
@@ -0,0 +1,93 @@
|
||||
import React from 'react';
|
||||
import {
|
||||
AbsoluteFill,
|
||||
interpolate,
|
||||
useCurrentFrame,
|
||||
useVideoConfig,
|
||||
} from 'remotion';
|
||||
|
||||
interface TitleProps {
|
||||
title: string;
|
||||
duration?: number; // 标题显示时长（秒）
|
||||
fadeOutStart?: number; // 开始淡出的时间（秒）
|
||||
}
|
||||
|
||||
/**
|
||||
* 片头标题组件
|
||||
* 在视频顶部显示标题，带淡入淡出效果
|
||||
*/
|
||||
export const Title: React.FC<TitleProps> = ({
|
||||
title,
|
||||
duration = 3,
|
||||
fadeOutStart = 2,
|
||||
}) => {
|
||||
const frame = useCurrentFrame();
|
||||
const { fps } = useVideoConfig();
|
||||
|
||||
const currentTimeInSeconds = frame / fps;
|
||||
|
||||
// 如果超过显示时长，不渲染
|
||||
if (currentTimeInSeconds > duration) {
|
||||
return null;
|
||||
}
|
||||
|
||||
// 淡入效果 (0-0.5秒)
|
||||
const fadeInOpacity = interpolate(
|
||||
currentTimeInSeconds,
|
||||
[0, 0.5],
|
||||
[0, 1],
|
||||
{ extrapolateRight: 'clamp' }
|
||||
);
|
||||
|
||||
// 淡出效果
|
||||
const fadeOutOpacity = interpolate(
|
||||
currentTimeInSeconds,
|
||||
[fadeOutStart, duration],
|
||||
[1, 0],
|
||||
{ extrapolateLeft: 'clamp', extrapolateRight: 'clamp' }
|
||||
);
|
||||
|
||||
const opacity = Math.min(fadeInOpacity, fadeOutOpacity);
|
||||
|
||||
// 轻微的下滑动画
|
||||
const translateY = interpolate(
|
||||
currentTimeInSeconds,
|
||||
[0, 0.5],
|
||||
[-20, 0],
|
||||
{ extrapolateRight: 'clamp' }
|
||||
);
|
||||
|
||||
return (
|
||||
<AbsoluteFill
|
||||
style={{
|
||||
justifyContent: 'flex-start',
|
||||
alignItems: 'center',
|
||||
paddingTop: '6%',
|
||||
opacity,
|
||||
}}
|
||||
>
|
||||
<h1
|
||||
style={{
|
||||
transform: `translateY(${translateY}px)`,
|
||||
textAlign: 'center',
|
||||
color: '#FFFFFF',
|
||||
fontSize: '72px',
|
||||
fontWeight: 900,
|
||||
fontFamily: '"PingFang SC", "Hiragino Sans GB", "Microsoft YaHei", "Noto Sans SC", sans-serif',
|
||||
textShadow: `
|
||||
0 0 10px rgba(0,0,0,0.9),
|
||||
0 0 20px rgba(0,0,0,0.7),
|
||||
0 4px 8px rgba(0,0,0,0.8),
|
||||
0 8px 16px rgba(0,0,0,0.5)
|
||||
`,
|
||||
margin: 0,
|
||||
padding: '0 5%',
|
||||
lineHeight: 1.3,
|
||||
letterSpacing: '4px',
|
||||
}}
|
||||
>
|
||||
{title}
|
||||
</h1>
|
||||
</AbsoluteFill>
|
||||
);
|
||||
};
|
||||
34
remotion/src/components/VideoLayer.tsx
Normal file
34
remotion/src/components/VideoLayer.tsx
Normal file
@@ -0,0 +1,34 @@
|
||||
import React from 'react';
|
||||
import { AbsoluteFill, OffthreadVideo, Audio, staticFile } from 'remotion';
|
||||
|
||||
interface VideoLayerProps {
|
||||
videoSrc: string;
|
||||
audioSrc?: string;
|
||||
}
|
||||
|
||||
/**
|
||||
* 视频图层组件
|
||||
* 渲染底层视频和音频，视频自动循环以匹配音频长度
|
||||
*/
|
||||
export const VideoLayer: React.FC<VideoLayerProps> = ({
|
||||
videoSrc,
|
||||
audioSrc,
|
||||
}) => {
|
||||
// 使用 staticFile 从 publicDir 加载视频
|
||||
const videoUrl = staticFile(videoSrc);
|
||||
|
||||
return (
|
||||
<AbsoluteFill>
|
||||
<OffthreadVideo
|
||||
src={videoUrl}
|
||||
loop
|
||||
style={{
|
||||
width: '100%',
|
||||
height: '100%',
|
||||
objectFit: 'cover',
|
||||
}}
|
||||
/>
|
||||
{audioSrc && <Audio src={staticFile(audioSrc)} />}
|
||||
</AbsoluteFill>
|
||||
);
|
||||
};
|
||||
4
remotion/src/index.ts
Normal file
4
remotion/src/index.ts
Normal file
@@ -0,0 +1,4 @@
|
||||
import { registerRoot } from 'remotion';
|
||||
import { RemotionRoot } from './Root';
|
||||
|
||||
registerRoot(RemotionRoot);
|
||||
66
remotion/src/utils/captions.ts
Normal file
66
remotion/src/utils/captions.ts
Normal file
@@ -0,0 +1,66 @@
|
||||
/**
|
||||
* 字幕数据类型定义和处理工具
|
||||
*/
|
||||
|
||||
export interface WordTimestamp {
|
||||
word: string;
|
||||
start: number;
|
||||
end: number;
|
||||
}
|
||||
|
||||
export interface Segment {
|
||||
text: string;
|
||||
start: number;
|
||||
end: number;
|
||||
words: WordTimestamp[];
|
||||
}
|
||||
|
||||
export interface CaptionsData {
|
||||
segments: Segment[];
|
||||
}
|
||||
|
||||
/**
|
||||
* 根据当前时间获取应该显示的字幕段落
|
||||
*/
|
||||
export function getCurrentSegment(
|
||||
captions: CaptionsData,
|
||||
currentTimeInSeconds: number
|
||||
): Segment | null {
|
||||
for (const segment of captions.segments) {
|
||||
if (currentTimeInSeconds >= segment.start && currentTimeInSeconds <= segment.end) {
|
||||
return segment;
|
||||
}
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
/**
|
||||
* 根据当前时间获取当前高亮的字的索引
|
||||
*/
|
||||
export function getCurrentWordIndex(
|
||||
segment: Segment,
|
||||
currentTimeInSeconds: number
|
||||
): number {
|
||||
for (let i = 0; i < segment.words.length; i++) {
|
||||
const word = segment.words[i];
|
||||
if (currentTimeInSeconds >= word.start && currentTimeInSeconds <= word.end) {
|
||||
return i;
|
||||
}
|
||||
// 如果当前时间在两个字之间，返回前一个字
|
||||
if (i < segment.words.length - 1) {
|
||||
const nextWord = segment.words[i + 1];
|
||||
if (currentTimeInSeconds > word.end && currentTimeInSeconds < nextWord.start) {
|
||||
return i;
|
||||
}
|
||||
}
|
||||
}
|
||||
// 如果超过最后一个字的结束时间，返回最后一个字
|
||||
if (segment.words.length > 0) {
|
||||
const lastWord = segment.words[segment.words.length - 1];
|
||||
if (currentTimeInSeconds >= lastWord.end) {
|
||||
return segment.words.length - 1;
|
||||
}
|
||||
}
|
||||
return -1;
|
||||
}
|
||||
|
||||
19
remotion/tsconfig.json
Normal file
19
remotion/tsconfig.json
Normal file
@@ -0,0 +1,19 @@
|
||||
{
|
||||
"compilerOptions": {
|
||||
"target": "ES2020",
|
||||
"module": "commonjs",
|
||||
"lib": ["ES2020", "DOM"],
|
||||
"jsx": "react-jsx",
|
||||
"strict": true,
|
||||
"esModuleInterop": true,
|
||||
"skipLibCheck": true,
|
||||
"forceConsistentCasingInFileNames": true,
|
||||
"resolveJsonModule": true,
|
||||
"declaration": true,
|
||||
"declarationMap": true,
|
||||
"outDir": "./dist",
|
||||
"rootDir": "."
|
||||
},
|
||||
"include": ["src/**/*", "render.ts"],
|
||||
"exclude": ["node_modules", "dist"]
|
||||
}
|
||||
9
run_qwen_tts.sh
Normal file
9
run_qwen_tts.sh
Normal file
@@ -0,0 +1,9 @@
|
||||
#!/bin/bash
|
||||
# Qwen3-TTS 声音克隆服务启动脚本
|
||||
# 端口: 8009
|
||||
# GPU: 0
|
||||
|
||||
cd /home/rongye/ProgramFiles/ViGent2/models/Qwen3-TTS
|
||||
|
||||
# 使用 qwen-tts conda 环境的 Python
|
||||
/home/rongye/ProgramFiles/miniconda3/envs/qwen-tts/bin/python qwen_tts_server.py
|
||||
Reference in New Issue
Block a user