Compare commits
2 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
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4a3dd2b225 | ||
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ee8cb9cfd2 |
@@ -107,7 +107,30 @@ playwright install chromium
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---
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## 步骤 6: 配置环境变量
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## 步骤 6: 配置 Supabase RLS 策略 (重要)
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> ⚠️ **注意**：为了支持前端直传文件，必须配置存储桶的行级安全策略 (RLS)。
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1. 确保 Supabase 容器正在运行 (`docker ps`).
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2. 将项目根目录下的 `supabase_rls.sql` (如果有) 或以下 SQL 内容在数据库中执行。
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3. **执行命令**:
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```bash
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# 进入后端目录
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cd /home/rongye/ProgramFiles/ViGent2/backend
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# 执行 SQL (允许 anon 角色上传/读取 materials 桶)
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docker exec -i supabase-db psql -U postgres <<EOF
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INSERT INTO storage.buckets (id, name, public) VALUES ('materials', 'materials', true) ON CONFLICT (id) DO NOTHING;
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INSERT INTO storage.buckets (id, name, public) VALUES ('outputs', 'outputs', true) ON CONFLICT (id) DO NOTHING;
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CREATE POLICY "Allow public uploads" ON storage.objects FOR INSERT TO anon WITH CHECK (bucket_id = 'materials');
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CREATE POLICY "Allow public read" ON storage.objects FOR SELECT TO anon USING (bucket_id = 'materials' OR bucket_id = 'outputs');
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EOF
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```
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---
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## 步骤 7: 配置环境变量
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```bash
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cd /home/rongye/ProgramFiles/ViGent2/backend
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@@ -121,6 +144,8 @@ cp .env.example .env
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| 配置项 | 默认值 | 说明 |
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|--------|--------|------|
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| `SUPABASE_URL` | `http://localhost:8008` | Supabase API 内部地址 |
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| `SUPABASE_PUBLIC_URL` | `https://api.hbyrkj.top` | Supabase API 公网地址 (前端访问) |
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| `LATENTSYNC_GPU_ID` | 1 | GPU 选择 (0 或 1) |
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| `LATENTSYNC_USE_SERVER` | false | 设为 true 以启用常驻服务加速 |
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| `LATENTSYNC_INFERENCE_STEPS` | 20 | 推理步数 (20-50) |
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@@ -129,7 +154,7 @@ cp .env.example .env
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---
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## 步骤 7: 安装前端依赖
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## 步骤 8: 安装前端依赖
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```bash
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cd /home/rongye/ProgramFiles/ViGent2/frontend
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@@ -143,7 +168,7 @@ npm run build
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---
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## 步骤 8: 测试运行
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## 步骤 9: 测试运行
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> 💡 先手动启动测试，确认一切正常后再配置 pm2 常驻服务。
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@@ -178,7 +203,7 @@ python -m scripts.server
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---
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## 步骤 9: 使用 pm2 管理常驻服务
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## 步骤 10: 使用 pm2 管理常驻服务
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> 推荐使用 pm2 管理所有服务，支持自动重启和日志管理。
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@@ -254,7 +279,7 @@ pm2 delete all # 删除所有服务
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---
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## 步骤 10: 配置 Nginx HTTPS (可选 - 公网访问)
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## 步骤 11: 配置 Nginx HTTPS (可选 - 公网访问)
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如果您需要通过公网域名 HTTPS 访问 (如 `https://vigent.hbyrkj.top`)，请参考以下 Nginx 配置。
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@@ -294,8 +319,42 @@ server {
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---
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---
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## 步骤 12: 配置阿里云 Nginx 网关 (关键)
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> ⚠️ **CRITICAL**: 如果使用 `api.hbyrkj.top` 等域名作为入口，必须在阿里云 (或公网入口) 的 Nginx 配置中解除上传限制。
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> **这是导致 500/413 错误的核心原因。**
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**关键配置项**：
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```nginx
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server {
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listen 443 ssl;
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server_name api.hbyrkj.top;
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# ... 其他 SSL 配置 ...
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# 允许大文件上传 (0 表示不限制，或设置为 100M, 500M)
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client_max_body_size 0;
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location / {
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proxy_pass http://127.0.0.1:YOUR_FRP_PORT;
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# 延长超时时间
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proxy_read_timeout 600s;
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proxy_send_timeout 600s;
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}
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}
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```
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**后果**：如果没有这个配置，上传会在 ~1MB 或 ~10MB 时直接断开，报 413 Payload Too Large 或 500/502 错误。
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---
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## 故障排除
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### GPU 不可用
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```bash
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278
Docs/DevLogs/Day11.md
Normal file
278
Docs/DevLogs/Day11.md
Normal file
@@ -0,0 +1,278 @@
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## 🔧 上传架构重构 (Direct Upload)
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### 🚨 问题描述 (10:30)
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**现象**：上传大于 7MB 的文件时，后端返回 500 Internal Server Error，实际为 `ClientDisconnect`。
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**ROOT CAUSE (关键原因)**：
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- **Aliyun Nginx 网关限制**：`api.hbyrkj.top` 域名的 Nginx 配置缺少 `client_max_body_size 0;`。
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- **默认限制**：Nginx 默认限制请求体为 1MB (或少量)，导致大文件上传时连接被网关强制截断。
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- **误判**：初期待查方向集中在 FRP 和 Backend Proxy 超时，实际是网关层的硬限制。
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### ✅ 解决方案：前端直传 Supabase + 网关配置 (14:00)
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**核心变更**：
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1. **网关配置**：在 Aliyun Nginx 的 `api.hbyrkj.top` 配置块中添加 `client_max_body_size 0;` (解除大小限制)。
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2. **架构优化**：移除后端文件转发逻辑，改由前端直接上传到 Supabase Storage (减少链路节点)。
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#### 1. 前端改造 (`frontend/src/app/page.tsx`)
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- 引入 `@supabase/supabase-js` 客户端。
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- 使用 `supabase.storage.from('materials').upload()` 直接上传。
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- 移除旧的 `XMLHttpRequest` 代理上传逻辑。
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- 添加文件重命名策略：`{timestamp}_{sanitized_filename}`。
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```typescript
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// V2: Direct Upload (Bypass Backend)
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const { data, error } = await supabase.storage
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.from('materials')
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.upload(path, file, {
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cacheControl: '3600',
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upsert: false
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});
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```
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#### 2. 后端适配 (`backend/app/api/materials.py`)
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- **上传接口**：(已废弃/保留用于极小文件) 主要流量走直传。
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- **列表接口**：更新为返回 **签名 URL (Signed URL)**，而非本地路径。
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- **兼容性**：前端直接接收 `path` 字段为完整 URL，无需再次拼接。
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#### 3. 权限控制 (RLS)
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- Supabase 默认禁止匿名写入。
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- 执行 SQL 策略允许 `anon` 角色对 `materials` 桶的 `INSERT` 和 `SELECT` 权限。
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```sql
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-- Allow anonymous uploads
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CREATE POLICY "Allow public uploads"
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ON storage.objects FOR INSERT
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TO anon WITH CHECK (bucket_id = 'materials');
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```
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### 结果
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- ✅ **彻底解决超时**：上传不再经过 Nginx/FRP，直接走 Supabase CDN。
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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。
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## 🔧 Supabase 部署与 RLS 配置
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### 相关文件
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- `supabase_rls.sql`: 定义存储桶权限的 SQL 脚本。
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- `docker-compose.yml`: 确认 Storage 服务配置正常。
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### 操作步骤
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1. 将 `supabase_rls.sql` 上传至服务器。
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2. 通过 Docker 执行 SQL：
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```bash
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cat supabase_rls.sql | docker exec -i supabase-db psql -U postgres
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```
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3. 验证前端上传成功。
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---
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## 🔐 用户隔离实现 (15:00)
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### 问题描述
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不同账户登录后能看到其他用户上传的素材和生成的视频，缺乏数据隔离。
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### 解决方案：存储路径前缀隔离
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#### 1. 素材模块 (`backend/app/api/materials.py`)
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```python
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# 上传时添加用户ID前缀
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storage_path = f"{user_id}/{timestamp}_{safe_name}"
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# 列表时只查询当前用户目录
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files_obj = await storage_service.list_files(
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bucket=storage_service.BUCKET_MATERIALS,
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path=user_id # 只列出用户目录下的文件
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)
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# 删除时验证权限
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if not material_id.startswith(f"{user_id}/"):
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raise HTTPException(403, "无权删除此素材")
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```
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#### 2. 视频模块 (`backend/app/api/videos.py`)
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```python
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# 生成视频时使用用户ID目录
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storage_path = f"{user_id}/{task_id}_output.mp4"
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# 列表/删除同样基于用户目录隔离
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```
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#### 3. 发布模块 (`backend/app/services/publish_service.py`)
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- Cookie 存储支持用户隔离：`cookies/{user_id}/{platform}.json`
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### 存储结构
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```
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Supabase Storage/
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├── materials/
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│ ├── {user_id_1}/
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│ │ ├── 1737000001_video1.mp4
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│ │ └── 1737000002_video2.mp4
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│ └── {user_id_2}/
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│ └── 1737000003_video3.mp4
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└── outputs/
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├── {user_id_1}/
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│ └── {task_id}_output.mp4
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└── {user_id_2}/
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└── ...
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```
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### 结果
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- ✅ 不同用户数据完全隔离
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- ✅ Cookie 和登录状态按用户存储
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- ✅ 删除操作验证所有权
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---
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## 🌐 Storage URL 修复 (16:00)
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### 问题描述
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生成的视频 URL 为 `http://localhost:8008/...`，前端无法访问。
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### 解决方案
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#### 1. 后端配置 (`backend/.env`)
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```ini
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SUPABASE_URL=http://localhost:8008 # 内部访问
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SUPABASE_PUBLIC_URL=https://api.hbyrkj.top # 公网访问
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```
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#### 2. URL 转换 (`backend/app/services/storage.py`)
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```python
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def _convert_to_public_url(self, url: str) -> str:
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"""将内部 URL 转换为公网可访问的 URL"""
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if settings.SUPABASE_PUBLIC_URL and settings.SUPABASE_URL:
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internal_url = settings.SUPABASE_URL.rstrip('/')
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public_url = settings.SUPABASE_PUBLIC_URL.rstrip('/')
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return url.replace(internal_url, public_url)
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return url
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```
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### 结果
|
||||
- ✅ 前端获取的 URL 可正常访问
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- ✅ 视频预览和下载功能正常
|
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|
||||
---
|
||||
|
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## ⚡ 发布服务优化 - 本地文件直读 (16:30)
|
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|
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### 问题描述
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发布视频时需要先通过 HTTP 下载 Supabase Storage 文件到临时目录，效率低且浪费资源。
|
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### 发现
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Supabase Storage 文件实际存储在本地磁盘：
|
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```
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/home/rongye/ProgramFiles/Supabase/volumes/storage/stub/stub/{bucket}/{path}/{internal_uuid}
|
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```
|
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|
||||
### 解决方案
|
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|
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#### 1. 添加本地路径获取方法 (`storage.py`)
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```python
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SUPABASE_STORAGE_LOCAL_PATH = Path("/home/rongye/ProgramFiles/Supabase/volumes/storage/stub/stub")
|
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|
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def get_local_file_path(self, bucket: str, path: str) -> Optional[str]:
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"""获取 Storage 文件的本地磁盘路径"""
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dir_path = SUPABASE_STORAGE_LOCAL_PATH / bucket / path
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if not dir_path.exists():
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return None
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files = list(dir_path.iterdir())
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return str(files[0]) if files else None
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```
|
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#### 2. 发布服务优先使用本地文件 (`publish_service.py`)
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```python
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# 解析 URL 获取 bucket 和 path
|
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match = re.search(r'/storage/v1/object/sign/([^/]+)/(.+?)\?', video_path)
|
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if match:
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bucket, storage_path = match.group(1), match.group(2)
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local_video_path = storage_service.get_local_file_path(bucket, storage_path)
|
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|
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if local_video_path and os.path.exists(local_video_path):
|
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logger.info(f"[发布] 直接使用本地文件: {local_video_path}")
|
||||
else:
|
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# Fallback: HTTP 下载
|
||||
```
|
||||
|
||||
### 结果
|
||||
- ✅ 发布速度显著提升（跳过下载步骤）
|
||||
- ✅ 减少临时文件占用
|
||||
- ✅ 保留 HTTP 下载作为 Fallback
|
||||
|
||||
---
|
||||
|
||||
## 🔧 Supabase Studio 配置 (17:00)
|
||||
|
||||
### 修改内容
|
||||
更新 `/home/rongye/ProgramFiles/Supabase/.env`：
|
||||
```ini
|
||||
# 修改前
|
||||
SUPABASE_PUBLIC_URL=http://localhost:8000
|
||||
|
||||
# 修改后
|
||||
SUPABASE_PUBLIC_URL=https://api.hbyrkj.top
|
||||
```
|
||||
|
||||
### 原因
|
||||
通过 `supabase.hbyrkj.top` 公网访问 Studio 时，需要正确的 API 公网地址。
|
||||
|
||||
### 操作
|
||||
```bash
|
||||
docker compose restart studio
|
||||
```
|
||||
|
||||
### 待解决
|
||||
- 🔄 Studio Settings 页面加载问题（401 Unauthorized）- 可能与 Nginx Basic Auth 配置冲突
|
||||
|
||||
---
|
||||
|
||||
## 📁 今日修改文件清单
|
||||
|
||||
| 文件 | 变更类型 | 说明 |
|
||||
|------|----------|------|
|
||||
| `backend/app/api/materials.py` | 修改 | 添加用户隔离 |
|
||||
| `backend/app/api/videos.py` | 修改 | 添加用户隔离 |
|
||||
| `backend/app/services/storage.py` | 修改 | URL转换 + 本地路径获取 |
|
||||
| `backend/app/services/publish_service.py` | 修改 | 本地文件直读优化 |
|
||||
| `backend/.env` | 修改 | 添加 SUPABASE_PUBLIC_URL |
|
||||
| `Supabase/.env` | 修改 | SUPABASE_PUBLIC_URL |
|
||||
| `frontend/src/app/page.tsx` | 修改 | 改用后端API上传 |
|
||||
|
||||
---
|
||||
|
||||
## 📅 明日任务规划 (Day 12)
|
||||
|
||||
### 🎯 目标：部署 Qwen3-TTS 0.6B 声音克隆系统
|
||||
|
||||
**任务背景**：
|
||||
- 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
|
||||
|
||||
**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
|
||||
|
||||
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
|
||||
- 兼容现有 `tts_service.py` 接口
|
||||
|
||||
3. **前端适配**
|
||||
- 添加"声音克隆"选项
|
||||
- 支持上传参考音频（3-10秒）
|
||||
- 音色预览功能
|
||||
|
||||
**预期成果**：
|
||||
- ✅ 用户可上传自己的声音样本
|
||||
- ✅ 生成的口播视频使用克隆后的声音
|
||||
- ✅ 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)
|
||||
347
Docs/DevLogs/Day12.md
Normal file
347
Docs/DevLogs/Day12.md
Normal file
@@ -0,0 +1,347 @@
|
||||
# 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
|
||||
const API_BASE = typeof window === 'undefined'
|
||||
? 'http://localhost:8006'
|
||||
: '';
|
||||
|
||||
// 防止重复跳转
|
||||
let isRedirecting = false;
|
||||
|
||||
const api = axios.create({
|
||||
baseURL: API_BASE,
|
||||
withCredentials: true, // 自动携带 HttpOnly 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;
|
||||
|
||||
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
|
||||
@@ -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
|
||||
└── ...
|
||||
|
||||
182
Docs/FRONTEND_DEV.md
Normal file
182
Docs/FRONTEND_DEV.md
Normal file
@@ -0,0 +1,182 @@
|
||||
# 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）
|
||||
253
Docs/QWEN3_TTS_DEPLOY.md
Normal file
253
Docs/QWEN3_TTS_DEPLOY.md
Normal file
@@ -0,0 +1,253 @@
|
||||
# Qwen3-TTS 0.6B 部署指南
|
||||
|
||||
> 本文档描述如何在 Ubuntu 服务器上部署 Qwen3-TTS 0.6B-Base 声音克隆模型。
|
||||
|
||||
## 系统要求
|
||||
|
||||
| 要求 | 规格 |
|
||||
|------|------|
|
||||
| GPU | NVIDIA RTX 3090 24GB (或更高) |
|
||||
| VRAM | ≥ 4GB (推理), ≥ 8GB (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** | 0.6B-Base (声音克隆) |
|
||||
| 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 0.6B-Base 模型 (2.4GB)
|
||||
modelscope download --model Qwen/Qwen3-TTS-12Hz-0.6B-Base --local_dir ./checkpoints/0.6B-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-0.6B-Base --local-dir ./checkpoints/0.6B-Base
|
||||
```
|
||||
|
||||
下载完成后，目录结构应如下：
|
||||
|
||||
```
|
||||
checkpoints/
|
||||
├── Tokenizer/ # ~651MB
|
||||
│ ├── config.json
|
||||
│ ├── model.safetensors
|
||||
│ └── ...
|
||||
└── 0.6B-Base/ # ~2.4GB
|
||||
├── 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/0.6B-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
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 目录结构
|
||||
|
||||
部署完成后，目录结构应如下：
|
||||
|
||||
```
|
||||
/home/rongye/ProgramFiles/ViGent2/models/Qwen3-TTS/
|
||||
├── checkpoints/
|
||||
│ ├── Tokenizer/ # 语音编解码器
|
||||
│ └── 0.6B-Base/ # 声音克隆模型
|
||||
├── qwen_tts/ # 源码
|
||||
│ ├── inference/
|
||||
│ ├── models/
|
||||
│ └── ...
|
||||
├── examples/
|
||||
│ └── myvoice.wav # 参考音频
|
||||
├── pyproject.toml
|
||||
├── requirements.txt
|
||||
└── test_inference.py # 测试脚本
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 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) | 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 0.6B 通常只需要 4-6GB 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)
|
||||
|
||||
### 模型加载失败
|
||||
|
||||
确保以下文件存在：
|
||||
- `checkpoints/0.6B-Base/config.json`
|
||||
- `checkpoints/0.6B-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
|
||||
|
||||
---
|
||||
|
||||
## 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)
|
||||
@@ -57,6 +57,10 @@ STUDIO_PORT=3003
|
||||
# 如果配置了 Nginx 反代: https://api.hbyrkj.top
|
||||
# 如果直连: http://8.148.25.142:8008
|
||||
API_EXTERNAL_URL=https://api.hbyrkj.top
|
||||
|
||||
# Studio 公网 API 地址 (通过公网访问 Studio 时必须配置)
|
||||
# 用于 Studio 前端调用 API
|
||||
SUPABASE_PUBLIC_URL=https://api.hbyrkj.top
|
||||
```
|
||||
|
||||
### 4. 启动服务
|
||||
@@ -67,7 +71,51 @@ docker compose up -d
|
||||
|
||||
---
|
||||
|
||||
## 第二部分：安全访问配置 (Nginx)
|
||||
## 第二部分：Storage 本地文件结构
|
||||
|
||||
### 1. 存储路径
|
||||
|
||||
Supabase Storage 使用本地文件系统存储，路径结构如下：
|
||||
|
||||
```
|
||||
/home/rongye/ProgramFiles/Supabase/volumes/storage/stub/stub/
|
||||
├── materials/ # 素材桶
|
||||
│ └── {user_id}/ # 用户目录 (隔离)
|
||||
│ └── {timestamp}_{filename}/
|
||||
│ └── {internal_uuid} # 实际文件 (Supabase 内部 UUID)
|
||||
└── outputs/ # 输出桶
|
||||
└── {user_id}/
|
||||
└── {task_id}_output.mp4/
|
||||
└── {internal_uuid}
|
||||
```
|
||||
|
||||
### 2. 用户隔离策略
|
||||
|
||||
所有用户数据通过路径前缀实现隔离：
|
||||
|
||||
| 资源类型 | 路径格式 | 示例 |
|
||||
|----------|----------|------|
|
||||
| 素材 | `{bucket}/{user_id}/{timestamp}_{filename}` | `materials/abc123/1737000001_video.mp4` |
|
||||
| 输出 | `{bucket}/{user_id}/{task_id}_output.mp4` | `outputs/abc123/uuid-xxx_output.mp4` |
|
||||
| Cookie | `cookies/{user_id}/{platform}.json` | `cookies/abc123/bilibili.json` |
|
||||
|
||||
### 3. 直接访问本地文件
|
||||
|
||||
后端可以直接读取本地文件（跳过 HTTP），提升发布等操作的效率：
|
||||
|
||||
```python
|
||||
# storage.py
|
||||
SUPABASE_STORAGE_LOCAL_PATH = Path("/home/rongye/ProgramFiles/Supabase/volumes/storage/stub/stub")
|
||||
|
||||
def get_local_file_path(self, bucket: str, path: str) -> Optional[str]:
|
||||
dir_path = SUPABASE_STORAGE_LOCAL_PATH / bucket / path
|
||||
files = list(dir_path.iterdir())
|
||||
return str(files[0]) if files else None
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 第三部分：安全访问配置 (Nginx)
|
||||
|
||||
建议在阿里云公网网关上配置 Nginx 反向代理，通过 Frp 隧道连接内网服务。
|
||||
|
||||
@@ -78,19 +126,36 @@ docker compose up -d
|
||||
### 2. Nginx 配置示例
|
||||
|
||||
```nginx
|
||||
# Studio (需要密码保护)
|
||||
# Studio (需要密码保护，但静态资源和内部API需排除)
|
||||
server {
|
||||
server_name supabase.hbyrkj.top;
|
||||
|
||||
|
||||
# SSL 配置略...
|
||||
|
||||
# 静态资源不需要认证
|
||||
location ~ ^/(favicon|_next|static)/ {
|
||||
auth_basic off;
|
||||
proxy_pass http://127.0.0.1:3003;
|
||||
proxy_set_header Host $host;
|
||||
proxy_http_version 1.1;
|
||||
}
|
||||
|
||||
# Studio 内部 API 调用不需要认证
|
||||
location /api/ {
|
||||
auth_basic off;
|
||||
proxy_pass http://127.0.0.1:3003;
|
||||
proxy_set_header Host $host;
|
||||
proxy_http_version 1.1;
|
||||
proxy_set_header Upgrade $http_upgrade;
|
||||
proxy_set_header Connection "upgrade";
|
||||
}
|
||||
|
||||
# 其他路径需要 Basic Auth 认证
|
||||
location / {
|
||||
# Basic Auth 保护后台
|
||||
auth_basic "Restricted Studio";
|
||||
auth_basic_user_file /etc/nginx/.htpasswd;
|
||||
|
||||
proxy_pass http://127.0.0.1:3003;
|
||||
|
||||
|
||||
# WebSocket 支持 (Realtime 必须)
|
||||
proxy_http_version 1.1;
|
||||
proxy_set_header Upgrade $http_upgrade;
|
||||
@@ -101,23 +166,39 @@ server {
|
||||
# API (公开访问)
|
||||
server {
|
||||
server_name api.hbyrkj.top;
|
||||
|
||||
|
||||
# SSL 配置略...
|
||||
|
||||
# ⚠️ 重要：解除上传大小限制
|
||||
client_max_body_size 0;
|
||||
|
||||
location / {
|
||||
proxy_pass http://127.0.0.1:8008;
|
||||
|
||||
|
||||
# 允许 WebSocket
|
||||
proxy_http_version 1.1;
|
||||
proxy_set_header Upgrade $http_upgrade;
|
||||
proxy_set_header Connection "upgrade";
|
||||
|
||||
# 大文件上传超时设置
|
||||
proxy_read_timeout 600s;
|
||||
proxy_send_timeout 600s;
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 3. 关键配置说明
|
||||
|
||||
| 配置项 | 作用 | 必要性 |
|
||||
|--------|------|--------|
|
||||
| `client_max_body_size 0` | 解除上传大小限制（默认 1MB） | **必须** |
|
||||
| `proxy_read_timeout 600s` | 大文件上传/下载超时 | 推荐 |
|
||||
| `proxy_http_version 1.1` | WebSocket 支持 | Realtime 必须 |
|
||||
| `auth_basic` | Studio 访问保护 | 推荐 |
|
||||
|
||||
---
|
||||
|
||||
## 第三部分：数据库与认证配置 (Database & Auth)
|
||||
## 第四部分：数据库与认证配置 (Database & Auth)
|
||||
|
||||
### 1. 初始化表结构 (Schema)
|
||||
|
||||
@@ -184,7 +265,7 @@ JWT_EXPIRE_HOURS=168
|
||||
|
||||
---
|
||||
|
||||
## 第四部分：常用维护命令
|
||||
## 第五部分：常用维护命令
|
||||
|
||||
**查看服务状态**:
|
||||
```bash
|
||||
|
||||
@@ -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**：2026-01-26
|
||||
**整体进度**：100%（Day 10 HTTPS 部署与细节完善）
|
||||
**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**：2026-01-28
|
||||
**整体进度**：100%（Day 12 iOS 兼容、移动端优化、Qwen3-TTS 部署）
|
||||
|
||||
## 📖 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 1-12 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-Day12)
|
||||
- [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)
|
||||
|
||||
---
|
||||
|
||||
@@ -148,21 +149,41 @@
|
||||
- [x] **安全加固** (Basic Auth 保护后台)
|
||||
- [x] **端口冲突解决** (迁移 Analytics/Kong)
|
||||
|
||||
### 阶段十七：上传架构重构 (Day 11)
|
||||
- [x] **直传改造** (前端直接上传 Supabase，绕过后端代理)
|
||||
- [x] **后端适配** (Signed URL 签名生成)
|
||||
- [x] **RLS 策略部署** (SQL 脚本自动化权限配置)
|
||||
- [x] **超时问题根治** (彻底解决 Nginx/FRP 30s 限制)
|
||||
- [x] **前端依赖更新** (@supabase/supabase-js 集成)
|
||||
|
||||
### 阶段十八：用户隔离与存储优化 (Day 11)
|
||||
- [x] **用户数据隔离** (素材/视频/Cookie 按用户ID目录隔离)
|
||||
- [x] **Storage URL 修复** (SUPABASE_PUBLIC_URL 配置，修复 localhost 问题)
|
||||
- [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 0.6B 部署** (声音克隆模型，GPU0，3秒参考音频快速克隆)
|
||||
|
||||
---
|
||||
|
||||
## 🛤️ 后续规划
|
||||
|
||||
### 🔴 优先待办
|
||||
- [ ] **Qwen3-TTS 集成到 ViGent2** - 前端 UI + 后端服务集成
|
||||
- [ ] 批量视频生成架构设计
|
||||
- [ ] 字幕样式编辑器集成
|
||||
|
||||
### 🟠 功能完善
|
||||
- [x] 定时发布功能 ✅ Day 7 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
|
||||
|
||||
@@ -241,7 +262,6 @@
|
||||
|
||||
## 📅 时间线
|
||||
|
||||
```
|
||||
Day 1: 项目初始化 + 核心功能 ✅ 完成
|
||||
- 后端 API 框架
|
||||
- 前端 UI
|
||||
@@ -317,5 +337,24 @@ Day 10: HTTPS 部署与细节完善 ✅ 完成
|
||||
- 账号列表 Bug 修复 (paths.py 白名单)
|
||||
- 阿里云 Nginx HTTPS 部署
|
||||
- UI 细节优化 (Title 更新)
|
||||
```
|
||||
|
||||
Day 11: 上传架构重构 ✅ 完成
|
||||
- **核心修复**: Aliyun Nginx `client_max_body_size 0` 配置
|
||||
- 500 错误根治 (Direct Upload + Gateway Config)
|
||||
- Supabase RLS 权限策略部署
|
||||
- 前端集成 supabase-js
|
||||
- 彻底解决大文件上传超时 (30s 限制)
|
||||
- **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 0.6B 部署** (声音克隆模型，GPU0)
|
||||
- **部署文档** (QWEN3_TTS_DEPLOY.md)
|
||||
|
||||
|
||||
14
README.md
14
README.md
@@ -11,9 +11,10 @@
|
||||
- 🎬 **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）
|
||||
- 📱 **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，支持管理员后台、注册/登录
|
||||
- 👥 **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
|
||||
|
||||
## 🛠️ 技术栈
|
||||
|
||||
@@ -21,7 +22,8 @@
|
||||
|------|------|
|
||||
| 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 |
|
||||
@@ -145,11 +147,9 @@ nohup python -m scripts.server > server.log 2>&1 &
|
||||
|
||||
## 📖 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)
|
||||
- [LatentSync 部署指南](models/LatentSync/DEPLOY.md)
|
||||
- [开发日志](Docs/DevLogs/)
|
||||
- [任务进度](Docs/task_complete.md)
|
||||
|
||||
|
||||
@@ -13,8 +13,9 @@ DEFAULT_TTS_VOICE=zh-CN-YunxiNeural
|
||||
|
||||
# =============== LatentSync 配置 ===============
|
||||
# GPU 选择 (0=第一块GPU, 1=第二块GPU)
|
||||
LATENTSYNC_GPU_ID=0
|
||||
LATENTSYNC_GPU_ID=1
|
||||
|
||||
# 使用本地模式 (true) 或远程 API (false)
|
||||
# 使用本地模式 (true) 或远程 API (false)
|
||||
LATENTSYNC_LOCAL=true
|
||||
|
||||
@@ -34,7 +35,7 @@ LATENTSYNC_GUIDANCE_SCALE=1.5
|
||||
LATENTSYNC_ENABLE_DEEPCACHE=true
|
||||
|
||||
# 随机种子 (设为 -1 则随机)
|
||||
LATENTSYNC_SEED=-1
|
||||
LATENTSYNC_SEED=1247
|
||||
|
||||
# =============== 上传配置 ===============
|
||||
# 最大上传文件大小 (MB)
|
||||
@@ -46,16 +47,17 @@ MAX_UPLOAD_SIZE_MB=500
|
||||
|
||||
# =============== Supabase 配置 ===============
|
||||
# 从 Supabase 项目设置 > API 获取
|
||||
SUPABASE_URL=your_supabase_url_here
|
||||
SUPABASE_KEY=your_supabase_anon_key_here
|
||||
SUPABASE_URL=http://localhost:8008/
|
||||
SUPABASE_PUBLIC_URL=https://api.hbyrkj.top
|
||||
SUPABASE_KEY=eyJhbGciOiAiSFMyNTYiLCAidHlwIjogIkpXVCJ9.eyJyb2xlIjogInNlcnZpY2Vfcm9sZSIsICJpc3MiOiAic3VwYWJhc2UiLCAiaWF0IjogMTc2OTQwNzU2NSwgImV4cCI6IDIwODQ3Njc1NjV9.LBPaimygpnM9o3mZ2Pi-iL8taJ90JjGbQ0HW6yFlmhg
|
||||
|
||||
# =============== JWT 配置 ===============
|
||||
# 用于签名 JWT Token 的密钥 (请更换为随机字符串)
|
||||
JWT_SECRET_KEY=generate_your_secure_random_key_here
|
||||
JWT_SECRET_KEY=F4MagRkf7nJsN-ag9AB7Q-30MbZRe7Iu4E9p9xRzyic
|
||||
JWT_ALGORITHM=HS256
|
||||
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=admin@example.com
|
||||
ADMIN_PASSWORD=change_this_password_immediately
|
||||
ADMIN_EMAIL=lamnickdavid@gmail.com
|
||||
ADMIN_PASSWORD=lam1988324
|
||||
|
||||
@@ -1,100 +1,331 @@
|
||||
from fastapi import APIRouter, UploadFile, File, HTTPException
|
||||
from fastapi import APIRouter, UploadFile, File, HTTPException, Request, BackgroundTasks, Depends
|
||||
from app.core.config import settings
|
||||
import shutil
|
||||
from app.core.deps import get_current_user
|
||||
from app.services.storage import storage_service
|
||||
import re
|
||||
import time
|
||||
import traceback
|
||||
import os
|
||||
import aiofiles
|
||||
from pathlib import Path
|
||||
from loguru import logger
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
|
||||
def sanitize_filename(filename: str) -> str:
|
||||
"""清理文件名，移除不安全字符"""
|
||||
# 移除路径分隔符和特殊字符
|
||||
safe_name = re.sub(r'[<>:"/\\|?*]', '_', filename)
|
||||
# 限制长度
|
||||
if len(safe_name) > 100:
|
||||
ext = Path(safe_name).suffix
|
||||
safe_name = safe_name[:100 - len(ext)] + ext
|
||||
return safe_name
|
||||
|
||||
async def process_and_upload(temp_file_path: str, original_filename: str, content_type: str, user_id: str):
|
||||
"""Background task to strip multipart headers and upload to Supabase"""
|
||||
try:
|
||||
logger.info(f"Processing raw upload: {temp_file_path} for user {user_id}")
|
||||
|
||||
# 1. Analyze file to find actual video content (strip multipart boundaries)
|
||||
# This is a simplified manual parser for a SINGLE file upload.
|
||||
# Structure:
|
||||
# --boundary
|
||||
# Content-Disposition: form-data; name="file"; filename="..."
|
||||
# Content-Type: video/mp4
|
||||
# \r\n\r\n
|
||||
# [DATA]
|
||||
# \r\n--boundary--
|
||||
|
||||
# We need to read the first few KB to find the header end
|
||||
start_offset = 0
|
||||
end_offset = 0
|
||||
boundary = b""
|
||||
|
||||
file_size = os.path.getsize(temp_file_path)
|
||||
|
||||
with open(temp_file_path, 'rb') as f:
|
||||
# Read first 4KB to find header
|
||||
head = f.read(4096)
|
||||
|
||||
# Find boundary
|
||||
first_line_end = head.find(b'\r\n')
|
||||
if first_line_end == -1:
|
||||
raise Exception("Could not find boundary in multipart body")
|
||||
|
||||
boundary = head[:first_line_end] # e.g. --boundary123
|
||||
logger.info(f"Detected boundary: {boundary}")
|
||||
|
||||
# Find end of headers (\r\n\r\n)
|
||||
header_end = head.find(b'\r\n\r\n')
|
||||
if header_end == -1:
|
||||
raise Exception("Could not find end of multipart headers")
|
||||
|
||||
start_offset = header_end + 4
|
||||
logger.info(f"Video data starts at offset: {start_offset}")
|
||||
|
||||
# Find end boundary (read from end of file)
|
||||
# It should be \r\n + boundary + -- + \r\n
|
||||
# We seek to end-200 bytes
|
||||
f.seek(max(0, file_size - 200))
|
||||
tail = f.read()
|
||||
|
||||
# The closing boundary is usually --boundary--
|
||||
# We look for the last occurrence of the boundary
|
||||
last_boundary_pos = tail.rfind(boundary)
|
||||
if last_boundary_pos != -1:
|
||||
# The data ends before \r\n + boundary
|
||||
# The tail buffer relative position needs to be converted to absolute
|
||||
end_pos_in_tail = last_boundary_pos
|
||||
# We also need to check for the preceding \r\n
|
||||
if end_pos_in_tail >= 2 and tail[end_pos_in_tail-2:end_pos_in_tail] == b'\r\n':
|
||||
end_pos_in_tail -= 2
|
||||
|
||||
# Absolute end offset
|
||||
end_offset = (file_size - 200) + last_boundary_pos
|
||||
# Correction for CRLF before boundary
|
||||
# Actually, simply: read until (file_size - len(tail) + last_boundary_pos) - 2
|
||||
end_offset = (max(0, file_size - 200) + last_boundary_pos) - 2
|
||||
else:
|
||||
logger.warning("Could not find closing boundary, assuming EOF")
|
||||
end_offset = file_size
|
||||
|
||||
logger.info(f"Video data ends at offset: {end_offset}. Total video size: {end_offset - start_offset}")
|
||||
|
||||
# 2. Extract and Upload to Supabase
|
||||
# Since we have the file on disk, we can just pass the file object (seeked) to upload_file?
|
||||
# Or if upload_file expects bytes/path, checking storage.py...
|
||||
# It takes `file_data` (bytes) or file-like?
|
||||
# supabase-py's `upload` method handles parsing if we pass a file object.
|
||||
# But we need to pass ONLY the video slice.
|
||||
# So we create a generator or a sliced file object?
|
||||
# Simpler: Read the slice into memory if < 1GB? Or copy to new temp file?
|
||||
# Copying to new temp file is safer for memory.
|
||||
|
||||
video_path = temp_file_path + "_video.mp4"
|
||||
with open(temp_file_path, 'rb') as src, open(video_path, 'wb') as dst:
|
||||
src.seek(start_offset)
|
||||
# Copy in chunks
|
||||
bytes_to_copy = end_offset - start_offset
|
||||
copied = 0
|
||||
while copied < bytes_to_copy:
|
||||
chunk_size = min(1024*1024*10, bytes_to_copy - copied) # 10MB chunks
|
||||
chunk = src.read(chunk_size)
|
||||
if not chunk:
|
||||
break
|
||||
dst.write(chunk)
|
||||
copied += len(chunk)
|
||||
|
||||
logger.info(f"Extracted video content to {video_path}")
|
||||
|
||||
# 3. Upload to Supabase with user isolation
|
||||
timestamp = int(time.time())
|
||||
safe_name = re.sub(r'[^a-zA-Z0-9._-]', '', original_filename)
|
||||
# 使用 user_id 作为目录前缀实现隔离
|
||||
storage_path = f"{user_id}/{timestamp}_{safe_name}"
|
||||
|
||||
# Use storage service (this calls Supabase which might do its own http request)
|
||||
# We read the cleaned video file
|
||||
with open(video_path, 'rb') as f:
|
||||
file_content = f.read() # Still reading into memory for simple upload call, but server has 32GB RAM so ok for 500MB
|
||||
await storage_service.upload_file(
|
||||
bucket=storage_service.BUCKET_MATERIALS,
|
||||
path=storage_path,
|
||||
file_data=file_content,
|
||||
content_type=content_type
|
||||
)
|
||||
|
||||
logger.info(f"Upload to Supabase complete: {storage_path}")
|
||||
|
||||
# Cleanup
|
||||
os.remove(temp_file_path)
|
||||
os.remove(video_path)
|
||||
|
||||
return storage_path
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Background upload processing failed: {e}\n{traceback.format_exc()}")
|
||||
raise
|
||||
|
||||
|
||||
@router.post("")
|
||||
async def upload_material(file: UploadFile = File(...)):
|
||||
if not file.filename.lower().endswith(('.mp4', '.mov', '.avi')):
|
||||
raise HTTPException(400, "Invalid format")
|
||||
async def upload_material(
|
||||
request: Request,
|
||||
background_tasks: BackgroundTasks,
|
||||
current_user: dict = Depends(get_current_user)
|
||||
):
|
||||
user_id = current_user["id"]
|
||||
logger.info(f"ENTERED upload_material (Streaming Mode) for user {user_id}. Headers: {request.headers}")
|
||||
|
||||
# 使用时间戳+原始文件名（保留原始名称，避免冲突）
|
||||
filename = "unknown_video.mp4" # Fallback
|
||||
content_type = "video/mp4"
|
||||
|
||||
# Try to parse filename from header if possible (unreliable in raw stream)
|
||||
# We will rely on post-processing or client hint
|
||||
# Frontend sends standard multipart.
|
||||
|
||||
# Create temp file
|
||||
timestamp = int(time.time())
|
||||
safe_name = sanitize_filename(file.filename)
|
||||
save_path = settings.UPLOAD_DIR / "materials" / f"{timestamp}_{safe_name}"
|
||||
|
||||
# Save file
|
||||
with open(save_path, "wb") as buffer:
|
||||
shutil.copyfileobj(file.file, buffer)
|
||||
|
||||
# Calculate size
|
||||
size_mb = save_path.stat().st_size / (1024 * 1024)
|
||||
|
||||
# 提取显示名称（去掉时间戳前缀）
|
||||
display_name = safe_name
|
||||
temp_filename = f"upload_{timestamp}.raw"
|
||||
temp_path = os.path.join("/tmp", temp_filename) # Use /tmp on Linux
|
||||
# Ensure /tmp exists (it does) but verify paths
|
||||
if os.name == 'nt': # Local dev
|
||||
temp_path = f"d:/tmp/{temp_filename}"
|
||||
os.makedirs("d:/tmp", exist_ok=True)
|
||||
|
||||
try:
|
||||
total_size = 0
|
||||
last_log = 0
|
||||
|
||||
return {
|
||||
"id": save_path.stem,
|
||||
"name": display_name,
|
||||
"path": f"uploads/materials/{save_path.name}",
|
||||
"size_mb": size_mb,
|
||||
"type": "video"
|
||||
}
|
||||
async with aiofiles.open(temp_path, 'wb') as f:
|
||||
async for chunk in request.stream():
|
||||
await f.write(chunk)
|
||||
total_size += len(chunk)
|
||||
|
||||
# Log progress every 20MB
|
||||
if total_size - last_log > 20 * 1024 * 1024:
|
||||
logger.info(f"Receiving stream... Processed {total_size / (1024*1024):.2f} MB")
|
||||
last_log = total_size
|
||||
|
||||
logger.info(f"Stream reception complete. Total size: {total_size} bytes. Saved to {temp_path}")
|
||||
|
||||
if total_size == 0:
|
||||
raise HTTPException(400, "Received empty body")
|
||||
|
||||
# Attempt to extract filename from the saved file's first bytes?
|
||||
# Or just accept it as "uploaded_video.mp4" for now to prove it works.
|
||||
# We can try to regex the header in the file content we just wrote.
|
||||
# Implemented in background task to return success immediately.
|
||||
|
||||
# Wait, if we return immediately, the user's UI might not show the file yet?
|
||||
# The prompt says "Wait for upload".
|
||||
# But to avoid User Waiting Timeout, maybe returning early is better?
|
||||
# NO, user expects the file to be in the list.
|
||||
# So we Must await the processing.
|
||||
# But "Processing" (Strip + Upload to Supabase) takes time.
|
||||
# Receiving took time.
|
||||
# If we await Supabase upload, does it timeout?
|
||||
# Supabase upload is outgoing. Usually faster/stable.
|
||||
|
||||
# Let's await the processing to ensure "List Materials" shows it.
|
||||
# We need to extract the filename for the list.
|
||||
|
||||
# Quick extract filename from first 4kb
|
||||
with open(temp_path, 'rb') as f:
|
||||
head = f.read(4096).decode('utf-8', errors='ignore')
|
||||
match = re.search(r'filename="([^"]+)"', head)
|
||||
if match:
|
||||
filename = match.group(1)
|
||||
logger.info(f"Extracted filename from body: {filename}")
|
||||
|
||||
# Run processing sync (in await)
|
||||
storage_path = await process_and_upload(temp_path, filename, content_type, user_id)
|
||||
|
||||
# Get signed URL (it exists now)
|
||||
signed_url = await storage_service.get_signed_url(
|
||||
bucket=storage_service.BUCKET_MATERIALS,
|
||||
path=storage_path
|
||||
)
|
||||
|
||||
size_mb = total_size / (1024 * 1024) # Approximate (includes headers)
|
||||
|
||||
# 从 storage_path 提取显示名
|
||||
display_name = storage_path.split('/')[-1] # 去掉 user_id 前缀
|
||||
if '_' in display_name:
|
||||
parts = display_name.split('_', 1)
|
||||
if parts[0].isdigit():
|
||||
display_name = parts[1]
|
||||
|
||||
return {
|
||||
"id": storage_path,
|
||||
"name": display_name,
|
||||
"path": signed_url,
|
||||
"size_mb": size_mb,
|
||||
"type": "video"
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
error_msg = f"Streaming upload failed: {str(e)}"
|
||||
detail_msg = f"Exception: {repr(e)}\nArgs: {e.args}\n{traceback.format_exc()}"
|
||||
logger.error(error_msg + "\n" + detail_msg)
|
||||
|
||||
# Write to debug file
|
||||
try:
|
||||
with open("debug_upload.log", "a") as logf:
|
||||
logf.write(f"\n--- Error at {time.ctime()} ---\n")
|
||||
logf.write(detail_msg)
|
||||
logf.write("\n-----------------------------\n")
|
||||
except:
|
||||
pass
|
||||
|
||||
if os.path.exists(temp_path):
|
||||
try:
|
||||
os.remove(temp_path)
|
||||
except:
|
||||
pass
|
||||
raise HTTPException(500, f"Upload failed. Check server logs. Error: {str(e)}")
|
||||
|
||||
|
||||
@router.get("")
|
||||
async def list_materials():
|
||||
materials_dir = settings.UPLOAD_DIR / "materials"
|
||||
files = []
|
||||
if materials_dir.exists():
|
||||
for f in materials_dir.glob("*"):
|
||||
try:
|
||||
stat = f.stat()
|
||||
# 提取显示名称：去掉时间戳前缀 (格式: {timestamp}_{原始文件名})
|
||||
display_name = f.name
|
||||
if '_' in f.name:
|
||||
parts = f.name.split('_', 1)
|
||||
if parts[0].isdigit():
|
||||
display_name = parts[1] # 原始文件名
|
||||
|
||||
files.append({
|
||||
"id": f.stem,
|
||||
"name": display_name,
|
||||
"path": f"uploads/materials/{f.name}",
|
||||
"size_mb": stat.st_size / (1024 * 1024),
|
||||
"type": "video",
|
||||
"created_at": stat.st_ctime
|
||||
})
|
||||
except Exception:
|
||||
continue
|
||||
# Sort by creation time desc
|
||||
files.sort(key=lambda x: x.get("created_at", 0), reverse=True)
|
||||
return {"materials": files}
|
||||
|
||||
|
||||
@router.delete("/{material_id}")
|
||||
async def delete_material(material_id: str):
|
||||
"""删除素材文件"""
|
||||
materials_dir = settings.UPLOAD_DIR / "materials"
|
||||
|
||||
# 查找匹配的文件（ID 是文件名不含扩展名）
|
||||
found = None
|
||||
for f in materials_dir.glob("*"):
|
||||
if f.stem == material_id:
|
||||
found = f
|
||||
break
|
||||
|
||||
if not found:
|
||||
raise HTTPException(404, "Material not found")
|
||||
|
||||
async def list_materials(current_user: dict = Depends(get_current_user)):
|
||||
user_id = current_user["id"]
|
||||
try:
|
||||
found.unlink()
|
||||
# 只列出当前用户目录下的文件
|
||||
files_obj = await storage_service.list_files(
|
||||
bucket=storage_service.BUCKET_MATERIALS,
|
||||
path=user_id
|
||||
)
|
||||
materials = []
|
||||
for f in files_obj:
|
||||
name = f.get('name')
|
||||
if not name or name == '.emptyFolderPlaceholder':
|
||||
continue
|
||||
display_name = name
|
||||
if '_' in name:
|
||||
parts = name.split('_', 1)
|
||||
if parts[0].isdigit():
|
||||
display_name = parts[1]
|
||||
# 完整路径包含 user_id
|
||||
full_path = f"{user_id}/{name}"
|
||||
signed_url = await storage_service.get_signed_url(
|
||||
bucket=storage_service.BUCKET_MATERIALS,
|
||||
path=full_path
|
||||
)
|
||||
metadata = f.get('metadata', {})
|
||||
size = metadata.get('size', 0)
|
||||
# created_at 在顶层，是 ISO 字符串
|
||||
created_at_str = f.get('created_at', '')
|
||||
created_at = 0
|
||||
if created_at_str:
|
||||
from datetime import datetime
|
||||
try:
|
||||
dt = datetime.fromisoformat(created_at_str.replace('Z', '+00:00'))
|
||||
created_at = int(dt.timestamp())
|
||||
except:
|
||||
pass
|
||||
materials.append({
|
||||
"id": full_path, # ID 使用完整路径
|
||||
"name": display_name,
|
||||
"path": signed_url,
|
||||
"size_mb": size / (1024 * 1024),
|
||||
"type": "video",
|
||||
"created_at": created_at
|
||||
})
|
||||
materials.sort(key=lambda x: x['id'], reverse=True)
|
||||
return {"materials": materials}
|
||||
except Exception as e:
|
||||
logger.error(f"List materials failed: {e}")
|
||||
return {"materials": []}
|
||||
|
||||
|
||||
@router.delete("/{material_id:path}")
|
||||
async def delete_material(material_id: str, current_user: dict = Depends(get_current_user)):
|
||||
user_id = current_user["id"]
|
||||
# 验证 material_id 属于当前用户
|
||||
if not material_id.startswith(f"{user_id}/"):
|
||||
raise HTTPException(403, "无权删除此素材")
|
||||
try:
|
||||
await storage_service.delete_file(
|
||||
bucket=storage_service.BUCKET_MATERIALS,
|
||||
path=material_id
|
||||
)
|
||||
return {"success": True, "message": "素材已删除"}
|
||||
except Exception as e:
|
||||
raise HTTPException(500, f"删除失败: {str(e)}")
|
||||
|
||||
|
||||
@@ -1,14 +1,19 @@
|
||||
from fastapi import APIRouter, HTTPException, BackgroundTasks
|
||||
from fastapi import APIRouter, HTTPException, BackgroundTasks, Depends, Request
|
||||
from pydantic import BaseModel
|
||||
from typing import Optional
|
||||
from pathlib import Path
|
||||
from loguru import logger
|
||||
import uuid
|
||||
import traceback
|
||||
import time
|
||||
import httpx
|
||||
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.storage import storage_service
|
||||
from app.core.config import settings
|
||||
from app.core.deps import get_current_user
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
@@ -47,42 +52,73 @@ async def _check_lipsync_ready(force: bool = False) -> bool:
|
||||
print(f"[LipSync] Health check: ready={_lipsync_ready}")
|
||||
return _lipsync_ready
|
||||
|
||||
async def _process_video_generation(task_id: str, req: GenerateRequest):
|
||||
async def _download_material(path_or_url: str, temp_path: Path):
|
||||
"""下载素材到临时文件 (流式下载，节省内存)"""
|
||||
if path_or_url.startswith("http"):
|
||||
# Download from URL
|
||||
timeout = httpx.Timeout(None) # Disable timeout for large files
|
||||
async with httpx.AsyncClient(timeout=timeout) as client:
|
||||
async with client.stream("GET", path_or_url) as resp:
|
||||
resp.raise_for_status()
|
||||
with open(temp_path, "wb") as f:
|
||||
async for chunk in resp.aiter_bytes():
|
||||
f.write(chunk)
|
||||
else:
|
||||
# Local file (legacy or absolute path)
|
||||
src = Path(path_or_url)
|
||||
if not src.is_absolute():
|
||||
src = settings.BASE_DIR.parent / path_or_url
|
||||
|
||||
if src.exists():
|
||||
import shutil
|
||||
shutil.copy(src, temp_path)
|
||||
else:
|
||||
raise FileNotFoundError(f"Material not found: {path_or_url}")
|
||||
|
||||
async def _process_video_generation(task_id: str, req: GenerateRequest, user_id: str):
|
||||
temp_files = [] # Track files to clean up
|
||||
try:
|
||||
start_time = time.time()
|
||||
|
||||
# Resolve path if it's relative
|
||||
input_material_path = Path(req.material_path)
|
||||
if not input_material_path.is_absolute():
|
||||
input_material_path = settings.BASE_DIR.parent / req.material_path
|
||||
|
||||
|
||||
tasks[task_id]["status"] = "processing"
|
||||
tasks[task_id]["progress"] = 5
|
||||
tasks[task_id]["message"] = "正在初始化..."
|
||||
|
||||
tasks[task_id]["message"] = "正在下载素材..."
|
||||
|
||||
# Prepare temp dir
|
||||
temp_dir = settings.UPLOAD_DIR / "temp"
|
||||
temp_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# 0. Download Material
|
||||
input_material_path = temp_dir / f"{task_id}_input.mp4"
|
||||
temp_files.append(input_material_path)
|
||||
|
||||
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]["progress"] = 10
|
||||
|
||||
|
||||
tts = TTSService()
|
||||
audio_path = settings.OUTPUT_DIR / f"{task_id}_audio.mp3"
|
||||
audio_path = temp_dir / f"{task_id}_audio.mp3"
|
||||
temp_files.append(audio_path)
|
||||
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")
|
||||
tasks[task_id]["progress"] = 25
|
||||
|
||||
|
||||
# 2. LipSync - 进度 25% -> 85%
|
||||
tasks[task_id]["message"] = "正在合成唇形 (LatentSync)..."
|
||||
tasks[task_id]["progress"] = 30
|
||||
|
||||
|
||||
lipsync = _get_lipsync_service()
|
||||
lipsync_video_path = settings.OUTPUT_DIR / f"{task_id}_lipsync.mp4"
|
||||
|
||||
lipsync_video_path = temp_dir / f"{task_id}_lipsync.mp4"
|
||||
temp_files.append(lipsync_video_path)
|
||||
|
||||
# 使用缓存的健康检查结果
|
||||
lipsync_start = time.time()
|
||||
is_ready = await _check_lipsync_ready()
|
||||
|
||||
|
||||
if is_ready:
|
||||
print(f"[LipSync] Starting LatentSync inference...")
|
||||
tasks[task_id]["progress"] = 35
|
||||
@@ -98,34 +134,72 @@ async def _process_video_generation(task_id: str, req: GenerateRequest):
|
||||
lipsync_time = time.time() - lipsync_start
|
||||
print(f"[Pipeline] LipSync completed in {lipsync_time:.1f}s")
|
||||
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
|
||||
|
||||
|
||||
video = VideoService()
|
||||
final_output = settings.OUTPUT_DIR / f"{task_id}_output.mp4"
|
||||
await video.compose(str(lipsync_video_path), str(audio_path), str(final_output))
|
||||
|
||||
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))
|
||||
|
||||
total_time = time.time() - start_time
|
||||
|
||||
# 4. Upload to Supabase with user isolation
|
||||
tasks[task_id]["message"] = "正在上传结果..."
|
||||
tasks[task_id]["progress"] = 95
|
||||
|
||||
# 使用 user_id 作为目录前缀实现隔离
|
||||
storage_path = f"{user_id}/{task_id}_output.mp4"
|
||||
with open(final_output_local_path, "rb") as f:
|
||||
file_data = f.read()
|
||||
await storage_service.upload_file(
|
||||
bucket=storage_service.BUCKET_OUTPUTS,
|
||||
path=storage_path,
|
||||
file_data=file_data,
|
||||
content_type="video/mp4"
|
||||
)
|
||||
|
||||
# Get Signed URL
|
||||
signed_url = await storage_service.get_signed_url(
|
||||
bucket=storage_service.BUCKET_OUTPUTS,
|
||||
path=storage_path
|
||||
)
|
||||
|
||||
print(f"[Pipeline] Total generation time: {total_time:.1f}s")
|
||||
|
||||
|
||||
tasks[task_id]["status"] = "completed"
|
||||
tasks[task_id]["progress"] = 100
|
||||
tasks[task_id]["message"] = f"生成完成！耗时 {total_time:.0f} 秒"
|
||||
tasks[task_id]["output"] = str(final_output)
|
||||
tasks[task_id]["download_url"] = f"/outputs/{final_output.name}"
|
||||
tasks[task_id]["output"] = storage_path
|
||||
tasks[task_id]["download_url"] = signed_url
|
||||
|
||||
except Exception as e:
|
||||
tasks[task_id]["status"] = "failed"
|
||||
tasks[task_id]["message"] = f"错误: {str(e)}"
|
||||
tasks[task_id]["error"] = traceback.format_exc()
|
||||
logger.error(f"Generate video failed: {e}")
|
||||
finally:
|
||||
# Cleanup temp files
|
||||
for f in temp_files:
|
||||
try:
|
||||
if f.exists():
|
||||
f.unlink()
|
||||
except Exception as e:
|
||||
print(f"Error cleaning up {f}: {e}")
|
||||
|
||||
@router.post("/generate")
|
||||
async def generate_video(req: GenerateRequest, background_tasks: BackgroundTasks):
|
||||
async def generate_video(
|
||||
req: GenerateRequest,
|
||||
background_tasks: BackgroundTasks,
|
||||
current_user: dict = Depends(get_current_user)
|
||||
):
|
||||
user_id = current_user["id"]
|
||||
task_id = str(uuid.uuid4())
|
||||
tasks[task_id] = {"status": "pending", "task_id": task_id, "progress": 0}
|
||||
background_tasks.add_task(_process_video_generation, task_id, req)
|
||||
tasks[task_id] = {"status": "pending", "task_id": task_id, "progress": 0, "user_id": user_id}
|
||||
background_tasks.add_task(_process_video_generation, task_id, req, user_id)
|
||||
return {"task_id": task_id}
|
||||
|
||||
@router.get("/tasks/{task_id}")
|
||||
@@ -144,54 +218,81 @@ async def lipsync_health():
|
||||
|
||||
|
||||
@router.get("/generated")
|
||||
async def list_generated_videos():
|
||||
"""从文件系统读取生成的视频列表（持久化）"""
|
||||
output_dir = settings.OUTPUT_DIR
|
||||
videos = []
|
||||
|
||||
if output_dir.exists():
|
||||
for f in output_dir.glob("*_output.mp4"):
|
||||
try:
|
||||
stat = f.stat()
|
||||
videos.append({
|
||||
"id": f.stem,
|
||||
"name": f.name,
|
||||
"path": f"/outputs/{f.name}",
|
||||
"size_mb": stat.st_size / (1024 * 1024),
|
||||
"created_at": stat.st_ctime
|
||||
})
|
||||
except Exception:
|
||||
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"""
|
||||
user_id = current_user["id"]
|
||||
try:
|
||||
# 只列出当前用户目录下的文件
|
||||
files_obj = await storage_service.list_files(
|
||||
bucket=storage_service.BUCKET_OUTPUTS,
|
||||
path=user_id
|
||||
)
|
||||
|
||||
videos = []
|
||||
for f in files_obj:
|
||||
name = f.get('name')
|
||||
if not name or name == '.emptyFolderPlaceholder':
|
||||
continue
|
||||
|
||||
# Sort by creation time desc (newest first)
|
||||
videos.sort(key=lambda x: x.get("created_at", 0), reverse=True)
|
||||
return {"videos": videos}
|
||||
|
||||
# 过滤非 output.mp4 文件
|
||||
if not name.endswith("_output.mp4"):
|
||||
continue
|
||||
|
||||
# 获取 ID (即文件名去除后缀)
|
||||
video_id = Path(name).stem
|
||||
|
||||
# 完整路径包含 user_id
|
||||
full_path = f"{user_id}/{name}"
|
||||
|
||||
# 获取签名链接
|
||||
signed_url = await storage_service.get_signed_url(
|
||||
bucket=storage_service.BUCKET_OUTPUTS,
|
||||
path=full_path
|
||||
)
|
||||
|
||||
metadata = f.get('metadata', {})
|
||||
size = metadata.get('size', 0)
|
||||
# created_at 在顶层，是 ISO 字符串，转换为 Unix 时间戳
|
||||
created_at_str = f.get('created_at', '')
|
||||
created_at = 0
|
||||
if created_at_str:
|
||||
from datetime import datetime
|
||||
try:
|
||||
dt = datetime.fromisoformat(created_at_str.replace('Z', '+00:00'))
|
||||
created_at = int(dt.timestamp())
|
||||
except:
|
||||
pass
|
||||
|
||||
videos.append({
|
||||
"id": video_id,
|
||||
"name": name,
|
||||
"path": signed_url, # Direct playable URL
|
||||
"size_mb": size / (1024 * 1024),
|
||||
"created_at": created_at
|
||||
})
|
||||
|
||||
# Sort by created_at desc (newest first)
|
||||
# Supabase API usually returns ISO string, simpler string sort works for ISO
|
||||
videos.sort(key=lambda x: x.get("created_at", ""), reverse=True)
|
||||
return {"videos": videos}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"List generated videos failed: {e}")
|
||||
return {"videos": []}
|
||||
|
||||
|
||||
@router.delete("/generated/{video_id}")
|
||||
async def delete_generated_video(video_id: str):
|
||||
async def delete_generated_video(video_id: str, current_user: dict = Depends(get_current_user)):
|
||||
"""删除生成的视频"""
|
||||
output_dir = settings.OUTPUT_DIR
|
||||
|
||||
# 查找匹配的文件
|
||||
found = None
|
||||
for f in output_dir.glob("*.mp4"):
|
||||
if f.stem == video_id:
|
||||
found = f
|
||||
break
|
||||
|
||||
if not found:
|
||||
raise HTTPException(404, "Video not found")
|
||||
|
||||
user_id = current_user["id"]
|
||||
try:
|
||||
found.unlink()
|
||||
# 同时删除相关的临时文件（如果存在）
|
||||
task_id = video_id.replace("_output", "")
|
||||
for suffix in ["_audio.mp3", "_lipsync.mp4"]:
|
||||
temp_file = output_dir / f"{task_id}{suffix}"
|
||||
if temp_file.exists():
|
||||
temp_file.unlink()
|
||||
|
||||
# video_id 通常是 uuid_output，完整路径需要加上 user_id
|
||||
storage_path = f"{user_id}/{video_id}.mp4"
|
||||
|
||||
await storage_service.delete_file(
|
||||
bucket=storage_service.BUCKET_OUTPUTS,
|
||||
path=storage_path
|
||||
)
|
||||
return {"success": True, "message": "视频已删除"}
|
||||
except Exception as e:
|
||||
raise HTTPException(500, f"删除失败: {str(e)}")
|
||||
|
||||
@@ -28,6 +28,7 @@ class Settings(BaseSettings):
|
||||
|
||||
# Supabase 配置
|
||||
SUPABASE_URL: str = ""
|
||||
SUPABASE_PUBLIC_URL: str = "" # 公网访问地址，用于生成前端可访问的 URL
|
||||
SUPABASE_KEY: str = ""
|
||||
|
||||
# JWT 配置
|
||||
|
||||
@@ -10,6 +10,28 @@ settings = config.settings
|
||||
|
||||
app = FastAPI(title="ViGent TalkingHead Agent")
|
||||
|
||||
from fastapi import Request
|
||||
from starlette.middleware.base import BaseHTTPMiddleware
|
||||
import time
|
||||
import traceback
|
||||
|
||||
class LoggingMiddleware(BaseHTTPMiddleware):
|
||||
async def dispatch(self, request: Request, call_next):
|
||||
start_time = time.time()
|
||||
logger.info(f"START Request: {request.method} {request.url}")
|
||||
logger.info(f"HEADERS: {dict(request.headers)}")
|
||||
try:
|
||||
response = await call_next(request)
|
||||
process_time = time.time() - start_time
|
||||
logger.info(f"END Request: {request.method} {request.url} - Status: {response.status_code} - Duration: {process_time:.2f}s")
|
||||
return response
|
||||
except Exception as e:
|
||||
process_time = time.time() - start_time
|
||||
logger.error(f"EXCEPTION during request {request.method} {request.url}: {str(e)}\n{traceback.format_exc()}")
|
||||
raise e
|
||||
|
||||
app.add_middleware(LoggingMiddleware)
|
||||
|
||||
app.add_middleware(
|
||||
CORSMiddleware,
|
||||
allow_origins=["*"],
|
||||
|
||||
@@ -2,12 +2,17 @@
|
||||
发布服务 (支持用户隔离)
|
||||
"""
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import tempfile
|
||||
import httpx
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Optional, List, Dict, Any
|
||||
from loguru import logger
|
||||
from app.core.config import settings
|
||||
from app.core.paths import get_user_cookie_dir, get_platform_cookie_path, get_legacy_cookie_dir, get_legacy_cookie_path
|
||||
from app.services.storage import storage_service
|
||||
|
||||
# Import platform uploaders
|
||||
from .uploader.bilibili_uploader import BilibiliUploader
|
||||
@@ -17,7 +22,7 @@ from .uploader.xiaohongshu_uploader import XiaohongshuUploader
|
||||
|
||||
class PublishService:
|
||||
"""Social media publishing service (with user isolation)"""
|
||||
|
||||
|
||||
# 支持的平台配置
|
||||
PLATFORMS: Dict[str, Dict[str, Any]] = {
|
||||
"bilibili": {"name": "B站", "url": "https://member.bilibili.com/platform/upload/video/frame", "enabled": True},
|
||||
@@ -113,13 +118,56 @@ class PublishService:
|
||||
logger.info(f"[发布] 视频: {video_path}")
|
||||
logger.info(f"[发布] 标题: {title}")
|
||||
logger.info(f"[发布] 用户: {user_id or 'legacy'}")
|
||||
|
||||
|
||||
temp_file = None
|
||||
try:
|
||||
# 处理视频路径
|
||||
if video_path.startswith('http://') or video_path.startswith('https://'):
|
||||
# 尝试从 URL 解析 bucket 和 path，直接使用本地文件
|
||||
local_video_path = None
|
||||
|
||||
# URL 格式: .../storage/v1/object/sign/{bucket}/{path}?token=...
|
||||
match = re.search(r'/storage/v1/object/sign/([^/]+)/(.+?)\?', video_path)
|
||||
if match:
|
||||
bucket = match.group(1)
|
||||
storage_path = match.group(2)
|
||||
logger.info(f"[发布] 解析 URL: bucket={bucket}, path={storage_path}")
|
||||
|
||||
# 尝试获取本地文件路径
|
||||
local_video_path = storage_service.get_local_file_path(bucket, storage_path)
|
||||
|
||||
if local_video_path and os.path.exists(local_video_path):
|
||||
logger.info(f"[发布] 直接使用本地文件: {local_video_path}")
|
||||
else:
|
||||
# 本地文件不存在，通过 HTTP 下载
|
||||
logger.info(f"[发布] 本地文件不存在，通过 HTTP 下载...")
|
||||
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4')
|
||||
temp_file.close()
|
||||
|
||||
# 将公网 URL 替换为内网 URL
|
||||
download_url = video_path
|
||||
if settings.SUPABASE_PUBLIC_URL and settings.SUPABASE_URL:
|
||||
public_url = settings.SUPABASE_PUBLIC_URL.rstrip('/')
|
||||
internal_url = settings.SUPABASE_URL.rstrip('/')
|
||||
download_url = video_path.replace(public_url, internal_url)
|
||||
|
||||
async with httpx.AsyncClient(timeout=httpx.Timeout(None)) as client:
|
||||
async with client.stream("GET", download_url) as resp:
|
||||
resp.raise_for_status()
|
||||
with open(temp_file.name, 'wb') as f:
|
||||
async for chunk in resp.aiter_bytes():
|
||||
f.write(chunk)
|
||||
local_video_path = temp_file.name
|
||||
logger.info(f"[发布] 视频已下载到: {local_video_path}")
|
||||
else:
|
||||
# 本地相对路径
|
||||
local_video_path = str(settings.BASE_DIR.parent / video_path)
|
||||
|
||||
# Select appropriate uploader
|
||||
if platform == "bilibili":
|
||||
uploader = BilibiliUploader(
|
||||
title=title,
|
||||
file_path=str(settings.BASE_DIR.parent / video_path),
|
||||
file_path=local_video_path,
|
||||
tags=tags,
|
||||
publish_date=publish_time,
|
||||
account_file=str(account_file),
|
||||
@@ -130,7 +178,7 @@ class PublishService:
|
||||
elif platform == "douyin":
|
||||
uploader = DouyinUploader(
|
||||
title=title,
|
||||
file_path=str(settings.BASE_DIR.parent / video_path),
|
||||
file_path=local_video_path,
|
||||
tags=tags,
|
||||
publish_date=publish_time,
|
||||
account_file=str(account_file),
|
||||
@@ -139,7 +187,7 @@ class PublishService:
|
||||
elif platform == "xiaohongshu":
|
||||
uploader = XiaohongshuUploader(
|
||||
title=title,
|
||||
file_path=str(settings.BASE_DIR.parent / video_path),
|
||||
file_path=local_video_path,
|
||||
tags=tags,
|
||||
publish_date=publish_time,
|
||||
account_file=str(account_file),
|
||||
@@ -157,7 +205,7 @@ class PublishService:
|
||||
result = await uploader.main()
|
||||
result['platform'] = platform
|
||||
return result
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.exception(f"[发布] 上传异常: {e}")
|
||||
return {
|
||||
@@ -165,6 +213,14 @@ class PublishService:
|
||||
"message": f"上传异常: {str(e)}",
|
||||
"platform": platform
|
||||
}
|
||||
finally:
|
||||
# 清理临时文件
|
||||
if temp_file and os.path.exists(temp_file.name):
|
||||
try:
|
||||
os.remove(temp_file.name)
|
||||
logger.info(f"[发布] 已清理临时文件: {temp_file.name}")
|
||||
except Exception as e:
|
||||
logger.warning(f"[发布] 清理临时文件失败: {e}")
|
||||
|
||||
async def login(self, platform: str, user_id: Optional[str] = None) -> Dict[str, Any]:
|
||||
"""
|
||||
|
||||
148
backend/app/services/storage.py
Normal file
148
backend/app/services/storage.py
Normal file
@@ -0,0 +1,148 @@
|
||||
from supabase import Client
|
||||
from app.core.supabase import get_supabase
|
||||
from app.core.config import settings
|
||||
from loguru import logger
|
||||
from typing import Optional, Union, Dict, List, Any
|
||||
from pathlib import Path
|
||||
import asyncio
|
||||
import functools
|
||||
import os
|
||||
|
||||
# Supabase Storage 本地存储根目录
|
||||
SUPABASE_STORAGE_LOCAL_PATH = Path("/home/rongye/ProgramFiles/Supabase/volumes/storage/stub/stub")
|
||||
|
||||
class StorageService:
|
||||
def __init__(self):
|
||||
self.supabase: Client = get_supabase()
|
||||
self.BUCKET_MATERIALS = "materials"
|
||||
self.BUCKET_OUTPUTS = "outputs"
|
||||
|
||||
def _convert_to_public_url(self, url: str) -> str:
|
||||
"""将内部 URL 转换为公网可访问的 URL"""
|
||||
if settings.SUPABASE_PUBLIC_URL and settings.SUPABASE_URL:
|
||||
# 去掉末尾斜杠进行替换
|
||||
internal_url = settings.SUPABASE_URL.rstrip('/')
|
||||
public_url = settings.SUPABASE_PUBLIC_URL.rstrip('/')
|
||||
return url.replace(internal_url, public_url)
|
||||
return url
|
||||
|
||||
def get_local_file_path(self, bucket: str, path: str) -> Optional[str]:
|
||||
"""
|
||||
获取 Storage 文件的本地磁盘路径
|
||||
|
||||
Supabase Storage 文件存储结构：
|
||||
{STORAGE_ROOT}/{bucket}/{path}/{internal_uuid}
|
||||
|
||||
Returns:
|
||||
本地文件路径，如果不存在返回 None
|
||||
"""
|
||||
try:
|
||||
# 构建目录路径
|
||||
dir_path = SUPABASE_STORAGE_LOCAL_PATH / bucket / path
|
||||
|
||||
if not dir_path.exists():
|
||||
logger.warning(f"Storage 目录不存在: {dir_path}")
|
||||
return None
|
||||
|
||||
# 目录下只有一个文件（internal_uuid）
|
||||
files = list(dir_path.iterdir())
|
||||
if not files:
|
||||
logger.warning(f"Storage 目录为空: {dir_path}")
|
||||
return None
|
||||
|
||||
local_path = str(files[0])
|
||||
logger.info(f"获取本地文件路径: {local_path}")
|
||||
return local_path
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取本地文件路径失败: {e}")
|
||||
return None
|
||||
|
||||
async def upload_file(self, bucket: str, path: str, file_data: bytes, content_type: str) -> str:
|
||||
"""
|
||||
异步上传文件到 Supabase Storage
|
||||
"""
|
||||
try:
|
||||
# 运行在线程池中，避免阻塞事件循环
|
||||
loop = asyncio.get_running_loop()
|
||||
await loop.run_in_executor(
|
||||
None,
|
||||
functools.partial(
|
||||
self.supabase.storage.from_(bucket).upload,
|
||||
path=path,
|
||||
file=file_data,
|
||||
file_options={"content-type": content_type, "upsert": "true"}
|
||||
)
|
||||
)
|
||||
logger.info(f"Storage upload success: {path}")
|
||||
return path
|
||||
except Exception as e:
|
||||
logger.error(f"Storage upload failed: {e}")
|
||||
raise e
|
||||
|
||||
async def get_signed_url(self, bucket: str, path: str, expires_in: int = 3600) -> str:
|
||||
"""异步获取签名访问链接"""
|
||||
try:
|
||||
loop = asyncio.get_running_loop()
|
||||
res = await loop.run_in_executor(
|
||||
None,
|
||||
lambda: self.supabase.storage.from_(bucket).create_signed_url(path, expires_in)
|
||||
)
|
||||
|
||||
# 兼容处理
|
||||
url = ""
|
||||
if isinstance(res, dict) and "signedURL" in res:
|
||||
url = res["signedURL"]
|
||||
elif isinstance(res, str):
|
||||
url = res
|
||||
else:
|
||||
logger.warning(f"Unexpected signed_url response: {res}")
|
||||
url = res.get("signedURL", "") if isinstance(res, dict) else str(res)
|
||||
|
||||
# 转换为公网可访问的 URL
|
||||
return self._convert_to_public_url(url)
|
||||
except Exception as e:
|
||||
logger.error(f"Get signed URL failed: {e}")
|
||||
return ""
|
||||
|
||||
async def get_public_url(self, bucket: str, path: str) -> str:
|
||||
"""获取公开访问链接"""
|
||||
try:
|
||||
loop = asyncio.get_running_loop()
|
||||
res = await loop.run_in_executor(
|
||||
None,
|
||||
lambda: self.supabase.storage.from_(bucket).get_public_url(path)
|
||||
)
|
||||
# 转换为公网可访问的 URL
|
||||
return self._convert_to_public_url(res)
|
||||
except Exception as e:
|
||||
logger.error(f"Get public URL failed: {e}")
|
||||
return ""
|
||||
|
||||
async def delete_file(self, bucket: str, path: str):
|
||||
"""异步删除文件"""
|
||||
try:
|
||||
loop = asyncio.get_running_loop()
|
||||
await loop.run_in_executor(
|
||||
None,
|
||||
lambda: self.supabase.storage.from_(bucket).remove([path])
|
||||
)
|
||||
logger.info(f"Deleted file: {bucket}/{path}")
|
||||
except Exception as e:
|
||||
logger.error(f"Delete file failed: {e}")
|
||||
pass
|
||||
|
||||
async def list_files(self, bucket: str, path: str) -> List[Any]:
|
||||
"""异步列出文件"""
|
||||
try:
|
||||
loop = asyncio.get_running_loop()
|
||||
res = await loop.run_in_executor(
|
||||
None,
|
||||
lambda: self.supabase.storage.from_(bucket).list(path)
|
||||
)
|
||||
return res or []
|
||||
except Exception as e:
|
||||
logger.error(f"List files failed: {e}")
|
||||
return []
|
||||
|
||||
storage_service = StorageService()
|
||||
246
frontend/package-lock.json
generated
246
frontend/package-lock.json
generated
@@ -8,6 +8,8 @@
|
||||
"name": "frontend",
|
||||
"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",
|
||||
@@ -68,7 +70,6 @@
|
||||
"integrity": "sha512-H3mcG6ZDLTlYfaSNi0iOKkigqMFvkTKlGUYlD8GW7nNOYRrevuA46iTypPyv+06V3fEmvvazfntkBU34L0azAw==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"dependencies": {
|
||||
"@babel/code-frame": "^7.28.6",
|
||||
"@babel/generator": "^7.28.6",
|
||||
@@ -1235,6 +1236,80 @@
|
||||
"dev": true,
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/@supabase/auth-js": {
|
||||
"version": "2.93.1",
|
||||
"resolved": "https://registry.npmjs.org/@supabase/auth-js/-/auth-js-2.93.1.tgz",
|
||||
"integrity": "sha512-pC0Ek4xk4z6q7A/3+UuZ/eYgfFUUQTg3DhapzrAgJnFGDJDFDyGCj6v9nIz8+3jfLqSZ3QKGe6AoEodYjShghg==",
|
||||
"dependencies": {
|
||||
"tslib": "2.8.1"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=20.0.0"
|
||||
}
|
||||
},
|
||||
"node_modules/@supabase/functions-js": {
|
||||
"version": "2.93.1",
|
||||
"resolved": "https://registry.npmjs.org/@supabase/functions-js/-/functions-js-2.93.1.tgz",
|
||||
"integrity": "sha512-Ott2IcIXHGupaC0nX9WNEiJAX4OdlGRu9upkkURaQHbaLdz9JuCcHxlwTERgtgjMpikbIWHfMM1M9QTQFYABiA==",
|
||||
"dependencies": {
|
||||
"tslib": "2.8.1"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=20.0.0"
|
||||
}
|
||||
},
|
||||
"node_modules/@supabase/postgrest-js": {
|
||||
"version": "2.93.1",
|
||||
"resolved": "https://registry.npmjs.org/@supabase/postgrest-js/-/postgrest-js-2.93.1.tgz",
|
||||
"integrity": "sha512-uRKKQJBDnfi6XFNFPNMh9+u3HT2PCgp065PcMPmG7e0xGuqvLtN89QxO2/SZcGbw2y1+mNBz0yUs5KmyNqF2fA==",
|
||||
"dependencies": {
|
||||
"tslib": "2.8.1"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=20.0.0"
|
||||
}
|
||||
},
|
||||
"node_modules/@supabase/realtime-js": {
|
||||
"version": "2.93.1",
|
||||
"resolved": "https://registry.npmjs.org/@supabase/realtime-js/-/realtime-js-2.93.1.tgz",
|
||||
"integrity": "sha512-2WaP/KVHPlQDjWM6qe4wOZz6zSRGaXw1lfXf4thbfvk3C3zPPKqXRyspyYnk3IhphyxSsJ2hQ/cXNOz48008tg==",
|
||||
"dependencies": {
|
||||
"@types/phoenix": "^1.6.6",
|
||||
"@types/ws": "^8.18.1",
|
||||
"tslib": "2.8.1",
|
||||
"ws": "^8.18.2"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=20.0.0"
|
||||
}
|
||||
},
|
||||
"node_modules/@supabase/storage-js": {
|
||||
"version": "2.93.1",
|
||||
"resolved": "https://registry.npmjs.org/@supabase/storage-js/-/storage-js-2.93.1.tgz",
|
||||
"integrity": "sha512-3KVwd4S1i1BVPL6KIywe5rnruNQXSkLyvrdiJmwnqwbCcDujQumARdGWBPesqCjOPKEU2M9ORWKAsn+2iLzquA==",
|
||||
"dependencies": {
|
||||
"iceberg-js": "^0.8.1",
|
||||
"tslib": "2.8.1"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=20.0.0"
|
||||
}
|
||||
},
|
||||
"node_modules/@supabase/supabase-js": {
|
||||
"version": "2.93.1",
|
||||
"resolved": "https://registry.npmjs.org/@supabase/supabase-js/-/supabase-js-2.93.1.tgz",
|
||||
"integrity": "sha512-FJTgS5s0xEgRQ3u7gMuzGObwf3jA4O5Ki/DgCDXx94w1pihLM4/WG3XFa4BaCJYfuzLxLcv6zPPA5tDvBUjAUg==",
|
||||
"dependencies": {
|
||||
"@supabase/auth-js": "2.93.1",
|
||||
"@supabase/functions-js": "2.93.1",
|
||||
"@supabase/postgrest-js": "2.93.1",
|
||||
"@supabase/realtime-js": "2.93.1",
|
||||
"@supabase/storage-js": "2.93.1"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=20.0.0"
|
||||
}
|
||||
},
|
||||
"node_modules/@swc/helpers": {
|
||||
"version": "0.5.15",
|
||||
"resolved": "https://registry.npmjs.org/@swc/helpers/-/helpers-0.5.15.tgz",
|
||||
@@ -1551,19 +1626,22 @@
|
||||
"version": "20.19.28",
|
||||
"resolved": "https://registry.npmjs.org/@types/node/-/node-20.19.28.tgz",
|
||||
"integrity": "sha512-VyKBr25BuFDzBFCK5sUM6ZXiWfqgCTwTAOK8qzGV/m9FCirXYDlmczJ+d5dXBAQALGCdRRdbteKYfJ84NGEusw==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"undici-types": "~6.21.0"
|
||||
}
|
||||
},
|
||||
"node_modules/@types/phoenix": {
|
||||
"version": "1.6.7",
|
||||
"resolved": "https://registry.npmjs.org/@types/phoenix/-/phoenix-1.6.7.tgz",
|
||||
"integrity": "sha512-oN9ive//QSBkf19rfDv45M7eZPi0eEXylht2OLEXicu5b4KoQ1OzXIw+xDSGWxSxe1JmepRR/ZH283vsu518/Q=="
|
||||
},
|
||||
"node_modules/@types/react": {
|
||||
"version": "19.2.8",
|
||||
"resolved": "https://registry.npmjs.org/@types/react/-/react-19.2.8.tgz",
|
||||
"integrity": "sha512-3MbSL37jEchWZz2p2mjntRZtPt837ij10ApxKfgmXCTuHWagYg7iA5bqPw6C8BMPfwidlvfPI/fxOc42HLhcyg==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"dependencies": {
|
||||
"csstype": "^3.2.2"
|
||||
}
|
||||
@@ -1578,6 +1656,14 @@
|
||||
"@types/react": "^19.2.0"
|
||||
}
|
||||
},
|
||||
"node_modules/@types/ws": {
|
||||
"version": "8.18.1",
|
||||
"resolved": "https://registry.npmjs.org/@types/ws/-/ws-8.18.1.tgz",
|
||||
"integrity": "sha512-ThVF6DCVhA8kUGy+aazFQ4kXQ7E1Ty7A3ypFOe0IcJV8O/M511G99AW24irKrW56Wt44yG9+ij8FaqoBGkuBXg==",
|
||||
"dependencies": {
|
||||
"@types/node": "*"
|
||||
}
|
||||
},
|
||||
"node_modules/@typescript-eslint/eslint-plugin": {
|
||||
"version": "8.53.0",
|
||||
"resolved": "https://registry.npmjs.org/@typescript-eslint/eslint-plugin/-/eslint-plugin-8.53.0.tgz",
|
||||
@@ -1623,7 +1709,6 @@
|
||||
"integrity": "sha512-npiaib8XzbjtzS2N4HlqPvlpxpmZ14FjSJrteZpPxGUaYPlvhzlzUZ4mZyABo0EFrOWnvyd0Xxroq//hKhtAWg==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"dependencies": {
|
||||
"@typescript-eslint/scope-manager": "8.53.0",
|
||||
"@typescript-eslint/types": "8.53.0",
|
||||
@@ -2123,7 +2208,6 @@
|
||||
"integrity": "sha512-NZyJarBfL7nWwIq+FDL6Zp/yHEhePMNnnJ0y3qfieCrmNvYct8uvtiV41UvlSe6apAfk0fY1FbWx+NwfmpvtTg==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"bin": {
|
||||
"acorn": "bin/acorn"
|
||||
},
|
||||
@@ -2368,6 +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",
|
||||
@@ -2394,6 +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",
|
||||
@@ -2464,7 +2565,6 @@
|
||||
}
|
||||
],
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"dependencies": {
|
||||
"baseline-browser-mapping": "^2.9.0",
|
||||
"caniuse-lite": "^1.0.30001759",
|
||||
@@ -2502,7 +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",
|
||||
@@ -2602,6 +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",
|
||||
@@ -2760,6 +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",
|
||||
@@ -2796,7 +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",
|
||||
@@ -2908,7 +3027,6 @@
|
||||
"version": "1.0.1",
|
||||
"resolved": "https://registry.npmjs.org/es-define-property/-/es-define-property-1.0.1.tgz",
|
||||
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"dev": true,
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"license": "MIT",
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"engines": {
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"node": ">= 0.4"
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@@ -2918,7 +3036,6 @@
|
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"resolved": "https://registry.npmjs.org/es-errors/-/es-errors-1.3.0.tgz",
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"dev": true,
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"node": ">= 0.4"
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@@ -2956,7 +3073,6 @@
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"resolved": "https://registry.npmjs.org/es-object-atoms/-/es-object-atoms-1.1.1.tgz",
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"dev": true,
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"es-errors": "^1.3.0"
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@@ -2969,7 +3085,6 @@
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"dev": true,
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"es-errors": "^1.3.0",
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@@ -3041,7 +3156,6 @@
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"@eslint-community/eslint-utils": "^4.8.0",
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"@eslint-community/regexpp": "^4.12.1",
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@@ -3227,7 +3341,6 @@
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"integrity": "sha512-whOE1HFo/qJDyX4SnXzP4N6zOWn79WhnCUY/iDR0mPfQZO8wcYE4JClzI2oZrhBnnMUCBCHZhO6VQyoBU95mZA==",
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"dependencies": {
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"@rtsao/scc": "^1.1.0",
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"array-includes": "^3.1.9",
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@@ -3586,6 +3699,26 @@
|
||||
"dev": true,
|
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"license": "ISC"
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||||
},
|
||||
"node_modules/follow-redirects": {
|
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"version": "1.15.11",
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"resolved": "https://registry.npmjs.org/follow-redirects/-/follow-redirects-1.15.11.tgz",
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"integrity": "sha512-deG2P0JfjrTxl50XGCDyfI97ZGVCxIpfKYmfyrQ54n5FO/0gfIES8C/Psl6kWVDolizcaaxZJnTS0QSMxvnsBQ==",
|
||||
"funding": [
|
||||
{
|
||||
"type": "individual",
|
||||
"url": "https://github.com/sponsors/RubenVerborgh"
|
||||
}
|
||||
],
|
||||
"license": "MIT",
|
||||
"engines": {
|
||||
"node": ">=4.0"
|
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},
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"peerDependenciesMeta": {
|
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"debug": {
|
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"optional": true
|
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}
|
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}
|
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|
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"node_modules/for-each": {
|
||||
"version": "0.3.5",
|
||||
"resolved": "https://registry.npmjs.org/for-each/-/for-each-0.3.5.tgz",
|
||||
@@ -3602,11 +3735,26 @@
|
||||
"url": "https://github.com/sponsors/ljharb"
|
||||
}
|
||||
},
|
||||
"node_modules/form-data": {
|
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"version": "4.0.5",
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"resolved": "https://registry.npmjs.org/form-data/-/form-data-4.0.5.tgz",
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"integrity": "sha512-8RipRLol37bNs2bhoV67fiTEvdTrbMUYcFTiy3+wuuOnUog2QBHCZWXDRijWQfAkhBj2Uf5UnVaiWwA5vdd82w==",
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"license": "MIT",
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"dependencies": {
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"asynckit": "^0.4.0",
|
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"combined-stream": "^1.0.8",
|
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"es-set-tostringtag": "^2.1.0",
|
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"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",
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"integrity": "sha512-7XHNxH7qX9xG5mIwxkhumTox/MIRNcOgDrxWsMt2pAr23WHp6MrRlN7FBSFpCpr+oVO0F744iUgR82nJMfG2SA==",
|
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"dev": true,
|
||||
"license": "MIT",
|
||||
"funding": {
|
||||
"url": "https://github.com/sponsors/ljharb"
|
||||
@@ -3667,7 +3815,6 @@
|
||||
"version": "1.3.0",
|
||||
"resolved": "https://registry.npmjs.org/get-intrinsic/-/get-intrinsic-1.3.0.tgz",
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"integrity": "sha512-9fSjSaos/fRIVIp+xSJlE6lfwhES7LNtKaCBIamHsjr2na1BiABJPo0mOjjz8GJDURarmCPGqaiVg5mfjb98CQ==",
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"dev": true,
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"license": "MIT",
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"dependencies": {
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"call-bind-apply-helpers": "^1.0.2",
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@@ -3692,7 +3839,6 @@
|
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"version": "1.0.1",
|
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"resolved": "https://registry.npmjs.org/get-proto/-/get-proto-1.0.1.tgz",
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"integrity": "sha512-sTSfBjoXBp89JvIKIefqw7U2CCebsc74kiY6awiGogKtoSGbgjYE/G/+l9sF3MWFPNc9IcoOC4ODfKHfxFmp0g==",
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"dev": true,
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"license": "MIT",
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||||
"dependencies": {
|
||||
"dunder-proto": "^1.0.1",
|
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@@ -3780,7 +3926,6 @@
|
||||
"version": "1.2.0",
|
||||
"resolved": "https://registry.npmjs.org/gopd/-/gopd-1.2.0.tgz",
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"integrity": "sha512-ZUKRh6/kUFoAiTAtTYPZJ3hw9wNxx+BIBOijnlG9PnrJsCcSjs1wyyD6vJpaYtgnzDrKYRSqf3OO6Rfa93xsRg==",
|
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"dev": true,
|
||||
"license": "MIT",
|
||||
"engines": {
|
||||
"node": ">= 0.4"
|
||||
@@ -3852,7 +3997,6 @@
|
||||
"version": "1.1.0",
|
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"resolved": "https://registry.npmjs.org/has-symbols/-/has-symbols-1.1.0.tgz",
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"integrity": "sha512-1cDNdwJ2Jaohmb3sg4OmKaMBwuC48sYni5HUw2DvsC8LjGTLK9h+eb1X6RyuOHe4hT0ULCW68iomhjUoKUqlPQ==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"engines": {
|
||||
"node": ">= 0.4"
|
||||
@@ -3865,7 +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"
|
||||
@@ -3881,7 +4024,6 @@
|
||||
"version": "2.0.2",
|
||||
"resolved": "https://registry.npmjs.org/hasown/-/hasown-2.0.2.tgz",
|
||||
"integrity": "sha512-0hJU9SCPvmMzIBdZFqNPXWa6dqh7WdH0cII9y+CyS8rG3nL48Bclra9HmKhVVUHyPWNH5Y7xDwAB7bfgSjkUMQ==",
|
||||
"dev": true,
|
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"license": "MIT",
|
||||
"dependencies": {
|
||||
"function-bind": "^1.1.2"
|
||||
@@ -3907,6 +4049,14 @@
|
||||
"hermes-estree": "0.25.1"
|
||||
}
|
||||
},
|
||||
"node_modules/iceberg-js": {
|
||||
"version": "0.8.1",
|
||||
"resolved": "https://registry.npmjs.org/iceberg-js/-/iceberg-js-0.8.1.tgz",
|
||||
"integrity": "sha512-1dhVQZXhcHje7798IVM+xoo/1ZdVfzOMIc8/rgVSijRK38EDqOJoGula9N/8ZI5RD8QTxNQtK/Gozpr+qUqRRA==",
|
||||
"engines": {
|
||||
"node": ">=20.0.0"
|
||||
}
|
||||
},
|
||||
"node_modules/ignore": {
|
||||
"version": "5.3.2",
|
||||
"resolved": "https://registry.npmjs.org/ignore/-/ignore-5.3.2.tgz",
|
||||
@@ -4864,7 +5014,6 @@
|
||||
"version": "1.1.0",
|
||||
"resolved": "https://registry.npmjs.org/math-intrinsics/-/math-intrinsics-1.1.0.tgz",
|
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"integrity": "sha512-/IXtbwEk5HTPyEwyKX6hGkYXxM9nbj64B+ilVJnC/R6B0pH5G4V3b0pVbL7DBj4tkhBAppbQUlf6F6Xl9LHu1g==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"engines": {
|
||||
"node": ">= 0.4"
|
||||
@@ -4894,6 +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",
|
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"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": {
|
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"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",
|
||||
@@ -5364,6 +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",
|
||||
@@ -5400,7 +5576,6 @@
|
||||
"resolved": "https://registry.npmjs.org/react/-/react-19.2.3.tgz",
|
||||
"integrity": "sha512-Ku/hhYbVjOQnXDZFv2+RibmLFGwFdeeKHFcOTlrt7xplBnya5OGn/hIRDsqDiSUcfORsDC7MPxwork8jBwsIWA==",
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"engines": {
|
||||
"node": ">=0.10.0"
|
||||
}
|
||||
@@ -5410,7 +5585,6 @@
|
||||
"resolved": "https://registry.npmjs.org/react-dom/-/react-dom-19.2.3.tgz",
|
||||
"integrity": "sha512-yELu4WmLPw5Mr/lmeEpox5rw3RETacE++JgHqQzd2dg+YbJuat3jH4ingc+WPZhxaoFzdv9y33G+F7Nl5O0GBg==",
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"dependencies": {
|
||||
"scheduler": "^0.27.0"
|
||||
},
|
||||
@@ -6112,7 +6286,6 @@
|
||||
"integrity": "sha512-5gTmgEY/sqK6gFXLIsQNH19lWb4ebPDLA4SdLP7dsWkIXHWlG66oPuVvXSGFPppYZz8ZDZq0dYYrbHfBCVUb1Q==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"engines": {
|
||||
"node": ">=12"
|
||||
},
|
||||
@@ -6275,7 +6448,6 @@
|
||||
"integrity": "sha512-jl1vZzPDinLr9eUt3J/t7V6FgNEw9QjvBPdysz9KfQDD41fQrC2Y4vKQdiaUpFT4bXlb1RHhLpp8wtm6M5TgSw==",
|
||||
"dev": true,
|
||||
"license": "Apache-2.0",
|
||||
"peer": true,
|
||||
"bin": {
|
||||
"tsc": "bin/tsc",
|
||||
"tsserver": "bin/tsserver"
|
||||
@@ -6331,7 +6503,6 @@
|
||||
"version": "6.21.0",
|
||||
"resolved": "https://registry.npmjs.org/undici-types/-/undici-types-6.21.0.tgz",
|
||||
"integrity": "sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==",
|
||||
"dev": true,
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/unrs-resolver": {
|
||||
@@ -6534,6 +6705,26 @@
|
||||
"node": ">=0.10.0"
|
||||
}
|
||||
},
|
||||
"node_modules/ws": {
|
||||
"version": "8.19.0",
|
||||
"resolved": "https://registry.npmjs.org/ws/-/ws-8.19.0.tgz",
|
||||
"integrity": "sha512-blAT2mjOEIi0ZzruJfIhb3nps74PRWTCz1IjglWEEpQl5XS/UNama6u2/rjFkDDouqr4L67ry+1aGIALViWjDg==",
|
||||
"engines": {
|
||||
"node": ">=10.0.0"
|
||||
},
|
||||
"peerDependencies": {
|
||||
"bufferutil": "^4.0.1",
|
||||
"utf-8-validate": ">=5.0.2"
|
||||
},
|
||||
"peerDependenciesMeta": {
|
||||
"bufferutil": {
|
||||
"optional": true
|
||||
},
|
||||
"utf-8-validate": {
|
||||
"optional": true
|
||||
}
|
||||
}
|
||||
},
|
||||
"node_modules/yallist": {
|
||||
"version": "3.1.1",
|
||||
"resolved": "https://registry.npmjs.org/yallist/-/yallist-3.1.1.tgz",
|
||||
@@ -6560,7 +6751,6 @@
|
||||
"integrity": "sha512-k7Nwx6vuWx1IJ9Bjuf4Zt1PEllcwe7cls3VNzm4CQ1/hgtFUK2bRNG3rvnpPUhFjmqJKAKtjV576KnUkHocg/g==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"funding": {
|
||||
"url": "https://github.com/sponsors/colinhacks"
|
||||
}
|
||||
|
||||
@@ -9,6 +9,8 @@
|
||||
"lint": "eslint"
|
||||
},
|
||||
"dependencies": {
|
||||
"@supabase/supabase-js": "^2.93.1",
|
||||
"axios": "^1.13.4",
|
||||
"next": "16.1.1",
|
||||
"react": "19.2.3",
|
||||
"react-dom": "19.2.3",
|
||||
|
||||
@@ -3,10 +3,7 @@
|
||||
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;
|
||||
@@ -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>
|
||||
|
||||
@@ -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 */
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
import type { Metadata } from "next";
|
||||
import type { Metadata, Viewport } from "next";
|
||||
import { Geist, Geist_Mono } from "next/font/google";
|
||||
import "./globals.css";
|
||||
|
||||
@@ -17,15 +17,27 @@ export const metadata: Metadata = {
|
||||
description: "ViGent Talking Head Agent",
|
||||
};
|
||||
|
||||
export const viewport: Viewport = {
|
||||
width: 'device-width',
|
||||
initialScale: 1,
|
||||
viewportFit: 'cover',
|
||||
themeColor: '#0f172a',
|
||||
};
|
||||
|
||||
export default function RootLayout({
|
||||
children,
|
||||
}: Readonly<{
|
||||
children: React.ReactNode;
|
||||
}>) {
|
||||
return (
|
||||
<html lang="en">
|
||||
<html lang="en" style={{ backgroundColor: '#0f172a' }}>
|
||||
<body
|
||||
className={`${geistSans.variable} ${geistMono.variable} antialiased`}
|
||||
style={{
|
||||
margin: 0,
|
||||
minHeight: '100dvh',
|
||||
background: 'linear-gradient(to bottom, #0f172a 0%, #0f172a 5%, #581c87 50%, #0f172a 95%, #0f172a 100%)',
|
||||
}}
|
||||
>
|
||||
{children}
|
||||
</body>
|
||||
|
||||
@@ -31,7 +31,7 @@ export default function LoginPage() {
|
||||
};
|
||||
|
||||
return (
|
||||
<div className="min-h-screen flex items-center justify-center bg-gradient-to-br from-slate-900 via-purple-900 to-slate-900">
|
||||
<div className="min-h-dvh flex items-center justify-center">
|
||||
<div className="w-full max-w-md p-8 bg-white/10 backdrop-blur-lg rounded-2xl shadow-2xl border border-white/20">
|
||||
<div className="text-center mb-8">
|
||||
<h1 className="text-3xl font-bold text-white mb-2">ViGent</h1>
|
||||
|
||||
@@ -3,6 +3,7 @@
|
||||
|
||||
import { useState, useEffect } from "react";
|
||||
import Link from "next/link";
|
||||
import api from "@/lib/axios";
|
||||
|
||||
const API_BASE = typeof window === 'undefined'
|
||||
? 'http://localhost:8006'
|
||||
@@ -33,6 +34,17 @@ interface GeneratedVideo {
|
||||
created_at: number;
|
||||
}
|
||||
|
||||
// 格式化日期（避免 Hydration 错误）
|
||||
const formatDate = (timestamp: number) => {
|
||||
const d = new Date(timestamp * 1000);
|
||||
const year = d.getFullYear();
|
||||
const month = String(d.getMonth() + 1).padStart(2, '0');
|
||||
const day = String(d.getDate()).padStart(2, '0');
|
||||
const hour = String(d.getHours()).padStart(2, '0');
|
||||
const minute = String(d.getMinutes()).padStart(2, '0');
|
||||
return `${year}/${month}/${day} ${hour}:${minute}`;
|
||||
};
|
||||
|
||||
export default function Home() {
|
||||
const [materials, setMaterials] = useState<Material[]>([]);
|
||||
const [selectedMaterial, setSelectedMaterial] = useState<string>("");
|
||||
@@ -48,7 +60,9 @@ export default function Home() {
|
||||
const [isUploading, setIsUploading] = useState(false);
|
||||
const [uploadProgress, setUploadProgress] = useState(0);
|
||||
const [uploadError, setUploadError] = useState<string | null>(null);
|
||||
const [uploadData, setUploadData] = useState<string>("");
|
||||
const [generatedVideos, setGeneratedVideos] = useState<GeneratedVideo[]>([]);
|
||||
|
||||
const [selectedVideoId, setSelectedVideoId] = useState<string | null>(null);
|
||||
|
||||
// 可选音色
|
||||
@@ -71,18 +85,8 @@ export default function Home() {
|
||||
setFetchError(null);
|
||||
setDebugData("Loading...");
|
||||
|
||||
// Add timestamp to prevent caching
|
||||
const url = `${API_BASE}/api/materials?t=${new Date().getTime()}`;
|
||||
const res = await fetch(url);
|
||||
|
||||
if (!res.ok) {
|
||||
throw new Error(`HTTP ${res.status} ${res.statusText}`);
|
||||
}
|
||||
|
||||
const text = await res.text(); // Get raw text first
|
||||
setDebugData(text.substring(0, 200) + (text.length > 200 ? "..." : "")); // Show preview
|
||||
|
||||
const data = JSON.parse(text);
|
||||
const { data } = await api.get(`/api/materials?t=${new Date().getTime()}`);
|
||||
setDebugData(JSON.stringify(data).substring(0, 200));
|
||||
setMaterials(data.materials || []);
|
||||
|
||||
if (data.materials?.length > 0) {
|
||||
@@ -100,11 +104,8 @@ export default function Home() {
|
||||
// 获取已生成的视频列表（持久化）
|
||||
const fetchGeneratedVideos = async () => {
|
||||
try {
|
||||
const res = await fetch(`${API_BASE}/api/videos/generated`);
|
||||
if (res.ok) {
|
||||
const data = await res.json();
|
||||
setGeneratedVideos(data.videos || []);
|
||||
}
|
||||
const { data } = await api.get('/api/videos/generated');
|
||||
setGeneratedVideos(data.videos || []);
|
||||
} catch (error) {
|
||||
console.error("获取历史视频失败:", error);
|
||||
}
|
||||
@@ -114,16 +115,10 @@ export default function Home() {
|
||||
const deleteMaterial = async (materialId: string) => {
|
||||
if (!confirm("确定要删除这个素材吗？")) return;
|
||||
try {
|
||||
const res = await fetch(`${API_BASE}/api/materials/${materialId}`, {
|
||||
method: "DELETE",
|
||||
});
|
||||
if (res.ok) {
|
||||
fetchMaterials();
|
||||
if (selectedMaterial === materialId) {
|
||||
setSelectedMaterial("");
|
||||
}
|
||||
} else {
|
||||
alert("删除失败");
|
||||
await api.delete(`/api/materials/${materialId}`);
|
||||
fetchMaterials();
|
||||
if (selectedMaterial === materialId) {
|
||||
setSelectedMaterial("");
|
||||
}
|
||||
} catch (error) {
|
||||
alert("删除失败: " + error);
|
||||
@@ -134,24 +129,18 @@ export default function Home() {
|
||||
const deleteVideo = async (videoId: string) => {
|
||||
if (!confirm("确定要删除这个视频吗？")) return;
|
||||
try {
|
||||
const res = await fetch(`${API_BASE}/api/videos/generated/${videoId}`, {
|
||||
method: "DELETE",
|
||||
});
|
||||
if (res.ok) {
|
||||
fetchGeneratedVideos();
|
||||
if (selectedVideoId === videoId) {
|
||||
setSelectedVideoId(null);
|
||||
setGeneratedVideo(null);
|
||||
}
|
||||
} else {
|
||||
alert("删除失败");
|
||||
await api.delete(`/api/videos/generated/${videoId}`);
|
||||
fetchGeneratedVideos();
|
||||
if (selectedVideoId === videoId) {
|
||||
setSelectedVideoId(null);
|
||||
setGeneratedVideo(null);
|
||||
}
|
||||
} catch (error) {
|
||||
alert("删除失败: " + error);
|
||||
}
|
||||
};
|
||||
|
||||
// 上传视频
|
||||
// 上传视频 - 使用 axios 支持进度显示
|
||||
const handleUpload = async (e: React.ChangeEvent<HTMLInputElement>) => {
|
||||
const file = e.target.files?.[0];
|
||||
if (!file) return;
|
||||
@@ -168,41 +157,37 @@ export default function Home() {
|
||||
setUploadProgress(0);
|
||||
setUploadError(null);
|
||||
|
||||
const formData = new FormData();
|
||||
formData.append('file', file);
|
||||
try {
|
||||
const formData = new FormData();
|
||||
formData.append('file', file);
|
||||
|
||||
// 使用 XMLHttpRequest 以获取上传进度
|
||||
const xhr = new XMLHttpRequest();
|
||||
await api.post('/api/materials', formData, {
|
||||
headers: { 'Content-Type': 'multipart/form-data' },
|
||||
onUploadProgress: (progressEvent) => {
|
||||
if (progressEvent.total) {
|
||||
const progress = Math.round((progressEvent.loaded / progressEvent.total) * 100);
|
||||
setUploadProgress(progress);
|
||||
}
|
||||
},
|
||||
});
|
||||
|
||||
xhr.upload.onprogress = (event) => {
|
||||
if (event.lengthComputable) {
|
||||
const progress = Math.round((event.loaded / event.total) * 100);
|
||||
setUploadProgress(progress);
|
||||
}
|
||||
};
|
||||
|
||||
xhr.onload = () => {
|
||||
setUploadProgress(100);
|
||||
setIsUploading(false);
|
||||
if (xhr.status >= 200 && xhr.status < 300) {
|
||||
fetchMaterials(); // 刷新素材列表
|
||||
setUploadProgress(100);
|
||||
} else {
|
||||
setUploadError(`上传失败: ${xhr.statusText}`);
|
||||
}
|
||||
};
|
||||
|
||||
xhr.onerror = () => {
|
||||
fetchMaterials();
|
||||
setUploadData("");
|
||||
} catch (err: any) {
|
||||
console.error("Upload failed:", err);
|
||||
setIsUploading(false);
|
||||
setUploadError('网络错误，上传失败');
|
||||
};
|
||||
|
||||
xhr.open('POST', `${API_BASE}/api/materials`);
|
||||
xhr.send(formData);
|
||||
const errorMsg = err.response?.data?.detail || err.message || String(err);
|
||||
setUploadError(`上传失败: ${errorMsg}`);
|
||||
}
|
||||
|
||||
// 清空 input 以便可以再次选择同一文件
|
||||
e.target.value = '';
|
||||
};
|
||||
|
||||
|
||||
|
||||
// 生成视频
|
||||
const handleGenerate = async () => {
|
||||
if (!selectedMaterial || !text.trim()) {
|
||||
@@ -222,35 +207,34 @@ export default function Home() {
|
||||
}
|
||||
|
||||
// 创建生成任务
|
||||
const res = await fetch(`${API_BASE}/api/videos/generate`, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
material_path: materialObj.path,
|
||||
text: text,
|
||||
voice: voice,
|
||||
add_subtitle: true,
|
||||
}),
|
||||
const { data } = await api.post('/api/videos/generate', {
|
||||
material_path: materialObj.path,
|
||||
text: text,
|
||||
voice: voice,
|
||||
add_subtitle: true,
|
||||
});
|
||||
|
||||
const data = await res.json();
|
||||
const taskId = data.task_id;
|
||||
|
||||
// 轮询任务状态
|
||||
const pollTask = async () => {
|
||||
const taskRes = await fetch(`${API_BASE}/api/videos/tasks/${taskId}`);
|
||||
const taskData: Task = await taskRes.json();
|
||||
setCurrentTask(taskData);
|
||||
try {
|
||||
const { data: taskData } = await api.get(`/api/videos/tasks/${taskId}`);
|
||||
setCurrentTask(taskData);
|
||||
|
||||
if (taskData.status === "completed") {
|
||||
setGeneratedVideo(`${API_BASE}${taskData.download_url}`);
|
||||
if (taskData.status === "completed") {
|
||||
setGeneratedVideo(`${API_BASE}${taskData.download_url}`);
|
||||
setIsGenerating(false);
|
||||
fetchGeneratedVideos(); // 刷新历史视频列表
|
||||
} else if (taskData.status === "failed") {
|
||||
alert("视频生成失败: " + taskData.message);
|
||||
setIsGenerating(false);
|
||||
} else {
|
||||
setTimeout(pollTask, 1000);
|
||||
}
|
||||
} catch (error) {
|
||||
console.error("轮询任务失败:", error);
|
||||
setIsGenerating(false);
|
||||
fetchGeneratedVideos(); // 刷新历史视频列表
|
||||
} else if (taskData.status === "failed") {
|
||||
alert("视频生成失败: " + taskData.message);
|
||||
setIsGenerating(false);
|
||||
} else {
|
||||
setTimeout(pollTask, 1000);
|
||||
}
|
||||
};
|
||||
|
||||
@@ -262,7 +246,7 @@ export default function Home() {
|
||||
};
|
||||
|
||||
return (
|
||||
<div className="min-h-screen bg-gradient-to-br from-slate-900 via-purple-900 to-slate-900">
|
||||
<div className="min-h-dvh">
|
||||
{/* Header <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">
|
||||
<h1 className="text-2xl font-bold text-white flex items-center gap-3">
|
||||
@@ -283,18 +267,18 @@ export default function Home() {
|
||||
</div>
|
||||
</header> */}
|
||||
<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>
|
||||
<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>
|
||||
ViGent
|
||||
</Link>
|
||||
<div className="flex items-center gap-4">
|
||||
<span className="px-4 py-2 bg-gradient-to-r from-purple-600 to-pink-600 text-white rounded-lg font-semibold">
|
||||
<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 bg-gradient-to-r from-purple-600 to-pink-600 text-white rounded-lg font-semibold">
|
||||
视频生成
|
||||
</span>
|
||||
<Link
|
||||
href="/publish"
|
||||
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"
|
||||
>
|
||||
发布管理
|
||||
</Link>
|
||||
@@ -302,14 +286,12 @@ export default function Home() {
|
||||
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>
|
||||
@@ -322,12 +304,12 @@ export default function Home() {
|
||||
{/* 左侧: 输入区域 */}
|
||||
<div className="space-y-6">
|
||||
{/* 素材选择 */}
|
||||
<div className="bg-white/5 rounded-2xl p-6 border border-white/10 backdrop-blur-sm">
|
||||
<div className="flex justify-between items-center mb-4">
|
||||
<h2 className="text-lg font-semibold text-white flex items-center gap-2">
|
||||
<div className="bg-white/5 rounded-2xl p-4 sm:p-6 border border-white/10 backdrop-blur-sm">
|
||||
<div className="flex justify-between items-center gap-2 mb-4">
|
||||
<h2 className="text-base sm:text-lg font-semibold text-white flex items-center gap-2 whitespace-nowrap">
|
||||
📹 选择素材视频
|
||||
</h2>
|
||||
<div className="flex gap-2">
|
||||
<div className="flex gap-1.5">
|
||||
{/* 隐藏的文件输入 */}
|
||||
<input
|
||||
type="file"
|
||||
@@ -338,16 +320,16 @@ export default function Home() {
|
||||
/>
|
||||
<label
|
||||
htmlFor="video-upload"
|
||||
className={`px-3 py-1 text-xs rounded cursor-pointer transition-all ${isUploading
|
||||
className={`px-2 py-1 text-xs rounded cursor-pointer transition-all whitespace-nowrap ${isUploading
|
||||
? "bg-gray-600 cursor-not-allowed text-gray-400"
|
||||
: "bg-gradient-to-r from-purple-600 to-pink-600 hover:from-purple-700 hover:to-pink-700 text-white"
|
||||
}`}
|
||||
>
|
||||
📤 上传视频
|
||||
📤 上传
|
||||
</label>
|
||||
<button
|
||||
onClick={fetchMaterials}
|
||||
className="px-3 py-1 text-xs bg-white/10 hover:bg-white/20 rounded text-gray-300"
|
||||
className="px-2 py-1 text-xs bg-white/10 hover:bg-white/20 rounded text-gray-300 whitespace-nowrap"
|
||||
>
|
||||
🔄 刷新
|
||||
</button>
|
||||
@@ -601,7 +583,7 @@ export default function Home() {
|
||||
className="flex-1 text-left"
|
||||
>
|
||||
<div className="text-white text-sm truncate">
|
||||
{new Date(v.created_at * 1000).toLocaleString('zh-CN')}
|
||||
{formatDate(v.created_at)}
|
||||
</div>
|
||||
<div className="text-gray-400 text-xs">
|
||||
{v.size_mb.toFixed(1)} MB
|
||||
|
||||
@@ -2,15 +2,28 @@
|
||||
|
||||
import { useState, useEffect } from "react";
|
||||
import useSWR from 'swr';
|
||||
|
||||
const fetcher = (url: string) => fetch(url).then((res) => res.json());
|
||||
import Link from "next/link";
|
||||
import api from "@/lib/axios";
|
||||
|
||||
// SWR fetcher 使用 axios（自动处理 401/403）
|
||||
const fetcher = (url: string) => api.get(url).then((res) => res.data);
|
||||
|
||||
// 动态获取 API 地址：服务端使用 localhost，客户端使用当前域名
|
||||
const API_BASE = typeof window === 'undefined'
|
||||
? 'http://localhost:8006'
|
||||
: '';
|
||||
|
||||
// 格式化日期（避免 Hydration 错误）
|
||||
const formatDate = (timestamp: number) => {
|
||||
const d = new Date(timestamp * 1000);
|
||||
const year = d.getFullYear();
|
||||
const month = String(d.getMonth() + 1).padStart(2, '0');
|
||||
const day = String(d.getDate()).padStart(2, '0');
|
||||
const hour = String(d.getHours()).padStart(2, '0');
|
||||
const minute = String(d.getMinutes()).padStart(2, '0');
|
||||
return `${year}/${month}/${day} ${hour}:${minute}`;
|
||||
};
|
||||
|
||||
interface Account {
|
||||
platform: string;
|
||||
name: string;
|
||||
@@ -46,8 +59,7 @@ export default function PublishPage() {
|
||||
|
||||
const fetchAccounts = async () => {
|
||||
try {
|
||||
const res = await fetch(`${API_BASE}/api/publish/accounts`);
|
||||
const data = await res.json();
|
||||
const { data } = await api.get('/api/publish/accounts');
|
||||
setAccounts(data.accounts || []);
|
||||
} catch (error) {
|
||||
console.error("获取账号失败:", error);
|
||||
@@ -56,13 +68,11 @@ export default function PublishPage() {
|
||||
|
||||
const fetchVideos = async () => {
|
||||
try {
|
||||
// 使用持久化的视频列表 API（从文件系统读取）
|
||||
const res = await fetch(`${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 +105,17 @@ export default function PublishPage() {
|
||||
|
||||
for (const platform of selectedPlatforms) {
|
||||
try {
|
||||
const res = await fetch(`${API_BASE}/api/publish`, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
video_path: selectedVideo,
|
||||
platform,
|
||||
title,
|
||||
tags: tagList,
|
||||
description: "",
|
||||
publish_time: scheduleMode === "scheduled" && publishTime
|
||||
? new Date(publishTime).toISOString()
|
||||
: null
|
||||
}),
|
||||
const { data: result } = await api.post('/api/publish', {
|
||||
video_path: selectedVideo,
|
||||
platform,
|
||||
title,
|
||||
tags: tagList,
|
||||
description: "",
|
||||
publish_time: scheduleMode === "scheduled" && publishTime
|
||||
? new Date(publishTime).toISOString()
|
||||
: null
|
||||
});
|
||||
|
||||
const result = await res.json();
|
||||
setPublishResults((prev) => [...prev, result]);
|
||||
// 发布成功后10秒自动清除结果
|
||||
if (result.success) {
|
||||
@@ -118,10 +123,11 @@ export default function PublishPage() {
|
||||
setPublishResults((prev) => prev.filter((r) => r !== result));
|
||||
}, 10000);
|
||||
}
|
||||
} catch (error) {
|
||||
} catch (error: any) {
|
||||
const message = error.response?.data?.detail || String(error);
|
||||
setPublishResults((prev) => [
|
||||
...prev,
|
||||
{ platform, success: false, message: String(error) },
|
||||
{ platform, success: false, message },
|
||||
]);
|
||||
}
|
||||
}
|
||||
@@ -166,21 +172,17 @@ export default function PublishPage() {
|
||||
setQrPlatform(platform); // 立即显示加载弹窗
|
||||
setQrCodeImage(null); // 清空旧二维码
|
||||
try {
|
||||
const res = await fetch(`${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: ${error.response?.data?.detail || error.message}`);
|
||||
} finally {
|
||||
setIsLoadingQR(false);
|
||||
}
|
||||
@@ -189,18 +191,15 @@ export default function PublishPage() {
|
||||
const handleLogout = async (platform: string) => {
|
||||
if (!confirm('确定要注销登录吗？')) return;
|
||||
try {
|
||||
const res = await fetch(`${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 +212,7 @@ 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 +247,31 @@ 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>
|
||||
<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>
|
||||
ViGent
|
||||
</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 +402,6 @@ 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 +436,61 @@ 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 && (
|
||||
|
||||
@@ -46,7 +46,7 @@ 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">
|
||||
@@ -69,7 +69,7 @@ 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>
|
||||
|
||||
50
frontend/src/lib/axios.ts
Normal file
50
frontend/src/lib/axios.ts
Normal file
@@ -0,0 +1,50 @@
|
||||
/**
|
||||
* 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;
|
||||
|
||||
// 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;
|
||||
|
||||
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
|
||||
|
||||
1. Definitions.
|
||||
|
||||
"License" shall mean the terms and conditions for use, reproduction,
|
||||
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|
||||
|
||||
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|
||||
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|
||||
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
|
||||
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|
||||
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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())
|
||||
Reference in New Issue
Block a user