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# 🎙️ ONNX VC - Standalone Real-Time Voice Changer
🌐 **Languages:** [English](README.md) | [Bahasa Indonesia](README.id.md)
A high-performance, low-latency, real-time AI voice conversion system powered by **ONNX Runtime** and **Retrieval-based Voice Conversion (RVC)**. Features a premium dashboard built with **Next.js App Router**, **TypeScript**, and **Tailwind CSS**, supporting full internationalization.
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## 📁 Repository Structure
* [server.py](file:///M:/Users/ahmad/project/onnx-voice-changer/server.py) — The main WebSocket backend and static HTTP server managing connection loops, audio resampling, and model execution.
* [server.py](file:///M:/Users/ahmad/project/onnx-voice-changer/server.py) — The main WebSocket backend server managing connection loops, audio resampling, and model execution.
* [start.bat](file:///M:/Users/ahmad/project/onnx-voice-changer/start.bat) — Windows launcher batch file that automatically resolves the Python virtual environment and executes the server.
* [requirements.txt](file:///M:/Users/ahmad/project/onnx-voice-changer/requirements.txt) — Python dependencies list.
* [frontend-next/](file:///M:/Users/ahmad/project/onnx-voice-changer/frontend-next) — The development workspace for the frontend client (Next.js, TypeScript).
* [frontend/](file:///M:/Users/ahmad/project/onnx-voice-changer/frontend) — Statically exported and optimized assets served by [server.py](file:///M:/Users/ahmad/project/onnx-voice-changer/server.py) backend.
* [frontend/](file:///M:/Users/ahmad/project/onnx-voice-changer/frontend) — The frontend client workspace built with Next.js (TypeScript, Tailwind CSS).
* [frontend-deprecated/](file:///M:/Users/ahmad/project/onnx-voice-changer/frontend-deprecated) — The old deprecated frontend code.
* [lib/](file:///M:/Users/ahmad/project/onnx-voice-changer/lib) — Core package containing inference models, ONNX conversion scripts, and prediction tools.
* [weights/](file:///M:/Users/ahmad/project/onnx-voice-changer/weights) — Directory for character voice model weights (e.g. `weights/HuTao/`).
* [pretrained/](file:///M:/Users/ahmad/project/onnx-voice-changer/pretrained) — Directory containing base pre-trained models.
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### 📋 Prerequisites
* **Python 3.10+**
* **FFmpeg** installed and added to the system PATH (Required for audio processing utilities).
* **Node.js 18+** & **npm** (Only required if you want to modify and compile the frontend workspace).
* **Node.js 18+** & **npm** (Required to run the Next.js frontend client).
* (Optional) **NVIDIA CUDA Toolkit** (v11.x/12.x) and **cuDNN** for GPU execution acceleration.
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### 🖥️ 4. Running the Frontend Client
Since the Python backend operates purely as a WebSocket API service, you must run the Next.js frontend client separately.
The frontend client runs as a standalone Next.js development server or built production server.
#### Option A: Development Server (Quick & Recommended)
1. Navigate to the frontend directory:
```bash
cd frontend-next
cd frontend
```
2. Install npm dependencies:
```bash
npm install
```
3. Spin up the dev server:
3. Start the development server:
```bash
npm run dev
```
Open your browser and navigate to **`http://localhost:3000`**.
#### Option B: Compiled Static Production Web Server
1. Navigate to `frontend-next` and build the application:
```bash
cd frontend-next
npm install
npm run build
```
*Note: This will compile static pages and copy them into the root `/frontend` folder.*
2. Serve the compiled output using a static file server of your choice:
- Using Node: `npx serve ../frontend -p 3000`
- Using Python: `python -m http.server 3000 --directory ../frontend`
Open **`http://localhost:3000`** in your browser.
Alternatively, to build and run the production server:
```bash
npm run build
npm run start
```
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| `--model` | Target folder name in `weights/` to load directly upon startup. | `None` |
### Step 2: Open the Frontend Dashboard
Make sure your frontend client is running (via `npm run dev` or a static server on `http://localhost:3000`), open it in your browser, and it will automatically connect to the WebSocket API backend.
Make sure your frontend client is running (via `npm run dev` or `npm run start` on `http://localhost:3000`), open it in your browser, and it will automatically connect to the WebSocket API backend.
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