# 🎙️ Standalone ONNX Real-Time Voice Changer Service

A high-performance, low-latency, real-time voice conversion system powered by **ONNX Runtime** and **Retrieval-based Voice Conversion (RVC)**. This application enables real-time voice conversion from a microphone/browser source to a designated target character model with minimal processing latency.

---

## ✨ Key Features
* **🚀 WebSocket Audio Pipeline:** Streaming audio transfer using binary WebSocket connections (raw PCM float32) for minimal overhead.
* **⚡ Multi-Backend ONNX Acceleration:** Supports execution providers including NVIDIA `CUDA`, AMD/Intel `DirectML`, and fallback `CPU`.
* **🎼 High-Fidelity DSP Pipeline:**
  * **Low-Cut Filter:** Active 1st order Butterworth high-pass filter at 80Hz to eliminate AC hum and rumble.
  * **Noise Gate:** Threshold-based noise suppression to bypass inference during silence (saving CPU/GPU cycles).
  * **Gain Controls:** Independent input/output digital gain staging.
* **🧠 Advanced Pitch Extraction:** Optimized 16kHz pitch prediction using the RMVPE (Retrieval-based Minimum Vocal Pitch Estimation) model.
* **🌐 Dual Routing Architecture:** Supports routing audio via the web browser (Web Audio API) or directly through the server's local audio hardware (using `sounddevice`).

---

## 🛠️ System Architecture

```mermaid
graph TD
    A[Microphone / Web Browser] -->|Web Audio API| B(WebSocket Connection)
    B -->|Raw Float32 PCM Chunk| C[server.py Backend]
    C -->|1. High-Pass Filter 80Hz| D[DSP Stage]
    D -->|2. Gain & Noise Gate| D
    D -->|3. Resample to 16kHz| E[Hubert/ContentVec ONNX]
    D -->|4. Pitch Estimation RMVPE| F[Pitch Predictor]
    E --> G[RVC ONNX Model Inference]
    F --> G
    G -->|Target Audio Chunks| H(WebSocket Connection)
    H -->|Play audio| I[Browser Speakers / Audio Device]
```

---

## 📁 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.
* [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/](file:///M:/Users/ahmad/project/onnx-voice-changer/frontend) — Contains client-side Web UI files:
  * [frontend/index.html](file:///M:/Users/ahmad/project/onnx-voice-changer/frontend/index.html) — Control interface layout.
  * [frontend/app.js](file:///M:/Users/ahmad/project/onnx-voice-changer/frontend/app.js) — WebSocket communication and client-side audio rendering.
  * [frontend/styles.css](file:///M:/Users/ahmad/project/onnx-voice-changer/frontend/styles.css) — Custom dashboard styling.
* [lib/](file:///M:/Users/ahmad/project/onnx-voice-changer/lib) — core package containing inference models and prediction tools.
* [weights/](file:///M:/Users/ahmad/project/onnx-voice-changer/weights) — Directory for voice model weights. Place your custom `.onnx` and `.pth` model sub-directories here.
* [pretrained/](file:///M:/Users/ahmad/project/onnx-voice-changer/pretrained) — Directory containing base pre-trained models such as `vec-768-layer-12.onnx` or `vec-256-layer-12.onnx`.

---

## 🚀 Getting Started

### 📋 Prerequisites
* **Python 3.10+** (Recommended)
* **FFmpeg** installed and added to the system PATH (Required for audio processing utilities).
* (Optional) **NVIDIA CUDA Toolkit** (v11.x/12.x) and **cuDNN** for GPU execution acceleration.

### 📦 Installation
1. Clone this repository to your local directory.
2. Initialize and activate a virtual environment (optional but recommended):
   ```bash
   python -m venv venv
   .\venv\Scripts\activate
   ```
3. Install the required dependencies:
   ```bash
   pip install -r requirements.txt
   ```
4. Place your ContentVec base model (`vec-768-layer-12.onnx` or `vec-256-layer-12.onnx`) inside the [pretrained/](file:///M:/Users/ahmad/project/onnx-voice-changer/pretrained) directory.
5. Place your character models in [weights/](file:///M:/Users/ahmad/project/onnx-voice-changer/weights) in structured folders (e.g., `weights/HuTao/` containing `HuTao.onnx` and `HuTao.pth`).

### 🏃 Running the Server

#### Option A: Quick Launch (Windows)
Simply double-click the [start.bat](file:///M:/Users/ahmad/project/onnx-voice-changer/start.bat) file. It will automatically detect Python, set up the directory paths, and launch the service.

#### Option B: Manual CLI execution
Execute the server using your terminal:
```bash
python server.py --host 127.0.0.1 --port 8765 --http_port 8000 --device cuda
```

### ⚙️ Command-Line Arguments
| Argument | Description | Default |
|---|---|---|
| `--host` | The address the WebSocket server binds to. | `127.0.0.1` |
| `--port` | WebSocket communication port. | `8765` |
| `--http_port`| Port serving the static frontend Web UI. | `8000` |
| `--device` | The ONNX Runtime execution device (`cpu`, `cuda`, `dml`). | `cuda` |
| `--model` | Target folder name in `weights/` to load directly upon startup. | `None` |

Once the server begins execution, it will spin up the local server, and your Web UI should open automatically at `http://localhost:8000`.

---

## 🔊 Audio DSP Details
To achieve low latency without output artifacts, the audio processing utilizes:
1. **Sliding Window Context Buffer:** Keeps a short historical buffer of the audio to feed the model the required context frames while minimizing output audio delay.
2. **Convolution Padding Fadeout:** 120ms of trailing silent padding is temporarily appended to input segments to avoid edge-fading anomalies inherent to RVC convolutional steps.
3. **Linear Resampling:** Low-overhead linear interpolation for quick sample rate adaptation.