https://github.com/theneodev/mdxnet
UVR using MDX Net
https://github.com/theneodev/mdxnet
Last synced: about 1 year ago
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UVR using MDX Net
- Host: GitHub
- URL: https://github.com/theneodev/mdxnet
- Owner: TheNeodev
- License: bsd-2-clause
- Created: 2025-03-17T05:25:34.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-17T05:47:15.000Z (about 1 year ago)
- Last Synced: 2025-03-17T06:29:51.626Z (about 1 year ago)
- Language: Python
- Size: 15.6 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# MDXNet
**Ultimate Vocal Remover powered by MDX Net**
[](LICENSE)
[](https://python.org)
[](https://github.com/TheNeodev/mdxnet)
[](https://github.com/kuielab/mdx-net)
MDXNet is a high-quality vocal separation tool that uses the MDX Net architecture. It leverages GPU acceleration (when available) and multi-threaded processing to deliver fast and efficient separation of vocals from audio files.
---
## Table of Contents
- [Features](#features)
- [Installation](#installation)
- [Downloading Models](#downloading-models)
- [Usage](#usage)
- [Python API](#python-api)
- [Contributing](#contributing)
- [License](#license)
- [Acknowledgments](#acknowledgments)
---
## Features
- **High-Quality Vocal Separation:** Utilizes MDX Net for precise separation.
- **GPU Acceleration:** Automatically uses GPU if available.
- **Multi-Threaded Processing:** Optimized for faster processing on multi-core systems.
---
## Installation
Install MDXNet directly from GitHub using pip:
```sh
pip install git+https://github.com/TheNeodev/mdxnet.git
```
Make sure you have Python 3.7 or later installed.
---
## Downloading Models
MDXNet requires pre-trained models to operate. Download the required models from the releases page:
- **Model Repository:**
[Download Models](https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/)
After downloading, place the model file (e.g., `uvr_models.onnx`) in a directory of your choice, and update the model path accordingly in your configuration.
---
## Usage
MDXNet can be used within your Python scripts.
### Python API
Below is an example of how to use the Python API for vocal separation:
```python
from mdxnet import MDXProcessor
# Define your model parameters
model_params = {
# Customize model parameters here
# e.g., "param1": value, "param2": value,
}
if __name__ == "__main__":
# Initialize the processor with the model path and parameters.
processor = MDXProcessor(
model_path="./uvr_models.onnx", # Update this path to your downloaded model
model_params=model_params,
processor=0 # Set processor index (use 0 for CPU, or specify GPU device index)
)
# Process the audio file to separate vocals and instrumental tracks.
main_path, invert_path = processor.process(
input_path="./Test.mp3", # Path to the input audio file
output_dir="./output", # Output directory for the separated tracks
denoise=True, # Enable denoising (set to False if not needed)
suffix="Vocals", # Suffix for the vocal track file
invert_suffix="Instrumental" # Suffix for the instrumental track file
)
print(f"Separated vocals saved to: {main_path}")
print(f"Instrumental track saved to: {invert_path}")
```
**Notes:**
- Ensure the model file is correctly placed and the path is updated.
- Customize the `model_params` dictionary based on your specific requirements.
---
## Contributing
Contributions are welcome! Please follow these steps:
1. Fork the repository.
2. Create a new branch for your feature or bugfix.
3. Make your changes and commit them with clear messages.
4. Submit a pull request detailing your changes.
For major changes, please open an issue first to discuss what you would like to change.
---
## License
This project is licensed under the [MIT License](LICENSE).
---
## Acknowledgments
- Thanks to the developers behind MDX Net/UVR for their groundbreaking work.
- Special thanks to all contributors and the community for continuous support.
---