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---
title: Vocal Isolator
emoji: 🗣
colorFrom: white
colorTo: blue
sdk: streamlit
sdk_version: 1.25.0
app_file: app.py
pinned: false
license: openrail
---
# Vocal Remover (for now)
A web-based tool for removing vocals from audio files using deep learning.
## Table of Contents
- [Overview](#overview)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Demo](#demo)
- [Technologies Used](#technologies-used)
- [Contributing](#contributing)
- [License](#license)
- [Useful Research Papers](#useful-research-papers)
## Overview
The Vocal Isolator is a user-friendly web application that leverages deep learning models to isolate vocals from audio files. It provides an easy and interactive way for users to upload their audio files and process them to obtain vocals-free versions.
## Features
- Upload audio files in various formats (WAV, MP3, OGG, FLAC).
- Process audio files to remove vocals using a pre-trained deep learning model.
- Display a progress bar during audio processing.
- Play the original and processed audio files in the browser.
- Downloadable WAV file
- Clean and intuitive user interface.
## Installation
1. Clone this repository:
```bash
git clone https://github.com/smotto/Sing-For-Me.git
cd Sing-For-Me
2. Install the required Python packages:
```bash
pip install -r requirements.txt
## Usage
1. Run the Streamlit app:
```bash
streamlit run main.py
2. Access the app in your web browser at http://localhost:8501.
## Demo
For a live demonstration, visit Demo Link.
## Technologies Used
* Python
* Streamlit
* PyTorch
* Soundfile and Librosa
## Contributing
Contributions are welcome! If you have suggestions, bug reports, or feature requests, please open an issue or submit a pull request.
## License
This project is licensed under the Apache 2.0 License.
## Useful Research Papers
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597)
- [WaveNet: A Generative Model for Raw Audio](https://arxiv.org/abs/1609.03499)
- [Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation](https://arxiv.org/abs/1806.03185)
- [KUIELab-MDX-Net: A Two-Stream Neural Network for Music Demixing](https://arxiv.org/abs/2111.12203) |