We consume, willingly or not, large amounts of media everyday, and that includes content that is emposed on us. Fast Music Remover gives you the choice to opt-out of them without missing out on the core content.
We're building a feature rich media processor that is efficient, modular and cross platform. It's being built for you! That means clean APIs for programmers, containerized on GHCR for remote users, with a Web UI providing seamless access to anyone interested!
Today, we support background music filtering and noise removal to enhance audio quality. In the near future, we plan to expand our capabilities by adding support for more ML models and DSP modules, as well as introducing realtime processing to empower you with the tools to take control of the media you consume.
If this resonates with you, consider contributing!
We offer a minimalistic UI to streamline access to the MediaProcessor's core features.
Fast-Music-Remover_DemoVideo.MP4
The original interview video is by Fisher College of Business, licensed under a Creative Commons Attribution license (reuse allowed).
Our immediate priority is to provide a stable first release with cross-platform support for Linux, macOS, and Windows. We’re focused on getting this early version out as soon as possible, and your feedback will help shape the direction of the project.
In parallel, we're developing cross-platform tooling to simplify manual setup for enthusiasts and contributors. The first release will feature a project launcher to manage dependency installation, project configuration, and starting the web application, handling all prerequisites locally for you. This will be offered in addition to our stable image on GHCR.
Following the first release, we plan to introduce a separate, unstable release with some features in alpha stage, such as realtime processing. At this stage, we'll also experiment with new ML models to expand the capabilities of our processing engine. Let us know if you have any requests!
We have a wide array of interesting technical challenges spanning multiple domains. Take part in building a free and open tool that directly addresses real-world challenges!
Check out our contributing guidelines for details on how to get started.
Tip
If you're just looking to test Fast Music Remover, you can skip these prerequisites and jump straight to the Docker Quick Start below!
To get started with Fast Music Remover, ensure that you have the following software installed on your system. These dependencies are necessary for running the backend server, compiling the C++ processor, and handling media files.
- Python 3.9+: Required for running the backend server.
- FFmpeg: For extracting, probing, and processing audio files.
- CMake: Needed to compile the C++
MediaProcessor. - nlohmann-json: A JSON library required for parsing configuration files in the
MediaProcessor. - libsndfile: Required for sampled audio file operations in the
MediaProcessor. - Docker and Docker Compose (optional but recommended for a quick setup):
Click here for installation commands for Ubuntu/Debian and macOS
FFmpeg:
-
On Ubuntu/Debian:
sudo apt update sudo apt install ffmpeg
-
On macOS:
brew install ffmpeg
After installing FFmpeg, ensure the correct path is set in the
config.jsonfile. By default, it is set to/usr/bin/ffmpeg. If you are using macOS and installed FFmpeg via Homebrew, update the path inconfig.jsonto:"ffmpeg_path": "/opt/homebrew/bin/ffmpeg"
CMake:
- On Ubuntu/Debian:
sudo apt update sudo apt install cmake
- On macOS:
brew install cmake
nlohmann-json:
- On Ubuntu/Debian:
sudo apt update sudo apt install nlohmann-json3-dev
- On macOS:
brew install nlohmann-json
libsndfile:
- On Ubuntu/Debian:
sudo apt update sudo apt install libsndfile1-dev
- On macOS:
brew install libsndfile
Docker and Docker Compose:
- On Ubuntu:
sudo apt install docker.io docker-compose
- On macOS:
brew install docker brew install docker-compose
Important
Ensure all the above dependencies are installed before proceeding with the setup.
To get started with Fast Music Remover, you have two options: running it directly via the provided Docker image or installing all the necessary dependencies manually.
Important
Ensure Docker is installed by running:
docker --versionWith Docker, you have two options to quickly try Fast Music Remover:
Pull the prebuilt image from the registry:
docker pull ghcr.io/omeryusufyagci/fast-music-remover:latestRun the container:
docker run -p 8080:8080 ghcr.io/omeryusufyagci/fast-music-remover:latestdocker-compose up --buildNote
You may need sudo to run this command, depending on how your system is setup.
Once the container is running, open http://localhost:8080 on your browser, and you can test it right away by submitting a URL or uploading a file from your local machine.
Whether you use the prebuilt image or build it locally, the containerized setup includes:
- A Flask backend to manage requests.
- The MediaProcessor (C++ binary).
- A minimalistic frontend for submitting and testing media, i.e. audio or video, both supported!
Once processing is finished, the frontend will provide a playback of the processed media.
For those who want more control or are looking to contribute, follow these steps to set up Fast Music Remover manually.
Ensure all dependencies mentioned in the Prerequisites section are installed before proceeding.
Install the Python dependencies with:
pip install -r requirements.txt- Navigate to the
MediaProcessordirectory:
cd MediaProcessor- Make a build directory and navigate into it:
mkdir build
cd build- Run CMake and compile (release build by default)
cmake ..
makeNote
If you encounter errors here, double-check that all prerequisites are installed.
After setting up the dependencies and compiling the C++ project, navigate back to the project root and start the backend server:
python3 app.py Tip
The server should be accessible at http://127.0.0.1:8080. Open this address in a web browser to get started.
Fast Music Remover is released under the MIT license.