This is the Windows app named Spleeter whose latest release can be downloaded as v2.3.0.zip. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named Spleeter with OnWorks for free.
Follow these instructions in order to run this app:
- 1. Downloaded this application in your PC.
- 2. Enter in our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 3. Upload this application in such filemanager.
- 4. Start any OS OnWorks online emulator from this website, but better Windows online emulator.
- 5. From the OnWorks Windows OS you have just started, goto our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 6. Download the application and install it.
- 7. Download Wine from your Linux distributions software repositories. Once installed, you can then double-click the app to run them with Wine. You can also try PlayOnLinux, a fancy interface over Wine that will help you install popular Windows programs and games.
Wine is a way to run Windows software on Linux, but with no Windows required. Wine is an open-source Windows compatibility layer that can run Windows programs directly on any Linux desktop. Essentially, Wine is trying to re-implement enough of Windows from scratch so that it can run all those Windows applications without actually needing Windows.
Spleeter is the Deezer source separation library with pretrained models written in Python and using Tensorflow. It makes it easy to train music source separation models (assuming you have a dataset of isolated sources), and provides already trained state of the art models for performing various flavours of separation. 2 stems and 4 stems models have state of the art performances on the musdb dataset. Spleeter is also very fast as it can perform separation of audio files to 4 stems 100x faster than real-time when run on a GPU. We designed Spleeter so you can use it straight from command line as well as directly in your own development pipeline as a Python library. It can be installed with Conda, with pip or be used with Docker.
- Makes it easy to train music source separation models
- Vocals (singing voice) / accompaniment separation (2 stems) model
- Vocals / drums / bass / other separation (4 stems) model
- Vocals / drums / bass / piano / other separation (5 stems) model
- 2 stems and 4 stems models have state of the art performances on the musdb dataset
- It can be installed with Conda, with pip or be used with Docker
This is an application that can also be fetched from https://sourceforge.net/projects/spleeter.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.