Spleeter
by bCloud LLC
Version 2.4.2 + Free Support on Ubuntu 26.04
Spleeter 2.4.2 is an AI-powered audio source separation tool developed for splitting music and audio tracks into individual components, also known as stems. It allows users to separate vocals, accompaniment, drums, bass, piano, and other musical elements using pretrained machine learning models.
The solution supports common audio processing workflows including vocal isolation, instrumental extraction, remixing, karaoke track creation, music analysis, and audio production. It is suitable for musicians, audio engineers, researchers, developers, and users working with music and machine learning applications.
Features of Spleeter 2.4.2:
- AI-powered audio source separation using pretrained models.
- Supports 2-stem separation for vocals and accompaniment.
- Supports 4-stem separation for vocals, drums, bass, and other audio.
- Supports 5-stem separation for vocals, drums, bass, piano, and other audio.
- Command-line interface for automated audio processing workflows.
- Supports common audio formats through FFmpeg integration.
Usage instructions for Spleeter:
$ sudo su
$ source /opt/miniforge3/etc/profile.d/conda.sh
$ conda activate spleeter
$ cd /opt/spleeter-test
$ spleeter separate -p spleeter:2stems -o output audio_example.mp3
Separated audio files are saved in:
/opt/spleeter-test/output/audio_example/
For 2-stem separation: spleeter separate -p spleeter:2stems -o output audio_example.mp3 For 4-stem separation: spleeter separate -p spleeter:4stems -o output4 audio_example.mp3 For 5-stem separation: spleeter separate -p spleeter:5stems -o output5 audio_example.mp3
Version: Spleeter 2.4.2
Disclaimer: Spleeter 2.4.2 is provided “as is” under applicable open-source licenses. Users are responsible for ensuring that they have the necessary rights and permissions to process audio content. The quality of audio separation may vary depending on the source recording, audio quality, and selected separation model. This solution is suitable for audio processing, music production, research, development, and machine learning use cases.