
ccMixter
The premier open-source repository for Creative Commons-licensed stems, acappellas, and collaborative remixes.

Deezer source separation library with pretrained models for audio decomposition.

Spleeter is a Deezer-developed source separation library, built in Python using TensorFlow. It provides pre-trained models for separating audio tracks into stems such as vocals, drums, bass, and other instruments. Spleeter offers separation capabilities including 2, 4, and 5 stem models. The architecture leverages deep learning models trained on large datasets, enabling it to decompose audio files with impressive speed, reaching 100x real-time on GPUs for 4-stem separation. It can be used via command-line or integrated as a Python library. Use cases include music production, remixing, audio restoration, and AI-driven music analysis. The tool supports pip and Docker installations.
Spleeter is a Deezer-developed source separation library, built in Python using TensorFlow.
Explore all tools that specialize in separate audio sources. This domain focus ensures Spleeter delivers optimized results for this specific requirement.
Explore all tools that specialize in extract audio stems. This domain focus ensures Spleeter delivers optimized results for this specific requirement.
Explore all tools that specialize in stem isolation. This domain focus ensures Spleeter delivers optimized results for this specific requirement.
Separates audio into 2, 4, or 5 stems (vocals, drums, bass, piano, other) using pre-trained deep learning models.
Leverages GPU acceleration to achieve separation speeds up to 100x faster than real-time for 4-stem separation.
Offers pre-trained models trained on extensive datasets, providing state-of-the-art source separation performance out-of-the-box.
Provides a user-friendly command-line interface for easy integration into existing audio processing workflows.
Allows direct integration into Python-based development pipelines, facilitating custom audio processing applications.
Enables containerized deployment of Spleeter, ensuring consistent performance across different environments.
Install ffmpeg and libsndfile using Conda: `conda install -c conda-forge ffmpeg libsndfile`
Install Spleeter with pip: `pip install spleeter`
Download an example audio file: `wget https://github.com/deezer/spleeter/raw/master/audio_example.mp3`
Separate the audio into two components: `spleeter separate -p spleeter:2stems -o output audio_example.mp3`
Check the output folder for separated audio files (vocals.wav and accompaniment.wav)
Explore advanced options and configurations via the command-line interface.
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"Spleeter is widely praised for its speed and quality in separating audio sources, though some users encounter issues with specific audio formats and hardware configurations."
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