{"categories":[{"label":"Sound/Audio","url":"https://skillfed.io/packages/category/multimedia-sound-audio"}],"enrichment":{"capability":"Separates audio files into multiple stems (vocals, instruments, drums, bass, etc.) using pre-trained deep learning models, available as a CLI tool or Python library.","skillfed_tags":["audio-processing","machine-learning","music-production"],"use_cases":["Generate karaoke tracks by separating vocals from instrumental stems in music files.","Extract individual instrument stems (drums, bass, guitar) from recordings for remixing or music production.","Denoise or remove echo/reverb from audio recordings using specialized UVR models.","Batch process large music libraries to create stem versions for archival or reuse.","Integrate stem separation into a larger audio analysis or music information retrieval pipeline."],"what_it_does":"Audio Separator is a Python package that uses pre-trained deep learning models to decompose audio files into separate stems\u2014such as vocals, drums, bass, and other instruments. It wraps models from Ultimate Vocal Remover (UVR) trained by @Anjok07, making them accessible both as a command-line tool and as a library for batch processing or integration into larger audio workflows. The package supports multiple model architectures (MDX-Net, VR Arch, Demucs, MDXC/RoFormer) and can run on CPU or accelerated hardware (NVIDIA CUDA, Apple Silicon CoreML, or experimental Windows DirectML).\n\nThe most common use case is separating a song into instrumental and vocal stems for karaoke production, but the underlying models support more granular decomposition into drums, bass, piano, guitar, and other sources. The package also handles audio preprocessing and format conversion via FFmpeg, making it straightforward to process diverse input files. With 22 runtime dependencies including torch, librosa, and ONNX tooling, it trades installation complexity for a complete, batteries-included audio processing pipeline.","worth_installing":"Yes, if you need audio stem separation and can accommodate the dependency footprint. The package is actively maintained, has no known vulnerabilities, uses a permissive MIT license, and offers both CLI and library interfaces. Install friction is low despite 22 dependencies. The main gotcha is the separate FFmpeg system requirement and the need to choose an appropriate hardware acceleration path (CUDA, CoreML, CPU, or experimental DirectML). Recommended for music production, karaoke generation, and audio research workflows."},"id":"audio-separator","links":{"html":"https://skillfed.io/packages/audio-separator","md":"https://skillfed.io/packages/audio-separator.md","pypi":"https://pypi.org/project/audio-separator/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-20","license_spdx":null,"license_treatment":"permissive","name":"audio-separator","python_support":"supports_current","summary":"Easy to use audio stem separation, using various models from UVR trained primarily by @Anjok07"},"popularity":{"monthly_downloads":363613,"position":7219,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.44.5"}