# ZFTurbo/Music-Source-Separation-Training

Repository for training models for music source separation.

Repository: https://github.com/ZFTurbo/Music-Source-Separation-Training
Canonical: https://ross.abutalabs.com/products/music-source-separation-training
Language: Python
License: MIT
License Family: permissive
Last push: 2026-08-25T19:07:48+00:00

## Health v2 (maintenance only)
Score: 83/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 68, longevity 73
- inputs: {"age_days": 1031, "days_push": 8, "days_rel": 135, "gap_med": 33.0, "n_releases_24m": 13}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1512, forks 219 (observed 2026-08-28T04:04:56.031258+00:00)

## What it is
A Python training framework for music source separation models, supporting many architectures such as MDX23C, Demucs, Band Split RoFormer, and Mel-Band RoFormer. It provides configurable training and inference code for separating audio into stems like vocals, drums, and bass.

## Use cases
- train a model to separate vocals from music
- split a song into stems like drums bass and vocals
- train a custom music source separation model
- remove vocals from a track with a deep learning model
- experiment with different audio separation architectures
- create karaoke versions of songs
- fine-tune a stem separation model on my own dataset

## When to choose
- you want to train or fine-tune source separation models with many architecture options
- you need a flexible, experiment-friendly training codebase for audio separation
- you want to run inference with pretrained separation models like Mel-Band RoFormer

## When to avoid
- you only need a ready-made GUI app to separate audio without training
- you need real-time separation in production rather than offline processing
- you work outside audio, e.g. image or text separation tasks

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, audio-processing, llm-training
- domain: machine-learning, deep-learning, media
- platform: python, cross-platform
- tags: music-source-separation, audio-separation, stem-separation, pytorch, model-training, demucs, roformer, karaoke, audio, gpu

## Member repositories
- ZFTurbo/Music-Source-Separation-Training (main) score 83

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:56.031258+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:32:19.373416+00:00, confidence not recorded.
  - readme: https://github.com/ZFTurbo/Music-Source-Separation-Training (fetched 2026-08-28T04:04:56.031258+00:00, sha bff1b5cd840b)
- Data as of 2026-08-30T08:39:29.467469+00:00.