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name: separation description: "Guides Spleeter pretrained music source separation with CLI and Python APIs for vocals, accompaniment, and 2/4/5-stem outputs." disable-model-invocation: true metadata: disco-role: operating license: MIT

Spleeter separation

Use this sub-skill when the task is to split local music/audio into pretrained Spleeter stems: vocals/accompaniment, 4-stem vocals/drums/bass/other, 5-stem vocals/drums/bass/piano/other, CLI spleeter separate, Python Separator, AudioAdapter, ffmpeg load/save, filename templates, MWF, or model first-run download/cache failures.

Route first

User need	Go to
Build or run separation commands, choose 2/4/5 stems, slice audio, batch files, choose output names/codecs, or validate stem outputs	workflow recipes
Use Separator, separate, separate_to_file, save_to_file, AudioAdapter, FFMPEGProcessAudioAdapter, or Codec in Python	API reference
Diagnose ffmpeg, bad audio paths, deprecated -i, output conflicts, model download/cache, TensorFlow warnings, Windows, or Apple Silicon issues	separation troubleshooting
Safely assemble and dry-run a public CLI call from installed Spleeter	scripts/separate_file.py
Install/runtime prerequisites, Python/TensorFlow constraints, ffmpeg checks, optional GPU note	root installation reference
Full CLI option catalog for separate, train, and evaluate	root CLI reference
Embedded descriptors, model cache/download variables, local JSON configs	root models/configuration reference

Separation boundaries

• Include spleeter separate FILES... with --output_path/-o, --params_filename/-p, --filename_format/-f, --codec/-c, --bitrate/-b, --offset/-s, --duration/-d, --mwf, --adapter/-a, and --verbose.
• Do not use deprecated -i or --inputs; Spleeter 2.4.2 exits with code 20 for that form. Pass audio files as positional arguments.
• Use model descriptors such as spleeter:2stems, spleeter:4stems, spleeter:5stems, and their -16kHz variants at a high level; read the root models/configuration reference for cache and custom descriptor details.
• Route custom model training, training CSVs, and Spleeter training configs to training.
• Route MUSDB evaluation, spleeter evaluate, musdb, museval, and metrics interpretation to evaluation.

Minimal operating plan

1. Confirm runtime basics: python -m spleeter --version, python -m spleeter separate --help, and system ffmpeg/ffprobe availability.
2. Choose a model descriptor: spleeter:2stems for vocals/accompaniment, spleeter:4stems for vocals/drums/bass/other, or spleeter:5stems when piano should be separated too.
3. Prefer a short --duration smoke run before a long batch. The first run for a descriptor may download and checksum pretrained model files.
4. Keep filename templates collision-safe: include {instrument} and, for multi-file jobs, usually {filename} or {foldername}.
5. Validate the expected stem files are present, non-empty, and decodable before using them downstream.