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muzic【数据与研究】Route Microsoft Muzic research workflows for music understanding, retrieval, symbolic generation, songwriting, and MusicAgent orchestration.
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Use this skill when a task names Muzic or asks for AI-music workflows that match the Muzic research projects: symbolic music understanding, cross-modal music retrieval, lyrics transcription augmentation, lyric-to-melody songwriting, symbolic music generation, structure/form/emotion control, or MusicAgent orchestration.
Muzic is a research monorepo, not a single pip-installable Python package. Most workflows are project-specific scripts with old dependency stacks, external datasets, checkpoints, or credentials. Start by routing to the right sub-skill, then validate inputs and runtime prerequisites before running heavyweight model code.
| User intent | Read |
|---|---|
| MusicBERT OctupleMIDI, symbolic music understanding, pretraining/fine-tuning/evaluation, PDAugment speech/MIDI augmentation, or CLaMP semantic search/zero-shot classification | sub-skills/music-understanding-retrieval/SKILL.md |
| DeepRapper rap lyrics, SongMASS lyric-to-melody or melody-to-lyric, TeleMelody, ReLyMe, ROC, lyric/chord templates, songwriting metrics | sub-skills/lyric-melody-songwriting/SKILL.md |
| GETMusic any-track generation/infilling, MuseCoco text-to-music, Museformer long structure, MeloForm form refinement, EmoGen emotion-controlled generation | sub-skills/symbolic-generation-structure/SKILL.md |
| MusicAgent installation, config, secrets, model/tool downloads, CLI or Gradio startup, plugin/tool selection | sub-skills/music-agent-workflows/SKILL.md |
| Repo-wide setup, dependency-family choices, old-stack caveats, and safe workspace checks | references/setup-and-environments.md and scripts/check_muzic_workspace.py |
| One-table map of Muzic subprojects, typical inputs, outputs, and owner sub-skill | references/project-map.md |
| Cross-cutting install, data, checkpoint, backend, and credential failures | references/troubleshooting.md |
Identify the subproject and task family from references/project-map.md.
Check whether the request is only planning/validation or an actual model run. Model runs often need checkpoints, datasets, old Torch/Fairseq/TensorFlow versions, CUDA, Java, system audio packages, or API credentials.
If the user has a Muzic checkout or prepared workspace, run the safe checker before expensive commands:
python scripts/check_muzic_workspace.py --workspace /path/to/muzic --expect musicbert clamp getmusic musicagent
Load the nearest sub-skill and its references. Use bundled validators/planners there before invoking original research scripts.
When giving commands, state the required working directory, input files, expected outputs, dependency family, and what is intentionally not verified.
requirements.txt blindly. It pins old and conflicting ML stacks; choose the smallest per-project environment described in references/setup-and-environments.md..env values in instructions or generated files.Root helper:
python scripts/check_muzic_workspace.py --help
Sub-skill helpers:
sub-skills/music-understanding-retrieval/scripts/validate_clamp_inputs.pysub-skills/music-understanding-retrieval/scripts/validate_pdaugment_layout.pysub-skills/lyric-melody-songwriting/scripts/plan_deeprapper_command.pysub-skills/lyric-melody-songwriting/scripts/check_songmass_telemelody_assets.pysub-skills/lyric-melody-songwriting/scripts/make_roc_input_template.pysub-skills/symbolic-generation-structure/scripts/validate_getmusic_request.pysub-skills/symbolic-generation-structure/scripts/plan_musecoco_pipeline.pysub-skills/music-agent-workflows/scripts/validate_musicagent_config.pyThese helpers are self-contained planning or layout checks. They do not load Muzic model weights, contact remote services, run training, or import source checkout modules.