L’interface est localisée ; les présentations et documents des Skills sont traduits automatiquement par IA dans votre langue. Le code, les commandes, chemins et termes techniques restent en version originale, et le document de l’auteur fait foi.
Explorer les Skills
Données et recherche · SKILL.md

muzic

muzic

Route Microsoft Muzic research workflows for music understanding, retrieval, symbolic generation, songwriting, and MusicAgent orchestration.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: muzic description: "Route Microsoft Muzic research workflows for music understanding, retrieval, symbolic generation, songwriting, and MusicAgent orchestration." metadata: disco-role: operating disable-model-invocation: true license: MIT

Muzic Repo Skill

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.

Quick routing

User intentRead
MusicBERT OctupleMIDI, symbolic music understanding, pretraining/fine-tuning/evaluation, PDAugment speech/MIDI augmentation, or CLaMP semantic search/zero-shot classificationsub-skills/music-understanding-retrieval/SKILL.md
DeepRapper rap lyrics, SongMASS lyric-to-melody or melody-to-lyric, TeleMelody, ReLyMe, ROC, lyric/chord templates, songwriting metricssub-skills/lyric-melody-songwriting/SKILL.md
GETMusic any-track generation/infilling, MuseCoco text-to-music, Museformer long structure, MeloForm form refinement, EmoGen emotion-controlled generationsub-skills/symbolic-generation-structure/SKILL.md
MusicAgent installation, config, secrets, model/tool downloads, CLI or Gradio startup, plugin/tool selectionsub-skills/music-agent-workflows/SKILL.md
Repo-wide setup, dependency-family choices, old-stack caveats, and safe workspace checksreferences/setup-and-environments.md and scripts/check_muzic_workspace.py
One-table map of Muzic subprojects, typical inputs, outputs, and owner sub-skillreferences/project-map.md
Cross-cutting install, data, checkpoint, backend, and credential failuresreferences/troubleshooting.md

First steps for any Muzic task

  1. Identify the subproject and task family from references/project-map.md.

  2. 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.

  3. 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
    
  4. Load the nearest sub-skill and its references. Use bundled validators/planners there before invoking original research scripts.

  5. When giving commands, state the required working directory, input files, expected outputs, dependency family, and what is intentionally not verified.

Setup stance

  • Do not install root requirements.txt blindly. It pins old and conflicting ML stacks; choose the smallest per-project environment described in references/setup-and-environments.md.
  • Treat full training and inference as data/checkpoint/backend dependent unless a sub-skill explicitly identifies a CPU-only validation path.
  • Do not assume Hugging Face, Google Drive, or dataset downloads are acceptable. Ask or plan explicitly before network-heavy downloads.
  • Do not hardcode credentials, local cache paths, or .env values in instructions or generated files.
  • Do not mutate system package managers, Conda base, or user environments without approval.

Safe bundled helpers

Root helper:

python scripts/check_muzic_workspace.py --help

Sub-skill helpers:

  • sub-skills/music-understanding-retrieval/scripts/validate_clamp_inputs.py
  • sub-skills/music-understanding-retrieval/scripts/validate_pdaugment_layout.py
  • sub-skills/lyric-melody-songwriting/scripts/plan_deeprapper_command.py
  • sub-skills/lyric-melody-songwriting/scripts/check_songmass_telemelody_assets.py
  • sub-skills/lyric-melody-songwriting/scripts/make_roc_input_template.py
  • sub-skills/symbolic-generation-structure/scripts/validate_getmusic_request.py
  • sub-skills/symbolic-generation-structure/scripts/plan_musecoco_pipeline.py
  • sub-skills/music-agent-workflows/scripts/validate_musicagent_config.py

These 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.

Provenance and routing metadata

Non-goals

  • This skill does not replace the Muzic research code, checkpoints, datasets, or external model assets.
  • This skill does not certify that optional CUDA/Triton/Java/system-audio/API-key workflows run in the current environment.
  • This skill does not export or import itself; import is intentionally disabled for this production run.
Skills enregistrées