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deepjazz

deepjazz

Use deepjazz for legacy MIDI jazz generation with music21 preprocessing, abstract grammar tokens, Keras/Theano LSTM training, safe diagnostics, and modernization guidance.

Independent GitHub snapshot · Automated classification · Original documentation governs

スキルの説明

GitHub

name: deepjazz description: "Use deepjazz for legacy MIDI jazz generation with music21 preprocessing, abstract grammar tokens, Keras/Theano LSTM training, safe diagnostics, and modernization guidance." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

deepjazz Repo Skill

Use this skill when a task involves the deepjazz repository or a deepjazz-style workflow: symbolic MIDI jazz generation, Metheny-style MIDI preprocessing, abstract grammar tokens, note cleanup, Keras/Theano LSTM generation, optional Theano GPU commands, or modernizing the legacy code.

This skill is self-contained. Do not rely on reading original repo scripts at runtime to recover constants, signatures, or workflow boundaries; use the bundled references and scripts below.

First route by task

User taskRoute
Inspect whether a MIDI file can work with deepjazz, change melody/accompaniment parts, or debug corpus extractionsub-skills/midi-preprocessing/
Explain or validate grammar tokens, run parse/unparse/cleanup smoke checks, or debug malformed generated grammarsub-skills/grammar-and-qa/
Run, adapt, or port the LSTM generation path; avoid playback/training hazards; inspect model settingssub-skills/lstm-generation/
Diagnose install/import/backend problems that cut across all workflowsreferences/troubleshooting.md
Check whether this skill matches a checkout/source revisionreferences/repo-provenance.md

Minimal safe start

  1. Treat deepjazz as a legacy script collection, not a modern installable package.

  2. For faithful execution, use a Python 2.7-era environment with NumPy/SciPy, music21, Keras 1.x, and Theano. Read references/dependency-environment.md before installing.

  3. Set KERAS_BACKEND=theano before importing legacy Keras.

  4. Run the bundled no-training environment check:

    python scripts/check_deepjazz_environment.py --expect-theano
    
  5. Validate MIDI structure before training:

    python sub-skills/midi-preprocessing/scripts/inspect_midi_structure.py --midi-file <input.mid>
    
  6. Validate grammar/QA behavior without training:

    python sub-skills/grammar-and-qa/scripts/grammar_roundtrip_smoke.py --seed 7
    
  7. Only run full generation after environment, data, and grammar checks pass. The original generation flow trains an LSTM, attempts realtime MIDI playback, and writes MIDI output.

Key distilled facts

  • Public source modules: generator, grammar, preprocess, qa, and lstm.
  • Verified public signatures: generate(data_fn, out_fn, N_epochs), build_model(corpus, val_indices, max_len, N_epochs=128), get_musical_data(data_fn), get_corpus_data(abstract_grammars), parse_melody(fullMeasureNotes, fullMeasureChords), unparse_grammar(m1_grammar, m1_chords), prune_grammar(curr_grammar), prune_notes(curr_notes), clean_up_notes(curr_notes).
  • Default generation settings: max_len=20, max_tries=1000, diversity=0.5, bpm=130, default epochs 128.
  • The README documents CPU execution and an optional NVIDIA/Theano GPU command; CPU is the safe baseline for this skill.
  • The default MIDI workflow is tailored to a Metheny fixture: melody part 5, accompaniment parts 0,1,6,7, and solo window offsets 476..548.

Shared references and scripts

Non-goals

  • Do not treat this as a generic modern music-generation framework.
  • Do not claim GPU verification unless a task explicitly verifies the legacy Theano CUDA path.
  • Do not run full generation as a quick smoke test; use bundled no-training helpers first.
  • Do not expect arbitrary MIDI files to work without adapting part and offset assumptions.
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