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name: lstm-generation description: "Run, adapt, or modernize deepjazz's legacy LSTM generation workflow safely." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

deepjazz LSTM Generation

Use this sub-skill when the task is to run, adapt, troubleshoot, or modernize the legacy deepjazz LSTM generation path: preprocess grammar corpus, build/train the Keras/Theano model, generate measure grammars, prune/QA, unparse to notes, and write MIDI.

This skill distills the relevant generation and model behavior into the bundled references below. Do not depend on reading legacy source files at runtime to recover constants, APIs, or control flow.

Route elsewhere

• For MIDI compatibility, melody/accompaniment part selection, and measure-window checks, use ../midi-preprocessing/.
• For grammar token semantics, grammar pruning details, unparse edge cases, and QA helper behavior, use ../grammar-and-qa/.

Start safely

1. Use a legacy environment for faithful execution: Python 2.7, Keras 1.2.x, Theano 0.9.x, NumPy 1.16.x, and music21 3.1.x. Set KERAS_BACKEND=theano before importing Keras when using the legacy stack.

2. Before any training or playback-capable command, run the safe diagnostic:

   python scripts/legacy_generation_check.py --check-imports --show-settings --expect-theano
   

   To inspect API signatures in a local deepjazz-style working copy without training, playback, or writes:

   python scripts/legacy_generation_check.py --repo-root <deepjazz-style-root> --check-imports --show-settings --expect-theano
   

3. Treat full generation as side-effectful: it trains an LSTM, attempts realtime MIDI playback, and writes a MIDI file. On headless servers, use an adapted entrypoint that disables playback before invoking generation.

4. Treat Theano CUDA as a legacy optional acceleration path only. The CPU path is the safe baseline; the CUDA command requires an old NVIDIA/CUDA/Theano stack and is not verified by this skill.

What to read or run

• Read references/generation-workflow.md for the public end-to-end generation flow, legacy CPU/GPU command shapes, constants, side effects, and headless one-epoch guidance.
• Read references/lstm-architecture.md for build_model(corpus, val_indices, max_len, N_epochs=128), one-hot shapes, layer stack, compile/fit parameters, and inference sampling shapes.
• Read references/modernization-notes.md when porting the model to Python 3 or tf.keras while preserving corpus/value mappings and the grammar pipeline.
• Read references/troubleshooting.md for backend mismatch, missing dependency, Python 3, Theano CUDA, playback, output-path, speed, and nondeterminism failures.
• Run scripts/legacy_generation_check.py for deterministic, no-training/no-playback dependency, backend, distilled-setting, and optional signature checks.

Core distilled facts

Area	Legacy behavior
Generator API	generate(data_fn, out_fn, N_epochs) trains, generates, plays, then writes MIDI.
Default CLI epoch count	128 if the command-line epoch argument is absent or invalid.
Default input	Metheny MIDI fixture path under a midi/ directory. Route compatibility checks to midi-preprocessing.
Default output naming	deepjazz_on_metheny...<N>_epoch(s).midi under a midi/ directory. Prefer explicit .midi output paths in adaptations.
Model settings	max_len=20, max_tries=1000, diversity=0.5.
Music settings	tempo marker bpm=130.
Training	Two stacked LSTM(128) layers with dropout, categorical crossentropy, RMSprop, batch size 128, nb_epoch=N_epochs.
Safety hazards	Full generation can be slow, nondeterministic, playback-dependent, and write output files. The bundled helper avoids all of those side effects.