README.md

metadata

title: Dynamics Needed
emoji: 🥁
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 6.24.0
app_file: app.py
python_version: '3.12'
short_description: Humanize drum dynamics
startup_duration_timeout: 30m

Dynamics Needed — demo Space

Web demo for structure-driven drum-velocity prediction: upload a drum-MIDI groove, pick a model and its musical context, and get back a version whose note velocities are predicted from structure and timing alone (never from the note's own loudness), plus a before/after velocity plot and an audio preview.

Three models, all trained on E-GMD:

• LightGBM — fast, deterministic gradient-boosted trees.
• Transformer · MDN — mixture-density head, temperature-controllable.
• Transformer · Categorical — softmax-over-bins head.

This directory is the source of truth

The demo source lives in the thesis monorepo at ml/demo/; the Hugging Face Space is a deploy target, not a second repo. Deploy with:

./deploy.sh <namespace>/<space-name>

deploy.sh assembles a self-contained bundle (this app + a vendored copy of the drum_dynamics package + the six ready-to-load model files from data/processed/ + the FluidR3_GM SoundFont) and pushes it with hf upload … --repo-type space. Model weights and the SoundFont are not committed to the monorepo; they are copied in at deploy time.

Hardware

Runs on ZeroGPU (the only free option for a Gradio Space on a non-PRO account). The models are tiny and CPU-only, so inference never requests a GPU — a single no-op @spaces.GPU function satisfies the ZeroGPU requirement without burning visitor quota.

Credits

Example grooves are clips from the E-GMD dataset (Google Magenta, CC-BY 4.0).