Download From Arbitrary Audio to EDM: Audio-Conditioned Retrieval of Discrete Rhythm Archetypes We present a system for transforming arbitrary audio into Electronic Dance Music (EDM) drum patterns while preserving the timbral identity of the source material. A Vector Quantized Variational Autoencoder (VQ-VAE) trained on 7,999 EDM drum loops learns a discrete codebook of 256 rhythm archetypes, validated through UMAP and hierarchical clustering to exhibit semantically meaningful structure. At inference, spectral features extracted from arbitrary input audio select the nearest archetype via nearest-neighbor retrieval in a shared audio feature space. A training sample from the selected archetype is reconstructed through the VQ-VAE, and a second decoder predicts per-hit velocity dynamics. The user's sounds are then placed at the reconstructed hit positions, scaled by predicted velocity. Applied to 2,000 files from the ESC-50 environmental sound dataset, the system activates 128 of 256 codebook entries (50% coverage), demonstrating broad responsiveness to diverse non-EDM audio.