⚡ AuraMIDI-v1 - Neural Music Transformer

AuraMIDI-v1 is a lightweight, high-performance symbolic music generation Transformer model trained using PyTorch and exported to ONNX for universal deployment across web, mobile, and desktop environments.

Trained on a curated dataset of 19,833 high-quality songs (~80 Million musical tokens) from the Lakh Clean MIDI Dataset, Google Magenta MAESTRO, and modern multi-genre stems (Synthwave, Cyberpunk, Lo-Fi Chill, Neo Jazz, Phonk, and EDM).

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📊 Model Architecture & Training Specs

• Model Name: AuraMIDI-v1
• Model Type: Causal Decoder-Only Transformer (GPT-style Music Architecture)
• Total Parameters: 48.60 Million
• Transformer Layers: 12 Layers (Pre-LN with GELU activation)
• Attention Heads: 16 Heads ($d_{head} = 32$)
• Hidden Size ($d_{model}$): 512
• Feedforward Dim: 2048
• Context Length: 1024 tokens
• Vocabulary Size: 10,000 (MidiTok REMI Tokenizer)
• Training Dataset: 19,833 songs / 79,454,977 tokens
• Training Hardware: NVIDIA Modern GPU with Mixed Precision (BF16 AMP)
• Training Epochs: 3 full epochs (~238 Million tokens processed)
• Final Validation Loss: 1.4065

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📦 Files in this Repository

File	Size	Description
auramidi_v1.pt	~194 MB	Full PyTorch checkpoint (Loss 1.40)
auramidi_v1.onnx	~166 MB	Monolithic standalone ONNX model for Web/Mobile
generate_neural.py	—	Autoregressive generation script with Top-K/Top-P sampling
model_architecture.py	—	PyTorch model architecture and tokenization pipeline

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🚀 Quick Start (Inference in Python)

pip install torch miditok pretty_midi

import torch
from generate_neural import generate_full_neural_composition

# Generate a complete 4-track neural track
midi_path = generate_full_neural_composition(
    genre="synthwave",
    key="Am",
    bpm=120,
    temperature=0.85,
    top_k=20,
    top_p=0.90
)

print(f"Generated neural MIDI: {midi_path}")