Version 1.0 - Gargantua Final Gold

🌌 Interstellar Notes v1.0.0 — Gargantua Final Gold Release

Welcome to the initial open-source release of Interstellar Notes!

This release introduces Gargantua Final Gold (gargantua_final_gold.pth), our flagship pre-trained model checkpoint for end-to-end Automatic Music Transcription (AMT), multi-channel CQT feature extraction, and DTW score-to-audio alignment.

---

🌟 Release Highlights

🧠 GargantuaAMT Architecture (gargantua_final_gold.pth)

• PyTorch Deep CRNN: Combines multi-channel 2D Convolutional layers with a Bidirectional LSTM backbone and attention-weighted logit sharpening.
• Sparse Bias Initialized: Optimized to minimize false positives by favoring negative logit bias on baseline quiet frames.
• Multi-instrument Support: Transcribes multiple active note pitches across isolated stem channels (e.g. Bass, Piano/Other).

⏱️ Dynamic Time Warping (DTW) Alignment Engine

• Automated frame-level score-to-performance alignment between raw .mp3 performances and MusicXML (.mxl) sheet music ground truth.
• Cost-based filtering and automated sanity check visualizer overlays.

🎨 Spectral Player Web Frontend

• Interactive React + Vite spectral visualizer web app located in spectral_player/ for real-time playback and inspection of spectro-temporal predictions.

---

📦 Assets in this Release

File Asset	Description	Size
gargantua_final_gold.pth	Flagship pre-trained PyTorch weights for GargantuaAMT	~118 MB
gargantua_v42_synthetic_gold_history.csv	Training loss & validation accuracy history log	~1 KB

---

🚀 Quick Usage Example

1. Download gargantua_final_gold.pth from the Assets section below.
2. Place the file inside the models/ directory of your local clone.
3. Load the model in Python:

import torch
from src.models.gargantua import GargantuaAMT

# Initialize Gargantua model architecture
model = GargantuaAMT(input_bins=440, n_notes=88, n_inst=3, in_channels=2)

# Load pre-trained weights
checkpoint = torch.load("models/gargantua_final_gold.pth", map_location="cpu")
model.load_state_dict(checkpoint)
model.eval()

print("Gargantua Final Gold successfully loaded!")