# WindOneSound 300M: FSD50K Part 5 Training (Grand Finale)

## Overview
This script fine-tunes the existing Part 4 Primitive Extractor checkpoint on the final FSD50K Part 5 dataset, completing the full 5-part training pipeline. The goal is to maximize the model's acoustic knowledge base and achieve peak generalization without catastrophic forgetting.

## Methodology & Configuration
- **Base Checkpoint:** `best_fsd50k_primitive_part4.pt`
- **Dataset:** FSD50K Part 5 (7,370 training samples, 819 validation samples)
- **Dual-Loss Objective:** 
  - **Weighted BCE Loss:** Handles multi-label classification with dynamically calculated class weights.
  - **MSE Reconstruction Loss:** Forces the model to rebuild the input BEATs features from the primitives, preserving the acoustic feature space.
  - **Total Loss:** `BCE_Loss + (0.1 * MSE_Loss)`
- **Optimizer & Scheduler:** AdamW (lr=2e-4, weight_decay=1e-4) with a 10% Linear Warmup followed by Cosine Annealing decay.
- **Epochs:** 10
- **Mobile Optimization:** Harmless PyTorch warnings are suppressed. The training loop outputs a clean, structured table after each epoch.

## Training Script
*(See the Python code block executed in Colab for the exact implementation)*

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**Date:** Wednesday, 8th of October