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model_checkpoints/Primitive_Extractor/primitiveE Readme.md

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+# Primitive Extractor
+
+## Overview
+The Primitive Extractor is the first trainable module in the WindOne Sound V3 architecture. Its primary function is to compress and refine the high-dimensional audio features extracted by the frozen BEATs model into a lower-dimensional, highly structured "primitive" representation.
+
+## Architecture & Flow
+- **Input Shape:** `[Batch, Time, 768]` (Raw features from BEATs)
+- **Output Shape:** `[Batch, Time, 512]` (Refined primitives)
+- **Total Parameters:** ~35 Million
+
+### 1. Convolutional Stack
+The input is transposed and passed through three 1D Convolutional layers with decreasing kernel sizes (7, 5, and 3). Each convolution is followed by Group Normalization (32 groups) and a GELU activation function. This step captures local, short-range temporal patterns and acoustic textures in the audio.
+
+### 2. Linear Projection
+The output of the conv stack (1728 dimensions) is projected down to 512 dimensions using a Linear layer, followed by Layer Normalization. This reduces the dimensionality while preserving the learned temporal features.
+
+### 3. Self-Attention & Residual Connection
+The 512-dimensional features are passed through an 8-head Multi-Head Self-Attention mechanism. This allows the model to capture long-range temporal dependencies across the audio sequence. Finally, a residual connection adds the attention output back to the input, followed by a final Layer Normalization.
+
+## Purpose
+By transforming the 768-dim BEATs embeddings into 512-dim primitives, this module creates a standardized, dense representation that the downstream Causal Graph Network and Memory modules can efficiently process.