# ⚠️ CAUTION: WindOneSound V3.2 Architecture & Training Guide

## 1. Executive Summary
WindOneSound is a **Multimodal Reasoning Engine**. It listens to audio, builds a causal graph of events, remembers context, and generates natural language. It is NOT a simple transcriber.

## 2. The Data Flow Pipeline

### Stage A: Perception (Frozen)
*   **Input:** Raw Audio → BEATs → `[B, T, 768]`.
*   **Module:** `PrimitiveExtractor`.
*   **Output:** Dense Primitives `[B, T, 512]`.
*   **Status:** FROZEN. Load weights from `primitive_extractor_esc50_best.pt`.

### Stage B: Reasoning Core (Trainable)
1.  **SourceMemory:** Biases features by speaker/source identity.
2.  **TimeConditioning:** Injects sinusoidal time embeddings. **CRITICAL:** Added INSIDE `CausalGraph`, not LLM.
3.  **CausalGraphNetwork:** Builds entity-event relationships using bidirectional attention. Uses Time Embeddings to order events.
4.  **COGENMemory:** Long-term storage with Top-K slot addressing and Decay Factor (0.995).
*   **Output:** `read_out` `[B, T, 512]` (Fully contextualized).

### Stage C: Hybrid Discretization (The Bridge)
*   **Module:** `VQPrimitiveTokenizer`.
*   **Outputs:**
    1.  `z_q`: Quantized continuous vectors `[B, T, 512]`.
    2.  `tokens`: Discrete integer IDs `[B, T]` (0-1023).
*   **Purpose:** Filters noise and creates a stable "Audio Vocabulary".

### Stage D: Generation Heads (Trainable)
*   **LLM Decoder:** Generates text.
    *   **Context Input:** HYBRID VECTOR (See Section 3).
    *   **Text Input:** Question/Prompt tokens.
*   **Voice Head:** Synthesizes Mel-Spectrograms from RAW `read_out` (no quantization artifacts).
*   **Agent Head:** Outputs action vectors from pooled `read_out`.

## 3. The Hybrid Context Strategy (VQ + Projection)

To maximize performance, we do NOT choose between VQ Tokens and Continuous Projections. We USE BOTH.

**Formula:**
`Final_Context[t] = Linear_Projection(z_q[t]) + Learned_Embedding(VQ_Token_ID[t])`

**Why?**
*   **Linear Projection (`z_q`):** Carries rich semantic detail (timbre, pitch, emotion). Essential for descriptive accuracy.
*   **Learned Embedding (`Token ID`):** Carries structural information (event boundaries, repetition patterns). Helps the LLM align text timestamps with audio events.

**Implementation Note:**
In `output_heads.py`, ensure `DualOutputHeads.forward` constructs this hybrid tensor before passing it to `self.text_decoder`.

## 4. Training Strategy (Strict Phasing)

### Phase 1: Validation (Done ✅)
*   Verify Primitive Extractor quality (>90% ESC-50 Acc).

### Phase 2: Codebook Stabilization (Done ✅)
*   Train VQ Tokenizer on frozen primitives. Goal: >95% code usage.

### Phase 3: Reasoning Warm-up
*   **Trainable:** SourceMemory, CausalGraph, COGENMemory.
*   **Frozen:** PE, VQ, LLM.
*   **Task:** Contrastive Learning or Classification to teach temporal/causal structure.

### Phase 4: Joint Multimodal Training
*   **Trainable:** Everything EXCEPT PrimitiveExtractor.
*   **Optimizer:** AdamW with Parameter Groups:
    *   VQ Codebook: LR=1e-5
    *   Graph/Memory: LR=1e-4
    *   LLM Decoder: LR=5e-4
    *   Heads: LR=1e-3
*   **Loss:** `L_text + 0.1*L_vq + 0.01*L_mel`

## 5. How Time Conditioning Works
Time embeddings are added to primitive features BEFORE the Causal Graph processes them. This allows the Graph's attention mechanism to distinguish between "Event A at t=0" and "Event B at t=5", enabling causal ordering (Cause → Effect). The LLM sees the *result* of this ordering, not the raw time signals.

## 6. QA Capability
The model answers questions via Conditional Generation:
1.  User asks: "Was there a dog?"
2.  Text Encoder processes question.
3.  Cross-Attention looks at Hybrid Audio Context.
4.  If "Dog" concepts are present in `z_q` and structured correctly by Graph, LLM predicts "Yes".

## 7. Common Pitfalls
*   **NaN Loss:** Clip gradients (max_norm=1.0). Lower VQ LR.
*   **Hallucinations:** Ensure `src_key_padding_mask` is used so LLM ignores silent audio frames.
*   **Memory Overflow:** Monitor COGEN novelty scores; adjust decay if slots saturate.