Multimodal Pipelines

Multimodal AI Pipelines (Vision & Audio)

PoC Architecture Design

This PoC explores the bridging mechanisms required to feed images (Vision) and Audio directly into an LLM, bypassing traditional OCR or Speech-to-Text APIs for a true Multimodal embedding space.

Core Mechanics

1. Vision-Language Bridging: Models like LLaVA or Qwen-VL use a Vision Encoder (e.g., CLIP ViT) to process an image into patch embeddings. These patches are passed through a projection layer (MLP) to align visual tokens with the text embedding space.
2. Audio-Text Pipelines: Audio streams are segmented and processed via Whisper (or native audio encoders) into spectogram embeddings.
3. Cross-Attention: The LLM's transformer blocks use cross-attention to attend to both the text tokens and the visual/audio tokens simultaneously during autoregressive generation.

Architecture Map

%%{init: {"theme": "default", "flowchart": {"useMaxWidth": true}}}%%
flowchart TD
    subgraph Inputs ["Multimodal Inputs"]
        A["Image Pixels"]
        B["Audio Waveform"]
        C["Text Prompt"]
    end
    
    subgraph Encoders ["Modality Encoders"]
        D["CLIP ViT Encoder"]
        E["Whisper Audio Encoder"]
        F["Text Tokenizer"]
    end
    
    subgraph Projection ["Alignment Layer"]
        G["Vision-Text Projector (MLP)"]
        H["Audio-Text Projector"]
    end
    
    subgraph Core ["LLM Backbone"]
        I["Self-Attention Blocks"]
        J["Autoregressive Decoding"]
    end
    
    A --> D
    B --> E
    C --> F
    D --> G
    E --> H
    F --> I
    G --> I
    H --> I
    I --> J
    J -->|"Output Tokens"| K["Action / Response"]