AdvancedLISA - Multimodal Vision+Audio AI

Model Description

AdvancedLISA is a sophisticated multimodal AI model that combines advanced vision and audio processing with reasoning capabilities. The model provides comprehensive scene understanding, emotion recognition, and multimodal analysis.

Key Capabilities

Multispectral Vision Processing: Processes 5-channel vision input (RGB + multispectral) with spatial reasoning
Advanced Audio Analysis: Comprehensive audio understanding including emotion, speaker, and content analysis
Multimodal Fusion: Cross-modal attention between vision and audio modalities
Reasoning Module: Transformer-based reasoning with sequence-to-sequence understanding
Emotion Recognition: Real-time emotion detection from audio input
Spatial Understanding: 3D spatial reasoning and object detection
Conversation Memory: Persistent memory across interaction sequences
Voice Synthesis: Independent voice generation capabilities

Model Details

Model Type: AdvancedLISA
Architecture: Vision+Audio Fusion with Reasoning
Parameters: 190,809,376 (191M)
Trainable Parameters: 190,809,376
Input Modalities:
- Vision: 5-channel multispectral images (224×224)
- Audio: Mel spectrograms (80 bins × 200 time steps)
Sequence Length: 30 frames/steps
Device: CPU/GPU compatible
Framework: PyTorch

Architecture Components

Component	Type	Parameters	Function
Vision Encoder	MultispectralVisionEncoder	15,544,195	Multispectral image processing + 3D spatial reasoning
Audio Encoder	AdvancedAudioEncoder	29,479,243	Audio analysis + emotion/speaker detection
Fusion Module	AdvancedFusionModule	16,803,334	Cross-modal attention and feature fusion
Reasoning Module	ReasoningModule	68,231,168	Transformer-based sequence reasoning
Voice Synthesis	IndependentVoiceSynthesis	8,061,965	Voice generation capabilities
Self Awareness	SelfAwarenessModule	22,579,201	Identity and context awareness
Conversation Memory	ConversationMemory	6,823,937	Persistent dialogue memory

Model Outputs

The model returns a comprehensive output dictionary:

{
    'vision_analysis': {
        'features': [batch, 30, 512],      # Core vision features
        'spatial_3d': [batch, 30, 6],      # 3D spatial understanding  
        'scene': [batch, 30, 1000],        # Scene classification
        'objects': [batch, 30, 80],        # Object detection
        'motion': [batch, 30, 4]           # Motion analysis
    },
    'audio_analysis': {
        'features': [batch, 30, 1024],     # Core audio features
        'spatial': [batch, 30, 4],         # Spatial audio
        'emotion': [batch, 30, 7],         # Emotion classification  
        'speaker': [batch, 30, 256],       # Speaker characteristics
        'content': [batch, 30, 128]        # Content analysis
    },
    'reasoning': [batch, 30, 1024],        # Fused reasoning output
    'timestamp': float,                    # Processing timestamp
    'rl_action': dict                      # Reinforcement learning actions
}

Performance

Inference Time: ~17.4s per sequence (CPU)
Throughput: ~0.06 sequences/second (CPU)
Memory Usage: ~191M parameters
Input Resolution: 224×224 images, 80-bin mel spectrograms
Sequence Length: Fixed at 30 frames

Note: GPU inference will be significantly faster

Usage

Basic Inference

import torch
import json
from pathlib import Path

# Load model configuration  
config_path = "Qybera/LisaV3.0/config.json"
with open(config_path, 'r') as f:
    config = json.load(f)

# Import and create model (requires lisa_model.py)
from lisa_model import create_lisa_model

model_config = {
    'model_config': {
        'vision_channels': 5,        # Multispectral input
        'audio_channels': 1,
        'vision_hidden': 512,
        'audio_hidden': 512,
        'fused_dim': 1024,
        'voice_hidden': 512,
        'vision_layers': 4,
        'audio_layers': 4,
        'reasoning_layers': 8,
        'mel_bins': 80,
        'max_memory': 50
    },
    'data_config': {
        'frame_size': [224, 224],
        'seq_len': 30,
        'n_mels': 80
    }
}

# Create and load model
model, device = create_lisa_model(model_config)

# Load trained weights
state_dict = torch.load("Qybera/LisaV3.0/pytorch_model.bin", map_location=device)
model.load_state_dict(state_dict)
model.eval()

# Prepare inputs (must be exactly sequence length 30)
vision_input = torch.randn(1, 30, 5, 224, 224).to(device)  # 5-channel multispectral
audio_input = torch.randn(1, 30, 1, 80, 200).to(device)    # Mel spectrograms

# Generate comprehensive analysis
with torch.no_grad():
    output = model(vision_input, audio_input)

# Access different analysis components
vision_features = output['vision_analysis']['features']  # [1, 30, 512]
audio_emotions = output['audio_analysis']['emotion']     # [1, 30, 7]
reasoning_output = output['reasoning']                   # [1, 30, 1024]

print(f"Vision features: {vision_features.shape}")
print(f"Detected emotions: {audio_emotions.shape}")
print(f"Reasoning output: {reasoning_output.shape}")

Batch Processing

# Process multiple sequences
batch_size = 2
vision_batch = torch.randn(batch_size, 30, 5, 224, 224).to(device)
audio_batch = torch.randn(batch_size, 30, 1, 80, 200).to(device)

with torch.no_grad():
    batch_output = model(vision_batch, audio_batch)

print(f"Batch processing: {batch_size} sequences")
print(f"Batch reasoning output: {batch_output['reasoning'].shape}")

Individual Component Access

# Access individual model components
vision_encoder = model.vision_encoder
audio_encoder = model.audio_encoder
reasoning_module = model.reasoning_module

# Use vision encoder separately
vision_analysis = vision_encoder(vision_input)
print("Vision analysis keys:", list(vision_analysis.keys()))

# Use audio encoder separately  
audio_analysis = audio_encoder(audio_input)
print("Audio analysis keys:", list(audio_analysis.keys()))

Input Requirements

⚠️ Important: The model expects exactly 30 frames/steps per sequence due to memory constraints.

Vision Input: [batch_size, 30, 5, 224, 224] - 5-channel multispectral images
Audio Input: [batch_size, 30, 1, 80, 200] - Mel spectrograms with 80 frequency bins
Batch Size: Flexible (tested up to batch_size=2)
Sequence Length: Fixed at 30 (longer sequences will cause errors)

Training Information

Framework: PyTorch
Final Training Loss: 0.611
Final Validation Loss: 0.639
Training Epochs: 50
Learning Rate: 2.14e-05 (with scheduling)
Optimizer: AdamW
Dataset: YouTube videos with multimodal processing

Limitations

Fixed Sequence Length: Must use exactly 30 frames per sequence
Memory Constraints: Cannot handle variable sequence lengths due to conversation memory implementation
CPU Performance: ~17s per inference on CPU (GPU recommended for real-time use)
Input Format: Requires specific multispectral (5-channel) vision input

Applications

Multimodal Scene Analysis: Comprehensive understanding of visual scenes with audio context
Emotion Recognition: Real-time emotion detection from audio input
Content Analysis: Understanding of both visual and audio content
Spatial Reasoning: 3D spatial understanding and object detection
Interactive AI: Conversation memory enables contextual interactions

Citation

@model{advancedlisa2025,
  title={AdvancedLISA: Multimodal Vision+Audio AI with Advanced Reasoning},
  author={LISA Development Team},
  year={2025},
  url={https://github.com/elijahnzeli1/LISA3D}-private
}

License

Apache-2.0 License - see LICENSE file for details

Model card updated based on comprehensive testing - September 2025

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