---
language:
- en
tags:
- multimodal
- vision
- audio
- multispectral
- emotion-recognition
- scene-understanding
- object-detection
- spatial-reasoning
- conversational-ai
widget:
- example_title: Vision+Audio Analysis Sample 1
  src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Vision+Audio Analysis Sample 2
  src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
pipeline_tag: reinforcement-learning
license: apache-2.0
base_model:
- Qybera/LisaV3
datasets:
- Qybera/pkl-video-audio
metrics:
- accuracy
---

# 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:

```python
{
    '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

```python
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

```python
# 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

```python
# 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

```bibtex
@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*