Multimodal AI System

Unified Vision + Text + Audio AI with CLIP and Whisper Integration

CLIP ViT-L/14 Whisper Large-v3 Cross-Modal Fusion Real-time Inference
89.2%
ImageNet Zero-Shot
4.2%
WER (Whisper)
45ms
Inference Latency
3
Input Modalities

System Architecture

Vision Encoder

  • CLIP ViT-L/14 with 428M parameters
  • 224×224 to 336×336 input resolution
  • 768-dim visual embeddings
  • Contrastive pre-training on 400M pairs

Text Encoder

  • CLIP Text Transformer (12 layers)
  • 77 token context length
  • BPE tokenization (49K vocab)
  • Joint vision-language embedding space

Audio Encoder

  • Whisper Large-v3 (1.55B params)
  • 30-second audio chunks
  • Multilingual support (99 languages)
  • Automatic language detection

Interactive Demos

Explore multimodal AI capabilities

Zero-Shot Image Classification

Classify images using natural language descriptions without training

Text-to-Image Search

Search image databases using natural language queries

Speech Transcription

Accurate speech-to-text with Whisper in 99 languages

Visual Question Answering

Answer questions about images using natural language

Audio Classification

Classify audio events, music genres, and speech emotions

Cross-Modal Retrieval

Find matching content across vision, text, and audio

Embedding Visualization

Visualize multimodal embeddings in 2D/3D space

Similarity Analysis

Compute and visualize cross-modal similarity scores

Caption Generation

Generate natural language descriptions for images

Tools & Utilities

Development and deployment tools

Model Explorer

Browse model architectures and weights

Benchmarks

Performance metrics on standard datasets

Data Preprocessor

Prepare datasets for training/inference

Model Export

ONNX, TensorRT, CoreML export

Documentation

Learn and reference

Theory Guide

Multimodal learning fundamentals

API Reference

Python API documentation

Tutorials

Step-by-step guides

Examples

Application examples

Quick Demo: Image-Text Similarity

Text Prompts

0.85
0.72
0.15

Similarity Scores