Multimodal Models: Text + Image + Audio

TL;DR

Multimodal AI models can understand and generate content across multiple formats—text, images, audio, and video. Unlike specialized models that only work with one type of data, multimodal models connect different modalities through shared representations. GPT-4 with vision can analyze images and answer questions about them, Whisper transcribes audio to text with high accuracy, and DALL-E generates images from text descriptions. These models enable powerful applications like visual search, accessibility tools, content moderation, and creative workflows—but they still have limitations around hallucinations, cost, and context understanding.

What Are Multimodal Models?

Most AI models are specialists. GPT-3.5 understands text. DALL-E 2 generates images. Whisper transcribes audio. But multimodal models break down these boundaries—they can work with multiple types of data simultaneously.

A multimodal model might:

• Take an image as input and generate text describing it
• Accept text and produce an image, audio, or video
• Analyze video by understanding both the visual frames and audio track
• Answer questions about uploaded documents, photos, or diagrams

Why does this matter? Real-world information isn't neatly separated into text-only or image-only formats. A product review includes photos. Medical diagnosis requires both patient history (text) and X-rays (images). Customer support might need to understand screenshots of error messages. Multimodal models handle these scenarios naturally.

How Multimodal Models Work

The key insight: different types of data can be mapped into a shared semantic space where similar concepts cluster together, regardless of modality.

Embeddings across modalities: Text encoders convert words into vectors. Image encoders convert pixels into vectors. When trained together (like CLIP), these encoders learn to place semantically similar concepts near each other—so the text "a photo of a cat" and an actual photo of a cat have similar vector representations.

Unified architectures: Modern multimodal models often use transformer architectures that can process different input types. GPT-4 with vision extends the text-only GPT-4 by adding vision encoders that convert images into tokens the language model can understand. The model then processes both text tokens and image tokens together.

Training approaches:

• Contrastive learning (CLIP): Train on millions of image-text pairs, learning to match images with their captions
• Joint training: Train a single model on mixed data—text, images, audio—learning connections between modalities
• Adapter layers: Add specialized input/output layers to existing models to handle new modalities

You don't need to understand the math, but knowing the concept helps: multimodal models learn shared representations that bridge different data types.

Text + Image Models

GPT-4 with Vision (GPT-4V)

GPT-4V extends GPT-4 to understand images. Upload a photo and ask questions about it—the model can describe what it sees, read text in images, identify objects, explain diagrams, and more.

Example use cases:

• Analyzing charts and extracting data
• Describing products in photos for e-commerce
• Reading handwritten notes or receipts
• Answering questions about scientific diagrams
• Identifying problems in photos (e.g., "What's wrong with this error message?")

API usage:

from openai import OpenAI
client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4-vision-preview",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://example.com/image.jpg"
                    }
                }
            ]
        }
    ],
    max_tokens=300
)

print(response.choices[0].message.content)

You can pass multiple images and ask comparative questions: "Which of these two layouts is more accessible?"

CLIP (Contrastive Language-Image Pre-training)

CLIP learns to connect images and text by training on hundreds of millions of image-caption pairs. It's not a chatbot—it's an embedding model that enables powerful search and classification.

What CLIP is good for:

• Visual search: Find images that match text queries ("sunset over mountains")
• Zero-shot classification: Classify images into categories the model never explicitly trained on
• Content moderation: Flag inappropriate images by comparing them to text descriptions

Example:

import torch
from PIL import Image
import clip

model, preprocess = clip.load("ViT-B/32")
image = preprocess(Image.open("photo.jpg")).unsqueeze(0)
text = clip.tokenize(["a dog", "a cat", "a bird"])

with torch.no_grad():
    image_features = model.encode_image(image)
    text_features = model.encode_text(text)

    # Calculate similarity
    similarity = (image_features @ text_features.T).softmax(dim=-1)
    print(similarity)  # [0.85, 0.10, 0.05] - probably a dog!

CLIP doesn't generate images or detailed descriptions—it creates embeddings that measure similarity between images and text.

DALL-E and Image Generation

DALL-E goes the opposite direction: text in, images out. Describe what you want, and it generates an image. DALL-E 3 (integrated with ChatGPT) produces highly detailed, accurate images from complex prompts.

Practical applications:

• Rapid prototyping for design concepts
• Creating marketing visuals
• Generating variations of existing images
• Illustrating blog posts or presentations

API example:

from openai import OpenAI
client = OpenAI()

response = client.images.generate(
    model="dall-e-3",
    prompt="A minimalist logo for a coffee shop, featuring a mountain silhouette and coffee cup, in earth tones",
    size="1024x1024",
    quality="standard",
    n=1
)

image_url = response.data[0].url

Text + Audio Models

Whisper for Speech Recognition

Whisper is OpenAI's speech-to-text model trained on 680,000 hours of multilingual audio. It's remarkably accurate, handles accents well, and can translate non-English speech to English text.

Use cases:

• Transcribing meetings, podcasts, interviews
• Generating subtitles for videos
• Voice commands and dictation
• Accessibility tools for hearing-impaired users

API example:

from openai import OpenAI
client = OpenAI()

audio_file = open("meeting.mp3", "rb")
transcript = client.audio.transcriptions.create(
    model="whisper-1",
    file=audio_file,
    response_format="text"
)

print(transcript)

Whisper can also provide timestamps, making it easy to create searchable transcripts where you can jump to specific moments.

Text-to-Speech (TTS)

Going the other direction, modern TTS models convert text into natural-sounding speech. OpenAI's TTS API supports multiple voices and is surprisingly lifelike.

Example:

from openai import OpenAI
client = OpenAI()

response = client.audio.speech.create(
    model="tts-1",
    voice="alloy",
    input="Welcome to the Field Guide to AI. Today we're exploring multimodal models."
)

response.stream_to_file("output.mp3")

Video Understanding

Video is the ultimate multimodal challenge: it combines visual frames, audio, and temporal relationships. Models like Google's Gemini 1.5 can analyze entire videos, understanding both what's happening visually and what's being said.

What video models can do:

• Summarize long videos
• Answer questions about specific moments
• Extract key scenes or highlights
• Analyze sentiment and emotions
• Generate transcripts with scene descriptions

Most video understanding is currently done by:

1. Sampling frames at intervals (e.g., 1 frame per second)
2. Transcribing audio with Whisper
3. Passing both to a vision-language model like GPT-4V
4. Combining insights across the timeline

Gemini 1.5 Pro can process up to 1 hour of video directly, understanding the full context without manual frame sampling.

Practical Applications

Accessibility

• Screen readers enhanced: Describe images, charts, and UI elements for visually impaired users
• Live captioning: Real-time transcription of meetings and events
• Sign language translation: Convert sign language to text (emerging capability)

Content Moderation

• Detect inappropriate images, videos, or audio at scale
• Flag potential safety issues before human review
• Understand context (e.g., educational vs. harmful content)

E-commerce and Retail

• Visual search: "Find me shoes that look like this"
• Product tagging: Automatically generate tags and descriptions from product photos
• Quality control: Identify defects in manufactured goods from images

Healthcare

• Analyze medical images (X-rays, MRIs) with clinical notes
• Transcribe doctor-patient conversations for records
• Assist in diagnosis by correlating symptoms (text) with scans (images)

Creative Tools

• Generate concept art and variations
• Create videos from text scripts (text → speech → video)
• Edit images with natural language instructions

Limitations and Considerations

Hallucinations: Multimodal models can confidently describe things that aren't in an image or misinterpret visual details. Always verify critical information.

Context misunderstanding: Models might miss subtle visual cues, cultural context, or sarcasm. They see pixels and hear sounds but don't truly "understand" like humans do.

Cost: Processing images, audio, and video is more expensive than text-only. GPT-4V charges per image, and costs scale with resolution and quantity.

Privacy: Uploading sensitive images, audio, or video to APIs means sharing data with providers. Consider privacy implications, especially in healthcare or personal applications.

Latency: Multimodal processing is slower than text-only. Analyzing a high-res image or transcribing a long audio file takes time.

Quality variance: Performance varies by modality and task. A model might excel at image captioning but struggle with technical diagrams or accented speech.

Using Multimodal APIs

Most major AI providers now offer multimodal capabilities:

OpenAI:

• GPT-4V (vision)
• DALL-E 3 (image generation)
• Whisper (speech-to-text)
• TTS (text-to-speech)

Google:

• Gemini 1.5 Pro (text, images, video, audio)
• Cloud Vision API (image analysis)
• Cloud Speech-to-Text

Anthropic:

• Claude 3 models support image inputs alongside text

Tips for working with multimodal APIs:

1. Prepare inputs properly: Resize large images, compress audio files, and ensure compatible formats
2. Be specific in prompts: "Describe the architectural style of this building" works better than "What is this?"
3. Handle errors gracefully: APIs may timeout or fail on corrupted media
4. Monitor costs: Track usage per modality—images and audio are pricier than text
5. Test edge cases: Try low-light images, accented speech, and ambiguous content to understand limitations

What's Next?

Multimodal AI is rapidly evolving. Current frontiers include:

• True multimodal generation: Models that can create coordinated text, images, and audio in one workflow
• Real-time processing: Live video analysis with minimal latency
• Embodied AI: Robots using multimodal models to understand and navigate the physical world
• Cross-modal retrieval: Search videos using text, find text documents using images, etc.

The distinction between "text models" and "image models" is blurring. Future AI systems will likely be multimodal by default, understanding and generating content across all formats seamlessly—just like humans do.

Key Takeaways

• Multimodal models bridge text, images, audio, and video through shared representations
• GPT-4V enables visual Q&A, DALL-E generates images from text, Whisper transcribes audio accurately
• CLIP connects images and text for powerful search and classification applications
• Practical use cases span accessibility, content moderation, e-commerce, healthcare, and creative tools
• Limitations include hallucinations, cost, latency, and context misunderstanding
• Major providers (OpenAI, Google, Anthropic) offer multimodal APIs with varying capabilities
• The future of AI is multimodal—expect models to work seamlessly across all data types