Leveraging GPT-4-Vision and Whisper for Efficient Video Analysis via Photo Grids

Below is an overview of how the described method leverages OpenAI’s APIs for video transcription and summarization:

Method Overview

1. Video Preprocessing:

   • The video is split into a fixed number of frames (e.g., a 3×3 grid creates 9 images) using a tool like FFMPEG.
   • Audio is simultaneously extracted.

2. Audio Transcription with Whisper:

   • The extracted audio is transcribed using Whisper (for example, using the Whisper v2 Large model via the API).
   • Whisper provides a text transcript, though note that it may occasionally hallucinate when the audio is very quiet.

3. Visual Analysis with GPT-4-Vision:

   • The frames are combined into a single “photo grid” image.
   • GPT-4-Vision processes the grid. A prompt instructs the model to “describe what’s likely going on in each frame” and to include details from the audio transcript.
   • Example prompt snippet:

     BASHCopied
     > The image shows video frames in sequence. Describe what’s likely going on in each frame.  
     > You can hear following in the audio track: ${audio_transcript}. Also mention what you can hear in the audio.

   • The model outputs frame-by-frame interpretations showing how it maps the visual sequence and audio cues.

4. Summarization with GPT-4-Turbo:

   • A follow-up prompt asks the model to combine the frame-by-frame description into an overall summary of the clip.
   • The prompt wording is carefully chosen (e.g., using “this time” instead of “summarize”) so the model preserves key details.

Key Advantages of This Approach

• Cost Efficiency:
  Combining frames into a single image and using the audio transcript means fewer API calls compared to processing many individual frames.

• Enhanced Contextual Understanding:
  GPT-4-Vision can use its broader context window to “stitch” together information from both visual inputs and the audio transcript, improving the overall understanding of the scene.

• Error Mitigation:
  Should the vision model miss details in a frame, the backup provided by Whisper’s transcription helps fill in gaps—unless the audio is entirely silent or contains only noise.

Example Code Segment for Post-Processing (Visual and Audio)

Below is a simplified version of the post-processing step using GPT-4-Vision:

PYTHONCopied
# Define a system prompt for GPT-4-Vision
system_prompt = (
    "The image shows video frames in sequence. "
    "Describe what’s likely going on in each frame. "
    "You can hear the following in the audio track: {audio_transcript}. "
    "Also mention what you can hear in the audio."
)

def generate_vision_description(temperature, system_prompt, audio_transcript, image_file):
    response = client.chat.completions.create(
        model="gpt-4-vision-model",
        temperature=temperature,
        messages=[
            { "role": "system", "content": system_prompt.format(audio_transcript=audio_transcript) },
            { "role": "user", "content": transcribe(image_file) }  # Hypothetical function to read the image input
        ]
    )
    return response.choices[0].message.content

# Example usage:
description = generate_vision_description(0, system_prompt, "transcribed audio here", "path/to/photo_grid.jpg")
print(description)

Note: This code is illustrative. Actual API endpoints, model names, and input formats might differ based on your current OpenAI API version and configuration.

On OpenAI API Topics

• Whisper API:
  It provides audio transcription with options to adjust details like noise handling. Developers can integrate it via the endpoint for audio transcriptions.

• GPT-4-Vision API:
  Designed to process image inputs, GPT-4-Vision can now inspect grids of images to interpret sequences or multiple frames at once. The method shown demonstrates a creative approach to guide its inference using a carefully phrased prompt.

• API-Based Summarization:
  Combining outputs from multimodal models (vision and speech) and then summarizing them using GPT-4-Turbo allows for richer, context-aware results while managing token usage effectively.

If you have any specific questions regarding how to integrate these APIs or wish to dive deeper into any parts of this method, feel free to ask!