Project 6: Multimodal Video Analysis and Summarization
Step 5: Generate Video Summaries
Integrate the visual and audio insights to create a concise yet comprehensive video summary. This crucial step combines the object and action recognition results from VideoMAE with the transcribed speech from Whisper to produce a coherent narrative. The summary should capture key visual elements (like detected objects, actions, and scene changes), important spoken content, and maintain the temporal flow of events.
This integration helps create a more complete understanding of the video content, as neither visual nor audio analysis alone can fully capture the video's meaning. For example, a business presentation video summary would include both the speaker's key points from the audio transcription and visual elements like shown graphs or demonstrations.
def generate_summary(transcription, visual_insights):
return f"The video depicts: {visual_insights}. The audio transcription is: '{transcription}'."
# Example summary
visual_insights = f"Predicted action: {predicted_class}"
summary = generate_summary(transcription, visual_insights)
print("Video Summary:")
print(summary)Let’s explain this code that generates video summaries:
1. Function Definition
The code defines a function called generate_summary that takes two parameters:
- transcription: Contains the text from the audio transcription
- visual_insights: Contains information about the visual elements detected in the video
2. Summary Generation
The function creates a simple formatted string that combines both visual and audio information in a structured way. It follows a template:
- "The video depicts: [visual information]. The audio transcription is: '[transcribed text]'"
3. Implementation Example
The code shows how to use this function:
- Creates visual_insights by formatting the predicted action class
- Calls generate_summary with the transcription and visual insights
- Prints the final summary
This integration is important because it combines both visual and audio elements to create a more complete understanding of the video content. For example, in a business presentation, the summary would include both the speaker's key points and any visual elements like graphs that were shown.
Step 5: Generate Video Summaries
Integrate the visual and audio insights to create a concise yet comprehensive video summary. This crucial step combines the object and action recognition results from VideoMAE with the transcribed speech from Whisper to produce a coherent narrative. The summary should capture key visual elements (like detected objects, actions, and scene changes), important spoken content, and maintain the temporal flow of events.
This integration helps create a more complete understanding of the video content, as neither visual nor audio analysis alone can fully capture the video's meaning. For example, a business presentation video summary would include both the speaker's key points from the audio transcription and visual elements like shown graphs or demonstrations.
def generate_summary(transcription, visual_insights):
return f"The video depicts: {visual_insights}. The audio transcription is: '{transcription}'."
# Example summary
visual_insights = f"Predicted action: {predicted_class}"
summary = generate_summary(transcription, visual_insights)
print("Video Summary:")
print(summary)Let’s explain this code that generates video summaries:
1. Function Definition
The code defines a function called generate_summary that takes two parameters:
- transcription: Contains the text from the audio transcription
- visual_insights: Contains information about the visual elements detected in the video
2. Summary Generation
The function creates a simple formatted string that combines both visual and audio information in a structured way. It follows a template:
- "The video depicts: [visual information]. The audio transcription is: '[transcribed text]'"
3. Implementation Example
The code shows how to use this function:
- Creates visual_insights by formatting the predicted action class
- Calls generate_summary with the transcription and visual insights
- Prints the final summary
This integration is important because it combines both visual and audio elements to create a more complete understanding of the video content. For example, in a business presentation, the summary would include both the speaker's key points and any visual elements like graphs that were shown.
Step 5: Generate Video Summaries
Integrate the visual and audio insights to create a concise yet comprehensive video summary. This crucial step combines the object and action recognition results from VideoMAE with the transcribed speech from Whisper to produce a coherent narrative. The summary should capture key visual elements (like detected objects, actions, and scene changes), important spoken content, and maintain the temporal flow of events.
This integration helps create a more complete understanding of the video content, as neither visual nor audio analysis alone can fully capture the video's meaning. For example, a business presentation video summary would include both the speaker's key points from the audio transcription and visual elements like shown graphs or demonstrations.
def generate_summary(transcription, visual_insights):
return f"The video depicts: {visual_insights}. The audio transcription is: '{transcription}'."
# Example summary
visual_insights = f"Predicted action: {predicted_class}"
summary = generate_summary(transcription, visual_insights)
print("Video Summary:")
print(summary)Let’s explain this code that generates video summaries:
1. Function Definition
The code defines a function called generate_summary that takes two parameters:
- transcription: Contains the text from the audio transcription
- visual_insights: Contains information about the visual elements detected in the video
2. Summary Generation
The function creates a simple formatted string that combines both visual and audio information in a structured way. It follows a template:
- "The video depicts: [visual information]. The audio transcription is: '[transcribed text]'"
3. Implementation Example
The code shows how to use this function:
- Creates visual_insights by formatting the predicted action class
- Calls generate_summary with the transcription and visual insights
- Prints the final summary
This integration is important because it combines both visual and audio elements to create a more complete understanding of the video content. For example, in a business presentation, the summary would include both the speaker's key points and any visual elements like graphs that were shown.
Step 5: Generate Video Summaries
Integrate the visual and audio insights to create a concise yet comprehensive video summary. This crucial step combines the object and action recognition results from VideoMAE with the transcribed speech from Whisper to produce a coherent narrative. The summary should capture key visual elements (like detected objects, actions, and scene changes), important spoken content, and maintain the temporal flow of events.
This integration helps create a more complete understanding of the video content, as neither visual nor audio analysis alone can fully capture the video's meaning. For example, a business presentation video summary would include both the speaker's key points from the audio transcription and visual elements like shown graphs or demonstrations.
def generate_summary(transcription, visual_insights):
return f"The video depicts: {visual_insights}. The audio transcription is: '{transcription}'."
# Example summary
visual_insights = f"Predicted action: {predicted_class}"
summary = generate_summary(transcription, visual_insights)
print("Video Summary:")
print(summary)Let’s explain this code that generates video summaries:
1. Function Definition
The code defines a function called generate_summary that takes two parameters:
- transcription: Contains the text from the audio transcription
- visual_insights: Contains information about the visual elements detected in the video
2. Summary Generation
The function creates a simple formatted string that combines both visual and audio information in a structured way. It follows a template:
- "The video depicts: [visual information]. The audio transcription is: '[transcribed text]'"
3. Implementation Example
The code shows how to use this function:
- Creates visual_insights by formatting the predicted action class
- Calls generate_summary with the transcription and visual insights
- Prints the final summary
This integration is important because it combines both visual and audio elements to create a more complete understanding of the video content. For example, in a business presentation, the summary would include both the speaker's key points and any visual elements like graphs that were shown.