veo-3-1: Automated Interaction Library

The veo-3-1 library provides a streamlined interface for demonstrating and integrating with the capabilities showcased at https://supermaker.ai/video/veo-3-1/. It automates common tasks and simplifies interaction with the platform.

Installation

Install veo-3-1 using pip: bash pip install veo-3-1

Basic Usage Examples

Here are a few examples demonstrating how to use the veo-3-1 library:

1. Generating a Video Summary:

This example demonstrates how to automatically generate a concise summary of a video using the veo-3-1 library. Assume the video is identified by a unique ID. python from veo_3_1 import VeoClient

client = VeoClient() # Replace with necessary authentication if required

video_id = "unique_video_123" summary = client.generate_video_summary(video_id)

if summary: print("Video Summary:") print(summary) else: print("Failed to generate video summary.")

2. Extracting Key Moments from a Video:

This example showcases how to extract key moments or highlights from a video based on predefined criteria (e.g., scenes with high activity). python from veo_3_1 import VeoClient

client = VeoClient() # Replace with necessary authentication if required

video_id = "another_unique_video_456" key_moments = client.extract_key_moments(video_id, threshold=0.7) # threshold is optional, defaults to 0.5

if key_moments: print("Key Moments (timestamps):") for timestamp in key_moments: print(timestamp) else: print("Failed to extract key moments.")

3. Applying a Visual Style Transfer:

This example demonstrates applying a specific visual style (e.g., a painting style) to a video. This requires a defined style ID. python from veo_3_1 import VeoClient

client = VeoClient() # Replace with necessary authentication if required

video_id = "yet_another_video_789" style_id = "impressionist_style_1" # This needs to be a valid style ID new_video_url = client.apply_visual_style(video_id, style_id)

if new_video_url: print("Video with style applied available at:") print(new_video_url) else: print("Failed to apply visual style.")

4. Generating a Transcript:

This example demonstrates automatic transcript generation from a video. python from veo_3_1 import VeoClient

client = VeoClient() # Replace with necessary authentication if required

video_id = "transcript_video_abc" transcript = client.generate_transcript(video_id)

if transcript: print("Video Transcript:") print(transcript) else: print("Failed to generate transcript.")

5. Checking Video Processing Status:

This example shows how to check the status of a video processing job initiated by one of the other functions. python from veo_3_1 import VeoClient

client = VeoClient() # Replace with necessary authentication if required

video_id = "processing_video_xyz" status = client.get_processing_status(video_id)

if status: print(f"Video Processing Status: {status}") else: print("Failed to retrieve processing status.")

Feature List

• Video Summarization: Automatically generate concise summaries of video content.
• Key Moment Extraction: Identify and extract key moments or highlights from videos.
• Visual Style Transfer: Apply visual styles to videos, changing their aesthetic appearance.
• Automatic Transcription: Generate text transcripts from video audio.
• Processing Status Monitoring: Track the status of video processing jobs.
• Simplified API: Easy-to-use functions for interacting with the underlying platform.
• Error Handling: Provides informative error messages for debugging.

License

MIT License

This project is a gateway to the veo-3-1 ecosystem. For advanced features and full capabilities, please visit: https://supermaker.ai/video/veo-3-1/