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Multimodal Video Analysis and AI Voiceover Generation Workflow

This workflow implements automated video analysis and voiceover generation. By extracting key frames from the video, it utilizes a multimodal large language model to generate narration scripts, and combines text-to-speech technology to synthesize high-quality voiceovers, ultimately uploading the audio files to the cloud. This process significantly reduces the difficulty and time costs associated with video commentary production, making it suitable for various fields such as education, marketing, and media. It helps users quickly generate vivid narration content, enhancing video production efficiency.

Workflow Diagram
Multimodal Video Analysis and AI Voiceover Generation Workflow Workflow diagram

Workflow Name

Multimodal Video Analysis and AI Voiceover Generation Workflow

Key Features and Highlights

This workflow automates the extraction of keyframes from video files, employs a multimodal large language model (LLM) to generate coherent narration scripts based on the extracted image frames, synthesizes high-quality voiceover audio via text-to-speech (TTS) technology, and uploads the final audio files to Google Drive. The entire process is highly automated, supports batch processing of video frames, ensures script coherence, and balances performance with service call limitations.

Core Problems Addressed

Traditional video content understanding and voiceover production often require extensive manual effort and specialized skills. This workflow leverages AI-powered visual understanding and language generation to automatically convert video content into vivid narration scripts and rapidly produce voiceovers, significantly lowering the barriers and time costs associated with video commentary creation.

Application Scenarios

  • Automated narration generation for educational and training videos
  • Rapid voiceover production for marketing videos
  • Intelligent summarization and narration creation for media content
  • Preliminary automatic script generation for multilingual video dubbing
  • Auxiliary tool for post-production in film and television

Main Process Steps

  1. Download Video: Retrieve video files from specified URLs using HTTP request nodes.
  2. Extract Keyframes: Use Python code nodes with OpenCV to uniformly extract up to 90 key image frames from the video.
  3. Batch Frame Processing: Split extracted frames into batches (15 frames per batch) and input them into the multimodal LLM to generate narration scripts for each segment.
  4. Image Preprocessing: Resize frames to meet model input requirements, ensuring optimal generation quality.
  5. Script Aggregation: Combine partial scripts generated from multiple batches into a complete narration text.
  6. Text-to-Speech Conversion: Invoke OpenAI’s audio generation API to convert the full script into MP3 format voiceover audio.
  7. Cloud Upload: Automatically upload the generated voiceover files to Google Drive for convenient storage and sharing.

Systems and Services Involved

  • OpenAI GPT-4o: Multimodal large language model for image understanding and text generation.
  • OpenAI TTS API: Text-to-speech service for voiceover synthesis.
  • Google Drive: Cloud file storage and management platform for saving generated audio files.
  • Pixabay: Source for sample video downloads.
  • OpenCV (Python code nodes): Video frame extraction and image processing.
  • n8n Node Components: Including HTTP request, code execution, batch processing, image editing, aggregation, wait, manual trigger, etc., to build a fully automated workflow.

Target Users and Value

  • Content Creators and Video Producers: Quickly and automatically generate video narration scripts and voiceovers, improving production efficiency.
  • Educational and Training Institutions: Automatically add intelligent narration to instructional videos, enhancing the learning experience.
  • Marketing Teams: Rapidly produce voiceover materials for marketing videos in bulk, reducing costs.
  • AI Developers and Automation Enthusiasts: Learn from a typical case of multimodal AI combined with multi-system integration.
  • Media and News Industry: Automate content summarization and voiceover production to boost news reporting efficiency.

Summary
This workflow integrates video processing, computer vision, multimodal language modeling, and TTS technologies to achieve fully automated intelligent conversion from video to voiceover. It not only lowers the threshold for video content understanding and voiceover production but also provides a powerful tool for automated video content creation across multiple industries. The workflow is designed with practicality and scalability in mind, allowing users to flexibly customize it according to their specific needs, making it highly valuable for widespread adoption.