Video Visual Understanding and Automated Dubbing Workflow
This workflow automates the production of video content narration, covering video downloading, frame extraction, narration script generation, and voiceover audio production. By combining multimodal large language models and text-to-speech technology, it significantly enhances the efficiency and quality of video narration, and automatically uploads the generated audio files to Google Drive for easy storage and sharing. It is suitable for fields such as media production, education and training, and marketing, simplifying the traditional content creation process.
Tags
Workflow Name
Video Visual Understanding and Automated Dubbing Workflow
Key Features and Highlights
This workflow delivers a fully automated pipeline from online video downloading and frame extraction to generating narration scripts based on a Multimodal Large Language Model (Multimodal LLM), and finally producing dubbing audio via Text-to-Speech (TTS) technology with automatic upload to Google Drive. Highlights include:
- Efficient and uniform extraction of key video frames using Python and OpenCV, with frame count control to optimize performance
- Batch processing of image frames through Langchain-integrated OpenAI GPT-4o model to generate coherent and stylistically consistent narration scripts
- High-quality automated dubbing using OpenAI’s speech synthesis API
- Automatic upload of generated audio files to Google Drive for convenient storage and sharing
Core Problems Addressed
Traditional video narration production is cumbersome, requiring manual script writing and voice recording, which is time-consuming and costly. This workflow combines automated visual content understanding with text generation to efficiently produce video narration scripts in batches and convert them into dubbing audio automatically, significantly reducing manual intervention and improving content production efficiency.
Application Scenarios
- Media content production: Rapid generation of professional narration scripts and dubbing for short videos and promotional clips
- Education and training: Automatic creation of course video narration audio
- Marketing: Batch production of product showcase videos with voiceover
- Content creators and video editors: Simplification of video narration script and dubbing workflows
Main Process Steps
- Video Download: Download online video resources via HTTP request nodes
- Video Frame Extraction: Use Python code nodes (OpenCV) to uniformly extract up to 90 key frames and convert them to Base64 format
- Frame Splitting and Batch Processing: Split frames into groups of 15 and send them in batches to the Multimodal LLM for processing
- Narration Script Generation: Utilize OpenAI GPT-4o model with multi-frame image inputs to generate coherent narration text segments, progressively merging them into a complete script
- Text-to-Speech Conversion: Call OpenAI’s speech synthesis API to convert the full script into MP3-format dubbing audio
- Upload and Storage: Upload the generated audio files to a designated Google Drive folder for easy access and sharing
Involved Systems and Services
- OpenAI GPT-4o Multimodal Language Model (text and image combined understanding and generation)
- OpenAI Text-to-Speech (TTS) Service
- Google Drive (storage and management of generated audio files)
- HTTP Request Nodes (video file downloading)
- Python/OpenCV (video frame extraction and image processing)
- n8n Automation Platform Nodes (workflow orchestration and data transformation)
Target Users and Value
- Content creators and video producers seeking rapid generation of professional narration scripts and dubbing to enhance production efficiency
- Marketing and media teams producing large volumes of high-quality dubbed video content
- Educational institutions automating the creation of course video narration audio
- Automation enthusiasts and developers exploring practical applications of multimodal AI combined with video content
This workflow leverages visual AI and natural language generation technologies to seamlessly connect video content understanding with audio generation, enabling an intelligent upgrade of content creation processes. We welcome you to experience and share your insights in the n8n community!
HeyGen AI Video Generation and Status Monitoring Workflow
This workflow enables automated personalized AI video generation and status monitoring. Users can easily configure AI avatars, voices, and text content, and the system will automatically send generation requests and poll the status in real-time until the video is completed and a usable link is provided. This process simplifies cumbersome API calls and enhances the efficiency of video content production, making it suitable for businesses, educational institutions, and content creators to quickly generate personalized videos while lowering the technical barrier.
Zoom AI Meeting Assistant
This workflow aims to enhance meeting efficiency by automatically retrieving Zoom meeting data and transcribing recordings. It utilizes AI to generate meeting minutes, extract tasks and to-dos, and intelligently create tasks in ClickUp while scheduling follow-up meetings. The entire process automates the flow from capturing meeting content to task assignment and scheduling, addressing issues such as the cumbersome nature of manually organizing meeting minutes, untimely task distribution, and time-consuming information transfer. It is suitable for organizations with frequent meetings and cross-departmental collaboration.
(G) LineChatBot + Google Sheets (as a memory)
This workflow implements the storage and management of user conversation history by building an intelligent chatbot based on the Line platform, ensuring continuity and contextual relevance in conversations. Utilizing Google Sheets as a lightweight database, the chatbot can automatically archive chat records and generate polite and friendly responses through advanced AI models, suitable for customer support and intelligent Q&A in the Thai language environment. This system effectively addresses the shortcomings of traditional chatbots in memory and data management, enhancing the user interaction experience.
AI-Driven Book Information Crawling and Organization Workflow
This workflow automatically captures book information from specified web pages using a no-code approach. It utilizes AI technology to extract structured data such as book titles, prices, stock status, and purchase links, and saves this information to Google Sheets. It addresses the issues of complex coding and inaccurate information extraction associated with traditional web crawlers. This solution is suitable for fields such as publishing, e-commerce, and market research, enhancing data acquisition efficiency, reducing manual intervention, and providing users with an intelligent data organization tool, significantly saving labor costs.
“Hey Siri, Ask Agent” Workflow
This workflow integrates with Apple Shortcuts, allowing users to interact with the smart assistant using the voice command "Hey Siri, AI Agent." The user's voice will be transcribed in real-time and sent to the system, which utilizes the OpenAI GPT-4 model to generate natural voice responses that are directly fed back to the user. This process addresses the user's desire for natural voice conversations, enhancing the convenience and efficiency of interactions in smart home and mobile office scenarios, while providing personalized real-time responses.
Automated Generation and Publishing Workflow for Multi-Type Service and Categorized Q&A Templates
This workflow automatically generates standard Q&A templates for different services by reading data from Google Sheets. It utilizes AI technology to intelligently complete some answers, enhancing the professionalism and naturalness of the content. The final Q&A is saved in JSON format and uploaded to Google Drive, facilitating one-click publishing to various content management systems. This helps businesses quickly build high-quality FAQ content, improve user experience and knowledge base quality, and address the time-consuming issue of manually writing Q&A.
GROQ LLAVA V1.5 7B
This workflow enables the automatic generation of detailed text descriptions after users send images via a Telegram bot, utilizing the GROQ LLAVA image understanding API for intelligent recognition. Users simply need to upload an image, and the system will convert it to Base64 format and call the API, ultimately replying to the user with the generated text. This process not only simplifies traditional image recognition methods but also enhances user experience, making it suitable for scenarios such as customer service automation, content management, educational tutoring, and visual assistance, allowing non-professional users to easily obtain information from images.
AirQuality Scheduler
AirQuality Scheduler is an automated tool that retrieves real-time air quality and pollen concentration data for specific locations on a daily schedule. Through an AI smart assistant, it generates personalized environmental health summaries and recommendations to help users effectively respond to environmental changes. This tool is suitable for individuals concerned about air pollution and pollen allergies, as well as health management organizations and businesses, providing scientifically sound and concise environmental health guidance to enhance quality of life.