HumationHuman + Automation
Back to Catalog

Automated YouTube video to blog post conversion with Gemini AI transcription

AttaAtta
1652 views
2/3/2026
Lead ManagementAutomationSalesforceCSVData Import
Official Page

This workflow automatically turns any YouTube video into a structured blog post with Gemini AI. By sending a simple POST request with a YouTube URL to a webhook, it downloads the video’s audio, transcribes the content, and generates a blog-ready article with a title, description, tags, and category. The final result, along with the full transcript and original video URL, is delivered to your chosen webhook or CMS.

How it works:

The workflow handles the entire process of transforming YouTube videos into complete blog posts using Gemini AI transcription and structured text generation. Once triggered, it:

  • Downloads the video’s audio
  • Transcribes the spoken content into text
  • Generates a blog post in the same language as the video’s original language
  • Creates:
    • A clear and engaging title
    • A short description
    • Suggested category and tags
    • The full transcript of the video
    • The original YouTube video URL

This makes it easy to repurpose video content into publish-ready articles in minutes.

This template is ideal for content creators, marketers, educators, and bloggers who want to quickly turn video content into written posts without manual transcription or editing.

Setup Instructions

  1. Install yt-dlp on your local machine or server where n8n runs. This is required to download YouTube audio.
  2. Get a Google Gemini API key and configure it in your AI nodes.
  3. Webhook Input Configuration:
    • Endpoint: The workflow starts with a Webhook Trigger.
    • Method: POST
    • Example Request Body:
{
	"videoUrl": "https://www.youtube.com/watch?v=lW5xEm7iSXk"
}
  1. Configure Output Webhook: Add your target endpoint in the last node where the blog post JSON is sent. This could be your CMS, a Notion database, or another integration.

Customization Guidance

  • Writing Style: Update the AI Agent’s prompt to adjust tone (e.g., casual, professional, SEO-optimized).
  • Metadata: Modify how categories and tags are generated to fit your website’s taxonomy.
  • Integration: Swap the final webhook with WordPress, Ghost, Notion, or Slack to fit your publishing workflow.
  • Transcript Handling: Save the full transcript separately if you also want searchable video archives.

Automated YouTube Video to Blog Post Conversion with Gemini AI Transcription

This n8n workflow automates the process of converting YouTube video content into structured blog posts using Google Gemini AI for transcription and content generation. It's designed to streamline content creation by transforming video dialogues into written articles.

What it does

This workflow performs the following key steps:

  1. Receives a Trigger: It starts by listening for an incoming webhook, which is expected to provide the necessary information to initiate the video processing.
  2. Executes a Command: It runs a shell command, likely to download the YouTube video or extract its audio for transcription.
  3. Reads/Writes Files from Disk: It interacts with the local file system, presumably to handle the downloaded video/audio file and any generated transcripts.
  4. Processes with Google Gemini AI: It utilizes the Google Gemini node, likely to transcribe the audio from the YouTube video.
  5. Generates Content with AI Agent: An AI Agent node (powered by LangChain) takes the transcription and other inputs to generate the blog post content. This step could involve summarizing, structuring, and enriching the transcribed text.
  6. Parses Structured Output: A Structured Output Parser node (from LangChain) is used to ensure the AI-generated content adheres to a predefined structure, such as JSON, making it ready for further use (e.g., publishing to a CMS).
  7. Edits Fields: A Set node is used to manipulate or add fields to the data, preparing it for the final response.
  8. Responds to Webhook: Finally, it sends a response back to the original webhook trigger, likely indicating the success or providing the generated blog post content.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance.
  • Google Gemini API Key: Access to the Google Gemini API for transcription and AI capabilities.
  • LangChain Integration: Ensure the @n8n/n8n-nodes-langchain package is installed in your n8n instance.
  • Shell Access: The n8n instance needs to be able to execute shell commands (e.g., yt-dlp or youtube-dl for video download, or a similar tool for audio extraction).
  • Storage: Sufficient disk space on your n8n host to temporarily store video/audio files.

Setup/Usage

  1. Import the Workflow: Download the workflow JSON and import it into your n8n instance.
  2. Configure Webhook: Activate the "Webhook" trigger node. Copy the test or production URL. This URL will be used to trigger the workflow with YouTube video information.
  3. Configure Credentials:
    • Google Gemini: Set up your Google Gemini API credentials in the "Google Gemini" and "Google Gemini Chat Model" nodes.
    • AI Agent: Configure the "AI Agent" node with any necessary API keys or model settings for your LangChain setup.
  4. Customize Shell Command: Adjust the "Execute Command" node to use your preferred tool for downloading YouTube videos and extracting audio (e.g., yt-dlp or youtube-dl). Ensure the command correctly takes the YouTube URL as input and outputs the audio file to a known location.
  5. Adjust File Paths: Update the "Read/Write Files from Disk" node to match the paths where your shell command saves and reads files.
  6. Define AI Agent Prompt: Customize the prompt in the "AI Agent" node to guide Gemini AI on how to structure and write the blog post from the transcribed content.
  7. Define Output Schema: Configure the "Structured Output Parser" node with the desired JSON schema for your blog post output.
  8. Test the Workflow: Send a test request to the webhook URL with a YouTube video URL to ensure the workflow executes as expected and generates the desired blog post content.
  9. Integrate: Connect the output of this workflow to your CMS, blog platform, or other services to automatically publish the generated content.

Related Templates

Track competitor SEO keywords with Decodo + GPT-4.1-mini + Google Sheets

This workflow automates competitor keyword research using OpenAI LLM and Decodo for intelligent web scraping. Who this is for SEO specialists, content strategists, and growth marketers who want to automate keyword research and competitive intelligence. Marketing analysts managing multiple clients or websites who need consistent SEO tracking without manual data pulls. Agencies or automation engineers using Google Sheets as an SEO data dashboard for keyword monitoring and reporting. What problem this workflow solves Tracking competitor keywords manually is slow and inconsistent. Most SEO tools provide limited API access or lack contextual keyword analysis. This workflow solves that by: Automatically scraping any competitor’s webpage with Decodo. Using OpenAI GPT-4.1-mini to interpret keyword intent, density, and semantic focus. Storing structured keyword insights directly in Google Sheets for ongoing tracking and trend analysis. What this workflow does Trigger — Manually start the workflow or schedule it to run periodically. Input Setup — Define the website URL and target country (e.g., https://dev.to, france). Data Scraping (Decodo) — Fetch competitor web content and metadata. Keyword Analysis (OpenAI GPT-4.1-mini) Extract primary and secondary keywords. Identify focus topics and semantic entities. Generate a keyword density summary and SEO strength score. Recommend optimization and internal linking opportunities. Data Structuring — Clean and convert GPT output into JSON format. Data Storage (Google Sheets) — Append structured keyword data to a Google Sheet for long-term tracking. Setup Prerequisites If you are new to Decode, please signup on this link visit.decodo.com n8n account with workflow editor access Decodo API credentials OpenAI API key Google Sheets account connected via OAuth2 Make sure to install the Decodo Community node. Create a Google Sheet Add columns for: primarykeywords, seostrengthscore, keyworddensity_summary, etc. Share with your n8n Google account. Connect Credentials Add credentials for: Decodo API credentials - You need to register, login and obtain the Basic Authentication Token via Decodo Dashboard OpenAI API (for GPT-4o-mini) Google Sheets OAuth2 Configure Input Fields Edit the “Set Input Fields” node to set your target site and region. Run the Workflow Click Execute Workflow in n8n. View structured results in your connected Google Sheet. How to customize this workflow Track Multiple Competitors → Use a Google Sheet or CSV list of URLs; loop through them using the Split In Batches node. Add Language Detection → Add a Gemini or GPT node before keyword analysis to detect content language and adjust prompts. Enhance the SEO Report → Expand the GPT prompt to include backlink insights, metadata optimization, or readability checks. Integrate Visualization → Connect your Google Sheet to Looker Studio for SEO performance dashboards. Schedule Auto-Runs → Use the Cron Node to run weekly or monthly for competitor keyword refreshes. Summary This workflow automates competitor keyword research using: Decodo for intelligent web scraping OpenAI GPT-4.1-mini for keyword and SEO analysis Google Sheets for live tracking and reporting It’s a complete AI-powered SEO intelligence pipeline ideal for teams that want actionable insights on keyword gaps, optimization opportunities, and content focus trends, without relying on expensive SEO SaaS tools.

Ranjan DailataBy Ranjan Dailata
161

Generate song lyrics and music from text prompts using OpenAI and Fal.ai Minimax

Spark your creativity instantly in any chat—turn a simple prompt like "heartbreak ballad" into original, full-length lyrics and a professional AI-generated music track, all without leaving your conversation. 📋 What This Template Does This chat-triggered workflow harnesses AI to generate detailed, genre-matched song lyrics (at least 600 characters) from user messages, then queues them for music synthesis via Fal.ai's minimax-music model. It polls asynchronously until the track is ready, delivering lyrics and audio URL back in chat. Crafts original, structured lyrics with verses, choruses, and bridges using OpenAI Submits to Fal.ai for melody, instrumentation, and vocals aligned to the style Handles long-running generations with smart looping and status checks Returns complete song package (lyrics + audio link) for seamless sharing 🔧 Prerequisites n8n account (self-hosted or cloud with chat integration enabled) OpenAI account with API access for GPT models Fal.ai account for AI music generation 🔑 Required Credentials OpenAI API Setup Go to platform.openai.com → API keys (sidebar) Click "Create new secret key" → Name it (e.g., "n8n Songwriter") Copy the key and add to n8n as "OpenAI API" credential type Test by sending a simple chat completion request Fal.ai HTTP Header Auth Setup Sign up at fal.ai → Dashboard → API Keys Generate a new API key → Copy it In n8n, create "HTTP Header Auth" credential: Name="Fal.ai", Header Name="Authorization", Header Value="Key [Your API Key]" Test with a simple GET to their queue endpoint (e.g., /status) ⚙️ Configuration Steps Import the workflow JSON into your n8n instance Assign OpenAI API credentials to the "OpenAI Chat Model" node Assign Fal.ai HTTP Header Auth to the "Generate Music Track", "Check Generation Status", and "Fetch Final Result" nodes Activate the workflow—chat trigger will appear in your n8n chat interface Test by messaging: "Create an upbeat pop song about road trips" 🎯 Use Cases Content Creators: YouTubers generating custom jingles for videos on the fly, streamlining production from idea to audio export Educators: Music teachers using chat prompts to create era-specific folk tunes for classroom discussions, fostering interactive learning Gift Personalization: Friends crafting anniversary R&B tracks from shared memories via quick chats, delivering emotional audio surprises Artist Brainstorming: Songwriters prototyping hip-hop beats in real-time during sessions, accelerating collaboration and iteration ⚠️ Troubleshooting Invalid JSON from AI Agent: Ensure the system prompt stresses valid JSON; test the agent standalone with a sample query Music Generation Fails (401/403): Verify Fal.ai API key has minimax-music access; check usage quotas in dashboard Status Polling Loops Indefinitely: Bump wait time to 45-60s for complex tracks; inspect fal.ai queue logs for bottlenecks Lyrics Under 600 Characters: Tweak agent prompt to enforce fuller structures like [V1][C][V2][B][C]; verify output length in executions

Daniel NkenchoBy Daniel Nkencho
601

Automate invoice processing with OCR, GPT-4 & Salesforce opportunity creation

PDF Invoice Extractor (AI) End-to-end pipeline: Watch Drive ➜ Download PDF ➜ OCR text ➜ AI normalize to JSON ➜ Upsert Buyer (Account) ➜ Create Opportunity ➜ Map Products ➜ Create OLI via Composite API ➜ Archive to OneDrive. --- Node by node (what it does & key setup) 1) Google Drive Trigger Purpose: Fire when a new file appears in a specific Google Drive folder. Key settings: Event: fileCreated Folder ID: google drive folder id Polling: everyMinute Creds: googleDriveOAuth2Api Output: Metadata { id, name, ... } for the new file. --- 2) Download File From Google Purpose: Get the file binary for processing and archiving. Key settings: Operation: download File ID: ={{ $json.id }} Creds: googleDriveOAuth2Api Output: Binary (default key: data) and original metadata. --- 3) Extract from File Purpose: Extract text from PDF (OCR as needed) for AI parsing. Key settings: Operation: pdf OCR: enable for scanned PDFs (in options) Output: JSON with OCR text at {{ $json.text }}. --- 4) Message a model (AI JSON Extractor) Purpose: Convert OCR text into strict normalized JSON array (invoice schema). Key settings: Node: @n8n/n8n-nodes-langchain.openAi Model: gpt-4.1 (or gpt-4.1-mini) Message role: system (the strict prompt; references {{ $json.text }}) jsonOutput: true Creds: openAiApi Output (per item): $.message.content → the parsed JSON (ensure it’s an array). --- 5) Create or update an account (Salesforce) Purpose: Upsert Buyer as Account using an external ID. Key settings: Resource: account Operation: upsert External Id Field: taxid_c External Id Value: ={{ $json.message.content.buyer.tax_id }} Name: ={{ $json.message.content.buyer.name }} Creds: salesforceOAuth2Api Output: Account record (captures Id) for downstream Opportunity. --- 6) Create an opportunity (Salesforce) Purpose: Create Opportunity linked to the Buyer (Account). Key settings: Resource: opportunity Name: ={{ $('Message a model').item.json.message.content.invoice.code }} Close Date: ={{ $('Message a model').item.json.message.content.invoice.issue_date }} Stage: Closed Won Amount: ={{ $('Message a model').item.json.message.content.summary.grand_total }} AccountId: ={{ $json.id }} (from Upsert Account output) Creds: salesforceOAuth2Api Output: Opportunity Id for OLI creation. --- 7) Build SOQL (Code / JS) Purpose: Collect unique product codes from AI JSON and build a SOQL query for PricebookEntry by Pricebook2Id. Key settings: pricebook2Id (hardcoded in script): e.g., 01sxxxxxxxxxxxxxxx Source lines: $('Message a model').first().json.message.content.products Output: { soql, codes } --- 8) Query PricebookEntries (Salesforce) Purpose: Fetch PricebookEntry.Id for each Product2.ProductCode. Key settings: Resource: search Query: ={{ $json.soql }} Creds: salesforceOAuth2Api Output: Items with Id, Product2.ProductCode (used for mapping). --- 9) Code in JavaScript (Build OLI payloads) Purpose: Join lines with PBE results and Opportunity Id ➜ build OpportunityLineItem payloads. Inputs: OpportunityId: ={{ $('Create an opportunity').first().json.id }} Lines: ={{ $('Message a model').first().json.message.content.products }} PBE rows: from previous node items Output: { body: { allOrNone:false, records:[{ OpportunityLineItem... }] } } Notes: Converts discount_total ➜ per-unit if needed (currently commented for standard pricing). Throws on missing PBE mapping or empty lines. --- 10) Create Opportunity Line Items (HTTP Request) Purpose: Bulk create OLIs via Salesforce Composite API. Key settings: Method: POST URL: https://<your-instance>.my.salesforce.com/services/data/v65.0/composite/sobjects Auth: salesforceOAuth2Api (predefined credential) Body (JSON): ={{ $json.body }} Output: Composite API results (per-record statuses). --- 11) Update File to One Drive Purpose: Archive the original PDF in OneDrive. Key settings: Operation: upload File Name: ={{ $json.name }} Parent Folder ID: onedrive folder id Binary Data: true (from the Download node) Creds: microsoftOneDriveOAuth2Api Output: Uploaded file metadata. --- Data flow (wiring) Google Drive Trigger → Download File From Google Download File From Google → Extract from File → Update File to One Drive Extract from File → Message a model Message a model → Create or update an account Create or update an account → Create an opportunity Create an opportunity → Build SOQL Build SOQL → Query PricebookEntries Query PricebookEntries → Code in JavaScript Code in JavaScript → Create Opportunity Line Items --- Quick setup checklist 🔐 Credentials: Connect Google Drive, OneDrive, Salesforce, OpenAI. 📂 IDs: Drive Folder ID (watch) OneDrive Parent Folder ID (archive) Salesforce Pricebook2Id (in the JS SOQL builder) 🧠 AI Prompt: Use the strict system prompt; jsonOutput = true. 🧾 Field mappings: Buyer tax id/name → Account upsert fields Invoice code/date/amount → Opportunity fields Product name must equal your Product2.ProductCode in SF. ✅ Test: Drop a sample PDF → verify: AI returns array JSON only Account/Opportunity created OLI records created PDF archived to OneDrive --- Notes & best practices If PDFs are scans, enable OCR in Extract from File. If AI returns non-JSON, keep “Return only a JSON array” as the last line of the prompt and keep jsonOutput enabled. Consider adding validation on parsing.warnings to gate Salesforce writes. For discounts/taxes in OLI: Standard OLI fields don’t support per-line discount amounts directly; model them in UnitPrice or custom fields. Replace the Composite API URL with your org’s domain or use the Salesforce node’s Bulk Upsert for simplicity.

Le NguyenBy Le Nguyen
942