This article provides a complete, practical walkthrough of the Manual Stickynote Automation Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   How to Build a Multilingual Audio Translation and Transcription Workflow in n8n with OpenAI and ElevenLabs
   
   Meta Description:  
   Learn how to create a powerful translation workflow in n8n that converts French text into speech, transcribes it back into text, and translates it into English using OpenAI and ElevenLabs APIs.
   
   Keywords:  
   n8n workflow, text-to-speech, voice translation, OpenAI Whisper, ElevenLabs, text transcription, multilingual workflows, French to English translation, OpenAI GPT, audio translation automation
   
   Third-Party APIs Used:
   
   - OpenAI API (ChatGPT for language translation and Whisper for audio transcription)
   - ElevenLabs API (Text-to-speech audio generation in multiple languages)
   
   —
   
   Article:
   
   Creating a Multilingual Audio Translation Workflow with n8n, OpenAI, and ElevenLabs
   
   In today’s interconnected world, global communication often demands creative and efficient solutions to language barriers. Whether you’re building a language-learning app or simply want to automate translations for customer support messages, combining technologies like text-to-speech, transcription, and artificial intelligence-powered translation can be a game changer.
   
   In this tutorial, we’ll explore a unique n8n workflow that automatically:
   
   1. Converts a French text into natural-sounding speech using ElevenLabs.
   2. Transcribes the generated audio back into text using OpenAI’s Whisper model.
   3. Translates the transcription into English using OpenAI’s GPT model.
   4. Converts the English translation back to speech using ElevenLabs.
   
   This end-to-end audio transformation pipeline brilliantly showcases how multiple APIs and AI models can be orchestrated within a visual automation platform like n8n.
   
   Let’s break down how it works.
   
   —
   
   Understanding the Workflow Structure
   
   The n8n workflow starts with a Manual Trigger node, which ensures the flow is initialized only when a user explicitly starts it. This makes testing and debugging much more straightforward.
   
   Step 1: Setting the French Text and Voice
   
   The Set node is used to define both the French input text and the ElevenLabs voice ID. The sample sentence used is:
   
   "Après, on a fait la sieste, Camille a travaillé pour French Today et j’ai étudié un peu, et puis Camille a proposé de suivre une visite guidée de l’Abbaye de Beauport qui commençait à 17 heures..."
   
   The voice ID links to a custom voice created in the ElevenLabs Voice Lab.
   
   Step 2: Generating French Audio
   
   Next, the “Generate French Audio” node sends a structured HTTP request to the ElevenLabs API to convert the French text into spoken audio using the “eleven_multilingual_v2” model configuration. Proper headers are implemented, including the audio/mpeg content type, along with streaming optimizations for low latency.
   
   Step 3: Transcribing the Audio with Whisper
   
   The generated audio file is passed to OpenAI’s Whisper transcription model within the “Transcribe Audio” node. This model converts spoken words back into written French text. OpenAI’s API requires multipart-form data for audio files and supports various languages by default.
   
   Step 4: Translating to English
   
   The transcribed French text is then fed into the “Translate Text to English” node, which uses OpenAI’s GPT model for machine translation. A simple prompt template — “Translate to English: {{ text }}” — makes this step flexible and reusable.
   
   Step 5: Converting English Text to Audio
   
   Finally, the translated English text is sent back to the ElevenLabs API to produce high-quality spoken audio in English. The “Translate English text to speech” node replicates the same voice and audio settings as the French version.
   
   —
   
   Why This Workflow Is Powerful
   
   This n8n setup seamlessly bridges the gap between different language formats and communication mediums (text ↔ speech ↔ text), with intelligent processing in between. Here’s why it’s so effective:
   
   - Flexibility: You can swap input languages or target translation languages by simply modifying one or two nodes.
   - Scalability: Ideal for batch-processing multilingual audio content.
   - Personalization: Easily connect to different voices in ElevenLabs’ Voice Lab.
   
   —
   
   How to Customize the Setup
   
   To get this workflow working for yourself, make sure to:
   
   - Add your own voice in ElevenLabs and insert the relevant voice ID in the Set node.
   - Create ElevenLabs Header Auth credentials with your API key defined as the xi-api-key.
   - Set up OpenAI credentials using your personal API key.
   
   Helpful sticky notes within the n8n workflow offer direct guidance for all setup steps, making initial configuration hassle-free.
   
   —
   
   Conclusion
   
   This n8n workflow shows just how powerful low-code automation can be when integrated with modern AI services. Whether you’re a language enthusiast, developer, or customer service innovator, this setup gives you an exciting foundation to build a multilingual, audio-aware system that can be adapted for various use cases — from e-learning to content localization.
   
   By combining the best of OpenAI’s language models with ElevenLabs’ cutting-edge speech synthesis, this workflow embodies the future of intelligent communication technologies.
   
   If you haven’t explored n8n for orchestrating AI-powered processes like this one, now is a great time to start.
   
   — 
   
   Ready to try it out? Get your OpenAI and ElevenLabs API keys, plug them into your n8n setup, and let this multilingual workflow speak for itself — quite literally!

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.