This article provides a complete, practical walkthrough of the Manual Code Create 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:  
   Create a Multilingual Audio Translation Pipeline Using n8n, ElevenLabs, and OpenAI
   
   Meta Description:  
   Learn how to set up a no-code workflow with n8n that converts French text into spoken audio, transcribes it back to text, translates it into English, and generates English speech. Leverage ElevenLabs for speech synthesis and OpenAI’s Whisper and GPT models for transcription and translation.
   
   Keywords:  
   n8n workflow, ElevenLabs API, OpenAI Whisper, GPT-4o-mini, text-to-speech, speech-to-text, audio translation pipeline, French to English translation, multilingual audio conversion, AI translation automation
   
   Third-Party APIs Used:
   
   - ElevenLabs Text-to-Speech API
   - ElevenLabs Voice Lab API (Voice ID setup)
   - OpenAI Whisper API (Audio Transcription)
   - OpenAI GPT (Chat Model, GPT-4o-mini for Translation)
   
   —
   
   Article:
   
   🗣️ From French Sentences to English Voices: How to Build a Multilingual Audio Pipeline with n8n, ElevenLabs, and OpenAI
   
   As AI-powered tools become more accessible, automating audio translation workflows has never been easier—especially with no-code platforms like n8n. In this article, we’ll dive into a sophisticated yet low-maintenance n8n workflow that takes a French paragraph, converts it into speech, transcribes it back to text, translates the transcription into English, and finally produces an English audio version.
   
   Whether you're building a multilingual podcast app, an education tool for language learners, or just want to explore what’s possible with voice AI, here's a practical example of how to get started.
   
   🔧 Workflow Overview
   
   This n8n workflow is a complete French-to-English speech translation pipeline, consisting of the following steps:
   
   1. A French text string is manually triggered.
   2. ElevenLabs Text-to-Speech (TTS) API converts it to French audio.
   3. OpenAI Whisper API transcribes the audio back into French text.
   4. That transcription is translated into English using OpenAI's GPT-4o-mini.
   5. ElevenLabs then generates English audio from the translated text.
   
   All of this is fully automated in a sequential pipeline that runs in one click.
   
   Let’s explore how each piece connects.
   
   🛠️ Step-by-Step Breakdown
   
   1. Manual Trigger & Input Setup  
      The workflow starts with a manual trigger that loads a hardcoded French paragraph like:
   
      > “Après, on a fait la sieste, Camille a travaillé pour French Today…”
   
      A Set node establishes two key variables:
      - voice_id: The ID of the ElevenLabs voice to synthesize speech.
      - text: The French text you want to process.
   
   2. Generate French Audio using ElevenLabs  
      The Set node hands off to an HTTP Request node that sends the French text to the ElevenLabs TTS API. This node includes authentication via an HTTP Header (xi-api-key), which you create using your ElevenLabs account.
   
      Configuration includes:
      - voice_id from your ElevenLabs Voice Lab
      - Model: "eleven_multilingual_v2"
      - Voice settings: adjustable stability & similarity boost
   
      The response is an MPEG audio stream of the French speech, which is used in subsequent steps.
   
   3. Add Metadata & Prepare for Transcription  
      Before transcription, a Code node adds a filename (audio.mp3) to the binary data, ensuring OpenAI Whisper can process the file correctly.
   
   4. Audio Transcription Using OpenAI Whisper  
      The next HTTP Request node interacts with OpenAI’s audio transcription endpoint at https://api.openai.com/v1/audio/transcriptions.
   
      Key Parameters:
      - Model: whisper-1
      - Content-Type: multipart/form-data
      - File: the MP3 file generated previously
   
      This returns the transcription as plain French text.
   
   5. Translate French Text to English via GPT-4o-mini  
      The transcription is routed into OpenAI’s GPT chat model via n8n’s LangChain integration. By sending a prompt that prepends “Translate to English:” to the French text, GPT-4o-mini returns a fluent English translation.
   
   6. English Audio Generation with ElevenLabs  
      From the translated text, another ElevenLabs TTS HTTP Request node synthesizes an English audio version. Once again using the same voice_id (which can be multilingual), the API returns an English-spoken version of the original French paragraph.
   
   📄 Setup Steps and Credentials Required
   
   To replicate this workflow, ensure the following:
   
   - ElevenLabs API Key:
     - Found under your Profile on elevenlabs.io
     - Used with HTTP Header authentication under the key name xi-api-key
   
   - ElevenLabs Voice ID:
     - Create or import a voice in your ElevenLabs Voice Lab
     - Copy the Voice ID into the ‘Set’ node
   
   - OpenAI API Key:
     - Used through the “OpenAi account” credential in the HTTP Request and LangChain nodes
   
   ✅ Use Cases
   
   - Language Learning: Convert educational text and hear it in two languages.
   - Tourism Apps: Translate spoken guides dynamically.
   - Accessibility Tools: Provide multilingual audio content.
   - Content Localization: Batch-process scripts for dubbing in multiple voice profiles.
   
   🔚 Final Thoughts
   
   This workflow showcases the power of combining language models and speech APIs without writing a single line of backend code. By modularizing tasks across ElevenLabs and OpenAI, you've effectively built a real-time audio translator—all orchestrated through n8n and its visual programming environment.
   
   Ready to try it yourself? With your API keys and a voice ID handy, this workflow can be up and running in minutes—streamlining tasks that used to take entire dev teams days to build.
   
   —
   
   Want to level it up? Add branching logic to detect the spoken language first or build a front-end interface for end users to input their own sentences.
   
   Happy automating! 🧠🎧🌍

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.