Seeking n8n Expert – Advanced Video Dubbing Workflow (Hybrid Local + Cloud Integration)

Jobs
1

Good morning,

We are looking for an experienced n8n developer to design and implement a complete automation workflow for a video dubbing platform.
This is a freelance, results-oriented project. The objective is to deliver a fully functional, documented, and production-ready workflow.

The project involves building a modular and scalable dubbing system inside n8n that manages all stages of the video dubbing process — from speech recognition and translation to voice synthesis, audio mixing, and final video generation.
The workflow should integrate both local tools and cloud APIs (Azure, OpenAI, Google Cloud), and support batch processing for large-scale operations.

Workflow Scope:

  1. Audio Extraction and Preparation

    • Separate voice, background, and sound effects using FFmpeg or equivalent tools.

    • Handle mono, stereo, and multi-channel formats.

    • Prepare clean tracks for speech recognition.

  2. Automatic Speech Recognition (ASR)

    • Local models: Whisper, Faster-Whisper, SpeechBrain.

    • Cloud APIs: Azure Speech-to-Text, Google Speech API, OpenAI Whisper API.

  3. Translation

    • Local tools: Argos Translate, M2M100, or similar.

    • Cloud APIs: Azure Translator, Google Translate, OpenAI GPT Translation.

    • Maintain time alignment between source and translated text.

  4. Text-to-Speech (TTS)

    • Local voices: Bark, Coqui TTS, XTTS, or similar.

    • Cloud TTS: Azure Neural Voices, Google Cloud TTS, OpenAI Voice.

    • Control voice parameters such as tone, pace, and expression.

  5. Audio Mixing and Video Assembly

    • Combine generated voice with background audio or effects.

    • Adjust synchronization, timing, and volume balance.

    • Export final video using FFmpeg or similar frameworks.

  6. Batch Processing

    • Implement automation for high-volume dubbing.

    • Include queue management, concurrency control, and progress tracking.

  7. Workflow Configuration and Control

    • Support both local and cloud execution modes.

    • Use environment variables for API credentials and model paths.

    • Include detailed logging, error handling, and retry mechanisms.

Requirements:

  • Proven experience developing advanced workflows with n8n.

  • Strong knowledge of REST APIs, OAuth2, and JSON data handling.

  • Practical experience with FFmpeg, SoX, or other multimedia automation tools.

  • Familiarity with AI-based ASR, translation, and TTS systems (both local and cloud).

  • Experience with batch and asynchronous task orchestration.

  • Ability to deliver a robust, production-ready workflow with clear documentation.

Deliverables:

  • Fully functional n8n workflow implementing the complete dubbing process.

  • Hybrid setup supporting both local and cloud services.

  • Batch processing capability for large-scale dubbing.

  • Documentation covering architecture, configuration, and dependencies.

  • Final validation and workflow testing.

Other Details:

This is a remote project with flexible timing but focused on technical precision and functional delivery.
If you have solid experience in multimedia automation and n8n workflow development, please reply with a short summary of your background and examples of relevant work.

4 Likes
2

Hi @RafaelP

This is an exciting challenge — at Hashlogics, we’ve delivered several AI-powered multimedia automation pipelines combining n8n, FFmpeg, and LLM/audio models across local and cloud setups.

One of our recent projects, the AI-Powered ESG & Sustainability Research Platform (case study), used similar hybrid logic — mixing local processing with cloud AI APIs (OpenAI, Google, Azure) and full orchestration in n8n with logging, retries, and async queuing.

We’re very comfortable designing workflows like the one you described:

  • ASR via Whisper / Azure Speech / Faster-Whisper.

  • Translation via GPT, M2M100, or Argos with time alignment.

  • TTS using Bark, Coqui, XTTS, or Azure Neural Voices.

  • Audio-Video assembly through FFmpeg with batch handling and progress tracking.

  • Configurable local + cloud execution modes, credential management, and monitoring.

We specialize in building production-ready n8n systems that balance performance, scalability, and maintainability — not just prototypes.

:globe_with_meridians: Portfolio
:star: Reviews
:date: Book a call

Would you like me to outline how we’d structure the dubbing pipeline (from audio extraction → ASR → translation → TTS → FFmpeg assembly) and estimate delivery milestones?

— Abdul Basit | CEO, Hashlogics

1 Like
3

Hi Rafael,

Good morning! This project sounds fascinating, and I’m confident I can deliver a fully functional, modular, and scalable video dubbing workflow using n8n.

I have solid experience designing complex automation pipelines with n8n, integrating both local and cloud services. My background includes:

  • Building multimedia workflows with FFmpeg for audio/video processing automation

  • Implementing AI-driven pipelines using cloud APIs such as Azure Cognitive Services, Google Cloud Speech & Translation APIs, and OpenAI for ASR, translation, and TTS

  • Orchestrating batch processing with queue management and concurrency control in n8n

  • Creating robust workflows with detailed error handling, logging, and retry mechanisms

  • Developing hybrid solutions that flexibly switch between local models (like Whisper) and cloud services based on environment configurations

Relevant examples:

  • Automated transcription and translation system for a media company integrating Whisper ASR with Google Translate and Azure TTS, processing thousands of audio files daily with progress monitoring

  • Scalable video captioning pipeline combining FFmpeg, AI models, and n8n workflows for batch video processing

  • Hybrid cloud/local AI workflows orchestrated with n8n for content localization

4

Hey Rafael,

I got you, I have been building all forms of automations for the past 2 years and have built 100s of flows for my clients. Have worked with all sorts of companies and gotten them 10s of thousands in revenue or savings by strategic flows. When you decide to work with me, not only will I build this flow out, but also give you a free consultation like I have for all my clients that led to these revenue jumps.

I have built a similar workflow like this for one of my clients. I can not only share that but also how you can streamline processes in your company for faster operations. All this with no strings attached on our first call.

Here, have a look at my website and you can book a call with me there!

Talk soon!

5

Hi @RafaelP :waving_hand:

I’m really interested in this project — I’ve built advanced AI automation and multimedia workflows in n8n, combining FFmpeg, Whisper, Azure, and OpenAI APIs for dubbing, translation, and voice synthesis before.

I’m ready to start and can deliver a robust, production-ready workflow with full documentation.
Let’s discuss your requirements in detail.

:e_mail: Email: [email protected]
:globe_with_meridians: Portfolio: https://www.muhammadz.fun

— Muhammad Bin Zohaib
Certified n8n Developer | AI Automation Engineer

6

Hello Rafael,

I build production voice and video automations in n8n. My background is voice AI and media pipelines in TypeScript with clear docs and observability.

Recent work:

  • Live voice app with low-latency ASR → TTS in production with daily traffic.

  • Podcast localization pipeline using Whisper/Azure STT, time-aligned translation, Azure Neural voices, and FFmpeg mixes to broadcast targets.

  • n8n batch jobs for media teams with queues, retries, idempotent steps, resumable runs, and Slack progress pings. Custom nodes for FFmpeg, Faster-Whisper, Coqui, and XTTS when needed.

How I’d approach your dubbing workflow:

  • Split and clean tracks so ASR hears only what matters. Silence trim and loudness checks.

  • Segment with stable timecodes. Keep alignment through translation and synthesis.

  • Tune voice tone, pace, and pauses on a small test set before scaling.

  • Mix with ducking and headroom. Prevent clipping and drift.

  • Run batches safely. Concurrency caps, backoff retries, and clear job states.

  • Hybrid by design. Local for volume, cloud for edge cases. One env toggle. Structured logs and cost visibility.

What this gives you:

  • Natural pacing that matches the source.

  • Reliable timing from first pass to final render.

  • Large-scale runs without stalls or double work.

  • A workflow you can switch from laptop to cloud without edits.

I’m mid-release this week but can open a build window Monday, Oct 13. First milestone is an end-to-end dub of a sample clip with clean export, then harden for volume with monitoring and docs. If helpful, I can share short clips and a one-page node map. I want you to succeed, so I’m here to help.

Sam

7

Hi @RafaelP,

I’m Georgiadis Shilisia, a no-code automation specialist with extensive experience in n8n workflow development and multimedia automation.

I’m very interested in your advanced video dubbing workflow project. I have expertise in:

• Building complex n8n workflows with advanced logic and error handling

• API integrations (OpenAI, Azure, Google Cloud services)

• Audio/video processing and automation

• Batch processing with queue management and concurrency control

• Hybrid local + cloud solutions with flexible environment configurations

• Database integrations (Supabase, PostgreSQL) for progress tracking

For your video dubbing workflow, I can deliver:

✓ ASR integration (Whisper/Azure) with accurate transcription

✓ Translation services with context preservation

✓ TTS implementation with natural voice synthesis

✓ FFmpeg integration for audio/video processing

✓ Robust error handling, logging, and retry mechanisms

✓ Scalable batch processing with monitoring

✓ Hybrid architecture that switches seamlessly between local and cloud

I’m available to start immediately and can deliver a production-ready workflow with full documentation. I’d be happy to discuss your specific requirements, timeline, and provide a detailed project plan.

Feel free to reach out via email at [email protected] or send me a direct message here to schedule a call or interview.

Looking forward to collaborating with you!

Best regards,

Georgiadis Shilisia

8

Hi ,

I’m Moataz Towfik, an Integration & Automation Engineer with strong hands-on experience in n8n, along with tools like TIBCO, MuleSoft, Jenkins, and Docker.

I’ve used n8n extensively to build custom automations, API integrations, and workflow orchestration for real-world business needs—especially in my roles at Vodafone and AlexBank. From simple triggers to complex branching flows, I know how to make n8n work efficiently and reliably.

If you’re looking for a freelancer who can jump in and deliver fast, scalable results—I’m ready.

LinkedIn: linkedin.com/in/moataz-towfik-012a5a238

Looking forward to hearing from you!

Best,
Moataz
:e_mail: [email protected]

9

Hello @RafaelP ,

Great! I can help you with this, We have a team of skilled -experience profession who are regularly engage in this type of automations and help to resolve more complex problems.

Let’s connect on a call to discuss this futher and explore how we can help you. Here is my upwork , linkedIn or Mail, we can on any of the platform that suits you the best.

Let me know you availabilty and your in-details project and questions (if you have any).

Thanks

Diya ~ Deligence Technologies