This article provides a complete, practical walkthrough of the Splitout Code Automation Triggered 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:
   Automating SRT Subtitle Translation with n8n and Google Translate
   
   Meta Description:
   Discover how to automate subtitle file translation using n8n with Google Translate. This guide walks through a low-code workflow that receives an SRT file, translates lines, and returns the translated file—all on autopilot.
   
   Keywords:
   n8n subtitle translation, automate SRT translation, Google Translate API, n8n workflow tutorial, subtitle localization, SRT translator, automate language translation, no-code automation, convert subtitles to other languages, Google Translate SRT
   
   Third-Party APIs Used:
   - Google Translate API (via OAuth2 credentials)
   
   Article:
   
   Automating SRT Subtitle Translation with n8n and Google Translate
   
   Translating video subtitles is a necessary but time-consuming task for content creators, educators, and streaming platforms targeting global audiences. Manually converting SRT (SubRip Subtitle) files line by line into other languages can be both tedious and error-prone. But with low-code automation platforms like n8n, it’s now possible to automate the entire translation process—all without manual intervention.
   
   This article explores a powerful n8n workflow titled “Translate” that leverages Google Translate to automatically convert SRT files into your desired language, then delivers the translated SRT file back to the user in seconds.
   
   Overview of the Workflow
   
   The workflow follows a clean and logical structure designed to:
   
   1. Receive an SRT file and target language from the user.
   2. Extract and parse each subtitle section.
   3. Translate the subtitle content using Google Translate.
   4. Recombine the translated content into a valid SRT format.
   5. Output the resulting translated file as a downloadable response.
   
   Let’s break down how each step functions and how n8n brings this together.
   
   Step 1: Receiving the SRT File
   
   The user uploads an SRT file using a form trigger node titled “Receive SRT File to Translate.” This form also includes a dropdown to select the desired target translation language—currently English (EN) or Japanese (JP). Additional languages can be added by modifying the dropdown options or bypassing them entirely with a hardcoded value in the translation node.
   
   Step 2: Extracting and Parsing Subtitle Text
   
   The binary SRT file is routed through a chain of nodes starting with "Expose Binary" and "Extract text from Binary File," which reads the raw subtitle data. This is followed by “Split SRT Lines,” a custom JavaScript node that parses the SRT content, grouping each subtitle entry (index, timestamps, and dialogue) into an array of entries for translation.
   
   This parsing ensures a clear distinction between timing metadata and the translatable text, which is essential for maintaining SRT format integrity post-translation.
   
   Step 3: Preparing Text for Translation
   
   Each subtitle block is split into two parts using “Prep Parts for Translate”: the header (which contains the index and timestamp) and the translatable text. This is done by identifying the second newline character in each block, allowing us to separate metadata from meaningful content.
   
   Step 4: Translating with Google Translate
   
   With the dialogue text isolated, the workflow sends it to the "Google Translate" node using dynamic input fields. The API request is authenticated using OAuth2 credentials connected to the user's Google account—ensuring secure and authorized access.
   
   Because the target translation language is dynamic (based on the form’s selection), the system is versatile and scalable to accommodate multiple languages.
   
   Step 5: Formatting Translated Text
   
   After receiving the translated text back, the workflow runs it through another custom script node, “Clean Translations & Group Titles.” This replaces character entities (e.g., ", ') with literal punctuation, and formats long sentences into 1–2 lines per subtitle block. It then recombines the original index/timestamp info with the translated dialogue.
   
   This helps preserve readability and ensures the translated file still adheres to typical subtitle format conventions.
   
   Step 6: Rebuilding the SRT File
   
   Once all subtitle blocks are translated and formatted properly, “Aggregate” and “Join completed text with double new line” nodes compile the entire array of subtitles into one string.
   
   The “Edit Fields” node re-injects the translated subtitle data back into the original file metadata, and the “Generate Binary” node encodes the final result into base64 format—suitable for binary file storage.
   
   Finally, “Convert to File” transforms this back into a downloadable .srt file, renamed to reflect the translation language (e.g., example_file EN.srt), and “Respond with file” sends the newly generated subtitle to the user via the web interface.
   
   Why This Workflow Matters
   
   This n8n implementation solves several real-world challenges:
   
   - ❯ Eliminates Manual Translation: Each dialogue is processed via the Google Translate API, reducing hours of manual work.
   - ❯ Preserves Subtitle Structure: By intelligently separating metadata from content, timing and formatting are never lost.
   - ❯ Language Flexibility: Additional languages can be supported by simply editing the dropdown or fixing the language value in the node.
   - ❯ Rapid Turnaround: Automating translation ensures that users can go from upload to final download within a minute or less, making content-localization far more accessible.
   
   Extending the Workflow
   
   Want to add more capabilities? Here are some ideas:
   
   - Add a language detection node to auto-identify the source language.
   - Trigger notifications (email, Slack) when a translation is complete.
   - Save translated files to cloud storage like Google Drive, AWS S3, or Dropbox for persistent use.
   - Add support for batch translation of multiple subtitles via looped executions.
   
   Conclusion
   
   This n8n workflow is a robust and flexible solution for automating SRT subtitle translation with minimal technical effort. By integrating Google Translate and leveraging n8n’s visual workflow interface, you can now localize your content automatically—saving time, improving scale, and ensuring consistent results.
   
   Whether you're a content creator, language service provider, or multimedia agency, automating SRT translation has never been easier.
   
   Ready to try it out? Integrate your Google Translate credentials and upload your first SRT file—let automation do the rest.
   
   If you're new to credentials setup for Google Translate in n8n, refer to the official guide here:  
   https://docs.n8n.io/integrations/builtin/credentials/google/
   
   Let your subtitles speak every language, effortlessly.
   
   — End —

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.