HumationHuman + Automation
Back to Catalog

Create .SRT subtitles & .LRC lyrics from audio with Whisper AI and GPT-5-nano

Václav ČiklVáclav Čikl
504 views
2/3/2026
Google DriveAutomationAttachmentsEmailNotion
Official Page

Overview

This workflow automates the entire process of creating professional subtitle (.SRT) and synced lyrics (.LRC) files from audio recordings. Upload your vocal track, let Whisper AI transcribe it with precise timestamps, and GPT-5-nano segments it into natural, singable lyric lines. With an optional quality control step, you can manually refine the output while maintaining perfect timestamp alignment.

Key Features

  • Whisper AI Transcription: Word-level timestamps with multi-language support via ISO codes
  • Intelligent Segmentation: GPT-5-nano formats transcriptions into natural lyric lines (2-8 words per line)
  • Quality Control Option: Download, edit, and re-upload corrections with smart timestamp matching
  • Advanced Alignment: Levenshtein distance algorithm preserves timestamps during manual edits
  • Dual Format Export: Generate both .SRT (video subtitles) and .LRC (synced lyrics) files
  • No Storage Needed: Files generated in-memory for instant download
  • Multi-Language: Supports various languages through Whisper API

Use Cases

  • Generate synced lyrics for music video releases on YouTube
  • Create .LRC files for Musixmatch, Apple Music, and Spotify
  • Prepare professional subtitles for social media content
  • Batch process subtitle files for catalog releases
  • Maintain consistent lyric formatting across artists
  • Streamline content delivery for streaming platforms
  • Speed up video editing workflow

Perfect For

  • For Musicians & Artists
  • For Record Labels
  • For Content Creators

What You'll Need

Required Setup

  • OpenAI API Key for Whisper transcription and GPT-5-nano segmentation

Recommended Input

  • Format: MP3 audio files (max 25MB)
  • Content: Clean vocal tracks work best (isolated vocals recommended, but whole tracks works still good)
  • Languages: Any language supported by Whisper (specify via ISO code)

How It Works

Automatic Mode (No Quality Check)

  1. Upload your MP3 vocal track to the workflow
  2. Transcription: Whisper AI processes audio with word-level timestamps
  3. Segmentation: GPT-5-nano formats text into natural lyric lines
  4. Generation: Workflow creates .SRT and .LRC files
  5. Download your ready-to-use subtitle files

Manual Quality Control Mode

  1. Upload your MP3 vocal track and enable quality check
  2. Transcription: Whisper AI processes audio with timestamps
  3. Initial Segmentation: GPT-5-nano creates first draft
  4. Download the .TXT file for review
  5. Edit lyrics in any text editor (keep line structure intact)
  6. Re-upload corrected .TXT file
  7. Smart Matching: Advanced diff algorithm aligns changes with original timestamps
  8. Download final .SRT and .LRC files with perfect timing

Technical Details

  • Transcription API: OpenAI Whisper (/v1/audio/transcriptions)
  • Segmentation Model: GPT-5-nano with custom lyric-focused prompt
  • System Prompt: "You are helping with preparing song lyrics for musicians. Take the following transcription and split it into lyric-like lines. Keep lines short (2–8 words), natural for singing/rap phrasing, and do not change the wording."
  • Timestamp Matching: Levenshtein distance + alignment algorithm
  • File Size Limit: 25MB (n8n platform default)
  • Processing: All in-memory, no disk storage
  • Cost: Based on Whisper API usage (varies with audio length)

Output Formats

.SRT (SubRip Subtitle)

Standard format for:

  • YouTube video subtitles
  • Video editing software (Premiere, DaVinci Resolve, etc.)
  • Media players (VLC, etc.)

.LRC (Lyric File)

Synced lyrics format for:

  • Musixmatch
  • Apple Music
  • Spotify
  • Music streaming services
  • Audio players with lyrics display

Pro Tips

💡 For Best Results:

  • Use isolated vocal tracks when possible (remove instrumentals)
  • Ensure clear recordings with minimal background noise
  • For quality check edits, only modify text content—don't change line breaks
  • Test with shorter tracks first to optimize your workflow

⚙️ Customization Options:

  • Adjust GPT segmentation style by modifying the system prompt
  • Add language detection or force specific languages in Whisper settings
  • Customize output file naming conventions in final nodes
  • Extend workflow with additional format exports if needed

Workflow Components

  1. Audio Input: Upload interface for MP3 files
  2. Whisper Transcribe: OpenAI API call with timestamp extraction
  3. Post-Processing: GPT-5-nano segmentation into lyric format
  4. Routing Quality Check: Decision point for manual review
  5. Timestamp Matching: Diff and alignment for corrected text
  6. Subtitles Preparation: JSON formatting for both output types
  7. File Generation: Convert to .SRT and .LRC formats
  8. Download Nodes: Export final files

Template Author:

Questions or need help with setup? 📧 Email:xciklv@gmail.com 💼 LinkedIn:https://www.linkedin.com/in/vaclavcikl/

Create SRT Subtitles & LRC Lyrics from Audio with Whisper AI and GPT-5 Nano

This n8n workflow automates the process of transcribing audio into text, generating SRT subtitle files and LRC lyric files, and then refining the generated text using an AI model. It's ideal for content creators, educators, or anyone needing accurate subtitles and lyrics from audio content.

What it does

This workflow performs the following steps:

  1. Triggers on Form Submission: Waits for an n8n form submission, likely containing an audio file or a URL to an audio file.
  2. Transcribes Audio: Sends the audio to an external API (presumably Whisper AI) for transcription, generating raw text.
  3. Conditional Processing: Checks if the transcription was successful.
  4. Generates SRT/LRC (if successful): If transcription is successful, it converts the raw text into structured SRT subtitle and LRC lyric file formats.
  5. Refines Text with LLM: Uses an OpenAI Chat Model (likely GPT-5 Nano, based on the directory name) via a Basic LLM Chain to refine and improve the transcribed text.
  6. Handles Errors (if transcription fails): If transcription fails, it might wait for a period before re-attempting or logging the error (though the specific error handling isn't explicitly detailed in the provided JSON, the If node suggests a branching path).

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance.
  • External Transcription API: Access to an audio transcription API (e.g., OpenAI Whisper API). This will require an API key and endpoint configured in the HTTP Request node.
  • OpenAI API Key: An OpenAI API key for the OpenAI Chat Model node to power the GPT-5 Nano (or similar) text refinement.
  • n8n Form Trigger: The n8n Form Trigger is used as the starting point, so you'll need to configure its input fields according to your needs (e.g., an audio file upload field or a URL field).

Setup/Usage

  1. Import the Workflow: Import the provided JSON into your n8n instance.
  2. Configure Credentials:
    • HTTP Request (Transcription): Edit the HTTP Request node (ID: 19). Configure it to point to your chosen audio transcription API endpoint. You will likely need to set the HTTP method (e.g., POST), URL, headers (e.g., Authorization with your API key), and the body containing the audio data or URL from the On form submission trigger.
    • OpenAI Chat Model: Edit the OpenAI Chat Model node (ID: 1153). Select or create an OpenAI credential and ensure it has access to the desired model (e.g., gpt-5-nano if available, or gpt-4, gpt-3.5-turbo).
  3. Configure n8n Form Trigger: Edit the On form submission node (ID: 1225) to define the input fields your form will collect (e.g., an "Audio File" field of type Binary or a "Audio URL" field of type String).
  4. Review Code Nodes: Inspect the Code nodes (ID: 834) and Convert to File nodes (ID: 1234) to understand how the data is being transformed into SRT and LRC formats. You may need to adjust these scripts based on the exact output format of your transcription API.
  5. Activate the Workflow: Once configured, activate the workflow.
  6. Submit the Form: Access the URL generated by the On form submission trigger and submit an audio file or URL to start the process.

The workflow will then process the audio, generate the subtitle and lyric files, and refine the text. The final output will be available from the last connected node, which you can then further integrate with other services (e.g., save to cloud storage, send via email, etc., by adding more nodes to the workflow).

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