This workflow contains community nodes that are only compatible with the self-hosted version of n8n.
This workflow automates the process of transcribing audio files and summarizing them using OpenAI models, with the final output stored neatly in Notion. Whether you're a researcher, content creator, student, or professional, this automation saves time by converting voice recordings into actionable summaries with zero manual effort.
Created by: Abdullah Dilshad
Contact: [email protected]
This template is ideal for:
This four-step workflow performs the following:
Step 1: Trigger: New Audio in Google Drive
Automatically triggers when a new audio file (MP3/WAV) is uploaded to a specified Google Drive folder.The file is then downloaded for processing.
Step 2: Transcribe Audio with Whisper
The audio file is sent to OpenAI’s Whisper model for high-accuracy transcription.
Step 3: Summarize Transcript with GPT-4
The transcript is passed to GPT-4, which generates a clean, concise summary.
Step 4: Store Summary in Notion
A new Notion page is created with the generated summary and optional metadata (file name, upload time, etc.).
Step 1: Google Drive Trigger
Connect your Google Drive account.
Select the folder you want to monitor.
This node detects new file uploads and passes the file for download.
Step 2: Download File
Downloads the new audio file for transcription.
Step 3: Transcribe Recording (OpenAI Whisper)
Connect your OpenAI API Key.
Ensure this node receives the binary audio file.
It will return the transcription as plain text.
Step 3: Transcribe Recording (OpenAI Whisper)
Connect your OpenAI API Key.
Ensure this node receives the binary audio file.
It will return the transcription as plain text.
Step 4: Summarize Transcript (GPT-4 via AI Agent)
Use your OpenAI API Key.
Configure a summarization prompt like:
"Summarize the following transcript in a clear and concise manner:"
Connect the output from Whisper into this GPT-4 prompt.
Step 5: Notion Integration
Connect your Notion account.
Choose or create a database to store summaries.
Map the GPT output (summary) to a "Text" or "Rich Text" property.
Optionally include metadata like filename, file upload date, etc.