Description
Overview
This podcast summary automation workflow systematically compiles daily summaries of the top podcast episodes within a specified genre using a no-code integration pipeline. Designed for content curators, researchers, and podcast enthusiasts, it addresses the challenge of efficiently extracting key insights from lengthy audio content by leveraging scheduled triggers and multi-API orchestration.
The process begins with a schedule trigger node set to activate daily at 8 AM, initiating the retrieval of top podcast episodes filtered by genre and country from the Taddy API.
Key Benefits
- Automates daily retrieval and summarization of top podcast episodes by genre using an orchestration pipeline.
- Reduces audio processing load by cropping specific segments before transcription and summarization.
- Utilizes AI-driven transcription and summarization for accurate and concise podcast content extraction.
- Delivers structured HTML email summaries to streamline content consumption and review.
Product Overview
This automation workflow activates on a scheduled trigger set to 8 AM daily, targeting a predefined podcast genre such as TECHNOLOGY. It sends a GraphQL query via HTTP POST to the Taddy API to retrieve the top 10 podcast episodes in the United States for the selected genre. The response includes episode metadata and audio URLs. Each episode is then processed individually by downloading the full audio file.
Subsequent processing involves cropping a 16-minute audio segment (from 8 to 24 minutes) via Aspose’s audio cutter API to optimize transcription efficiency. The workflow polls the API until the cropped audio is available, then downloads it for transcription.
OpenAI’s Whisper API transcribes the cropped audio into text, which is then summarized by an AI language model configured to generate concise 3-4 paragraph summaries focusing on key points. The workflow compiles these summaries into an HTML table format and sends the compiled content via Gmail using OAuth2 authentication. Error handling for download readiness is managed through conditional checks and wait nodes, with default platform retry behavior for other failures.
Features and Outcomes
Core Automation
This orchestration pipeline processes podcast episodes by ingesting audio, applying segment cropping, and generating AI-driven summaries. It uses deterministic branching to handle download availability checks and sequential processing of transcription and summarization nodes.
- Processes multiple podcast episodes individually with parallel splitting for efficient throughput.
- Implements single-pass audio segment extraction to reduce transcription load.
- Applies AI summarization with controlled output length and focus on major points.
Integrations and Intake
The workflow integrates with Taddy’s GraphQL API for podcast metadata retrieval, Aspose’s audio cutter API for segment extraction, and OpenAI’s Whisper and GPT models for transcription and summarization. Authentication is managed via API keys for Taddy and OAuth2 for Gmail.
- Taddy API supplies top podcast data filtered by genre and country.
- Aspose API performs audio cropping with multipart-form data and status polling.
- OpenAI API handles transcription and summarization with predefined credentials.
Outputs and Consumption
The final output is a structured HTML email containing a table with podcast series names, episode titles linked to audio URLs, and AI-generated summaries. This synchronous email delivery facilitates immediate user access to summarized content.
- HTML table format with columns: Podcast, Episode (clickable link), Summary.
- Email dispatched via Gmail node using OAuth2 authentication.
- Summaries limited to 3-4 paragraphs emphasizing key episode content.
Workflow — End-to-End Execution
Step 1: Trigger
The workflow initiates via a schedule trigger node configured to activate daily at 8 AM. This time-based trigger ensures consistent daily execution without manual intervention.
Step 2: Processing
After setting the target podcast genre, the workflow sends a POST request with a GraphQL query to the Taddy API to fetch top podcast episodes. The response is split into individual episodes for parallel processing. Basic presence checks ensure the availability of required audio URLs before download.
Step 3: Analysis
Each episode’s audio is downloaded, then a 16-minute segment is extracted using Aspose’s audio cutter API. The workflow polls for the cropped audio’s availability, waiting if necessary. Once ready, the cropped audio is transcribed by OpenAI’s Whisper API, and the resulting text is summarized by an AI model instructed to focus on primary points in a limited paragraph format.
Step 4: Delivery
The compiled summaries are formatted into an HTML table and sent via Gmail using OAuth2 credentials. The email subject is statically set to “Podcast Review,” and the email body contains the structured podcast summaries with clickable episode links.
Use Cases
Scenario 1
A content curator needs daily summaries of technology podcasts to monitor industry trends. This workflow automates the retrieval, transcription, and summarization of top episodes, delivering concise insights via email, eliminating manual listening and note-taking.
Scenario 2
A researcher studying media coverage prefers summarized podcast content over full audio. The workflow extracts a representative audio segment, transcribes, and summarizes it, providing structured summaries that support rapid qualitative analysis.
Scenario 3
An enterprise communications team requires daily briefing emails summarizing popular podcasts in news and current affairs. Using this automation, they receive formatted email summaries with direct audio links, supporting informed decision-making and content curation.
How to use
To deploy the podcast summary automation workflow, import it into n8n and configure credentials for Taddy API (X-USER-ID and X-API-KEY) and Gmail OAuth2. Set the desired podcast genre in the designated node. Adjust the schedule trigger to the preferred daily runtime. Upon execution, the workflow will fetch top podcasts, process audio segments, transcribe and summarize content, and send the compiled email. Expect daily emails containing structured HTML tables with episode summaries and audio links.
Comparison — Manual Process vs. Automation Workflow
| Attribute | Manual/Alternative | This Workflow |
|---|---|---|
| Steps required | Multiple manual steps: search, download, crop, transcribe, summarize, format, email. | Single automated pipeline executing all steps sequentially and conditionally. |
| Consistency | Variable quality and completeness depending on user attention and tools. | Deterministic and repeatable processing with defined data checks. |
| Scalability | Limited by manual effort and time constraints. | Processes multiple episodes in parallel with scalable API integration. |
| Maintenance | Requires frequent manual updates and monitoring of individual tools. | Centralized workflow with configurable nodes and credential management. |
Technical Specifications
| Environment | n8n workflow automation platform |
|---|---|
| Tools / APIs | Taddy API, Aspose Audio Cutter API, OpenAI Whisper and GPT models, Gmail API |
| Execution Model | Scheduled trigger with sequential and parallel node execution |
| Input Formats | GraphQL JSON response, audio files (mp3) |
| Output Formats | HTML email with table containing podcast metadata and summaries |
| Data Handling | Transient processing of audio and text; no persistent storage within workflow |
| Known Constraints | Relies on external API availability and response times |
| Credentials | API keys for Taddy, OAuth2 for Gmail, OpenAI API key for transcription and summarization |
Implementation Requirements
- Valid API credentials for Taddy API (X-USER-ID and X-API-KEY) configured in HTTP request headers.
- OAuth2 credentials for Gmail account with permission to send emails via API.
- OpenAI API key with access to Whisper transcription and GPT summarization models.
Configuration & Validation
- Set the podcast genre string in the Genre node to a supported value (e.g., TECHNOLOGY).
- Verify Taddy API credential headers contain valid user ID and API key before running.
- Confirm Gmail OAuth2 credentials are authorized and linked correctly in the Gmail node.
Data Provenance
- Triggered by Schedule node at configured daily time.
- Podcast metadata sourced from TaddyTopDaily node querying Taddy API via GraphQL.
- Audio processing through Request Audio Crop and Whisper Transcribe Audio nodes, with summaries generated by Summarize Podcast node using OpenAI credentials.
FAQ
How is the podcast summary automation workflow triggered?
The workflow is triggered by a scheduled node set to run daily at 8 AM, initiating the entire podcast retrieval and summarization pipeline automatically.
Which tools or models does the orchestration pipeline use?
It integrates Taddy API for podcast data, Aspose Audio Cutter API for segment cropping, and OpenAI’s Whisper for transcription along with a GPT-based model for summarization.
What does the response look like for client consumption?
The response is an HTML-formatted email containing a table with podcast series names, episode titles linked to audio files, and AI-generated summaries.
Is any data persisted by the workflow?
No data is persistently stored within the workflow; all audio and text processing is transient, with results delivered immediately via email.
How are errors handled in this integration flow?
Conditional nodes check for audio availability with wait-and-retry logic; otherwise, the workflow relies on n8n’s default error handling and retry mechanisms.
Conclusion
This podcast summary automation workflow provides a systematic and repeatable method to extract, transcribe, and summarize top podcast episodes daily within a chosen genre. It delivers structured HTML summaries via email, enabling efficient content review without manual audio processing. The workflow’s reliance on external APIs such as Taddy, Aspose, and OpenAI introduces dependencies on their availability and response times. Nonetheless, it offers a precise, automated solution for daily podcast content synthesis with configurable scheduling and genre selection.
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