Turn BBC News articles into podcasts using Hugging Face and Google Gemini

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Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

Turn BBC News Articles into Podcasts using Hugging Face and Google Gemini Effortlessly transform BBC news articles into engaging podcasts with this automated n8n workflow. Who is this for? This tem...

Best for

Content Creation automation workflows
Multimodal AI automation workflows
advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.manualtrigger, n8n-nodes-base.splitout, n8n-nodes-base.html, n8n-nodes-base.aggregate, n8n-nodes-base.httprequest, n8n-nodes-base.filter, n8n-nodes-base.if, @n8n/n8n-nodes-langchain.chainllm

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Onur.

Original n8n.io source

1.1 Workflow description

Title: Turn BBC News articles into podcasts using Hugging Face and Google Gemini
Workflow name: Turn BBC News articles into podcasts using Hugging Face and Google Gemini

Turn BBC News Articles into Podcasts using Hugging Face and Google Gemini

Effortlessly transform BBC news articles into engaging podcasts with this automated n8n workflow.

Who is this for?

This template is perfect for:

Content creators who want to quickly produce podcasts from current events.
Students looking for an efficient way to create audio content for projects or assignments.
Individuals interested in generating their own podcasts without technical expertise.

Setup Information

Install n8n: If you haven't already, download and install n8n from n8n.io.
Import the Workflow: Copy the JSON code for this workflow and import it into your n8n instance.
Configure Credentials:
- Gemini API: Set up your Gemini API credentials in the workflow's LLM nodes.
- Hugging Face Token: Obtain an access token from Hugging Face and add it to the HTTP Request node for the text-to-speech model.
Customize (Optional):
- Filtering Criteria: Adjust the News Classifier node to fine-tune the selection of news articles based on your preferences.
- Output Options: Modify the workflow to save the generated audio file to a cloud storage service or publish it to a podcast hosting platform.

Prerequisites

An active n8n instance.
Basic understanding of n8n workflows (no coding required).
API credentials for Gemini and a Hugging Face account with an access token.

What problem does it solve?

This workflow eliminates the manual effort involved in creating podcasts from news articles. It automates the entire process, from fetching and filtering news to generating the final audio file.

What are the benefits?

Time-saving: Create podcasts in minutes, not hours.
Easy to use: No coding or technical skills required.
Customizable: Adapt the workflow to your specific needs and preferences.
Cost-effective: Leverage free or low-cost services like Gemini and Hugging Face.

How does it work?

The workflow fetches news articles from the BBC website.
It filters articles based on their suitability for a podcast.
It extracts the full content of the selected articles.
It uses Gemini LLM to create a podcast script.
It converts the script to speech using Hugging Face's text-to-speech model.
The final podcast audio is ready for use.

Nodes in the Workflow

Fetch BBC News Page: Retrieves the main BBC News page.
News Classifier: Categorizes news articles using Gemini LLM.
Fetch BBC News Detail: Extracts detailed content from suitable articles.
Basic Podcast LLM Chain: Generates a podcast script using Gemini LLM.
HTTP Request: Converts the script to speech using Hugging Face.

Add Story

I'm excited to share this workflow with the n8n community and help content creators and students easily produce engaging podcasts!

Additional Tips

Explore the n8n documentation and community resources for more advanced customization options.
Experiment with different filtering criteria and LLM prompts to achieve your desired podcast style.

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - When clicking ‘Test workflow’

Type / Role: n8n-nodes-base.manualTrigger - manualTrigger
Config choices: Version 1

Block 2 - Split Out

Type / Role: n8n-nodes-base.splitOut - splitOut
Config choices: Version 1

Block 3 - Extract News Block

Type / Role: n8n-nodes-base.html - html
Config choices: Version 1.2

Block 4 - Extract News Content

Type / Role: n8n-nodes-base.html - html
Config choices: Version 1.2

Block 5 - Aggregate

Type / Role: n8n-nodes-base.aggregate - aggregate
Config choices: Version 1

Block 6 - Fetch BBC News Detail

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.2

Block 7 - Extract Detail

Type / Role: n8n-nodes-base.html - html
Config choices: Version 1.2

Block 8 - Filter Empty Detail

Type / Role: n8n-nodes-base.filter - filter
Config choices: Version 2.2

Block 9 - If script exists

Type / Role: n8n-nodes-base.if - if
Config choices: Version 2

Block 10 - Basic Podcast LLM Chain

Type / Role: @n8n/n8n-nodes-langchain.chainLlm - chainLlm
Config choices: Version 1.5

Block 11 - Gemini

Type / Role: @n8n/n8n-nodes-langchain.lmChatGoogleGemini - lmChatGoogleGemini
Config choices: Version 1

Block 12 - Output Parser

Type / Role: @n8n/n8n-nodes-langchain.outputParserStructured - outputParserStructured
Config choices: Version 1.2

Block 13 - News Classifier

Type / Role: @n8n/n8n-nodes-langchain.textClassifier - textClassifier
Config choices: Version 1

Block 14 - Fetch BBC News Page

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 1

Block 15 - Sticky Note

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 16 - Sticky Note1

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 17 - Sticky Note2

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 18 - Sticky Note3

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 19 - Sticky Note4

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 20 - Sticky Note5

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 21 - Limit 10 Items

Type / Role: n8n-nodes-base.limit - limit
Config choices: Version 1

Block 22 - Hugging Face Text-to-Speech.

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.2

3. Summary Table

Workflow	Turn BBC News articles into podcasts using Hugging Face and Google Gemini
Complexity	advanced
Nodes	22
Categories	Content Creation, Multimodal AI
Author	Onur
Published	22 Feb 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/2972/2972.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
4. Test with sample data

Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
5. Activate and monitor

Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Download workflow JSON Original n8n.io source Content Creation workflows Multimodal AI workflows

Frequently asked questions

What does Turn BBC News articles into podcasts using Hugging Face and Google Gemini do?

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Content Creation, Multimodal AI use case.

Onur

15 workflows

Nodes

n8n-nodes-base.manualtrigger n8n-nodes-base.splitout n8n-nodes-base.html n8n-nodes-base.aggregate n8n-nodes-base.httprequest n8n-nodes-base.filter n8n-nodes-base.if @n8n/n8n-nodes-langchain.chainllm

Complexity

advanced

Published 22 Feb 2025

Likes 0

View on n8n.io Download Workflow

Install path: /data/workflows/2972/2972.json

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Turn BBC News articles into podcasts using Hugging Face and Google Gemini

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

Turn BBC News Articles into Podcasts using Hugging Face and Google Gemini

Who is this for?

Setup Information

Prerequisites

What problem does it solve?

What are the benefits?

How does it work?

Nodes in the Workflow

Add Story

Additional Tips

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - When clicking ‘Test workflow’

Block 2 - Split Out

Block 3 - Extract News Block

Block 4 - Extract News Content

Block 5 - Aggregate

Block 6 - Fetch BBC News Detail

Block 7 - Extract Detail

Block 8 - Filter Empty Detail

Block 9 - If script exists

Block 10 - Basic Podcast LLM Chain

Block 11 - Gemini

Block 12 - Output Parser

Block 13 - News Classifier

Block 14 - Fetch BBC News Page

Block 15 - Sticky Note

Block 16 - Sticky Note1

Block 17 - Sticky Note2

Block 18 - Sticky Note3

Block 19 - Sticky Note4

Block 20 - Sticky Note5

Block 21 - Limit 10 Items

Block 22 - Hugging Face Text-to-Speech.

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions