Text-To-Audio News Conversion With Generative AI

Solution Overview

The surge in demand for audio content has prompted news organizations to seek efficient ways to deliver daily summaries. For example, podcasts have 9 million listeners per year in the U.S. alone. However, automating this process is challenging because it involves managing dynamic article data and converting it into high-quality audio experiences.

With MongoDB and generative AI, you can build a news automation solution to streamline and scale podcast creation. MongoDB serves as the core data layer for the system, efficiently managing news articles as flexible, schema-less documents within a single collection. These documents capture both static information—such as title, content, and publication date—and dynamic metrics that monitor article performance and popularity over time, such as the number of qualified reads. You can also store derived insights, such as sentiment analysis and key entities, in your MongoDB collection and enrich them with a generative AI pipeline.

This adaptable structure provides a robust framework to query and extract the latest news and metadata. You can then transform this information into audio podcasts by integrating advanced language models. With this foundation in place, you can unlock AI-driven business opportunities, attract new customers and increase revenue streams.

Reference Architectures

To implement this framework, you need MongoDB for data storage and AI-powered speech synthesis for audio creation. You can use Google’s NotebookLM model to refine news text with accurate intonation and pacing. The diagram below outlines the workflow for converting news summaries into audio:

Figure 1. AI-based text-to-audio conversion architecture

1. Retrieve Articles: Use aggregation and Atlas Vector Search to fetch relevant news articles from the database.

2. Generate Podcast Script: Pass the articles through an AI pipeline to create a structured, multi-voice podcast script.

3. Convert to Audio: Use advanced text-to-speech models to transform the script into high-quality audio, stored as a .wav file.

4. Optimize Delivery: Cache the generated podcast to ensure seamless, on-demand playback for users.

This framework delivers high-quality, human-like narration in MP3 format, providing users with a professional and engaging listening experience.

Build the Solution

Follow these steps to build a text-to-audio solution using the MongoDB ist.media GitHub repository. You can use this framework as inspiration to build your own customized text-to-audio pipeline.

Key Learnings

To create a media solution that converts news data into audio content, you need a system that is flexible, fast, and able to scale easily. MongoDB makes this possible through these core strengths:

• The document model handles diverse attributes: News data combines various attributes, including static fields such as ID, title, date and body, dynamic metadata such as read count, AI-generated insights such as keywords and article sentiment, and embeddings for semantic search. The document model supports all these elements, removing database limitations and allowing the system to evolve smoothly.

• Speed ensures operational efficiency: By processing complete, self-contained documents, MongoDB avoids complex operations, enabling faster analysis and near real-time transformation of articles into audio content.

• Scalable systems enable growth: MongoDB Atlas handles both small changes and large amounts of data smoothly, ensuring high performance and reliability as your media application grows.

• Flexible systems empower developers: Without fixed schemas, developers can easily add new information, like AI insights, audience metrics, or editorial updates. This makes it simple to adapt and respond to evolving news consumption.

Authors

• Benjamin Lorenz, MongoDB

• Diego Canales, MongoDB

Learn More

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