Leveraging transcription for accurate podcast content search and matching
The project
After building a stable and reliable platform that provides real-time searching and analysis on a large dataset of news articles and posts from various sources, Clipit began expanding its datasets to include new content types—specifically, podcasts.
As the podcast industry continues to grow rapidly, monitoring these audio channels has become increasingly essential for PR and communication professionals, as well as marketers.
This project focused on gathering new content from podcasts by developing a service that processes RSS podcast feeds added by media analysts at Clipit retrieves the MP3 files, transcribes them into text, and presents the content based on specific matching criteria.
The goal
The main goal was to enrich the content currently provided to clients by including new audio items and offering specific functionalities that allow for smooth navigation throughout podcast audio files. This functionality provides organizations with deeper insights into the value and relevance of podcast content related to set search queries, leveraging the power of precise content matching and sentiment analysis.
The challenge
Developing efficient methods for scanning and matching podcast content was crucial for effective content curation and user engagement.
Extending and refining current implementations to handle diverse content types, such as audio, while maintaining existing performance standards.
Ensuring a seamless and intuitive user experience when presenting new types of content on the platform.
Choosing the most accurate speech-to-text solution to ensure reliable transcription and accessibility.
Maintaining the platform’s high-performance standards, even with the addition of new content processing methods.
The result
In the initial phase, the team carefully evaluated several speech-to-text solutions and selected the most suitable one for accurate and reliable podcast transcription.
During implementation, a new audio grabber service was developed to manage file downloading, type identification, audio extraction, and transcription. This service was integrated into Clipit’s codebase, with improvements made to the matching process to handle specific timestamps, ensuring accuracy and relevance in search results.
In the final phase, the focus shifted to enhancing the user experience on the dashboard by offering playback options for entire podcasts or specific matched segments. This integration not only expands the range of monitored media but also provides actionable insights through advanced transcription and matching techniques, making podcasts a valuable component of the real-time analytics platform.
Clipit successfully integrated podcasts into its real-time analytics platform by developing a new service that processes RSS feeds, transcribes audio, and presents the content based on specific matching criteria. This project enriched the platform's offerings, ensuring smooth navigation and maintaining performance standards.
