Transcriptions dataset

2024-12-12 · Source: Data Stories · Field: Technology & Digital — Data Science & Analytics, Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

Data Stories, a podcast, has transcribed its 170 episodes, totaling 1,539,957 spoken words, into full written text. This extensive archive includes specific word counts, such as 61 mentions of "weather," 923 mentions of "maps," and 48 mentions of "AI." The project, a collaboration with Miska Knapek, now offers a new archive page for browsing and searching episodes, a data tour, and access to the underlying data and code on GitHub. This initiative significantly enhances the discoverability and accessibility of the podcast's content.

Key takeaway

For podcast producers or content managers with extensive audio libraries, transcribing your back catalog can significantly improve content discoverability and SEO. Consider publishing the raw text and metadata to enable advanced search and analysis, potentially revealing unexpected insights into your content's thematic landscape and audience interests.

Key insights

Transcribing podcast archives enhances content discoverability and provides valuable linguistic data.

Principles

• Full transcription aids content searchability.
• Quantitative analysis reveals topic prevalence.

Method

Transcribe audio content, then analyze word frequencies and make data publicly available.

In practice

• Transcribe existing audio archives.
• Publish data and code for transparency.

Topics

• Podcast Transcription
• Data Stories Archive
• Word Frequency Analysis
• Data Exploration Tools

Code references

• MoritzStefaner/data-stories-archive

Best for: Data Scientist, Software Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Stories.