How to transcribe a podcast with AI: A step-by-step guide

Podcast transcription converts spoken episodes into written, speaker-labeled, time-stamped text that improves accessibility, search visibility, content reuse, and archive searchability, while also supporting transcript features in services such as Apple Podcasts and Spotify. The guide recommends a workflow of exporting high-quality audio, selecting AI, human, or hybrid transcription, configuring settings such as language detection, speaker diarization, multichannel processing, key terms, and audio-event tags, reviewing the output, and exporting it in an appropriate format. Clean recordings with separate speaker tracks, limited overlap, minimal background music, and supplied names or jargon can improve accuracy, while human review is advised for difficult audio, specialized terminology, or sensitive quotations. HTML and plain text are suited to websites and written content, SRT and VTT support captions and podcast platforms, and JSON provides detailed timing data for developers. Transcripts can also be repurposed into show notes, blog posts, social media excerpts, chapters, and clips, with complete website transcripts serving as an additional search asset.

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