The Problem
A huge share of crypto discourse happens in long-form audio — podcasts and live shows — that is completely invisible to search and analytics.
Knowing which speakers discuss which coins, and when, means transcribing hours of audio, separating who said what, and joining it to live market data. None of that exists off the shelf.
Our Solution
We built a pipeline that turns crypto podcasts into structured, searchable intelligence.
From audio to attributed transcript
- Episodes are tracked in a lightweight queue that only processes new, unhandled recordings.
- Long recordings are split into manageable chunks and transcribed with cloud speech-to-text using speaker diarization — so every line is attributed to a speaker, building per-speaker profiles across episodes.
- Transcripts are persisted to cloud storage and a relational database.
Joined to live market data
A parallel job continuously ingests the top cryptocurrencies and their metadata from a market-data API into a canonical reference table — giving the transcript layer a controlled vocabulary to match against, so conversations can be linked to specific coins.
Outcome
Hours of unsearchable crypto audio become structured, speaker-attributed intelligence joined to live market data — making it possible to see which voices are talking about which coins across the podcast landscape.
Tools & Technologies
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