Back office ops · Production
Building Semantic Search on Podcast Transcripts: Audio Transcription with OpenAI Whisper and Storage in ApertureDB (Part 1)
The problem
The author hosts 20+ AI-focused podcasts and wanted an easy way to search and rediscover knowledge shared across episodes, rather than manually revisiting recordings.
Workflow diagram · grounded in source
1
Upload podcast audio files
trigger
“After you upload the podcast's audio files in the directory, it's time to run the Whisper model and generate the transcripts.”
2
Whisper speech-to-text
ai_action
“The following code snippet iterates through all the audio files you uploaded, transcribes them using Whisper, and saves the transcriptions as text files.”
3
Create transcript metadata CSV
output
“I created a CSV file named 'transcripts_ingestion.csv' (see the screenshot below) containing columns such as 'filename', 'type of file', 'format of file',' page_url', 'constraint_page_url'”
4
Ingest transcripts into ApertureDB
integration
“Next step is to ingest the transcription data into the database instance.”
5
Verify stored transcriptions
validation
“After ingesting the data, we can query the database to ensure the transcriptions have been stored correctly.”
Reported outcome
Part 1 of the series transcribed podcast episodes using OpenAI Whisper and stored them in ApertureDB, with 23 episodes successfully ingested and the majority of transcription content described as accurate.
Reported metrics
Podcast episodes stored in database23
Transcription accuracymajority of the content was pretty accurate
Reported stack
ApertureDBGoogle Colab
Source
https://mlops.community/blog/semantic-search-to-glean-valuable-insights-from-podcasts-part-1
Read source ↗Frequently asked questions
What did this team achieve with this AI workflow?
Part 1 of the series transcribed podcast episodes using OpenAI Whisper and stored them in ApertureDB, with 23 episodes successfully ingested and the majority of transcription content described as accurate.
What tools did this team use?
ApertureDB, Google Colab.
What results were reported?
Podcast episodes stored in database: 23; Transcription accuracy: majority of the content was pretty accurate (source-reported, not independently verified).
How is this back office ops AI workflow structured?
Upload podcast audio files → Whisper speech-to-text → Create transcript metadata CSV → Ingest transcripts into ApertureDB → Verify stored transcriptions.