CONTENT CREATION
Generate audiograms from podcast highlight clips
When a new episode audio file lands in S3, this picks the most quotable 30-60 second moments, renders them as waveform audiograms.
How it runs
The automated pipeline, trigger to output.
- TriggerNew episode audio uploaded to S3AWS S3
- ActionTranscribe audio to timestamped transcriptOpenAI
- ActionScore and select top quotable segmentsOpenAI
- ActionRender waveform audiograms with captions
- LogicDrop clips below confidence threshold
- OutputWrite finished clips to review prefix in S3AWS S3
What it does
Turns a long-form podcast episode into a handful of short, captioned audiogram video clips you can post to social. It finds the punchiest moments from the transcript, renders each as a branded waveform video with burned-in captions, and stages them for review.
When to use it
Run it on every episode when your team wants social-ready short clips without an editor manually scrubbing the timeline. Best for shows that publish weekly and need 3-5 promo clips per drop.
How it works
- 1A new episode audio file uploaded to your S3 bucket triggers the run.
- 2The audio is transcribed to a timestamped transcript via OpenAI Whisper.
- 3An LLM scores segments and selects the 4 most quotable 30-60s windows.
- 4Each window is rendered as a waveform audiogram with burned-in captions.
- 5A filter drops any clip whose confidence score falls below threshold.
- 6The finished MP4 clips are written back to an S3 "ready-for-review" prefix.
Set it up
What you configure once, before turning it on.
- 1Connect AWS S3Buckets, objects, signed URLs.
- 2Connect OpenAIModels, embeddings, files.
- 3Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 4Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 5Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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