Who should use the AI Transcription workflow?
Teams or solo builders working on creativity tasks who want a repeatable process instead of one-off tool experiments.
Execution Map
4-step pipeline — follow each step in order
1Setup · Step 1 of 4
Open task pageAudio-to-MIDI transcription
Prepare inputs and settings through Audio-to-MIDI transcription before running ai transcription.
Why this matters
Audio-to-MIDI transcription sets up the foundation for ai transcription; clean inputs here reduce downstream rework.
What you get
Inputs, context, and settings are ready so the workflow can move into execution without blockers.
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2Core · Step 2 of 4
Open task pageAI Transcription
Execute ai transcription with AI Transcription to produce the primary final deliverable.
Why this matters
This is the core step where ai transcription actually happens, so it determines baseline quality for everything after it.
What you get
A first-pass final deliverable is generated and ready for refinement in the next steps.
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3Refine · Step 3 of 4
Open task pageEdit videos by editing the text transcription
Refine and validate ai transcription output using Edit videos by editing the text transcription before final delivery.
Why this matters
Edit videos by editing the text transcription adds quality control so issues are caught before the workflow is finalized.
What you get
The final deliverable is improved, validated, and prepared for final delivery.
4Deliver · Step 4 of 4
Open task pagePodcast Transcription
Package and ship the output through Podcast Transcription so ai transcription reaches end users.
Why this matters
Podcast Transcription is what turns intermediate output into a usable, publishable result for real users.
What you get
A finalized final deliverable is ready for publishing, handoff, or integration.