Who should use the Automatic Speech Recognition workflow?
Teams or solo builders working on work tasks who want a repeatable process instead of one-off tool experiments.
Execution Map
7-step pipeline — follow each step in order
1Setup · Step 1 of 7
Open task pageSpeech recognition
Prepare inputs and settings through Speech recognition before running automatic speech recognition.
Why this matters
Speech recognition sets up the foundation for automatic speech recognition; 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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2Input · Step 2 of 7
Open task pagePerforming speech recognition completely offline
Use Performing speech recognition completely offline to build supporting assets that improve automatic speech recognition quality.
Why this matters
Performing speech recognition completely offline strengthens automatic speech recognition by feeding better supporting material into the pipeline.
What you get
Supporting assets from performing speech recognition completely offline are prepared and connected to the main workflow.
3Input · Step 3 of 7
Open task pageSpeech-to-Text
Use Speech-to-Text to build supporting assets that improve automatic speech recognition quality.
Why this matters
Speech-to-Text strengthens automatic speech recognition by feeding better supporting material into the pipeline.
What you get
Supporting assets from speech-to-text are prepared and connected to the main workflow.
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4Core · Step 4 of 7
Open task pageAutomatic Speech Recognition
Execute automatic speech recognition with Automatic Speech Recognition to produce the primary audio output.
Why this matters
This is the core step where automatic speech recognition actually happens, so it determines baseline quality for everything after it.
What you get
A first-pass audio output is generated and ready for refinement in the next steps.
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5Refine · Step 5 of 7
Open task pageLLM Inference Acceleration
Refine and validate automatic speech recognition output using LLM Inference Acceleration before final delivery.
Why this matters
LLM Inference Acceleration adds quality control so issues are caught before the workflow is finalized.
What you get
The audio output is improved, validated, and prepared for final delivery.
6Refine · Step 6 of 7
Open task pageReal-time Semantic Segmentation
Refine and validate automatic speech recognition output using Real-time Semantic Segmentation before final delivery.
Why this matters
Real-time Semantic Segmentation adds quality control so issues are caught before the workflow is finalized.
What you get
The audio output is improved, validated, and prepared for final delivery.
7Deliver · Step 7 of 7
Open task pageSpeech Synthesis
Package and ship the output through Speech Synthesis so automatic speech recognition reaches end users.
Why this matters
Speech Synthesis is what turns intermediate output into a usable, publishable result for real users.
What you get
A finalized audio output is ready for publishing, handoff, or integration.
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