Step01
Set Up Live Transcription
Continuously capture audio from the user's microphone and convert it into a live text stream. This stream will be the input for your decision model.
IntermediateProduct
How to Build a Real-Time Voice-Controlled To-Do List App with Jev
Create a fluid, hands-free to-do list that updates instantly as you speak. Use a multi-step classification process to understand commands, match tasks, and execute functions in real-time without needing to pause.
Before you start
Step by step
The workflow
Follow the sequence once, then adapt the prompts, checks, and handoffs to your own setup.
5 steps
Step02
Classify Action Intent
Feed the transcribed text stream to a Jev model. Configure it to analyze the incoming words and decide when a complete, actionable command has been stated, without requiring the user to pause.
This step is crucial for creating a fluid user experience. The model needs to determine the boundary of a command within a continuous stream of speech.
Step03
Fuzzy Match the Task
Once Jev decides a command has been given, take the relevant text (e.g., 'oat milk') and use a second Jev call to perform a fuzzy match against the existing items in your to-do list. The model should return the best match and a confidence score.
Step04
Match the Function
Simultaneously, use a third Jev call to identify the operation the user wants to perform (e.g., 'remove', 'complete', 'low priority'). Match this operation to a corresponding function in your application's code, like removeTask() or setPriority().
Example prompt
Book dentist appointment, remove. Buy oat milk, complete. Review pull request, low priority.
Step05
Execute the Action
Using the matched task from Step 3 and the matched function from Step 4, execute the corresponding code to update the to-do list state. Update the UI after the action succeeds. End-to-end latency depends on transcription, model calls, and your application.
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