Put what you have learned in this course into practice with the structured exercise below. Working through it end-to-end is the single best way to consolidate the concepts.

Exercise: Speech To Text Ai in the Real World

Pick one realistic scenario from your own work, study, or curiosity and apply the techniques covered in earlier lessons. Aim for a small, completable artifact — a notebook, a working script, a written design, or a side-by-side comparison.

Suggested Workflow

1. Frame the goal: write a one-sentence problem statement that the techniques in this course can address.
2. Pick the right tool: revisit the lessons above and choose the specific method, model, or pattern that best fits.
3. Build an MVP: produce the simplest version that proves the approach works. Keep it small.
4. Evaluate: how did it perform? What surprised you? What broke?
5. Iterate: refine one decision (prompt, parameter, tool) and re-evaluate.

Self-Check Questions

• Could you explain the chosen approach to a teammate in under two minutes?
• Where might this break in production? What guardrails would you add?
• What is the next thing you would try if you had another day?

Stretch Goal

Document your run as a short write-up. Compare against alternative approaches and note the trade-offs.

This practice loop converts passive reading into durable skill — the exact transfer-of-learning step most learners skip.

Concrete Starter Project: Build a Meeting Notes Generator

Goal: Build a web app that accepts an audio file, transcribes it with speaker diarization, and generates structured meeting notes using an LLM.

Steps:

1. Create a Next.js API route that accepts audio file uploads up to 100MB.
2. Send the audio to AssemblyAI with speaker_labels: true and auto_chapters: true.
3. Poll the AssemblyAI API until transcription is complete.
4. Pass the transcript (with speaker labels) to GPT-4o to extract: summary, action items, decisions, and next steps.
5. Display the output in a clean two-column layout: full transcript on the left, AI summary on the right.

Extend it:

• Add speaker name assignment (let users label "Speaker A" as "Alice" and see the transcript update).
• Cache transcripts by file hash to avoid re-processing identical uploads.
• Email the meeting notes to attendees using Resend after processing completes.

What you will learn: End-to-end audio AI pipeline design including file handling, async processing, speaker identification, and LLM-based analysis.