Phase 5: Speech Foundations · ~75 minutes · Python

Whisper — Architecture & Fine-Tuning

Whisper is a 30-second-window transformer encoder-decoder, trained on 680k hours of multilingual weakly-supervised audio-text pairs. One architecture, multiple tasks, robust across 99 languages. The 2026 reference ASR.

Hiring signal: Understanding of whisper — architecture & fine-tuning internals

What you will learn

Implement whisper — architecture & fine-tuning from scratch
Understand the math and intuition behind the algorithm
Use production libraries for the same task
Ship a reusable artifact

Introduction

Type: Build Languages: Python Prerequisites: Phase 6 · 04 (ASR), Phase 5 · 10 (Attention), Phase 7 · 05 (Full Transformer) Time: ~75 minutes

The Problem

Whisper, released by OpenAI in September 2022, was the first ASR model to ship as a commodity: paste audio, get text, 99 languages, robust to noise, runs on a laptop. By 2024 OpenAI had shipped Large-v3 and Turbo variants; by 2026, Whisper is the default baseline for everything from podcast transcription to voice assistants to YouTube subtitles.

But Whisper is not a pipeline you can treat as a black box forever. Domain shift kills it — technical jargon, speaker accents, proper nouns, short clips, silence. You need to know:

What it actually is inside.
How to give it chunked, streaming, or long-form audio correctly.
When to fine-tune and how.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build It, Use It, Pitfalls that still ship in 2026, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

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