Phase 2: Diffusion Models · ~45 minutes · Python

Audio Generation

Audio is a 1-D signal at 16-48 kHz. A five-second clip is 80-240k samples. No transformer attends to that sequence directly. The solution for every production audio model in 2026 is the same: a neural codec (Encodec, SoundStream, DAC) compresses audio to discrete tokens at 50-75 Hz, and a transformer or diffusion model generates tokens.

Hiring signal: Understanding of audio generation internals

What you will learn

Implement audio generation 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 · 02 (Audio Features), Phase 6 · 04 (ASR), Phase 8 · 06 (DDPM) Time: ~45 minutes

The Problem

Three audio generation tasks:

Text-to-speech. Given text, produce speech. Clean speech is narrow-band and has strong phonetic structure — solved well by transformer-over-tokens. VALL-E (Microsoft), NaturalSpeech 3, ElevenLabs, OpenAI TTS.
Music generation. Given a prompt (text, melody, chord progression, genre), produce music. Much broader distribution. MusicGen (Meta), Stable Audio 2.5, Suno v4, Udio, Riffusion.
Audio effects / sound design. Given a prompt, produce ambient sound or Foley. AudioGen, AudioLDM 2, Stable Audio Open.

All three run on the same substrate: neural audio codec + token-AR or diffusion generator.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Production landscape, Build It, Pitfalls, Use It, Ship It, Exercises, Key Terms, Production note: audio is a streaming problem, Further Reading — plus a hands-on lab, quiz, and project artifact.

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Key terms in this lesson

Token