Phase 5: Speech Foundations · ~45 minutes · Python
Audio Fundamentals — Waveforms, Sampling, Fourier Transform
Waveforms are the raw signal. Spectrograms are the representation. Mel features are the ML-friendly form. Every modern ASR and TTS pipeline walks this ladder, and the first rung is understanding sampling and Fourier.
Hiring signal: Understanding of audio fundamentals — waveforms, sampling, fourier transform internals
What you will learn
- Implement audio fundamentals — waveforms, sampling, fourier transform from scratch
- Understand the math and intuition behind the algorithm
- Use production libraries for the same task
- Ship a reusable artifact
Introduction
Type: Learn Languages: Python Prerequisites: Phase 1 · 06 (Vectors & Matrices), Phase 1 · 14 (Probability Distributions) Time: ~45 minutes
The Problem
A microphone produces a pressure-vs-time signal. Your neural net consumes tensors. Between them sits a stack of conventions that, when violated, produce silent bugs: the model trains fine but the WER doubles, or TTS ships a hiss, or a voice cloning system memorizes the microphone instead of the speaker.
Every bug in speech systems traces back to one of three questions:
- What sample rate was the data recorded at, and what does the model expect?
- Is the signal aliased?
- Are you operating on raw samples or on a frequency representation?
Get these right and the rest of Phase 6 is tractable. Get them wrong and even Whisper-Large-v4 produces garbage.
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