Audio Processing

Audio processing is the foundation layer beneath every voice feature: how sound becomes data a model can work with. Audio is captured as a waveform — thousands of amplitude samples per second — and usually transformed into a spectrogram, a time-frequency picture that machine learning models treat much like an image. Concepts like sample rate, channels, codecs, and chunking determine what your pipeline can and can’t do before any AI is involved.

You need this grounding because audio pipelines fail in ways text pipelines don’t. A transcription that works on a clean WAV file falls apart on a compressed phone recording; a real-time voice agent stutters because you buffered audio in the wrong chunk sizes; costs explode because you sent stereo 48 kHz audio where mono 16 kHz would do. Understanding the representation lets you diagnose these problems instead of blindly swapping models, and it explains the constraints — file-size limits, supported formats — that every speech API imposes.

In practice, you’ll use ffmpeg and libraries like pydub or librosa to inspect, convert, resample, and split audio before it touches a model. You’ll build the standard preprocessing steps — normalize format, downmix to mono, segment long recordings at silence boundaries — that feed the speech-to-text and text-to-speech topics coming next. Treat this as the plumbing course: unglamorous, but it’s what makes the Whisper pipeline you build later actually reliable.