How Audio Codecs Turn Sound Into Tokens
Traditional spoken dialogue systems rely on a cascaded architecture. This setup chains multiple independent components together: Voice Activity Detection (VAD) identifies when a user speaks, Speech-to-Text (STT) transcribes the audio waveform into text, a text-based Large Language Model (LLM) generates a textual response, and Text-to-Speech (TTS) synthesizes that output back into an audio signal. This chain reaction causes two massive headaches. For starters, stacking all these independent programs creates a noticeable, awkward lag. On top of that, converting a human voice into plain text completely strips away the soul of the conversation. You lose emotional nuance, the sarcasm, the sighs, and the background environment. To overcome these limitations, modern architectures integrate audio processing directly into the transformer trunk. By treating brief snippets of audio waveforms as discrete tokens, a single model can process text and sound simultaneously.
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