Neural Audio Codecs — EnCodec, SNAC, Mimi, DAC and the Semantic-Acoustic Split
2026 audio generation is almost all tokens. EnCodec, SNAC, Mimi, and DAC turn continuous waveforms into discrete sequences that a transformer can predict. The semantic-vs-acoustic token split — first-codebook as semantic, rest as acoustic — is the most important architectural shift since the Transformer for audio. Language models work on discrete tokens. Audio is continuous. If you want an LLM-style model for speech / music — MusicGen, Moshi, Sesame CSM, VibeVoice, Orpheus — you first need a neural audio codec: a learned encoder that discretizes audio into a small vocabulary of tokens, and a matching decoder that reconstructs the waveform. Two families have emerged: Reconstruction-first codecs — EnCodec, DAC. Optimize perceptual audio quality. Tokens are "acoustic" — they capture everything including speaker identity, timbre, background noise. Semantic-first codecs — Mimi (Kyutai), SpeechTokenizer. Force the first codebook to encode linguistic / phonetic content (often by distilling from WavLM). Subsequent codebooks are acoustic detail. The 2024-2026 insight: a pure reconstruction codec gives you blurry speech when you try to generate from text. The LLM over codec tokens has to learn both language structure AND acoustic structure in the same codebook, which doesn't scale. Separating them — semantic codebook 0, acoustic codebooks 1-N — is what makes Moshi and Sesame CSM work. Four codec landscape: EnCodec, DAC, SNAC (multi-scale), Mimi (semantic+acoustic) Rather than…
Neural Audio Codecs — EnCodec, SNAC, Mimi, DAC and the Semantic-Acoustic Split: 2026 audio generation is almost all tokens. EnCodec, SNAC, Mimi, and DAC turn…
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