Neural audio codecs compress audio into discrete tokens, enabling efficient storage and language model-style generation. How it works: Encoder compresses audio waveform to low-bitrate discrete codes, decoder reconstructs from codes. Vector quantization creates codebook of audio tokens. Key models: EnCodec (Meta), SoundStream (Google), DAC (Descript Audio Codec). Technical details: Residual Vector Quantization (RVQ) uses multiple codebooks for refinement, convolutional encoder/decoder, trainable codebooks. Compression rates: 1.5-24 kbps (vs 1400 kbps for CD), extreme compression with good quality. For generation: Audio tokens become vocabulary for language models. Generate token sequences, decode to audio. Foundation for AudioLM, MusicLM, Bark. Advantages: Unified representation for all audio (speech, music, sounds), compatible with transformer architectures, efficient generation. Applications: Audio compression, audio generation, neural voice synthesis, music generation. Comparison to traditional codecs: MP3/AAC use hand-designed transforms, neural codecs learn optimal compression. Revolutionary for audio AI.