Large language models (LLMs) have significantly advanced audio processing
through audio codecs that convert audio into discrete tokens, enabling the
application of language modelling techniques to audio data. However,
traditional codecs often operate at high bitrates or within narrow domains such
as speech and lack the semantic clues required for efficient language
modelling. Addressing these challenges, we introduce SemantiCodec, a novel
codec designed to compress audio into fewer than a hundred tokens per second
across diverse audio types, including speech, general audio, and music, without
compromising quality. SemantiCodec features a dual-encoder architecture: a
semantic encoder using a self-supervised AudioMAE, discretized using k-means
clustering on extensive audio data, and an acoustic encoder to capture the
remaining details. The semantic and acoustic encoder outputs are used to
reconstruct audio via a diffusion-model-based decoder. SemantiCodec is
presented in three variants with token rates of 25, 50, and 100 per second,
supporting a range of ultra-low bit rates between 0.31 kbps and 1.43 kbps.
Experimental results demonstrate that SemantiCodec significantly outperforms
the state-of-the-art Descript codec on reconstruction quality. Our results also
suggest that SemantiCodec contains significantly richer semantic information
than all evaluated audio codecs, even at significantly lower bitrates. Our code
and demos are available at this https URL

利用语言模型和音频编解码技术，SemantiCodec 是一种能够以非常低的比特率将各种类型的音频压缩为每秒少于一百个标记的新型编解码器，具有更高的重构质量和更丰富的语义信息。