SemantiCodec: An Ultra Low Bitrate Semantic Audio Codec for General Sound
This work addresses the need for efficient, low-bitrate audio compression with rich semantic clues for applications in audio processing and language modeling, representing a novel advancement rather than an incremental improvement.
The paper tackles the problem of high bitrates and lack of semantic information in traditional audio codecs by introducing SemantiCodec, which compresses audio into fewer than 100 tokens per second across diverse types like speech and music, achieving ultra-low bitrates of 0.31-1.40 kbps and outperforming the state-of-the-art Descript codec in reconstruction quality.
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 sound, and music, without compromising quality. SemantiCodec features a dual-encoder architecture: a semantic encoder using a self-supervised pre-trained Audio Masked Autoencoder (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 1.40 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 state-of-the-art audio codecs, even at significantly lower bitrates. Our code and demos are available at https://haoheliu.github.io/SemantiCodec/.