WavTokenizer: an Efficient Acoustic Discrete Codec Tokenizer for Audio Language Modeling

Language models have been effectively applied to modeling natural signals, such as images, video, speech, and audio. A crucial component of these models is the tokenizer, which compresses high-dimensional natural signals into lower-dimensional discrete tokens. In this paper, we introduce WavTokenizer, which offers several advantages over previous state-of-the-art (SOTA) acoustic codec models in the audio domain: 1) extreme compression. By compressing the layers of quantizers and the temporal dimension of the discrete codec, one-second audio of 24kHz sampling rate requires only a single quantizer with 40 or 75 tokens. 2) improved subjective reconstruction quality. Despite the reduced number of tokens, WavTokenizer achieves SOTA reconstruction quality with outstanding UTMOS scores and also inherently contains richer semantic information. Specifically, we achieve these results by designing a broader VQ space, extending contextual windows, improving attention networks, and introducing a powerful multi-scale discriminator and an inverse Fourier transform structure. We conduct extensive reconstruction experiments in the domains of speech, audio, and music. WavTokenizer exhibits competitive to superior performance across various objective and subjective metrics compared to SOTA models. We also evaluate WavTokenizer on semantic representation, VQ utilization, and adaptability to generative models. Comprehensive ablation studies confirm the necessity of each module in WavTokenizer.