TaDiCodec: Text-aware Diffusion Speech Tokenizer for Speech Language
  Modeling
Authors: name: Yuancheng Wang, name: Dekun Chen, name: Xueyao Zhang, name: Junan Zhang, name: Jiaqi Li, name: Zhizheng Wu

Speech tokenizers serve as foundational components for speech language
models, yet current designs exhibit several limitations, including: 1)
dependence on multi-layer residual vector quantization structures or high frame
rates, 2) reliance on auxiliary pre-trained models for semantic distillation,
and 3) requirements for complex two-stage training processes. In this work, we
introduce the Text-aware Diffusion Transformer Speech Codec (TaDiCodec), a
novel approach designed to overcome these challenges. TaDiCodec employs
end-to-end optimization for quantization and reconstruction through a diffusion
autoencoder, while integrating text guidance into the diffusion decoder to
enhance reconstruction quality and achieve optimal compression. TaDiCodec
achieves an extremely low frame rate of 6.25 Hz and a corresponding bitrate of
0.0875 kbps with a single-layer codebook for 24 kHz speech, while maintaining
superior performance on critical speech generation evaluation metrics such as
Word Error Rate (WER), speaker similarity (SIM), and speech quality (UTMOS).
Notably, TaDiCodec employs a single-stage, end-to-end training paradigm, and
obviating the need for auxiliary pre-trained models. We also validate the
compatibility of TaDiCodec in language model based zero-shot text-to-speech
with both autoregressive modeling and masked generative modeling, demonstrating
its effectiveness and efficiency for speech language modeling, as well as a
significantly small reconstruction-generation gap. We will open source our code
and model checkpoints. Audio samples are are available at
https:/tadicodec.github.io/. We release code and model checkpoints at
https:/github.com/HeCheng0625/Diffusion-Speech-Tokenizer.