Current speech large language models build upon discrete speech
representations, which can be categorized into semantic tokens and acoustic
tokens. However, existing speech tokens are not specifically designed for
speech language modeling. To assess the suitability of speech tokens for
building speech language models, we established the first benchmark,
SLMTokBench. Our results indicate that neither semantic nor acoustic tokens are
ideal for this purpose. Therefore, we propose SpeechTokenizer, a unified speech
tokenizer for speech large language models. SpeechTokenizer adopts the
Encoder-Decoder architecture with residual vector quantization (RVQ). Unifying
semantic and acoustic tokens, SpeechTokenizer disentangles different aspects of
speech information hierarchically across different RVQ layers. Furthermore, We
construct a Unified Speech Language Model (USLM) leveraging SpeechTokenizer.
Experiments show that SpeechTokenizer performs comparably to EnCodec in speech
reconstruction and demonstrates strong performance on the SLMTokBench
benchmark. Also, USLM outperforms VALL-E in zero-shot Text-to-Speech tasks.
Code and models are available at
this https URL

通过对语音令牌进行编码解码架构的统一建模和分层信息表征，提出了 SpeechTokenizer，用于语音大型语言模型的训练，其在语音重构和零样本语音合成任务中表现出色，相较于传统的语义和声学令牌在建模效果上有显著优势。