SpeechTokenizer: Unified Speech Tokenizer for Speech Language Models
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Keywords: speech, audio, multi-modal, large language model
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Abstract: 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
https://github.com/ZhangXInFD/SpeechTokenizer/.
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Primary Area: representation learning for computer vision, audio, language, and other modalities
Submission Number: 338
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