SpeechTokenizer: Unified Speech Tokenizer for Speech Large Language Models
CoRR(2023)
摘要
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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关键词
unified speechtokenizer speechtokenizer,speechtokenizer large language
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