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Language models (LMs) have recently flourished in natural language processingand computer vision, generating high-fidelity texts or images in various tasks.In contrast, the current speech generative models are still strugglingregarding speech quality and task generalization. This paper presents Vec-TokSpeech, an extensible framework that resembles multiple speech generationtasks, generating expressive and high-fidelity speech. Specifically, we proposea novel speech codec based on speech vectors and semantic tokens. Speechvectors contain acoustic details contributing to high-fidelity speechreconstruction, while semantic tokens focus on the linguistic content ofspeech, facilitating language modeling. Based on the proposed speech codec,Vec-Tok Speech leverages an LM to undertake the core of speech generation.Moreover, Byte-Pair Encoding (BPE) is introduced to reduce the token length andbit rate for lower exposure bias and longer context coverage, improving theperformance of LMs. Vec-Tok Speech can be used for intra- and cross-lingualzero-shot voice conversion (VC), zero-shot speaking style transfertext-to-speech (TTS), speech-to-speech translation (S2ST), speech denoising,and speaker de-identification and anonymization. Experiments show that Vec-TokSpeech, built on 50k hours of speech, performs better than other SOTA models.Code will be available at https://github.com/BakerBunker/VecTok .

Vec-Tok Speech: speech vectorization and tokenization for neural speech generation