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%2C a hierarchical transformer designed for efficient speech language modeling. GPST quantizes audio waveforms into two distinct types of discrete speech representations and integrates them within a hierarchical transformer architecture%2C allowing for a unified one-stage generation process and enhancing Hi-Res audio generation capabilities. By training on large corpora of speeches in an end-to-end unsupervised manner%2C GPST can generate syntactically consistent speech with diverse speaker identities. Given a brief 3-second prompt%2C GPST can produce natural and coherent personalized speech%2C demonstrating in-context learning abilities. Moreover%2C our approach can be easily extended to spoken cross-lingual speech generation by incorporating multi-lingual semantic tokens and universal acoustic tokens. Experimental results indicate that GPST significantly outperforms the existing speech language models in terms of word error rate%2C speech quality%2C and speaker similarity. See url{https:%2F%2Fyoungsheen.github.io%2FGPST%2Fdemo} for demo samples.?quality=80&w=800)
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