Generative Pre-Trained Speech Language Model with Efficient Hierarchical Transformer

Abstract

While recent advancements in speech language modeling have achieved significant progress, they face remarkable challenges in modelling the long acoustic sequence of neural audio codecs. Previous speech language models are compelled to learn acoustic tokens through a multi-stage generation process, which hinders their performance due to error propagation and information loss. In this paper, we introduce \textbf{G}enerative \textbf{P}re-Trained \textbf{S}peech Language Model (GPST), 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, allowing for a unified one-stage generation process and enhancing Hi-Res audio generation capabilities. By training on large corpora of raw audio waveforms in an end-to-end unsupervised manner, GPST can generate syntactically consistent speech with diverse speaker identity unconditionally. When provided a brief 3-second prompt, GPST is able to produce natural and coherent personalized speech, demonstrating in-context learning abilities. Moreover, 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, speech quality and speaker similarity.