Audio-text pre-training (ATP) has witnessed remarkable strides across a variety of downstream tasks. Yet, most existing pretrained audio models only specialize in either discriminative tasks or generative tasks. In this study, we develop SLIT, a novel ATP framework which transfers flexibly to both audio-text understanding and generation tasks, bootstrapping audio-text pre-training from frozen pretrained audio encoders and large language models. To bridge the modality gap during pre-training, we leverage Q-Former, which undergoes a multi-stage pre-training process. The first stage enhances audio-text representation learning from a frozen audio encoder, while the second stage boosts audio-to-text generative learning with a frozen language model. Furthermore, we introduce an ATP instruction tuning strategy, which enables flexible and informative feature extraction tailered to the given instructions for different tasks. Experiments show that SLIT achieves superior performances on a variety of audio-text understanding and generation tasks, and even demonstrates strong generalization capabilities when directly applied to zero-shot scenarios.

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