The paper introduces Audio Flamingo, a novel audio language model designed to enhance the understanding of audio, including non-speech sounds and non-verbal speech. The model is equipped with three key capabilities: strong audio understanding, in-context learning (ICL) and retrieval augmented generation (RAG), and multi-turn dialogue abilities. To achieve these capabilities, the authors propose a series of training techniques, architectural designs, and data strategies. Extensive evaluations across various audio understanding tasks demonstrate the effectiveness of Audio Flamingo, setting new state-of-the-art benchmarks. The model's ability to understand audio, adapt to new tasks, and engage in multi-turn dialogues is highlighted, with detailed results and comparisons provided in the paper. The authors also discuss the neural architecture and hyperparameters used in the experiments, and outline future directions for improving the model's performance and expanding its capabilities.The paper introduces Audio Flamingo, a novel audio language model designed to enhance the understanding of audio, including non-speech sounds and non-verbal speech. The model is equipped with three key capabilities: strong audio understanding, in-context learning (ICL) and retrieval augmented generation (RAG), and multi-turn dialogue abilities. To achieve these capabilities, the authors propose a series of training techniques, architectural designs, and data strategies. Extensive evaluations across various audio understanding tasks demonstrate the effectiveness of Audio Flamingo, setting new state-of-the-art benchmarks. The model's ability to understand audio, adapt to new tasks, and engage in multi-turn dialogues is highlighted, with detailed results and comparisons provided in the paper. The authors also discuss the neural architecture and hyperparameters used in the experiments, and outline future directions for improving the model's performance and expanding its capabilities.