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Audio Flamingo 2 (AF2) is introduced as an Audio-Language Model (ALM) that enhances audio understanding and reasoning by utilizing a custom CLAP model, synthetic Audio QA data, and a multi-stage curriculum learning strategy. AF2 achieves state-of-the-art performance on over 20 benchmarks with a 3B parameter model, outperforming larger models. The work also introduces LongAudio, a new dataset for training ALMs on long audio segments (30 secs to 5 mins), and demonstrates exceptional performance on the LongAudioBench benchmark after fine-tuning AF2.
A 3B parameter model, Audio Flamingo 2, now rivals larger proprietary models in audio understanding and reasoning, even handling audio segments up to 5 minutes long.
Understanding and reasoning over non-speech sounds and music are crucial for both humans and AI agents to interact effectively with their environments. In this paper, we introduce Audio Flamingo 2 (AF2), an Audio-Language Model (ALM) with advanced audio understanding and reasoning capabilities. AF2 leverages (i) a custom CLAP model, (ii) synthetic Audio QA data for fine-grained audio reasoning, and (iii) a multi-stage curriculum learning strategy. AF2 achieves state-of-the-art performance with only a 3B parameter small language model, surpassing large open-source and proprietary models across over 20 benchmarks. Next, for the first time, we extend audio understanding to long audio segments (30 secs to 5 mins) and propose LongAudio, a large and novel dataset for training ALMs on long audio captioning and question-answering tasks. Fine-tuning AF2 on LongAudio leads to exceptional performance on our proposed LongAudioBench, an expert annotated benchmark for evaluating ALMs on long audio understanding capabilities. We conduct extensive ablation studies to confirm the efficacy of our approach. Project Website: https://research.nvidia.com/labs/adlr/AF2/.
NVIDIA