Audio Flamingo 2: An Audio-Language Model with Long-Audio Understanding
  and Expert Reasoning Abilities
Authors: name: Sreyan Ghosh, name: Zhifeng Kong, name: Sonal Kumar, name: S Sakshi, name: Jaehyeon Kim, name: Wei Ping, name: Rafael Valle, name: Dinesh Manocha, name: Bryan Catanzaro

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/.