Audio Flamingo 3: Advancing Audio Intelligence with Fully Open Large
  Audio Language Models
Authors: name: Arushi Goel, name: Sreyan Ghosh, name: Jaehyeon Kim, name: Sonal Kumar, name: Zhifeng Kong, name: Sang-gil Lee, name: Chao-Han Huck Yang, name: Ramani Duraiswami, name: Dinesh Manocha, name: Rafael Valle, name: Bryan Catanzaro

We present Audio Flamingo 3 (AF3), a fully open state-of-the-art (SOTA) large
audio-language model that advances reasoning and understanding across speech,
sound, and music. AF3 introduces: (i) AF-Whisper, a unified audio encoder
trained using a novel strategy for joint representation learning across all 3
modalities of speech, sound, and music; (ii) flexible, on-demand thinking,
allowing the model to do chain-of-thought-type reasoning before answering;
(iii) multi-turn, multi-audio chat; (iv) long audio understanding and reasoning
(including speech) up to 10 minutes; and (v) voice-to-voice interaction. To
enable these capabilities, we propose several large-scale training datasets
curated using novel strategies, including AudioSkills-XL, LongAudio-XL,
AF-Think, and AF-Chat, and train AF3 with a novel five-stage curriculum-based
training strategy. Trained on only open-source audio data, AF3 achieves new
SOTA results on over 20+ (long) audio understanding and reasoning benchmarks,
surpassing both open-weight and closed-source models trained on much larger
datasets.