Competitive Audio-Language Models with Data-Efficient Single-Stage Training on Public Data
Abstract
Large language models (LLMs) have transformed NLP, yet their integration with audio remains underexplored despite audio's centrality to human communication. We introduce Falcon3-Audio, a family of Audio-Language Models (ALMs) built on instruction-tuned LLMs and Whisper encoders. Using a remarkably small amount of public audio data, less than 30K hours (5K unique), Falcon3-Audio-7B matches the best reported performance among open-weight models on the MMAU benchmark, with a score of 64.14, matching R1-AQA, while distinguishing itself through superior data and parameter efficiency, single-stage training, and transparency. Notably, our smallest 1B model remains competitive with larger open models ranging from 2B to 13B parameters. Through extensive ablations, we find that common complexities such as curriculum learning, multiple audio encoders, and intricate cross-attention connectors are not required for strong performance, even compared to models trained on over 500K hours of data.
Cite
@article{arxiv.2509.07526,
title = {Competitive Audio-Language Models with Data-Efficient Single-Stage Training on Public Data},
author = {Gokul Karthik Kumar and Rishabh Saraf and Ludovick Lepauloux and Abdul Muneer and Billel Mokeddem and Hakim Hacid},
journal= {arXiv preprint arXiv:2509.07526},
year = {2026}
}
Comments
Accepted at ASRU 2025