Audio Language Model

Audio Language Models (ALMs) aim to bridge the gap between audio and text data, enabling computers to understand and process sound in a more nuanced way than previously possible. Current research focuses on improving ALMs' performance in various tasks, such as zero-shot audio classification, captioning, and text-to-audio generation, often leveraging contrastive learning and transformer-based architectures. This work is driven by the need for more robust and efficient models capable of handling diverse audio types and languages, with applications ranging from improved speech recognition to more sophisticated human-computer interaction. The development of larger, more versatile ALMs and associated benchmarks is a key area of ongoing effort.