Recently, instruction-following audio-language models have received broad attention
for audio interaction with humans. However, the absence of pre-trained audio models
capable of handling diverse audio types and tasks has hindered progress in this field.
Consequently, most existing works have only been able to support a limited range of inter-
action capabilities. In this paper, we develop the Qwen-Audio model and address this
limitation by scaling up audio-language pre-training to cover over 30 tasks and various
audio types, such as human speech, natural sounds, music, and songs, to facilitate uni-
versal audio understanding abilities. However, directly co-training all tasks and datasets
can lead to interference issues, as the textual labels associated with different datasets
exhibit considerable variations due to differences in task focus, language, granularity of
annotation, and text structure. To overcome the one-to-many interference, we carefully
Qwen-Audio