What is the impact of varying the number of training samples on the accuracy of Qwen-Audio versus Whisper-Larg

Abstract

Abstract: 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 interaction 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

Research Question

What is the impact of varying the number of training samples on the accuracy of Qwen-Audio versus Whisper-Large-V3 and OpenPangu-7B-MLA in low-resource language speech understanding tasks as measured by WER on MMSU?

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