task and multi-dataset co-training arises from the considerable variation in textual labels associated with
different datasets. This variation stems from differences in task objectives, languages, annotation granularity,
and text structure (structured or unstructured). To address this one-to-many challenge, we have carefully
designed a multi-task training framework that conditions the decoder on a sequence of hierarchical tags. This
design encourages knowledge sharing and helps mitigate interference through shared and specified tags,
respectively. Furthermore, we incorporate the speech recognition with the word-level time-stamp prediction
(SRWT) task for training, which is usually ignored in previous multi-task learning research. We find the task
not only improves the grounding and grounding-based QA tasks beyond speech signals such as sound and
music, but also improves the performance of ASR. As shown in Figure 1, extensive evaluation demonstrates
Qwen-Audio