  Recent advancements in audio generation have enabled the creation of
high-fidelity audio clips from free-form textual descriptions. However,
temporal relationships, a critical feature for audio content, are currently
underrepresented in mainstream models, resulting in an imprecise temporal
controllability. Specifically, users cannot accurately control the timestamps
of sound events using free-form text. We acknowledge that a significant factor
is the absence of high-quality, temporally-aligned audio-text datasets, which
are essential for training models with temporal control. The more
temporally-aligned the annotations, the better the models can understand the
precise relationship between audio outputs and temporal textual prompts.
Therefore, we present a strongly aligned audio-text dataset, AudioTime. It
provides text annotations rich in temporal information such as timestamps,
duration, frequency, and ordering, covering almost all aspects of temporal
control. Additionally, we offer a comprehensive test set and evaluation metric
to assess the temporal control performance of various models. Examples are
available on the https://zeyuxie29.github.io/AudioTime/
