  Recent work has studied text-to-audio synthesis using large amounts of paired
text-audio data. However, audio recordings with high-quality text annotations
can be difficult to acquire. In this work, we approach text-to-audio synthesis
using unlabeled videos and pretrained language-vision models. We propose to
learn the desired text-audio correspondence by leveraging the visual modality
as a bridge. We train a conditional diffusion model to generate the audio track
of a video, given a video frame encoded by a pretrained contrastive
language-image pretraining (CLIP) model. At test time, we first explore
performing a zero-shot modality transfer and condition the diffusion model with
a CLIP-encoded text query. However, we observe a noticeable performance drop
with respect to image queries. To close this gap, we further adopt a pretrained
diffusion prior model to generate a CLIP image embedding given a CLIP text
embedding. Our results show the effectiveness of the proposed method, and that
the pretrained diffusion prior can reduce the modality transfer gap. While we
focus on text-to-audio synthesis, the proposed model can also generate audio
from image queries, and it shows competitive performance against a
state-of-the-art image-to-audio synthesis model in a subjective listening test.
This study offers a new direction of approaching text-to-audio synthesis that
leverages the naturally-occurring audio-visual correspondence in videos and the
power of pretrained language-vision models.
