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Mainstream Audio Analytics models are trained to learn under the paradigm ofone class label to many recordings focusing on one task. Learning under suchrestricted supervision limits the flexibility of models because they requirelabeled audio for training and can only predict the predefined categories.Instead, we propose to learn audio concepts from natural language supervision.We call our approach Contrastive Language-Audio Pretraining (CLAP), whichlearns to connect language and audio by using two encoders and a contrastivelearning to bring audio and text descriptions into a joint multimodal space. Wetrained CLAP with 128k audio and text pairs and evaluated it on 16 downstreamtasks across 8 domains, such as Sound Event Classification, Music tasks, andSpeech-related tasks. Although CLAP was trained with significantly less pairsthan similar computer vision models, it establishes SoTA for Zero-Shotperformance. Additionally, we evaluated CLAP in a supervised learning setup andachieve SoTA in 5 tasks. Hence, CLAP's Zero-Shot capability removes the need oftraining with class labels, enables flexible class prediction at inferencetime, and generalizes to multiple downstream tasks.

CLAP: Learning Audio Concepts From Natural Language Supervision