Zero-Shot Audio Classification Using HuggingFace CLAP Open-Source Model

Author(s): Youssef Hosni

Originally published on Towards AI.

Zero-shot audio classification tasks present a significant challenge in machine learning, particularly when labeled data is scarce. This article explores the application of Hugging Face’s open-source models, specifically the Contrastive Language-Audio Pretraining (CLAP) models, in addressing this task.

The CLAP models leverage contrastive learning techniques to learn representations of audio data without relying on labeled examples during training. The article covers the setup of working environments, building an audio classification pipeline, and considerations such as sampling rates for transformer models. It delves into the architecture and training process of the CLAP models, highlighting their effectiveness in zero-shot audio classification tasks.

Readers interested in zero-shot learning, audio classification, and leveraging pre-trained models for natural language and audio processing tasks will find this article informative and valuable for their research and practical applications.

Setting Up Working EnvironmentsBuild Audio Classification PipelineSampling Rate for Transformer ModelsZero-Shot Audio Classification

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Published via Towards AI