LAION-AI/CLAP

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2256 stars · 214 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

CLAP (Contrastive Language-Audio Pretraining) is a Python library from LAION that learns joint audio and text embeddings using contrastive learning, trained on the LAION-Audio-630K dataset. It enables extracting latent representations of audio and text for downstream tasks such as text-to-audio retrieval and zero-shot audio classification, and is also integrated into HuggingFace Transformers.

Use cases

• find audio clips matching a text description
• compute embeddings for audio files and text queries
• zero-shot classify sounds or music by label prompts
• build a text-to-audio search engine
• extract audio features for a downstream model
• retrieve music by natural language queries

When to choose

• you need joint audio-text embeddings for retrieval or classification
• you want zero-shot audio classification without training a custom classifier
• you want a CLIP-style model for the audio modality with pretrained checkpoints

When to avoid

• you need image or video understanding rather than audio
• you need speech-to-text transcription rather than semantic audio representation
• you cannot run GPU inference or handle large model checkpoints

Facets

library · maturity stable

machine-learning audio-processing nlp search-engine machine-learning deep-learning python cross-platform contrastive-learning multimodal audio-text-retrieval zero-shot-classification laion pytorch pretrained-models embedding audio natural-language-processing gpu

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