{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"Extracts learned audio and text embeddings using contrastive language-audio pretraining, enabling cross-modal retrieval and downstream audio-text tasks.","skillfed_tags":["audio-embeddings","multimodal-learning","zero-shot-classification"],"use_cases":["Zero-shot audio classification by embedding audio and comparing to embeddings of class labels as text.","Cross-modal retrieval: find audio clips matching a text query or vice versa by embedding both and ranking by similarity.","Audio-text dataset annotation: embed a large audio corpus and use text queries to discover or label relevant clips.","Feature extraction for downstream models: use CLAP embeddings as fixed input features for audio classification or tagging tasks.","Music or speech recognition: use pretrained checkpoints tuned for music or speech to embed domain-specific audio."],"what_it_does":"CLAP is a PyPI package that wraps Contrastive Language-Audio Pretraining models, allowing you to generate fixed-size embeddings for both audio files and text descriptions. The embeddings are trained to align semantically\u2014audio and text describing the same content will have similar embeddings in the learned space. You load a pretrained checkpoint (trained on AudioSet, music, speech, or combinations thereof), then call methods to embed audio from files or raw data, or to embed text strings. The package handles the model architecture, quantization, and checkpoint management internally.\n\nThe typical workflow is to extract embeddings for a corpus of audio and text, then use those embeddings for tasks like zero-shot audio classification (by comparing audio embeddings to class-label text embeddings), cross-modal retrieval, or as features for downstream supervised models. The package depends on librosa for audio loading, transformers for text encoding, and PyTorch for the model itself, plus 15 other utilities for data handling and training infrastructure.","worth_installing":"Yes, with conditions. Install if you need audio-text embeddings and are comfortable with the 18 dependencies and aging maintenance status. The package is permissively licensed, has no known vulnerabilities, and offers multiple pretrained models for different audio domains. However, verify that the HuggingFace checkpoints you need are still available, and be aware that the last release was 467 days ago\u2014if you encounter bugs or need updates, community support may be limited."},"id":"laion-clap","links":{"html":"https://skillfed.io/packages/laion-clap","md":"https://skillfed.io/packages/laion-clap.md","pypi":"https://pypi.org/project/laion-clap/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-05-04","license_spdx":null,"license_treatment":"permissive","name":"laion-clap","python_support":"supports_current","summary":"Contrastive Language-Audio Pretraining Model from LAION"},"popularity":{"monthly_downloads":94890,"position":13300,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.1.7"}