{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"}],"enrichment":{"capability":"PyTorch implementation of the CREPE pitch tracker that estimates fundamental frequency and periodicity from audio signals using pre-trained neural network models.","skillfed_tags":["audio-processing","pitch-detection","deep-learning"],"use_cases":["Extract pitch contours from speech recordings for prosody analysis or voice conversion applications.","Analyze melody in music recordings for transcription, similarity matching, or music information retrieval tasks.","Generate pitch embeddings from audio as pretrained features for downstream voice or music classification models.","Batch process large audio corpora to compute pitch statistics or detect voiced/unvoiced regions.","Build real-time pitch tracking pipelines for live audio applications using GPU acceleration."],"what_it_does":"torchcrepe is a PyTorch port of the CREPE convolutional pitch estimation model, originally developed for robust pitch tracking in speech and music. It loads pre-trained model weights (available in 'tiny' and 'full' variants) converted from the original TensorFlow implementation and uses them to extract pitch and periodicity estimates from audio.\n\nThe package provides multiple entry points: direct pitch prediction from audio arrays, batch processing from files, and extraction of intermediate embeddings from the fifth max-pooling layer. It includes post-processing tools for filtering noisy periodicity values, thresholding unreliable pitch estimates, and decoding strategies (Viterbi, weighted argmax, argmax) to reduce octave errors. A command-line interface and file-to-file convenience functions support workflow integration.","worth_installing":"Yes, if you need robust pitch estimation from audio. The package is well-established (first released 2020-08-01), has low install friction, carries no known vulnerabilities, and remains actively maintained. The aging status (455 days since last release) reflects stable maturity rather than abandonment. MIT licensing poses no restrictions. Primary gotcha: PyTorch must be installed separately and configured for your hardware before use."},"id":"torchcrepe","links":{"html":"https://skillfed.io/packages/torchcrepe","md":"https://skillfed.io/packages/torchcrepe.md","pypi":"https://pypi.org/project/torchcrepe/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-05-16","license_spdx":null,"license_treatment":"permissive","name":"torchcrepe","python_support":"unspecified","summary":"Pytorch implementation of CREPE pitch tracker"},"popularity":{"monthly_downloads":295755,"position":7915,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.0.24"}