{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/6"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/7"},{"label":"Multimedia","url":"https://skillfed.io/packages/category/multimedia"},{"label":"Sound/Audio","url":"https://skillfed.io/packages/category/multimedia-sound-audio"}],"enrichment":{"capability":"Audiomentations applies randomized audio transformations\u2014noise injection, pitch shifting, time stretching, filtering, and distortion\u2014to augment training data for machine learning models that work with audio.","skillfed_tags":["audio-ml","data-augmentation","signal-processing"],"use_cases":["Augment speech data during training to improve robustness to background noise and acoustic variation.","Generate synthetic pitch and tempo variations to train music information retrieval models.","Simulate room acoustics and microphone artifacts to make audio classifiers generalize across recording conditions.","Add realistic distortion and compression artifacts to train models that handle low-quality or compressed audio.","Combine multiple transforms in a pipeline to create diverse training samples from a small audio dataset."],"what_it_does":"Audiomentations is a Python library for augmenting audio data by applying randomized transformations to waveforms. It provides a Compose-based API inspired by albumentations, letting you chain transforms like noise injection, pitch shifting, time stretching, filtering, and distortion with configurable probability. The library runs on CPU, supports both mono and multichannel audio, and integrates directly into training loops for TensorFlow/Keras and PyTorch.\n\nThe package solves the problem of generating synthetic audio variations during training to improve model robustness and generalization. Rather than manually coding audio effects, you declare a pipeline of transforms with parameter ranges, and the library applies them randomly to each sample during training. It is designed for practitioners building real-world audio ML systems\u2014speech recognition, audio classification, sound event detection\u2014where models trained only on clean lab data often fail in production.","worth_installing":"Yes. Audiomentations is actively maintained, has no security vulnerabilities, installs with low friction, and provides a well-documented, production-ready API for audio augmentation. It is particularly valuable if you are training audio ML models and need to generate synthetic variations without writing custom signal processing code. The MIT license removes legal barriers. Install it if audio data augmentation is part of your training pipeline."},"id":"audiomentations","links":{"html":"https://skillfed.io/packages/audiomentations","md":"https://skillfed.io/packages/audiomentations.md","pypi":"https://pypi.org/project/audiomentations/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-09-13","license_spdx":null,"license_treatment":"permissive","name":"audiomentations","python_support":"supports_current","summary":"A Python library for audio data augmentation. Inspired by albumentations. Useful for machine learning."},"popularity":{"monthly_downloads":211335,"position":9481,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.43.1"}