{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/2"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/3"},{"label":"Multimedia","url":"https://skillfed.io/packages/category/multimedia"},{"label":"Sound/Audio","url":"https://skillfed.io/packages/category/multimedia-sound-audio"}],"enrichment":{"capability":"Provides PyTorch-native audio data augmentation transforms that run on CPU or GPU, designed to integrate directly into neural network models as differentiable modules.","skillfed_tags":["audio-augmentation","gpu-accelerated","deep-learning"],"use_cases":["Augment audio batches during training of speech recognition or audio classification models to improve robustness.","Apply differentiable transforms as part of an end-to-end learnable audio processing pipeline.","Add background noise, pitch variation, or filtering to synthetic training data for audio ML tasks.","Accelerate augmentation on GPU to reduce training bottlenecks when processing large audio datasets.","Compose multiple transforms (gain, filtering, polarity inversion) into a single augmentation routine."],"what_it_does":"torch-audiomentations is a PyTorch library for augmenting audio data during training. It provides a collection of transforms\u2014gain adjustment, filtering, pitch shifting, noise addition, and more\u2014that extend nn.Module and can be composed into a pipeline. Most transforms are differentiable and support batches of multichannel audio, running efficiently on both CPU and GPU. The library is designed to avoid becoming a bottleneck in model training.\n\nThe transforms support three application modes (per_batch, per_example, per_channel) and probability-based triggering, giving fine control over how augmentation is applied to different samples in a batch. It is inspired by the audiomentations library but optimized for PyTorch workflows. The package is in early development (alpha status), so APIs may change, and some features like target data processing and multi-GPU support remain experimental or unsupported.","worth_installing":"Yes, if you are training audio models with PyTorch and need GPU-accelerated augmentation. The library is mature enough for production use (1164 GitHub stars, top 5000 PyPI packages), has no known vulnerabilities, and offers a permissive license. However, be aware that maintenance is aging (last release 576 days ago), multiprocessing and multi-GPU setups have known limitations, and some transforms have edge cases (e.g., PitchShift at low sample rates). For single-GPU training on CPU or GPU, it is a solid choice."},"id":"torch-audiomentations","links":{"html":"https://skillfed.io/packages/torch-audiomentations","md":"https://skillfed.io/packages/torch-audiomentations.md","pypi":"https://pypi.org/project/torch-audiomentations/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-01-15","license_spdx":null,"license_treatment":"permissive","name":"torch-audiomentations","python_support":"supports_current","summary":"A Pytorch library for audio data augmentation. Inspired by audiomentations. Useful for deep learning."},"popularity":{"monthly_downloads":2125883,"position":3273,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.12.0"}