{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence"},{"label":"Sound/Audio","url":"https://skillfed.io/packages/category/multimedia-sound-audio"}],"enrichment":{"capability":"Provides PyTorch-based audio processing, transforms, and dataloaders for machine learning tasks, with GPU acceleration and autograd support for trainable audio features.","skillfed_tags":["audio-ml","gpu-accelerated","pytorch-extension"],"use_cases":["Build end-to-end speech recognition or audio classification models with GPU acceleration and automatic differentiation.","Load and preprocess standard audio datasets (with built-in dataloaders) for training neural networks.","Apply common audio transforms (spectrograms, mel-frequency cepstral coefficients, resampling) as differentiable layers in PyTorch models.","Align audio with transcriptions using forced alignment for speech processing pipelines.","Ensure compatibility with Kaldi-based audio processing through compliance interfaces."],"what_it_does":"torchaudio is a PyTorch extension for audio and speech processing designed specifically for machine learning workflows. It provides GPU-accelerated audio transforms (Spectrogram, MelSpectrogram, MFCC, MuLawEncoding/Decoding, Resample), dataloaders for common audio datasets, forced alignment, and compliance interfaces that align with other libraries like Kaldi. All computations use PyTorch operations, making it feel like a natural extension of the PyTorch ecosystem with full autograd support for trainable features.\n\nThe library transitioned into maintenance phase starting with version 2.8, removing redundant features to reduce scope and focus on its core strength: processing audio data for ML. It is not a general signal-processing library but rather a specialized tool for building audio-based machine learning models. It supports modern Python versions (3.10\u20133.14) across macOS, Linux, and Windows, with compiled wheels for efficient installation.","worth_installing":"Yes, if you are building audio or speech ML models with PyTorch and need GPU acceleration and autograd support. The permissive BSD license and active maintenance are favorable. Be aware that it is in maintenance mode (feature removals since 2.8), so verify that remaining APIs match your needs. Install friction is moderate due to compiled wheels, but pre-built distributions are available for common platforms and Python versions."},"id":"torchaudio","links":{"html":"https://skillfed.io/packages/torchaudio","md":"https://skillfed.io/packages/torchaudio.md","pypi":"https://pypi.org/project/torchaudio/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-23","license_spdx":null,"license_treatment":"permissive","name":"torchaudio","python_support":"unspecified","summary":"An audio package for PyTorch"},"popularity":{"monthly_downloads":12339584,"position":1325,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"2.11.0"}