{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"}],"enrichment":{"capability":"Computes Short Term Objective Intelligibility (STOI) as a PyTorch loss function for training speech enhancement models, wrapping the classical and extended STOI metrics in differentiable form.","skillfed_tags":["speech-processing","loss-function","audio-ml"],"use_cases":["Train a speech denoising network using NegSTOILoss as the primary loss function to optimize for intelligibility rather than raw signal fidelity.","Combine NegSTOILoss with other losses in a multi-objective speech enhancement model to balance intelligibility and artifact reduction.","Validate speech enhancement models on STOI-like criteria during training without switching to a separate evaluation pipeline.","Experiment with speech quality metrics in PyTorch workflows where torchaudio and torch are already in use."],"what_it_does":"torch_stoi is a PyTorch implementation of the Short Term Objective Intelligibility (STOI) metric, designed specifically for use as a differentiable loss function in neural network training. It wraps the classical and extended STOI measures\u2014metrics originally developed to predict speech intelligibility in noisy conditions\u2014into a form that PyTorch can backpropagate through. The package depends on numpy, torch, pystoi, and torchaudio to compute the metric.\n\nThe implementation is intentionally approximate rather than exact: it trades perfect fidelity to the original STOI algorithm for computational efficiency and differentiability in a training loop. The documentation explicitly warns that it is intended only as a loss function, not as a replacement for the original metric when exact reproducibility matters. This makes it suitable for training speech enhancement or denoising models where STOI-like objectives guide the network, but not for rigorous evaluation benchmarking.","worth_installing":"Yes, if you are training a PyTorch speech enhancement model and want STOI as a loss function. Install friction is low and the license is permissive. However, the dormant maintenance status (no releases for 683 days) and the documented approximation\u2014not exact replication\u2014of the original metric mean you should verify that the implementation's behavior matches your training requirements before committing to it in production. No known security vulnerabilities."},"id":"torch-stoi","links":{"html":"https://skillfed.io/packages/torch-stoi","md":"https://skillfed.io/packages/torch-stoi.md","pypi":"https://pypi.org/project/torch-stoi/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2024-09-30","license_spdx":null,"license_treatment":"permissive","name":"torch-stoi","python_support":"unspecified","summary":"Computes Short Term Objective Intelligibility in PyTorch"},"popularity":{"monthly_downloads":427554,"position":6749,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.2.3"}