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Data manipulation and transformation for audio signal processing, powered by PyTorch
The main features of pytorch/audio are: Computer Audition, Deep Learning, Deep Learning Ecosystems, Deep Learning Frameworks, Machine Learning Frameworks, Natural Language Processing, Speech and Audio, Speech and Audio Processing.
Projects with overlapping indexed features include: pytorch/pytorch — PyTorch is a machine learning framework centered on a GPU-ready tensor library that supports multi-dimensional array… pytorch/ignite — Ignite is a high-level training framework for PyTorch neural networks that serves as a training engine and deep… catalyst-team/catalyst — Accelerated deep learning R&D. graal-research/poutyne — A simplified framework and utilities for PyTorch. bsautermeister/tensorlight — TensorLight - A high-level framework for TensorFlow. deepmind/sonnet — Sonnet is a modular machine learning framework and TensorFlow library used for building, training, and managing deep…
Ignite is a high-level training framework for PyTorch neural networks that serves as a training engine and deep learning lifecycle manager. It provides a structured system for organizing and automating training and evaluation loops, managing data iterators and triggering event handlers at specific milestones during the model training process. The project distinguishes itself through a comprehensive suite of tools for distributed training and model evaluation. It includes utilities for synchronizing gradients and coordinating collective communication across multiple GPUs or nodes, as well as a
PyTorch is a machine learning framework centered on a GPU-ready tensor library that supports multi-dimensional array operations across both CPU and accelerator hardware. It provides a foundational infrastructure for mathematical computation and dynamic neural network construction, utilizing a tape-based automatic differentiation system that allows for flexible, non-static graph execution. The framework is designed for deep integration with Python, enabling natural usage alongside standard scientific computing ecosystems. It distinguishes itself through a comprehensive distributed training sui
Sonnet is a modular machine learning framework and TensorFlow library used for building, training, and managing deep learning models. It functions as a system for composing neural networks from reusable modules and layers that encapsulate their own parameters and internal states. The project provides specialized tools for distributed model training, enabling the synchronization of gradients across multiple hardware devices. It also serves as a model state management system, allowing for the persistence of neural network weights and the export of portable models that separate the computation g
