Data manipulation and transformation for audio signal processing, powered by PyTorch
The aim of torchaudio is to apply PyTorch to the audio domain. By supporting PyTorch, torchaudio follows the same philosophy of providing strong GPU acceleration, having a focus on trainable features through the autograd system, and having consistent style (tensor names and dimension names). Therefore, it is primarily a machine learning library and not a general signal processing library. The benefits of PyTorch can be seen in torchaudio through having all the computations be through PyTorch operations which makes it easy to use and feel like a natural extension.
The aim of torchaudio is to apply PyTorch to the audio domain. By supporting PyTorch, torchaudio follows the same philosophy of providing strong GPU acceleration, having a focus on trainable features through the autograd system, and having consistent style (tensor names and dimension names). Therefore, it is primarily a machine learning library and not a general signal processing library. The benefits of PyTorch can be seen in torchaudio through having all the computations be through PyTorch operations which makes it easy to use and feel like a natural extension.
To install py39-torchaudio, run the following command in macOS terminal (Applications->Utilities->Terminal)
sudo port install py39-torchaudio
To see what files were installed by py39-torchaudio, run:
port contents py39-torchaudio
To later upgrade py39-torchaudio, run:
sudo port selfupdate && sudo port upgrade py39-torchaudio
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