[ 原始碼： pytorch-audio ]
套件：python3-torchaudio（0.7.2-1）
Data manipulation and transformation for audio signal processing
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 is 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.
其他與 python3-torchaudio 有關的套件
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- dep: libc6 (>= 2.14) [amd64]
- GNU C 函式庫：共用函式庫
同時作為一個虛擬套件由這些套件填實： libc6-udeb
- dep: libc6 (>= 2.17) [arm64]
- dep: libc6 (>= 2.22) [ppc64el]
- dep: libc6 (>= 2.4) [armhf, s390x]
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- dep: libgcc-s1 (>= 3.0) [除 armhf]
- GCC 支援函式庫
- dep: libgcc-s1 (>= 3.5) [armhf]
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- dep: libsox3 (>= 14.4.2~)
- SoX library of audio effects and processing
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- dep: libstdc++6 (>= 5.2) [armhf]
- GNU Standard C++ Library v3
- dep: libstdc++6 (>= 9) [除 armhf]
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- dep: libtorch1.7 (>= 1.7.1)
- Tensors and Dynamic neural networks in Python with strong GPU acceleration
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- dep: python3
- interactive high-level object-oriented language (default python3 version)
- dep: python3 (<< 3.10)
- dep: python3 (>= 3.9~)
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- dep: python3-torch (>= 1.6.0)
- Tensors and Dynamic neural networks in Python with strong GPU acceleration
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- dep: python3.9
- Interactive high-level object-oriented language (version 3.9)