# Torchaudio

torchaudio: an audio library for PyTorch
========================================

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The aim of torchaudio is to apply [PyTorch](https://github.com/pytorch/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.

- [Support audio I/O (Load files, Save files)](http://pytorch.org/audio/main/)
  - Load a variety of audio formats, such as `wav`, `mp3`, `ogg`, `flac`, `opus`, `sphere`, into a torch Tensor using SoX
  - [Kaldi (ark/scp)](http://pytorch.org/audio/main/kaldi_io.html)
- [Dataloaders for common audio datasets](http://pytorch.org/audio/main/datasets.html)
- Common audio transforms
    - [Spectrogram, AmplitudeToDB, MelScale, MelSpectrogram, MFCC, MuLawEncoding, MuLawDecoding, Resample](http://pytorch.org/audio/main/transforms.html)
- Compliance interfaces: Run code using PyTorch that align with other libraries
    - [Kaldi: spectrogram, fbank, mfcc](https://pytorch.org/audio/main/compliance.kaldi.html)

Installation
------------

Please refer to https://pytorch.org/audio/main/installation.html for installation and build process of TorchAudio.

依赖pytorch，点击下面连接选择指定版本，生成安装命令：
https://pytorch.org/get-started/locally/

Quick Usage
-----------

```python
import torchaudio

waveform, sample_rate = torchaudio.load('foo.wav')  # load tensor from file
torchaudio.save('foo_save.wav', waveform, sample_rate)  # save tensor to file
```

Backend Dispatch
----------------

By default in OSX and Linux, torchaudio uses SoX as a backend to load and save files.
The backend can be changed to [SoundFile](https://pysoundfile.readthedocs.io/en/latest/)
using the following. See [SoundFile](https://pysoundfile.readthedocs.io/en/latest/)
for installation instructions.

```python
import torchaudio
torchaudio.set_audio_backend("soundfile")  # switch backend

waveform, sample_rate = torchaudio.load('foo.wav')  # load tensor from file, as usual
torchaudio.save('foo_save.wav', waveform, sample_rate)  # save tensor to file, as usual
```

**Note**
- SoundFile currently does not support mp3.
- "soundfile" backend is not supported by TorchScript.

API Reference
-------------

API Reference is located here: http://pytorch.org/audio/main/