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README updates (quick cleanup) #184

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12 changes: 6 additions & 6 deletions README.md
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Expand Up @@ -4,16 +4,16 @@ torchaudio: an audio library for PyTorch
[![Build Status](https://travis-ci.org/pytorch/audio.svg?branch=master)](https://travis-ci.org/pytorch/audio)

The aim of torchaudio is to apply [PyTorch](https://github.com/pytorch/pytorch) to
the audio domain. By supporting PyTorch, torchaudio will follow the same philosophy
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 will be primarily a machine learning library and not a general signal
processing library. The benefits of Pytorch will be seen in torchaudio through
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.

- [Support audio I/O (Load files, Save files)](http://pytorch.org/audio/)
- Load the following formats into a torch Tensor
- Load the following formats into a torch Tensor using sox
- mp3, wav, aac, ogg, flac, avr, cdda, cvs/vms,
- aiff, au, amr, mp2, mp4, ac3, avi, wmv,
- mpeg, ircam and any other format supported by libsox.
Expand Down Expand Up @@ -73,8 +73,8 @@ Conventions
-----------

With torchaudio being a machine learning library and built on top of PyTorch,
torchaudio is standardized around the following naming conventions. In particular,
tensors are assumed to have channel as the first dimension and time as the last
torchaudio is standardized around the following naming conventions. Tensors are
assumed to have channel as the first dimension and time as the last
dimension (when applicable). This makes it consistent with PyTorch's dimensions.
For size names, the prefix `n_` is used (e.g. "a tensor of size (`n_freq`, `n_mel`)")
whereas dimension names do not have this prefix (e.g. "a tensor of
Expand Down