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There are some issues on CMake-based integration after 2021-06-19.

```
  Imported target "torch" includes non-existent path
    "/opt/conda/conda-bld/pytorch_1624259172741/work/torch/lib"
  in its INTERFACE_INCLUDE_DIRECTORIES.  Possible reasons include:
  * The path was deleted, renamed, or moved to another location.
  * An install or uninstall procedure did not complete successfully.
  * The installation package was faulty and references files it does not provide.
```

This is being worked on PR like https://github.com/pytorch/pytorch/pull/60403.
The nightly builds are still causing the error, so as a workaround, 
we pin the nightly build version for Linux/macOS unittest.
The issue still happens on Windows unittest and binary builds.

This code was added as a part of investigation of inconsistent behavior
of file-like object support. Now the investigation is done and this code
does not provide much insight yet adding a bunch of lines to the log, so
this PR removes it.

Numpy is a dependency of pytorch so we should let it get resolved as it
install pytorch
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>

Signed-off-by: Eli Uriegas <eliuriegas@fb.com>

* Switch to cmake for build
* Hide symbols

So that when a test fails in flaky manner, we can look into the difference.

* Do not install sox system-wide, so that it will never get mixed up with static one torchaudio builds
* Do not install cmake system-wide
* Move libsndfile installation to Docker image

This reverts commit 84966ae1.

This reverts commit 1ecbc249.

Reason: The macOS CI jobs are stuck with xdist recently.

Upstream is on 9.4.1 and we were experiencing issues when conda-build
was attempting to check for overlinking and failed out due to some files
not existing in xcode 9.0
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>

librosa 0.8.0 has been released which include fix for
https://github.com/librosa/librosa/issues/1160 at
https://github.com/librosa/librosa/pull/1164

We have been running unit test with editable installation. (i.e. `python setup.py develop`), with which we missed issues like #842. 

This CC makes installation in CI non-editable, and change test directory structure so that the source code will not shadow the installed version of `torchaudio`. With simple `pytest test`, `pytest` modifies `sys.path` and prepend checked out repository, which shadows the installed version.

To remedy this, the whole test suites has been moved from `./test` to `./test/torchaudio_unittest`. This adds nice module structure to our test code and we can do absolute import in each test module, which makes it possible again to run test with `python -m unittest torchaudio_unittest/XXX.py`

This change does not affect the regular development process (`python setup.py develop` && `pytest test`)

Currently our test suites automatically/silently skip tests on C++ extension
if it is not available. This is nice in local env, but in CI these tests should be
enforced and reported as failure if C++ extension is not available.

This PR adds switch for making tests fail if C++ extension is not available,
and make CI for *nix fail if that's the case.

Currently macOS CI job takes twice the time of linux unit tests.
This PR uses `pytest-xdist` to run unit tests in parallel on macOS CI job. 

Interestingly applying the same technique to linux unit tests make the test execution longer.
So the change is only applied to macOS.

macOS 27 mins -> 17 mins 😸
linux 14 mins -> 1 hours 😾

* Pin llvmlite

* Add comments

* Use cmake for third party

* Apply patch to libmad

* Update gitignore

* Update docker test image

In #728, linux unit test switches to libsox provided by apt.
For CPU jobs this is fine because all the job steps share the same Docker container,
but on CPU job, each job step runs a script in a new Docker container, so
libsox installed in a step is not available to the subsequent steps.

To fix this, this PR moves the installation of libsox and sox to Docker build.

This is a part of PRs to add new "sox_io" backend #726, and depends on #718.

This PR adds `info` function to "sox_io" backend, which allows users to fetch some metadata of an audio file. 
At this moment, the information retrieved are;

 - Number of samples in the audio file
 - Sampling rate
 - Number of channels