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* Fix HF model integration

Previously, when testing wav2vec models from HF transformers, all the model were
instantiated as `Wav2Vec2ForCTC` class, while some of them were supposed to be
`Wav2Vec2Model`.

Fixing this revealed that model importer cannot correctly handle `Wav2Vec2Model` import.

This PR fixes these issues.

Summary: Update fairseq reference from master to main elsewhere

Reviewed By: alexeib

Differential Revision: D30938472

fbshipit-source-id: 243b98550207f241c9d3265bf3d4060350aaf0a8
Co-authored-by: Diana Liskovich <dianaml@fb.com>

Apply tweak around the test names so that it's easier to see which tests are failing.

Before: `test_import_finetuned_model_2`
After:  `test_import_finetuned_model_2_wav2vec2_large_lv60k`

* [BC-Breaking] Update `extract_features` of Wav2Vec2Model

Originally, `extract_features` method was returning the result from
the convolutional feature extractor module.

The features commonly used in downstream tasks are outputs from intermediate
layers of transformer block in encoder.

This commit update the behavior of `extract_features` to allow selectively
retrieve such features.

Make the structure of library files somewhat similar to PyTorch core, which has the following pattern

```
torch/_C.so
torch/lib/libc10.so
torch/lib/libtorch.so
...
```

```
torchaudio/_torchaudio.so
torchaudio/lib/libtorchaudio.so
```

* Split `libtorchaudio` and `_torchaudio`

This change extract the core implementation from `_torchaudio` to `libtorchaudio`,
so that `libtorchaudio` is reusable in TorchScript-based app.

`_torchaudio` is a wrapper around `libtorchaudio` and only provides PyBind11-based
features. (currently file-like object support in I/O)

* Removed `BUILD_LIBTORCHAUDIO` option

When invoking `cmake`, `libtorchaudio` is always built, so this option is removed.

The new assumptions around the library discoverability

- In regular OSS workflow (`pip`/`conda`-based binary installation), both `libtorchaudio` and `_torchaudio` are present.
    In this case,`libtorchaudio` has to be loaded manually with `torch.ops.load_library` and/or `torch.classes.load_library` otherwise importing `_torchaudio` would not be able to resolve the symbols defined in `libtorchaudio`.
- When `torchaudio` is deployed with PEX format (single zip file)
  - We expect that`libtorchaudio.so` exists as a file in some search path configured by client code.
  - `_torchaudio` is still importable and because we do not know where `libtorchaudio` will exist, we will let the dynamic loader resolve the dependency from `_torchaudio` to `libtorchaudio`, which should work as long as `libtorchaudio` is in a library search path (search path is not modifiable from already-running Python process).

CCI says circleci/python:3.8 is deprecaetd. Migrating to cimg/python:3.8.

> CircleCI’s latest pre-built container images were designed from the ground up to help your team build more reliably.
> Our new images were built specifically for continuous integration projects and they are our most deterministic, performant, and efficient images yet.

> As of Dec 31, 2021, legacy images will no longer be supported on CircleCI.

Related links

- https://circleci.com/blog/announcing-our-next-generation-convenience-images-smaller-faster-more-deterministic
- https://discuss.circleci.com/t/legacy-convenience-image-deprecation/41034
- https://circleci.com/docs/2.0/next-gen-migration-guide

* Fix comparison between signed and unsigned integer expressions
* Remove unused variable

Applies style fixes identified in fbcode.

* fix build error on ROCM

* Update CMakeLists.txt
Co-authored-by: Nikita Shulga <nikita.shulga@gmail.com>

* address comments and fix cuda detction on rocm
Co-authored-by: Nikita Shulga <nikita.shulga@gmail.com>

* Update the way to access libsox global config

Preparation for splitting `libtorchaudio` and `_torchaudio`.

When two libraries are compiled separately, and each code does
`#include <sox.h>` independently, two copies of libsox's static global
variables (`sox_globals_t`) are created.

Our code should be referring to the same instance. To achieve this,
`_torchaudio` should be accessing the global variable defined in
`libtorchaudio` via the custom utility functions, and it should not
directly use `sox_get_globals`.

This PR moves the code related to PyBind11 to the dedicated directory `torchaudio/csrc/pybind`.

Before, features related to PyBind11 (I/O for file-like object) was implemented in `torchaudio/csrc/sox` and the binding was defined in `torchaudio/csrc/pybind.cpp`. We used macro definition `TORCH_API_INCLUDE_EXTENSION_H` to turn on/off the feature, in addition to including/excluding `torchaudio/csrc/pybind.cpp` in the list of compiled source.

In the previous manner, in C++ example, one had to rebuild libtorchaudio separately, but by splitting them completely at compile time, it should conceptually possible to distribute libtorchaudio within torchaudio Python package and reuse it for C++ example.

* put output tensor on device in `get_whitenoise()`

* Update `get_spectrogram()` so that window uses same device as waveform

* put window on proper device in `test_griffinlim()`

Cherry-pick of
https://github.com/pytorch/vision/commit/d2460a75de237cfef8e5c3415f7bb0ad8467c0e5
into this repo

Fixes https://github.com/pytorch/audio/issues/1750

Downloading CUDA-11.1 from conda-forge takes a while

* Remove some obsolete conditions about CUDA-10.0 from `pkg_helpers.bash`.
* Use `USE_CUDA` instead of `FORCE_CUDA` in `pkg_helpers.bash`
* Do not define `NO_CUDA_PACKAGE` in build_wheel.sh and build_cuda.sh
* Add conda-forge for Win cuda-11.1 builds
* Pass USE_CUDA / TORCH_CUDA_ARCH_LIST to conda build
* Add selected CUDA to path
* Don't define USE_CUDA for ROCM

TODO: Fix Windows CUDA builds

It's wrong to depend on `${TORCH_LIBRARIES}` as it pulls in explicit
`libcuda.so.1` dependency, which violates the assumption that GPU
accelerated libraries should be loadable with no NVIDIA drivers installed

Instead, make it depend on `torch` target, which includes all necessary
Torch C++ API dependences

Calling `torch.[ops|classes].load_library(<PATH_TO_LIBRARY_FILE>)` is problematic in case `torchaudio`
is deployed with PEX format, because the library file does not exist as a file.

Our extension module, when it exists, is guaranteed to have PyBind11 binding even when no function is bound.
This allows to load the library using the regular `import` statement in Python, and it works even in PEX format.
When the library is loaded, the static initialization kicks in and the custom kernels bound via TorchScript also
become available. This removes the need to call `torch.[ops|classe].load_library`.

This works even when the implementation of custom kernel is stripped from `_torchaudio.so` so long as `_torchaudio.so` properly depend on the library that has the kernel implementations and static initialization.

Needed to support CUDA builds on CPU machine

Parse `TORCH_CUDA_ARCH_LIST` as new-CUDA-language Cmake-3.18+ style [CMAKE_CUDA_ARCHITECTURES](https://cmake.org/cmake/help/latest/prop_tgt/CUDA_ARCHITECTURES.html#prop_tgt:CUDA_ARCHITECTURES)

Copy `cuda_install.bat` from
https://github.com/pytorch/vision/blob/3d9d264198e2167030dc04bf453aeca47daa37db/packaging/windows/internal/cuda_install.bat

Add `windows_intall_cuda` step and include it in `binary_windows_conda`
and `binary_windows_wheel` workflows

That marks `.circleci/.config.yml` as auto-generated that excluding it
from code-reviews

Introduce a helper function `torch_script` that performs scripting in the recommended way.

* Default to BUILD_SOX=1 in non-Windows systems

Since the adaptation of CMake and restricting to the static linking of libsox,
the build process has become much robust with libsox integration enabled.

This commit makes it default behavior to build libsox integration in non-Windows systems.
The build process still checks BUILD_SOX env var so, setting `BUILD_SOX=0` disables it.

- Support three solutions for MVDR beamforming ("ref_channel", "stv_evd", "stv_power").
- Support single-channel and multi-channel time-frequency masks
- Add unit tests