Refactor MVDR module (#2383)

Summary:
- Use `apply_beamforming`, `rtf_evd`, `rtf_power`, `mvdr_weights_souden`, `mvdr_weights_rtf` methods under `torchaudio.functional` to replace the class methods.
- Refactor docstrings in `PSD` and `MVDR`.
- Put `_get_mvdr_vector` outside of `MVDR` class as it doesn't call self methods inside.
- Since MVDR uses einsum for matrix operations, packing and unpacking batches are not necessary. It can be tested by the [batch_consistency_test](https://github.com/pytorch/audio/blob/main/test/torchaudio_unittest/transforms/batch_consistency_test.py#L202). Removed it from the code.

Pull Request resolved: https://github.com/pytorch/audio/pull/2383

Reviewed By: carolineechen, mthrok

Differential Revision: D36338373

Pulled By: nateanl

fbshipit-source-id: a48a6ae2825657e5967a19656245596cdf037c5f

torchaudio/functional/functional.py

@@ -1748,17 +1748,18 @@ def psd(
     .. properties:: Autograd TorchScript
 
     Args:
-        specgram (Tensor): Multi-channel complex-valued spectrum.
-            Tensor of dimension `(..., channel, freq, time)`
-        mask (Tensor or None, optional): Real-valued time-frequency mask
-            for normalization. Tensor of dimension `(..., freq, time)`
+        specgram (torch.Tensor): Multi-channel complex-valued spectrum.
+            Tensor with dimensions `(..., channel, freq, time)`.
+        mask (torch.Tensor or None, optional): Time-Frequency mask for normalization.
+            Tensor with dimensions `(..., freq, time)` if multi_mask is ``False`` or
+            with dimensions `(..., channel, freq, time)` if multi_mask is ``True``.
             (Default: ``None``)
-        normalize (bool, optional): whether to normalize the mask along the time dimension. (Default: ``True``)
-        eps (float, optional): a value added to the denominator in mask normalization. (Default: ``1e-10``)
+        normalize (bool, optional): If ``True``, normalize the mask along the time dimension. (Default: ``True``)
+        eps (float, optional): Value to add to the denominator in mask normalization. (Default: ``1e-15``)
 
     Returns:
-        Tensor: The complex-valued PSD matrix of the input spectrum.
-        Tensor of dimension `(..., freq, channel, channel)`
+        torch.Tensor: The complex-valued PSD matrix of the input spectrum.
+        Tensor with dimensions `(..., freq, channel, channel)`
     """
     specgram = specgram.transpose(-3, -2)  # shape (freq, channel, time)
     # outer product:
@@ -1780,14 +1781,14 @@ def _compute_mat_trace(input: torch.Tensor, dim1: int = -1, dim2: int = -2) -> t
     r"""Compute the trace of a Tensor along ``dim1`` and ``dim2`` dimensions.
 
     Args:
-        input (torch.Tensor): Tensor of dimension `(..., channel, channel)`
-        dim1 (int, optional): the first dimension of the diagonal matrix
-            (Default: -1)
-        dim2 (int, optional): the second dimension of the diagonal matrix
-            (Default: -2)
+        input (torch.Tensor): Tensor with dimensions `(..., channel, channel)`.
+        dim1 (int, optional): The first dimension of the diagonal matrix.
+            (Default: ``-1``)
+        dim2 (int, optional): The second dimension of the diagonal matrix.
+            (Default: ``-2``)
 
     Returns:
-        Tensor: trace of the input Tensor
+        Tensor: The trace of the input Tensor.
     """
     assert input.ndim >= 2, "The dimension of the tensor must be at least 2."
     assert input.shape[dim1] == input.shape[dim2], "The size of ``dim1`` and ``dim2`` must be the same."
@@ -1799,12 +1800,12 @@ def _tik_reg(mat: torch.Tensor, reg: float = 1e-7, eps: float = 1e-8) -> torch.T
     """Perform Tikhonov regularization (only modifying real part).
 
     Args:
-        mat (torch.Tensor): input matrix (..., channel, channel)
-        reg (float, optional): regularization factor (Default: 1e-8)
-        eps (float, optional): a value to avoid the correlation matrix is all-zero (Default: ``1e-8``)
+        mat (torch.Tensor): Input matrix with dimensions `(..., channel, channel)`.
+        reg (float, optional): Regularization factor. (Default: 1e-8)
+        eps (float, optional): Value to avoid the correlation matrix is all-zero. (Default: ``1e-8``)
 
     Returns:
-        Tensor: regularized matrix (..., channel, channel)
+        Tensor: Regularized matrix with dimensions `(..., channel, channel)`.
     """
     # Add eps
     C = mat.size(-1)