Fix documentation of functional and transforms (#3134)

Summary:
Address #3101. The documentation for `power=1` should represent magnitude instead of energy.

Pull Request resolved: https://github.com/pytorch/audio/pull/3134

Reviewed By: mthrok

Differential Revision: D43910652

Pulled By: nateanl

fbshipit-source-id: e0768438e819222a5dde6b86c5123ab0e8af59fb

torchaudio/functional/functional.py

@@ -83,7 +83,7 @@ def spectrogram(
         hop_length (int): Length of hop between STFT windows
         win_length (int): Window size
         power (float or None): Exponent for the magnitude spectrogram,
-            (must be > 0) e.g., 1 for energy, 2 for power, etc.
+            (must be > 0) e.g., 1 for magnitude, 2 for power, etc.
             If None, then the complex spectrum is returned instead.
         normalized (bool or str): Whether to normalize by magnitude after stft. If input is str, choices are
             ``"window"`` and ``"frame_length"``, if specific normalization type is desirable. ``True`` maps to
@@ -286,7 +286,7 @@ def griffinlim(
             Default: ``win_length // 2``)
         win_length (int): Window size. (Default: ``n_fft``)
         power (float): Exponent for the magnitude spectrogram,
-            (must be > 0) e.g., 1 for energy, 2 for power, etc.
+            (must be > 0) e.g., 1 for magnitude, 2 for power, etc.
         n_iter (int): Number of iteration for phase recovery process.
         momentum (float): The momentum parameter for fast Griffin-Lim.
             Setting this to 0 recovers the original Griffin-Lim method.

torchaudio/transforms/_transforms.py

@@ -36,7 +36,7 @@ class Spectrogram(torch.nn.Module):
         window_fn (Callable[..., Tensor], optional): A function to create a window tensor
             that is applied/multiplied to each frame/window. (Default: ``torch.hann_window``)
         power (float or None, optional): Exponent for the magnitude spectrogram,
-            (must be > 0) e.g., 1 for energy, 2 for power, etc.
+            (must be > 0) e.g., 1 for magnitude, 2 for power, etc.
             If None, then the complex spectrum is returned instead. (Default: ``2``)
         normalized (bool or str, optional): Whether to normalize by magnitude after stft. If input is str, choices are
             ``"window"`` and ``"frame_length"``, if specific normalization type is desirable. ``True`` maps to
@@ -227,7 +227,7 @@ class GriffinLim(torch.nn.Module):
         window_fn (Callable[..., Tensor], optional): A function to create a window tensor
             that is applied/multiplied to each frame/window. (Default: ``torch.hann_window``)
         power (float, optional): Exponent for the magnitude spectrogram,
-            (must be > 0) e.g., 1 for energy, 2 for power, etc. (Default: ``2``)
+            (must be > 0) e.g., 1 for magnitude, 2 for power, etc. (Default: ``2``)
         wkwargs (dict or None, optional): Arguments for window function. (Default: ``None``)
         momentum (float, optional): The momentum parameter for fast Griffin-Lim.
             Setting this to 0 recovers the original Griffin-Lim method.
@@ -560,7 +560,7 @@ class MelSpectrogram(torch.nn.Module):
         window_fn (Callable[..., Tensor], optional): A function to create a window tensor
             that is applied/multiplied to each frame/window. (Default: ``torch.hann_window``)
         power (float, optional): Exponent for the magnitude spectrogram,
-            (must be > 0) e.g., 1 for energy, 2 for power, etc. (Default: ``2``)
+            (must be > 0) e.g., 1 for magnitude, 2 for power, etc. (Default: ``2``)
         normalized (bool, optional): Whether to normalize by magnitude after stft. (Default: ``False``)
         wkwargs (Dict[..., ...] or None, optional): Arguments for window function. (Default: ``None``)
         center (bool, optional): whether to pad :attr:`waveform` on both sides so