Update docs (#1567)

torchaudio/transforms.py

@@ -54,11 +54,11 @@ class Spectrogram(torch.nn.Module):
         wkwargs (dict or None, optional): Arguments for window function. (Default: ``None``)
         center (bool, optional): whether to pad :attr:`waveform` on both sides so
             that the :math:`t`-th frame is centered at time :math:`t \times \text{hop\_length}`.
-            Default: ``True``
+            (Default: ``True``)
         pad_mode (string, optional): controls the padding method used when
-            :attr:`center` is ``True``. Default: ``"reflect"``
+            :attr:`center` is ``True``. (Default: ``"reflect"``)
         onesided (bool, optional): controls whether to return half of results to
-            avoid redundancy Default: ``True``
+            avoid redundancy (Default: ``True``)
         return_complex (bool, optional):
             Indicates whether the resulting complex-valued Tensor should be represented with
             native complex dtype, such as `torch.cfloat` and `torch.cdouble`, or real dtype
@@ -345,7 +345,7 @@ class InverseMelScale(torch.nn.Module):
         tolerance_change (float, optional): Difference in losses to stop optimization at. (Default: ``1e-8``)
         sgdargs (dict or None, optional): Arguments for the SGD optimizer. (Default: ``None``)
         norm (Optional[str]): If 'slaney', divide the triangular mel weights by the width of the mel band
-        (area normalization). (Default: ``None``)
+            (area normalization). (Default: ``None``)
         mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``)
     """
     __constants__ = ['n_stft', 'n_mels', 'sample_rate', 'f_min', 'f_max', 'max_iter', 'tolerance_loss',
@@ -436,25 +436,28 @@ class MelSpectrogram(torch.nn.Module):
 
     Args:
         sample_rate (int, optional): Sample rate of audio signal. (Default: ``16000``)
+        n_fft (int, optional): Size of FFT, creates ``n_fft // 2 + 1`` bins. (Default: ``400``)
         win_length (int or None, optional): Window size. (Default: ``n_fft``)
         hop_length (int or None, optional): Length of hop between STFT windows. (Default: ``win_length // 2``)
-        n_fft (int, optional): Size of FFT, creates ``n_fft // 2 + 1`` bins. (Default: ``400``)
         f_min (float, optional): Minimum frequency. (Default: ``0.``)
         f_max (float or None, optional): Maximum frequency. (Default: ``None``)
         pad (int, optional): Two sided padding of signal. (Default: ``0``)
         n_mels (int, optional): Number of mel filterbanks. (Default: ``128``)
         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``)
+        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
             that the :math:`t`-th frame is centered at time :math:`t \times \text{hop\_length}`.
-            Default: ``True``
+            (Default: ``True``)
         pad_mode (string, optional): controls the padding method used when
-            :attr:`center` is ``True``. Default: ``"reflect"``
+            :attr:`center` is ``True``. (Default: ``"reflect"``)
         onesided (bool, optional): controls whether to return half of results to
-            avoid redundancy. Default: ``True``
+            avoid redundancy. (Default: ``True``)
         norm (Optional[str]): If 'slaney', divide the triangular mel weights by the width of the mel band
-        (area normalization). (Default: ``None``)
+            (area normalization). (Default: ``None``)
         mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``)
 
     Example