Move batch from vocoder transform to functional (#350)

* fixing errors in docstring.

* move batch to functional.

Author: vincentqb

torchaudio/functional.py

@@ -469,13 +469,13 @@ def phase_vocoder(complex_specgrams, rate, phase_advance):
     factor of ``rate``.
 
     Args:
-        complex_specgrams (torch.Tensor): Dimension of `(channel, freq, time, complex=2)`
+        complex_specgrams (torch.Tensor): Dimension of `(..., freq, time, complex=2)`
         rate (float): Speed-up factor
         phase_advance (torch.Tensor): Expected phase advance in each bin. Dimension
             of (freq, 1)
 
     Returns:
-        complex_specgrams_stretch (torch.Tensor): Dimension of `(channel,
+        complex_specgrams_stretch (torch.Tensor): Dimension of `(...,
         freq, ceil(time/rate), complex=2)`
 
     Example
@@ -490,6 +490,10 @@ def phase_vocoder(complex_specgrams, rate, phase_advance):
         torch.Size([2, 1025, 231, 2])
     """
 
+    # pack batch
+    shape = complex_specgrams.size()
+    complex_specgrams = complex_specgrams.reshape([-1] + list(shape[-3:]))
+
     time_steps = torch.arange(0,
                               complex_specgrams.size(-2),
                               rate,
@@ -527,6 +531,9 @@ def phase_vocoder(complex_specgrams, rate, phase_advance):
 
     complex_specgrams_stretch = torch.stack([real_stretch, imag_stretch], dim=-1)
 
+    # unpack batch
+    complex_specgrams_stretch = complex_specgrams_stretch.reshape(shape[:-3] + complex_specgrams_stretch.shape[1:])
+
     return complex_specgrams_stretch
 
 
@@ -775,6 +782,10 @@ def mask_along_axis(specgram, mask_param, mask_value, axis):
         torch.Tensor: Masked spectrogram of dimensions (channel, freq, time)
     """
 
+    # pack batch
+    shape = specgram.size()
+    specgram = specgram.reshape([-1] + list(shape[-2:]))
+
     value = torch.rand(1) * mask_param
     min_value = torch.rand(1) * (specgram.size(axis) - value)
 
@@ -789,7 +800,10 @@ def mask_along_axis(specgram, mask_param, mask_value, axis):
     else:
         raise ValueError('Only Frequency and Time masking are supported')
 
-    return specgram
+    # unpack batch
+    specgram = specgram.reshape(shape[:-2] + specgram.shape[-2:])
+
+    return specgram.reshape(shape[:-2] + specgram.shape[-2:])
 
 
 def compute_deltas(specgram, win_length=5, mode="replicate"):

torchaudio/transforms.py

@@ -380,9 +380,9 @@ def __init__(self, power=1.0):
     def forward(self, complex_tensor):
         r"""
         Args:
-            complex_tensor (Tensor): Tensor shape of `(*, complex=2)`
+            complex_tensor (Tensor): Tensor shape of `(..., complex=2)`
         Returns:
-            Tensor: norm of the input tensor, shape of `(*, )`
+            Tensor: norm of the input tensor, shape of `(..., )`
         """
         return F.complex_norm(complex_tensor, self.power)
 
@@ -438,14 +438,14 @@ def forward(self, complex_specgrams, overriding_rate=None):
         # type: (Tensor, Optional[float]) -> Tensor
         r"""
         Args:
-            complex_specgrams (Tensor): complex spectrogram (*, channel, freq, time, complex=2)
+            complex_specgrams (Tensor): complex spectrogram (..., freq, time, complex=2)
             overriding_rate (float or None): speed up to apply to this batch.
                 If no rate is passed, use ``self.fixed_rate``
 
         Returns:
-            (Tensor): Stretched complex spectrogram of dimension (*, channel, freq, ceil(time/rate), complex=2)
+            (Tensor): Stretched complex spectrogram of dimension (..., freq, ceil(time/rate), complex=2)
         """
-        assert complex_specgrams.size(-1) == 2, "complex_specgrams should be a complex tensor, shape (*, complex=2)"
+        assert complex_specgrams.size(-1) == 2, "complex_specgrams should be a complex tensor, shape (..., complex=2)"
 
         if overriding_rate is None:
             rate = self.fixed_rate
@@ -458,16 +458,12 @@ def forward(self, complex_specgrams, overriding_rate=None):
         if rate == 1.0:
             return complex_specgrams
 
-        shape = complex_specgrams.size()
-        complex_specgrams = complex_specgrams.reshape([-1] + list(shape[-3:]))
-        complex_specgrams = F.phase_vocoder(complex_specgrams, rate, self.phase_advance)
-
-        return complex_specgrams.reshape(shape[:-3] + complex_specgrams.shape[-3:])
+        return F.phase_vocoder(complex_specgrams, rate, self.phase_advance)
 
 
 class _AxisMasking(torch.nn.Module):
-    r"""
-    Apply masking to a spectrogram.
+    r"""Apply masking to a spectrogram.
+
     Args:
         mask_param (int): Maximum possible length of the mask
         axis: What dimension the mask is applied on
@@ -486,26 +482,22 @@ def forward(self, specgram, mask_value=0.):
         # type: (Tensor, float) -> Tensor
         r"""
         Args:
-            specgram (torch.Tensor): Tensor of dimension (*, channel, freq, time)
+            specgram (torch.Tensor): Tensor of dimension (..., freq, time)
 
         Returns:
-            torch.Tensor: Masked spectrogram of dimensions (*, channel, freq, time)
+            torch.Tensor: Masked spectrogram of dimensions (..., freq, time)
         """
 
         # if iid_masks flag marked and specgram has a batch dimension
         if self.iid_masks and specgram.dim() == 4:
             return F.mask_along_axis_iid(specgram, self.mask_param, mask_value, self.axis + 1)
         else:
-            shape = specgram.size()
-            specgram = specgram.reshape([-1] + list(shape[-2:]))
-            specgram = F.mask_along_axis(specgram, self.mask_param, mask_value, self.axis)
-
-            return specgram.reshape(shape[:-2] + specgram.shape[-2:])
+            return F.mask_along_axis(specgram, self.mask_param, mask_value, self.axis)
 
 
 class FrequencyMasking(_AxisMasking):
-    r"""
-    Apply masking to a spectrogram in the frequency domain.
+    r"""Apply masking to a spectrogram in the frequency domain.
+
     Args:
         freq_mask_param (int): maximum possible length of the mask.
             Indices uniformly sampled from [0, freq_mask_param).
@@ -518,8 +510,8 @@ def __init__(self, freq_mask_param, iid_masks=False):
 
 
 class TimeMasking(_AxisMasking):
-    r"""
-    Apply masking to a spectrogram in the time domain.
+    r"""Apply masking to a spectrogram in the time domain.
+
     Args:
         time_mask_param (int): maximum possible length of the mask.
             Indices uniformly sampled from [0, time_mask_param).