Remove lazy behavior from MelScale

test/torchaudio_unittest/transforms/batch_consistency_test.py

@@ -33,15 +33,15 @@ def test_batch_Resample(self):
         self.assertEqual(computed, expected)
 
     def test_batch_MelScale(self):
-        specgram = torch.randn(2, 31, 2786)
+        specgram = torch.randn(2, 201, 256)
 
         # Single then transform then batch
         expected = torchaudio.transforms.MelScale()(specgram).repeat(3, 1, 1, 1)
 
         # Batch then transform
         computed = torchaudio.transforms.MelScale()(specgram.repeat(3, 1, 1, 1))
 
-        # shape = (3, 2, 201, 1394)
+        # shape = (3, 2, 128, 256)
         self.assertEqual(computed, expected)
 
     def test_batch_InverseMelScale(self):

test/torchaudio_unittest/transforms/torchscript_consistency_impl.py

@@ -59,10 +59,6 @@ def test_AmplitudeToDB(self):
         spec = torch.rand((6, 201))
         self._assert_consistency(T.AmplitudeToDB(), spec)
 
-    def test_MelScale_invalid(self):
-        with self.assertRaises(ValueError):
-            torch.jit.script(T.MelScale())
-
     def test_MelScale(self):
         spec_f = torch.rand((1, 201, 6))
         self._assert_consistency(T.MelScale(n_stft=201), spec_f)

test/torchaudio_unittest/transforms/transforms_test.py

@@ -55,17 +55,17 @@ def test_AmplitudeToDB(self):
         self.assertEqual(mag_to_db_torch, power_to_db_torch)
 
     def test_melscale_load_save(self):
-        specgram = torch.ones(1, 1000, 100)
+        specgram = torch.ones(1, 201, 100)
         melscale_transform = transforms.MelScale()
         melscale_transform(specgram)
 
-        melscale_transform_copy = transforms.MelScale(n_stft=1000)
+        melscale_transform_copy = transforms.MelScale()
         melscale_transform_copy.load_state_dict(melscale_transform.state_dict())
 
         fb = melscale_transform.fb
         fb_copy = melscale_transform_copy.fb
 
-        self.assertEqual(fb_copy.size(), (1000, 128))
+        self.assertEqual(fb_copy.size(), (201, 128))
         self.assertEqual(fb, fb_copy)
 
     def test_melspectrogram_load_save(self):

test/torchaudio_unittest/transforms/transforms_test_impl.py

@@ -1,5 +1,3 @@
-import warnings
-
 import torch
 import torchaudio.transforms as T
 
@@ -63,22 +61,6 @@ def test_InverseMelScale(self):
         assert _get_ratio(relative_diff < 1e-3) > 5e-3
         assert _get_ratio(relative_diff < 1e-5) > 1e-5
 
-    def test_melscale_unset_weight_warning(self):
-        """Issue a warning if MelScale initialized without a weight
-
-        As part of the deprecation of lazy intialization behavior (#1510),
-        issue a warning if `n_stft` is not set.
-        """
-        with warnings.catch_warnings(record=True) as caught_warnings:
-            warnings.simplefilter("always")
-            T.MelScale(n_mels=64, sample_rate=8000)
-        assert len(caught_warnings) == 1
-
-        with warnings.catch_warnings(record=True) as caught_warnings:
-            warnings.simplefilter("always")
-            T.MelScale(n_mels=64, sample_rate=8000, n_stft=201)
-        assert len(caught_warnings) == 0
-
     @nested_params(
         ["sinc_interpolation", "kaiser_window"],
         [16000, 44100],

torchaudio/transforms.py

@@ -244,9 +244,8 @@ class MelScale(torch.nn.Module):
         sample_rate (int, optional): Sample rate of audio signal. (Default: ``16000``)
         f_min (float, optional): Minimum frequency. (Default: ``0.``)
         f_max (float or None, optional): Maximum frequency. (Default: ``sample_rate // 2``)
-        n_stft (int, optional): Number of bins in STFT. Calculated from first input
-            if None is given.  See ``n_fft`` in :class:`Spectrogram`. (Default: ``None``)
-        norm (Optional[str]): If 'slaney', divide the triangular mel weights by the width of the mel band
+        n_stft (int, optional): Number of bins in STFT. See ``n_fft`` in :class:`Spectrogram`. (Default: ``201``)
+        norm (str or None, optional): If 'slaney', divide the triangular mel weights by the width of the mel band
         (area normalization). (Default: ``None``)
         mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``)
     """
@@ -257,7 +256,7 @@ def __init__(self,
                  sample_rate: int = 16000,
                  f_min: float = 0.,
                  f_max: Optional[float] = None,
-                 n_stft: Optional[int] = None,
+                 n_stft: int = 201,
                  norm: Optional[str] = None,
                  mel_scale: str = "htk") -> None:
         super(MelScale, self).__init__()
@@ -269,35 +268,11 @@ def __init__(self,
         self.mel_scale = mel_scale
 
         assert f_min <= self.f_max, 'Require f_min: {} < f_max: {}'.format(f_min, self.f_max)
-
-        if n_stft is None or n_stft == 0:
-            warnings.warn(
-                'Initialization of torchaudio.transforms.MelScale with an unset weight '
-                '`n_stft=None` is deprecated and will be removed in release 0.10. '
-                'Please set a proper `n_stft` value. Typically this is `n_fft // 2 + 1`. '
-                'Refer to https://github.com/pytorch/audio/issues/1510 '
-                'for more details.'
-            )
-
-        fb = torch.empty(0) if n_stft is None else F.create_fb_matrix(
+        fb = F.create_fb_matrix(
             n_stft, self.f_min, self.f_max, self.n_mels, self.sample_rate, self.norm,
             self.mel_scale)
         self.register_buffer('fb', fb)
 
-    def __prepare_scriptable__(self):
-        r"""If `self.fb` is empty, the `forward` method will try to resize the parameter,
-        which does not work once the transform is scripted. However, this error does not happen
-        until the transform is executed. This is inconvenient especially if the resulting
-        TorchScript object is executed in other environments. Therefore, we check the
-        validity of `self.fb` here and fail if the resulting TS does not work.
-
-        Returns:
-            MelScale: self
-        """
-        if self.fb.numel() == 0:
-            raise ValueError("n_stft must be provided at construction")
-        return self
-
     def forward(self, specgram: Tensor) -> Tensor:
         r"""
         Args:
@@ -311,14 +286,6 @@ def forward(self, specgram: Tensor) -> Tensor:
         shape = specgram.size()
         specgram = specgram.reshape(-1, shape[-2], shape[-1])
 
-        if self.fb.numel() == 0:
-            tmp_fb = F.create_fb_matrix(specgram.size(1), self.f_min, self.f_max,
-                                        self.n_mels, self.sample_rate, self.norm,
-                                        self.mel_scale)
-            # Attributes cannot be reassigned outside __init__ so workaround
-            self.fb.resize_(tmp_fb.size())
-            self.fb.copy_(tmp_fb)
-
         # (channel, frequency, time).transpose(...) dot (frequency, n_mels)
         # -> (channel, time, n_mels).transpose(...)
         mel_specgram = torch.matmul(specgram.transpose(1, 2), self.fb).transpose(1, 2)