BC-Breaking: Remove deprecated normalized argument from griffinlim

test/torchaudio_unittest/functional/batch_consistency_test.py

@@ -52,14 +52,13 @@ def test_griffinlim(self):
         hop = 200
         window = torch.hann_window(ws)
         power = 2
-        normalize = False
         momentum = 0.99
         n_iter = 32
         length = 1000
         torch.random.manual_seed(0)
         batch = torch.rand(self.batch_size, 1, 201, 6)
         self.assert_batch_consistency(
-            F.griffinlim, batch, window, n_fft, hop, ws, power, normalize,
+            F.griffinlim, batch, window, n_fft, hop, ws, power,
             n_iter, momentum, length, 0, atol=5e-5)
 
     @parameterized.expand(list(itertools.product(

test/torchaudio_unittest/functional/librosa_compatibility_test.py

@@ -38,7 +38,7 @@ def test_griffinlim(self):
         init = 'random' if rand_init else None
 
         specgram = F.spectrogram(tensor, 0, window, n_fft, hop, ws, 2, normalize).sqrt()
-        ta_out = F.griffinlim(specgram, window, n_fft, hop, ws, 1, normalize,
+        ta_out = F.griffinlim(specgram, window, n_fft, hop, ws, 1,
                               n_iter, momentum, length, rand_init)
         lr_out = librosa.griffinlim(specgram.squeeze(0).numpy(), n_iter=n_iter, hop_length=hop,
                                     momentum=momentum, init=init, length=length)

test/torchaudio_unittest/functional/torchscript_consistency_impl.py

@@ -41,12 +41,11 @@ def func(tensor):
             hop = 200
             window = torch.hann_window(ws, device=tensor.device, dtype=tensor.dtype)
             power = 2.
-            normalize = False
             momentum = 0.99
             n_iter = 32
             length = 1000
             rand_int = False
-            return F.griffinlim(tensor, window, n_fft, hop, ws, power, normalize, n_iter, momentum, length, rand_int)
+            return F.griffinlim(tensor, window, n_fft, hop, ws, power, n_iter, momentum, length, rand_int)
 
         tensor = torch.rand((1, 201, 6))
         self._assert_consistency(func, tensor)

torchaudio/functional/functional.py

@@ -118,7 +118,6 @@ def griffinlim(
         hop_length: int,
         win_length: int,
         power: float,
-        normalized: bool,
         n_iter: int,
         momentum: float,
         length: Optional[int],
@@ -148,7 +147,6 @@ def griffinlim(
         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.
-        normalized (bool): Whether to normalize by magnitude after stft.
         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.
@@ -162,12 +160,6 @@ def griffinlim(
     assert momentum < 1, 'momentum={} > 1 can be unstable'.format(momentum)
     assert momentum >= 0, 'momentum={} < 0'.format(momentum)
 
-    if normalized:
-        warnings.warn(
-            "The argument normalized is not used in Griffin-Lim, "
-            "and will be removed in v0.9.0 release. To suppress this warning, "
-            "please use `normalized=False`.")
-
     # pack batch
     shape = specgram.size()
     specgram = specgram.reshape([-1] + list(shape[-2:]))

torchaudio/transforms.py

@@ -122,7 +122,6 @@ class GriffinLim(torch.nn.Module):
             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``)
         momentum (float, optional): The momentum parameter for fast Griffin-Lim.
             Setting this to 0 recovers the original Griffin-Lim method.
@@ -158,7 +157,6 @@ def __init__(self,
                  hop_length: Optional[int] = None,
                  window_fn: Callable[..., Tensor] = torch.hann_window,
                  power: float = 2.,
-                 normalized: bool = False,
                  wkwargs: Optional[dict] = None,
                  momentum: float = 0.99,
                  length: Optional[int] = None,
@@ -174,7 +172,6 @@ def __init__(self,
         self.hop_length = hop_length if hop_length is not None else self.win_length // 2
         window = window_fn(self.win_length) if wkwargs is None else window_fn(self.win_length, **wkwargs)
         self.register_buffer('window', window)
-        self.normalized = normalized
         self.length = length
         self.power = power
         self.momentum = momentum / (1 + momentum)
@@ -191,7 +188,7 @@ def forward(self, specgram: Tensor) -> Tensor:
             Tensor: waveform of (..., time), where time equals the ``length`` parameter if given.
         """
         return F.griffinlim(specgram, self.window, self.n_fft, self.hop_length, self.win_length, self.power,
-                            self.normalized, self.n_iter, self.momentum, self.length, self.rand_init)
+                            self.n_iter, self.momentum, self.length, self.rand_init)
 
 
 class AmplitudeToDB(torch.nn.Module):