Update MelResNet (#751)

* update varible names and docstring

* update format

* update docsting and output value

Co-authored-by: Ji Chen <[email protected]>

Author: jimchen90

test/test_models.py

@@ -36,18 +36,20 @@ def test_mfcc(self):
 class TestMelResNet(common_utils.TorchaudioTestCase):
 
     def test_waveform(self):
+        """Validate the output dimensions of a _MelResNet block.
+        """
 
-        batch_size = 2
-        num_features = 200
-        input_dims = 100
-        output_dims = 128
-        res_blocks = 10
-        hidden_dims = 128
-        pad = 2
+        n_batch = 2
+        n_time = 200
+        n_freq = 100
+        n_output = 128
+        n_res_block = 10
+        n_hidden = 128
+        kernel_size = 5
 
-        model = _MelResNet(res_blocks, input_dims, hidden_dims, output_dims, pad)
+        model = _MelResNet(n_res_block, n_freq, n_hidden, n_output, kernel_size)
 
-        x = torch.rand(batch_size, input_dims, num_features)
+        x = torch.rand(n_batch, n_freq, n_time)
         out = model(x)
 
-        assert out.size() == (batch_size, output_dims, num_features - pad * 2)
+        assert out.size() == (n_batch, n_output, n_time - kernel_size + 1)

torchaudio/models/_wavernn.py

@@ -5,101 +5,83 @@
 
 
 class _ResBlock(nn.Module):
-    r"""This is a ResNet block layer. This layer is based on the paper "Deep Residual Learning
-    for Image Recognition". Kaiming He,  Xiangyu Zhang, Shaoqing Ren, Jian Sun. CVPR, 2016.
-    It is a block used in WaveRNN. WaveRNN is based on the paper "Efficient Neural Audio Synthesis".
-    Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart,
-    Florian Stimberg, Aaron van den Oord, Sander Dieleman, Koray Kavukcuoglu. arXiv:1802.08435, 2018.
+    r"""ResNet block based on "Deep Residual Learning for Image Recognition"
+
+    The paper link is https://arxiv.org/pdf/1512.03385.pdf.
 
     Args:
-        num_dims: the number of compute dimensions in the input (default=128).
+        n_freq: the number of bins in a spectrogram (default=128)
 
-    Examples::
-        >>> resblock = _ResBlock(num_dims=128)
-        >>> input = torch.rand(10, 128, 512)
-        >>> output = resblock(input)
+    Examples
+        >>> resblock = _ResBlock()
+        >>> input = torch.rand(10, 128, 512)  # a random spectrogram
+        >>> output = resblock(input)  # shape: (10, 128, 512)
     """
 
-    def __init__(self, num_dims: int = 128) -> None:
+    def __init__(self, n_freq: int = 128) -> None:
         super().__init__()
 
         self.resblock_model = nn.Sequential(
-            nn.Conv1d(in_channels=num_dims, out_channels=num_dims, kernel_size=1, bias=False),
-            nn.BatchNorm1d(num_dims),
+            nn.Conv1d(in_channels=n_freq, out_channels=n_freq, kernel_size=1, bias=False),
+            nn.BatchNorm1d(n_freq),
             nn.ReLU(inplace=True),
-            nn.Conv1d(in_channels=num_dims, out_channels=num_dims, kernel_size=1, bias=False),
-            nn.BatchNorm1d(num_dims)
+            nn.Conv1d(in_channels=n_freq, out_channels=n_freq, kernel_size=1, bias=False),
+            nn.BatchNorm1d(n_freq)
         )
 
-    def forward(self, x: Tensor) -> Tensor:
+    def forward(self, specgram: Tensor) -> Tensor:
         r"""Pass the input through the _ResBlock layer.
-
         Args:
-            x: the input sequence to the _ResBlock layer (required).
+            specgram (Tensor): the input sequence to the _ResBlock layer (n_batch, n_freq, n_time).
 
-        Shape:
-            - x: :math:`(N, S, T)`.
-            - output: :math:`(N, S, T)`.
-        where N is the batch size, S is the number of input sequence,
-        T is the length of input sequence.
+        Return:
+            Tensor shape: (n_batch, n_freq, n_time)
         """
 
-        residual = x
-        return self.resblock_model(x) + residual
+        return self.resblock_model(specgram) + specgram
 
 
 class _MelResNet(nn.Module):
-    r"""This is a MelResNet layer based on a stack of ResBlocks. It is a block used in WaveRNN.
-    WaveRNN is based on the paper "Efficient Neural Audio Synthesis". Nal Kalchbrenner, Erich Elsen,
-    Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, Florian Stimberg, Aaron van den Oord,
-    Sander Dieleman, Koray Kavukcuoglu. arXiv:1802.08435, 2018.
+    r"""MelResNet layer uses a stack of ResBlocks on spectrogram.
 
     Args:
-        res_blocks: the number of ResBlock in stack (default=10).
-        input_dims: the number of input sequence (default=100).
-        hidden_dims: the number of compute dimensions (default=128).
-        output_dims: the number of output sequence (default=128).
-        pad: the number of kernal size (pad * 2 + 1) in the first Conv1d layer (default=2).
-
-    Examples::
-        >>> melresnet = _MelResNet(res_blocks=10, input_dims=100,
-                                   hidden_dims=128, output_dims=128, pad=2)
-        >>> input = torch.rand(10, 100, 512)
-        >>> output = melresnet(input)
+        n_res_block: the number of ResBlock in stack (default=10)
+        n_freq: the number of bins in a spectrogram (default=128)
+        n_hidden: the number of hidden dimensions (default=128)
+        n_output: the number of output dimensions (default=128)
+        kernel_size: the number of kernel size in the first Conv1d layer (default=5)
+
+    Examples
+        >>> melresnet = _MelResNet()
+        >>> input = torch.rand(10, 128, 512)  # a random spectrogram
+        >>> output = melresnet(input)  # shape: (10, 128, 508)
     """
 
-    def __init__(self, res_blocks: int = 10,
-                 input_dims: int = 100,
-                 hidden_dims: int = 128,
-                 output_dims: int = 128,
-                 pad: int = 2) -> None:
+    def __init__(self,
+                 n_res_block: int = 10,
+                 n_freq: int = 128,
+                 n_hidden: int = 128,
+                 n_output: int = 128,
+                 kernel_size: int = 5) -> None:
         super().__init__()
 
-        kernel_size = pad * 2 + 1
-        ResBlocks = []
-
-        for i in range(res_blocks):
-            ResBlocks.append(_ResBlock(hidden_dims))
+        ResBlocks = [_ResBlock(n_hidden) for _ in range(n_res_block)]
 
         self.melresnet_model = nn.Sequential(
-            nn.Conv1d(in_channels=input_dims, out_channels=hidden_dims, kernel_size=kernel_size, bias=False),
-            nn.BatchNorm1d(hidden_dims),
+            nn.Conv1d(in_channels=n_freq, out_channels=n_hidden, kernel_size=kernel_size, bias=False),
+            nn.BatchNorm1d(n_hidden),
             nn.ReLU(inplace=True),
             *ResBlocks,
-            nn.Conv1d(in_channels=hidden_dims, out_channels=output_dims, kernel_size=1)
+            nn.Conv1d(in_channels=n_hidden, out_channels=n_output, kernel_size=1)
         )
 
-    def forward(self, x: Tensor) -> Tensor:
+    def forward(self, specgram: Tensor) -> Tensor:
         r"""Pass the input through the _MelResNet layer.
-
         Args:
-            x: the input sequence to the _MelResNet layer (required).
+            specgram (Tensor): the input sequence to the _MelResNet layer (n_batch, n_freq, n_time).
 
-        Shape:
-            - x: :math:`(N, S, T)`.
-            - output: :math:`(N, P, T - 2 * pad)`.
-        where N is the batch size, S is the number of input sequence,
-        P is the number of output sequence, T is the length of input sequence.
+        Return:
+            Tensor shape: (n_batch, n_output, n_time - kernel_size + 1)
         """
 
-        return self.melresnet_model(x)
+        return self.melresnet_model(specgram)