Add MelResNet Block

test/test_models.py

@@ -1,5 +1,5 @@
 import torch
-from torchaudio.models import Wav2Letter
+from torchaudio.models import Wav2Letter, _MelResNet
 
 
 class TestWav2Letter:
@@ -29,3 +29,23 @@ def test_mfcc(self):
         out = model(x)
 
         assert out.size() == (batch_size, num_classes, 2)
+
+
+class TestMelResNet:
+
+    def test_waveform(self):
+
+        batch_size = 2
+        num_features = 200
+        input_dims = 100
+        output_dims = 128
+        res_blocks = 10
+        hidden_dims = 128
+        pad = 2
+
+        model = _MelResNet(res_blocks, input_dims, hidden_dims, output_dims, pad)
+
+        x = torch.rand(batch_size, input_dims, num_features)
+        out = model(x)
+
+        assert out.size() == (batch_size, output_dims, num_features - pad * 2)

torchaudio/models/__init__.py

@@ -1 +1,2 @@
 from .wav2letter import *
+from ._wavernn import *

torchaudio/models/_wavernn.py

@@ -0,0 +1,107 @@
+from typing import Optional
+
+from torch import Tensor
+from torch import nn
+
+__all__ = ["_ResBlock", "_MelResNet"]
+
+
+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.
+
+    Args:
+        num_dims: the number of compute dimensions in the input (default=128).
+
+    Examples::
+        >>> resblock = _ResBlock(num_dims=128)
+        >>> input = torch.rand(10, 128, 512)
+        >>> output = resblock(input)
+    """
+
+    def __init__(self, num_dims: 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.ReLU(inplace=True),
+            nn.Conv1d(in_channels=num_dims, out_channels=num_dims, kernel_size=1, bias=False),
+            nn.BatchNorm1d(num_dims)
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        r"""Pass the input through the _ResBlock layer.
+
+        Args:
+            x: the input sequence to the _ResBlock layer (required).
+
+        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.
+        """
+
+        residual = x
+        return self.resblock_model(x) + residual
+
+
+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.
+
+    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)
+    """
+
+    def __init__(self, res_blocks: int = 10,
+                 input_dims: int = 100,
+                 hidden_dims: int = 128,
+                 output_dims: int = 128,
+                 pad: int = 2) -> None:
+        super().__init__()
+
+        kernel_size = pad * 2 + 1
+        ResBlocks = []
+
+        for i in range(res_blocks):
+            ResBlocks.append(_ResBlock(hidden_dims))
+
+        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.ReLU(inplace=True),
+            *ResBlocks,
+            nn.Conv1d(in_channels=hidden_dims, out_channels=output_dims, kernel_size=1)
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        r"""Pass the input through the _MelResNet layer.
+
+        Args:
+            x: the input sequence to the _MelResNet layer (required).
+
+        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 ouput sequence, T is the length of input sequence.
+        """
+
+        return self.melresnet_model(x)