Initial commit: FunASR Speech Recognition Toolkit

Add complete FunASR codebase including models, runtime, and documentation.

funasr/models/mossformer/mossformer_decoder.py

@@ -0,0 +1,56 @@
+import torch
+import torch.nn as nn
+
+
+class MossFormerDecoder(nn.ConvTranspose1d):
+    """A decoder layer that consists of ConvTranspose1d.
+
+    Arguments
+    ---------
+    kernel_size : int
+        Length of filters.
+    in_channels : int
+        Number of  input channels.
+    out_channels : int
+        Number of output channels.
+
+
+    Example
+    ---------
+    >>> x = torch.randn(2, 100, 1000)
+    >>> decoder = Decoder(kernel_size=4, in_channels=100, out_channels=1)
+    >>> h = decoder(x)
+    >>> h.shape
+    torch.Size([2, 1003])
+    """
+
+    def __init__(self, *args, **kwargs):
+        """Initialize MossFormerDecoder.
+        
+            Args:
+                *args: Variable positional arguments.
+                **kwargs: Additional keyword arguments.
+            """
+        super(MossFormerDecoder, self).__init__(*args, **kwargs)
+
+    def forward(self, x):
+        """Return the decoded output.
+
+        Arguments
+        ---------
+        x : torch.Tensor
+            Input tensor with dimensionality [B, N, L].
+                where, B = Batchsize,
+                       N = number of filters
+                       L = time points
+        """
+
+        if x.dim() not in [2, 3]:
+            raise RuntimeError("{} accept 3/4D tensor as input".format(self.__name__))
+        x = super().forward(x if x.dim() == 3 else torch.unsqueeze(x, 1))
+
+        if torch.squeeze(x).dim() == 1:
+            x = torch.squeeze(x, dim=1)
+        else:
+            x = torch.squeeze(x)
+        return x