init ctc decoder

ctc.py

@@ -0,0 +1,60 @@
+import torch
+import torch.nn.functional as F
+
+
+class CTC(torch.nn.Module):
+    """CTC module.
+
+    Args:
+        odim: dimension of outputs
+        encoder_output_size: number of encoder projection units
+        dropout_rate: dropout rate (0.0 ~ 1.0)
+        reduce: reduce the CTC loss into a scalar
+    """
+
+    def __init__(
+        self,
+        odim: int,
+        encoder_output_size: int,
+        dropout_rate: float = 0.0,
+        reduce: bool = True,
+        blank_id: int = 0,
+        **kwargs,
+    ):
+        super().__init__()
+        eprojs = encoder_output_size
+        self.dropout_rate = dropout_rate
+        self.ctc_lo = torch.nn.Linear(eprojs, odim)
+        self.blank_id = blank_id
+        self.ctc_loss = torch.nn.CTCLoss(reduction="none", blank=blank_id)
+        self.reduce = reduce
+
+    def softmax(self, hs_pad):
+        """softmax of frame activations
+
+        Args:
+            Tensor hs_pad: 3d tensor (B, Tmax, eprojs)
+        Returns:
+            torch.Tensor: softmax applied 3d tensor (B, Tmax, odim)
+        """
+        return F.softmax(self.ctc_lo(hs_pad), dim=2)
+
+    def log_softmax(self, hs_pad):
+        """log_softmax of frame activations
+
+        Args:
+            Tensor hs_pad: 3d tensor (B, Tmax, eprojs)
+        Returns:
+            torch.Tensor: log softmax applied 3d tensor (B, Tmax, odim)
+        """
+        return F.log_softmax(self.ctc_lo(hs_pad), dim=2)
+
+    def argmax(self, hs_pad):
+        """argmax of frame activations
+
+        Args:
+            torch.Tensor hs_pad: 3d tensor (B, Tmax, eprojs)
+        Returns:
+            torch.Tensor: argmax applied 2d tensor (B, Tmax)
+        """
+        return torch.argmax(self.ctc_lo(hs_pad), dim=2)