encoder interfaces

nn/encoder/__init__.py

@@ -1,3 +1,5 @@
 """
 Code to create encoders (for hybrid, the encoder of encoder-decoder-attention, or also transducer).
 """
+
+from .base import *

nn/encoder/base.py

@@ -13,17 +13,55 @@
 you only care about some encoded vector of type :class:`Tensor`.
 """
 
+from typing import Tuple
+from abc import ABC
 from ... import nn
 
 
-class IEncoder(nn.Module):
+class IEncoder(nn.Module, ABC):
   """
   Generic encoder interface
+
+  The encoder is a function x -> y.
+  The input can potentially be sparse or dense.
+  The output is dense with feature dim `out_dim`.
   """
 
+  out_dim: nn.Dim
+
   @nn.scoped
   def __call__(self, source: nn.Tensor) -> nn.Tensor:
     """
     Encode the input
     """
     raise NotImplementedError
+
+
+class ISeqFramewiseEncoder(nn.Module, ABC):
+  """
+  This specializes IEncoder that it operates on a sequence.
+  The output sequence length here is the same as the input.
+  """
+
+  out_dim: nn.Dim
+
+  @nn.scoped
+  def __call__(self, source: nn.Tensor, *, spatial_dim: nn.Dim) -> nn.Tensor:
+    raise NotImplementedError
+
+
+class ISeqDownsamplingEncoder(nn.Module, ABC):
+  """
+  This is more specific than IEncoder in that it operates on a sequence.
+  The output sequence length here is shorter than the input.
+
+  This is a common scenario for speech recognition
+  where the input might be on 10ms/frame
+  and the output might cover 30ms/frame or 60ms/frame or so.
+  """
+
+  out_dim: nn.Dim
+
+  @nn.scoped
+  def __call__(self, source: nn.Tensor, *, in_spatial_dim: nn.Dim) -> Tuple[nn.Tensor, nn.Dim]:
+    raise NotImplementedError