Minor fixes

egs/librispeech/ASR/pruned_transducer_stateless/joiner.py

@@ -38,7 +38,8 @@ class Joiner(nn.Module):
         Returns:
           Return a tensor of shape (N, T, s_range, C).
         """
-        assert encoder_out.ndim == decoder_out.ndim == 4
+        assert encoder_out.ndim == decoder_out.ndim
+        assert encoder_out.ndim in (2, 4)
         assert encoder_out.shape == decoder_out.shape
 
         logit = encoder_out + decoder_out

egs/librispeech/ASR/pruned_transducer_stateless2/conformer.py

@@ -250,12 +250,12 @@ class Conformer(EncoderInterface):
         x: torch.Tensor,
         x_lens: torch.Tensor,
         states: List[Tensor],
-        warmup: float = 1.0,
+        processed_lens: Optional[Tensor] = None,
-        chunk_size: int = 16,
         left_context: int = 64,
         right_context: int = 4,
+        chunk_size: int = 16,
         simulate_streaming: bool = False,
-        processed_lens: Optional[Tensor] = None,
+        warmup: float = 1.0,
     ) -> Tuple[torch.Tensor, torch.Tensor, List[torch.Tensor]]:
         """
         Args:
@@ -271,13 +271,8 @@ class Conformer(EncoderInterface):
             the second element is the conv_cache which has a shape of
             (encoder_layers, cnn_module_kernel-1, batch, conv_dim).
             Note: states will be modified in this function.
-          warmup:
+          processed_lens:
-            A floating point value that gradually increases from 0 throughout
+            How many frames (after subsampling) have been processed for each sequence.
-            training; when it is >= 1.0 we are "fully warmed up".  It is used
-            to turn modules on sequentially.
-          chunk_size:
-            The chunk size for decoding, this will be used to simulate streaming
-            decoding using masking.
           left_context:
             How many previous frames the attention can see in current chunk.
             Note: It's not that each individual frame has `left_context` frames
@@ -286,13 +281,18 @@ class Conformer(EncoderInterface):
             How many future frames the attention can see in current chunk.
             Note: It's not that each individual frame has `right_context` frames
             of right context, some have more.
+          chunk_size:
+            The chunk size for decoding, this will be used to simulate streaming
+            decoding using masking.
           simulate_streaming:
             If setting True, it will use a masking strategy to simulate streaming
             fashion (i.e. every chunk data only see limited left context and
             right context). The whole sequence is supposed to be send at a time
             When using simulate_streaming.
-          processed_lens:
+          warmup:
-            How many frames (after subsampling) have been processed for each sequence.
+            A floating point value that gradually increases from 0 throughout
+            training; when it is >= 1.0 we are "fully warmed up".  It is used
+            to turn modules on sequentially.
         Returns:
           Return a tuple containing 2 tensors:
             - logits, its shape is (batch_size, output_seq_len, output_dim)

egs/librispeech/ASR/pruned_transducer_stateless2/joiner.py

@@ -52,8 +52,10 @@ class Joiner(nn.Module):
         Returns:
           Return a tensor of shape (N, T, s_range, C).
         """
-        assert encoder_out.ndim == decoder_out.ndim == 4
+
-        assert encoder_out.shape[:-1] == decoder_out.shape[:-1]
+        assert encoder_out.ndim == decoder_out.ndim
+        assert encoder_out.ndim in (2, 4)
+        assert encoder_out.shape == decoder_out.shape
 
         if project_input:
             logit = self.encoder_proj(encoder_out) + self.decoder_proj(

egs/librispeech/ASR/transducer_stateless/conformer.py

@@ -246,11 +246,11 @@ class Conformer(Transformer):
         x: torch.Tensor,
         x_lens: torch.Tensor,
         states: List[torch.Tensor],
-        chunk_size: int = 16,
+        processed_lens: Optional[Tensor] = None,
         left_context: int = 64,
         right_context: int = 0,
+        chunk_size: int = 16,
         simulate_streaming: bool = False,
-        processed_lens: Optional[Tensor] = None,
     ) -> Tuple[torch.Tensor, torch.Tensor, List[torch.Tensor]]:
         """
         Args:
@@ -266,9 +266,8 @@ class Conformer(Transformer):
             the second element is the conv_cache which has a shape of
             (encoder_layers, cnn_module_kernel-1, batch, conv_dim).
             Note: states will be modified in this function.
-          chunk_size:
+          processed_lens:
-            The chunk size for decoding, this will be used to simulate streaming
+            How many frames (after subsampling) have been processed for each sequence.
-            decoding using masking.
           left_context:
             How many previous frames the attention can see in current chunk.
             Note: It's not that each individual frame has `left_context` frames
@@ -277,13 +276,14 @@ class Conformer(Transformer):
             How many future frames the attention can see in current chunk.
             Note: It's not that each individual frame has `right_context` frames
             of right context, some have more.
+          chunk_size:
+            The chunk size for decoding, this will be used to simulate streaming
+            decoding using masking.
           simulate_streaming:
             If setting True, it will use a masking strategy to simulate streaming
             fashion (i.e. every chunk data only see limited left context and
             right context). The whole sequence is supposed to be send at a time
             When using simulate_streaming.
-          processed_lens:
-            How many frames (after subsampling) have been processed for each sequence.
         Returns:
           Return a tuple containing 2 tensors:
             - logits, its shape is (batch_size, output_seq_len, output_dim)