make states in forward streaming optional

egs/librispeech/ASR/pruned_transducer_stateless/decode.py

@@ -377,7 +377,6 @@ def decode_one_batch(
         encoder_out, encoder_out_lens, _ = model.encoder.streaming_forward(
             x=feature,
             x_lens=feature_lens,
-            states=[],
             chunk_size=params.decode_chunk_size,
             left_context=params.left_context,
             simulate_streaming=True,

egs/librispeech/ASR/pruned_transducer_stateless2/conformer.py

@@ -249,7 +249,7 @@ class Conformer(EncoderInterface):
         self,
         x: torch.Tensor,
         x_lens: torch.Tensor,
-        states: List[Tensor],
+        states: Optional[List[Tensor]] = None,
         processed_lens: Optional[Tensor] = None,
         left_context: int = 64,
         right_context: int = 4,
@@ -311,6 +311,8 @@ class Conformer(EncoderInterface):
         lengths = (((x_lens - 1) >> 1) - 1) >> 1
 
         if not simulate_streaming:
+            assert states is not None
+            assert processed_lens is not None
             assert (
                 len(states) == 2
                 and states[0].shape
@@ -332,8 +334,6 @@ class Conformer(EncoderInterface):
 
             src_key_padding_mask = make_pad_mask(lengths)
 
-            assert processed_lens is not None
-
             processed_mask = torch.arange(left_context, device=x.device).expand(
                 x.size(0), left_context
             )
@@ -366,6 +366,8 @@ class Conformer(EncoderInterface):
                 x = x[0:-right_context, ...]
                 lengths -= right_context
         else:
+            assert states is None
+            states = []  # just to make torch.script.jit happy
             # this branch simulates streaming decoding using mask as we are
             # using in training time.
             src_key_padding_mask = make_pad_mask(lengths)

egs/librispeech/ASR/pruned_transducer_stateless2/decode.py

@@ -391,7 +391,6 @@ def decode_one_batch(
         encoder_out, encoder_out_lens, _ = model.encoder.streaming_forward(
             x=feature,
             x_lens=feature_lens,
-            states=[],
             chunk_size=params.decode_chunk_size,
             left_context=params.left_context,
             simulate_streaming=True,

egs/librispeech/ASR/pruned_transducer_stateless3/decode.py

@@ -377,7 +377,6 @@ def decode_one_batch(
         encoder_out, encoder_out_lens, _ = model.encoder.streaming_forward(
             x=feature,
             x_lens=feature_lens,
-            states=[],
             chunk_size=params.decode_chunk_size,
             left_context=params.left_context,
             simulate_streaming=True,

egs/librispeech/ASR/pruned_transducer_stateless4/decode.py

@@ -403,7 +403,6 @@ def decode_one_batch(
         encoder_out, encoder_out_lens, _ = model.encoder.streaming_forward(
             x=feature,
             x_lens=feature_lens,
-            states=[],
             chunk_size=params.decode_chunk_size,
             left_context=params.left_context,
             simulate_streaming=True,

egs/librispeech/ASR/transducer_stateless/conformer.py

@@ -245,7 +245,7 @@ class Conformer(Transformer):
         self,
         x: torch.Tensor,
         x_lens: torch.Tensor,
-        states: List[torch.Tensor],
+        states: Optional[List[torch.Tensor]] = None,
         processed_lens: Optional[Tensor] = None,
         left_context: int = 64,
         right_context: int = 0,
@@ -302,6 +302,8 @@ class Conformer(Transformer):
         lengths = (((x_lens - 1) >> 1) - 1) >> 1
 
         if not simulate_streaming:
+            assert states is not None
+            assert processed_lens is not None
             assert (
                 len(states) == 2
                 and states[0].shape
@@ -322,8 +324,6 @@ class Conformer(Transformer):
             lengths -= 2  # we will cut off 1 frame on each side of encoder_embed output
             src_key_padding_mask = make_pad_mask(lengths)
 
-            assert processed_lens is not None
-
             processed_mask = torch.arange(left_context, device=x.device).expand(
                 x.size(0), left_context
             )
@@ -352,6 +352,8 @@ class Conformer(Transformer):
                 right_context=right_context,
             )  # (T, B, F)
         else:
+            assert states is None
+            states = []  # just to make torch.script.jit happy
             src_key_padding_mask = make_pad_mask(lengths)
             x = self.encoder_embed(x)
             x, pos_emb = self.encoder_pos(x)