Minor fixes for torch.jit.script support (#1329)

egs/aishell/ASR/transducer_stateless/decoder.py

@@ -70,6 +70,10 @@ def __init__(
  groups=embedding_dim,
  bias=False,
  )
+ else:
+ # To avoid `RuntimeError: Module 'Decoder' has no attribute 'conv'`
+ # when inference with torch.jit.script and context_size == 1
+ self.conv = nn.Identity()
 
  def forward(self, y: torch.Tensor, need_pad: bool = True) -> torch.Tensor:
  """

egs/libriheavy/ASR/zipformer_prompt_asr/decoder.py

@@ -95,6 +95,10 @@ def __init__(
  max_abs=1.0,
  prob=0.05,
  )
+ else:
+ # To avoid `RuntimeError: Module 'Decoder' has no attribute 'conv'`
+ # when inference with torch.jit.script and context_size == 1
+ self.conv = nn.Identity()
 
  def forward(self, y: torch.Tensor, need_pad: bool = True) -> torch.Tensor:
  """

egs/librispeech/ASR/pruned_transducer_stateless/decoder.py

@@ -74,6 +74,10 @@ def __init__(
  groups=embedding_dim,
  bias=False,
  )
+ else:
+ # To avoid `RuntimeError: Module 'Decoder' has no attribute 'conv'`
+ # when inference with torch.jit.script and context_size == 1
+ self.conv = nn.Identity()
  self.output_linear = nn.Linear(embedding_dim, vocab_size)
 
  def forward(self, y: torch.Tensor, need_pad: bool = True) -> torch.Tensor:

egs/librispeech/ASR/transducer_stateless/decoder.py

@@ -71,6 +71,10 @@ def __init__(
  groups=embedding_dim,
  bias=False,
  )
+ else:
+ # To avoid `RuntimeError: Module 'Decoder' has no attribute 'conv'`
+ # when inference with torch.jit.script and context_size == 1
+ self.conv = nn.Identity()
 
  def forward(self, y: torch.Tensor, need_pad: bool = True) -> torch.Tensor:
  """

egs/librispeech/ASR/zipformer/decoder.py

@@ -17,7 +17,6 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-
 from scaling import Balancer
 
 
@@ -95,6 +94,10 @@ def __init__(
  max_abs=1.0,
  prob=0.05,
  )
+ else:
+ # To avoid `RuntimeError: Module 'Decoder' has no attribute 'conv'`
+ # when inference with torch.jit.script and context_size == 1
+ self.conv = nn.Identity()
 
  def forward(self, y: torch.Tensor, need_pad: bool = True) -> torch.Tensor:
  """