diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/joiner.py b/egs/librispeech/ASR/pruned_transducer_stateless2/joiner.py
index a1226f712..752a5f774 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless2/joiner.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless2/joiner.py
@@ -35,7 +35,8 @@ class Joiner(nn.Module):
         self.output_linear = ScaledLinear(joiner_dim, vocab_size)
 
     def forward(
-        self, encoder_out: torch.Tensor, decoder_out: torch.Tensor
+            self, encoder_out: torch.Tensor, decoder_out: torch.Tensor,
+            project_input: bool = True
     ) -> torch.Tensor:
         """
         Args:
@@ -43,13 +44,20 @@ class Joiner(nn.Module):
             Output from the encoder. Its shape is (N, T, s_range, C).
           decoder_out:
             Output from the decoder. Its shape is (N, T, s_range, C).
+           project_input:
+            If true, apply input projections encoder_proj and decoder_proj.
+            If this is false, it is the user's responsibility to do this
+            manually.
         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]
 
-        logit = self.encoder_proj(encoder_out) + self.decoder_proj(decoder_out)
+        if project_input:
+            logit = self.encoder_proj(encoder_out) + self.decoder_proj(decoder_out)
+        else:
+            logit = encoder_out + decoder_out
 
         logit = self.output_linear(torch.tanh(logit))
 
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/model.py b/egs/librispeech/ASR/pruned_transducer_stateless2/model.py
index 1dd20c546..a9178c8b3 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless2/model.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless2/model.py
@@ -164,11 +164,17 @@ class Transducer(nn.Module):
         # am_pruned : [B, T, prune_range, encoder_dim]
         # lm_pruned : [B, T, prune_range, decoder_dim]
         am_pruned, lm_pruned = k2.do_rnnt_pruning(
-            am=encoder_out, lm=decoder_out, ranges=ranges
+            am=self.joiner.encoder_proj(encoder_out),
+            lm=self.joiner.decoder_proj(decoder_out),
+            ranges=ranges
         )
 
         # logits : [B, T, prune_range, vocab_size]
-        logits = self.joiner(am_pruned, lm_pruned)
+
+        # project_input=False since we applied the decoder's input projections
+        # prior to do_rnnt_pruning (this is an optimization for speed).
+        logits = self.joiner(am_pruned, lm_pruned,
+                             project_input=False)
 
         pruned_loss = k2.rnnt_loss_pruned(
             logits=logits,