diff --git a/sherpa-onnx/csrc/offline-transducer-greedy-search-nemo-decoder.cc b/sherpa-onnx/csrc/offline-transducer-greedy-search-nemo-decoder.cc
index cff6b81d9e..9db537c0f8 100644
--- a/sherpa-onnx/csrc/offline-transducer-greedy-search-nemo-decoder.cc
+++ b/sherpa-onnx/csrc/offline-transducer-greedy-search-nemo-decoder.cc
@@ -62,34 +62,34 @@ static OfflineTransducerDecoderResult DecodeOne(
         encoder_shape.data(), encoder_shape.size());
 
     for (int32_t q = 0; q != max_symbols_per_frame; ++q) {
-        Ort::Value logit = model->RunJoiner(View(&cur_encoder_out),
-                                            View(&decoder_output_pair.first));
-
-        float *p_logit = logit.GetTensorMutableData<float>();
-        if (blank_penalty > 0) {
-          p_logit[blank_id] -= blank_penalty;
-        }
-
-        auto y = static_cast<int32_t>(std::distance(
-            static_cast<const float *>(p_logit),
-            std::max_element(static_cast<const float *>(p_logit),
-                             static_cast<const float *>(p_logit) + vocab_size)));
-
-        if (y != blank_id) {
-          ans.tokens.push_back(y);
-          ans.timestamps.push_back(t);
-
-          decoder_input_pair = BuildDecoderInput(y, model->Allocator());
-
-          decoder_output_pair =
-              model->RunDecoder(std::move(decoder_input_pair.first),
-                                std::move(decoder_input_pair.second),
-                                std::move(decoder_output_pair.second));
-        } else {
-           break;
-        } // if (y != blank_id)
+      Ort::Value logit = model->RunJoiner(View(&cur_encoder_out),
+                                          View(&decoder_output_pair.first));
+
+      float *p_logit = logit.GetTensorMutableData<float>();
+      if (blank_penalty > 0) {
+        p_logit[blank_id] -= blank_penalty;
+      }
+
+      auto y = static_cast<int32_t>(std::distance(
+          static_cast<const float *>(p_logit),
+          std::max_element(static_cast<const float *>(p_logit),
+                           static_cast<const float *>(p_logit) + vocab_size)));
+
+      if (y != blank_id) {
+        ans.tokens.push_back(y);
+        ans.timestamps.push_back(t);
+
+        decoder_input_pair = BuildDecoderInput(y, model->Allocator());
+
+        decoder_output_pair =
+            model->RunDecoder(std::move(decoder_input_pair.first),
+                              std::move(decoder_input_pair.second),
+                              std::move(decoder_output_pair.second));
+      } else {
+        break;
+      }  // if (y != blank_id)
     }
-  }    // for (int32_t i = 0; i != num_rows; ++i)
+  }  // for (int32_t i = 0; i != num_rows; ++i)
 
   return ans;
 }
@@ -104,14 +104,24 @@ OfflineTransducerGreedySearchNeMoDecoder::Decode(
   int32_t dim1 = static_cast<int32_t>(shape[1]);
   int32_t dim2 = static_cast<int32_t>(shape[2]);
 
-  const int32_t *p_length = encoder_out_length.GetTensorData<int32_t>();
+  auto length_type =
+      encoder_out_length.GetTensorTypeAndShapeInfo().GetElementType();
+  if ((length_type != ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32) &&
+      (length_type != ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64)) {
+    SHERPA_ONNX_LOGE("Unsupported encoder_out_length data type: %d",
+                     static_cast<int32_t>(length_type));
+    SHERPA_ONNX_EXIT(-1);
+  }
+
   const float *p = encoder_out.GetTensorData<float>();
 
   std::vector<OfflineTransducerDecoderResult> ans(batch_size);
 
   for (int32_t i = 0; i != batch_size; ++i) {
     const float *this_p = p + dim1 * dim2 * i;
-    int32_t this_len = p_length[i];
+    int32_t this_len = length_type == ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32
+                           ? encoder_out_length.GetTensorData<int32_t>()[i]
+                           : encoder_out_length.GetTensorData<int64_t>()[i];
 
     ans[i] = DecodeOne(this_p, this_len, dim2, model_, blank_penalty_);
   }