Judge before UseCachedDecoderOut (#431)

Co-authored-by: hiedean <hiedean@tju.edu.cn>

@@ -89,9 +89,24 @@ void OnlineTransducerGreedySearchDecoder::Decode(
   int32_t num_frames = static_cast<int32_t>(encoder_out_shape[1]);
   int32_t num_frames = static_cast<int32_t>(encoder_out_shape[1]);
   int32_t vocab_size = model_->VocabSize();
   int32_t vocab_size = model_->VocabSize();
 
 
-  Ort::Value decoder_input = model_->BuildDecoderInput(*result);
-  Ort::Value decoder_out = model_->RunDecoder(std::move(decoder_input));
-  UseCachedDecoderOut(*result, &decoder_out);
+  Ort::Value decoder_out{nullptr};
+  bool is_batch_decoder_out_cached = true;
+  for (const auto &r : *result) {
+    if (!r.decoder_out) {
+      is_batch_decoder_out_cached = false;
+      break;
+    }
+  }
+  if (is_batch_decoder_out_cached) {
+    auto &r = result->front();
+    std::vector<int64_t> decoder_out_shape = r.decoder_out.GetTensorTypeAndShapeInfo().GetShape();
+    decoder_out_shape[0] = batch_size;
+    decoder_out = Ort::Value::CreateTensor<float>(model_->Allocator(), decoder_out_shape.data(), decoder_out_shape.size());
+    UseCachedDecoderOut(*result, &decoder_out);
+  } else {
+    Ort::Value decoder_input = model_->BuildDecoderInput(*result);
+    decoder_out = model_->RunDecoder(std::move(decoder_input));
+  }
 
 
   for (int32_t t = 0; t != num_frames; ++t) {
   for (int32_t t = 0; t != num_frames; ++t) {
     Ort::Value cur_encoder_out =
     Ort::Value cur_encoder_out =