from local

egs/aishell/ASR/transformer_ctc/decode.py

@@ -175,6 +175,37 @@ def decode_one_batch_greedy(
     token_dict: dict,
     ) -> Dict[str, List[List[int]]]:
     
+    if HLG is not None:
+        device = HLG.device
+    else:
+        device = H.device
+
+    feature = batch["inputs"]
+    assert feature.ndim == 3
+    feature = feature.to(device)
+    # at entry, feature is (N, T, C)
+
+    supervisions = batch["supervisions"]
+
+    nnet_output, memory, memory_key_padding_mask = model(feature, supervisions)
+    # nnet_output is (N, T, C)
+
+    supervision_segments = torch.stack(
+        (
+            supervisions["sequence_idx"],
+            supervisions["start_frame"] // params.subsampling_factor,
+            supervisions["num_frames"] // params.subsampling_factor,
+        ),
+        1,
+    ).to(torch.int32)
+
+    if H is None:
+        assert HLG is not None
+        decoding_graph = HLG
+    else:
+        assert HLG is None
+        decoding_graph = H
+
     if params.method == 'greedy-search' or params.method == 'ctc-decoding':
         batch_size = nnet_output.size(0)
         for i in range(batch_size):