Implement greedy search in batch mode for transducer decoding. (#262)

egs/librispeech/ASR/pruned_transducer_stateless/beam_search.py

@@ -106,7 +106,7 @@ def fast_beam_search(
 def greedy_search(
     model: Transducer, encoder_out: torch.Tensor, max_sym_per_frame: int
 ) -> List[int]:
-    """
+    """Greedy search for a single utterance.
     Args:
       model:
         An instance of `Transducer`.
@@ -178,6 +178,64 @@ def greedy_search(
     return hyp
 
 
+def greedy_search_batch(
+    model: Transducer, encoder_out: torch.Tensor
+) -> List[List[int]]:
+    """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1.
+    Args:
+      model:
+        The transducer model.
+      encoder_out:
+        Output from the encoder. Its shape is (N, T, C), where N >= 1.
+    Returns:
+      Return a list-of-list integers containing the decoded results.
+      len(ans) equals to encoder_out.size(0).
+    """
+    assert encoder_out.ndim == 3
+    assert encoder_out.size(0) >= 1, encoder_out.size(0)
+
+    device = model.device
+
+    batch_size = encoder_out.size(0)
+    T = encoder_out.size(1)
+
+    blank_id = model.decoder.blank_id
+    context_size = model.decoder.context_size
+
+    hyps = [[blank_id] * context_size for _ in range(batch_size)]
+
+    decoder_input = torch.tensor(
+        hyps,
+        device=device,
+        dtype=torch.int64,
+    )  # (batch_size, context_size)
+
+    decoder_out = model.decoder(decoder_input, need_pad=False)
+    # decoder_out: (batch_size, 1, decoder_out_dim)
+    for t in range(T):
+        current_encoder_out = encoder_out[:, t : t + 1, :].unsqueeze(2)  # noqa
+        # current_encoder_out's shape: (batch_size, 1, 1, encoder_out_dim)
+        logits = model.joiner(current_encoder_out, decoder_out.unsqueeze(1))
+        # logits'shape (batch_size, 1, 1, vocab_size)
+
+        logits = logits.squeeze(1).squeeze(1)  # (batch_size, vocab_size)
+        assert logits.ndim == 2, logits.shape
+        y = logits.argmax(dim=1).tolist()
+        emitted = False
+        for i, v in enumerate(y):
+            if v != blank_id:
+                hyps[i].append(v)
+                emitted = True
+        if emitted:
+            # update decoder output
+            decoder_input = [h[-context_size:] for h in hyps]
+            decoder_input = torch.tensor(decoder_input, device=device)
+            decoder_out = model.decoder(decoder_input, need_pad=False)
+
+    ans = [h[context_size:] for h in hyps]
+    return ans
+
+
 @dataclass
 class Hypothesis:
     # The predicted tokens so far.

egs/librispeech/ASR/pruned_transducer_stateless/decode.py

@@ -71,6 +71,7 @@ from beam_search import (
     beam_search,
     fast_beam_search,
     greedy_search,
+    greedy_search_batch,
     modified_beam_search,
 )
 from train import get_params, get_transducer_model
@@ -261,6 +262,16 @@ def decode_one_batch(
         )
         for hyp in sp.decode(hyp_tokens):
             hyps.append(hyp.split())
+    elif (
+        params.decoding_method == "greedy_search"
+        and params.max_sym_per_frame == 1
+    ):
+        hyp_tokens = greedy_search_batch(
+            model=model,
+            encoder_out=encoder_out,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
     else:
         batch_size = encoder_out.size(0)