Support using log_add in LG decoding with fast_beam_search.

egs/librispeech/ASR/pruned_transducer_stateless/beam_search.py

@@ -86,6 +86,7 @@ def fast_beam_search_nbest(
     num_paths: int,
     nbest_scale: float = 0.5,
     use_double_scores: bool = True,
+    use_max: bool = True,
 ) -> List[List[int]]:
     """It limits the maximum number of symbols per frame to 1.
 
@@ -121,6 +122,8 @@ def fast_beam_search_nbest(
       use_double_scores:
         True to use double precision for computation. False to use
         single precision.
+      use_max:
+        False to use log-add to compute total scores. True to use max.
     Returns:
       Return the decoded result.
     """
@@ -141,14 +144,47 @@ def fast_beam_search_nbest(
         nbest_scale=nbest_scale,
     )
 
-    # at this point, nbest.fsa.scores are all zeros.
+    # The following code is modified from nbest.intersect()
+    word_fsa = k2.invert(nbest.fsa)
+    if hasattr(lattice, "aux_labels"):
+        # delete token IDs as it is not needed
+        del word_fsa.aux_labels
+    word_fsa.scores.zero_()
+    word_fsa_with_epsilon_loops = k2.linear_fsa_with_self_loops(word_fsa)
+    path_to_utt_map = nbest.shape.row_ids(1)
 
-    nbest = nbest.intersect(lattice)
+    if hasattr(lattice, "aux_labels"):
-    # Now nbest.fsa.scores contains acoustic scores
+        # lattice has token IDs as labels and word IDs as aux_labels.
+        # inv_lattice has word IDs as labels and token IDs as aux_labels
+        inv_lattice = k2.invert(lattice)
+        inv_lattice = k2.arc_sort(inv_lattice)
+    else:
+        inv_lattice = k2.arc_sort(lattice)
 
-    max_indexes = nbest.tot_scores().argmax()
+    if inv_lattice.shape[0] == 1:
+        path_lattice = k2.intersect_device(
+            inv_lattice,
+            word_fsa_with_epsilon_loops,
+            b_to_a_map=torch.zeros_like(path_to_utt_map),
+            sorted_match_a=True,
+        )
+    else:
+        path_lattice = k2.intersect_device(
+            inv_lattice,
+            word_fsa_with_epsilon_loops,
+            b_to_a_map=path_to_utt_map,
+            sorted_match_a=True,
+        )
 
-    best_path = k2.index_fsa(nbest.fsa, max_indexes)
+    # path_lattice has word IDs as labels and token IDs as aux_labels
+    path_lattice = k2.top_sort(k2.connect(path_lattice))
+    tot_scores = path_lattice.get_tot_scores(
+        use_double_scores=use_double_scores,
+        log_semiring=(False if use_max else True),
+    )
+    ragged_tot_scores = k2.RaggedTensor(nbest.shape, tot_scores)
+    best_hyp_indexes = ragged_tot_scores.argmax()
+    best_path = k2.index_fsa(nbest.fsa, best_hyp_indexes)
 
     hyps = get_texts(best_path)
 

egs/librispeech/ASR/pruned_transducer_stateless/decode.py

@@ -154,7 +154,7 @@ def get_parser():
 
     parser.add_argument(
         "--lang-dir",
-        type=str,
+        type=Path,
         default="data/lang_bpe_500",
         help="The lang dir containing word table and LG graph",
     )
@@ -195,8 +195,8 @@ def get_parser():
         type=str2bool,
         default=False,
         help="""Whether to use an LG graph for FSA-based beam search.
-        Used only when --decoding_method is fast_beam_search. If setting true,
+        Used only when --decoding_method is fast_beam_search. If true,
-        it assumes there is an LG.pt file in lang_dir.""",
+        it uses lang_dir/LG.pt during decoding.""",
     )
 
     parser.add_argument(
@@ -320,6 +320,7 @@ def decode_one_batch(
     # at entry, feature is (N, T, C)
 
     supervisions = batch["supervisions"]
+
     feature_lens = supervisions["num_frames"].to(device)
 
     encoder_out, encoder_out_lens = model.encoder(
@@ -351,6 +352,7 @@ def decode_one_batch(
                 max_states=params.max_states,
                 num_paths=params.num_paths,
                 nbest_scale=params.nbest_scale,
+                use_max=params.use_max,
             )
             for hyp in hyp_tokens:
                 hyps.append([word_table[i] for i in hyp])
@@ -563,7 +565,10 @@ def main():
         params.suffix += f"-beam-{params.beam}"
         params.suffix += f"-max-contexts-{params.max_contexts}"
         params.suffix += f"-max-states-{params.max_states}"
-        params.suffix += f"-use-max-{params.use_max}"
+        if params.use_LG:
+            params.suffix += f"-use-max-{params.use_max}"
+            params.suffix += f"-nbest-scale-{params.nbest_scale}"
+            params.suffix += f"-num-paths-{params.num_paths}"
     elif "beam_search" in params.decoding_method:
         params.suffix += (
             f"-{params.decoding_method}-beam-size-{params.beam_size}"
@@ -632,8 +637,10 @@ def main():
         if params.use_LG:
             lexicon = Lexicon(params.lang_dir)
             word_table = lexicon.word_table
+            lg_filename = params.lang_dir / "LG.pt"
+            logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(f"{params.lang_dir}/LG.pt", map_location=device)
+                torch.load(lg_filename, map_location=device)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else: