Minor fixes

egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py

@@ -608,7 +608,7 @@ def greedy_search(
         # logits is (1, 1, 1, vocab_size)
 
         if blank_penalty != 0:
-            logits[:,:,:,0] -= blank_penalty
+            logits[:, :, :, 0] -= blank_penalty
 
         y = logits.argmax().item()
         if y not in (blank_id, unk_id):
@@ -1748,7 +1748,7 @@ def beam_search(
                 )
 
                 if blank_penalty != 0:
-                    logits[:,:,:,0] -= blank_penalty
+                    logits[:, :, :, 0] -= blank_penalty
 
                 # TODO(fangjun): Scale the blank posterior
                 log_prob = (logits / temperature).log_softmax(dim=-1)

egs/wenetspeech/ASR/pruned_transducer_stateless7/decode.py

@@ -123,9 +123,9 @@ from beam_search import (
     greedy_search_batch,
     modified_beam_search,
 )
+from lhotse.cut import Cut
 from train import add_model_arguments, get_params, get_transducer_model
 
-from lhotse.cut import Cut
 from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler
 from icefall.checkpoint import (
     average_checkpoints,
@@ -324,7 +324,7 @@ def decode_one_batch(
     params: AttributeDict,
     model: nn.Module,
     lexicon: Lexicon,
-    #  graph_compiler: CharCtcTrainingGraphCompiler,
+    graph_compiler: CharCtcTrainingGraphCompiler,
     batch: dict,
     decoding_graph: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[List[str]]]:
@@ -431,7 +431,10 @@ def decode_one_batch(
             hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
     elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1:
         hyp_tokens = greedy_search_batch(
-            model=model, encoder_out=encoder_out, encoder_out_lens=encoder_out_lens, blank_penalty=params.blank_penalty,
+            model=model,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            blank_penalty=params.blank_penalty,
         )
         for i in range(encoder_out.size(0)):
             hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
@@ -461,7 +464,9 @@ def decode_one_batch(
                 )
             elif params.decoding_method == "beam_search":
                 hyp = beam_search(
-                    model=model, encoder_out=encoder_out_i, beam=params.beam_size,
+                    model=model,
+                    encoder_out=encoder_out_i,
+                    beam=params.beam_size,
                     blank_penalty=params.blank_penalty,
                 )
             else:
@@ -493,6 +498,7 @@ def decode_dataset(
     params: AttributeDict,
     model: nn.Module,
     lexicon: Lexicon,
+    graph_compiler: CharCtcTrainingGraphCompiler,
     decoding_graph: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[Tuple[List[str], List[str]]]]:
     """Decode dataset.
@@ -538,6 +544,7 @@ def decode_dataset(
             model=model,
             lexicon=lexicon,
             decoding_graph=decoding_graph,
+            graph_compiler=graph_compiler,
             batch=batch,
         )
 
@@ -660,6 +667,11 @@ def main():
     params.blank_id = lexicon.token_table["<blk>"]
     params.vocab_size = max(lexicon.tokens) + 1
 
+    graph_compiler = CharCtcTrainingGraphCompiler(
+        lexicon=lexicon,
+        device=device,
+    )
+
     logging.info(params)
 
     logging.info("About to create model")
@@ -747,8 +759,6 @@ def main():
 
     if "fast_beam_search" in params.decoding_method:
         if params.decoding_method == "fast_beam_search_nbest_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(
@@ -756,11 +766,9 @@ def main():
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:
-            #  word_table = None
             decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
     else:
         decoding_graph = None
-        #  word_table = None
 
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
@@ -791,8 +799,6 @@ def main():
 
     test_sets = ["DEV", "TEST_NET", "TEST_MEETING"]
     test_dl = [dev_dl, test_net_dl, test_meeting_dl]
-    # test_sets = ["TEST_MEETING"]
-    # test_dl = [test_meeting_dl]
 
     for test_set, test_dl in zip(test_sets, test_dl):
         results_dict = decode_dataset(
@@ -800,12 +806,14 @@ def main():
             params=params,
             model=model,
             lexicon=lexicon,
-            #  word_table=word_table,
+            graph_compiler=graph_compiler,
             decoding_graph=decoding_graph,
         )
 
         save_results(
-            params=params, test_set_name=test_set, results_dict=results_dict,
+            params=params,
+            test_set_name=test_set,
+            results_dict=results_dict,
         )
 
     logging.info("Done!")