remove hlg related modifications

egs/librispeech/ASR/local/compile_hlg.py

@@ -47,19 +47,10 @@ def get_args():
         """,
     )
 
-    parser.add_argument(
-        "--h-graph",
-        type=str,
-        help="""one of ["H", "Trivial"]
-        H: k2.ctc_topo
-        Trivial: k2.trivial_graph
-        """,
-    )
-
     return parser.parse_args()
 
 
-def compile_HLG(lang_dir: str, h_graph: str = "H") -> k2.Fsa:
+def compile_HLG(lang_dir: str) -> k2.Fsa:
     """
     Args:
       lang_dir:
@@ -71,14 +62,7 @@ def compile_HLG(lang_dir: str, h_graph: str = "H") -> k2.Fsa:
     lexicon = Lexicon(lang_dir)
     max_token_id = max(lexicon.tokens)
     logging.info(f"Building ctc_topo. max_token_id: {max_token_id}")
-
-    if h_graph == "H":
-        H = k2.ctc_topo(max_token_id)
-    elif h_graph == "Trivial":
-        H = k2.trivial_graph(max_token_id - 1)
-    else:
-        raise ValueError(f"Unsupported h_graph: {h_graph}")
-
+    H = k2.ctc_topo(max_token_id)
     L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt"))
 
     if Path("data/lm/G_3_gram.pt").is_file():
@@ -154,17 +138,15 @@ def main():
     args = get_args()
     lang_dir = Path(args.lang_dir)
 
-    if (lang_dir / f"{args.h_graph}LG.pt").is_file():
-        logging.info(
-            f"{lang_dir}/{args.h_graph}LG.pt already exists - skipping"
-        )
+    if (lang_dir / "HLG.pt").is_file():
+        logging.info(f"{lang_dir}/HLG.pt already exists - skipping")
         return
 
     logging.info(f"Processing {lang_dir}")
 
     HLG = compile_HLG(lang_dir)
-    logging.info(f"Saving {args.h_graph}LG.pt to {lang_dir}")
-    torch.save(HLG.as_dict(), f"{lang_dir}/{args.h_graph}LG.pt")
+    logging.info(f"Saving HLG.pt to {lang_dir}")
+    torch.save(HLG.as_dict(), f"{lang_dir}/HLG.pt")
 
 
 if __name__ == "__main__":

egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py

@@ -614,15 +614,14 @@ def greedy_search_batch(
         logits = model.joiner(
             current_encoder_out, decoder_out.unsqueeze(1), project_input=False
         )
-        # logits'shape (batch_size, 1, 1, vocab_size)
 
+        # logits'shape (batch_size, 1, 1, vocab_size)
         logits = logits.squeeze(1).squeeze(1)  # (batch_size, vocab_size)
 
         if ngram_rescoring:
             all_logits[start:end] = logits
 
             assert logits.ndim == 2, logits.shape
-            logits_argmax = logits.argmax(dim=1)
             logits_softmax = logits.softmax(dim=1)
 
 
@@ -729,9 +728,6 @@ def greedy_search_batch(
         subsampling_factor=1,
     )
 
-    lm_weight = 0.5  # (TODO): tuning this.
-    lattice.scores = lattice.scores - lattice.lm_scores * (1 - lm_weight)
-
     best_path = one_best_decoding(
         lattice=lattice,
         use_double_scores=True,

egs/librispeech/ASR/pruned_transducer_stateless6/decode.py

@@ -659,8 +659,6 @@ def main():
             )
             decoding_graph = k2.add_epsilon_self_loops(decoding_graph)
 
-        decoding_graph.lm_scores = decoding_graph.scores.clone()
-
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")