Rename lattice_score_scale to nbest_scale.

docs/source/recipes/librispeech/conformer_ctc.rst

@@ -299,9 +299,9 @@ The commonly used options are:
     .. code-block::
 
       $ cd egs/librispeech/ASR
-      $ ./conformer_ctc/decode.py --method attention-decoder --max-duration 30 --lattice-score-scale 0.5
+      $ ./conformer_ctc/decode.py --method attention-decoder --max-duration 30 --nbest-scale 0.5
 
-  - ``--lattice-score-scale``
+  - ``--nbest-scale``
 
     It is used to scale down lattice scores so that there are more unique
     paths for rescoring.
@@ -577,7 +577,7 @@ The command to run HLG decoding + LM rescoring + attention decoder rescoring is:
     --G ./tmp/icefall_asr_librispeech_conformer_ctc/data/lm/G_4_gram.pt \
     --ngram-lm-scale 1.3 \
     --attention-decoder-scale 1.2 \
-    --lattice-score-scale 0.5 \
+    --nbest-scale 0.5 \
     --num-paths 100 \
     --sos-id 1 \
     --eos-id 1 \

egs/librispeech/ASR/RESULTS.md

@@ -40,7 +40,7 @@ python conformer_ctc/train.py --bucketing-sampler True \
                               --full-libri True \
                               --world-size 4
 
-python conformer_ctc/decode.py --lattice-score-scale 0.5 \
+python conformer_ctc/decode.py --nbest-scale 0.5 \
                                --epoch 34 \
                                --avg 20 \
                                --method attention-decoder \

egs/librispeech/ASR/conformer_ctc/decode.py

@@ -106,7 +106,7 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--lattice-score-scale",
+        "--nbest-scale",
         type=float,
         default=0.5,
         help="""The scale to be applied to `lattice.scores`.
@@ -250,12 +250,12 @@ def decode_one_batch(
             num_paths=params.num_paths,
             ref_texts=supervisions["text"],
             word_table=word_table,
-            lattice_score_scale=params.lattice_score_scale,
+            nbest_scale=params.nbest_scale,
             oov="<UNK>",
         )
         hyps = get_texts(best_path)
         hyps = [[word_table[i] for i in ids] for ids in hyps]
-        key = f"oracle_{params.num_paths}_lattice_score_scale_{params.lattice_score_scale}"  # noqa
+        key = f"oracle_{params.num_paths}_nbest_scale_{params.nbest_scale}"  # noqa
         return {key: hyps}
 
     if params.method in ["1best", "nbest"]:
@@ -269,9 +269,9 @@ def decode_one_batch(
                 lattice=lattice,
                 num_paths=params.num_paths,
                 use_double_scores=params.use_double_scores,
-                lattice_score_scale=params.lattice_score_scale,
+                nbest_scale=params.nbest_scale,
             )
-            key = f"no_rescore-scale-{params.lattice_score_scale}-{params.num_paths}"  # noqa
+            key = f"no_rescore-nbest-scale-{params.nbest_scale}-{params.num_paths}"  # noqa
 
         hyps = get_texts(best_path)
         hyps = [[word_table[i] for i in ids] for ids in hyps]
@@ -293,7 +293,7 @@ def decode_one_batch(
             G=G,
             num_paths=params.num_paths,
             lm_scale_list=lm_scale_list,
-            lattice_score_scale=params.lattice_score_scale,
+            nbest_scale=params.nbest_scale,
         )
     elif params.method == "whole-lattice-rescoring":
         best_path_dict = rescore_with_whole_lattice(
@@ -319,7 +319,7 @@ def decode_one_batch(
             memory_key_padding_mask=memory_key_padding_mask,
             sos_id=sos_id,
             eos_id=eos_id,
-            lattice_score_scale=params.lattice_score_scale,
+            nbest_scale=params.nbest_scale,
         )
     else:
         assert False, f"Unsupported decoding method: {params.method}"

egs/librispeech/ASR/conformer_ctc/pretrained.py

@@ -125,7 +125,7 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--lattice-score-scale",
+        "--nbest-scale",
         type=float,
         default=0.5,
         help="""
@@ -336,7 +336,7 @@ def main():
             memory_key_padding_mask=memory_key_padding_mask,
             sos_id=params.sos_id,
             eos_id=params.eos_id,
-            lattice_score_scale=params.lattice_score_scale,
+            nbest_scale=params.nbest_scale,
             ngram_lm_scale=params.ngram_lm_scale,
             attention_scale=params.attention_decoder_scale,
         )

egs/librispeech/ASR/tdnn_lstm_ctc/decode.py

@@ -97,7 +97,7 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--lattice-score-scale",
+        "--nbest-scale",
         type=float,
         default=0.5,
         help="""The scale to be applied to `lattice.scores`.
@@ -229,7 +229,7 @@ def decode_one_batch(
                 lattice=lattice,
                 num_paths=params.num_paths,
                 use_double_scores=params.use_double_scores,
-                lattice_score_scale=params.lattice_score_scale,
+                nbest_scale=params.nbest_scale,
             )
             key = f"no_rescore-{params.num_paths}"
         hyps = get_texts(best_path)
@@ -248,7 +248,7 @@ def decode_one_batch(
             G=G,
             num_paths=params.num_paths,
             lm_scale_list=lm_scale_list,
-            lattice_score_scale=params.lattice_score_scale,
+            nbest_scale=params.nbest_scale,
         )
     else:
         best_path_dict = rescore_with_whole_lattice(

icefall/decode.py

@@ -180,7 +180,7 @@ class Nbest(object):
         lattice: k2.Fsa,
         num_paths: int,
         use_double_scores: bool = True,
-        lattice_score_scale: float = 0.5,
+        nbest_scale: float = 0.5,
     ) -> "Nbest":
         """Construct an Nbest object by **sampling** `num_paths` from a lattice.
 
@@ -206,7 +206,7 @@ class Nbest(object):
           Return an Nbest instance.
         """
         saved_scores = lattice.scores.clone()
-        lattice.scores *= lattice_score_scale
+        lattice.scores *= nbest_scale
         # path is a ragged tensor with dtype torch.int32.
         # It has three axes [utt][path][arc_pos]
         path = k2.random_paths(
@@ -446,7 +446,7 @@ def nbest_decoding(
     lattice: k2.Fsa,
     num_paths: int,
     use_double_scores: bool = True,
-    lattice_score_scale: float = 1.0,
+    nbest_scale: float = 1.0,
 ) -> k2.Fsa:
     """It implements something like CTC prefix beam search using n-best lists.
 
@@ -474,7 +474,7 @@ def nbest_decoding(
       use_double_scores:
         True to use double precision floating point in the computation.
         False to use single precision.
-      lattice_score_scale:
+      nbest_scale:
         It's the scale applied to the `lattice.scores`. A smaller value
         leads to more unique paths at the risk of missing the correct path.
     Returns:
@@ -484,7 +484,7 @@ def nbest_decoding(
         lattice=lattice,
         num_paths=num_paths,
         use_double_scores=use_double_scores,
-        lattice_score_scale=lattice_score_scale,
+        nbest_scale=nbest_scale,
     )
     # nbest.fsa.scores contains 0s
 
@@ -505,7 +505,7 @@ def nbest_oracle(
     ref_texts: List[str],
     word_table: k2.SymbolTable,
     use_double_scores: bool = True,
-    lattice_score_scale: float = 0.5,
+    nbest_scale: float = 0.5,
     oov: str = "<UNK>",
 ) -> Dict[str, List[List[int]]]:
     """Select the best hypothesis given a lattice and a reference transcript.
@@ -517,7 +517,7 @@ def nbest_oracle(
     The decoding result returned from this function is the best result that
     we can obtain using n-best decoding with all kinds of rescoring techniques.
 
-    This function is useful to tune the value of `lattice_score_scale`.
+    This function is useful to tune the value of `nbest_scale`.
 
     Args:
       lattice:
@@ -533,7 +533,7 @@ def nbest_oracle(
       use_double_scores:
         True to use double precision for computation. False to use
         single precision.
-      lattice_score_scale:
+      nbest_scale:
         It's the scale applied to the lattice.scores. A smaller value
         yields more unique paths.
       oov:
@@ -549,7 +549,7 @@ def nbest_oracle(
         lattice=lattice,
         num_paths=num_paths,
         use_double_scores=use_double_scores,
-        lattice_score_scale=lattice_score_scale,
+        nbest_scale=nbest_scale,
     )
 
     hyps = nbest.build_levenshtein_graphs()
@@ -590,7 +590,7 @@ def rescore_with_n_best_list(
     G: k2.Fsa,
     num_paths: int,
     lm_scale_list: List[float],
-    lattice_score_scale: float = 1.0,
+    nbest_scale: float = 1.0,
     use_double_scores: bool = True,
 ) -> Dict[str, k2.Fsa]:
     """Rescore an n-best list with an n-gram LM.
@@ -607,7 +607,7 @@ def rescore_with_n_best_list(
         Size of nbest list.
       lm_scale_list:
         A list of float representing LM score scales.
-      lattice_score_scale:
+      nbest_scale:
         Scale to be applied to ``lattice.score`` when sampling paths
         using ``k2.random_paths``.
       use_double_scores:
@@ -631,7 +631,7 @@ def rescore_with_n_best_list(
         lattice=lattice,
         num_paths=num_paths,
         use_double_scores=use_double_scores,
-        lattice_score_scale=lattice_score_scale,
+        nbest_scale=nbest_scale,
     )
     # nbest.fsa.scores are all 0s at this point
 
@@ -769,7 +769,7 @@ def rescore_with_attention_decoder(
     memory_key_padding_mask: Optional[torch.Tensor],
     sos_id: int,
     eos_id: int,
-    lattice_score_scale: float = 1.0,
+    nbest_scale: float = 1.0,
     ngram_lm_scale: Optional[float] = None,
     attention_scale: Optional[float] = None,
     use_double_scores: bool = True,
@@ -796,7 +796,7 @@ def rescore_with_attention_decoder(
         The token ID for SOS.
       eos_id:
         The token ID for EOS.
-      lattice_score_scale:
+      nbest_scale:
         It's the scale applied to `lattice.scores`. A smaller value
         leads to more unique paths at the risk of missing the correct path.
       ngram_lm_scale:
@@ -812,7 +812,7 @@ def rescore_with_attention_decoder(
         lattice=lattice,
         num_paths=num_paths,
         use_double_scores=use_double_scores,
-        lattice_score_scale=lattice_score_scale,
+        nbest_scale=nbest_scale,
     )
     # nbest.fsa.scores are all 0s at this point
 

test/test_decode.py

@@ -43,7 +43,7 @@ def test_nbest_from_lattice():
         lattice=lattice,
         num_paths=10,
         use_double_scores=True,
-        lattice_score_scale=0.5,
+        nbest_scale=0.5,
     )
     # each lattice has only 4 distinct paths that have different word sequences:
     # 10->30