Add fast_beam_search_nbest. (#420)

* Add fast_beam_search_nbest.

* Fix CI errors.

* Fix CI errors.

* More fixes.

* Small fixes.

* Support using log_add in LG decoding with fast_beam_search.

* Support LG decoding in pruned_transducer_stateless

* Support LG for pruned_transducer_stateless2.

* Support LG for fast beam search.

* Minor fixes.

.github/scripts/run-librispeech-pruned-transducer-stateless5-2022-05-13.sh

@@ -32,6 +32,12 @@ for sym in 1 2 3; do
     --max-sym-per-frame $sym \
     --checkpoint $repo/exp/pretrained.pt \
     --bpe-model $repo/data/lang_bpe_500/bpe.model \
+    --num-encoder-layers 18 \
+    --dim-feedforward 2048 \
+    --nhead 8 \
+    --encoder-dim 512 \
+    --decoder-dim 512 \
+    --joiner-dim 512
     $repo/test_wavs/1089-134686-0001.wav \
     $repo/test_wavs/1221-135766-0001.wav \
     $repo/test_wavs/1221-135766-0002.wav

.github/workflows/test.yml

@@ -33,13 +33,13 @@ jobs:
         # disable macOS test for now.
         os: [ubuntu-18.04]
         python-version: [3.7, 3.8]
-        torch: ["1.8.0", "1.10.0"]
+        torch: ["1.8.0", "1.11.0"]
-        torchaudio: ["0.8.0", "0.10.0"]
+        torchaudio: ["0.8.0", "0.11.0"]
-        k2-version: ["1.9.dev20211101"]
+        k2-version: ["1.15.1.dev20220427"]
         exclude:
           - torch: "1.8.0"
-            torchaudio: "0.10.0"
+            torchaudio: "0.11.0"
-          - torch: "1.10.0"
+          - torch: "1.11.0"
             torchaudio: "0.8.0"
 
       fail-fast: false
@@ -67,7 +67,7 @@ jobs:
           # numpy 1.20.x does not support python 3.6
           pip install numpy==1.19
           pip install torch==${{ matrix.torch }}+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html
-          if [[ ${{ matrix.torchaudio }} == "0.10.0" ]]; then
+          if [[ ${{ matrix.torchaudio }} == "0.11.0" ]]; then
             pip install torchaudio==${{ matrix.torchaudio }}+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html
           else
             pip install torchaudio==${{ matrix.torchaudio }}

egs/librispeech/ASR/pruned_transducer_stateless/beam_search.py

@@ -75,6 +75,202 @@ def fast_beam_search_one_best(
     return hyps
 
 
+def fast_beam_search_nbest_LG(
+    model: Transducer,
+    decoding_graph: k2.Fsa,
+    encoder_out: torch.Tensor,
+    encoder_out_lens: torch.Tensor,
+    beam: float,
+    max_states: int,
+    max_contexts: int,
+    num_paths: int,
+    nbest_scale: float = 0.5,
+    use_double_scores: bool = True,
+) -> List[List[int]]:
+    """It limits the maximum number of symbols per frame to 1.
+
+    The process to get the results is:
+     - (1) Use fast beam search to get a lattice
+     - (2) Select `num_paths` paths from the lattice using k2.random_paths()
+     - (3) Unique the selected paths
+     - (4) Intersect the selected paths with the lattice and compute the
+           shortest path from the intersection result
+     - (5) The path with the largest score is used as the decoding output.
+
+    Args:
+      model:
+        An instance of `Transducer`.
+      decoding_graph:
+        Decoding graph used for decoding, may be a TrivialGraph or a HLG.
+      encoder_out:
+        A tensor of shape (N, T, C) from the encoder.
+      encoder_out_lens:
+        A tensor of shape (N,) containing the number of frames in `encoder_out`
+        before padding.
+      beam:
+        Beam value, similar to the beam used in Kaldi..
+      max_states:
+        Max states per stream per frame.
+      max_contexts:
+        Max contexts pre stream per frame.
+      num_paths:
+        Number of paths to extract from the decoded lattice.
+      nbest_scale:
+        It's the scale applied to the lattice.scores. A smaller value
+        yields more unique paths.
+      use_double_scores:
+        True to use double precision for computation. False to use
+        single precision.
+    Returns:
+      Return the decoded result.
+    """
+    lattice = fast_beam_search(
+        model=model,
+        decoding_graph=decoding_graph,
+        encoder_out=encoder_out,
+        encoder_out_lens=encoder_out_lens,
+        beam=beam,
+        max_states=max_states,
+        max_contexts=max_contexts,
+    )
+
+    nbest = Nbest.from_lattice(
+        lattice=lattice,
+        num_paths=num_paths,
+        use_double_scores=use_double_scores,
+        nbest_scale=nbest_scale,
+    )
+
+    # 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)
+
+    if hasattr(lattice, "aux_labels"):
+        # 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)
+
+    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,
+        )
+
+    # 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=True,  # Note: we always use True
+    )
+    # See https://github.com/k2-fsa/icefall/pull/420 for why
+    # we always use log_semiring=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)
+
+    return hyps
+
+
+def fast_beam_search_nbest(
+    model: Transducer,
+    decoding_graph: k2.Fsa,
+    encoder_out: torch.Tensor,
+    encoder_out_lens: torch.Tensor,
+    beam: float,
+    max_states: int,
+    max_contexts: int,
+    num_paths: int,
+    nbest_scale: float = 0.5,
+    use_double_scores: bool = True,
+) -> List[List[int]]:
+    """It limits the maximum number of symbols per frame to 1.
+
+    The process to get the results is:
+     - (1) Use fast beam search to get a lattice
+     - (2) Select `num_paths` paths from the lattice using k2.random_paths()
+     - (3) Unique the selected paths
+     - (4) Intersect the selected paths with the lattice and compute the
+           shortest path from the intersection result
+     - (5) The path with the largest score is used as the decoding output.
+
+    Args:
+      model:
+        An instance of `Transducer`.
+      decoding_graph:
+        Decoding graph used for decoding, may be a TrivialGraph or a HLG.
+      encoder_out:
+        A tensor of shape (N, T, C) from the encoder.
+      encoder_out_lens:
+        A tensor of shape (N,) containing the number of frames in `encoder_out`
+        before padding.
+      beam:
+        Beam value, similar to the beam used in Kaldi..
+      max_states:
+        Max states per stream per frame.
+      max_contexts:
+        Max contexts pre stream per frame.
+      num_paths:
+        Number of paths to extract from the decoded lattice.
+      nbest_scale:
+        It's the scale applied to the lattice.scores. A smaller value
+        yields more unique paths.
+      use_double_scores:
+        True to use double precision for computation. False to use
+        single precision.
+    Returns:
+      Return the decoded result.
+    """
+    lattice = fast_beam_search(
+        model=model,
+        decoding_graph=decoding_graph,
+        encoder_out=encoder_out,
+        encoder_out_lens=encoder_out_lens,
+        beam=beam,
+        max_states=max_states,
+        max_contexts=max_contexts,
+    )
+
+    nbest = Nbest.from_lattice(
+        lattice=lattice,
+        num_paths=num_paths,
+        use_double_scores=use_double_scores,
+        nbest_scale=nbest_scale,
+    )
+
+    # at this point, nbest.fsa.scores are all zeros.
+
+    nbest = nbest.intersect(lattice)
+    # Now nbest.fsa.scores contains acoustic scores
+
+    max_indexes = nbest.tot_scores().argmax()
+
+    best_path = k2.index_fsa(nbest.fsa, max_indexes)
+
+    hyps = get_texts(best_path)
+
+    return hyps
+
+
 def fast_beam_search_nbest_oracle(
     model: Transducer,
     decoding_graph: k2.Fsa,

egs/librispeech/ASR/pruned_transducer_stateless/decode.py

@@ -50,20 +50,44 @@ Usage:
     --exp-dir ./pruned_transducer_stateless/exp \
     --max-duration 600 \
     --decoding-method fast_beam_search \
-    --beam 4 \
+    --beam 20.0 \
-    --max-contexts 4 \
+    --max-contexts 8 \
-    --max-states 8
+    --max-states 64
 
-(5) fast beam search using LG
+(5) fast beam search (nbest)
 ./pruned_transducer_stateless/decode.py \
     --epoch 28 \
     --avg 15 \
     --exp-dir ./pruned_transducer_stateless/exp \
-    --use-LG True \
-    --use-max False \
     --max-duration 600 \
-    --decoding-method fast_beam_search \
+    --decoding-method fast_beam_search_nbest \
-    --beam 8 \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64 \
+    --num-paths 200 \
+    --nbest-scale 0.5
+
+(6) fast beam search (nbest oracle WER)
+./pruned_transducer_stateless/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless/exp \
+    --max-duration 600 \
+    --decoding-method fast_beam_search_nbest_oracle \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64 \
+    --num-paths 200 \
+    --nbest-scale 0.5
+
+(7) fast beam search (with LG)
+./pruned_transducer_stateless/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless/exp \
+    --max-duration 600 \
+    --decoding-method fast_beam_search_nbest_LG \
+    --beam 20.0 \
     --max-contexts 8 \
     --max-states 64
 """
@@ -82,6 +106,9 @@ import torch.nn as nn
 from asr_datamodule import LibriSpeechAsrDataModule
 from beam_search import (
     beam_search,
+    fast_beam_search_nbest,
+    fast_beam_search_nbest_LG,
+    fast_beam_search_nbest_oracle,
     fast_beam_search_one_best,
     greedy_search,
     greedy_search_batch,
@@ -99,7 +126,6 @@ from icefall.utils import (
     AttributeDict,
     setup_logger,
     store_transcripts,
-    str2bool,
     write_error_stats,
 )
 
@@ -153,7 +179,7 @@ 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",
     )
@@ -167,6 +193,11 @@ def get_parser():
           - beam_search
           - modified_beam_search
           - fast_beam_search
+          - fast_beam_search_nbest
+          - fast_beam_search_nbest_oracle
+          - fast_beam_search_nbest_LG
+        If you use fast_beam_search_nbest_LG, you have to specify
+        `--lang-dir`, which should contain `LG.pt`.
         """,
     )
 
@@ -182,30 +213,13 @@ def get_parser():
     parser.add_argument(
         "--beam",
         type=float,
-        default=4,
+        default=20.0,
         help="""A floating point value to calculate the cutoff score during beam
         search (i.e., `cutoff = max-score - beam`), which is the same as the
         `beam` in Kaldi.
-        Used only when --decoding-method is fast_beam_search""",
+        Used only when --decoding-method is fast_beam_search,
-    )
+        fast_beam_search_nbest, fast_beam_search_nbest_LG,
-
+        and fast_beam_search_nbest_oracle
-    parser.add_argument(
-        "--use-LG",
-        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,
-        it assumes there is an LG.pt file in lang_dir.""",
-    )
-
-    parser.add_argument(
-        "--use-max",
-        type=str2bool,
-        default=False,
-        help="""If True, use max-op to select the hypothesis that have the
-        max log_prob in case of duplicate hypotheses.
-        If False, use log_add.
-        Used only for beam_search, modified_beam_search, and fast_beam_search
         """,
     )
 
@@ -214,7 +228,7 @@ def get_parser():
         type=float,
         default=0.01,
         help="""
-        Used only when --decoding_method is fast_beam_search.
+        Used only when --decoding_method is fast_beam_search_nbest_LG.
         It specifies the scale for n-gram LM scores.
         """,
     )
@@ -222,9 +236,10 @@ def get_parser():
     parser.add_argument(
         "--max-contexts",
         type=int,
-        default=4,
+        default=8,
         help="""Used only when --decoding-method is
-        fast_beam_search""",
+        fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle""",
     )
 
     parser.add_argument(
@@ -232,7 +247,8 @@ def get_parser():
         type=int,
         default=8,
         help="""Used only when --decoding-method is
-        fast_beam_search""",
+        fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle""",
     )
 
     parser.add_argument(
@@ -250,6 +266,24 @@ def get_parser():
         Used only when --decoding_method is greedy_search""",
     )
 
+    parser.add_argument(
+        "--num-paths",
+        type=int,
+        default=200,
+        help="""Number of paths for nbest decoding.
+        Used only when the decoding method is fast_beam_search_nbest,
+        fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--nbest-scale",
+        type=float,
+        default=0.5,
+        help="""Scale applied to lattice scores when computing nbest paths.
+        Used only when the decoding method is fast_beam_search_nbest,
+        fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
+    )
+
     return parser
 
 
@@ -286,7 +320,8 @@ def decode_one_batch(
         The word symbol table.
       decoding_graph:
         The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
-        only when --decoding_method is fast_beam_search.
+        only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
+        fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
     Returns:
       Return the decoding result. See above description for the format of
       the returned dict.
@@ -299,6 +334,7 @@ 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(
@@ -316,10 +352,49 @@ def decode_one_batch(
             max_contexts=params.max_contexts,
             max_states=params.max_states,
         )
-        if params.use_LG:
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif params.decoding_method == "fast_beam_search_nbest_LG":
+        hyp_tokens = fast_beam_search_nbest_LG(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            nbest_scale=params.nbest_scale,
+        )
         for hyp in hyp_tokens:
             hyps.append([word_table[i] for i in hyp])
-        else:
+    elif params.decoding_method == "fast_beam_search_nbest":
+        hyp_tokens = fast_beam_search_nbest(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif params.decoding_method == "fast_beam_search_nbest_oracle":
+        hyp_tokens = fast_beam_search_nbest_oracle(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            ref_texts=sp.encode(supervisions["text"]),
+            nbest_scale=params.nbest_scale,
+        )
         for hyp in sp.decode(hyp_tokens):
             hyps.append(hyp.split())
     elif (
@@ -339,7 +414,6 @@ def decode_one_batch(
             encoder_out=encoder_out,
             encoder_out_lens=encoder_out_lens,
             beam=params.beam_size,
-            use_max=params.use_max,
         )
         for hyp in sp.decode(hyp_tokens):
             hyps.append(hyp.split())
@@ -361,7 +435,6 @@ def decode_one_batch(
                     model=model,
                     encoder_out=encoder_out_i,
                     beam=params.beam_size,
-                    use_max=params.use_max,
                 )
             else:
                 raise ValueError(
@@ -371,14 +444,17 @@ def decode_one_batch(
 
     if params.decoding_method == "greedy_search":
         return {"greedy_search": hyps}
-    elif params.decoding_method == "fast_beam_search":
+    elif "fast_beam_search" in params.decoding_method:
-        return {
+        key = f"beam_{params.beam}_"
-            (
+        key += f"max_contexts_{params.max_contexts}_"
-                f"beam_{params.beam}_"
+        key += f"max_states_{params.max_states}"
-                f"max_contexts_{params.max_contexts}_"
+        if "nbest" in params.decoding_method:
-                f"max_states_{params.max_states}"
+            key += f"_num_paths_{params.num_paths}_"
-            ): hyps
+            key += f"nbest_scale_{params.nbest_scale}"
-        }
+            if "LG" in params.decoding_method:
+                key += f"_ngram_lm_scale_{params.ngram_lm_scale}"
+
+        return {key: hyps}
     else:
         return {f"beam_size_{params.beam_size}": hyps}
 
@@ -406,7 +482,8 @@ def decode_dataset(
         The word symbol table.
       decoding_graph:
         The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
-        only when --decoding_method is fast_beam_search.
+        only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
+        fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
     Returns:
       Return a dict, whose key may be "greedy_search" if greedy search
       is used, or it may be "beam_7" if beam size of 7 is used.
@@ -424,7 +501,7 @@ def decode_dataset(
     if params.decoding_method == "greedy_search":
         log_interval = 50
     else:
-        log_interval = 10
+        log_interval = 20
 
     results = defaultdict(list)
     for batch_idx, batch in enumerate(dl):
@@ -517,6 +594,9 @@ def main():
         "greedy_search",
         "beam_search",
         "fast_beam_search",
+        "fast_beam_search_nbest",
+        "fast_beam_search_nbest_LG",
+        "fast_beam_search_nbest_oracle",
         "modified_beam_search",
     )
     params.res_dir = params.exp_dir / params.decoding_method
@@ -527,16 +607,18 @@ def main():
         params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
 
     if "fast_beam_search" in params.decoding_method:
-        params.suffix += f"-use-LG-{params.use_LG}"
         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 "nbest" in params.decoding_method:
+            params.suffix += f"-nbest-scale-{params.nbest_scale}"
+            params.suffix += f"-num-paths-{params.num_paths}"
+            if "LG" in params.decoding_method:
+                params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}"
     elif "beam_search" in params.decoding_method:
         params.suffix += (
             f"-{params.decoding_method}-beam-size-{params.beam_size}"
         )
-        params.suffix += f"-use-max-{params.use_max}"
     else:
         params.suffix += f"-context-{params.context_size}"
         params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
@@ -596,12 +678,14 @@ def main():
     model.eval()
     model.device = device
 
-    if params.decoding_method == "fast_beam_search":
+    if "fast_beam_search" in params.decoding_method:
-        if params.use_LG:
+        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(
-                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:

egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py

@@ -37,7 +37,7 @@ def fast_beam_search_one_best(
 ) -> List[List[int]]:
     """It limits the maximum number of symbols per frame to 1.
 
-    A lattice is first obtained using modified beam search, and then
+    A lattice is first obtained using fast beam search, and then
     the shortest path within the lattice is used as the final output.
 
     Args:
@@ -74,6 +74,202 @@ def fast_beam_search_one_best(
     return hyps
 
 
+def fast_beam_search_nbest_LG(
+    model: Transducer,
+    decoding_graph: k2.Fsa,
+    encoder_out: torch.Tensor,
+    encoder_out_lens: torch.Tensor,
+    beam: float,
+    max_states: int,
+    max_contexts: int,
+    num_paths: int,
+    nbest_scale: float = 0.5,
+    use_double_scores: bool = True,
+) -> List[List[int]]:
+    """It limits the maximum number of symbols per frame to 1.
+
+    The process to get the results is:
+     - (1) Use fast beam search to get a lattice
+     - (2) Select `num_paths` paths from the lattice using k2.random_paths()
+     - (3) Unique the selected paths
+     - (4) Intersect the selected paths with the lattice and compute the
+           shortest path from the intersection result
+     - (5) The path with the largest score is used as the decoding output.
+
+    Args:
+      model:
+        An instance of `Transducer`.
+      decoding_graph:
+        Decoding graph used for decoding, may be a TrivialGraph or a HLG.
+      encoder_out:
+        A tensor of shape (N, T, C) from the encoder.
+      encoder_out_lens:
+        A tensor of shape (N,) containing the number of frames in `encoder_out`
+        before padding.
+      beam:
+        Beam value, similar to the beam used in Kaldi..
+      max_states:
+        Max states per stream per frame.
+      max_contexts:
+        Max contexts pre stream per frame.
+      num_paths:
+        Number of paths to extract from the decoded lattice.
+      nbest_scale:
+        It's the scale applied to the lattice.scores. A smaller value
+        yields more unique paths.
+      use_double_scores:
+        True to use double precision for computation. False to use
+        single precision.
+    Returns:
+      Return the decoded result.
+    """
+    lattice = fast_beam_search(
+        model=model,
+        decoding_graph=decoding_graph,
+        encoder_out=encoder_out,
+        encoder_out_lens=encoder_out_lens,
+        beam=beam,
+        max_states=max_states,
+        max_contexts=max_contexts,
+    )
+
+    nbest = Nbest.from_lattice(
+        lattice=lattice,
+        num_paths=num_paths,
+        use_double_scores=use_double_scores,
+        nbest_scale=nbest_scale,
+    )
+
+    # 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)
+
+    if hasattr(lattice, "aux_labels"):
+        # 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)
+
+    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,
+        )
+
+    # 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=True,  # Note: we always use True
+    )
+    # See https://github.com/k2-fsa/icefall/pull/420 for why
+    # we always use log_semiring=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)
+
+    return hyps
+
+
+def fast_beam_search_nbest(
+    model: Transducer,
+    decoding_graph: k2.Fsa,
+    encoder_out: torch.Tensor,
+    encoder_out_lens: torch.Tensor,
+    beam: float,
+    max_states: int,
+    max_contexts: int,
+    num_paths: int,
+    nbest_scale: float = 0.5,
+    use_double_scores: bool = True,
+) -> List[List[int]]:
+    """It limits the maximum number of symbols per frame to 1.
+
+    The process to get the results is:
+     - (1) Use fast beam search to get a lattice
+     - (2) Select `num_paths` paths from the lattice using k2.random_paths()
+     - (3) Unique the selected paths
+     - (4) Intersect the selected paths with the lattice and compute the
+           shortest path from the intersection result
+     - (5) The path with the largest score is used as the decoding output.
+
+    Args:
+      model:
+        An instance of `Transducer`.
+      decoding_graph:
+        Decoding graph used for decoding, may be a TrivialGraph or a HLG.
+      encoder_out:
+        A tensor of shape (N, T, C) from the encoder.
+      encoder_out_lens:
+        A tensor of shape (N,) containing the number of frames in `encoder_out`
+        before padding.
+      beam:
+        Beam value, similar to the beam used in Kaldi..
+      max_states:
+        Max states per stream per frame.
+      max_contexts:
+        Max contexts pre stream per frame.
+      num_paths:
+        Number of paths to extract from the decoded lattice.
+      nbest_scale:
+        It's the scale applied to the lattice.scores. A smaller value
+        yields more unique paths.
+      use_double_scores:
+        True to use double precision for computation. False to use
+        single precision.
+    Returns:
+      Return the decoded result.
+    """
+    lattice = fast_beam_search(
+        model=model,
+        decoding_graph=decoding_graph,
+        encoder_out=encoder_out,
+        encoder_out_lens=encoder_out_lens,
+        beam=beam,
+        max_states=max_states,
+        max_contexts=max_contexts,
+    )
+
+    nbest = Nbest.from_lattice(
+        lattice=lattice,
+        num_paths=num_paths,
+        use_double_scores=use_double_scores,
+        nbest_scale=nbest_scale,
+    )
+
+    # at this point, nbest.fsa.scores are all zeros.
+
+    nbest = nbest.intersect(lattice)
+    # Now nbest.fsa.scores contains acoustic scores
+
+    max_indexes = nbest.tot_scores().argmax()
+
+    best_path = k2.index_fsa(nbest.fsa, max_indexes)
+
+    hyps = get_texts(best_path)
+
+    return hyps
+
+
 def fast_beam_search_nbest_oracle(
     model: Transducer,
     decoding_graph: k2.Fsa,
@@ -89,7 +285,7 @@ def fast_beam_search_nbest_oracle(
 ) -> List[List[int]]:
     """It limits the maximum number of symbols per frame to 1.
 
-    A lattice is first obtained using modified beam search, and then
+    A lattice is first obtained using fast beam search, and then
     we select `num_paths` linear paths from the lattice. The path
     that has the minimum edit distance with the given reference transcript
     is used as the output.

egs/librispeech/ASR/pruned_transducer_stateless2/decode.py

@@ -43,16 +43,53 @@ Usage:
     --decoding-method modified_beam_search \
     --beam-size 4
 
-(4) fast beam search
+(4) fast beam search (one best)
 ./pruned_transducer_stateless2/decode.py \
     --epoch 28 \
     --avg 15 \
     --exp-dir ./pruned_transducer_stateless2/exp \
     --max-duration 600 \
     --decoding-method fast_beam_search \
-    --beam 4 \
+    --beam 20.0 \
-    --max-contexts 4 \
+    --max-contexts 8 \
-    --max-states 8
+    --max-states 64
+
+(5) fast beam search (nbest)
+./pruned_transducer_stateless2/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless2/exp \
+    --max-duration 600 \
+    --decoding-method fast_beam_search_nbest \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64 \
+    --num-paths 200 \
+    --nbest-scale 0.5
+
+(6) fast beam search (nbest oracle WER)
+./pruned_transducer_stateless2/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless2/exp \
+    --max-duration 600 \
+    --decoding-method fast_beam_search_nbest_oracle \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64 \
+    --num-paths 200 \
+    --nbest-scale 0.5
+
+(7) fast beam search (with LG)
+./pruned_transducer_stateless2/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless2/exp \
+    --max-duration 600 \
+    --decoding-method fast_beam_search_nbest_LG \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64
 """
 
 
@@ -69,6 +106,9 @@ import torch.nn as nn
 from asr_datamodule import LibriSpeechAsrDataModule
 from beam_search import (
     beam_search,
+    fast_beam_search_nbest,
+    fast_beam_search_nbest_LG,
+    fast_beam_search_nbest_oracle,
     fast_beam_search_one_best,
     greedy_search,
     greedy_search_batch,
@@ -81,6 +121,7 @@ from icefall.checkpoint import (
     find_checkpoints,
     load_checkpoint,
 )
+from icefall.lexicon import Lexicon
 from icefall.utils import (
     AttributeDict,
     setup_logger,
@@ -136,6 +177,13 @@ def get_parser():
         help="Path to the BPE model",
     )
 
+    parser.add_argument(
+        "--lang-dir",
+        type=Path,
+        default="data/lang_bpe_500",
+        help="The lang dir containing word table and LG graph",
+    )
+
     parser.add_argument(
         "--decoding-method",
         type=str,
@@ -145,6 +193,11 @@ def get_parser():
           - beam_search
           - modified_beam_search
           - fast_beam_search
+          - fast_beam_search_nbest
+          - fast_beam_search_nbest_oracle
+          - fast_beam_search_nbest_LG
+        If you use fast_beam_search_nbest_LG, you have to specify
+        `--lang-dir`, which should contain `LG.pt`.
         """,
     )
 
@@ -160,27 +213,42 @@ def get_parser():
     parser.add_argument(
         "--beam",
         type=float,
-        default=4,
+        default=20.0,
         help="""A floating point value to calculate the cutoff score during beam
         search (i.e., `cutoff = max-score - beam`), which is the same as the
         `beam` in Kaldi.
-        Used only when --decoding-method is fast_beam_search""",
+        Used only when --decoding-method is fast_beam_search,
+        fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle
+        """,
+    )
+
+    parser.add_argument(
+        "--ngram-lm-scale",
+        type=float,
+        default=0.01,
+        help="""
+        Used only when --decoding_method is fast_beam_search_nbest_LG.
+        It specifies the scale for n-gram LM scores.
+        """,
     )
 
     parser.add_argument(
         "--max-contexts",
         type=int,
-        default=4,
+        default=8,
         help="""Used only when --decoding-method is
-        fast_beam_search""",
+        fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle""",
     )
 
     parser.add_argument(
         "--max-states",
         type=int,
-        default=8,
+        default=64,
         help="""Used only when --decoding-method is
-        fast_beam_search""",
+        fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle""",
     )
 
     parser.add_argument(
@@ -198,6 +266,24 @@ def get_parser():
         Used only when --decoding_method is greedy_search""",
     )
 
+    parser.add_argument(
+        "--num-paths",
+        type=int,
+        default=200,
+        help="""Number of paths for nbest decoding.
+        Used only when the decoding method is fast_beam_search_nbest,
+        fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--nbest-scale",
+        type=float,
+        default=0.5,
+        help="""Scale applied to lattice scores when computing nbest paths.
+        Used only when the decoding method is fast_beam_search_nbest,
+        fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
+    )
+
     return parser
 
 
@@ -206,6 +292,7 @@ def decode_one_batch(
     model: nn.Module,
     sp: spm.SentencePieceProcessor,
     batch: dict,
+    word_table: Optional[k2.SymbolTable] = None,
     decoding_graph: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[List[str]]]:
     """Decode one batch and return the result in a dict. The dict has the
@@ -229,9 +316,12 @@ def decode_one_batch(
         It is the return value from iterating
         `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
         for the format of the `batch`.
+      word_table:
+        The word symbol table.
       decoding_graph:
         The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
-        only when --decoding_method is fast_beam_search.
+        only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
+        fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
     Returns:
       Return the decoding result. See above description for the format of
       the returned dict.
@@ -263,6 +353,49 @@ def decode_one_batch(
         )
         for hyp in sp.decode(hyp_tokens):
             hyps.append(hyp.split())
+    elif params.decoding_method == "fast_beam_search_nbest_LG":
+        hyp_tokens = fast_beam_search_nbest_LG(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in hyp_tokens:
+            hyps.append([word_table[i] for i in hyp])
+    elif params.decoding_method == "fast_beam_search_nbest":
+        hyp_tokens = fast_beam_search_nbest(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif params.decoding_method == "fast_beam_search_nbest_oracle":
+        hyp_tokens = fast_beam_search_nbest_oracle(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            ref_texts=sp.encode(supervisions["text"]),
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
     elif (
         params.decoding_method == "greedy_search"
         and params.max_sym_per_frame == 1
@@ -318,6 +451,17 @@ def decode_one_batch(
                 f"max_states_{params.max_states}"
             ): hyps
         }
+    elif "fast_beam_search" in params.decoding_method:
+        key = f"beam_{params.beam}_"
+        key += f"max_contexts_{params.max_contexts}_"
+        key += f"max_states_{params.max_states}"
+        if "nbest" in params.decoding_method:
+            key += f"_num_paths_{params.num_paths}_"
+            key += f"nbest_scale_{params.nbest_scale}"
+            if "LG" in params.decoding_method:
+                key += f"_ngram_lm_scale_{params.ngram_lm_scale}"
+
+        return {key: hyps}
     else:
         return {f"beam_size_{params.beam_size}": hyps}
 
@@ -327,6 +471,7 @@ def decode_dataset(
     params: AttributeDict,
     model: nn.Module,
     sp: spm.SentencePieceProcessor,
+    word_table: Optional[k2.SymbolTable] = None,
     decoding_graph: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[Tuple[List[str], List[str]]]]:
     """Decode dataset.
@@ -340,9 +485,12 @@ def decode_dataset(
         The neural model.
       sp:
         The BPE model.
+      word_table:
+        The word symbol table.
       decoding_graph:
         The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
-        only when --decoding_method is fast_beam_search.
+        only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
+        fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
     Returns:
       Return a dict, whose key may be "greedy_search" if greedy search
       is used, or it may be "beam_7" if beam size of 7 is used.
@@ -360,7 +508,7 @@ def decode_dataset(
     if params.decoding_method == "greedy_search":
         log_interval = 50
     else:
-        log_interval = 10
+        log_interval = 20
 
     results = defaultdict(list)
     for batch_idx, batch in enumerate(dl):
@@ -370,6 +518,7 @@ def decode_dataset(
             params=params,
             model=model,
             sp=sp,
+            word_table=word_table,
             decoding_graph=decoding_graph,
             batch=batch,
         )
@@ -452,6 +601,9 @@ def main():
         "greedy_search",
         "beam_search",
         "fast_beam_search",
+        "fast_beam_search_nbest",
+        "fast_beam_search_nbest_LG",
+        "fast_beam_search_nbest_oracle",
         "modified_beam_search",
     )
     params.res_dir = params.exp_dir / params.decoding_method
@@ -465,6 +617,11 @@ def main():
         params.suffix += f"-beam-{params.beam}"
         params.suffix += f"-max-contexts-{params.max_contexts}"
         params.suffix += f"-max-states-{params.max_states}"
+        if "nbest" in params.decoding_method:
+            params.suffix += f"-nbest-scale-{params.nbest_scale}"
+            params.suffix += f"-num-paths-{params.num_paths}"
+            if "LG" in params.decoding_method:
+                params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}"
     elif "beam_search" in params.decoding_method:
         params.suffix += (
             f"-{params.decoding_method}-beam-size-{params.beam_size}"
@@ -528,10 +685,24 @@ def main():
     model.eval()
     model.device = device
 
-    if params.decoding_method == "fast_beam_search":
+    if "fast_beam_search" in params.decoding_method:
-        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+        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(
+                torch.load(lg_filename, map_location=device)
+            )
+            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}")
@@ -553,6 +724,7 @@ def main():
             params=params,
             model=model,
             sp=sp,
+            word_table=word_table,
             decoding_graph=decoding_graph,
         )
 

egs/librispeech/ASR/pruned_transducer_stateless3/decode.py

@@ -43,16 +43,53 @@ Usage:
     --decoding-method modified_beam_search \
     --beam-size 4
 
-(4) fast beam search
+(4) fast beam search (one best)
 ./pruned_transducer_stateless3/decode.py \
     --epoch 28 \
     --avg 15 \
     --exp-dir ./pruned_transducer_stateless3/exp \
     --max-duration 600 \
     --decoding-method fast_beam_search \
-        --beam 4 \
+    --beam 20.0 \
-        --max-contexts 4 \
+    --max-contexts 8 \
-        --max-states 8
+    --max-states 64
+
+(5) fast beam search (nbest)
+./pruned_transducer_stateless3/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless3/exp \
+    --max-duration 600 \
+    --decoding-method fast_beam_search_nbest \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64 \
+    --num-paths 200 \
+    --nbest-scale 0.5
+
+(6) fast beam search (nbest oracle WER)
+./pruned_transducer_stateless3/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless3/exp \
+    --max-duration 600 \
+    --decoding-method fast_beam_search_nbest_oracle \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64 \
+    --num-paths 200 \
+    --nbest-scale 0.5
+
+(7) fast beam search (with LG)
+./pruned_transducer_stateless3/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless3/exp \
+    --max-duration 600 \
+    --decoding-method fast_beam_search_nbest_LG \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64
 """
 
 
@@ -69,6 +106,8 @@ import torch.nn as nn
 from asr_datamodule import AsrDataModule
 from beam_search import (
     beam_search,
+    fast_beam_search_nbest,
+    fast_beam_search_nbest_LG,
     fast_beam_search_nbest_oracle,
     fast_beam_search_one_best,
     greedy_search,
@@ -83,6 +122,7 @@ from icefall.checkpoint import (
     find_checkpoints,
     load_checkpoint,
 )
+from icefall.lexicon import Lexicon
 from icefall.utils import (
     AttributeDict,
     setup_logger,
@@ -138,6 +178,13 @@ def get_parser():
         help="Path to the BPE model",
     )
 
+    parser.add_argument(
+        "--lang-dir",
+        type=Path,
+        default="data/lang_bpe_500",
+        help="The lang dir containing word table and LG graph",
+    )
+
     parser.add_argument(
         "--decoding-method",
         type=str,
@@ -147,7 +194,11 @@ def get_parser():
           - beam_search
           - modified_beam_search
           - fast_beam_search
+          - fast_beam_search_nbest
           - fast_beam_search_nbest_oracle
+          - fast_beam_search_nbest_LG
+        If you use fast_beam_search_nbest_LG, you have to specify
+        `--lang-dir`, which should contain `LG.pt`.
         """,
     )
 
@@ -163,28 +214,42 @@ def get_parser():
     parser.add_argument(
         "--beam",
         type=float,
-        default=4,
+        default=20.0,
         help="""A floating point value to calculate the cutoff score during beam
         search (i.e., `cutoff = max-score - beam`), which is the same as the
         `beam` in Kaldi.
-        Used only when --decoding-method is
+        Used only when --decoding-method is fast_beam_search,
-        fast_beam_search or fast_beam_search_nbest_oracle""",
+        fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle
+        """,
+    )
+
+    parser.add_argument(
+        "--ngram-lm-scale",
+        type=float,
+        default=0.01,
+        help="""
+        Used only when --decoding_method is fast_beam_search_nbest_LG.
+        It specifies the scale for n-gram LM scores.
+        """,
     )
 
     parser.add_argument(
         "--max-contexts",
         type=int,
-        default=4,
+        default=8,
         help="""Used only when --decoding-method is
-        fast_beam_search or fast_beam_search_nbest_oracle""",
+        fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle""",
     )
 
     parser.add_argument(
         "--max-states",
         type=int,
-        default=8,
+        default=64,
         help="""Used only when --decoding-method is
-        fast_beam_search or fast_beam_search_nbest_oracle""",
+        fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle""",
     )
 
     parser.add_argument(
@@ -205,10 +270,10 @@ def get_parser():
     parser.add_argument(
         "--num-paths",
         type=int,
-        default=100,
+        default=200,
-        help="""Number of paths for computed nbest oracle WER
+        help="""Number of paths for nbest decoding.
-        when the decoding method is fast_beam_search_nbest_oracle.
+        Used only when the decoding method is fast_beam_search_nbest,
-        """,
+        fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
     )
 
     parser.add_argument(
@@ -216,9 +281,10 @@ def get_parser():
         type=float,
         default=0.5,
         help="""Scale applied to lattice scores when computing nbest paths.
-        Used only when the decoding_method is fast_beam_search_nbest_oracle.
+        Used only when the decoding method is fast_beam_search_nbest,
-        """,
+        fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
     )
+
     return parser
 
 
@@ -227,6 +293,7 @@ def decode_one_batch(
     model: nn.Module,
     sp: spm.SentencePieceProcessor,
     batch: dict,
+    word_table: Optional[k2.SymbolTable] = None,
     decoding_graph: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[List[str]]]:
     """Decode one batch and return the result in a dict. The dict has the
@@ -250,10 +317,12 @@ def decode_one_batch(
         It is the return value from iterating
         `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
         for the format of the `batch`.
+      word_table:
+        The word symbol table.
       decoding_graph:
         The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
-        only when --decoding_method is
+        only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
-        fast_beam_search or fast_beam_search_nbest_oracle.
+        fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
     Returns:
       Return the decoding result. See above description for the format of
       the returned dict.
@@ -285,6 +354,34 @@ def decode_one_batch(
         )
         for hyp in sp.decode(hyp_tokens):
             hyps.append(hyp.split())
+    elif params.decoding_method == "fast_beam_search_nbest_LG":
+        hyp_tokens = fast_beam_search_nbest_LG(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in hyp_tokens:
+            hyps.append([word_table[i] for i in hyp])
+    elif params.decoding_method == "fast_beam_search_nbest":
+        hyp_tokens = fast_beam_search_nbest(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
     elif params.decoding_method == "fast_beam_search_nbest_oracle":
         hyp_tokens = fast_beam_search_nbest_oracle(
             model=model,
@@ -355,16 +452,25 @@ def decode_one_batch(
                 f"max_states_{params.max_states}"
             ): hyps
         }
-    elif params.decoding_method == "fast_beam_search_nbest_oracle":
+    elif params.decoding_method == "fast_beam_search":
         return {
             (
                 f"beam_{params.beam}_"
                 f"max_contexts_{params.max_contexts}_"
-                f"max_states_{params.max_states}_"
+                f"max_states_{params.max_states}"
-                f"num_paths_{params.num_paths}_"
-                f"nbest_scale_{params.nbest_scale}"
             ): hyps
         }
+    elif "fast_beam_search" in params.decoding_method:
+        key = f"beam_{params.beam}_"
+        key += f"max_contexts_{params.max_contexts}_"
+        key += f"max_states_{params.max_states}"
+        if "nbest" in params.decoding_method:
+            key += f"_num_paths_{params.num_paths}_"
+            key += f"nbest_scale_{params.nbest_scale}"
+            if "LG" in params.decoding_method:
+                key += f"_ngram_lm_scale_{params.ngram_lm_scale}"
+
+        return {key: hyps}
     else:
         return {f"beam_size_{params.beam_size}": hyps}
 
@@ -374,6 +480,7 @@ def decode_dataset(
     params: AttributeDict,
     model: nn.Module,
     sp: spm.SentencePieceProcessor,
+    word_table: Optional[k2.SymbolTable] = None,
     decoding_graph: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[Tuple[List[str], List[str]]]]:
     """Decode dataset.
@@ -387,9 +494,12 @@ def decode_dataset(
         The neural model.
       sp:
         The BPE model.
+      word_table:
+        The word symbol table.
       decoding_graph:
         The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
-        only when --decoding_method is fast_beam_search.
+        only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
+        fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
     Returns:
       Return a dict, whose key may be "greedy_search" if greedy search
       is used, or it may be "beam_7" if beam size of 7 is used.
@@ -407,7 +517,7 @@ def decode_dataset(
     if params.decoding_method == "greedy_search":
         log_interval = 50
     else:
-        log_interval = 10
+        log_interval = 20
 
     results = defaultdict(list)
     for batch_idx, batch in enumerate(dl):
@@ -417,6 +527,7 @@ def decode_dataset(
             params=params,
             model=model,
             sp=sp,
+            word_table=word_table,
             decoding_graph=decoding_graph,
             batch=batch,
         )
@@ -499,6 +610,8 @@ def main():
         "greedy_search",
         "beam_search",
         "fast_beam_search",
+        "fast_beam_search_nbest",
+        "fast_beam_search_nbest_LG",
         "fast_beam_search_nbest_oracle",
         "modified_beam_search",
     )
@@ -509,16 +622,15 @@ def main():
     else:
         params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
 
-    if params.decoding_method == "fast_beam_search":
+    if "fast_beam_search" in params.decoding_method:
         params.suffix += f"-beam-{params.beam}"
         params.suffix += f"-max-contexts-{params.max_contexts}"
         params.suffix += f"-max-states-{params.max_states}"
-    elif params.decoding_method == "fast_beam_search_nbest_oracle":
+        if "nbest" in params.decoding_method:
-        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"-num-paths-{params.num_paths}"
             params.suffix += f"-nbest-scale-{params.nbest_scale}"
+            params.suffix += f"-num-paths-{params.num_paths}"
+            if "LG" in params.decoding_method:
+                params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}"
     elif "beam_search" in params.decoding_method:
         params.suffix += (
             f"-{params.decoding_method}-beam-size-{params.beam_size}"
@@ -539,9 +651,9 @@ def main():
     sp = spm.SentencePieceProcessor()
     sp.load(params.bpe_model)
 
-    # <blk> and <unk> is defined in local/train_bpe_model.py
+    # <blk> and <unk> are defined in local/train_bpe_model.py
     params.blank_id = sp.piece_to_id("<blk>")
-    params.unk_id = sp.unk_id()
+    params.unk_id = sp.piece_to_id("<unk>")
     params.vocab_size = sp.get_piece_size()
 
     logging.info(params)
@@ -583,13 +695,24 @@ def main():
     model.device = device
     model.unk_id = params.unk_id
 
-    if params.decoding_method in (
+    if "fast_beam_search" in params.decoding_method:
-        "fast_beam_search",
+        if params.decoding_method == "fast_beam_search_nbest_LG":
-        "fast_beam_search_nbest_oracle",
+            lexicon = Lexicon(params.lang_dir)
-    ):
+            word_table = lexicon.word_table
-        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+            lg_filename = params.lang_dir / "LG.pt"
+            logging.info(f"Loading {lg_filename}")
+            decoding_graph = k2.Fsa.from_dict(
+                torch.load(lg_filename, map_location=device)
+            )
+            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}")
@@ -612,6 +735,7 @@ def main():
             params=params,
             model=model,
             sp=sp,
+            word_table=word_table,
             decoding_graph=decoding_graph,
         )
 

egs/librispeech/ASR/pruned_transducer_stateless4/decode.py

@@ -44,16 +44,53 @@ Usage:
     --decoding-method modified_beam_search \
     --beam-size 4
 
-(4) fast beam search
+(4) fast beam search (one best)
 ./pruned_transducer_stateless4/decode.py \
     --epoch 30 \
     --avg 15 \
     --exp-dir ./pruned_transducer_stateless4/exp \
     --max-duration 600 \
     --decoding-method fast_beam_search \
-    --beam 4 \
+    --beam 20.0 \
-    --max-contexts 4 \
+    --max-contexts 8 \
-    --max-states 8
+    --max-states 64
+
+(5) fast beam search (nbest)
+./pruned_transducer_stateless4/decode.py \
+    --epoch 30 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless3/exp \
+    --max-duration 600 \
+    --decoding-method fast_beam_search_nbest \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64 \
+    --num-paths 200 \
+    --nbest-scale 0.5
+
+(6) fast beam search (nbest oracle WER)
+./pruned_transducer_stateless4/decode.py \
+    --epoch 30 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless4/exp \
+    --max-duration 600 \
+    --decoding-method fast_beam_search_nbest_oracle \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64 \
+    --num-paths 200 \
+    --nbest-scale 0.5
+
+(7) fast beam search (with LG)
+./pruned_transducer_stateless4/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless4/exp \
+    --max-duration 600 \
+    --decoding-method fast_beam_search_nbest_LG \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64
 """
 
 
@@ -70,6 +107,9 @@ import torch.nn as nn
 from asr_datamodule import LibriSpeechAsrDataModule
 from beam_search import (
     beam_search,
+    fast_beam_search_nbest,
+    fast_beam_search_nbest_LG,
+    fast_beam_search_nbest_oracle,
     fast_beam_search_one_best,
     greedy_search,
     greedy_search_batch,
@@ -83,6 +123,7 @@ from icefall.checkpoint import (
     find_checkpoints,
     load_checkpoint,
 )
+from icefall.lexicon import Lexicon
 from icefall.utils import (
     AttributeDict,
     setup_logger,
@@ -150,6 +191,13 @@ def get_parser():
         help="Path to the BPE model",
     )
 
+    parser.add_argument(
+        "--lang-dir",
+        type=Path,
+        default="data/lang_bpe_500",
+        help="The lang dir containing word table and LG graph",
+    )
+
     parser.add_argument(
         "--decoding-method",
         type=str,
@@ -159,6 +207,11 @@ def get_parser():
           - beam_search
           - modified_beam_search
           - fast_beam_search
+          - fast_beam_search_nbest
+          - fast_beam_search_nbest_oracle
+          - fast_beam_search_nbest_LG
+        If you use fast_beam_search_nbest_LG, you have to specify
+        `--lang-dir`, which should contain `LG.pt`.
         """,
     )
 
@@ -174,27 +227,42 @@ def get_parser():
     parser.add_argument(
         "--beam",
         type=float,
-        default=4,
+        default=20.0,
         help="""A floating point value to calculate the cutoff score during beam
         search (i.e., `cutoff = max-score - beam`), which is the same as the
         `beam` in Kaldi.
-        Used only when --decoding-method is fast_beam_search""",
+        Used only when --decoding-method is fast_beam_search,
+        fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle
+        """,
+    )
+
+    parser.add_argument(
+        "--ngram-lm-scale",
+        type=float,
+        default=0.01,
+        help="""
+        Used only when --decoding_method is fast_beam_search_nbest_LG.
+        It specifies the scale for n-gram LM scores.
+        """,
     )
 
     parser.add_argument(
         "--max-contexts",
         type=int,
-        default=4,
+        default=8,
         help="""Used only when --decoding-method is
-        fast_beam_search""",
+        fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle""",
     )
 
     parser.add_argument(
         "--max-states",
         type=int,
-        default=8,
+        default=64,
         help="""Used only when --decoding-method is
-        fast_beam_search""",
+        fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle""",
     )
 
     parser.add_argument(
@@ -212,6 +280,24 @@ def get_parser():
         Used only when --decoding_method is greedy_search""",
     )
 
+    parser.add_argument(
+        "--num-paths",
+        type=int,
+        default=200,
+        help="""Number of paths for nbest decoding.
+        Used only when the decoding method is fast_beam_search_nbest,
+        fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--nbest-scale",
+        type=float,
+        default=0.5,
+        help="""Scale applied to lattice scores when computing nbest paths.
+        Used only when the decoding method is fast_beam_search_nbest,
+        fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
+    )
+
     return parser
 
 
@@ -220,6 +306,7 @@ def decode_one_batch(
     model: nn.Module,
     sp: spm.SentencePieceProcessor,
     batch: dict,
+    word_table: Optional[k2.SymbolTable] = None,
     decoding_graph: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[List[str]]]:
     """Decode one batch and return the result in a dict. The dict has the
@@ -243,9 +330,12 @@ def decode_one_batch(
         It is the return value from iterating
         `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
         for the format of the `batch`.
+      word_table:
+        The word symbol table.
       decoding_graph:
         The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
-        only when --decoding_method is fast_beam_search.
+        only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
+        fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
     Returns:
       Return the decoding result. See above description for the format of
       the returned dict.
@@ -277,6 +367,49 @@ def decode_one_batch(
         )
         for hyp in sp.decode(hyp_tokens):
             hyps.append(hyp.split())
+    elif params.decoding_method == "fast_beam_search_nbest_LG":
+        hyp_tokens = fast_beam_search_nbest_LG(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in hyp_tokens:
+            hyps.append([word_table[i] for i in hyp])
+    elif params.decoding_method == "fast_beam_search_nbest":
+        hyp_tokens = fast_beam_search_nbest(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif params.decoding_method == "fast_beam_search_nbest_oracle":
+        hyp_tokens = fast_beam_search_nbest_oracle(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            ref_texts=sp.encode(supervisions["text"]),
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
     elif (
         params.decoding_method == "greedy_search"
         and params.max_sym_per_frame == 1
@@ -324,14 +457,17 @@ def decode_one_batch(
 
     if params.decoding_method == "greedy_search":
         return {"greedy_search": hyps}
-    elif params.decoding_method == "fast_beam_search":
+    elif "fast_beam_search" in params.decoding_method:
-        return {
+        key = f"beam_{params.beam}_"
-            (
+        key += f"max_contexts_{params.max_contexts}_"
-                f"beam_{params.beam}_"
+        key += f"max_states_{params.max_states}"
-                f"max_contexts_{params.max_contexts}_"
+        if "nbest" in params.decoding_method:
-                f"max_states_{params.max_states}"
+            key += f"_num_paths_{params.num_paths}_"
-            ): hyps
+            key += f"nbest_scale_{params.nbest_scale}"
-        }
+            if "LG" in params.decoding_method:
+                key += f"_ngram_lm_scale_{params.ngram_lm_scale}"
+
+        return {key: hyps}
     else:
         return {f"beam_size_{params.beam_size}": hyps}
 
@@ -341,6 +477,7 @@ def decode_dataset(
     params: AttributeDict,
     model: nn.Module,
     sp: spm.SentencePieceProcessor,
+    word_table: Optional[k2.SymbolTable] = None,
     decoding_graph: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[Tuple[List[str], List[str]]]]:
     """Decode dataset.
@@ -354,9 +491,12 @@ def decode_dataset(
         The neural model.
       sp:
         The BPE model.
+      word_table:
+        The word symbol table.
       decoding_graph:
         The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
-        only when --decoding_method is fast_beam_search.
+        only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
+        fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
     Returns:
       Return a dict, whose key may be "greedy_search" if greedy search
       is used, or it may be "beam_7" if beam size of 7 is used.
@@ -374,7 +514,7 @@ def decode_dataset(
     if params.decoding_method == "greedy_search":
         log_interval = 50
     else:
-        log_interval = 10
+        log_interval = 20
 
     results = defaultdict(list)
     for batch_idx, batch in enumerate(dl):
@@ -385,6 +525,7 @@ def decode_dataset(
             model=model,
             sp=sp,
             decoding_graph=decoding_graph,
+            word_table=word_table,
             batch=batch,
         )
 
@@ -466,6 +607,9 @@ def main():
         "greedy_search",
         "beam_search",
         "fast_beam_search",
+        "fast_beam_search_nbest",
+        "fast_beam_search_nbest_LG",
+        "fast_beam_search_nbest_oracle",
         "modified_beam_search",
     )
     params.res_dir = params.exp_dir / params.decoding_method
@@ -479,6 +623,11 @@ def main():
         params.suffix += f"-beam-{params.beam}"
         params.suffix += f"-max-contexts-{params.max_contexts}"
         params.suffix += f"-max-states-{params.max_states}"
+        if "nbest" in params.decoding_method:
+            params.suffix += f"-nbest-scale-{params.nbest_scale}"
+            params.suffix += f"-num-paths-{params.num_paths}"
+            if "LG" in params.decoding_method:
+                params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}"
     elif "beam_search" in params.decoding_method:
         params.suffix += (
             f"-{params.decoding_method}-beam-size-{params.beam_size}"
@@ -592,10 +741,24 @@ def main():
     model.to(device)
     model.eval()
 
-    if params.decoding_method == "fast_beam_search":
+    if "fast_beam_search" in params.decoding_method:
-        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+        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(
+                torch.load(lg_filename, map_location=device)
+            )
+            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}")
@@ -617,6 +780,7 @@ def main():
             params=params,
             model=model,
             sp=sp,
+            word_table=word_table,
             decoding_graph=decoding_graph,
         )
 

egs/librispeech/ASR/pruned_transducer_stateless5/decode.py

@@ -44,16 +44,53 @@ Usage:
     --decoding-method modified_beam_search \
     --beam-size 4
 
-(4) fast beam search
+(4) fast beam search (one best)
 ./pruned_transducer_stateless5/decode.py \
     --epoch 28 \
     --avg 15 \
     --exp-dir ./pruned_transducer_stateless5/exp \
     --max-duration 600 \
     --decoding-method fast_beam_search \
-    --beam 4 \
+    --beam 20.0 \
-    --max-contexts 4 \
+    --max-contexts 8 \
-    --max-states 8
+    --max-states 64
+
+(5) fast beam search (nbest)
+./pruned_transducer_stateless5/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless5/exp \
+    --max-duration 600 \
+    --decoding-method fast_beam_search_nbest \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64 \
+    --num-paths 200 \
+    --nbest-scale 0.5
+
+(6) fast beam search (nbest oracle WER)
+./pruned_transducer_stateless5/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless5/exp \
+    --max-duration 600 \
+    --decoding-method fast_beam_search_nbest_oracle \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64 \
+    --num-paths 200 \
+    --nbest-scale 0.5
+
+(7) fast beam search (with LG)
+./pruned_transducer_stateless5/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless5/exp \
+    --max-duration 600 \
+    --decoding-method fast_beam_search_nbest_LG \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64
 """
 
 
@@ -70,6 +107,9 @@ import torch.nn as nn
 from asr_datamodule import LibriSpeechAsrDataModule
 from beam_search import (
     beam_search,
+    fast_beam_search_nbest,
+    fast_beam_search_nbest_LG,
+    fast_beam_search_nbest_oracle,
     fast_beam_search_one_best,
     greedy_search,
     greedy_search_batch,
@@ -83,6 +123,7 @@ from icefall.checkpoint import (
     find_checkpoints,
     load_checkpoint,
 )
+from icefall.lexicon import Lexicon
 from icefall.utils import (
     AttributeDict,
     setup_logger,
@@ -128,7 +169,7 @@ def get_parser():
     parser.add_argument(
         "--use-averaged-model",
         type=str2bool,
-        default=False,
+        default=True,
         help="Whether to load averaged model. Currently it only supports "
         "using --epoch. If True, it would decode with the averaged model "
         "over the epoch range from `epoch-avg` (excluded) to `epoch`."
@@ -150,6 +191,13 @@ def get_parser():
         help="Path to the BPE model",
     )
 
+    parser.add_argument(
+        "--lang-dir",
+        type=Path,
+        default="data/lang_bpe_500",
+        help="The lang dir containing word table and LG graph",
+    )
+
     parser.add_argument(
         "--decoding-method",
         type=str,
@@ -159,6 +207,11 @@ def get_parser():
           - beam_search
           - modified_beam_search
           - fast_beam_search
+          - fast_beam_search_nbest
+          - fast_beam_search_nbest_oracle
+          - fast_beam_search_nbest_LG
+        If you use fast_beam_search_nbest_LG, you have to specify
+        `--lang-dir`, which should contain `LG.pt`.
         """,
     )
 
@@ -174,27 +227,42 @@ def get_parser():
     parser.add_argument(
         "--beam",
         type=float,
-        default=4,
+        default=20.0,
         help="""A floating point value to calculate the cutoff score during beam
         search (i.e., `cutoff = max-score - beam`), which is the same as the
         `beam` in Kaldi.
-        Used only when --decoding-method is fast_beam_search""",
+        Used only when --decoding-method is fast_beam_search,
+        fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle
+        """,
+    )
+
+    parser.add_argument(
+        "--ngram-lm-scale",
+        type=float,
+        default=0.01,
+        help="""
+        Used only when --decoding_method is fast_beam_search_nbest_LG.
+        It specifies the scale for n-gram LM scores.
+        """,
     )
 
     parser.add_argument(
         "--max-contexts",
         type=int,
-        default=4,
+        default=8,
         help="""Used only when --decoding-method is
-        fast_beam_search""",
+        fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle""",
     )
 
     parser.add_argument(
         "--max-states",
         type=int,
-        default=8,
+        default=64,
         help="""Used only when --decoding-method is
-        fast_beam_search""",
+        fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle""",
     )
 
     parser.add_argument(
@@ -212,6 +280,24 @@ def get_parser():
         Used only when --decoding_method is greedy_search""",
     )
 
+    parser.add_argument(
+        "--num-paths",
+        type=int,
+        default=200,
+        help="""Number of paths for nbest decoding.
+        Used only when the decoding method is fast_beam_search_nbest,
+        fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--nbest-scale",
+        type=float,
+        default=0.5,
+        help="""Scale applied to lattice scores when computing nbest paths.
+        Used only when the decoding method is fast_beam_search_nbest,
+        fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
+    )
+
     add_model_arguments(parser)
 
     return parser
@@ -222,6 +308,7 @@ def decode_one_batch(
     model: nn.Module,
     sp: spm.SentencePieceProcessor,
     batch: dict,
+    word_table: Optional[k2.SymbolTable] = None,
     decoding_graph: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[List[str]]]:
     """Decode one batch and return the result in a dict. The dict has the
@@ -245,9 +332,12 @@ def decode_one_batch(
         It is the return value from iterating
         `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
         for the format of the `batch`.
+      word_table:
+        The word symbol table.
       decoding_graph:
         The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
-        only when --decoding_method is fast_beam_search.
+        only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
+        fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
     Returns:
       Return the decoding result. See above description for the format of
       the returned dict.
@@ -279,6 +369,49 @@ def decode_one_batch(
         )
         for hyp in sp.decode(hyp_tokens):
             hyps.append(hyp.split())
+    elif params.decoding_method == "fast_beam_search_nbest_LG":
+        hyp_tokens = fast_beam_search_nbest_LG(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in hyp_tokens:
+            hyps.append([word_table[i] for i in hyp])
+    elif params.decoding_method == "fast_beam_search_nbest":
+        hyp_tokens = fast_beam_search_nbest(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif params.decoding_method == "fast_beam_search_nbest_oracle":
+        hyp_tokens = fast_beam_search_nbest_oracle(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            ref_texts=sp.encode(supervisions["text"]),
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
     elif (
         params.decoding_method == "greedy_search"
         and params.max_sym_per_frame == 1
@@ -326,14 +459,17 @@ def decode_one_batch(
 
     if params.decoding_method == "greedy_search":
         return {"greedy_search": hyps}
-    elif params.decoding_method == "fast_beam_search":
+    elif "fast_beam_search" in params.decoding_method:
-        return {
+        key = f"beam_{params.beam}_"
-            (
+        key += f"max_contexts_{params.max_contexts}_"
-                f"beam_{params.beam}_"
+        key += f"max_states_{params.max_states}"
-                f"max_contexts_{params.max_contexts}_"
+        if "nbest" in params.decoding_method:
-                f"max_states_{params.max_states}"
+            key += f"_num_paths_{params.num_paths}_"
-            ): hyps
+            key += f"nbest_scale_{params.nbest_scale}"
-        }
+            if "LG" in params.decoding_method:
+                key += f"_ngram_lm_scale_{params.ngram_lm_scale}"
+
+        return {key: hyps}
     else:
         return {f"beam_size_{params.beam_size}": hyps}
 
@@ -343,6 +479,7 @@ def decode_dataset(
     params: AttributeDict,
     model: nn.Module,
     sp: spm.SentencePieceProcessor,
+    word_table: Optional[k2.SymbolTable] = None,
     decoding_graph: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[Tuple[List[str], List[str]]]]:
     """Decode dataset.
@@ -356,9 +493,12 @@ def decode_dataset(
         The neural model.
       sp:
         The BPE model.
+      word_table:
+        The word symbol table.
       decoding_graph:
         The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
-        only when --decoding_method is fast_beam_search.
+        only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
+        fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
     Returns:
       Return a dict, whose key may be "greedy_search" if greedy search
       is used, or it may be "beam_7" if beam size of 7 is used.
@@ -387,6 +527,7 @@ def decode_dataset(
             model=model,
             sp=sp,
             decoding_graph=decoding_graph,
+            word_table=word_table,
             batch=batch,
         )
 
@@ -468,6 +609,9 @@ def main():
         "greedy_search",
         "beam_search",
         "fast_beam_search",
+        "fast_beam_search_nbest",
+        "fast_beam_search_nbest_LG",
+        "fast_beam_search_nbest_oracle",
         "modified_beam_search",
     )
     params.res_dir = params.exp_dir / params.decoding_method
@@ -481,6 +625,11 @@ def main():
         params.suffix += f"-beam-{params.beam}"
         params.suffix += f"-max-contexts-{params.max_contexts}"
         params.suffix += f"-max-states-{params.max_states}"
+        if "nbest" in params.decoding_method:
+            params.suffix += f"-nbest-scale-{params.nbest_scale}"
+            params.suffix += f"-num-paths-{params.num_paths}"
+            if "LG" in params.decoding_method:
+                params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}"
     elif "beam_search" in params.decoding_method:
         params.suffix += (
             f"-{params.decoding_method}-beam-size-{params.beam_size}"
@@ -594,10 +743,24 @@ def main():
     model.to(device)
     model.eval()
 
-    if params.decoding_method == "fast_beam_search":
+    if "fast_beam_search" in params.decoding_method:
-        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+        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(
+                torch.load(lg_filename, map_location=device)
+            )
+            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}")
@@ -619,6 +782,7 @@ def main():
             params=params,
             model=model,
             sp=sp,
+            word_table=word_table,
             decoding_graph=decoding_graph,
         )
 

icefall/decode.py

@@ -308,9 +308,7 @@ class Nbest(object):
             del word_fsa.aux_labels
 
         word_fsa.scores.zero_()
-        word_fsa_with_epsilon_loops = k2.remove_epsilon_and_add_self_loops(
+        word_fsa_with_epsilon_loops = k2.linear_fsa_with_self_loops(word_fsa)
-            word_fsa
-        )
 
         path_to_utt_map = self.shape.row_ids(1)
 
@@ -609,7 +607,7 @@ def rescore_with_n_best_list(
       num_paths:
         Size of nbest list.
       lm_scale_list:
-        A list of float representing LM score scales.
+        A list of floats representing LM score scales.
       nbest_scale:
         Scale to be applied to ``lattice.score`` when sampling paths
         using ``k2.random_paths``.