Support pure CTC decoding requiring neither a lexicion nor an n-gram LM.

egs/librispeech/ASR/conformer_ctc/decode.py

@@ -23,6 +23,7 @@ from pathlib import Path
 from typing import Dict, List, Optional, Tuple
 
 import k2
+import sentencepiece as spm
 import torch
 import torch.nn as nn
 from asr_datamodule import LibriSpeechAsrDataModule
@@ -77,6 +78,9 @@ def get_parser():
         default="attention-decoder",
         help="""Decoding method.
         Supported values are:
+            - (0) ctc. Use CTC decoding. It uses a sentence piece
+              model, i.e., lang_dir/bpe.model, to convert word pieces to words.
+              It needs neither a lexicon nor an n-gram LM.
             - (1) 1best. Extract the best path from the decoding lattice as the
               decoding result.
             - (2) nbest. Extract n paths from the decoding lattice; the path
@@ -128,14 +132,26 @@ def get_parser():
         """,
     )
 
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="conformer_ctc/exp",
+        help="The experiment dir",
+    )
+
+    parser.add_argument(
+        "--lang-dir",
+        type=str,
+        default="data/lang_bpe",
+        help="The lang dir",
+    )
+
     return parser
 
 
 def get_params() -> AttributeDict:
     params = AttributeDict(
         {
-            "exp_dir": Path("conformer_ctc/exp"),
-            "lang_dir": Path("data/lang_bpe"),
             "lm_dir": Path("data/lm"),
             # parameters for conformer
             "subsampling_factor": 4,
@@ -159,13 +175,15 @@ def get_params() -> AttributeDict:
 def decode_one_batch(
     params: AttributeDict,
     model: nn.Module,
-    HLG: k2.Fsa,
+    HLG: Optional[k2.Fsa],
+    H: Optional[k2.Fsa],
+    bpe_model: Optional[spm.SentencePieceProcessor],
     batch: dict,
     word_table: k2.SymbolTable,
     sos_id: int,
     eos_id: int,
     G: Optional[k2.Fsa] = None,
-) -> Dict[str, List[List[int]]]:
+) -> Dict[str, List[List[str]]]:
     """Decode one batch and return the result in a dict. The dict has the
     following format:
 
@@ -190,7 +208,11 @@ def decode_one_batch(
       model:
         The neural model.
       HLG:
-        The decoding graph.
+        The decoding graph. Used only when params.method is NOT ctc-decoding.
+      H:
+        The ctc topo. Used only when params.method is ctc-decoding.
+      bpe_model:
+        The BPE model. Used only when params.method is ctc-decoding.
       batch:
         It is the return value from iterating
         `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
@@ -209,7 +231,10 @@ def decode_one_batch(
       Return the decoding result. See above description for the format of
       the returned dict.
     """
+    if HLG is not None:
         device = HLG.device
+    else:
+        device = H.device
     feature = batch["inputs"]
     assert feature.ndim == 3
     feature = feature.to(device)
@@ -229,9 +254,17 @@ def decode_one_batch(
         1,
     ).to(torch.int32)
 
+    if H is None:
+        assert HLG is not None
+        decoding_graph = HLG
+    else:
+        assert HLG is None
+        assert bpe_model is not None
+        decoding_graph = H
+
     lattice = get_lattice(
         nnet_output=nnet_output,
-        HLG=HLG,
+        decoding_graph=decoding_graph,
         supervision_segments=supervision_segments,
         search_beam=params.search_beam,
         output_beam=params.output_beam,
@@ -240,6 +273,24 @@ def decode_one_batch(
         subsampling_factor=params.subsampling_factor,
     )
 
+    if params.method == "ctc-decoding":
+        best_path = one_best_decoding(
+            lattice=lattice, use_double_scores=params.use_double_scores
+        )
+        # Note: `best_path.aux_labels` contains token IDs, not word IDs
+        # since we are using H, not HLG here.
+        #
+        # token_ids is a lit-of-list of IDs
+        token_ids = get_texts(best_path)
+
+        # hyps is a list of str, e.g., [['xxx yyy zzz'], ...]
+        hyps = bpe_model.decode(token_ids)
+
+        # hyps is a list of list of str, e.g., [['xxx', 'yyy', 'zzz'], ... ]
+        hyps = [s.split() for s in hyps]
+        key = f"ctc-decoding"
+        return {key: hyps}
+
     if params.method == "nbest-oracle":
         # Note: You can also pass rescored lattices to it.
         # We choose the HLG decoded lattice for speed reasons
@@ -340,12 +391,14 @@ def decode_dataset(
     dl: torch.utils.data.DataLoader,
     params: AttributeDict,
     model: nn.Module,
-    HLG: k2.Fsa,
+    HLG: Optional[k2.Fsa],
+    H: Optional[k2.Fsa],
+    bpe_model: Optional[spm.SentencePieceProcessor],
     word_table: k2.SymbolTable,
     sos_id: int,
     eos_id: int,
     G: Optional[k2.Fsa] = None,
-) -> Dict[str, List[Tuple[List[int], List[int]]]]:
+) -> Dict[str, List[Tuple[List[str], List[str]]]]:
     """Decode dataset.
 
     Args:
@@ -356,7 +409,11 @@ def decode_dataset(
       model:
         The neural model.
       HLG:
-        The decoding graph.
+        The decoding graph. Used only when params.method is NOT ctc-decoding.
+      H:
+        The ctc topo. Used only when params.method is ctc-decoding.
+      bpe_model:
+        The BPE model. Used only when params.method is ctc-decoding.
       word_table:
         It is the word symbol table.
       sos_id:
@@ -391,6 +448,8 @@ def decode_dataset(
             params=params,
             model=model,
             HLG=HLG,
+            H=H,
+            bpe_model=bpe_model,
             batch=batch,
             word_table=word_table,
             G=G,
@@ -469,6 +528,8 @@ def main():
     parser = get_parser()
     LibriSpeechAsrDataModule.add_arguments(parser)
     args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+    args.lang_dir = Path(args.lang_dir)
 
     params = get_params()
     params.update(vars(args))
@@ -496,6 +557,18 @@ def main():
     sos_id = graph_compiler.sos_id
     eos_id = graph_compiler.eos_id
 
+    if params.method == "ctc-decoding":
+        HLG = None
+        H = k2.ctc_topo(
+            max_token=max_token_id,
+            modified=False,
+            device=device,
+        )
+        bpe_model = spm.SentencePieceProcessor()
+        bpe_model.load(str(params.lang_dir / "bpe.model"))
+    else:
+        H = None
+        bpe_model = None
         HLG = k2.Fsa.from_dict(
             torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu")
         )
@@ -593,6 +666,8 @@ def main():
             params=params,
             model=model,
             HLG=HLG,
+            H=H,
+            bpe_model=bpe_model,
             word_table=lexicon.word_table,
             G=G,
             sos_id=sos_id,

egs/librispeech/ASR/conformer_ctc/pretrained.py

@@ -301,7 +301,7 @@ def main():
 
     lattice = get_lattice(
         nnet_output=nnet_output,
-        HLG=HLG,
+        decoding_graph=HLG,
         supervision_segments=supervision_segments,
         search_beam=params.search_beam,
         output_beam=params.output_beam,

egs/librispeech/ASR/tdnn_lstm_ctc/decode.py

@@ -146,7 +146,7 @@ def decode_one_batch(
     batch: dict,
     lexicon: Lexicon,
     G: Optional[k2.Fsa] = None,
-) -> Dict[str, List[List[int]]]:
+) -> Dict[str, List[List[str]]]:
     """Decode one batch and return the result in a dict. The dict has the
     following format:
 
@@ -210,7 +210,7 @@ def decode_one_batch(
 
     lattice = get_lattice(
         nnet_output=nnet_output,
-        HLG=HLG,
+        decoding_graph=HLG,
         supervision_segments=supervision_segments,
         search_beam=params.search_beam,
         output_beam=params.output_beam,
@@ -272,7 +272,7 @@ def decode_dataset(
     HLG: k2.Fsa,
     lexicon: Lexicon,
     G: Optional[k2.Fsa] = None,
-) -> Dict[str, List[Tuple[List[int], List[int]]]]:
+) -> Dict[str, List[Tuple[List[str], List[str]]]]:
     """Decode dataset.
 
     Args:

egs/librispeech/ASR/tdnn_lstm_ctc/pretrained.py

@@ -232,7 +232,7 @@ def main():
 
     lattice = get_lattice(
         nnet_output=nnet_output,
-        HLG=HLG,
+        decoding_graph=HLG,
         supervision_segments=supervision_segments,
         search_beam=params.search_beam,
         output_beam=params.output_beam,

egs/yesno/ASR/tdnn/decode.py

@@ -124,7 +124,7 @@ def decode_one_batch(
 
     lattice = get_lattice(
         nnet_output=nnet_output,
-        HLG=HLG,
+        decoding_graph=HLG,
         supervision_segments=supervision_segments,
         search_beam=params.search_beam,
         output_beam=params.output_beam,

egs/yesno/ASR/tdnn/pretrained.py

@@ -175,7 +175,7 @@ def main():
 
     lattice = get_lattice(
         nnet_output=nnet_output,
-        HLG=HLG,
+        decoding_graph=HLG,
         supervision_segments=supervision_segments,
         search_beam=params.search_beam,
         output_beam=params.output_beam,

icefall/decode.py

@@ -66,7 +66,7 @@ def _intersect_device(
 
 def get_lattice(
     nnet_output: torch.Tensor,
-    HLG: k2.Fsa,
+    decoding_graph: k2.Fsa,
     supervision_segments: torch.Tensor,
     search_beam: float,
     output_beam: float,
@@ -79,8 +79,9 @@ def get_lattice(
     Args:
       nnet_output:
         It is the output of a neural model of shape `(N, T, C)`.
-      HLG:
+      decoding_graph:
-        An Fsa, the decoding graph. See also `compile_HLG.py`.
+        An Fsa, the decoding graph. It can be either an HLG
+        (see `compile_HLG.py`) or an H (see `k2.ctc_topo`).
       supervision_segments:
         A 2-D **CPU** tensor of dtype `torch.int32` with 3 columns.
         Each row contains information for a supervision segment. Column 0
@@ -117,7 +118,7 @@ def get_lattice(
     )
 
     lattice = k2.intersect_dense_pruned(
-        HLG,
+        decoding_graph,
         dense_fsa_vec,
         search_beam=search_beam,
         output_beam=output_beam,