add otc related scripts using phone instead of bpe

egs/librispeech/WSASR/conformer_ctc2/decode_phone.py

@@ -26,7 +26,6 @@ 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
@@ -41,7 +40,6 @@ from icefall.checkpoint import (
 from icefall.decode import get_lattice, one_best_decoding
 from icefall.env import get_env_info
 from icefall.lexicon import Lexicon
-from icefall.otc_graph_compiler import OtcTrainingGraphCompiler
 from icefall.utils import (
     AttributeDict,
     get_texts,
@@ -94,7 +92,7 @@ def get_parser():
     parser.add_argument(
         "--avg",
         type=int,
-        default=1,
+        default=5,
         help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
         "'--epoch' and '--iter'",
@@ -195,7 +193,7 @@ def remove_duplicates_and_blank(hyp: List[int]) -> List[int]:
 def decode_one_batch(
     params: AttributeDict,
     model: nn.Module,
-    HLG: Optional[k2.Fsa],
+    HLG: k2.Fsa,
     batch: dict,
     word_table: k2.SymbolTable,
     G: Optional[k2.Fsa] = None,
@@ -239,10 +237,7 @@ def decode_one_batch(
       Return the decoding result. See above description for the format of
       the returned dict. Note: If it decodes to nothing, then return None.
     """
-    if HLG is not None:
+    device = HLG.device
-        device = HLG.device
-    else:
-        device = H.device
     feature = batch["inputs"]
     assert feature.ndim == 3
     feature = feature.to(device)
@@ -271,7 +266,6 @@ def decode_one_batch(
         1,
     ).to(torch.int32)
 
-    assert HLG is not None
     decoding_graph = HLG
 
     lattice = get_lattice(
@@ -303,7 +297,7 @@ def decode_dataset(
     dl: torch.utils.data.DataLoader,
     params: AttributeDict,
     model: nn.Module,
-    HLG: Optional[k2.Fsa],
+    HLG: k2.Fsa,
     word_table: k2.SymbolTable,
     G: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
@@ -452,7 +446,7 @@ def main():
 
     lexicon = Lexicon(params.lang_dir)
     # remove otc_token from decoding units
-    max_token_id = len(lexicon.tokens)  - 1
+    max_token_id = len(lexicon.tokens) - 1
     num_classes = max_token_id + 1  # +1 for the blank
 
     device = torch.device("cpu")
@@ -463,9 +457,7 @@ def main():
 
     params.num_classes = num_classes
 
-    HLG = k2.Fsa.from_dict(
+    HLG = k2.Fsa.from_dict(torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu"))
-        torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu")
-    )
     HLG = HLG.to(device)
     assert HLG.requires_grad is False
 

egs/librispeech/WSASR/conformer_ctc2/train_phone.py

@@ -899,15 +899,6 @@ def run(rank, world_size, args):
     if torch.cuda.is_available():
         device = torch.device("cuda", rank)
 
-    if params.show_alignment:
-        HLG = k2.Fsa.from_dict(torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu"))
-        HLG = HLG.to(device)
-        assert HLG.requires_grad is False
-        if not hasattr(HLG, "lm_scores"):
-            HLG.lm_scores = HLG.scores.clone()
-        params.HLG = HLG
-
-
     lexicon = Lexicon(params.lang_dir)
     graph_compiler = OtcPhoneTrainingGraphCompiler(
         lexicon,
@@ -1118,7 +1109,6 @@ def main():
     args.exp_dir = Path(args.exp_dir)
     args.otc_token = f"{args.otc_token}"
 
-
     world_size = args.world_size
     assert world_size >= 1
     if world_size > 1: