Update ctc-decoding on pretrained.py and conformer_ctc.rst

docs/source/recipes/librispeech/conformer_ctc.rst

@@ -551,7 +551,7 @@ The command to run CTC decoding is:
   $ cd egs/librispeech/ASR
   $ ./conformer_ctc/pretrained.py \
     --checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretrained.pt \
-    --lang-dir ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe \
+    --bpe-model ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/bpe.model \
     --method ctc-decoding \
     ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac \
     ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac \
@@ -595,7 +595,8 @@ The command to run HLG decoding is:
   $ cd egs/librispeech/ASR
   $ ./conformer_ctc/pretrained.py \
     --checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretrained.pt \
-    --lang-dir ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe \
+    --words-file ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/words.txt \
+    --HLG ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt \
     ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac \
     ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac \
     ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac
@@ -637,7 +638,8 @@ The command to run HLG decoding + LM rescoring is:
   $ cd egs/librispeech/ASR
   $ ./conformer_ctc/pretrained.py \
     --checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretrained.pt \
-    --lang-dir ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe \
+    --words-file ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/words.txt \
+    --HLG ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt \
     --method whole-lattice-rescoring \
     --G ./tmp/icefall_asr_librispeech_conformer_ctc/data/lm/G_4_gram.pt \
     --ngram-lm-scale 0.8 \
@@ -684,7 +686,8 @@ The command to run HLG decoding + LM rescoring + attention decoder rescoring is:
   $ cd egs/librispeech/ASR
   $ ./conformer_ctc/pretrained.py \
     --checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretrained.pt \
-    --lang-dir ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe \
+    --words-file ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/words.txt \
+    --HLG ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt \
     --method attention-decoder \
     --G ./tmp/icefall_asr_librispeech_conformer_ctc/data/lm/G_4_gram.pt \
     --ngram-lm-scale 1.3 \

egs/librispeech/ASR/conformer_ctc/pretrained.py

@@ -20,6 +20,7 @@
 import argparse
 import logging
 import math
+import os
 from typing import List
 
 import k2
@@ -36,7 +37,6 @@ from icefall.decode import (
     rescore_with_attention_decoder,
     rescore_with_whole_lattice,
 )
-from icefall.lexicon import Lexicon
 from icefall.utils import AttributeDict, get_texts
 
 
@@ -55,10 +55,21 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--lang-dir",
+        "--words-file",
         type=str,
-        required=True,
+        help="Path to words.txt",
-        help="Path to lang dir.",
+    )
+
+    parser.add_argument(
+        "--HLG",
+        type=str,
+        help="Path to HLG.pt.",
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        help="Path to bpe.model.",
     )
 
     parser.add_argument(
@@ -287,17 +298,19 @@ def main():
 
     if params.method == "ctc-decoding":
         logging.info("Use CTC decoding")
-        lexicon = Lexicon(params.lang_dir)
+        if not os.path.exists(params.bpe_model):
-        max_token_id = max(lexicon.tokens)
+            raise ValueError("The path to bpe.model is required!")
+
+        bpe_model = spm.SentencePieceProcessor()
+        bpe_model.load(params.bpe_model)
+        max_token_id = bpe_model.get_piece_size() - 1
+
         H = k2.ctc_topo(
             max_token=max_token_id,
             modified=False,
             device=device,
         )
 
-        bpe_model = spm.SentencePieceProcessor()
-        bpe_model.load(params.lang_dir + "/bpe.model")
-
         lattice = get_lattice(
             nnet_output=nnet_output,
             decoding_graph=H,
@@ -320,10 +333,13 @@ def main():
         "whole-lattice-rescoring",
         "attention-decoder",
     ]:
-        logging.info(f"Loading HLG from {params.lang_dir}/HLG.pt")
+        if not os.path.exists(params.HLG):
-        HLG = k2.Fsa.from_dict(
+            raise ValueError("The path to HLG.pt is required!")
-            torch.load(params.lang_dir + "/HLG.pt", map_location="cpu")
+        if not os.path.exists(params.words_file):
-        )
+            raise ValueError("The path to words.txt is required!")
+
+        logging.info(f"Loading HLG from {params.HLG}")
+        HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
         HLG = HLG.to(device)
         if not hasattr(HLG, "lm_scores"):
             # For whole-lattice-rescoring and attention-decoder
@@ -386,9 +402,7 @@ def main():
             best_path = next(iter(best_path_dict.values()))
 
         hyps = get_texts(best_path)
-        word_sym_table = k2.SymbolTable.from_file(
+        word_sym_table = k2.SymbolTable.from_file(params.words_file)
-            params.lang_dir + "/words.txt"
-        )
         hyps = [[word_sym_table[i] for i in ids] for ids in hyps]
     else:
         raise ValueError(f"Unsupported decoding method: {params.method}")