updated the lstm_transducer_stateless recipes

also revoked previous changes in conformer_ctc3/jit_pretrained.py

egs/librispeech/ASR/conformer_ctc3/jit_pretrained.py

@@ -24,7 +24,7 @@ Usage (for non-streaming mode):
 (1) ctc-decoding
 ./conformer_ctc3/pretrained.py \
   --nn-model-filename ./conformer_ctc3/exp/cpu_jit.pt \
-  --tokens data/lang_bpe_500/tokens.txt \
+  --bpe-model data/lang_bpe_500/bpe.model \
   --method ctc-decoding \
   --sample-rate 16000 \
   /path/to/foo.wav \
@@ -71,6 +71,7 @@ from typing import List
 
 import k2
 import kaldifeat
+import sentencepiece as spm
 import torch
 import torchaudio
 from decode import get_decoding_params
@@ -115,9 +116,11 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--tokens",
+        "--bpe-model",
         type=str,
-        help="Path to the tokens.txt.",
+        help="""Path to bpe.model.
+        Used only when method is ctc-decoding.
+        """,
     )
 
     parser.add_argument(
@@ -126,9 +129,10 @@ def get_parser():
         default="1best",
         help="""Decoding method.
         Possible values are:
-        (0) ctc-decoding - Use CTC decoding. It uses a tokens.txt file 
-            to convert tokens to actual words or characters. It needs 
-            neither a lexicon nor an n-gram LM.
+        (0) ctc-decoding - 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 - Use the best path as decoding output. Only
             the transformer encoder output is used for decoding.
             We call it HLG decoding.
@@ -277,7 +281,6 @@ def main():
     waves = [w.to(device) for w in waves]
 
     logging.info("Decoding started")
-    hyps = []
     features = fbank(waves)
     feature_lengths = [f.size(0) for f in features]
 
@@ -297,17 +300,10 @@ def main():
 
     if params.method == "ctc-decoding":
         logging.info("Use CTC decoding")
+        bpe_model = spm.SentencePieceProcessor()
+        bpe_model.load(params.bpe_model)
         max_token_id = params.num_classes - 1
 
-        # Load tokens.txt here
-        token_table = k2.SymbolTable.from_file(params.tokens)
-
-        def token_ids_to_words(token_ids: List[int]) -> str:
-            text = ""
-            for i in token_ids:
-                text += token_table[i]
-            return text.replace("▁", " ").strip()
-
         H = k2.ctc_topo(
             max_token=max_token_id,
             modified=False,
@@ -328,9 +324,9 @@ def main():
         best_path = one_best_decoding(
             lattice=lattice, use_double_scores=params.use_double_scores
         )
-        hyp_tokens = get_texts(best_path)
-        for hyp in hyp_tokens:
-            hyps.append(token_ids_to_words(hyp))
+        token_ids = get_texts(best_path)
+        hyps = bpe_model.decode(token_ids)
+        hyps = [s.split() for s in hyps]
     elif params.method in [
         "1best",
         "nbest-rescoring",
@@ -395,16 +391,16 @@ def main():
             )
             best_path = next(iter(best_path_dict.values()))
 
+        hyps = get_texts(best_path)
         word_sym_table = k2.SymbolTable.from_file(params.words_file)
-        hyp_tokens = get_texts(best_path)
-        for hyp in hyp_tokens:
-            hyps.append(" ".join([word_sym_table[i] for i in hyp]))
+        hyps = [[word_sym_table[i] for i in ids] for ids in hyps]
     else:
         raise ValueError(f"Unsupported decoding method: {params.method}")
 
     s = "\n"
     for filename, hyp in zip(params.sound_files, hyps):
-        s += f"{filename}:\n{hyp}\n\n"
+        words = " ".join(hyp)
+        s += f"{filename}:\n{words}\n\n"
     logging.info(s)
 
     logging.info("Decoding Done")

egs/librispeech/ASR/lstm_transducer_stateless/export.py

@@ -26,7 +26,7 @@ Usage:
 
 ./lstm_transducer_stateless/export.py \
   --exp-dir ./lstm_transducer_stateless/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
+  --tokens data/lang_bpe_500/tokens.txt \
   --epoch 35 \
   --avg 10 \
   --jit-trace 1
@@ -38,7 +38,7 @@ It will generate 3 files: `encoder_jit_trace.pt`,
 
 ./lstm_transducer_stateless/export.py \
   --exp-dir ./lstm_transducer_stateless/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
+  --tokens data/lang_bpe_500/tokens.txt \
   --epoch 35 \
   --avg 10
 
@@ -79,7 +79,7 @@ import argparse
 import logging
 from pathlib import Path
 
-import sentencepiece as spm
+import k2
 import torch
 import torch.nn as nn
 from scaling_converter import convert_scaled_to_non_scaled
@@ -91,7 +91,7 @@ from icefall.checkpoint import (
     find_checkpoints,
     load_checkpoint,
 )
-from icefall.utils import str2bool
+from icefall.utils import num_tokens, str2bool
 
 
 def get_parser():
@@ -148,10 +148,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        default="data/lang_bpe_500/bpe.model",
-        help="Path to the BPE model",
+        default="data/lang_bpe_500/tokens.txt",
+        help="Path to the tokens.txt.",
     )
 
     parser.add_argument(
@@ -266,12 +266,13 @@ def main():
 
     logging.info(f"device: {device}")
 
-    sp = spm.SentencePieceProcessor()
-    sp.load(params.bpe_model)
+    # Load tokens.txt here
+    token_table = k2.SymbolTable.from_file(params.tokens)
 
-    # <blk> is defined in local/train_bpe_model.py
-    params.blank_id = sp.piece_to_id("<blk>")
-    params.vocab_size = sp.get_piece_size()
+    # Load id of the <blk> token and the vocab size, <blk> is 
+    # defined in local/train_bpe_model.py
+    params.blank_id = token_table["<blk>"]
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(params)
 

egs/librispeech/ASR/lstm_transducer_stateless/pretrained.py

@@ -20,7 +20,7 @@ Usage:
 (1) greedy search
 ./lstm_transducer_stateless/pretrained.py \
     --checkpoint ./lstm_transducer_stateless/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --tokens ./data/lang_bpe_500/tokens.txt \
     --method greedy_search \
     /path/to/foo.wav \
     /path/to/bar.wav
@@ -28,7 +28,7 @@ Usage:
 (2) beam search
 ./lstm_transducer_stateless/pretrained.py \
     --checkpoint ./lstm_transducer_stateless/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --tokens ./data/lang_bpe_500/tokens.txt \
     --method beam_search \
     --beam-size 4 \
     /path/to/foo.wav \
@@ -37,7 +37,7 @@ Usage:
 (3) modified beam search
 ./lstm_transducer_stateless/pretrained.py \
     --checkpoint ./lstm_transducer_stateless/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --tokens ./data/lang_bpe_500/tokens.txt \
     --method modified_beam_search \
     --beam-size 4 \
     /path/to/foo.wav \
@@ -46,7 +46,7 @@ Usage:
 (4) fast beam search
 ./lstm_transducer_stateless/pretrained.py \
     --checkpoint ./lstm_transducer_stateless/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --tokens ./data/lang_bpe_500/tokens.txt \
     --method fast_beam_search \
     --beam-size 4 \
     /path/to/foo.wav \
@@ -66,7 +66,6 @@ from typing import List
 
 import k2
 import kaldifeat
-import sentencepiece as spm
 import torch
 import torchaudio
 from beam_search import (
@@ -79,6 +78,8 @@ from beam_search import (
 from torch.nn.utils.rnn import pad_sequence
 from train import add_model_arguments, get_params, get_transducer_model
 
+from icefall.utils import num_tokens
+
 
 def get_parser():
     parser = argparse.ArgumentParser(
@@ -95,9 +96,9 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        help="""Path to bpe.model.""",
+        help="Path to the tokens.txt.",
     )
 
     parser.add_argument(
@@ -214,13 +215,14 @@ def main():
 
     params.update(vars(args))
 
-    sp = spm.SentencePieceProcessor()
-    sp.load(params.bpe_model)
+    # Load tokens.txt here
+    token_table = k2.SymbolTable.from_file(params.tokens)
 
+    # Load id of the <blk> token and the vocab size
     # <blk> is defined in local/train_bpe_model.py
-    params.blank_id = sp.piece_to_id("<blk>")
-    params.unk_id = sp.piece_to_id("<unk>")
-    params.vocab_size = sp.get_piece_size()
+    params.blank_id = token_table["<blk>"]
+    params.unk_id = token_table["<unk>"]
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(f"{params}")
 
@@ -275,6 +277,12 @@ def main():
         msg += f" with beam size {params.beam_size}"
     logging.info(msg)
 
+    def token_ids_to_words(token_ids: List[int]) -> str:
+        text = ""
+        for i in token_ids:
+            text += token_table[i]
+        return text.replace("▁", " ").strip()
+
     if params.method == "fast_beam_search":
         decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
         hyp_tokens = fast_beam_search_one_best(
@@ -286,8 +294,8 @@ def main():
             max_contexts=params.max_contexts,
             max_states=params.max_states,
         )
-        for hyp in sp.decode(hyp_tokens):
-            hyps.append(hyp.split())
+        for hyp in hyp_tokens:
+            hyps.append(token_ids_to_words(hyp))
     elif params.method == "modified_beam_search":
         hyp_tokens = modified_beam_search(
             model=model,
@@ -296,16 +304,16 @@ def main():
             beam=params.beam_size,
         )
 
-        for hyp in sp.decode(hyp_tokens):
-            hyps.append(hyp.split())
+        for hyp in hyp_tokens:
+            hyps.append(token_ids_to_words(hyp))
     elif params.method == "greedy_search" and params.max_sym_per_frame == 1:
         hyp_tokens = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
             encoder_out_lens=encoder_out_lens,
         )
-        for hyp in sp.decode(hyp_tokens):
-            hyps.append(hyp.split())
+        for hyp in hyp_tokens:
+            hyps.append(token_ids_to_words(hyp))
     else:
         for i in range(num_waves):
             # fmt: off
@@ -326,12 +334,11 @@ def main():
             else:
                 raise ValueError(f"Unsupported method: {params.method}")
 
-            hyps.append(sp.decode(hyp).split())
+            hyps.append(token_ids_to_words(hyp))
 
     s = "\n"
     for filename, hyp in zip(params.sound_files, hyps):
-        words = " ".join(hyp)
-        s += f"{filename}:\n{words}\n\n"
+        s += f"{filename}:\n{hyp}\n\n"
     logging.info(s)
 
     logging.info("Decoding Done")

egs/librispeech/ASR/lstm_transducer_stateless2/export-for-ncnn.py

@@ -29,7 +29,7 @@ popd
 
 ./lstm_transducer_stateless2/export-for-ncnn.py \
   --exp-dir $repo/exp \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
   --epoch 99 \
   --avg 1 \
   --use-averaged-model 0 \
@@ -49,7 +49,7 @@ import argparse
 import logging
 from pathlib import Path
 
-import sentencepiece as spm
+import k2
 import torch
 from scaling_converter import convert_scaled_to_non_scaled
 from train import add_model_arguments, get_params, get_transducer_model
@@ -60,7 +60,7 @@ from icefall.checkpoint import (
     find_checkpoints,
     load_checkpoint,
 )
-from icefall.utils import setup_logger, str2bool
+from icefall.utils import num_tokens, setup_logger, str2bool
 
 
 def get_parser():
@@ -106,10 +106,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        default="data/lang_bpe_500/bpe.model",
-        help="Path to the BPE model",
+        default="data/lang_bpe_500/tokens.txt",
+        help="Path to the tokens.txt.",
     )
 
     parser.add_argument(
@@ -221,12 +221,13 @@ def main():
 
     logging.info(f"device: {device}")
 
-    sp = spm.SentencePieceProcessor()
-    sp.load(params.bpe_model)
+    # Load tokens.txt here
+    token_table = k2.SymbolTable.from_file(params.tokens)
 
-    # <blk> is defined in local/train_bpe_model.py
-    params.blank_id = sp.piece_to_id("<blk>")
-    params.vocab_size = sp.get_piece_size()
+    # Load id of the <blk> token and the vocab size, <blk> is 
+    # defined in local/train_bpe_model.py
+    params.blank_id = token_table["<blk>"]
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(params)
 

egs/librispeech/ASR/lstm_transducer_stateless2/export-onnx.py

@@ -28,7 +28,7 @@ popd
 2. Export the model to ONNX
 
 ./lstm_transducer_stateless2/export-onnx.py \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
   --use-averaged-model 0 \
   --epoch 99 \
   --avg 1 \
@@ -52,8 +52,8 @@ import logging
 from pathlib import Path
 from typing import Dict, Optional, Tuple
 
+import k2
 import onnx
-import sentencepiece as spm
 import torch
 import torch.nn as nn
 from decoder import Decoder
@@ -68,7 +68,7 @@ from icefall.checkpoint import (
     find_checkpoints,
     load_checkpoint,
 )
-from icefall.utils import setup_logger, str2bool
+from icefall.utils import num_tokens, setup_logger, str2bool
 
 
 def get_parser():
@@ -125,10 +125,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        default="data/lang_bpe_500/bpe.model",
-        help="Path to the BPE model",
+        default="data/lang_bpe_500/tokens.txt",
+        help="Path to the tokens.txt.",
     )
 
     parser.add_argument(
@@ -437,12 +437,13 @@ def main():
 
     logging.info(f"device: {device}")
 
-    sp = spm.SentencePieceProcessor()
-    sp.load(params.bpe_model)
+    # Load tokens.txt here
+    token_table = k2.SymbolTable.from_file(params.tokens)
 
-    # <blk> is defined in local/train_bpe_model.py
-    params.blank_id = sp.piece_to_id("<blk>")
-    params.vocab_size = sp.get_piece_size()
+    # Load id of the <blk> token and the vocab size, <blk> is 
+    # defined in local/train_bpe_model.py
+    params.blank_id = token_table["<blk>"]
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(params)
 

egs/librispeech/ASR/lstm_transducer_stateless2/export.py

@@ -27,7 +27,7 @@ Usage:
 
 ./lstm_transducer_stateless2/export.py \
   --exp-dir ./lstm_transducer_stateless2/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
+  --tokens ./data/lang_bpe_500/tokens.txt \
   --epoch 35 \
   --avg 10 \
   --jit-trace 1
@@ -39,7 +39,7 @@ It will generate 3 files: `encoder_jit_trace.pt`,
 
 ./lstm_transducer_stateless2/export.py \
   --exp-dir ./lstm_transducer_stateless2/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
+  --tokens ./data/lang_bpe_500/tokens.txt \
   --epoch 35 \
   --avg 10
 
@@ -80,7 +80,7 @@ import argparse
 import logging
 from pathlib import Path
 
-import sentencepiece as spm
+import k2
 import torch
 import torch.nn as nn
 from scaling_converter import convert_scaled_to_non_scaled
@@ -92,7 +92,7 @@ from icefall.checkpoint import (
     find_checkpoints,
     load_checkpoint,
 )
-from icefall.utils import str2bool
+from icefall.utils import num_tokens, str2bool
 
 
 def get_parser():
@@ -149,10 +149,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        default="data/lang_bpe_500/bpe.model",
-        help="Path to the BPE model",
+        default="data/lang_bpe_500/tokens.txt",
+        help="Path to the tokens.txt.",
     )
 
     parser.add_argument(
@@ -267,12 +267,13 @@ def main():
 
     logging.info(f"device: {device}")
 
-    sp = spm.SentencePieceProcessor()
-    sp.load(params.bpe_model)
+    # Load tokens.txt here
+    token_table = k2.SymbolTable.from_file(params.tokens)
 
-    # <blk> is defined in local/train_bpe_model.py
-    params.blank_id = sp.piece_to_id("<blk>")
-    params.vocab_size = sp.get_piece_size()
+    # Load id of the <blk> token and the vocab size, <blk> is 
+    # defined in local/train_bpe_model.py
+    params.blank_id = token_table["<blk>"]
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(params)
 

egs/librispeech/ASR/lstm_transducer_stateless2/pretrained.py

@@ -20,7 +20,7 @@ Usage:
 (1) greedy search
 ./lstm_transducer_stateless2/pretrained.py \
     --checkpoint ./lstm_transducer_stateless2/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --tokens ./data/lang_bpe_500/tokens.txt \
     --method greedy_search \
     /path/to/foo.wav \
     /path/to/bar.wav
@@ -28,7 +28,7 @@ Usage:
 (2) beam search
 ./lstm_transducer_stateless2/pretrained.py \
     --checkpoint ./lstm_transducer_stateless2/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --tokens ./data/lang_bpe_500/tokens.txt \
     --method beam_search \
     --beam-size 4 \
     /path/to/foo.wav \
@@ -37,7 +37,7 @@ Usage:
 (3) modified beam search
 ./lstm_transducer_stateless2/pretrained.py \
     --checkpoint ./lstm_transducer_stateless2/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --tokens ./data/lang_bpe_500/tokens.txt \
     --method modified_beam_search \
     --beam-size 4 \
     /path/to/foo.wav \
@@ -46,7 +46,7 @@ Usage:
 (4) fast beam search
 ./lstm_transducer_stateless2/pretrained.py \
     --checkpoint ./lstm_transducer_stateless2/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --tokens ./data/lang_bpe_500/tokens.txt \
     --method fast_beam_search \
     --beam-size 4 \
     /path/to/foo.wav \
@@ -69,7 +69,6 @@ from typing import List
 
 import k2
 import kaldifeat
-import sentencepiece as spm
 import torch
 import torchaudio
 from beam_search import (
@@ -82,6 +81,8 @@ from beam_search import (
 from torch.nn.utils.rnn import pad_sequence
 from train import add_model_arguments, get_params, get_transducer_model
 
+from icefall.utils import num_tokens
+
 
 def get_parser():
     parser = argparse.ArgumentParser(
@@ -98,9 +99,9 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        help="""Path to bpe.model.""",
+        help="""Path to tokens.txt.""",
     )
 
     parser.add_argument(
@@ -217,13 +218,14 @@ def main():
 
     params.update(vars(args))
 
-    sp = spm.SentencePieceProcessor()
-    sp.load(params.bpe_model)
+    # Load tokens.txt here
+    token_table = k2.SymbolTable.from_file(params.tokens)
 
+    # Load id of the <blk> token and the vocab size
     # <blk> is defined in local/train_bpe_model.py
-    params.blank_id = sp.piece_to_id("<blk>")
-    params.unk_id = sp.piece_to_id("<unk>")
-    params.vocab_size = sp.get_piece_size()
+    params.blank_id = token_table["<blk>"]
+    params.unk_id = token_table["<unk>"]
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(f"{params}")
 
@@ -278,6 +280,12 @@ def main():
         msg += f" with beam size {params.beam_size}"
     logging.info(msg)
 
+    def token_ids_to_words(token_ids: List[int]) -> str:
+        text = ""
+        for i in token_ids:
+            text += token_table[i]
+        return text.replace("▁", " ").strip()
+
     if params.method == "fast_beam_search":
         decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
         hyp_tokens = fast_beam_search_one_best(
@@ -289,8 +297,8 @@ def main():
             max_contexts=params.max_contexts,
             max_states=params.max_states,
         )
-        for hyp in sp.decode(hyp_tokens):
-            hyps.append(hyp.split())
+        for hyp in hyp_tokens:
+            hyps.append(token_ids_to_words(hyp))
     elif params.method == "modified_beam_search":
         hyp_tokens = modified_beam_search(
             model=model,
@@ -299,16 +307,16 @@ def main():
             beam=params.beam_size,
         )
 
-        for hyp in sp.decode(hyp_tokens):
-            hyps.append(hyp.split())
+        for hyp in hyp_tokens:
+            hyps.append(token_ids_to_words(hyp))
     elif params.method == "greedy_search" and params.max_sym_per_frame == 1:
         hyp_tokens = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
             encoder_out_lens=encoder_out_lens,
         )
-        for hyp in sp.decode(hyp_tokens):
-            hyps.append(hyp.split())
+        for hyp in hyp_tokens:
+            hyps.append(token_ids_to_words(hyp))
     else:
         for i in range(num_waves):
             # fmt: off
@@ -329,12 +337,11 @@ def main():
             else:
                 raise ValueError(f"Unsupported method: {params.method}")
 
-            hyps.append(sp.decode(hyp).split())
+            hyps.append(token_ids_to_words(hyp))
 
     s = "\n"
     for filename, hyp in zip(params.sound_files, hyps):
-        words = " ".join(hyp)
-        s += f"{filename}:\n{words}\n\n"
+        s += f"{filename}:\n{hyp}\n\n"
     logging.info(s)
 
     logging.info("Decoding Done")

egs/librispeech/ASR/lstm_transducer_stateless3/export.py

@@ -26,7 +26,7 @@ Usage:
 
 ./lstm_transducer_stateless3/export.py \
   --exp-dir ./lstm_transducer_stateless3/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
+  --tokens data/lang_bpe_500/tokens.txt \
   --epoch 40 \
   --avg 20 \
   --jit-trace 1
@@ -38,7 +38,7 @@ It will generate 3 files: `encoder_jit_trace.pt`,
 
 ./lstm_transducer_stateless3/export.py \
   --exp-dir ./lstm_transducer_stateless3/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
+  --tokens data/lang_bpe_500/tokens.txt \
   --epoch 40 \
   --avg 20
 
@@ -79,7 +79,7 @@ import argparse
 import logging
 from pathlib import Path
 
-import sentencepiece as spm
+import k2
 import torch
 import torch.nn as nn
 from scaling_converter import convert_scaled_to_non_scaled
@@ -91,7 +91,7 @@ from icefall.checkpoint import (
     find_checkpoints,
     load_checkpoint,
 )
-from icefall.utils import str2bool
+from icefall.utils import num_tokens, str2bool
 
 
 def get_parser():
@@ -148,10 +148,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        default="data/lang_bpe_500/bpe.model",
-        help="Path to the BPE model",
+        default="data/lang_bpe_500/tokens.txt",
+        help="Path to tokens.txt.",
     )
 
     parser.add_argument(
@@ -266,12 +266,13 @@ def main():
 
     logging.info(f"device: {device}")
 
-    sp = spm.SentencePieceProcessor()
-    sp.load(params.bpe_model)
+    # Load tokens.txt here
+    token_table = k2.SymbolTable.from_file(params.tokens)
 
-    # <blk> is defined in local/train_bpe_model.py
-    params.blank_id = sp.piece_to_id("<blk>")
-    params.vocab_size = sp.get_piece_size()
+    # Load id of the <blk> token and the vocab size, <blk> is 
+    # defined in local/train_bpe_model.py
+    params.blank_id = token_table["<blk>"]
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(params)
 

egs/librispeech/ASR/lstm_transducer_stateless3/pretrained.py

@@ -20,7 +20,7 @@ Usage:
 (1) greedy search
 ./lstm_transducer_stateless3/pretrained.py \
     --checkpoint ./lstm_transducer_stateless3/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --tokens data/lang_bpe_500/tokens.txt \
     --method greedy_search \
     /path/to/foo.wav \
     /path/to/bar.wav
@@ -28,7 +28,7 @@ Usage:
 (2) beam search
 ./lstm_transducer_stateless3/pretrained.py \
     --checkpoint ./lstm_transducer_stateless3/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --tokens data/lang_bpe_500/tokens.txt \
     --method beam_search \
     --beam-size 4 \
     /path/to/foo.wav \
@@ -37,7 +37,7 @@ Usage:
 (3) modified beam search
 ./lstm_transducer_stateless3/pretrained.py \
     --checkpoint ./lstm_transducer_stateless3/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --tokens data/lang_bpe_500/tokens.txt \
     --method modified_beam_search \
     --beam-size 4 \
     /path/to/foo.wav \
@@ -79,6 +79,8 @@ from beam_search import (
 from torch.nn.utils.rnn import pad_sequence
 from train import add_model_arguments, get_params, get_transducer_model
 
+from icefall.utils import num_tokens
+
 
 def get_parser():
     parser = argparse.ArgumentParser(
@@ -95,9 +97,9 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        help="""Path to bpe.model.""",
+        help="""Path to tokens.txt.""",
     )
 
     parser.add_argument(
@@ -214,13 +216,14 @@ def main():
 
     params.update(vars(args))
 
-    sp = spm.SentencePieceProcessor()
-    sp.load(params.bpe_model)
+    # Load tokens.txt here
+    token_table = k2.SymbolTable.from_file(params.tokens)
 
+    # Load id of the <blk> token and the vocab size
     # <blk> is defined in local/train_bpe_model.py
-    params.blank_id = sp.piece_to_id("<blk>")
-    params.unk_id = sp.piece_to_id("<unk>")
-    params.vocab_size = sp.get_piece_size()
+    params.blank_id = token_table["<blk>"]
+    params.unk_id = token_table["<unk>"]
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(f"{params}")
 
@@ -275,6 +278,12 @@ def main():
         msg += f" with beam size {params.beam_size}"
     logging.info(msg)
 
+    def token_ids_to_words(token_ids: List[int]) -> str:
+        text = ""
+        for i in token_ids:
+            text += token_table[i]
+        return text.replace("▁", " ").strip()
+
     if params.method == "fast_beam_search":
         decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
         hyp_tokens = fast_beam_search_one_best(
@@ -286,8 +295,8 @@ def main():
             max_contexts=params.max_contexts,
             max_states=params.max_states,
         )
-        for hyp in sp.decode(hyp_tokens):
-            hyps.append(hyp.split())
+        for hyp in hyp_tokens:
+            hyps.append(token_ids_to_words(hyp))
     elif params.method == "modified_beam_search":
         hyp_tokens = modified_beam_search(
             model=model,
@@ -296,16 +305,16 @@ def main():
             beam=params.beam_size,
         )
 
-        for hyp in sp.decode(hyp_tokens):
-            hyps.append(hyp.split())
+        for hyp in hyp_tokens:
+            hyps.append(token_ids_to_words(hyp))
     elif params.method == "greedy_search" and params.max_sym_per_frame == 1:
         hyp_tokens = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
             encoder_out_lens=encoder_out_lens,
         )
-        for hyp in sp.decode(hyp_tokens):
-            hyps.append(hyp.split())
+        for hyp in hyp_tokens:
+            hyps.append(token_ids_to_words(hyp))
     else:
         for i in range(num_waves):
             # fmt: off
@@ -326,12 +335,11 @@ def main():
             else:
                 raise ValueError(f"Unsupported method: {params.method}")
 
-            hyps.append(sp.decode(hyp).split())
+            hyps.append(token_ids_to_words(hyp))
 
     s = "\n"
     for filename, hyp in zip(params.sound_files, hyps):
-        words = " ".join(hyp)
-        s += f"{filename}:\n{words}\n\n"
+        s += f"{filename}:\n{hyp}\n\n"
     logging.info(s)
 
     logging.info("Decoding Done")