updates for the zipformer_mmi and transducer_stateless recipes

.github/scripts/run-librispeech-transducer-stateless2-2022-04-19.sh

@@ -28,7 +28,7 @@ for sym in 1 2 3; do
     --method greedy_search \
     --max-sym-per-frame $sym \
     --checkpoint $repo/exp/pretrained.pt \
-    --bpe-model $repo/data/lang_bpe_500/bpe.model \
+    --tokens $repo/data/lang_bpe_500/tokens.txt \
     $repo/test_wavs/1089-134686-0001.wav \
     $repo/test_wavs/1221-135766-0001.wav \
     $repo/test_wavs/1221-135766-0002.wav
@@ -41,7 +41,7 @@ for method in fast_beam_search modified_beam_search beam_search; do
     --method $method \
     --beam-size 4 \
     --checkpoint $repo/exp/pretrained.pt \
-    --bpe-model $repo/data/lang_bpe_500/bpe.model \
+    --tokens $repo/data/lang_bpe_500/tokens.txt \
     $repo/test_wavs/1089-134686-0001.wav \
     $repo/test_wavs/1221-135766-0001.wav \
     $repo/test_wavs/1221-135766-0002.wav

.github/scripts/run-librispeech-zipformer-mmi-2022-12-08.sh

@@ -37,7 +37,7 @@ log "Export to torchscript model"
 ./zipformer_mmi/export.py \
   --exp-dir $repo/exp \
   --use-averaged-model false \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
   --epoch 99 \
   --avg 1 \
   --jit 1
@@ -61,7 +61,7 @@ for method in 1best nbest nbest-rescoring-LG nbest-rescoring-3-gram nbest-rescor
     --method $method \
     --checkpoint $repo/exp/pretrained.pt \
     --lang-dir $repo/data/lang_bpe_500 \
-    --bpe-model $repo/data/lang_bpe_500/bpe.model \
+    --tokens $repo/data/lang_bpe_500/tokens.txt \
     $repo/test_wavs/1089-134686-0001.wav \
     $repo/test_wavs/1221-135766-0001.wav \
     $repo/test_wavs/1221-135766-0002.wav

.github/scripts/run-pre-trained-transducer-stateless-librispeech-100h.sh

@@ -28,7 +28,7 @@ for sym in 1 2 3; do
     --method greedy_search \
     --max-sym-per-frame $sym \
     --checkpoint $repo/exp/pretrained.pt \
-    --bpe-model $repo/data/lang_bpe_500/bpe.model \
+    --tokens $repo/data/lang_bpe_500/tokens.txt \
     $repo/test_wavs/1089-134686-0001.wav \
     $repo/test_wavs/1221-135766-0001.wav \
     $repo/test_wavs/1221-135766-0002.wav
@@ -41,7 +41,7 @@ for method in modified_beam_search beam_search fast_beam_search; do
     --method $method \
     --beam-size 4 \
     --checkpoint $repo/exp/pretrained.pt \
-    --bpe-model $repo/data/lang_bpe_500/bpe.model \
+    --tokens $repo/data/lang_bpe_500/tokens.txt \
     $repo/test_wavs/1089-134686-0001.wav \
     $repo/test_wavs/1221-135766-0001.wav \
     $repo/test_wavs/1221-135766-0002.wav

.github/scripts/run-pre-trained-transducer-stateless-librispeech-960h.sh

@@ -28,7 +28,7 @@ for sym in 1 2 3; do
     --method greedy_search \
     --max-sym-per-frame $sym \
     --checkpoint $repo/exp/pretrained.pt \
-    --bpe-model $repo/data/lang_bpe_500/bpe.model \
+    --tokens $repo/data/lang_bpe_500/tokens.txt \
     $repo/test_wavs/1089-134686-0001.wav \
     $repo/test_wavs/1221-135766-0001.wav \
     $repo/test_wavs/1221-135766-0002.wav
@@ -41,7 +41,7 @@ for method in modified_beam_search beam_search fast_beam_search; do
     --method $method \
     --beam-size 4 \
     --checkpoint $repo/exp/pretrained.pt \
-    --bpe-model $repo/data/lang_bpe_500/bpe.model \
+    --tokens $repo/data/lang_bpe_500/tokens.txt \
     $repo/test_wavs/1089-134686-0001.wav \
     $repo/test_wavs/1221-135766-0001.wav \
     $repo/test_wavs/1221-135766-0002.wav

.github/scripts/run-pre-trained-transducer-stateless.sh

@@ -28,7 +28,7 @@ for sym in 1 2 3; do
     --method greedy_search \
     --max-sym-per-frame $sym \
     --checkpoint $repo/exp/pretrained.pt \
-    --bpe-model $repo/data/lang_bpe_500/bpe.model \
+    --tokens $repo/data/lang_bpe_500/tokens.txt \
     $repo/test_wavs/1089-134686-0001.wav \
     $repo/test_wavs/1221-135766-0001.wav \
     $repo/test_wavs/1221-135766-0002.wav
@@ -41,7 +41,7 @@ for method in fast_beam_search modified_beam_search beam_search; do
     --method $method \
     --beam-size 4 \
     --checkpoint $repo/exp/pretrained.pt \
-    --bpe-model $repo/data/lang_bpe_500/bpe.model \
+    --tokens $repo/data/lang_bpe_500/tokens.txt \
     $repo/test_wavs/1089-134686-0001.wav \
     $repo/test_wavs/1221-135766-0001.wav \
     $repo/test_wavs/1221-135766-0002.wav

.github/scripts/run-pre-trained-transducer.sh

@@ -27,7 +27,7 @@ log "Beam search decoding"
   --method beam_search \
   --beam-size 4 \
   --checkpoint $repo/exp/pretrained.pt \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
   $repo/test_wavs/1089-134686-0001.wav \
   $repo/test_wavs/1221-135766-0001.wav \
   $repo/test_wavs/1221-135766-0002.wav

egs/librispeech/ASR/transducer/export.py

@@ -22,7 +22,7 @@
 Usage:
 ./transducer/export.py \
   --exp-dir ./transducer/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
+  --tokens data/lang_bpe_500/tokens.txt \
   --epoch 34 \
   --avg 11
 
@@ -46,7 +46,7 @@ import argparse
 import logging
 from pathlib import Path
 
-import sentencepiece as spm
+import k2
 import torch
 from conformer import Conformer
 from decoder import Decoder
@@ -55,7 +55,7 @@ from model import Transducer
 
 from icefall.checkpoint import average_checkpoints, load_checkpoint
 from icefall.env import get_env_info
-from icefall.utils import AttributeDict, str2bool
+from icefall.utils import AttributeDict, num_tokens, str2bool
 
 
 def get_parser():
@@ -90,10 +90,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        default="data/lang_bpe_500/bpe.model",
+        default="data/lang_bpe_500/tokens.txt",
-        help="Path to the BPE model",
+        help="Path to the tokens.txt.",
     )
 
     parser.add_argument(
@@ -191,12 +191,14 @@ def main():
 
     logging.info(f"device: {device}")
 
-    sp = spm.SentencePieceProcessor()
+    # Load tokens.txt here
-    sp.load(params.bpe_model)
+    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.blank_id = token_table["<blk>"]
-    params.vocab_size = sp.get_piece_size()
+    params.unk_id = token_table["<unk>"]
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(params)
 

egs/librispeech/ASR/transducer/pretrained.py

@@ -19,7 +19,7 @@ Usage:
 
 ./transducer/pretrained.py \
         --checkpoint ./transducer/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 \
@@ -36,8 +36,8 @@ import logging
 import math
 from typing import List
 
+import k2
 import kaldifeat
-import sentencepiece as spm
 import torch
 import torchaudio
 from beam_search import beam_search, greedy_search
@@ -48,7 +48,7 @@ from model import Transducer
 from torch.nn.utils.rnn import pad_sequence
 
 from icefall.env import get_env_info
-from icefall.utils import AttributeDict
+from icefall.utils import AttributeDict, num_tokens
 
 
 def get_parser():
@@ -66,11 +66,9 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        help="""Path to bpe.model.
+        help="Path to tokens.txt.",
-        Used only when method is ctc-decoding.
-        """,
     )
 
     parser.add_argument(
@@ -204,12 +202,14 @@ def main():
 
     params.update(vars(args))
 
-    sp = spm.SentencePieceProcessor()
+    # Load tokens.txt here
-    sp.load(params.bpe_model)
+    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.blank_id = token_table["<blk>"]
-    params.vocab_size = sp.get_piece_size()
+    params.unk_id = token_table["<unk>"]
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(f"{params}")
 
@@ -257,6 +257,12 @@ def main():
             x=features, x_lens=feature_lengths
         )
 
+    def token_ids_to_words(token_ids: List[int]) -> str:
+        text = ""
+        for i in token_ids:
+            text += token_table[i]
+        return text.replace("▁", " ").strip()
+
     num_waves = encoder_out.size(0)
     hyps = []
     for i in range(num_waves):
@@ -272,12 +278,11 @@ 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{hyp}\n\n"
-        s += f"{filename}:\n{words}\n\n"
     logging.info(s)
 
     logging.info("Decoding Done")

egs/librispeech/ASR/transducer_stateless/export.py

@@ -22,7 +22,7 @@
 Usage:
 ./transducer_stateless/export.py \
   --exp-dir ./transducer_stateless/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
+  --tokens data/lang_bpe_500/tokens.txt \
   --epoch 20 \
   --avg 10
 
@@ -46,7 +46,7 @@ import argparse
 import logging
 from pathlib import Path
 
-import sentencepiece as spm
+import k2
 import torch
 import torch.nn as nn
 from conformer import Conformer
@@ -56,7 +56,7 @@ from model import Transducer
 
 from icefall.checkpoint import average_checkpoints, load_checkpoint
 from icefall.env import get_env_info
-from icefall.utils import AttributeDict, str2bool
+from icefall.utils import AttributeDict, num_tokens, str2bool
 
 
 def get_parser():
@@ -91,10 +91,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        default="data/lang_bpe_500/bpe.model",
+        default="data/lang_bpe_500/tokens.txt",
-        help="Path to the BPE model",
+        help="Path to the tokens.txt.",
     )
 
     parser.add_argument(
@@ -191,12 +191,14 @@ def main():
 
     logging.info(f"device: {device}")
 
-    sp = spm.SentencePieceProcessor()
+    # Load tokens.txt here
-    sp.load(params.bpe_model)
+    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.blank_id = token_table["<blk>"]
-    params.vocab_size = sp.get_piece_size()
+    params.unk_id = token_table["<unk>"]
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(params)
 

egs/librispeech/ASR/transducer_stateless/pretrained.py

@@ -20,7 +20,7 @@ Usage:
 (1) greedy search
 ./transducer_stateless/pretrained.py \
     --checkpoint ./transducer_stateless/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --tokens data/lang_bpe_500/tokens.txt \
     --method greedy_search \
     --max-sym-per-frame 1 \
     /path/to/foo.wav \
@@ -29,7 +29,7 @@ Usage:
 (2) beam search
 ./transducer_stateless/pretrained.py \
     --checkpoint ./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 \
@@ -38,7 +38,7 @@ Usage:
 (3) modified beam search
 ./transducer_stateless/pretrained.py \
     --checkpoint ./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 \
@@ -47,7 +47,7 @@ Usage:
 (4) fast beam search
 ./transducer_stateless/pretrained.py \
     --checkpoint ./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 \
@@ -67,7 +67,6 @@ from typing import List
 
 import k2
 import kaldifeat
-import sentencepiece as spm
 import torch
 import torchaudio
 from beam_search import (
@@ -80,6 +79,8 @@ from beam_search import (
 from torch.nn.utils.rnn import pad_sequence
 from train import get_params, get_transducer_model
 
+from icefall.utils import num_tokens
+
 
 def get_parser():
     parser = argparse.ArgumentParser(
@@ -96,9 +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(
@@ -213,12 +214,14 @@ def main():
 
     params.update(vars(args))
 
-    sp = spm.SentencePieceProcessor()
+    # Load tokens.txt here
-    sp.load(params.bpe_model)
+    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.blank_id = token_table["<blk>"]
-    params.vocab_size = sp.get_piece_size()
+    params.unk_id = token_table["<unk>"]
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(f"{params}")
 
@@ -273,6 +276,12 @@ 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_list = fast_beam_search_one_best(
@@ -318,12 +327,11 @@ def main():
                 raise ValueError(f"Unsupported method: {params.method}")
             hyp_list.append(hyp)
 
-    hyps = [sp.decode(hyp).split() for hyp in hyp_list]
+    hyps = [token_ids_to_words(hyp) for hyp in hyp_list]
 
     s = "\n"
     for filename, hyp in zip(params.sound_files, hyps):
-        words = " ".join(hyp)
+        s += f"{filename}:\n{hyp}\n\n"
-        s += f"{filename}:\n{words}\n\n"
     logging.info(s)
 
     logging.info("Decoding Done")

egs/librispeech/ASR/transducer_stateless2/export.py

@@ -22,7 +22,7 @@
 Usage:
 ./transducer_stateless2/export.py \
   --exp-dir ./transducer_stateless2/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
+  --tokens data/lang_bpe_500/tokens.txt \
   --epoch 20 \
   --avg 10
 
@@ -46,12 +46,12 @@ import argparse
 import logging
 from pathlib import Path
 
-import sentencepiece as spm
+import k2
 import torch
 from train import get_params, get_transducer_model
 
 from icefall.checkpoint import average_checkpoints, load_checkpoint
-from icefall.utils import str2bool
+from icefall.utils import num_tokens, str2bool
 
 
 def get_parser():
@@ -86,10 +86,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        default="data/lang_bpe_500/bpe.model",
+        default="data/lang_bpe_500/tokens.txt",
-        help="Path to the BPE model",
+        help="Path to the tokens.txt",
     )
 
     parser.add_argument(
@@ -123,12 +123,14 @@ def main():
 
     logging.info(f"device: {device}")
 
-    sp = spm.SentencePieceProcessor()
+    # Load tokens.txt here
-    sp.load(params.bpe_model)
+    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.blank_id = token_table["<blk>"]
-    params.vocab_size = sp.get_piece_size()
+    params.unk_id = token_table["<unk>"]
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(params)
 

egs/librispeech/ASR/transducer_stateless2/pretrained.py

@@ -20,7 +20,7 @@ Usage:
 (1) greedy search
 ./transducer_stateless2/pretrained.py \
     --checkpoint ./transducer_stateless2/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --tokens data/lang_bpe_500/tokens.txt \
     --method greedy_search \
     --max-sym-per-frame 1 \
     /path/to/foo.wav \
@@ -29,7 +29,7 @@ Usage:
 (2) beam search
 ./transducer_stateless2/pretrained.py \
     --checkpoint ./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 \
@@ -38,7 +38,7 @@ Usage:
 (3) modified beam search
 ./transducer_stateless2/pretrained.py \
     --checkpoint ./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 \
@@ -47,7 +47,7 @@ Usage:
 (4) fast beam search
 ./transducer_stateless2/pretrained.py \
     --checkpoint ./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 \
@@ -67,7 +67,6 @@ from typing import List
 
 import k2
 import kaldifeat
-import sentencepiece as spm
 import torch
 import torchaudio
 from beam_search import (
@@ -80,6 +79,8 @@ from beam_search import (
 from torch.nn.utils.rnn import pad_sequence
 from train import get_params, get_transducer_model
 
+from icefall.utils import num_tokens
+
 
 def get_parser():
     parser = argparse.ArgumentParser(
@@ -96,9 +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(
@@ -213,12 +214,14 @@ def main():
 
     params.update(vars(args))
 
-    sp = spm.SentencePieceProcessor()
+    # Load tokens.txt here
-    sp.load(params.bpe_model)
+    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.blank_id = token_table["<blk>"]
-    params.vocab_size = sp.get_piece_size()
+    params.unk_id = token_table["<unk>"]
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(f"{params}")
 
@@ -273,6 +276,12 @@ 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_list = fast_beam_search_one_best(
@@ -318,12 +327,11 @@ def main():
                 raise ValueError(f"Unsupported method: {params.method}")
             hyp_list.append(hyp)
 
-    hyps = [sp.decode(hyp).split() for hyp in hyp_list]
+    hyps = [token_ids_to_words(hyp) for hyp in hyp_list]
 
     s = "\n"
     for filename, hyp in zip(params.sound_files, hyps):
-        words = " ".join(hyp)
+        s += f"{filename}:\n{hyp}\n\n"
-        s += f"{filename}:\n{words}\n\n"
     logging.info(s)
 
     logging.info("Decoding Done")

egs/librispeech/ASR/transducer_stateless_multi_datasets/export.py

@@ -22,7 +22,7 @@
 Usage:
 ./transducer_stateless_multi_datasets/export.py \
   --exp-dir ./transducer_stateless_multi_datasets/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
+  --tokens data/lang_bpe_500/tokens.txt \
   --epoch 20 \
   --avg 10
 
@@ -47,7 +47,7 @@ import argparse
 import logging
 from pathlib import Path
 
-import sentencepiece as spm
+import k2
 import torch
 import torch.nn as nn
 from conformer import Conformer
@@ -57,7 +57,7 @@ from model import Transducer
 
 from icefall.checkpoint import average_checkpoints, load_checkpoint
 from icefall.env import get_env_info
-from icefall.utils import AttributeDict, str2bool
+from icefall.utils import AttributeDict, num_tokens, str2bool
 
 
 def get_parser():
@@ -92,10 +92,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        default="data/lang_bpe_500/bpe.model",
+        default="data/lang_bpe_500/tokens.txt",
-        help="Path to the BPE model",
+        help="Path to the tokens.txt.",
     )
 
     parser.add_argument(
@@ -192,12 +192,14 @@ def main():
 
     logging.info(f"device: {device}")
 
-    sp = spm.SentencePieceProcessor()
+    # Load tokens.txt here
-    sp.load(params.bpe_model)
+    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.blank_id = token_table["<blk>"]
-    params.vocab_size = sp.get_piece_size()
+    params.unk_id = token_table["<unk>"]
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(params)
 

egs/librispeech/ASR/transducer_stateless_multi_datasets/pretrained.py

@@ -20,7 +20,7 @@ Usage:
 (1) greedy search
 ./transducer_stateless_multi_datasets/pretrained.py \
     --checkpoint ./transducer_stateless_multi_datasets/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --tokens data/lang_bpe_500/tokens.txt \
     --method greedy_search \
     --max-sym-per-frame 1 \
     /path/to/foo.wav \
@@ -29,7 +29,7 @@ Usage:
 (2) beam search
 ./transducer_stateless_multi_datasets/pretrained.py \
     --checkpoint ./transducer_stateless_multi_datasets/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 \
@@ -38,7 +38,7 @@ Usage:
 (3) modified beam search
 ./transducer_stateless_multi_datasets/pretrained.py \
     --checkpoint ./transducer_stateless_multi_datasets/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 \
@@ -47,7 +47,7 @@ Usage:
 (4) fast beam search
 ./transducer_stateless_multi_datasets/pretrained.py \
     --checkpoint ./transducer_stateless_multi_datasets/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 \
@@ -67,7 +67,6 @@ from typing import List
 
 import k2
 import kaldifeat
-import sentencepiece as spm
 import torch
 import torchaudio
 from beam_search import (
@@ -80,6 +79,8 @@ from beam_search import (
 from torch.nn.utils.rnn import pad_sequence
 from train import get_params, get_transducer_model
 
+from icefall.utils import num_tokens
+
 
 def get_parser():
     parser = argparse.ArgumentParser(
@@ -96,9 +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(
@@ -213,12 +214,14 @@ def main():
 
     params.update(vars(args))
 
-    sp = spm.SentencePieceProcessor()
+    # Load tokens.txt here
-    sp.load(params.bpe_model)
+    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.blank_id = token_table["<blk>"]
-    params.vocab_size = sp.get_piece_size()
+    params.unk_id = token_table["<unk>"]
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(f"{params}")
 
@@ -273,6 +276,12 @@ 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_list = fast_beam_search_one_best(
@@ -318,12 +327,11 @@ def main():
                 raise ValueError(f"Unsupported method: {params.method}")
             hyp_list.append(hyp)
 
-    hyps = [sp.decode(hyp).split() for hyp in hyp_list]
+    hyps = [token_ids_to_words(hyp) for hyp in hyp_list]
 
     s = "\n"
     for filename, hyp in zip(params.sound_files, hyps):
-        words = " ".join(hyp)
+        s += f"{filename}:\n{hyp}\n\n"
-        s += f"{filename}:\n{words}\n\n"
     logging.info(s)
 
     logging.info("Decoding Done")

egs/librispeech/ASR/zipformer_mmi/export.py

@@ -26,7 +26,7 @@ Usage:
 
 ./zipformer_mmi/export.py \
   --exp-dir ./zipformer_mmi/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
+  --tokens data/lang_bpe_500/tokens.txt \
   --epoch 30 \
   --avg 9 \
   --jit 1
@@ -45,7 +45,7 @@ for how to use the exported models outside of icefall.
 
 ./zipformer_mmi/export.py \
   --exp-dir ./zipformer_mmi/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
+  --tokens data/lang_bpe_500/tokens.txt \
   --epoch 20 \
   --avg 10
 
@@ -86,7 +86,7 @@ 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_ctc_model, get_params
@@ -97,7 +97,7 @@ from icefall.checkpoint import (
     find_checkpoints,
     load_checkpoint,
 )
-from icefall.utils import str2bool
+from icefall.utils import num_tokens, str2bool
 
 
 def get_parser():
@@ -154,10 +154,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        default="data/lang_bpe_500/bpe.model",
+        default="data/lang_bpe_500/tokens.txt",
-        help="Path to the BPE model",
+        help="Path to the tokens.txt.",
     )
 
     parser.add_argument(
@@ -190,12 +190,14 @@ def main():
 
     logging.info(f"device: {device}")
 
-    sp = spm.SentencePieceProcessor()
+    # Load tokens.txt here
-    sp.load(params.bpe_model)
+    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.blank_id = token_table["<blk>"]
-    params.vocab_size = sp.get_piece_size()
+    params.unk_id = token_table["<unk>"]
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(params)
 

egs/librispeech/ASR/zipformer_mmi/pretrained.py

@@ -21,7 +21,7 @@ You can generate the checkpoint with the following command:
 
 ./zipformer_mmi/export.py \
   --exp-dir ./zipformer_mmi/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
+  --tokens data/lang_bpe_500/tokens.txt \
   --epoch 20 \
   --avg 10
 
@@ -30,14 +30,14 @@ Usage of this script:
 (1) 1best
 ./zipformer_mmi/pretrained.py \
     --checkpoint ./zipformer_mmi/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --tokens data/lang_bpe_500/tokens.txt \
     --method 1best \
     /path/to/foo.wav \
     /path/to/bar.wav
 (2) nbest
 ./zipformer_mmi/pretrained.py \
     --checkpoint ./zipformer_mmi/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --tokens data/lang_bpe_500/tokens.txt \
     --nbest-scale 1.2 \
     --method nbest \
     /path/to/foo.wav \
@@ -45,7 +45,7 @@ Usage of this script:
 (3) nbest-rescoring-LG
 ./zipformer_mmi/pretrained.py \
     --checkpoint ./zipformer_mmi/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --tokens data/lang_bpe_500/tokens.txt \
     --nbest-scale 1.2 \
     --method nbest-rescoring-LG \
     /path/to/foo.wav \
@@ -53,7 +53,7 @@ Usage of this script:
 (4) nbest-rescoring-3-gram
 ./zipformer_mmi/pretrained.py \
     --checkpoint ./zipformer_mmi/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --tokens data/lang_bpe_500/tokens.txt \
     --nbest-scale 1.2 \
     --method nbest-rescoring-3-gram \
     /path/to/foo.wav \
@@ -61,7 +61,7 @@ Usage of this script:
 (5) nbest-rescoring-4-gram
 ./zipformer_mmi/pretrained.py \
     --checkpoint ./zipformer_mmi/exp/pretrained.pt \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --tokens data/lang_bpe_500/tokens.txt \
     --nbest-scale 1.2 \
     --method nbest-rescoring-4-gram \
     /path/to/foo.wav \
@@ -83,7 +83,6 @@ from typing import List
 
 import k2
 import kaldifeat
-import sentencepiece as spm
 import torch
 import torchaudio
 from decode import get_decoding_params
@@ -97,7 +96,7 @@ from icefall.decode import (
     one_best_decoding,
 )
 from icefall.mmi_graph_compiler import MmiTrainingGraphCompiler
-from icefall.utils import get_texts
+from icefall.utils import get_texts, num_tokens
 
 
 def get_parser():
@@ -115,9 +114,9 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        help="""Path to bpe.model.""",
+        help="""Path to tokens.txt.""",
     )
 
     parser.add_argument(
@@ -247,13 +246,14 @@ def main():
     params.update(get_decoding_params())
     params.update(vars(args))
 
-    sp = spm.SentencePieceProcessor()
+    # Load tokens.txt here
-    sp.load(params.bpe_model)
+    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.blank_id = token_table["<blk>"]
-    params.unk_id = sp.piece_to_id("<unk>")
+    params.unk_id = token_table["<unk>"]
-    params.vocab_size = sp.get_piece_size()
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(f"{params}")
 
@@ -298,8 +298,6 @@ def main():
     features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
     feature_lengths = torch.tensor(feature_lengths, device=device)
 
-    bpe_model = spm.SentencePieceProcessor()
-    bpe_model.load(str(params.lang_dir / "bpe.model"))
     mmi_graph_compiler = MmiTrainingGraphCompiler(
         params.lang_dir,
         uniq_filename="lexicon.txt",
@@ -313,6 +311,12 @@ def main():
     if not hasattr(HP, "lm_scores"):
         HP.lm_scores = HP.scores.clone()
 
+    def token_ids_to_words(token_ids: List[int]) -> str:
+        text = ""
+        for i in token_ids:
+            text += token_table[i]
+        return text.replace("▁", " ").strip()
+
     method = params.method
     assert method in (
         "1best",
@@ -390,14 +394,11 @@ def main():
     #
     # 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 = [token_ids_to_words(ids) for ids in token_ids]
-    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]
     s = "\n"
     for filename, hyp in zip(params.sound_files, hyps):
-        words = " ".join(hyp)
+        s += f"{filename}:\n{hyp}\n\n"
-        s += f"{filename}:\n{words}\n\n"
     logging.info(s)
 
     logging.info("Decoding Done")