add transducer-stateless-aishell recipe to readme

README.md

@@ -107,6 +107,17 @@ The best CER we currently have is:
 
 We provide a Colab notebook to run a pre-trained conformer CTC model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1WnG17io5HEZ0Gn_cnh_VzK5QYOoiiklC?usp=sharing)
 
+#### Transducer Stateless Model
+
+The best CER we currently have is:
+
+|     | test |
+|-----|------|
+| CER | 5.7 |
+
+
+We provide a Colab notebook to run a pre-trained TransducerStateless model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/14XaT2MhnBkK-3_RqqWq3K90Xlbin-GZC#scrollTo=I30mgIz31SUF)
+
 #### TDNN LSTM CTC Model
 
 The CER for this model is:

egs/aishell/ASR/transducer_stateless/decode.py

@@ -40,6 +40,7 @@ from icefall.utils import (
     setup_logger,
     store_transcripts,
     write_error_stats,
+    str2bool,
 )
 
 
@@ -108,6 +109,16 @@ def get_parser():
         default=3,
         help="Maximum number of symbols per frame",
     )
+    parser.add_argument(
+        "--export",
+        type=str2bool,
+        default=False,
+        help="""When enabled, the averaged model is saved to
+        conformer_ctc/exp/pretrained.pt. Note: only model.state_dict() is saved.
+        pretrained.pt contains a dict {"model": model.state_dict()},
+        which can be loaded by `icefall.checkpoint.load_checkpoint()`.
+        """,
+    )
 
     return parser
 
@@ -417,6 +428,13 @@ def main():
         model.to(device)
         model.load_state_dict(average_checkpoints(filenames, device=device))
 
+    if params.export:
+        logging.info(f"Export averaged model to {params.exp_dir}/pretrained.pt")
+        torch.save(
+            {"model": model.state_dict()}, f"{params.exp_dir}/pretrained.pt"
+        )
+        return
+
     model.to(device)
     model.eval()
     model.device = device

egs/aishell/ASR/transducer_stateless/pretrained.py

@@ -45,9 +45,9 @@ import argparse
 import logging
 import math
 from typing import List
+from pathlib import Path
 
 import kaldifeat
-import sentencepiece as spm
 import torch
 import torchaudio
 from beam_search import beam_search, greedy_search
@@ -59,6 +59,8 @@ from torch.nn.utils.rnn import pad_sequence
 
 from icefall.env import get_env_info
 from icefall.utils import AttributeDict
+from icefall.lexicon import Lexicon
+from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler
 
 
 def get_parser():
@@ -76,9 +78,9 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--lang-dir",
         type=str,
-        help="""Path to bpe.model.
+        help="""Path to lang.
         Used only when method is ctc-decoding.
         """,
     )
@@ -220,18 +222,10 @@ def read_sound_files(
 def main():
     parser = get_parser()
     args = parser.parse_args()
+    args.lang_dir = Path(args.lang_dir)
 
     params = get_params()
-
     params.update(vars(args))
-
-    sp = spm.SentencePieceProcessor()
-    sp.load(params.bpe_model)
-
-    # <blk> is defined in local/train_bpe_model.py
-    params.blank_id = sp.piece_to_id("<blk>")
-    params.vocab_size = sp.get_piece_size()
-
     logging.info(f"{params}")
 
     device = torch.device("cpu")
@@ -240,6 +234,15 @@ def main():
 
     logging.info(f"device: {device}")
 
+    lexicon = Lexicon(params.lang_dir)
+    graph_compiler = CharCtcTrainingGraphCompiler(
+        lexicon=lexicon,
+        device=device,
+    )
+
+    params.blank_id = graph_compiler.texts_to_ids("<blk>")[0][0]
+    params.vocab_size = max(lexicon.tokens) + 1
+
     logging.info("Creating model")
     model = get_transducer_model(params)
 
@@ -303,7 +306,7 @@ def main():
         else:
             raise ValueError(f"Unsupported method: {params.method}")
 
-        hyps.append(sp.decode(hyp).split())
+        hyps.append([lexicon.token_table[i] for i in hyp])
 
     s = "\n"
     for filename, hyp in zip(params.sound_files, hyps):