update decoding files

egs/tal_csasr/ASR/lstm_transducer_stateless3/decode.py

@@ -704,10 +704,12 @@ def main():
     tal_csasr = TAL_CSASRAsrDataModule(args)
 
     dev_cuts = tal_csasr.valid_cuts()
+    dev_cuts = dev_cuts.subset(first=300)
     dev_cuts = dev_cuts.map(text_normalize_for_cut)
     dev_dl = tal_csasr.valid_dataloaders(dev_cuts)
 
     test_cuts = tal_csasr.test_cuts()
+    test_cuts = test_cuts.subset(first=300)
     test_cuts = test_cuts.map(text_normalize_for_cut)
     test_dl = tal_csasr.test_dataloaders(test_cuts)
 

egs/tal_csasr/ASR/lstm_transducer_stateless3/streaming_decode.py

@@ -55,6 +55,7 @@ Usage:
       --max-contexts 4 \
       --max-states 8
 """
+import re
 import argparse
 import logging
 import warnings
@@ -66,11 +67,13 @@ import numpy as np
 import sentencepiece as spm
 import torch
 import torch.nn as nn
-from asr_datamodule import LibriSpeechAsrDataModule
+from asr_datamodule import TAL_CSASRAsrDataModule
 from beam_search import Hypothesis, HypothesisList, get_hyps_shape
 from kaldifeat import Fbank, FbankOptions
 from lhotse import CutSet
+from lhotse.cut import Cut
 from lstm import LOG_EPSILON, stack_states, unstack_states
+from local.text_normalize import text_normalize
 from stream import Stream
 from torch.nn.utils.rnn import pad_sequence
 from train import add_model_arguments, get_params, get_transducer_model
@@ -82,6 +85,8 @@ from icefall.checkpoint import (
     load_checkpoint,
 )
 from icefall.decode import one_best_decoding
+from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler
+from icefall.lexicon import Lexicon
 from icefall.utils import (
     AttributeDict,
     get_texts,
@@ -143,10 +148,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--lang-dir",
         type=str,
-        default="data/lang_bpe_500/bpe.model",
-        help="Path to the BPE model",
+        default="data/lang_char",
+        help="Path to the dir containing bpe.model and tokens.txt",
     )
 
     parser.add_argument(
@@ -617,12 +622,25 @@ def create_streaming_feature_extractor() -> Fbank:
     opts.mel_opts.num_bins = 80
     return Fbank(opts)
 
+def filter_zh_en(text: str):
+    import re
+    pattern = re.compile(r"([\u4e00-\u9fff])")
+    
+    chars = pattern.split(text.upper())
+    chars_new = []
+    for char in chars:
+        if char != "":
+            tokens = char.strip().split(" ")
+            chars_new.extend(tokens)
+    return chars_new
 
 def decode_dataset(
     cuts: CutSet,
     model: nn.Module,
     params: AttributeDict,
     sp: spm.SentencePieceProcessor,
+    lexicon: Lexicon,
+    graph_compiler: CharCtcTrainingGraphCompiler,
     decoding_graph: Optional[k2.Fsa] = None,
 ):
     """Decode dataset.
@@ -691,11 +709,12 @@ def decode_dataset(
             )
 
             for i in sorted(finished_streams, reverse=True):
+                hyp = streams[i].decoding_result()
                 decode_results.append(
                     (
                         streams[i].id,
-                        streams[i].ground_truth.split(),
-                        sp.decode(streams[i].decoding_result()).split(),
+                        filter_zh_en(streams[i].ground_truth),
+                        sp.decode([lexicon.token_table[idx] for idx in hyp]),
                     )
                 )
                 del streams[i]
@@ -712,11 +731,12 @@ def decode_dataset(
         )
 
         for i in sorted(finished_streams, reverse=True):
+            hyp = streams[i].decoding_result()
             decode_results.append(
                 (
                     streams[i].id,
-                    streams[i].ground_truth.split(),
-                    sp.decode(streams[i].decoding_result()).split(),
+                    filter_zh_en(streams[i].ground_truth),
+                    [sp.decode(lexicon.token_table[idx]) for idx in hyp],
                 )
             )
             del streams[i]
@@ -781,7 +801,7 @@ def save_results(
 @torch.no_grad()
 def main():
     parser = get_parser()
-    LibriSpeechAsrDataModule.add_arguments(parser)
+    TAL_CSASRAsrDataModule.add_arguments(parser)
     args = parser.parse_args()
     args.exp_dir = Path(args.exp_dir)
 
@@ -822,13 +842,17 @@ def main():
 
     logging.info(f"Device: {device}")
 
+    bpe_model = params.lang_dir + "/bpe.model"
     sp = spm.SentencePieceProcessor()
-    sp.load(params.bpe_model)
+    sp.load(bpe_model)
 
-    # <blk> and <unk> are 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()
+    lexicon = Lexicon(params.lang_dir)
+    graph_compiler = CharCtcTrainingGraphCompiler(
+        lexicon=lexicon,
+        device=device,
+    )
+    params.blank_id = lexicon.token_table["<blk>"]
+    params.vocab_size = max(lexicon.tokens) + 1
 
     params.device = device
 
@@ -924,13 +948,23 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    librispeech = LibriSpeechAsrDataModule(args)
+    def text_normalize_for_cut(c: Cut):
+        # Text normalize for each sample
+        text = c.supervisions[0].text
+        text = text.strip("\n").strip("\t")
+        c.supervisions[0].text = text_normalize(text)
+        return c
     
-    test_clean_cuts = librispeech.test_clean_cuts()
-    test_other_cuts = librispeech.test_other_cuts()
+    tal_csasr = TAL_CSASRAsrDataModule(args)
 
-    test_sets = ["test-clean", "test-other"]
-    test_cuts = [test_clean_cuts, test_other_cuts]
+    dev_cuts = tal_csasr.valid_cuts()
+    dev_cuts = dev_cuts.map(text_normalize_for_cut)
+    
+    test_cuts = tal_csasr.test_cuts()
+    test_cuts = test_cuts.map(text_normalize_for_cut)
+
+    test_sets = ["dev", "test"]
+    test_cuts = [dev_cuts, test_cuts]
 
     for test_set, test_cut in zip(test_sets, test_cuts):
         results_dict = decode_dataset(
@@ -938,6 +972,8 @@ def main():
             model=model,
             params=params,
             sp=sp,
+            lexicon=lexicon,
+            graph_compiler=graph_compiler,
             decoding_graph=decoding_graph,
         )