Minor fixes to the decode.py

egs/aishell/ASR/pruned_transducer_stateless7/decode.py

@@ -58,6 +58,7 @@ Usage:
 
 import argparse
 import logging
+import os
 from collections import defaultdict
 from pathlib import Path
 from typing import Dict, List, Optional, Tuple
@@ -76,6 +77,8 @@ from beam_search import (
 )
 from train import add_model_arguments, get_params, get_transducer_model
 
+from icefall import ContextGraph, LmScorer, NgramLm
+from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler
 from icefall.checkpoint import (
     average_checkpoints,
     average_checkpoints_with_averaged_model,
@@ -211,6 +214,26 @@ def get_parser():
         Used only when --decoding_method is greedy_search""",
     )
 
+    parser.add_argument(
+        "--context-score",
+        type=float,
+        default=2,
+        help="""
+        The bonus score of each token for the context biasing words/phrases.
+        Used only when --decoding_method is modified_beam_search.
+        """,
+    )
+
+    parser.add_argument(
+        "--context-file",
+        type=str,
+        default="",
+        help="""
+        The path of the context biasing lists, one word/phrase each line
+        Used only when --decoding_method is modified_beam_search.
+        """,
+    )
+
     add_model_arguments(parser)
 
     return parser
@@ -222,6 +245,7 @@ def decode_one_batch(
     token_table: k2.SymbolTable,
     batch: dict,
     decoding_graph: Optional[k2.Fsa] = None,
+    context_graph: Optional[ContextGraph] = None,
 ) -> Dict[str, List[List[str]]]:
     """Decode one batch and return the result in a dict. The dict has the
     following format:
@@ -285,6 +309,7 @@ def decode_one_batch(
             encoder_out=encoder_out,
             encoder_out_lens=encoder_out_lens,
             beam=params.beam_size,
+            context_graph=context_graph,
         )
     else:
         hyp_tokens = []
@@ -324,7 +349,12 @@ def decode_one_batch(
             ): hyps
         }
     else:
-        return {f"beam_size_{params.beam_size}": hyps}
+        key = f"beam_size_{params.beam_size}"
+        if params.has_contexts:
+            key += f"-context-score-{params.context_score}"
+        else:
+            key += "-no-context-words"
+        return {key: hyps}
 
 
 def decode_dataset(
@@ -333,6 +363,7 @@ def decode_dataset(
     model: nn.Module,
     token_table: k2.SymbolTable,
     decoding_graph: Optional[k2.Fsa] = None,
+    context_graph: Optional[ContextGraph] = None,
 ) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
     """Decode dataset.
 
@@ -377,6 +408,7 @@ def decode_dataset(
             model=model,
             token_table=token_table,
             decoding_graph=decoding_graph,
+            context_graph=context_graph,
             batch=batch,
         )
 
@@ -407,16 +439,17 @@ def save_results(
     for key, results in results_dict.items():
         recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
         results = sorted(results)
-        store_transcripts(filename=recog_path, texts=results)
+        # we compute CER for aishell dataset.
+        results_char = []
+        for res in results:
+            results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
+
+        store_transcripts(filename=recog_path, texts=results_char)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
         errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-        # we compute CER for aishell dataset.
-        results_char = []
-        for res in results:
-            results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results_char, enable_log=True
@@ -457,6 +490,12 @@ def main():
         "fast_beam_search",
         "modified_beam_search",
     )
+
+    if os.path.exists(params.context_file):
+        params.has_contexts = True
+    else:
+        params.has_contexts = False
+
     params.res_dir = params.exp_dir / params.decoding_method
 
     if params.iter > 0:
@@ -470,6 +509,10 @@ def main():
         params.suffix += f"-max-states-{params.max_states}"
     elif "beam_search" in params.decoding_method:
         params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}"
+        if params.has_contexts:
+            params.suffix += f"-context-score-{params.context_score}"
+        else:
+            params.suffix += "-no-contexts-words"
     else:
         params.suffix += f"-context-{params.context_size}"
         params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
@@ -490,6 +533,11 @@ def main():
     params.blank_id = 0
     params.vocab_size = max(lexicon.tokens) + 1
 
+    graph_compiler = CharCtcTrainingGraphCompiler(
+        lexicon=lexicon,
+        device=device,
+    )
+
     logging.info(params)
 
     logging.info("About to create model")
@@ -586,6 +634,19 @@ def main():
     else:
         decoding_graph = None
 
+    if params.decoding_method == "modified_beam_search":
+        if os.path.exists(params.context_file):
+            contexts_text = []
+            for line in open(params.context_file).readlines():
+                contexts_text.append(line.strip())
+            contexts = graph_compiler.texts_to_ids(contexts_text)
+            context_graph = ContextGraph(params.context_score)
+            context_graph.build(contexts)
+        else:
+            context_graph = None
+    else:
+        context_graph = None
+
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
@@ -608,6 +669,7 @@ def main():
             model=model,
             token_table=lexicon.token_table,
             decoding_graph=decoding_graph,
+            context_graph=context_graph,
         )
 
         save_results(

egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py

@@ -15,7 +15,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import logging
 import warnings
 from dataclasses import dataclass, field
 from typing import Dict, List, Optional, Tuple, Union

egs/librispeech/ASR/pruned_transducer_stateless4/decode.py

@@ -131,8 +131,6 @@ from pathlib import Path
 from typing import Dict, List, Optional, Tuple
 
 import k2
-import kaldifst
-import graphviz
 import sentencepiece as spm
 import torch
 import torch.nn as nn
@@ -576,7 +574,10 @@ def decode_one_batch(
         return {key: (hyps, timestamps)}
     else:
         key = f"beam_size_{params.beam_size}"
-        key += f"-context-score-{params.context_score}"
+        if params.has_contexts:
+            key += f"-context-score-{params.context_score}"
+        else:
+            key += "-no-context-words"
         return {key: (hyps, timestamps)}
 
 
@@ -626,7 +627,7 @@ def decode_dataset(
     if params.decoding_method == "greedy_search":
         log_interval = 50
     else:
-        log_interval = 1
+        log_interval = 20
 
     results = defaultdict(list)
     for batch_idx, batch in enumerate(dl):
@@ -759,6 +760,12 @@ def main():
         "fast_beam_search_nbest_oracle",
         "modified_beam_search",
     )
+
+    if os.path.exists(params.context_file):
+        params.has_contexts = True
+    else:
+        params.has_contexts = False
+
     params.res_dir = params.exp_dir / params.decoding_method
 
     if params.iter > 0:
@@ -781,7 +788,10 @@ def main():
             params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}"
     elif "beam_search" in params.decoding_method:
         params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}"
-        params.suffix += f"-context-score-{params.context_score}"
+        if params.has_contexts:
+            params.suffix += f"-context-score-{params.context_score}"
+        else:
+            params.suffix += "-no-context-words"
     else:
         params.suffix += f"-context-{params.context_size}"
         params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
@@ -938,14 +948,8 @@ def main():
     test_clean_dl = librispeech.test_dataloaders(test_clean_cuts)
     test_other_dl = librispeech.test_dataloaders(test_other_cuts)
 
-    test_book_cuts = librispeech.test_book_cuts()
+    test_sets = ["test-clean", "test-other"]
-    test_book_dl = librispeech.test_dataloaders(test_book_cuts)
+    test_dl = [test_clean_dl, test_other_dl]
-
-    test_book2_cuts = librispeech.test_book2_cuts()
-    test_book2_dl = librispeech.test_dataloaders(test_book2_cuts)
-
-    test_sets = ["test-book", "test-book2", "test-clean", "test-other"]
-    test_dl = [test_book_dl, test_book2_dl, test_clean_dl, test_other_dl]
 
     for test_set, test_dl in zip(test_sets, test_dl):
         results_dict = decode_dataset(

egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py

@@ -389,7 +389,6 @@ class LibriSpeechAsrDataModule:
         )
         sampler = DynamicBucketingSampler(
             cuts,
-            num_buckets=2,
             max_duration=self.args.max_duration,
             shuffle=False,
         )
@@ -468,25 +467,6 @@ class LibriSpeechAsrDataModule:
             self.args.manifest_dir / "librispeech_cuts_test-clean.jsonl.gz"
         )
 
-    @lru_cache()
-    def test_book_cuts(self) -> CutSet:
-        logging.info("About to get test-books cuts")
-        return load_manifest_lazy(self.args.manifest_dir / "libri_books_feats.jsonl.gz")
-
-    @lru_cache()
-    def test_book_test_cuts(self) -> CutSet:
-        logging.info("About to get test-books cuts")
-        return load_manifest_lazy(
-            self.args.manifest_dir / "libri_book_test_feats.jsonl.gz"
-        )
-
-    @lru_cache()
-    def test_book2_cuts(self) -> CutSet:
-        logging.info("About to get test-books2 cuts")
-        return load_manifest_lazy(
-            self.args.manifest_dir / "libri_books2_feats.jsonl.gz"
-        )
-
     @lru_cache()
     def test_other_cuts(self) -> CutSet:
         logging.info("About to get test-other cuts")

egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py

@@ -396,21 +396,14 @@ class WenetSpeechAsrDataModule:
     @lru_cache()
     def valid_cuts(self) -> CutSet:
         logging.info("About to get dev cuts")
-        return load_manifest_lazy(self.args.manifest_dir / "cuts_DEV2.jsonl.gz")
+        return load_manifest_lazy(self.args.manifest_dir / "cuts_DEV.jsonl.gz")
 
     @lru_cache()
     def test_net_cuts(self) -> List[CutSet]:
         logging.info("About to get TEST_NET cuts")
-        return load_manifest_lazy(self.args.manifest_dir / "cuts_TEST_NET2.jsonl.gz")
+        return load_manifest_lazy(self.args.manifest_dir / "cuts_TEST_NET.jsonl.gz")
 
     @lru_cache()
     def test_meeting_cuts(self) -> List[CutSet]:
         logging.info("About to get TEST_MEETING cuts")
-        return load_manifest_lazy(
+        return load_manifest_lazy(self.args.manifest_dir / "cuts_TEST_MEETING.jsonl.gz")
-            self.args.manifest_dir / "cuts_TEST_MEETING2.jsonl.gz"
-        )
-
-    @lru_cache()
-    def test_car_cuts(self) -> List[CutSet]:
-        logging.info("About to get TEST_CAR cuts")
-        return load_manifest_lazy(self.args.manifest_dir / "car_test_feats.jsonl.gz")

egs/wenetspeech/ASR/pruned_transducer_stateless5/decode.py

@@ -533,9 +533,12 @@ def decode_one_batch(
             ): hyps
         }
     else:
-        return {
+        key = f"beam_size_{params.beam_size}"
-            f"beam_size_{params.beam_size}_context_score_{params.context_score}": hyps
+        if params.has_contexts:
-        }
+            key += f"-context-score-{params.context_score}"
+        else:
+            key += "-no-context-words"
+        return {key: hyps}
 
 
 def decode_dataset(
@@ -674,6 +677,12 @@ def main():
         "modified_beam_search_lm_shallow_fusion",
         "modified_beam_search_LODR",
     )
+
+    if os.path.exists(params.context_file):
+        params.has_contexts = True
+    else:
+        params.has_contexts = False
+
     params.res_dir = params.exp_dir / params.decoding_method
 
     params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
@@ -683,7 +692,10 @@ def main():
         params.suffix += f"-max-states-{params.max_states}"
     elif "beam_search" in params.decoding_method:
         params.suffix += f"-beam-{params.beam_size}"
-        params.suffix += f"-context-score-{params.context_score}"
+        if params.has_contexts:
+            params.suffix += f"-context-score-{params.context_score}"
+        else:
+            params.suffix += "-no-contexts-words"
     else:
         params.suffix += f"-context-{params.context_size}"
         params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
@@ -851,14 +863,10 @@ def main():
 
     if params.decoding_method == "modified_beam_search":
         if os.path.exists(params.context_file):
-            contexts = []
+            contexts_text = []
             for line in open(params.context_file).readlines():
-                context_list = graph_compiler.texts_to_ids(line.strip())
+                contexts_text.append(line.strip())
-                tmp = []
+            contexts = graph_compiler.texts_to_ids(contexts_text)
-                for context in context_list:
-                    for x in context:
-                        tmp.append(x)
-                contexts.append(tmp)
             context_graph = ContextGraph(params.context_score)
             context_graph.build(contexts)
         else:
@@ -882,11 +890,8 @@ def main():
     test_meeting_cuts = wenetspeech.test_meeting_cuts()
     test_meeting_dl = wenetspeech.test_dataloaders(test_meeting_cuts)
 
-    test_car_cuts = wenetspeech.test_car_cuts()
+    test_sets = ["DEV", "TEST_NET", "TEST_MEETING"]
-    test_car_dl = wenetspeech.test_dataloaders(test_car_cuts)
+    test_dls = [dev_dl, test_net_dl, test_meeting_dl]
-
-    test_sets = ["CAR", "TEST_NET", "DEV", "TEST_MEETING"]
-    test_dls = [test_car_dl, test_net_dl, dev_dl, test_meeting_dl]
 
     for test_set, test_dl in zip(test_sets, test_dls):
         results_dict = decode_dataset(