update

examples/wenet/toolbox_infer.py

@@ -57,31 +57,31 @@ def main():
     nn_model_file = local_model_dir / m_dict["nn_model_file"]
     tokens_file = local_model_dir / m_dict["tokens_file"]
 
-    # recognizer = models.load_recognizer(
-    #     repo_id=m_dict["repo_id"],
-    #     nn_model_file=nn_model_file.as_posix(),
-    #     tokens_file=tokens_file.as_posix(),
-    #     sub_folder=m_dict["sub_folder"],
-    #     local_model_dir=local_model_dir,
-    #     recognizer_type=m_dict["recognizer_type"],
-    #     decoding_method="greedy_search",
-    #     num_active_paths=2,
-    # )
-
-    feat_config = sherpa.FeatureConfig(normalize_samples=False)
-    feat_config.fbank_opts.frame_opts.samp_freq = args.sample_rate
-    feat_config.fbank_opts.mel_opts.num_bins = 80
-    feat_config.fbank_opts.frame_opts.dither = 0
-
-    config = sherpa.OfflineRecognizerConfig(
-        nn_model=nn_model_file.as_posix(),
-        tokens=tokens_file.as_posix(),
-        use_gpu=False,
-        feat_config=feat_config,
+    recognizer = models.load_recognizer(
+        repo_id=m_dict["repo_id"],
+        nn_model_file=nn_model_file.as_posix(),
+        tokens_file=tokens_file.as_posix(),
+        sub_folder=m_dict["sub_folder"],
+        local_model_dir=local_model_dir,
+        recognizer_type=m_dict["recognizer_type"],
         decoding_method="greedy_search",
         num_active_paths=2,
     )
-    recognizer = sherpa.OfflineRecognizer(config)
+
+    # feat_config = sherpa.FeatureConfig(normalize_samples=False)
+    # feat_config.fbank_opts.frame_opts.samp_freq = args.sample_rate
+    # feat_config.fbank_opts.mel_opts.num_bins = 80
+    # feat_config.fbank_opts.frame_opts.dither = 0
+    #
+    # config = sherpa.OfflineRecognizerConfig(
+    #     nn_model=nn_model_file.as_posix(),
+    #     tokens=tokens_file.as_posix(),
+    #     use_gpu=False,
+    #     feat_config=feat_config,
+    #     decoding_method="greedy_search",
+    #     num_active_paths=2,
+    # )
+    # recognizer = sherpa.OfflineRecognizer(config)
 
     text = decode.decode_by_recognizer(recognizer=recognizer,
                                        filename=out_filename.as_posix(),

toolbox/k2_sherpa/models.py

@@ -56,12 +56,11 @@ def download_model(repo_id: str,
     return nn_model_file, tokens_file
 
 
-@lru_cache(maxsize=10)
 def load_sherpa_offline_recognizer(nn_model_file: str,
                                    tokens_file: str,
                                    sample_rate: int = 16000,
                                    num_active_paths: int = 2,
-                                   decoding_method: EnumDecodingMethod = EnumDecodingMethod.greedy_search,
+                                   decoding_method: str = "greedy_search",
                                    num_mel_bins: int = 80,
                                    frame_dither: int = 0,
                                    ):
@@ -90,7 +89,7 @@ def load_recognizer(repo_id: str,
                     sub_folder: str,
                     local_model_dir: str,
                     recognizer_type: str,
-                    decoding_method: EnumDecodingMethod = EnumDecodingMethod.greedy_search,
+                    decoding_method: str = "greedy_search",
                     num_active_paths: int = 4,
                     ):
     if not os.path.exists(local_model_dir):