update

examples/wenet/toolbox_infer.py

@@ -57,24 +57,24 @@ 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_sherpa_offline_recognizer(
-        nn_model_file=nn_model_file.as_posix(),
-        tokens_file=tokens_file.as_posix(),
-        decoding_method="greedy_search",
-        num_active_paths=2,
-    )
-
-    # recognizer = models.load_recognizer(
-    #     repo_id=m_dict["repo_id"],
+    # recognizer = models.load_sherpa_offline_recognizer(
     #     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 = 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

toolbox/k2_sherpa/models.py

@@ -56,31 +56,6 @@ def download_model(repo_id: str,
     return nn_model_file, tokens_file
 
 
-def load_sherpa_offline_recognizer22(nn_model_file: str,
-                                   tokens_file: str,
-                                   sample_rate: int = 16000,
-                                   num_active_paths: int = 2,
-                                   decoding_method: str = "greedy_search",
-                                   num_mel_bins: int = 80,
-                                   frame_dither: int = 0,
-                                   ):
-    feat_config = sherpa.FeatureConfig(normalize_samples=False)
-    feat_config.fbank_opts.frame_opts.samp_freq = sample_rate
-    feat_config.fbank_opts.mel_opts.num_bins = num_mel_bins
-    feat_config.fbank_opts.frame_opts.dither = frame_dither
-
-    config = sherpa.OfflineRecognizerConfig(
-        nn_model=nn_model_file,
-        tokens=tokens_file,
-        use_gpu=False,
-        feat_config=feat_config,
-        decoding_method="greedy_search",
-        num_active_paths=2,
-    )
-    recognizer = sherpa.OfflineRecognizer(config)
-    return recognizer
-
-
 def load_sherpa_offline_recognizer(nn_model_file: str,
                                    tokens_file: str,
                                    sample_rate: int = 16000,