update

examples/wenet/toolbox_infer.py

@@ -82,7 +82,7 @@ def main():
     nn_model_file = local_model_dir / m_dict["nn_model_file"]
     tokens_file = local_model_dir / m_dict["tokens_file"]
 
-    recognizer = load_sherpa_offline_recognizer(
+    recognizer = models.load_sherpa_offline_recognizer(
         nn_model_file=nn_model_file.as_posix(),
         tokens_file=tokens_file.as_posix(),
         decoding_method="greedy_search",

toolbox/k2_sherpa/models.py

@@ -64,6 +64,31 @@ def load_sherpa_offline_recognizer(nn_model_file: str,
                                    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 = 80
+    feat_config.fbank_opts.frame_opts.dither = 0
+
+    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_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()
     feat_config.fbank_opts.frame_opts.samp_freq = sample_rate
     feat_config.fbank_opts.mel_opts.num_bins = num_mel_bins