update

examples/wenet/infer.py

@@ -58,7 +58,6 @@ def main():
         decoding_method="greedy_search",
         num_active_paths=2,
     )
-
     recognizer = sherpa.OfflineRecognizer(config)
 
     signal, sample_rate = librosa.load(args.filename, sr=args.sample_rate)

examples/wenet/toolbox_infer.py

@@ -69,24 +69,37 @@ 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"],
+    # 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,
         decoding_method="greedy_search",
         num_active_paths=2,
     )
+    recognizer = sherpa.OfflineRecognizer(config)
 
     s = recognizer.create_stream()
-
     s.accept_wave_file(
         temp_file.as_posix()
     )
     recognizer.decode_stream(s)
-
     text = s.result.text.strip()
     text = text.lower()
     print("text: {}".format(text))