test

funasr/models/lcbnet/model.py

@@ -413,7 +413,6 @@ class LCBNet(nn.Module):
             logging.info("enable beam_search")
             self.init_beam_search(**kwargs)
             self.nbest = kwargs.get("nbest", 1)
-        pdb.set_trace()
 
         meta_data = {}
         if isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank":  # fbank
@@ -431,6 +430,7 @@ class LCBNet(nn.Module):
                                                             tokenizer=tokenizer)
             time2 = time.perf_counter()
             meta_data["load_data"] = f"{time2 - time1:0.3f}"
+            pdb.set_trace()
             speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
                                                    frontend=frontend)
             time3 = time.perf_counter()

funasr/utils/load_utils.py

@@ -31,14 +31,13 @@ def load_audio_text_image_video(data_or_path_or_list, fs: int = 16000, audio_fs:
             return [load_audio_text_image_video(audio, fs=fs, audio_fs=audio_fs, data_type=data_type, **kwargs) for audio in data_or_path_or_list]
     if isinstance(data_or_path_or_list, str) and data_or_path_or_list.startswith('http'): # download url to local file
         data_or_path_or_list = download_from_url(data_or_path_or_list)
-    pdb.set_trace()
+
     if isinstance(data_or_path_or_list, str) and os.path.exists(data_or_path_or_list): # local file
         if data_type is None or data_type == "sound":
             data_or_path_or_list, audio_fs = torchaudio.load(data_or_path_or_list)
             if kwargs.get("reduce_channels", True):
                 data_or_path_or_list = data_or_path_or_list.mean(0)
         elif data_type == "text" and tokenizer is not None:
-            pdb.set_trace()
             data_or_path_or_list = tokenizer.encode(data_or_path_or_list)
         elif data_type == "image": # undo
             pass
@@ -68,7 +67,7 @@ def load_audio_text_image_video(data_or_path_or_list, fs: int = 16000, audio_fs:
     else:
         pass
         # print(f"unsupport data type: {data_or_path_or_list}, return raw data")
-    pdb.set_trace()  
+
     if audio_fs != fs and data_type != "text":
         resampler = torchaudio.transforms.Resample(audio_fs, fs)
         data_or_path_or_list = resampler(data_or_path_or_list[None, :])[0, :]
@@ -112,6 +111,7 @@ def extract_fbank(data, data_len = None, data_type: str="sound", frontend=None,
     # import pdb;
     # pdb.set_trace()
     # if data_type == "sound":
+    pdb.set_trace()
     data, data_len = frontend(data, data_len, **kwargs)
     
     if isinstance(data_len, (list, tuple)):