atsr

funasr/auto/auto_model.py

@@ -41,7 +41,7 @@ def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None):
     chars = string.ascii_letters + string.digits
     if isinstance(data_in, str) and data_in.startswith('http'): # url
         data_in = download_from_url(data_in)
-    pdb.set_trace()
+
     if isinstance(data_in, str) and os.path.exists(data_in): # wav_path; filelist: wav.scp, file.jsonl;text.txt;
         _, file_extension = os.path.splitext(data_in)
         file_extension = file_extension.lower()

funasr/models/lcbnet/model.py

@@ -426,6 +426,7 @@ class LCBNet(nn.Module):
                                                             tokenizer=tokenizer)
             time2 = time.perf_counter()
             meta_data["load_data"] = f"{time2 - time1:0.3f}"
+            pdb.set_trace()
             audio_sample_list = sample_list[0]
             ocr_sample_list = sample_list[1]
             speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
@@ -441,7 +442,7 @@ class LCBNet(nn.Module):
         encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
         if isinstance(encoder_out, tuple):
             encoder_out = encoder_out[0]
-        
+        pdb.set_trace()
         ocr_list_new = [[x + 1 if x != 0 else x for x in sublist] for sublist in ocr_sample_list]
         ocr = torch.tensor(ocr_list_new).to(device=kwargs["device"])
         ocr_lengths = ocr.new_full([1], dtype=torch.long, fill_value=ocr.size(1)).to(device=kwargs["device"])

funasr/utils/load_utils.py

@@ -31,7 +31,7 @@ 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)
@@ -67,7 +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, :]