diff --git a/funasr/utils/load_utils.py b/funasr/utils/load_utils.py
index 218f370a7..5ccd2161d 100644
--- a/funasr/utils/load_utils.py
+++ b/funasr/utils/load_utils.py
@@ -81,7 +81,7 @@ def load_audio_text_image_video(
         data_or_path_or_list = download_from_url(data_or_path_or_list)
 
     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":
+        if data_type is None or data_type in ["sound", "kaldi_ark_or_sound"]:
             # if use_ffmpeg:
             #     data_or_path_or_list = _load_audio_ffmpeg(data_or_path_or_list, sr=fs)
             #     data_or_path_or_list = torch.from_numpy(data_or_path_or_list).squeeze()  # [n_samples,]
@@ -113,7 +113,7 @@ def load_audio_text_image_video(
         data_or_path_or_list = tokenizer.encode(data_or_path_or_list)
     elif isinstance(data_or_path_or_list, np.ndarray):  # audio sample point
         data_or_path_or_list = torch.from_numpy(data_or_path_or_list).squeeze()  # [n_samples,]
-    elif isinstance(data_or_path_or_list, str) and data_type == "kaldi_ark":
+    elif isinstance(data_or_path_or_list, str) and data_type in ["kaldi_ark", "kaldi_ark_or_sound"]:
         data_mat = kaldiio.load_mat(data_or_path_or_list)
         if isinstance(data_mat, tuple):
             audio_fs, mat = data_mat
@@ -124,33 +124,7 @@ def load_audio_text_image_video(
             mat = mat / 32768
         if mat.ndim == 2:
             mat = mat[:, 0]
-        data_or_path_or_list = mat
-    elif isinstance(data_or_path_or_list, str) and data_type == "kaldi_ark_or_sound":
-        if ".ark:" in data_or_path_or_list:
-            data_mat = kaldiio.load_mat(data_or_path_or_list)
-            if isinstance(data_mat, tuple):
-                if isinstance(data_mat[0], int):
-                    audio_fs, mat = data_mat
-                else:
-                    mat, audio_fs = data_mat
-            else:
-                mat = data_mat
-            if mat.dtype == "int16" or mat.dtype == "int32":
-                mat = mat.astype(np.float64)
-                mat = mat / 32768
-            if mat.ndim == 2:
-                mat = mat[:, 0]
-            data_or_path_or_list = torch.from_numpy(mat)
-        else:
-            try:
-                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)
-            except:
-                data_or_path_or_list = _load_audio_ffmpeg(data_or_path_or_list, sr=fs)
-                data_or_path_or_list = torch.from_numpy(
-                    data_or_path_or_list
-                ).squeeze()  # [n_samples,]
+        data_or_path_or_list = torch.from_numpy(mat)
     elif isinstance(data_or_path_or_list, bytes):  # audio bytes
         data_or_path_or_list = load_bytes(data_or_path_or_list)
     else: