diff --git a/funasr/utils/export_utils.py b/funasr/utils/export_utils.py
index 0691ef2d5..ca04d75ce 100644
--- a/funasr/utils/export_utils.py
+++ b/funasr/utils/export_utils.py
@@ -1,12 +1,10 @@
 import os
 import torch
 import functools
-import onnx
-from onnxconverter_common import float16
 
 import warnings
-warnings.filterwarnings("ignore")
 
+warnings.filterwarnings("ignore")
 
 
 def export(
@@ -44,14 +42,13 @@ def export(
                 print(f"export_dir: {export_dir}")
                 _torchscripts(m, path=export_dir, device="cuda")
 
-
-        elif type=='onnx_fp16':
+        elif type == "onnx_fp16":
             assert (
                 torch.cuda.is_available()
-            ), "Currently onnx_fp16 optimization for FunASR only supports GPU"  
+            ), "Currently onnx_fp16 optimization for FunASR only supports GPU"
 
             if hasattr(m, "encoder") and hasattr(m, "decoder"):
-                _onnx_opt_for_encdec(m, path=export_dir, enable_fp16=True)                      
+                _onnx_opt_for_encdec(m, path=export_dir, enable_fp16=True)
 
     return export_dir
 
@@ -73,7 +70,6 @@ def _onnx(
     else:
         dummy_input = tuple([input.to(device) for input in dummy_input])
 
-
     verbose = kwargs.get("verbose", False)
 
     if isinstance(model.export_name, str):
@@ -94,8 +90,13 @@ def _onnx(
     )
 
     if quantize:
-        from onnxruntime.quantization import QuantType, quantize_dynamic
-        import onnx
+        try:
+            from onnxruntime.quantization import QuantType, quantize_dynamic
+            import onnx
+        except:
+            raise RuntimeError(
+                "You are quantizing the onnx model, please install onnxruntime first. via \n`pip install onnx`\n`pip install onnxruntime`."
+            )
 
         quant_model_path = model_path.replace(".onnx", "_quant.onnx")
         onnx_model = onnx.load(model_path)
@@ -117,19 +118,21 @@ def _onnx(
 
 def _torchscripts(model, path, device="cuda"):
     dummy_input = model.export_dummy_inputs()
-    
+
     if device == "cuda":
         model = model.cuda()
         if isinstance(dummy_input, torch.Tensor):
             dummy_input = dummy_input.cuda()
         else:
             dummy_input = tuple([i.cuda() for i in dummy_input])
-    
+
     model_script = torch.jit.trace(model, dummy_input)
     if isinstance(model.export_name, str):
         model_script.save(os.path.join(path, f"{model.export_name}".replace("onnx", "torchscript")))
     else:
-        model_script.save(os.path.join(path, f"{model.export_name()}".replace("onnx", "torchscript")))
+        model_script.save(
+            os.path.join(path, f"{model.export_name()}".replace("onnx", "torchscript"))
+        )
 
 
 def _bladedisc_opt(model, model_inputs, enable_fp16=True):
@@ -225,7 +228,6 @@ def _bladedisc_opt_for_encdec(model, path, enable_fp16):
     model_script.save(os.path.join(path, f"{model.export_name}_blade.torchscript"))
 
 
-
 def _onnx_opt_for_encdec(model, path, enable_fp16):
 
     # Get input data
@@ -267,16 +269,19 @@ def _onnx_opt_for_encdec(model, path, enable_fp16):
             input_names=model.export_input_names(),
             output_names=model.export_output_names(),
             dynamic_axes=model.export_dynamic_axes(),
-        )    
-
+        )
 
     # fp32 to fp16
     fp16_model_path = f"{path}/{model.export_name}_hook_fp16.onnx"
     print("*" * 50)
     print(f"[_onnx_opt_for_encdec(fp16)]: {fp16_model_path}\n\n")
     if os.path.exists(fp32_model_path) and not os.path.exists(fp16_model_path):
+        try:
+            from onnxconverter_common import float16
+        except:
+            raise RuntimeError(
+                "You are converting the onnx model to fp16, please install onnxconverter-common first. via `pip install onnxconverter-common`."
+            )
         fp32_onnx_model = onnx.load(fp32_model_path)
         fp16_onnx_model = float16.convert_float_to_float16(fp32_onnx_model, keep_io_types=True)
-        onnx.save(
-            fp16_onnx_model, fp16_model_path
-        )
+        onnx.save(fp16_onnx_model, fp16_model_path)
diff --git a/funasr/utils/load_utils.py b/funasr/utils/load_utils.py
index 52753c4e2..1d80fcf36 100644
--- a/funasr/utils/load_utils.py
+++ b/funasr/utils/load_utils.py
@@ -10,7 +10,6 @@ import torchaudio
 import time
 import logging
 from torch.nn.utils.rnn import pad_sequence
-from pydub import AudioSegment
 
 try:
     from funasr.download.file import download_from_url
@@ -20,6 +19,11 @@ import pdb
 import subprocess
 from subprocess import CalledProcessError, run
 
+try:
+    from pydub import AudioSegment
+except:
+    pass
+
 
 def is_ffmpeg_installed():
     try:
@@ -166,7 +170,12 @@ def validate_frame_rate(
     byte_data = BytesIO(input)
 
     # 使用 pydub 加载音频
-    audio = AudioSegment.from_file(byte_data)
+    try:
+        audio = AudioSegment.from_file(byte_data)
+    except:
+        raise RuntimeError(
+            "You are decoding the pcm data, please install pydub first. via `pip install pydub`."
+        )
 
     # 确保采样率为 16000 Hz
     if audio.frame_rate != fs: