diff --git a/Ref_Audio_Selector/config.ini b/Ref_Audio_Selector/config.ini
index 0ba2aa2..26b47e9 100644
--- a/Ref_Audio_Selector/config.ini
+++ b/Ref_Audio_Selector/config.ini
@@ -7,6 +7,8 @@ time_log_print_type = file
 time_log_print_dir = Ref_Audio_Selector/log/performance
 # 参考音频目录
 reference_audio_dir = refer_audio
+# 临时文件目录
+temp_dir = Ref_Audio_Selector/temp
 
 [AudioSample]
 # list转换待选参考音频目录
diff --git a/Ref_Audio_Selector/config_param/config_params.py b/Ref_Audio_Selector/config_param/config_params.py
index 050bb1d..9c585d4 100644
--- a/Ref_Audio_Selector/config_param/config_params.py
+++ b/Ref_Audio_Selector/config_param/config_params.py
@@ -9,6 +9,8 @@ time_log_print_type = config.get_base('time_log_print_type')
 time_log_print_dir = config.get_base('time_log_print_dir')
 # 参考音频目录
 reference_audio_dir = config.get_base('reference_audio_dir')
+# 临时文件目录
+temp_dir = config.get_base('temp_dir')
 
 # [AudioSample]
 # list转换待选参考音频目录
diff --git a/Ref_Audio_Selector/tool/speaker_verification/voice_similarity.py b/Ref_Audio_Selector/tool/speaker_verification/voice_similarity.py
index e4a5ed9..f30ace2 100644
--- a/Ref_Audio_Selector/tool/speaker_verification/voice_similarity.py
+++ b/Ref_Audio_Selector/tool/speaker_verification/voice_similarity.py
@@ -1,5 +1,9 @@
 import argparse
 import os
+import soundfile as sf
+import torchaudio
+import torchaudio.transforms as T
+import Ref_Audio_Selector.config_param.config_params as params
 from Ref_Audio_Selector.common.time_util import timeit_decorator
 
 from modelscope.pipelines import pipeline
@@ -17,10 +21,13 @@ def compare_audio_and_generate_report(reference_audio_path, comparison_dir_path,
     comparison_audio_paths = [os.path.join(comparison_dir_path, f) for f in os.listdir(comparison_dir_path) if
                               f.endswith('.wav')]
 
+    # 因为这个模型是基于16k音频数据训练的，为了避免后续比较时，每次都对参考音频进行重采样，所以，提前进行了采样
+    reference_audio_16k = ensure_16k_wav(reference_audio_path)
+
     # Step 2: 用参考音频依次比较音频目录下的每个音频，获取相似度分数及对应路径
     similarity_scores = []
     for audio_path in comparison_audio_paths:
-        score = sv_pipeline([reference_audio_path, audio_path])['score']
+        score = sv_pipeline([reference_audio_16k, audio_path])['score']
         similarity_scores.append({
             'score': score,
             'path': audio_path
@@ -42,6 +49,56 @@ def compare_audio_and_generate_report(reference_audio_path, comparison_dir_path,
         f.write(content)
 
 
+def ensure_16k_wav(audio_file_path, target_sample_rate=16000):
+    # 读取音频文件信息
+    sample_rate, audio_data = sf.read(audio_file_path)
+
+    # 检查采样率是否为16kHz
+    if sample_rate == target_sample_rate:
+        # 是16kHz采样率，直接返回原始文件路径
+        return audio_file_path
+
+    # 设置临时文件名
+    temp_file_path = os.path.join(params.temp_dir, os.path.basename(audio_file_path))
+
+    # 重采样至16kHz并保存到临时文件
+    sf.write(temp_file_path, audio_data, samplerate=target_sample_rate, format="WAV")
+
+    return temp_file_path
+
+
+def ensure_16k_wav_2(audio_file_path, target_sample_rate=16000):
+    """
+    输入一个音频文件地址，判断其采样率并决定是否进行重采样，然后将结果保存到指定的输出文件。
+
+    参数:
+        audio_file_path (str): 音频文件路径。
+        output_file_path (str): 保存重采样后音频数据的目标文件路径。
+        target_sample_rate (int, optional): 目标采样率，默认为16000Hz。
+    """
+    # 读取音频文件并获取其采样率
+    waveform, sample_rate = torchaudio.load(audio_file_path)
+
+    # 判断是否需要重采样
+    if sample_rate == target_sample_rate:
+        return audio_file_path
+    else:
+
+        # 创建Resample实例
+        resampler = T.Resample(orig_freq=sample_rate, new_freq=target_sample_rate)
+
+        # 应用重采样
+        resampled_waveform = resampler(waveform)
+
+        # 设置临时文件名
+        temp_file_path = os.path.join(params.temp_dir, os.path.basename(audio_file_path))
+
+        # 保存重采样后的音频到指定文件
+        torchaudio.save(temp_file_path, resampled_waveform, target_sample_rate)
+
+    return temp_file_path
+
+
 def parse_arguments():
     parser = argparse.ArgumentParser(description="Audio processing script arguments")