diff --git a/Ref_Audio_Selector/tool/text_comparison/text_comparison.py b/Ref_Audio_Selector/tool/text_comparison/text_comparison.py
index edb0c7d..156fa53 100644
--- a/Ref_Audio_Selector/tool/text_comparison/text_comparison.py
+++ b/Ref_Audio_Selector/tool/text_comparison/text_comparison.py
@@ -8,14 +8,19 @@ bert_path = os.environ.get(
     "bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
 )
 
+# Set device to GPU if available, else CPU
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+print(f'使用计算设备: {device}')
+
 tokenizer = AutoTokenizer.from_pretrained(bert_path)
-model = AutoModel.from_pretrained(bert_path)
+model = AutoModel.from_pretrained(bert_path).to(device)
 
 
 def calculate_similarity(text1, text2, max_length=512):
     # 预处理文本，设置最大长度
-    inputs1 = tokenizer(text1, padding=True, truncation=True, max_length=max_length, return_tensors='pt')
-    inputs2 = tokenizer(text2, padding=True, truncation=True, max_length=max_length, return_tensors='pt')
+    inputs1 = tokenizer(text1, padding=True, truncation=True, max_length=max_length, return_tensors='pt').to(device)
+    inputs2 = tokenizer(text2, padding=True, truncation=True, max_length=max_length, return_tensors='pt').to(device)
 
     # 获取句子向量（这里是取CLS token的向量并展平为一维）
     with torch.no_grad():