diff --git a/paddlespeech/cli/vector/infer.py b/paddlespeech/cli/vector/infer.py
index de4d66218..205d61f92 100644
--- a/paddlespeech/cli/vector/infer.py
+++ b/paddlespeech/cli/vector/infer.py
@@ -42,19 +42,19 @@ from paddlespeech.s2t.utils.utility import UpdateConfig
 from paddlespeech.vector.modules.sid_model import SpeakerIdetification
 
 pretrained_models = {
-    # The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]".
-    # e.g. "conformer_wenetspeech-zh-16k" and "panns_cnn6-32k".
-    # Command line and python api use "{model_name}[_{dataset}]" as --model, usage:
-    # "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav"
-    "ecapa_tdnn-16k": {
+    # The tags for pretrained_models should be "{model_name}[-{dataset}][-{sr}][-...]".
+    # e.g. "ecapa_tdnn-voxceleb12-16k".
+    # Command line and python api use "{model_name}[-{dataset}]" as --model, usage:
+    # "paddlespeech vector --task spk --model ecapa_tdnn-voxceleb12-16k --sr 16000 --input ./input.wav"
+    "ecapa_tdnn-voxceleb12-16k": {
         'url':
-        'https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr1_conformer_wenetspeech_ckpt_0.1.1.model.tar.gz',
+        'https://paddlespeech.bj.bcebos.com/vector/voxceleb/sv0_ecapa_tdnn_voxceleb12_ckpt_0_1_0.tar.gz',
         'md5':
-        '76cb19ed857e6623856b7cd7ebbfeda4',
+        '85ff08ce0ef406b8c6d7b5ffc5b2b48f',
         'cfg_path':
-        'model.yaml',
+        'conf/model.yaml',
         'ckpt_path':
-        'exp/conformer/checkpoints/wenetspeech',
+        'model/model',
     },
 }
 
@@ -202,6 +202,14 @@ class VectorExecutor(BaseExecutor):
             The support models includes \n\t\t{}'.format(tag, "\n\t\t".join(support_models))
 
         res_path = os.path.join(MODEL_HOME, tag)
+        decompressed_path = download_and_decompress(pretrained_models[tag],
+                                                    res_path)
+
+        decompressed_path = os.path.abspath(decompressed_path)
+        logger.info(
+            'Use pretrained model stored in: {}'.format(decompressed_path))
+
+        return decompressed_path
 
     def _init_from_path(self,
                         model_type: str='ecapa_tdnn-voxceleb12',
@@ -216,7 +224,12 @@ class VectorExecutor(BaseExecutor):
         if cfg_path is None or ckpt_path is None:
             sample_rate_str = "16k" if sample_rate == 16000 else "8k"
             tag = model_type + "-" + sample_rate_str
+            logger.info(f"load the pretrained model: {tag}")
             res_path = self._get_pretrained_path(tag)
+            self.res_path = res_path
+
+            self.cfg_path = os.path.join(res_path, pretrained_models[tag]['cfg_path'])
+            self.ckpt_path = os.path.join(res_path, pretrained_models[tag]['ckpt_path'] + '.pdparams')
         else:
             self.cfg_path = os.path.abspath(cfg_path)
             self.ckpt_path = os.path.abspath(ckpt_path + ".pdparams")
@@ -226,7 +239,7 @@ class VectorExecutor(BaseExecutor):
         logger.info(f"start to read the ckpt from {self.ckpt_path}")
         logger.info(f"read the config from {self.cfg_path}")
         logger.info(f"get the res path {self.res_path}")
-
+        
         # stage 2: read and config and init the model body
         self.config = CfgNode(new_allowed=True)
         self.config.merge_from_file(self.cfg_path)