diff --git a/examples/voxceleb/sv0/local/data.sh b/examples/voxceleb/sv0/local/data.sh
index 04cf7ecde..fd50cd40c 100755
--- a/examples/voxceleb/sv0/local/data.sh
+++ b/examples/voxceleb/sv0/local/data.sh
@@ -136,10 +136,3 @@ if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then
       exit 1
    fi
 fi
-
-
-
-
-
-
-
diff --git a/paddlespeech/vector/exps/ecapa_tdnn/train.py b/paddlespeech/vector/exps/ecapa_tdnn/train.py
index 257b97abe..9f48a4c43 100644
--- a/paddlespeech/vector/exps/ecapa_tdnn/train.py
+++ b/paddlespeech/vector/exps/ecapa_tdnn/train.py
@@ -23,13 +23,13 @@ from paddle.io import DistributedBatchSampler
 from yacs.config import CfgNode
 
 from paddleaudio.compliance.librosa import melspectrogram
-from paddleaudio.datasets.voxceleb import VoxCeleb
 from paddlespeech.s2t.utils.log import Log
 from paddlespeech.vector.io.augment import build_augment_pipeline
 from paddlespeech.vector.io.augment import waveform_augment
 from paddlespeech.vector.io.batch import batch_pad_right
 from paddlespeech.vector.io.batch import feature_normalize
 from paddlespeech.vector.io.batch import waveform_collate_fn
+from paddlespeech.vector.io.dataset import VoxCelebDataset
 from paddlespeech.vector.models.ecapa_tdnn import EcapaTdnn
 from paddlespeech.vector.modules.loss import AdditiveAngularMargin
 from paddlespeech.vector.modules.loss import LogSoftmaxWrapper
@@ -37,6 +37,7 @@ from paddlespeech.vector.modules.sid_model import SpeakerIdetification
 from paddlespeech.vector.training.scheduler import CyclicLRScheduler
 from paddlespeech.vector.training.seeding import seed_everything
 from paddlespeech.vector.utils.time import Timer
+# from paddleaudio.datasets.voxceleb import VoxCeleb
 
 logger = Log(__name__).getlog()
 
@@ -54,8 +55,14 @@ def main(args, config):
 
     # stage2: data prepare, such vox1 and vox2 data, and augment noise data and pipline
     # note: some cmd must do in rank==0, so wo will refactor the data prepare code
-    train_dataset = VoxCeleb('train', target_dir=args.data_dir)
-    dev_dataset = VoxCeleb('dev', target_dir=args.data_dir)
+    train_dataset = VoxCelebDataset(
+        csv_path=os.path.join(args.data_dir, "vox/csv/train.csv"),
+        spk_id2label_path=os.path.join(args.data_dir,
+                                       "vox/meta/spk_id2label.txt"))
+    dev_dataset = VoxCelebDataset(
+        csv_path=os.path.join(args.data_dir, "vox/csv/dev.csv"),
+        spk_id2label_path=os.path.join(args.data_dir,
+                                       "vox/meta/spk_id2label.txt"))
 
     if config.augment:
         augment_pipeline = build_augment_pipeline(target_dir=args.data_dir)
diff --git a/paddlespeech/vector/io/augment.py b/paddlespeech/vector/io/augment.py
index 3baace139..8b7ccc11d 100644
--- a/paddlespeech/vector/io/augment.py
+++ b/paddlespeech/vector/io/augment.py
@@ -21,13 +21,14 @@ import paddle
 import paddle.nn as nn
 import paddle.nn.functional as F
 
-from paddleaudio.datasets.rirs_noises import OpenRIRNoise
 from paddlespeech.s2t.utils.log import Log
+from paddlespeech.vector.io.dataset import RIRSNoiseDataset
 from paddlespeech.vector.io.signal_processing import compute_amplitude
 from paddlespeech.vector.io.signal_processing import convolve1d
 from paddlespeech.vector.io.signal_processing import dB_to_amplitude
 from paddlespeech.vector.io.signal_processing import notch_filter
 from paddlespeech.vector.io.signal_processing import reverberate
+# from paddleaudio.datasets.rirs_noises import OpenRIRNoise
 
 logger = Log(__name__).getlog()
 
@@ -839,8 +840,10 @@ def build_augment_pipeline(target_dir=None) -> List[paddle.nn.Layer]:
         List[paddle.nn.Layer]: all augment process
     """
     logger.info("start to build the augment pipeline")
-    noise_dataset = OpenRIRNoise('noise', target_dir=target_dir)
-    rir_dataset = OpenRIRNoise('rir', target_dir=target_dir)
+    noise_dataset = RIRSNoiseDataset(csv_path=os.path.join(
+        target_dir, "rir_noise/csv/noise.csv"))
+    rir_dataset = OpenRIRNoise(csv_path=os.path.join(target_dir,
+                                                     "rir_noise/csv/rir.csv"))
 
     wavedrop = TimeDomainSpecAugment(
         sample_rate=16000,