diff --git a/dataset/rir_noise/rir_noise.py b/dataset/rir_noise/rir_noise.py
index e7b122890..bb0440a75 100644
--- a/dataset/rir_noise/rir_noise.py
+++ b/dataset/rir_noise/rir_noise.py
@@ -81,6 +81,10 @@ def create_manifest(data_dir, manifest_path_prefix):
                         },
                         ensure_ascii=False))
         manifest_path = manifest_path_prefix + '.' + dtype
+
+        if not os.path.exists(os.path.dirname(manifest_path)):
+            os.makedirs(os.path.dirname(manifest_path))
+
         with codecs.open(manifest_path, 'w', 'utf-8') as fout:
             for line in json_lines:
                 fout.write(line + '\n')
diff --git a/paddlespeech/vector/io/dataset.py b/paddlespeech/vector/io/dataset.py
new file mode 100644
index 000000000..c8a4299e9
--- /dev/null
+++ b/paddlespeech/vector/io/dataset.py
@@ -0,0 +1,120 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import collections
+
+from paddle.io import Dataset
+
+from paddleaudio import load as load_audio
+
+
+class VoxCelebDataset(Dataset):
+    meta_info = collections.namedtuple(
+        'META_INFO', ('id', 'duration', 'wav', 'start', 'stop', 'spk_id'))
+
+    def __init__(self, csv_path, spk_id2label_path, config):
+        super().__init__()
+        self.csv_path = csv_path
+        self.spk_id2label_path = spk_id2label_path
+        self.config = config
+        self.data = self.load_data_csv()
+        self.spk_id2label = self.load_speaker_to_label()
+
+    def load_data_csv(self):
+        data = []
+        with open(self.csv_path, 'r') as rf:
+            for line in rf.readlines()[1:]:
+                audio_id, duration, wav, start, stop, spk_id = line.strip(
+                ).split(',')
+                data.append(
+                    self.meta_info(audio_id,
+                                   float(duration), wav,
+                                   int(start), int(stop), spk_id))
+        return data
+
+    def load_speaker_to_label(self):
+        with open(self.spk_id2label_path, 'r') as f:
+            for line in f.readlines():
+                spk_id, label = line.strip().split(' ')
+                self.spk_id2label[spk_id] = int(label)
+
+    def convert_to_record(self, idx: int):
+        sample = self.data[idx]
+
+        record = {}
+        # To show all fields in a namedtuple: `type(sample)._fields`
+        for field in type(sample)._fields:
+            record[field] = getattr(sample, field)
+
+        waveform, sr = load_audio(record['wav'])
+
+        # random select a chunk audio samples from the audio
+        if self.config.random_chunk:
+            num_wav_samples = waveform.shape[0]
+            num_chunk_samples = int(self.config.chunk_duration * sr)
+            start = random.randint(0, num_wav_samples - num_chunk_samples - 1)
+            stop = start + num_chunk_samples
+        else:
+            start = record['start']
+            stop = record['stop']
+
+        # we only return the waveform as feat
+        waveform = waveform[start:stop]
+        record.update({'feat': waveform})
+        record.update({'label': self.spk_id2label[record['spk_id']]})
+
+        return record
+
+    def __getitem__(self, idx):
+        return self.convert_to_record(idx)
+
+    def __len__(self):
+        return len(self.data)
+
+
+class RIRSNoiseDataset(Dataset):
+    meta_info = collections.namedtuple('META_INFO', ('id', 'duration', 'wav'))
+
+    def __init__(self, csv_path):
+        super().__init__()
+        self.csv_path = csv_path
+        self.data = self.load_csv_data()
+
+    def load_csv_data(self):
+        data = []
+        with open(self.csv_path, 'r') as rf:
+            for line in rf.readlines()[1:]:
+                audio_id, duration, wav = line.strip().split(',')
+                data.append(self.meta_info(audio_id, float(duration), wav))
+
+        random.shuffle(data)
+        return data
+
+    def convert_to_record(self, idx: int):
+        sample = self.data[idx]
+
+        record = {}
+        # To show all fields in a namedtuple: `type(sample)._fields`
+        for field in type(sample)._fields:
+            record[field] = getattr(sample, field)
+
+        waveform, sr = load_audio(record['wav'])
+
+        record.update({'feat': waveform})
+        return record
+
+    def __getitem__(self, idx):
+        return self.convert_to_record(idx)
+
+    def __len__(self):
+        return len(self.data)