diff --git a/deepspeech/io/batchfy.py b/deepspeech/io/batchfy.py
index 36c1ec31..54c6f0e1 100644
--- a/deepspeech/io/batchfy.py
+++ b/deepspeech/io/batchfy.py
@@ -421,7 +421,7 @@ def make_batchset(
             key=lambda data: int(data[1][batch_sort_key][batch_sort_axis]["shape"][0]),
             reverse=not shortest_first, )
         logger.info("# utts: " + str(len(sorted_data)))
-        
+
         if count == "seq":
             batches = batchfy_by_seq(
                 sorted_data,
@@ -466,4 +466,4 @@ def make_batchset(
     logger.info("# minibatches: " + str(len(batches)))
 
     # batch: List[List[Tuple[str, dict]]]
-    return batches
\ No newline at end of file
+    return batches
diff --git a/deepspeech/io/collator.py b/deepspeech/io/collator.py
index 2ef11966..4900350e 100644
--- a/deepspeech/io/collator.py
+++ b/deepspeech/io/collator.py
@@ -23,7 +23,7 @@ from deepspeech.frontend.featurizer.speech_featurizer import SpeechFeaturizer
 from deepspeech.frontend.normalizer import FeatureNormalizer
 from deepspeech.frontend.speech import SpeechSegment
 from deepspeech.frontend.utility import IGNORE_ID
-from deepspeech.io.utility import pad_sequence
+from deepspeech.io.utility import pad_list
 from deepspeech.utils.log import Log
 
 __all__ = ["SpeechCollator"]
@@ -286,13 +286,12 @@ class SpeechCollator():
             texts.append(tokens)
             text_lens.append(tokens.shape[0])
 
-        padded_audios = pad_sequence(
-            audios, padding_value=0.0).astype(np.float32)  #[B, T, D]
-        audio_lens = np.array(audio_lens).astype(np.int64)
-        padded_texts = pad_sequence(
-            texts, padding_value=IGNORE_ID).astype(np.int64)
-        text_lens = np.array(text_lens).astype(np.int64)
-        return utts, padded_audios, audio_lens, padded_texts, text_lens
+        #[B, T, D]
+        xs_pad = pad_list(audios, 0.0).astype(np.float32)
+        ilens = np.array(audio_lens).astype(np.int64)
+        ys_pad = pad_list(texts, IGNORE_ID).astype(np.int64)
+        olens = np.array(text_lens).astype(np.int64)
+        return utts, xs_pad, ilens, ys_pad, olens
 
     @property
     def manifest(self):
diff --git a/deepspeech/io/dataloader.py b/deepspeech/io/dataloader.py
index 0c5034ca..2e6b6a02 100644
--- a/deepspeech/io/dataloader.py
+++ b/deepspeech/io/dataloader.py
@@ -11,6 +11,7 @@
 # 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 numpy as np
 from paddle.io import DataLoader
 
 from deepspeech.frontend.utility import read_manifest
@@ -30,11 +31,11 @@ class CustomConverter():
 
     Args:
         subsampling_factor (int): The subsampling factor.
-        dtype (paddle.dtype): Data type to convert.
-
+        dtype (np.dtype): Data type to convert.
+        
     """
 
-    def __init__(self, subsampling_factor=1, dtype=paddle.float32):
+    def __init__(self, subsampling_factor=1, dtype=np.float32):
         """Construct a CustomConverter object."""
         self.subsampling_factor = subsampling_factor
         self.ignore_id = -1
@@ -52,7 +53,7 @@ class CustomConverter():
         """
         # batch should be located in list
         assert len(batch) == 1
-        xs, ys = batch[0]
+        (xs, ys), utts = batch[0]
 
         # perform subsampling
         if self.subsampling_factor > 1:
@@ -74,15 +75,14 @@ class CustomConverter():
         else:
             xs_pad = pad_list(xs, 0).astype(self.dtype)
 
-        ilens = paddle.to_tensor(ilens)
-
         # NOTE: this is for multi-output (e.g., speech translation)
         ys_pad = pad_list(
             [np.array(y[0][:]) if isinstance(y, tuple) else y for y in ys],
             self.ignore_id)
 
-        olens = np.array([y.shape[0] for y in ys])
-        return xs_pad, ilens, ys_pad, olens
+        olens = np.array(
+            [y[0].shape[0] if isinstance(y, tuple) else y.shape[0] for y in ys])
+        return utts, xs_pad, ilens, ys_pad, olens
 
 
 class BatchDataLoader():
@@ -166,7 +166,7 @@ class BatchDataLoader():
         # we used an empty collate function instead which returns list
         self.train_loader = DataLoader(
             dataset=TransformDataset(
-                self.data, lambda data: self.converter([self.load(data)])),
+                self.data, lambda data: self.converter([self.load(data, return_uttid=True)])),
             batch_size=1,
             shuffle=not use_sortagrad if train_mode else False,
             collate_fn=lambda x: x[0],
diff --git a/deepspeech/io/dataset.py b/deepspeech/io/dataset.py
index a30666b4..c5b6e737 100644
--- a/deepspeech/io/dataset.py
+++ b/deepspeech/io/dataset.py
@@ -16,7 +16,6 @@ from typing import Optional
 from paddle.io import Dataset
 from yacs.config import CfgNode
 
-
 from deepspeech.utils.log import Log
 
 __all__ = ["ManifestDataset", "TripletManifestDataset", "TransformDataset"]
diff --git a/deepspeech/io/utility.py b/deepspeech/io/utility.py
index 915813f3..91abdf08 100644
--- a/deepspeech/io/utility.py
+++ b/deepspeech/io/utility.py
@@ -14,7 +14,9 @@
 from collections import OrderedDict
 from typing import List
 
+import kaldiio
 import numpy as np
+import soundfile
 
 from deepspeech.frontend.augmentor.augmentation import AugmentationPipeline
 from deepspeech.utils.log import Log
@@ -383,3 +385,91 @@ class LoadInputsAndTargets():
         else:
             raise NotImplementedError(
                 "Not supported: loader_type={}".format(filetype))
+
+
+class SoundHDF5File():
+    """Collecting sound files to a HDF5 file
+
+    >>> f = SoundHDF5File('a.flac.h5', mode='a')
+    >>> array = np.random.randint(0, 100, 100, dtype=np.int16)
+    >>> f['id'] = (array, 16000)
+    >>> array, rate = f['id']
+
+
+    :param: str filepath:
+    :param: str mode:
+    :param: str format: The type used when saving wav. flac, nist, htk, etc.
+    :param: str dtype:
+
+    """
+
+    def __init__(self,
+                 filepath,
+                 mode="r+",
+                 format=None,
+                 dtype="int16",
+                 **kwargs):
+        self.filepath = filepath
+        self.mode = mode
+        self.dtype = dtype
+
+        self.file = h5py.File(filepath, mode, **kwargs)
+        if format is None:
+            # filepath = a.flac.h5 -> format = flac
+            second_ext = os.path.splitext(os.path.splitext(filepath)[0])[1]
+            format = second_ext[1:]
+            if format.upper() not in soundfile.available_formats():
+                # If not found, flac is selected
+                format = "flac"
+
+        # This format affects only saving
+        self.format = format
+
+    def __repr__(self):
+        return '<SoundHDF5 file "{}" (mode {}, format {}, type {})>'.format(
+            self.filepath, self.mode, self.format, self.dtype)
+
+    def create_dataset(self, name, shape=None, data=None, **kwds):
+        f = io.BytesIO()
+        array, rate = data
+        soundfile.write(f, array, rate, format=self.format)
+        self.file.create_dataset(
+            name, shape=shape, data=np.void(f.getvalue()), **kwds)
+
+    def __setitem__(self, name, data):
+        self.create_dataset(name, data=data)
+
+    def __getitem__(self, key):
+        data = self.file[key][()]
+        f = io.BytesIO(data.tobytes())
+        array, rate = soundfile.read(f, dtype=self.dtype)
+        return array, rate
+
+    def keys(self):
+        return self.file.keys()
+
+    def values(self):
+        for k in self.file:
+            yield self[k]
+
+    def items(self):
+        for k in self.file:
+            yield k, self[k]
+
+    def __iter__(self):
+        return iter(self.file)
+
+    def __contains__(self, item):
+        return item in self.file
+
+    def __len__(self, item):
+        return len(self.file)
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.file.close()
+
+    def close(self):
+        self.file.close()