diff --git a/.notebook/python_test.ipynb b/.notebook/python_test.ipynb
index af55de5a4..5617c8765 100644
--- a/.notebook/python_test.ipynb
+++ b/.notebook/python_test.ipynb
@@ -637,7 +637,7 @@
   {
    "cell_type": "code",
    "execution_count": 59,
-   "id": "threaded-grove",
+   "id": "legitimate-overhead",
    "metadata": {},
    "outputs": [
     {
@@ -660,7 +660,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "equal-vanilla",
+   "id": "genuine-feeding",
    "metadata": {},
    "outputs": [
     {
@@ -705,7 +705,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "gorgeous-stanford",
+   "id": "bizarre-story",
    "metadata": {},
    "outputs": [
     {
@@ -728,7 +728,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "geological-actor",
+   "id": "appointed-brooklyn",
    "metadata": {},
    "outputs": [
     {
@@ -748,7 +748,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "miniature-ethnic",
+   "id": "occasional-utilization",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -758,7 +758,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "honest-clarity",
+   "id": "trained-indonesian",
    "metadata": {},
    "outputs": [
     {
@@ -776,7 +776,7 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "id": "environmental-stewart",
+   "id": "following-brave",
    "metadata": {},
    "outputs": [
     {
@@ -809,7 +809,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "trying-brazil",
+   "id": "prospective-blind",
    "metadata": {},
    "outputs": [
     {
@@ -839,7 +839,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "chronic-interval",
+   "id": "minus-ethernet",
    "metadata": {},
    "outputs": [
     {
@@ -868,7 +868,7 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "widespread-basin",
+   "id": "ordinary-closer",
    "metadata": {},
    "outputs": [
     {
@@ -900,7 +900,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "clinical-lighting",
+   "id": "demographic-mumbai",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -911,7 +911,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "federal-supervision",
+   "id": "conscious-stuff",
    "metadata": {},
    "outputs": [
     {
@@ -931,7 +931,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "parallel-trademark",
+   "id": "virgin-dublin",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -941,7 +941,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "extended-fishing",
+   "id": "sized-homework",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -951,7 +951,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "baking-auckland",
+   "id": "disciplinary-headquarters",
    "metadata": {},
    "outputs": [
     {
@@ -969,10 +969,103 @@
     "np.allclose(x, samples)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "persistent-synthetic",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "hydraulic-reach",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.uniform?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "suitable-house",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "random.uniform?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "printable-carter",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.RandomState?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "considered-interval",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "random.sample?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "ideal-hurricane",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['3', '5'], dtype='<U1')"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.random.choice(['5','4', '3'], 2, replace=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "skilled-cooler",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['3', '4']"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "random.sample(['5','4', '3'], 2)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "changed-storage",
+   "id": "adopted-hardware",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 13a9daaa5..02c084bb8 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -18,6 +18,9 @@
     -   id: check-yaml
     -   id: check-json
     -   id: pretty-format-json
+        args:
+        - --no-sort-keys
+        - --autofix
     -   id: check-merge-conflict
     -   id: flake8
         aergs:
@@ -49,3 +52,7 @@
         language: system
         files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto|py)$
         #exclude: (?=decoders/swig).*(\.cpp|\.h)$
+-   repo: https://github.com/asottile/reorder_python_imports
+    rev: v2.4.0
+    hooks:
+      - id: reorder-python-imports
diff --git a/deepspeech/frontend/augmentor/augmentation.py b/deepspeech/frontend/augmentor/augmentation.py
index aa1c4841d..16dc8ec35 100644
--- a/deepspeech/frontend/augmentor/augmentation.py
+++ b/deepspeech/frontend/augmentor/augmentation.py
@@ -14,7 +14,9 @@
 """Contains the data augmentation pipeline."""
 
 import json
-import random
+import numpy as np
+
+# audio augment
 from deepspeech.frontend.augmentor.volume_perturb import VolumePerturbAugmentor
 from deepspeech.frontend.augmentor.shift_perturb import ShiftPerturbAugmentor
 from deepspeech.frontend.augmentor.speed_perturb import SpeedPerturbAugmentor
@@ -23,6 +25,8 @@ from deepspeech.frontend.augmentor.impulse_response import ImpulseResponseAugmen
 from deepspeech.frontend.augmentor.resample import ResampleAugmentor
 from deepspeech.frontend.augmentor.online_bayesian_normalization import \
     OnlineBayesianNormalizationAugmentor
+# feature augment
+from deepspeech.frontend.augmentor.spec_augment import SpecAugmentor
 
 
 class AugmentationPipeline():
@@ -84,9 +88,12 @@ class AugmentationPipeline():
     """
 
     def __init__(self, augmentation_config: str, random_seed=0):
-        self._rng = random.Random(random_seed)
+        self._rng = np.random.RandomState(random_seed)
+        self._spec_types = ('specaug')
         self._augmentors, self._rates = self._parse_pipeline_from(
-            augmentation_config)
+            augmentation_config, 'audio')
+        self._spec_augmentors, self._spec_rates = self._parse_pipeline_from(
+            augmentation_config, 'feature')
 
     def transform_audio(self, audio_segment):
         """Run the pre-processing pipeline for data augmentation.
@@ -100,15 +107,41 @@ class AugmentationPipeline():
             if self._rng.uniform(0., 1.) < rate:
                 augmentor.transform_audio(audio_segment)
 
-    def _parse_pipeline_from(self, config_json):
+    def transform_feature(self, spec_segment):
+        """spectrogram augmentation.
+         
+        Args:
+            spec_segment (np.ndarray): audio feature, (D, T).
+        """
+        for augmentor, rate in zip(self._augmentors, self._rates):
+            if self._rng.uniform(0., 1.) < rate:
+                spec_segment = augmentor.transform_feature(spec_segment)
+        return spec_segment
+
+    def _parse_pipeline_from(self, config_json, aug_type='audio'):
         """Parse the config json to build a augmentation pipelien."""
+        assert aug_type in ('audio', 'feature'), aug_type
         try:
             configs = json.loads(config_json)
+            audio_confs = []
+            feature_confs = []
+            for config in configs:
+                if config["type"] in self._spec_types:
+                    feature_confs.append(config)
+                else:
+                    audio_confs.append(config)
+
+            if aug_type == 'audio':
+                aug_confs = audio_confs
+            elif aug_type == 'feature':
+                aug_confs = feature_confs
+
             augmentors = [
                 self._get_augmentor(config["type"], config["params"])
-                for config in configs
+                for config in aug_confs
             ]
-            rates = [config["prob"] for config in configs]
+            rates = [config["prob"] for config in aug_confs]
+
         except Exception as e:
             raise ValueError("Failed to parse the augmentation config json: "
                              "%s" % str(e))
@@ -130,5 +163,7 @@ class AugmentationPipeline():
             return NoisePerturbAugmentor(self._rng, **params)
         elif augmentor_type == "impulse":
             return ImpulseResponseAugmentor(self._rng, **params)
+        elif augmentor_type == "specaug":
+            return SpecAugmentor(self._rng, **params)
         else:
             raise ValueError("Unknown augmentor type [%s]." % augmentor_type)
diff --git a/deepspeech/frontend/augmentor/base.py b/deepspeech/frontend/augmentor/base.py
index 4d48d6def..250d32dc4 100644
--- a/deepspeech/frontend/augmentor/base.py
+++ b/deepspeech/frontend/augmentor/base.py
@@ -43,15 +43,13 @@ class AugmentorBase():
         pass
 
     @abstractmethod
-    def transform_spectrogram(self, spec_segment):
-        """Adds various effects to the input spectrogram segment. Such effects
+    def transform_feature(self, spec_segment):
+        """Adds various effects to the input audo feature segment. Such effects
         will augment the training data to make the model invariant to certain
         types of time_mask or freq_mask in the real world, improving model's
         generalization ability.
         
-        Note that this is an in-place transformation.
-
-        :param spec_segment: Spectrogram segment to add effects to.
-        :type spec_segment: Spectrogram
+        Args:
+            spec_segment (Spectrogram): Spectrogram segment to add effects to.
         """
         pass
diff --git a/deepspeech/frontend/augmentor/impulse_response.py b/deepspeech/frontend/augmentor/impulse_response.py
index 40aa3d47e..fb359445f 100644
--- a/deepspeech/frontend/augmentor/impulse_response.py
+++ b/deepspeech/frontend/augmentor/impulse_response.py
@@ -39,6 +39,7 @@ class ImpulseResponseAugmentor(AugmentorBase):
         :param audio_segment: Audio segment to add effects to.
         :type audio_segment: AudioSegmenet|SpeechSegment
         """
-        impulse_json = self._rng.sample(self._impulse_manifest, 1)[0]
+        impulse_json = self._rng.choice(
+            self._impulse_manifest, 1, replace=False)[0]
         impulse_segment = AudioSegment.from_file(impulse_json['audio_filepath'])
         audio_segment.convolve(impulse_segment, allow_resample=True)
diff --git a/deepspeech/frontend/augmentor/noise_perturb.py b/deepspeech/frontend/augmentor/noise_perturb.py
index 350370b8f..e24da4c3e 100644
--- a/deepspeech/frontend/augmentor/noise_perturb.py
+++ b/deepspeech/frontend/augmentor/noise_perturb.py
@@ -45,7 +45,7 @@ class NoisePerturbAugmentor(AugmentorBase):
         :param audio_segment: Audio segment to add effects to.
         :type audio_segment: AudioSegmenet|SpeechSegment
         """
-        noise_json = self._rng.sample(self._noise_manifest, 1)[0]
+        noise_json = self._rng.choice(self._noise_manifest, 1, replace=False)[0]
         if noise_json['duration'] < audio_segment.duration:
             raise RuntimeError("The duration of sampled noise audio is smaller "
                                "than the audio segment to add effects to.")
diff --git a/deepspeech/frontend/augmentor/spec_augment.py b/deepspeech/frontend/augmentor/spec_augment.py
new file mode 100644
index 000000000..2b648daba
--- /dev/null
+++ b/deepspeech/frontend/augmentor/spec_augment.py
@@ -0,0 +1,169 @@
+# Copyright (c) 2021 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.
+"""Contains the volume perturb augmentation model."""
+import logging
+import numpy as np
+
+from deepspeech.frontend.augmentor.base import AugmentorBase
+
+logger = logging.getLogger(__name__)
+
+
+class SpecAugmentor(AugmentorBase):
+    """Augmentation model for Time warping, Frequency masking, Time masking.
+
+    SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
+        https://arxiv.org/abs/1904.08779
+        
+    SpecAugment on Large Scale Datasets
+        https://arxiv.org/abs/1912.05533
+    
+    """
+
+    def __init__(self,
+                 rng,
+                 F,
+                 T,
+                 n_freq_masks,
+                 n_time_masks,
+                 p=1.0,
+                 W=40,
+                 adaptive_number_ratio=0,
+                 adaptive_size_ratio=0,
+                 max_n_time_masks=20):
+        """SpecAugment class.
+        Args:
+            rng (random.Random): random generator object.
+            F (int): parameter for frequency masking
+            T (int): parameter for time masking
+            n_freq_masks (int): number of frequency masks
+            n_time_masks (int): number of time masks
+            p (float): parameter for upperbound of the time mask
+            W (int): parameter for time warping
+            adaptive_number_ratio (float): adaptive multiplicity ratio for time masking
+            adaptive_size_ratio (float): adaptive size ratio for time masking
+            max_n_time_masks (int): maximum number of time masking
+        """
+        super().__init__()
+        self._rng = rng
+
+        self.W = W
+        self.F = F
+        self.T = T
+        self.n_freq_masks = n_freq_masks
+        self.n_time_masks = n_time_masks
+        self.p = p
+
+        # adaptive SpecAugment
+        self.adaptive_number_ratio = adaptive_number_ratio
+        self.adaptive_size_ratio = adaptive_size_ratio
+        self.max_n_time_masks = max_n_time_masks
+
+        if adaptive_number_ratio > 0:
+            self.n_time_masks = 0
+            logger.info('n_time_masks is set ot zero for adaptive SpecAugment.')
+        if adaptive_size_ratio > 0:
+            self.T = 0
+            logger.info('T is set to zero for adaptive SpecAugment.')
+
+        self._freq_mask = None
+        self._time_mask = None
+
+    def librispeech_basic(self):
+        self.W = 80
+        self.F = 27
+        self.T = 100
+        self.n_freq_masks = 1
+        self.n_time_masks = 1
+        self.p = 1.0
+
+    def librispeech_double(self):
+        self.W = 80
+        self.F = 27
+        self.T = 100
+        self.n_freq_masks = 2
+        self.n_time_masks = 2
+        self.p = 1.0
+
+    def switchboard_mild(self):
+        self.W = 40
+        self.F = 15
+        self.T = 70
+        self.n_freq_masks = 2
+        self.n_time_masks = 2
+        self.p = 0.2
+
+    def switchboard_strong(self):
+        self.W = 40
+        self.F = 27
+        self.T = 70
+        self.n_freq_masks = 2
+        self.n_time_masks = 2
+        self.p = 0.2
+
+    @property
+    def freq_mask(self):
+        return self._freq_mask
+
+    @property
+    def time_mask(self):
+        return self._time_mask
+
+    def time_warp(xs, W=40):
+        raise NotImplementedError
+
+    def mask_freq(self, xs, replace_with_zero=False):
+        n_bins = xs.shape[0]
+        for i in range(0, self.n_freq_masks):
+            f = int(self._rng.uniform(low=0, high=self.F))
+            f_0 = int(self._rng.uniform(low=0, high=n_bins - f))
+            xs[f_0:f_0 + f, :] = 0
+            assert f_0 <= f_0 + f
+            self._freq_mask = (f_0, f_0 + f)
+        return xs
+
+    def mask_time(self, xs, replace_with_zero=False):
+        n_frames = xs.shape[1]
+
+        if self.adaptive_number_ratio > 0:
+            n_masks = int(n_frames * self.adaptive_number_ratio)
+            n_masks = min(n_masks, self.max_n_time_masks)
+        else:
+            n_masks = self.n_time_masks
+
+        if self.adaptive_size_ratio > 0:
+            T = self.adaptive_size_ratio * n_frames
+        else:
+            T = self.T
+
+        for i in range(n_masks):
+            t = int(self._rng.uniform(low=0, high=T))
+            t = min(t, int(n_frames * self.p))
+            t_0 = int(self._rng.uniform(low=0, high=n_frames - t))
+            xs[:, t_0:t_0 + t] = 0
+            assert t_0 <= t_0 + t
+            self._time_mask = (t_0, t_0 + t)
+        return xs
+
+    def transform_feature(self, xs: np.ndarray):
+        """
+        Args:
+            xs (FloatTensor): `[F, T]`
+        Returns:
+            xs (FloatTensor): `[F, T]`
+        """
+        # xs = self.time_warp(xs)
+        xs = self.mask_freq(xs)
+        xs = self.mask_time(xs)
+        return xs
diff --git a/deepspeech/io/dataset.py b/deepspeech/io/dataset.py
index 4550b058a..2bd8ddb8a 100644
--- a/deepspeech/io/dataset.py
+++ b/deepspeech/io/dataset.py
@@ -192,7 +192,7 @@ class ManifestDataset(Dataset):
 
         self._normalizer = FeatureNormalizer(
             mean_std_filepath) if mean_std_filepath else None
-        self._audio_augmentation_pipeline = AugmentationPipeline(
+        self._augmentation_pipeline = AugmentationPipeline(
             augmentation_config=augmentation_config, random_seed=random_seed)
         self._speech_featurizer = SpeechFeaturizer(
             unit_type=unit_type,
@@ -295,11 +295,14 @@ class ManifestDataset(Dataset):
                 self._subfile_from_tar(audio_file), transcript)
         else:
             speech_segment = SpeechSegment.from_file(audio_file, transcript)
-        self._audio_augmentation_pipeline.transform_audio(speech_segment)
+        # audio augment
+        self._augmentation_pipeline.transform_audio(speech_segment)
         specgram, transcript_part = self._speech_featurizer.featurize(
             speech_segment, self._keep_transcription_text)
         if self._normalizer:
             specgram = self._normalizer.apply(specgram)
+        # specgram augment
+        specgram = self._augmentation_pipeline.transform_feature(specgram)
         return specgram, transcript_part
 
     def _instance_reader_creator(self, manifest):
diff --git a/docs/src/augmentation.md b/docs/src/augmentation.md
index e4b7c0012..6df8061d7 100644
--- a/docs/src/augmentation.md
+++ b/docs/src/augmentation.md
@@ -5,6 +5,7 @@ Data augmentation has often been a highly effective technique to boost the deep
 
 Six optional augmentation components are provided to be selected, configured and inserted into the processing pipeline.
 
+* Audio
   - Volume Perturbation
   - Speed Perturbation
   - Shifting Perturbation
@@ -12,6 +13,10 @@ Six optional augmentation components are provided to be selected, configured and
   - Noise Perturbation (need background noise audio files)
   - Impulse Response (need impulse audio files)
 
+* Feature
+  - SpecAugment
+  - Adaptive SpecAugment
+
 In order to inform the trainer of what augmentation components are needed and what their processing orders are, it is required to prepare in advance an *augmentation configuration file* in [JSON](http://www.json.org/) format. For example:
 
 ```
@@ -31,6 +36,6 @@ In order to inform the trainer of what augmentation components are needed and wh
 
 When the `augment_conf_file` argument is set to the path of the above example configuration file, every audio clip in every epoch will be processed: with 60% of chance, it will first be speed perturbed with a uniformly random sampled speed-rate between 0.95 and 1.05, and then with 80% of chance it will be shifted in time with a random sampled offset between -5 ms and 5 ms. Finally this newly synthesized audio clip will be feed into the feature extractor for further training.
 
-For other configuration examples, please refer to `examples/conf/augmentation.config.example`.
+For other configuration examples, please refer to `examples/conf/augmentation.example.json`.
 
 Be careful when utilizing the data augmentation technique, as improper augmentation will do harm to the training, due to the enlarged train-test gap.
diff --git a/docs/src/data_preparation.md b/docs/src/data_preparation.md
index 7b6142bde..a3d1b3eb4 100644
--- a/docs/src/data_preparation.md
+++ b/docs/src/data_preparation.md
@@ -40,4 +40,4 @@ python3 utils/build_vocab.py \
 --manifest_paths examples/librispeech/data/manifest.train
 ```
 
-It will write a vocabuary file `examples/librispeech/data/eng_vocab.txt` with all transcription text in `examples/librispeech/data/manifest.train`, without vocabulary truncation (`--count_threshold 0`).
+It will write a vocabuary file `examples/librispeech/data/vocab.txt` with all transcription text in `examples/librispeech/data/manifest.train`, without vocabulary truncation (`--count_threshold 0`).
diff --git a/docs/src/install.md b/docs/src/install.md
index bd4d5a432..72b7b6988 100644
--- a/docs/src/install.md
+++ b/docs/src/install.md
@@ -8,10 +8,10 @@ To avoid the trouble of environment setup, [running in Docker container](#runnin
 
 ## Setup
 
-- Make sure these libraries or tools installed: `pkg-config`, `flac`, `ogg`, `vorbis`, `boost` and `swig`, e.g. installing them via `apt-get`:
+- Make sure these libraries or tools installed: `pkg-config`, `flac`, `ogg`, `vorbis`, `boost`, `sox, and `swig`, e.g. installing them via `apt-get`:
 
 ```bash
-sudo apt-get install -y pkg-config libflac-dev libogg-dev libvorbis-dev libboost-dev swig python3-dev
+sudo apt-get install -y sox pkg-config libflac-dev libogg-dev libvorbis-dev libboost-dev swig python3-dev
 ```
 
 or, installing them via `yum`:
diff --git a/examples/aishell/s0/conf/augmentation.config b/examples/aishell/s0/conf/augmentation.config
deleted file mode 100644
index 6c24da549..000000000
--- a/examples/aishell/s0/conf/augmentation.config
+++ /dev/null
@@ -1,8 +0,0 @@
-[
-    {
-        "type": "shift",
-        "params": {"min_shift_ms": -5,
-                   "max_shift_ms": 5},
-        "prob": 1.0
-    }
-]
diff --git a/examples/aishell/s0/conf/augmentation.json b/examples/aishell/s0/conf/augmentation.json
new file mode 100644
index 000000000..a1a759e67
--- /dev/null
+++ b/examples/aishell/s0/conf/augmentation.json
@@ -0,0 +1,10 @@
+[
+  {
+    "type": "shift",
+    "params": {
+      "min_shift_ms": -5,
+      "max_shift_ms": 5
+    },
+    "prob": 1.0
+  }
+]
diff --git a/examples/aishell/s0/conf/deepspeech2.yaml b/examples/aishell/s0/conf/deepspeech2.yaml
index 821c183e5..e06ae0239 100644
--- a/examples/aishell/s0/conf/deepspeech2.yaml
+++ b/examples/aishell/s0/conf/deepspeech2.yaml
@@ -5,7 +5,7 @@ data:
   test_manifest: data/manifest.test
   mean_std_filepath: data/mean_std.npz
   vocab_filepath: data/vocab.txt 
-  augmentation_config: conf/augmentation.config
+  augmentation_config: conf/augmentation.json
   batch_size: 64 # one gpu
   max_duration: 27.0
   min_duration: 0.0
diff --git a/examples/aishell/s1/conf/augmentation.json b/examples/aishell/s1/conf/augmentation.json
new file mode 100644
index 000000000..aa16afb2e
--- /dev/null
+++ b/examples/aishell/s1/conf/augmentation.json
@@ -0,0 +1,34 @@
+[
+  {
+    "type": "speed",
+    "params": {
+      "min_speed_rate": 0.9,
+      "max_speed_rate": 1.1,
+      "num_rates": 3
+    },
+    "prob": 1.0
+  },
+  {
+    "type": "shift",
+    "params": {
+      "min_shift_ms": -5,
+      "max_shift_ms": 5
+    },
+    "prob": 1.0
+  },
+  {
+    "type": "specaug",
+    "params": {
+      "F": 10,
+      "T": 50,
+      "n_freq_masks": 2,
+      "n_time_masks": 2,
+      "p": 1.0,
+      "W": 80,
+      "adaptive_number_ratio": 0,
+      "adaptive_size_ratio": 0,
+      "max_n_time_masks": 20
+    },
+    "prob": 0.0
+  }
+]
diff --git a/examples/aishell/s1/conf/conformer.yaml b/examples/aishell/s1/conf/conformer.yaml
new file mode 100644
index 000000000..fced75d7e
--- /dev/null
+++ b/examples/aishell/s1/conf/conformer.yaml
@@ -0,0 +1,110 @@
+# https://yaml.org/type/float.html
+data:
+  train_manifest: data/manifest.train
+  dev_manifest: data/manifest.dev
+  test_manifest: data/manifest.test
+  vocab_filepath: data/vocab.txt 
+  unit_type: 'char'
+  spm_model_prefix: ''
+  mean_std_filepath: ""
+  augmentation_config: conf/augmentation.json
+  batch_size: 16
+  min_input_len: 0.5
+  max_input_len: 20.0
+  min_output_len: 0.0
+  max_output_len: 400
+  min_output_input_ratio: 0.05
+  max_output_input_ratio: 10.0
+  raw_wav: True  # use raw_wav or kaldi feature
+  specgram_type: fbank #linear, mfcc, fbank
+  feat_dim: 80
+  delta_delta: False
+  target_sample_rate: 16000
+  max_freq: None
+  n_fft: None
+  stride_ms: 10.0
+  window_ms: 25.0
+  use_dB_normalization: True
+  target_dB: -20
+  random_seed: 0
+  keep_transcription_text: False
+  sortagrad: True 
+  shuffle_method: batch_shuffle
+  num_workers: 0
+
+
+# network architecture
+model:
+    cmvn_file: "data/mean_std.npz"
+    cmvn_file_type: "npz"
+    # encoder related
+    encoder: conformer
+    encoder_conf:
+        output_size: 256    # dimension of attention
+        attention_heads: 4
+        linear_units: 2048  # the number of units of position-wise feed forward
+        num_blocks: 12      # the number of encoder blocks
+        dropout_rate: 0.1
+        positional_dropout_rate: 0.1
+        attention_dropout_rate: 0.0
+        input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
+        normalize_before: True
+        use_cnn_module: True
+        cnn_module_kernel: 15
+        activation_type: 'swish'
+        pos_enc_layer_type: 'rel_pos'
+        selfattention_layer_type: 'rel_selfattn'
+
+    # decoder related
+    decoder: transformer
+    decoder_conf:
+        attention_heads: 4
+        linear_units: 2048
+        num_blocks: 6
+        dropout_rate: 0.1
+        positional_dropout_rate: 0.1
+        self_attention_dropout_rate: 0.0
+        src_attention_dropout_rate: 0.0
+
+    # hybrid CTC/attention
+    model_conf:
+        ctc_weight: 0.3
+        lsm_weight: 0.1     # label smoothing option
+        length_normalized_loss: false
+
+
+training:
+  n_epoch: 240
+  accum_grad: 4
+  global_grad_clip: 5.0
+  optim: adam
+  optim_conf:
+    lr: 0.002
+    weight_decay: 1e-06
+  scheduler: warmuplr     # pytorch v1.1.0+ required
+  scheduler_conf:
+    warmup_steps: 25000
+    lr_decay: 1.0
+  log_interval: 100
+
+
+decoding:
+  batch_size: 16
+  error_rate_type: wer
+  decoding_method: attention  # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
+  lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
+  alpha: 2.5
+  beta: 0.3
+  beam_size: 10
+  cutoff_prob: 1.0
+  cutoff_top_n: 0
+  num_proc_bsearch: 8
+  ctc_weight: 0.0 # ctc weight for attention rescoring decode mode.
+  decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
+      # <0: for decoding, use full chunk.
+      # >0: for decoding, use fixed chunk size as set.
+      # 0: used for training, it's prohibited here. 
+  num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
+  simulate_streaming: False  # simulate streaming inference. Defaults to False.
+
+
diff --git a/examples/aug_conf/augmentation.config b/examples/aug_conf/augmentation.config
deleted file mode 100644
index 6c24da549..000000000
--- a/examples/aug_conf/augmentation.config
+++ /dev/null
@@ -1,8 +0,0 @@
-[
-    {
-        "type": "shift",
-        "params": {"min_shift_ms": -5,
-                   "max_shift_ms": 5},
-        "prob": 1.0
-    }
-]
diff --git a/examples/aug_conf/augmentation.config.example b/examples/aug_conf/augmentation.config.example
deleted file mode 100644
index 2902125ab..000000000
--- a/examples/aug_conf/augmentation.config.example
+++ /dev/null
@@ -1,40 +0,0 @@
-[
-    {
-        "type": "noise",
-        "params": {"min_snr_dB": 40,
-                   "max_snr_dB": 50,
-                   "noise_manifest_path": "datasets/manifest.noise"},
-        "prob": 0.6
-    },
-    {
-        "type": "impulse",
-        "params": {"impulse_manifest_path": "datasets/manifest.impulse"},
-        "prob": 0.5
-    },
-    {
-        "type": "speed",
-        "params": {"min_speed_rate": 0.95,
-                   "max_speed_rate": 1.05,
-                   "num_rates": 3},
-        "prob": 0.5
-    },
-    {
-        "type": "shift",
-        "params": {"min_shift_ms": -5,
-                   "max_shift_ms": 5},
-        "prob": 1.0
-    },
-    {
-        "type": "volume",
-        "params": {"min_gain_dBFS": -10,
-                   "max_gain_dBFS": 10},
-        "prob": 0.0
-    },
-    {
-        "type": "bayesian_normal",
-        "params": {"target_db": -20,
-                   "prior_db": -20,
-                   "prior_samples": 100},
-        "prob": 0.0
-    }
-]
diff --git a/examples/aug_conf/augmentation.example.json b/examples/aug_conf/augmentation.example.json
new file mode 100644
index 000000000..efae2e5e3
--- /dev/null
+++ b/examples/aug_conf/augmentation.example.json
@@ -0,0 +1,67 @@
+[
+  {
+    "type": "noise",
+    "params": {
+      "min_snr_dB": 40,
+      "max_snr_dB": 50,
+      "noise_manifest_path": "datasets/manifest.noise"
+    },
+    "prob": 0.6
+  },
+  {
+    "type": "impulse",
+    "params": {
+      "impulse_manifest_path": "datasets/manifest.impulse"
+    },
+    "prob": 0.5
+  },
+  {
+    "type": "speed",
+    "params": {
+      "min_speed_rate": 0.95,
+      "max_speed_rate": 1.05,
+      "num_rates": 3
+    },
+    "prob": 0.5
+  },
+  {
+    "type": "shift",
+    "params": {
+      "min_shift_ms": -5,
+      "max_shift_ms": 5
+    },
+    "prob": 1.0
+  },
+  {
+    "type": "volume",
+    "params": {
+      "min_gain_dBFS": -10,
+      "max_gain_dBFS": 10
+    },
+    "prob": 0.0
+  },
+  {
+    "type": "bayesian_normal",
+    "params": {
+      "target_db": -20,
+      "prior_db": -20,
+      "prior_samples": 100
+    },
+    "prob": 0.0
+  },
+  {
+    "type": "specaug",
+    "params": {
+      "F": 10,
+      "T": 50,
+      "n_freq_masks": 2,
+      "n_time_masks": 2,
+      "p": 1.0,
+      "W": 80,
+      "adaptive_number_ratio": 0,
+      "adaptive_size_ratio": 0,
+      "max_n_time_masks": 20
+    },
+    "prob": 0.0
+  }
+]
diff --git a/examples/aug_conf/augmentation.json b/examples/aug_conf/augmentation.json
new file mode 100644
index 000000000..a1a759e67
--- /dev/null
+++ b/examples/aug_conf/augmentation.json
@@ -0,0 +1,10 @@
+[
+  {
+    "type": "shift",
+    "params": {
+      "min_shift_ms": -5,
+      "max_shift_ms": 5
+    },
+    "prob": 1.0
+  }
+]
diff --git a/examples/librispeech/s0/conf/augmentation.config b/examples/librispeech/s0/conf/augmentation.config
deleted file mode 100644
index 6c24da549..000000000
--- a/examples/librispeech/s0/conf/augmentation.config
+++ /dev/null
@@ -1,8 +0,0 @@
-[
-    {
-        "type": "shift",
-        "params": {"min_shift_ms": -5,
-                   "max_shift_ms": 5},
-        "prob": 1.0
-    }
-]
diff --git a/examples/librispeech/s0/conf/augmentation.json b/examples/librispeech/s0/conf/augmentation.json
new file mode 100644
index 000000000..a1a759e67
--- /dev/null
+++ b/examples/librispeech/s0/conf/augmentation.json
@@ -0,0 +1,10 @@
+[
+  {
+    "type": "shift",
+    "params": {
+      "min_shift_ms": -5,
+      "max_shift_ms": 5
+    },
+    "prob": 1.0
+  }
+]
diff --git a/examples/librispeech/s0/conf/deepspeech2.yaml b/examples/librispeech/s0/conf/deepspeech2.yaml
index 15fd4cbe3..81313e611 100644
--- a/examples/librispeech/s0/conf/deepspeech2.yaml
+++ b/examples/librispeech/s0/conf/deepspeech2.yaml
@@ -5,7 +5,7 @@ data:
   test_manifest: data/manifest.test-clean
   mean_std_filepath: data/mean_std.npz
   vocab_filepath: data/vocab.txt 
-  augmentation_config: conf/augmentation.config
+  augmentation_config: conf/augmentation.json
   batch_size: 20
   max_duration: 27.0
   min_duration: 0.0
diff --git a/examples/tiny/s0/conf/augmentation.config b/examples/tiny/s0/conf/augmentation.config
deleted file mode 100644
index 6c24da549..000000000
--- a/examples/tiny/s0/conf/augmentation.config
+++ /dev/null
@@ -1,8 +0,0 @@
-[
-    {
-        "type": "shift",
-        "params": {"min_shift_ms": -5,
-                   "max_shift_ms": 5},
-        "prob": 1.0
-    }
-]
diff --git a/examples/tiny/s0/conf/augmentation.json b/examples/tiny/s0/conf/augmentation.json
new file mode 100644
index 000000000..a1a759e67
--- /dev/null
+++ b/examples/tiny/s0/conf/augmentation.json
@@ -0,0 +1,10 @@
+[
+  {
+    "type": "shift",
+    "params": {
+      "min_shift_ms": -5,
+      "max_shift_ms": 5
+    },
+    "prob": 1.0
+  }
+]
diff --git a/examples/tiny/s0/conf/deepspeech2.yaml b/examples/tiny/s0/conf/deepspeech2.yaml
index c7dd83f3c..59d0d79e2 100644
--- a/examples/tiny/s0/conf/deepspeech2.yaml
+++ b/examples/tiny/s0/conf/deepspeech2.yaml
@@ -5,7 +5,7 @@ data:
   test_manifest: data/manifest.tiny
   mean_std_filepath: data/mean_std.npz
   vocab_filepath: data/vocab.txt 
-  augmentation_config: conf/augmentation.config
+  augmentation_config: conf/augmentation.json
   batch_size: 4
   max_duration: 27.0
   min_duration: 0.0
diff --git a/examples/tiny/s1/conf/augmentation.config b/examples/tiny/s1/conf/augmentation.config
deleted file mode 100644
index 6c24da549..000000000
--- a/examples/tiny/s1/conf/augmentation.config
+++ /dev/null
@@ -1,8 +0,0 @@
-[
-    {
-        "type": "shift",
-        "params": {"min_shift_ms": -5,
-                   "max_shift_ms": 5},
-        "prob": 1.0
-    }
-]
diff --git a/examples/tiny/s1/conf/augmentation.json b/examples/tiny/s1/conf/augmentation.json
new file mode 100644
index 000000000..a1a759e67
--- /dev/null
+++ b/examples/tiny/s1/conf/augmentation.json
@@ -0,0 +1,10 @@
+[
+  {
+    "type": "shift",
+    "params": {
+      "min_shift_ms": -5,
+      "max_shift_ms": 5
+    },
+    "prob": 1.0
+  }
+]
diff --git a/examples/tiny/s1/conf/conformer.yaml b/examples/tiny/s1/conf/conformer.yaml
index e4c6f33c1..b1101736d 100644
--- a/examples/tiny/s1/conf/conformer.yaml
+++ b/examples/tiny/s1/conf/conformer.yaml
@@ -7,7 +7,7 @@ data:
   unit_type: 'spm'
   spm_model_prefix: 'data/bpe_unigram_200'
   mean_std_filepath: ""
-  augmentation_config: conf/augmentation.config
+  augmentation_config: conf/augmentation.json
   batch_size: 4
   min_input_len: 0.5
   max_input_len: 20.0