diff --git a/deepspeech/exps/deepspeech2/config.py b/deepspeech/exps/deepspeech2/config.py
index 050a50b00a70e2a9546886b28d530a6b5e694fe3..7d2250fc705c9652bcdf745b2aa4c1f115dac2b7 100644
--- a/deepspeech/exps/deepspeech2/config.py
+++ b/deepspeech/exps/deepspeech2/config.py
@@ -11,80 +11,26 @@
 # 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.
-from yacs.config import CfgNode as CN
+from yacs.config import CfgNode
 
 from deepspeech.models.deepspeech2 import DeepSpeech2Model
+from deepspeech.io.dataset import ManifestDataset
+from deepspeech.io.collator import SpeechCollator
+from deepspeech.exps.deepspeech2.model import DeepSpeech2Trainer
+from deepspeech.exps.deepspeech2.model import DeepSpeech2Tester
 
-_C = CN()
-_C.data = CN(
-    dict(
-        train_manifest="",
-        dev_manifest="",
-        test_manifest="",
-        max_duration=float('inf'),
-        min_duration=0.0,
-    ))
 
-_C.collator =CN(
-    dict(
-        unit_type="char",
-        vocab_filepath="",
-        spm_model_prefix="",
-        mean_std_filepath="",
-        augmentation_config="",
-        random_seed=0,
-        specgram_type='linear',  # 'linear', 'mfcc', 'fbank'
-        feat_dim=0,  # 'mfcc', 'fbank'
-        delta_delta=False,  # 'mfcc', 'fbank'
-        stride_ms=10.0,  # ms
-        window_ms=20.0,  # ms
-        n_fft=None,  # fft points
-        max_freq=None,  # None for samplerate/2
-        target_sample_rate=16000,  # target sample rate
-        use_dB_normalization=True,
-        target_dB=-20,
-        dither=1.0,  # feature dither
-        keep_transcription_text=False,
-        batch_size=32,  # batch size
-        num_workers=0,  # data loader workers
-        sortagrad=False,  # sorted in first epoch when True
-        shuffle_method="batch_shuffle",  # 'batch_shuffle', 'instance_shuffle'
-    ))
+_C = CfgNode()
 
-_C.model = CN(
-    dict(
-        num_conv_layers=2,  #Number of stacking convolution layers.
-        num_rnn_layers=3,  #Number of stacking RNN layers.
-        rnn_layer_size=1024,  #RNN layer size (number of RNN cells).
-        use_gru=True,  #Use gru if set True. Use simple rnn if set False.
-        share_rnn_weights=True  #Whether to share input-hidden weights between forward and backward directional RNNs.Notice that for GRU, weight sharing is not supported.
-    ))
+_C.data = ManifestDataset.params()
 
+_C.collator = SpeechCollator.params()
 
-DeepSpeech2Model.params(_C.model)
+_C.model = DeepSpeech2Model.params()
 
-_C.training = CN(
-    dict(
-        lr=5e-4,  # learning rate
-        lr_decay=1.0,  # learning rate decay
-        weight_decay=1e-6,  # the coeff of weight decay
-        global_grad_clip=5.0,  # the global norm clip
-        n_epoch=50,  # train epochs
-    ))
+_C.training = DeepSpeech2Trainer.params()
 
-_C.decoding = CN(
-    dict(
-        alpha=2.5,  # Coef of LM for beam search.
-        beta=0.3,  # Coef of WC for beam search.
-        cutoff_prob=1.0,  # Cutoff probability for pruning.
-        cutoff_top_n=40,  # Cutoff number for pruning.
-        lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm',  # Filepath for language model.
-        decoding_method='ctc_beam_search',  # Decoding method. Options: ctc_beam_search, ctc_greedy
-        error_rate_type='wer',  # Error rate type for evaluation. Options `wer`, 'cer'
-        num_proc_bsearch=8,  # # of CPUs for beam search.
-        beam_size=500,  # Beam search width.
-        batch_size=128,  # decoding batch size
-    ))
+_C.decoding = DeepSpeech2Tester.params()
 
 
 def get_cfg_defaults():
diff --git a/deepspeech/exps/deepspeech2/model.py b/deepspeech/exps/deepspeech2/model.py
index 1eefc871bb6036f85afbcc0a61ca7013a41eb91d..c11d1e2591df334e8b9616ac9c8a1440a475c469 100644
--- a/deepspeech/exps/deepspeech2/model.py
+++ b/deepspeech/exps/deepspeech2/model.py
@@ -34,10 +34,28 @@ from deepspeech.utils import layer_tools
 from deepspeech.utils import mp_tools
 from deepspeech.utils.log import Log
 
+from typing import Optional
+from yacs.config import CfgNode
 logger = Log(__name__).getlog()
 
 
 class DeepSpeech2Trainer(Trainer):
+    @classmethod
+    def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
+        # training config
+        default = CfgNode(
+                    dict(
+                        lr=5e-4,  # learning rate
+                        lr_decay=1.0,  # learning rate decay
+                        weight_decay=1e-6,  # the coeff of weight decay
+                        global_grad_clip=5.0,  # the global norm clip
+                        n_epoch=50,  # train epochs
+                    ))
+
+        if config is not None:
+            config.merge_from_other_cfg(default)
+        return default
+
     def __init__(self, config, args):
         super().__init__(config, args)
 
@@ -184,6 +202,27 @@ class DeepSpeech2Trainer(Trainer):
 
 
 class DeepSpeech2Tester(DeepSpeech2Trainer):
+    @classmethod
+    def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
+        # testing config
+        default = CfgNode(
+                dict(
+                    alpha=2.5,  # Coef of LM for beam search.
+                    beta=0.3,  # Coef of WC for beam search.
+                    cutoff_prob=1.0,  # Cutoff probability for pruning.
+                    cutoff_top_n=40,  # Cutoff number for pruning.
+                    lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm',  # Filepath for language model.
+                    decoding_method='ctc_beam_search',  # Decoding method. Options: ctc_beam_search, ctc_greedy
+                    error_rate_type='wer',  # Error rate type for evaluation. Options `wer`, 'cer'
+                    num_proc_bsearch=8,  # # of CPUs for beam search.
+                    beam_size=500,  # Beam search width.
+                    batch_size=128,  # decoding batch size
+                ))
+
+        if config is not None:
+            config.merge_from_other_cfg(default)
+        return default
+
     def __init__(self, config, args):
         super().__init__(config, args)