diff --git a/codes/models/asr/w2v_wrapper.py b/codes/models/asr/w2v_wrapper.py
index bc6ab2ac..15b9c93a 100644
--- a/codes/models/asr/w2v_wrapper.py
+++ b/codes/models/asr/w2v_wrapper.py
@@ -4,7 +4,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from transformers import Wav2Vec2ForCTC, Wav2Vec2CTCTokenizer
-from transformers.models.wav2vec2.modeling_wav2vec2 import Wav2Vec2Attention
+from transformers.models.wav2vec2.modeling_wav2vec2 import Wav2Vec2Attention, Wav2Vec2Model
 
 from data.audio.unsupervised_audio_dataset import load_audio
 from models.tacotron2.text import symbols, sequence_to_text
@@ -156,6 +156,18 @@ class Wav2VecWrapper(nn.Module):
                     module.dropout = new_dropout_rate
 
 
+class Wav2VecBaseWrapper(nn.Module):
+    def __init__(self, basis_model='facebook/wav2vec2-large'):
+        super().__init__()
+        self.w2v = Wav2Vec2Model.from_pretrained(basis_model)
+
+    def forward(self, audio):
+        audio = (audio - audio.mean()) / torch.sqrt(audio.var() + 1e-7)
+        audio = audio.squeeze(1)  # Get rid of the channels; w2v re-adds them.
+        outputs = self.w2v(input_values=audio)
+        return outputs.last_hidden_state
+
+
 @register_model
 def register_wav2vec_feature_extractor(opt_net, opt):
     return Wav2VecFeatureExtractor(**opt_get(opt_net, ['kwargs'], {}))
@@ -166,6 +178,11 @@ def register_wav2vec2_finetune(opt_net, opt):
     return Wav2VecWrapper(**opt_get(opt_net, ['kwargs'], {}))
 
 
+@register_model
+def register_wav2vec2(opt_net, opt):
+    return Wav2VecBaseWrapper(**opt_get(opt_net, ['kwargs'], {}))
+
+
 if __name__ == '__main__':
     fe = Wav2VecFeatureExtractor(basis_model='facebook/wav2vec2-large-960h')
     w2v = Wav2VecWrapper(basis_model='facebook/wav2vec2-large-960h', freeze_transformer=True, remove_feature_extractor=True, ramp_dropout_mode=True)