diff --git a/flash/audio/classification/transforms.py b/flash/audio/classification/transforms.py
index 6189b07f16..02a9ed2cbc 100644
--- a/flash/audio/classification/transforms.py
+++ b/flash/audio/classification/transforms.py
@@ -11,7 +11,6 @@
 # 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 os
 from typing import Callable, Dict, Tuple
 
 import torch
@@ -19,10 +18,7 @@
 
 from flash.core.data.data_source import DefaultDataKeys
 from flash.core.data.transforms import ApplyToKeys, kornia_collate, merge_transforms
-from flash.core.utilities.imports import _KORNIA_AVAILABLE, _TORCHAUDIO_AVAILABLE, _TORCHVISION_AVAILABLE
-
-if _KORNIA_AVAILABLE:
-    import kornia as K
+from flash.core.utilities.imports import _TORCHAUDIO_AVAILABLE, _TORCHVISION_AVAILABLE
 
 if _TORCHVISION_AVAILABLE:
     import torchvision
@@ -35,19 +31,6 @@
 def default_transforms(spectrogram_size: Tuple[int, int]) -> Dict[str, Callable]:
     """The default transforms for audio classification for spectrograms: resize the spectrogram,
     convert the spectrogram and target to a tensor, and collate the batch."""
-    if _KORNIA_AVAILABLE and os.getenv("FLASH_TESTING", "0") != "1":
-        #  Better approach as all transforms are applied on tensor directly
-        return {
-            "to_tensor_transform": nn.Sequential(
-                ApplyToKeys(DefaultDataKeys.INPUT, torchvision.transforms.ToTensor()),
-                ApplyToKeys(DefaultDataKeys.TARGET, torch.as_tensor),
-            ),
-            "post_tensor_transform": ApplyToKeys(
-                DefaultDataKeys.INPUT,
-                K.geometry.Resize(spectrogram_size),
-            ),
-            "collate": kornia_collate,
-        }
     return {
         "pre_tensor_transform": ApplyToKeys(DefaultDataKeys.INPUT, T.Resize(spectrogram_size)),
         "to_tensor_transform": nn.Sequential(
@@ -60,13 +43,12 @@ def default_transforms(spectrogram_size: Tuple[int, int]) -> Dict[str, Callable]
 
 def train_default_transforms(spectrogram_size: Tuple[int, int], time_mask_param: int,
                              freq_mask_param: int) -> Dict[str, Callable]:
-    """During training we apply the default transforms with aditional ``TimeMasking`` and ``Frequency Masking``"""
-    if os.getenv("FLASH_TESTING", "0") != 1:
-        transforms = {
-            "post_tensor_transform": nn.Sequential(
-                ApplyToKeys(DefaultDataKeys.INPUT, TAudio.TimeMasking(time_mask_param=time_mask_param)),
-                ApplyToKeys(DefaultDataKeys.INPUT, TAudio.FrequencyMasking(freq_mask_param=freq_mask_param))
-            )
-        }
+    """During training we apply the default transforms with additional ``TimeMasking`` and ``Frequency Masking``"""
+    transforms = {
+        "post_tensor_transform": nn.Sequential(
+            ApplyToKeys(DefaultDataKeys.INPUT, TAudio.TimeMasking(time_mask_param=time_mask_param)),
+            ApplyToKeys(DefaultDataKeys.INPUT, TAudio.FrequencyMasking(freq_mask_param=freq_mask_param))
+        )
+    }
 
     return merge_transforms(default_transforms(spectrogram_size), transforms)