diff --git a/tonic/audio_augmentations.py b/tonic/audio_augmentations.py
index ab490cd..a77a9ea 100644
--- a/tonic/audio_augmentations.py
+++ b/tonic/audio_augmentations.py
@@ -6,13 +6,12 @@
 import librosa
 import numpy as np
 import torch
+import torchaudio
 
-from tonic.audio_transforms import FixLength
-
-# import torchaudio
 # from qut_noise import QUTNoise
-# from torchaudio.utils import download_asset
+from torchaudio.utils import download_asset
 
+from tonic.audio_transforms import FixLength
 
 __all__ = [
     "RandomTimeStretch",
@@ -366,38 +365,37 @@ def __call__(self, audio: np.ndarray):
 #         return noise_then_audio
 
 
-# @dataclass
-# class RIR:
-#     """Convolves a RIR (room impluse response, sound of hand clapping in an empty room) to the data
-#     sample.
+@dataclass
+class RIR:
+    """Convolves a RIR (room impluse response) to the data sample.
 
-#     Parameters:
-#         samplerate (float): sample rate of the sample
-#         caching (bool): if we are caching the DiskCached dataset will dynamically pass copy index of data item to the transform (to set aug_index). Otherwise the aug_index will be chosen randomly in every call of transform
+    Parameters:
+        samplerate (float): sample rate of the sample
+        rir_audio (str): path to a sample room impluse response in the .wav format
+        caching (bool): if we are caching the DiskCached dataset will dynamically pass copy index of data item to the transform (to set aug_index). Otherwise the aug_index will be chosen randomly in every call of transform
 
-#     Args:
-#         audio (np.ndarray): data sample
-#     Returns:
-#         np.ndarray: data sample convolved with RIR
-#     """
+    Args:
+        audio (np.ndarray): data sample
+    Returns:
+        np.ndarray: data sample convolved with RIR
+    """
 
-#     samplerate: float
-#     caching: bool = False
+    samplerate: float
+    rir_audio: str
+    caching: bool = False
 
-#     def __call__(self, audio):
-#         SAMPLE_RIR = download_asset(
-#             "tutorial-assets/Lab41-SRI-VOiCES-rm1-impulse-mc01-stu-clo-8000hz.wav"
-#         )
-#         rir_raw, rir_sample_rate = torchaudio.load(SAMPLE_RIR)
-#         rir = rir_raw[:, int(rir_sample_rate * 1.01) : int(rir_sample_rate * 1.3)]
-#         rir = rir / torch.norm(rir, p=2)
-#         RIR = torch.flip(rir, [1])
+    def __call__(self, audio):
+        SAMPLE_RIR = download_asset(self.rir_audio)
+        rir_raw, rir_sample_rate = torchaudio.load(SAMPLE_RIR)
+        rir = rir_raw[:, int(rir_sample_rate * 1.01) : int(rir_sample_rate * 1.3)]
+        rir = rir / torch.norm(rir, p=2)
+        RIR = torch.flip(rir, [1])
 
-#         t_audio = torch.nn.functional.pad(
-#             torch.from_numpy(audio), (RIR.shape[1] - 1, 0)
-#         )
-#         rir_augmented = torch.nn.functional.conv1d(t_audio[None, ...], RIR[None, ...])[
-#             0
-#         ].numpy()
+        t_audio = torch.nn.functional.pad(
+            torch.from_numpy(audio), (RIR.shape[1] - 1, 0)
+        )
+        rir_augmented = torch.nn.functional.conv1d(t_audio[None, ...], RIR[None, ...])[
+            0
+        ].numpy()
 
-#         return rir_augmented
+        return rir_augmented