diff --git a/i6_models/parts/best_rq/__init__.py b/i6_models/parts/best_rq/__init__.py
new file mode 100644
index 00000000..fb9a81db
--- /dev/null
+++ b/i6_models/parts/best_rq/__init__.py
@@ -0,0 +1,2 @@
+from .mask import *
+from .quantizer import *
diff --git a/i6_models/parts/best_rq/mask.py b/i6_models/parts/best_rq/mask.py
index 59106a8c..176ec2c3 100644
--- a/i6_models/parts/best_rq/mask.py
+++ b/i6_models/parts/best_rq/mask.py
@@ -1,25 +1,51 @@
+from typing import Optional, Tuple
+
 import torch
 import torch.nn as nn
-from typing import Optional
 import numpy as np
 
+__all__ = ["RandomMask"]
+
 
 class RandomMask(nn.Module):
-    def __init__(self, input_dim, mask_replace_val):
+    """
+    randomly mask out consecutive frames time dimension, the masked frames can be either
+    replaced with zeros or with learnable embeddings.
+    simplified version from Fairseq compute_mask_indices function,
+    C.f. https://github.com/facebookresearch/fairseq/blob/ecbf110e1eb43861214b05fa001eff584954f65a/fairseq/data/data_utils.py#L399
+    """
 
+    def __init__(
+        self,
+        input_dim: int,
+        mask_replace_val: str,
+        mask_prob: float,
+        mask_length: int,
+        min_masks: int = 0,
+    ):
+        """
+        :param input_dim: number of feature dimension of input
+        :param mask_replace_val: the way to replace masked frames, either with zeros or lernable embeddings
+        :param mask_prob: percentage of frames to be masked out
+        :param mask_length: the length of each mask span
+        :param min_masks: minimum number of masks
+        """
+        super().__init__()
+
+        assert mask_replace_val in ["lernable", "zero"], "not implemented yet"
         if mask_replace_val == "lernable":
             self.mask_emb = nn.Parameter(torch.FloatTensor(input_dim).uniform_())
-        elif mask_replace_val == 0:
+        elif mask_replace_val == "zero":
             self.mask_emb = torch.zeros(input_dim)
+        self.mask_prob = mask_prob
+        self.mask_length = mask_length
+        self.min_masks = min_masks
 
     def forward(
         self,
         tensor: torch.tensor,
         padding_mask: Optional[torch.Tensor],
-        mask_prob: float,
-        mask_length: int,
-        min_masks: int = 0,
-    ):
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         ndim_batch, ndim_time, _ = tensor.size()
 
         mask = torch.zeros((ndim_batch, ndim_time), dtype=torch.bool)
@@ -34,22 +60,24 @@ def forward(
 
             num_mask = int(
                 # add a random number for probabilistic rounding
-                mask_prob * seq_len / float(mask_length)
+                self.mask_prob * seq_len / float(self.mask_length)
                 + np.random.rand()
             )
-            num_mask = max(min_masks, num_mask)
+            num_mask = max(self.min_masks, num_mask)
 
-            min_len = mask_length
+            min_len = self.mask_length
             if seq_len - min_len <= num_mask:
                 min_len = seq_len - num_mask - 1
             mask_idc = np.random.choice(seq_len - min_len, num_mask, replace=False)
 
-            mask_idc = np.asarray([mask_idc[j] + mask_length for j in range(len(mask_idc))])
-        mask_idcs.append(mask_idc)
+            mask_idc = np.asarray(
+                [mask_idc[j] + offset for j in range(len(mask_idc)) for offset in range(self.mask_length)]
+            )
+            mask_idcs.append(mask_idc)
 
         for i, mask_idc in enumerate(mask_idcs):
             mask[i, mask_idc] = True
 
-        tensor[mask] = self.mask_emb
+        tensor[mask] = self.mask_emb.to(tensor.device)
 
-        return tensor
+        return tensor, torch.tensor(mask).to(tensor.device)
diff --git a/i6_models/parts/best_rq/quantizer.py b/i6_models/parts/best_rq/quantizer.py
index 0d039ba5..8633eb42 100644
--- a/i6_models/parts/best_rq/quantizer.py
+++ b/i6_models/parts/best_rq/quantizer.py
@@ -3,22 +3,35 @@
 import torch.nn.functional as F
 from torch.linalg import vector_norm
 
+__all__ = [
+    "RandomProjectionQuantizer",
+]
+
 
 class RandomProjectionQuantizer(nn.Module):
-    def __init__(self, input_dim, cb_dim, cb_vocab):
+    """
+    implement the fixed random projection quantizer from BestRQ
+    C.f. https://arxiv.org/pdf/2202.01855 for theoretic background
+    code adapted from https://github.com/speechbrain/speechbrain/blob/16b6420d4ff23210cfca2e888be8853264e0cb17/speechbrain/nnet/quantisers.py#L127
+    """
+
+    def __init__(self, input_dim, codebook_dim, codebook_num_vars):
+        """
+        :param input_dim: number of feature dimension of input
+        :param codebook_dim: number of dimension for vocab in the codebook
+        :param codebook_num_vars: vocab size of the codebook
+        """
         super().__init__()
 
         self.input_dim = input_dim
-        self.cb_dim = cb_dim
-        self.cb_vocab = cb_vocab
 
-        # Section 3.1 "projection matrix A use Xavier initialization"
-        P_init = torch.empty((input_dim, cb_dim))
+        # projection matrix use Xavier initialization
+        P_init = torch.empty((input_dim, codebook_dim))
         self.register_buffer("P", nn.init.xavier_uniform_(P_init))
 
         # normalize random matrix for codebook
-        self.register_buffer("CB", F.normalize(torch.randn(cb_vocab, cb_dim)))
+        self.register_buffer("CB", F.normalize(torch.randn(codebook_num_vars, codebook_dim)))
 
-    def forward(self, x):
+    def forward(self, x: torch.tensor) -> torch.tensor:
         x = F.normalize(x @ self.P)
         return vector_norm((self.CB.unsqueeze(1) - x.unsqueeze(1)), dim=-1).argmin(dim=1)