diff --git a/i6_models/parts/best_rq/mask.py b/i6_models/parts/best_rq/mask.py
index 2e88479b..62b09ae3 100644
--- a/i6_models/parts/best_rq/mask.py
+++ b/i6_models/parts/best_rq/mask.py
@@ -45,7 +45,7 @@ def forward(
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         ndim_batch, ndim_time, _ = tensor.size()
 
-        mask = torch.zeros((ndim_batch, ndim_time), dtype=torch.bool)
+        mask = torch.ones((ndim_batch, ndim_time), dtype=torch.bool)
 
         mask_idcs = []
         for i in range(ndim_batch):
@@ -67,7 +67,7 @@ def forward(
             mask_idc = np.random.choice(seq_len - min_len, num_mask, replace=False)
 
             for j in mask_idc:
-                mask[i, j : j + self.mask_length] = True
+                mask[i, j : j + self.mask_length] = False
 
         tensor[mask] = self.mask_emb.to(tensor.device)
 
diff --git a/i6_models/parts/best_rq/quantizer.py b/i6_models/parts/best_rq/quantizer.py
index 8633eb42..ce4b77d7 100644
--- a/i6_models/parts/best_rq/quantizer.py
+++ b/i6_models/parts/best_rq/quantizer.py
@@ -12,7 +12,7 @@ class RandomProjectionQuantizer(nn.Module):
     """
     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
+    code adapted from https://github.com/speechbrain/speechbrain/blob/7edb1397d8f92bb4fcaf17eb08e366e5b639ae88/speechbrain/nnet/quantisers.py#L127
     """
 
     def __init__(self, input_dim, codebook_dim, codebook_num_vars):
@@ -33,5 +33,5 @@ def __init__(self, input_dim, codebook_dim, codebook_num_vars):
         self.register_buffer("CB", F.normalize(torch.randn(codebook_num_vars, codebook_dim)))
 
     def forward(self, x: torch.tensor) -> torch.tensor:
-        x = F.normalize(x @ self.P)
+        x = F.normalize(x @ self.P, dim=2)
         return vector_norm((self.CB.unsqueeze(1) - x.unsqueeze(1)), dim=-1).argmin(dim=1)