diff --git a/wenet/ssl/bestrq/mask.py b/wenet/ssl/bestrq/mask.py
index f5e62eea0..c8905953e 100644
--- a/wenet/ssl/bestrq/mask.py
+++ b/wenet/ssl/bestrq/mask.py
@@ -1,53 +1,54 @@
-import torch
-
-
-def _sampler(pdf: torch.Tensor, num_samples: int,
-             device=torch.device('cpu')) -> torch.Tensor:
-    size = pdf.size()
-    z = -torch.log(torch.rand(size, device=device))
-    _, indices = torch.topk(pdf + z, num_samples)
-    return indices
-
-
-def compute_mask_indices(
-    size: torch.Size,
-    mask_prob: float,
-    mask_length: int,
-    min_masks: int = 0,
-    device=torch.device('cpu')) -> torch.Tensor:
-
-    assert len(size) == 2
-    batch_size, seq_length = size
-
-    # compute number of masked span in batch
-    num_masked_spans = mask_prob * float(seq_length) / float(
-        mask_length) + torch.rand(1)[0]
-    num_masked_spans = int(num_masked_spans)
-    num_masked_spans = max(num_masked_spans, min_masks)
-
-    # num_masked <= seq_length
-    if num_masked_spans * mask_length > seq_length:
-        num_masked_spans = seq_length // mask_length
-
-    pdf = torch.ones(batch_size, seq_length - (mask_length - 1), device=device)
-    mask_idxs = _sampler(pdf, num_masked_spans, device=device)
-
-    mask_idxs = mask_idxs.unsqueeze(-1).repeat(1, 1, mask_length).view(
-        batch_size,
-        num_masked_spans * mask_length)  # [B,num_masked_spans*mask_length]
-
-    offset = torch.arange(mask_length, device=device).view(1, 1, -1).repeat(
-        1, num_masked_spans, 1)  # [1,num_masked_spans,mask_length]
-    offset = offset.view(1, num_masked_spans * mask_length)
-
-    mask_idxs = mask_idxs + offset  # [B,num_masked_spans, mask_length]
-
-    ones = torch.ones(batch_size,
-                      seq_length,
-                      dtype=torch.bool,
-                      device=mask_idxs.device)
-    # masks to fill
-    full_mask = torch.zeros_like(ones,
-                                 dtype=torch.bool,
-                                 device=mask_idxs.device)
-    return torch.scatter(full_mask, dim=1, index=mask_idxs, src=ones)
+import torch
+
+
+def _sampler(pdf: torch.Tensor, num_samples: int,
+             device=torch.device('cpu')) -> torch.Tensor:
+    size = pdf.size()
+    z = -torch.log(torch.rand(size, device=device))
+    _, indices = torch.topk(pdf + z, num_samples)
+    return indices
+
+
+def compute_mask_indices(
+        size: torch.Size,
+        mask_prob: float,
+        mask_length: int,
+        min_masks: int = 0,
+        device=torch.device('cpu'),
+) -> torch.Tensor:
+
+    assert len(size) == 2
+    batch_size, seq_length = size
+
+    # compute number of masked span in batch
+    num_masked_spans = mask_prob * float(seq_length) / float(
+        mask_length) + torch.rand(1)[0]
+    num_masked_spans = int(num_masked_spans)
+    num_masked_spans = max(num_masked_spans, min_masks)
+
+    # num_masked <= seq_length
+    if num_masked_spans * mask_length > seq_length:
+        num_masked_spans = seq_length // mask_length
+
+    pdf = torch.ones(batch_size, seq_length - (mask_length - 1), device=device)
+    mask_idxs = _sampler(pdf, num_masked_spans, device=device)
+
+    mask_idxs = mask_idxs.unsqueeze(-1).repeat(1, 1, mask_length).view(
+        batch_size,
+        num_masked_spans * mask_length)  # [B,num_masked_spans*mask_length]
+
+    offset = torch.arange(mask_length, device=device).view(1, 1, -1).repeat(
+        1, num_masked_spans, 1)  # [1,num_masked_spans,mask_length]
+    offset = offset.view(1, num_masked_spans * mask_length)
+
+    mask_idxs = mask_idxs + offset  # [B,num_masked_spans, mask_length]
+
+    ones = torch.ones(batch_size,
+                      seq_length,
+                      dtype=torch.bool,
+                      device=mask_idxs.device)
+    # masks to fill
+    full_mask = torch.zeros_like(ones,
+                                 dtype=torch.bool,
+                                 device=mask_idxs.device)
+    return torch.scatter(full_mask, dim=1, index=mask_idxs, src=ones)