diff --git a/egs/libritts/CODEC/encodec/binary.py b/egs/libritts/CODEC/encodec/binary.py
index 0f552fb99..003bcfaf5 100644
--- a/egs/libritts/CODEC/encodec/binary.py
+++ b/egs/libritts/CODEC/encodec/binary.py
@@ -132,7 +132,7 @@ def test():
     for rep in range(4):
         length: int = torch.randint(10, 2_000, (1,)).item()
         bits: int = torch.randint(1, 16, (1,)).item()
-        tokens: List[int] = torch.randint(2 ** bits, (length,)).tolist()
+        tokens: List[int] = torch.randint(2**bits, (length,)).tolist()
         rebuilt: List[int] = []
         buf = io.BytesIO()
         packer = BitPacker(bits, buf)
diff --git a/egs/libritts/CODEC/encodec/loss.py b/egs/libritts/CODEC/encodec/loss.py
index 8675841b2..9cf1d42d2 100644
--- a/egs/libritts/CODEC/encodec/loss.py
+++ b/egs/libritts/CODEC/encodec/loss.py
@@ -235,7 +235,7 @@ class MelSpectrogramReconstructionLoss(torch.nn.Module):
         super().__init__()
         self.wav_to_specs = []
         for i in range(5, 12):
-            s = 2 ** i
+            s = 2**i
             self.wav_to_specs.append(
                 MelSpectrogram(
                     sample_rate=sampling_rate,
diff --git a/egs/libritts/CODEC/encodec/modules/seanet.py b/egs/libritts/CODEC/encodec/modules/seanet.py
index 38f2f8728..76999b298 100644
--- a/egs/libritts/CODEC/encodec/modules/seanet.py
+++ b/egs/libritts/CODEC/encodec/modules/seanet.py
@@ -161,7 +161,7 @@ class SEANetEncoder(nn.Module):
                     SEANetResnetBlock(
                         mult * n_filters,
                         kernel_sizes=[residual_kernel_size, 1],
-                        dilations=[dilation_base ** j, 1],
+                        dilations=[dilation_base**j, 1],
                         norm=norm,
                         norm_params=norm_params,
                         activation=activation,
@@ -311,7 +311,7 @@ class SEANetDecoder(nn.Module):
                     SEANetResnetBlock(
                         mult * n_filters // 2,
                         kernel_sizes=[residual_kernel_size, 1],
-                        dilations=[dilation_base ** j, 1],
+                        dilations=[dilation_base**j, 1],
                         activation=activation,
                         activation_params=activation_params,
                         norm=norm,
diff --git a/egs/libritts/CODEC/encodec/quantization/ac.py b/egs/libritts/CODEC/encodec/quantization/ac.py
index 99b62d14b..8d8a770ca 100644
--- a/egs/libritts/CODEC/encodec/quantization/ac.py
+++ b/egs/libritts/CODEC/encodec/quantization/ac.py
@@ -41,7 +41,7 @@ def build_stable_quantized_cdf(
     if roundoff:
         pdf = (pdf / roundoff).floor() * roundoff
     # interpolate with uniform distribution to achieve desired minimum probability.
-    total_range = 2 ** total_range_bits
+    total_range = 2**total_range_bits
     cardinality = len(pdf)
     alpha = min_range * cardinality / total_range
     assert alpha <= 1, "you must reduce min_range"
@@ -51,7 +51,7 @@ def build_stable_quantized_cdf(
     if min_range < 2:
         raise ValueError("min_range must be at least 2.")
     if check:
-        assert quantized_cdf[-1] <= 2 ** total_range_bits, quantized_cdf[-1]
+        assert quantized_cdf[-1] <= 2**total_range_bits, quantized_cdf[-1]
         if (
             (quantized_cdf[1:] - quantized_cdf[:-1]) < min_range
         ).any() or quantized_cdf[0] < min_range:
@@ -142,7 +142,7 @@ class ArithmeticCoder:
             quantized_cdf (Tensor): use `build_stable_quantized_cdf`
                 to build this from your pdf estimate.
         """
-        while self.delta < 2 ** self.total_range_bits:
+        while self.delta < 2**self.total_range_bits:
             self.low *= 2
             self.high = self.high * 2 + 1
             self.max_bit += 1
@@ -150,10 +150,10 @@ class ArithmeticCoder:
         range_low = 0 if symbol == 0 else quantized_cdf[symbol - 1].item()
         range_high = quantized_cdf[symbol].item() - 1
         effective_low = int(
-            math.ceil(range_low * (self.delta / (2 ** self.total_range_bits)))
+            math.ceil(range_low * (self.delta / (2**self.total_range_bits)))
         )
         effective_high = int(
-            math.floor(range_high * (self.delta / (2 ** self.total_range_bits)))
+            math.floor(range_high * (self.delta / (2**self.total_range_bits)))
         )
         assert self.low <= self.high
         self.high = self.low + effective_high
@@ -238,7 +238,7 @@ class ArithmeticDecoder:
                 to build this from your pdf estimate. This must be **exatly**
                 the same cdf as the one used at encoding time.
         """
-        while self.delta < 2 ** self.total_range_bits:
+        while self.delta < 2**self.total_range_bits:
             bit = self.unpacker.pull()
             if bit is None:
                 return None
@@ -255,10 +255,10 @@ class ArithmeticDecoder:
             range_low = quantized_cdf[mid - 1].item() if mid > 0 else 0
             range_high = quantized_cdf[mid].item() - 1
             effective_low = int(
-                math.ceil(range_low * (self.delta / (2 ** self.total_range_bits)))
+                math.ceil(range_low * (self.delta / (2**self.total_range_bits)))
             )
             effective_high = int(
-                math.floor(range_high * (self.delta / (2 ** self.total_range_bits)))
+                math.floor(range_high * (self.delta / (2**self.total_range_bits)))
             )
             low = effective_low + self.low
             high = effective_high + self.low
diff --git a/egs/libritts/CODEC/encodec/quantization/core_vq.py b/egs/libritts/CODEC/encodec/quantization/core_vq.py
index 0b342f2b0..4719e20f7 100644
--- a/egs/libritts/CODEC/encodec/quantization/core_vq.py
+++ b/egs/libritts/CODEC/encodec/quantization/core_vq.py
@@ -76,7 +76,7 @@ def kmeans(samples, num_clusters: int, num_iters: int = 10):
 
     for _ in range(num_iters):
         diffs = rearrange(samples, "n d -> n () d") - rearrange(means, "c d -> () c d")
-        dists = -(diffs ** 2).sum(dim=-1)
+        dists = -(diffs**2).sum(dim=-1)
 
         buckets = dists.max(dim=-1).indices
         bins = torch.bincount(buckets, minlength=num_clusters)