diff --git a/kokoro/istftnet.py b/kokoro/istftnet.py
index 929c478..01289b2 100644
--- a/kokoro/istftnet.py
+++ b/kokoro/istftnet.py
@@ -1,4 +1,5 @@
 # https://github.com/yl4579/StyleTTS2/blob/main/Modules/istftnet.py
+import math
 from scipy.signal import get_window
 from torch.nn.utils import weight_norm
 import numpy as np
@@ -259,9 +260,9 @@ class Generator(nn.Module):
         self.num_upsamples = len(upsample_rates)
         self.m_source = SourceModuleHnNSF(
                     sampling_rate=24000,
-                    upsample_scale=np.prod(upsample_rates) * gen_istft_hop_size,
+                    upsample_scale=math.prod(upsample_rates) * gen_istft_hop_size,
                     harmonic_num=8, voiced_threshod=10)
-        self.f0_upsamp = nn.Upsample(scale_factor=np.prod(upsample_rates) * gen_istft_hop_size)
+        self.f0_upsamp = nn.Upsample(scale_factor=math.prod(upsample_rates) * gen_istft_hop_size)
         self.noise_convs = nn.ModuleList()
         self.noise_res = nn.ModuleList()
         self.ups = nn.ModuleList()
@@ -276,7 +277,7 @@ class Generator(nn.Module):
                 self.resblocks.append(AdaINResBlock1(ch, k, d, style_dim))
             c_cur = upsample_initial_channel // (2 ** (i + 1))
             if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1:])
+                stride_f0 = math.prod(upsample_rates[i + 1:])
                 self.noise_convs.append(nn.Conv1d(
                     gen_istft_n_fft + 2, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=(stride_f0+1) // 2))
                 self.noise_res.append(AdaINResBlock1(c_cur, 7, [1,3,5], style_dim))