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fix lint

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lyuxiang.lx
2024-09-05 16:15:34 +08:00
parent eeebc45313
commit 90433f5373
35 changed files with 189 additions and 122 deletions
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11
cosyvoice/hifigan/generator.py
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@@ -38,6 +38,8 @@ This code is modified from https://github.com/jik876/hifi-gan
https://github.com/NVIDIA/BigVGAN
"""
class ResBlock(torch.nn.Module):
"""Residual block module in HiFiGAN/BigVGAN."""
def __init__(
@@ -100,6 +102,7 @@ class ResBlock(torch.nn.Module):
remove_weight_norm(self.convs1[idx])
remove_weight_norm(self.convs2[idx])
class SineGen(torch.nn.Module):
""" Definition of sine generator
SineGen(samp_rate, harmonic_num = 0,
@@ -286,8 +289,7 @@ class HiFTGenerator(nn.Module):
self.source_resblocks = nn.ModuleList()
downsample_rates = [1] + upsample_rates[::-1][:-1]
downsample_cum_rates = np.cumprod(downsample_rates)
for i, (u, k, d) in enumerate(zip(downsample_cum_rates[::-1], source_resblock_kernel_sizes,
source_resblock_dilation_sizes)):
for i, (u, k, d) in enumerate(zip(downsample_cum_rates[::-1], source_resblock_kernel_sizes, source_resblock_dilation_sizes)):
if u == 1:
self.source_downs.append(
Conv1d(istft_params["n_fft"] + 2, base_channels // (2 ** (i + 1)), 1, 1)
@@ -304,7 +306,7 @@ class HiFTGenerator(nn.Module):
self.resblocks = nn.ModuleList()
for i in range(len(self.ups)):
ch = base_channels // (2**(i + 1))
for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
for _, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
self.resblocks.append(ResBlock(ch, k, d))
self.conv_post = weight_norm(Conv1d(ch, istft_params["n_fft"] + 2, 7, 1, padding=3))
@@ -332,7 +334,8 @@ class HiFTGenerator(nn.Module):
magnitude = torch.clip(magnitude, max=1e2)
real = magnitude * torch.cos(phase)
img = magnitude * torch.sin(phase)
inverse_transform = torch.istft(torch.complex(real, img), self.istft_params["n_fft"], self.istft_params["hop_len"], self.istft_params["n_fft"], window=self.stft_window.to(magnitude.device))
inverse_transform = torch.istft(torch.complex(real, img), self.istft_params["n_fft"], self.istft_params["hop_len"],
self.istft_params["n_fft"], window=self.stft_window.to(magnitude.device))
return inverse_transform
def forward(self, x: torch.Tensor, cache_source: torch.Tensor = torch.zeros(1, 1, 0)) -> torch.Tensor: