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Refactor: Format Code with Ruff and Update Deprecated G2PW Link (#2255)

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* ruff check --fix

* ruff format --line-length 120 --target-version py39

* Change the link for G2PW Model

* update pytorch version and colab
This commit is contained in:
XXXXRT666
2025-04-07 09:42:47 +01:00
committed by GitHub
parent 9da7e17efe
commit 53cac93589
132 changed files with 8185 additions and 6648 deletions
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52
GPT_SoVITS/module/modules.py
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@@ -52,11 +52,7 @@ class ConvReluNorm(nn.Module):
self.conv_layers = nn.ModuleList()
self.norm_layers = nn.ModuleList()
self.conv_layers.append(
nn.Conv1d(
in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
)
)
self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
self.norm_layers.append(LayerNorm(hidden_channels))
self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
for _ in range(n_layers - 1):
@@ -156,9 +152,7 @@ class WN(torch.nn.Module):
self.drop = nn.Dropout(p_dropout)
if gin_channels != 0:
cond_layer = torch.nn.Conv1d(
gin_channels, 2 * hidden_channels * n_layers, 1
)
cond_layer = torch.nn.Conv1d(gin_channels, 2 * hidden_channels * n_layers, 1)
self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
for i in range(n_layers):
@@ -479,9 +473,7 @@ class ConvFlow(nn.Module):
self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
self.proj = nn.Conv1d(
filter_channels, self.half_channels * (num_bins * 3 - 1), 1
)
self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
self.proj.weight.data.zero_()
self.proj.bias.data.zero_()
@@ -495,9 +487,7 @@ class ConvFlow(nn.Module):
h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
self.filter_channels
)
unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(self.filter_channels)
unnormalized_derivatives = h[..., 2 * self.num_bins :]
x1, logabsdet = piecewise_rational_quadratic_transform(
@@ -616,9 +606,7 @@ class MultiHeadAttention(nn.Module):
self.w_ks = nn.Linear(d_model, n_head * d_k)
self.w_vs = nn.Linear(d_model, n_head * d_v)
self.attention = ScaledDotProductAttention(
temperature=np.power(d_model, 0.5), dropout=dropout
)
self.attention = ScaledDotProductAttention(temperature=np.power(d_model, 0.5), dropout=dropout)
self.fc = nn.Linear(n_head * d_v, d_model)
self.dropout = nn.Dropout(dropout)
@@ -649,9 +637,7 @@ class MultiHeadAttention(nn.Module):
output, attn = self.attention(q, k, v, mask=slf_mask)
output = output.view(n_head, sz_b, len_x, d_v)
output = (
output.permute(1, 2, 0, 3).contiguous().view(sz_b, len_x, -1)
) # b x lq x (n*dv)
output = output.permute(1, 2, 0, 3).contiguous().view(sz_b, len_x, -1) # b x lq x (n*dv)
output = self.fc(output)
@@ -741,9 +727,7 @@ class MelStyleEncoder(nn.Module):
if mask is not None:
mask = (mask.int() == 0).squeeze(1)
max_len = x.shape[1]
slf_attn_mask = (
mask.unsqueeze(1).expand(-1, max_len, -1) if mask is not None else None
)
slf_attn_mask = mask.unsqueeze(1).expand(-1, max_len, -1) if mask is not None else None
# spectral
x = self.spectral(x)
@@ -785,9 +769,7 @@ class MelStyleEncoderVAE(nn.Module):
mu = self.fc1(enc_out)
logvar = self.fc2(enc_out)
posterior = D.Normal(mu, torch.exp(logvar))
kl_divergence = D.kl_divergence(
posterior, D.Normal(torch.zeros_like(mu), torch.ones_like(logvar))
)
kl_divergence = D.kl_divergence(posterior, D.Normal(torch.zeros_like(mu), torch.ones_like(logvar)))
loss_kl = kl_divergence.mean()
z = posterior.rsample()
@@ -825,9 +807,7 @@ class ActNorm(nn.Module):
def forward(self, x, x_mask=None, g=None, reverse=False, **kwargs):
if x_mask is None:
x_mask = torch.ones(x.size(0), 1, x.size(2)).to(
device=x.device, dtype=x.dtype
)
x_mask = torch.ones(x.size(0), 1, x.size(2)).to(device=x.device, dtype=x.dtype)
x_len = torch.sum(x_mask, [1, 2])
if not self.initialized:
self.initialize(x, x_mask)
@@ -856,9 +836,7 @@ class ActNorm(nn.Module):
v = m_sq - (m**2)
logs = 0.5 * torch.log(torch.clamp_min(v, 1e-6))
bias_init = (
(-m * torch.exp(-logs)).view(*self.bias.shape).to(dtype=self.bias.dtype)
)
bias_init = (-m * torch.exp(-logs)).view(*self.bias.shape).to(dtype=self.bias.dtype)
logs_init = (-logs).view(*self.logs.shape).to(dtype=self.logs.dtype)
self.bias.data.copy_(bias_init)
@@ -873,9 +851,7 @@ class InvConvNear(nn.Module):
self.n_split = n_split
self.no_jacobian = no_jacobian
w_init = torch.linalg.qr(
torch.FloatTensor(self.n_split, self.n_split).normal_()
)[0]
w_init = torch.linalg.qr(torch.FloatTensor(self.n_split, self.n_split).normal_())[0]
if torch.det(w_init) < 0:
w_init[:, 0] = -1 * w_init[:, 0]
self.weight = nn.Parameter(w_init)
@@ -890,11 +866,7 @@ class InvConvNear(nn.Module):
x_len = torch.sum(x_mask, [1, 2])
x = x.view(b, 2, c // self.n_split, self.n_split // 2, t)
x = (
x.permute(0, 1, 3, 2, 4)
.contiguous()
.view(b, self.n_split, c // self.n_split, t)
)
x = x.permute(0, 1, 3, 2, 4).contiguous().view(b, self.n_split, c // self.n_split, t)
if reverse:
if hasattr(self, "weight_inv"):