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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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27
GPT_SoVITS/AR/modules/transformer_onnx.py
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@@ -42,12 +42,8 @@ class LayerNorm(nn.Module):
self.eps = eps
self.elementwise_affine = elementwise_affine
if self.elementwise_affine:
self.weight = nn.Parameter(
torch.empty(self.normalized_shape, **factory_kwargs)
)
self.bias = nn.Parameter(
torch.empty(self.normalized_shape, **factory_kwargs)
)
self.weight = nn.Parameter(torch.empty(self.normalized_shape, **factory_kwargs))
self.bias = nn.Parameter(torch.empty(self.normalized_shape, **factory_kwargs))
else:
self.register_parameter("weight", None)
self.register_parameter("bias", None)
@@ -74,15 +70,10 @@ class LayerNorm(nn.Module):
)
assert embedding is None
return F.layer_norm(
input, self.normalized_shape, self.weight, self.bias, self.eps
)
return F.layer_norm(input, self.normalized_shape, self.weight, self.bias, self.eps)
def extra_repr(self) -> str:
return (
"{normalized_shape}, eps={eps}, "
"elementwise_affine={elementwise_affine}".format(**self.__dict__)
)
return "{normalized_shape}, eps={eps}, elementwise_affine={elementwise_affine}".format(**self.__dict__)
class IdentityNorm(nn.Module):
@@ -121,6 +112,7 @@ class TransformerEncoder(nn.Module):
>>> src = torch.rand(10, 32, 512)
>>> out = transformer_encoder(src)
"""
__constants__ = ["norm"]
def __init__(self, encoder_layer, num_layers, norm=None):
@@ -154,6 +146,7 @@ class TransformerEncoder(nn.Module):
class TransformerEncoderLayer(nn.Module):
__constants__ = ["batch_first", "norm_first"]
def __init__(
self,
d_model: int,
@@ -184,13 +177,9 @@ class TransformerEncoderLayer(nn.Module):
linear2_cls=linear2_self_attention_cls,
**factory_kwargs,
)
self.linear1 = linear1_feedforward_cls(
d_model, dim_feedforward, **factory_kwargs
)
self.linear1 = linear1_feedforward_cls(d_model, dim_feedforward, **factory_kwargs)
self.dropout = nn.Dropout(dropout)
self.linear2 = linear2_feedforward_cls(
dim_feedforward, d_model, **factory_kwargs
)
self.linear2 = linear2_feedforward_cls(dim_feedforward, d_model, **factory_kwargs)
self.norm_first = norm_first
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)