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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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38
GPT_SoVITS/module/core_vq.py
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@@ -30,6 +30,7 @@
# SOFTWARE.
"""Core vector quantization implementation."""
import typing as tp
from einops import rearrange, repeat
@@ -121,9 +122,7 @@ class EuclideanCodebook(nn.Module):
):
super().__init__()
self.decay = decay
init_fn: tp.Union[tp.Callable[..., torch.Tensor], tp.Any] = (
uniform_init if not kmeans_init else torch.zeros
)
init_fn: tp.Union[tp.Callable[..., torch.Tensor], tp.Any] = uniform_init if not kmeans_init else torch.zeros
embed = init_fn(codebook_size, dim)
self.codebook_size = codebook_size
@@ -151,9 +150,7 @@ class EuclideanCodebook(nn.Module):
# broadcast_tensors(self.buffers())
def replace_(self, samples, mask):
modified_codebook = torch.where(
mask[..., None], sample_vectors(samples, self.codebook_size), self.embed
)
modified_codebook = torch.where(mask[..., None], sample_vectors(samples, self.codebook_size), self.embed)
self.embed.data.copy_(modified_codebook)
def expire_codes_(self, batch_samples):
@@ -174,11 +171,7 @@ class EuclideanCodebook(nn.Module):
def quantize(self, x):
embed = self.embed.t()
dist = -(
x.pow(2).sum(1, keepdim=True)
- 2 * x @ embed
+ embed.pow(2).sum(0, keepdim=True)
)
dist = -(x.pow(2).sum(1, keepdim=True) - 2 * x @ embed + embed.pow(2).sum(0, keepdim=True))
embed_ind = dist.max(dim=-1).indices
return embed_ind
@@ -222,8 +215,7 @@ class EuclideanCodebook(nn.Module):
embed_sum = x.t() @ embed_onehot
ema_inplace(self.embed_avg, embed_sum.t(), self.decay)
cluster_size = (
laplace_smoothing(self.cluster_size, self.codebook_size, self.epsilon)
* self.cluster_size.sum()
laplace_smoothing(self.cluster_size, self.codebook_size, self.epsilon) * self.cluster_size.sum()
)
embed_normalized = self.embed_avg / cluster_size.unsqueeze(1)
self.embed.data.copy_(embed_normalized)
@@ -264,12 +256,8 @@ class VectorQuantization(nn.Module):
_codebook_dim: int = default(codebook_dim, dim)
requires_projection = _codebook_dim != dim
self.project_in = (
nn.Linear(dim, _codebook_dim) if requires_projection else nn.Identity()
)
self.project_out = (
nn.Linear(_codebook_dim, dim) if requires_projection else nn.Identity()
)
self.project_in = nn.Linear(dim, _codebook_dim) if requires_projection else nn.Identity()
self.project_out = nn.Linear(_codebook_dim, dim) if requires_projection else nn.Identity()
self.epsilon = epsilon
self.commitment_weight = commitment_weight
@@ -330,13 +318,9 @@ class ResidualVectorQuantization(nn.Module):
def __init__(self, *, num_quantizers, **kwargs):
super().__init__()
self.layers = nn.ModuleList(
[VectorQuantization(**kwargs) for _ in range(num_quantizers)]
)
self.layers = nn.ModuleList([VectorQuantization(**kwargs) for _ in range(num_quantizers)])
def forward(
self, x, n_q: tp.Optional[int] = None, layers: tp.Optional[list] = None
):
def forward(self, x, n_q: tp.Optional[int] = None, layers: tp.Optional[list] = None):
quantized_out = 0.0
residual = x
@@ -359,9 +343,7 @@ class ResidualVectorQuantization(nn.Module):
out_losses, out_indices = map(torch.stack, (all_losses, all_indices))
return quantized_out, out_indices, out_losses, out_quantized
def encode(
self, x: torch.Tensor, n_q: tp.Optional[int] = None, st: tp.Optional[int] = None
) -> torch.Tensor:
def encode(self, x: torch.Tensor, n_q: tp.Optional[int] = None, st: tp.Optional[int] = None) -> torch.Tensor:
residual = x
all_indices = []
n_q = n_q or len(self.layers)