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chore(format): run black on dev (#583)

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Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
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github-actions[bot]
2024-07-18 18:32:24 +09:00
committed by GitHub
parent 9f0b7a048d
commit 20402c367b
1 changed files with 17 additions and 10 deletions
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ChatTTS/model/dvae.py
+17 -10
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@@ -95,11 +95,14 @@ class GFSQ(nn.Module):
feat = self.quantizer.get_output_from_indices(x)
return feat.transpose_(1, 2) if self.transpose else feat
def __call__(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
def __call__(
self, x: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
return super().__call__(x)
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
def forward(
self, x: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
if self.transpose:
x.transpose_(1, 2)
feat, ind = self.quantizer(x)
@@ -177,7 +180,7 @@ class MelSpectrogramFeatures(torch.nn.Module):
n_fft=1024,
hop_length=256,
n_mels=100,
padding: Literal["center", "same"]="center",
padding: Literal["center", "same"] = "center",
):
super().__init__()
if padding not in ["center", "same"]:
@@ -221,10 +224,10 @@ class DVAE(nn.Module):
if encoder_config is not None:
self.downsample_conv = nn.Sequential(
nn.Conv1d(100, dim, 3, 1, 1),
nn.GELU(),
nn.Conv1d(dim, dim, 4, 2, 1),
nn.GELU()
nn.Conv1d(100, dim, 3, 1, 1),
nn.GELU(),
nn.Conv1d(dim, dim, 4, 2, 1),
nn.GELU(),
)
self.preprocessor_mel = MelSpectrogramFeatures()
self.encoder: Optional[DVAEDecoder] = DVAEDecoder(**encoder_config)
@@ -241,11 +244,15 @@ class DVAE(nn.Module):
self.coef.cpu().numpy().astype(np.float32).tobytes()
)
def __call__(self, inp: torch.Tensor, mode: Literal["encode", "decode"]="decode") -> torch.Tensor:
def __call__(
self, inp: torch.Tensor, mode: Literal["encode", "decode"] = "decode"
) -> torch.Tensor:
return super().__call__(inp, mode)
@torch.inference_mode()
def forward(self, inp: torch.Tensor, mode: Literal["encode", "decode"]="decode") -> torch.Tensor:
def forward(
self, inp: torch.Tensor, mode: Literal["encode", "decode"] = "decode"
) -> torch.Tensor:
if mode == "encode" and hasattr(self, "encoder") and self.vq_layer is not None:
mel = self.preprocessor_mel(inp)
x: torch.Tensor = self.downsample_conv(mel / self.coef)