From 20402c367b7db0d526b910f71f0da57177336536 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Thu, 18 Jul 2024 18:32:24 +0900 Subject: [PATCH] chore(format): run black on dev (#583) Co-authored-by: github-actions[bot] --- ChatTTS/model/dvae.py | 27 +++++++++++++++++---------- 1 file changed, 17 insertions(+), 10 deletions(-) diff --git a/ChatTTS/model/dvae.py b/ChatTTS/model/dvae.py index 707465c..a5cb5a0 100644 --- a/ChatTTS/model/dvae.py +++ b/ChatTTS/model/dvae.py @@ -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)