Introduce Docker and Windows CI Workflow, Pre-commit Formatting, and Language Resource Auto-Download (#2351)

* Docker Auto-Build Workflow

* Rename

* Update

* Fix Bugs

* Disable Progress Bar When workflows triggered

* Fix Wget

* Fix Bugs

* Fix Bugs

* Update Wget

* Update Workflows

* Accelerate Docker Image Building

* Fix Install.sh

* Add Skip-Check For Action Runner

* Fix Dockerfile

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* Delete File in Runner

* Add Sort

* Delete More Files

* Delete More

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* Add Pre-Commit Hook
Update Docker

* Add Code Spell Check

* [pre-commit.ci] trigger

* [pre-commit.ci] trigger

* [pre-commit.ci] trigger

* Fix Bugs

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* Disable Progress Bar and Logs while using GitHub Actions

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* Fix Bugs

* update conda

* fix bugs

* Fix Bugs

* fix bugs

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* Quiet Installation

* fix bugs

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* fix bug

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* Fix pre-commit.ci and Docker

* fix bugs

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* Update Docker & Pre-Commit

* fix  bugs

* Update Req

* Update Req

* Update OpenCC

* update precommit

* .

* Update .pre-commit-config.yaml

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update Docs and fix bugs

* Fix \

* Fix MacOS

* .

* test

* .

* Add Tag Alias

* .

* fix bugs

* fix bugs

* make image smaller

* update pre-commit config

* .

* .

* fix bugs

* use miniconda

* Fix Wrong Path

* .

* debug

* debug

* revert

* Fix Bugs

* Update Docs, Add Dict Auto Download in install.sh

* update docker_build

* Update Docs for Install.sh

* update docker docs about architecture

* Add Xcode-Commandline-Tool Installation

* Update Docs

1. Add Missing VC17
2. Modufied the Order of FFmpeg Installation and Requirements Installation
3. Remove Duplicate FFmpeg

* Fix Wrong Cuda Version

* Update TESTED ENV

* Add PYTHONNOUSERSITE(-s)

* Fix Wrapper

* Update install.sh For Robustness

* Ignore .git

* Preload CUDNN For Ctranslate2

* Remove Gradio Warnings

* Update Colab

* Fix OpenCC Problems

* Update Win DLL Strategy

* Fix Onnxruntime-gpu NVRTC Error

* Fix Path Problems

* Add Windows Packages Workflow

* WIP

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* WIP

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* WIP

* WIP

* WIP

* WIP

* WIP

* WIP

* WIP

* WIP

* WIP

* WIP

* WIP

* WIP

* Fix Path

* Fix Path

* Enable Logging

* Set 7-Zip compression level to maximum (-mx=9)

* Use Multithread in ONNX Session

* Fix Tag Bugs

* Add Time

* Add Time

* Add Time

* Compress More

* Copy DLL to Solve VC Runtime DLL Missing Issues

* Expose FFmpeg Errors, Copy Only Part of Visual C++ Runtime

* Update build_windows_packages.ps1

* Update build_windows_packages.ps1

* Update build_windows_packages.ps1

* Update build_windows_packages.ps1

* WIP

* WIP

* WIP

* Update build_windows_packages.ps1

* Update install.sh

* Update build_windows_packages.ps1

* Update docker-publish.yaml

* Update install.sh

* Update Dockerfile

* Update docker_build.sh

* Update miniconda_install.sh

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Update Colab-WebUI.ipynb

* Update Colab-Inference.ipynb

* Update docker-compose.yaml

* 更新 build_windows_packages.ps1

* Update install.sh

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

GPT_SoVITS/module/models.py

@@ -414,7 +414,8 @@ class Generator(torch.nn.Module):
         upsample_rates,
         upsample_initial_channel,
         upsample_kernel_sizes,
-        gin_channels=0,is_bias=False,
+        gin_channels=0,
+        is_bias=False,
     ):
         super(Generator, self).__init__()
         self.num_kernels = len(resblock_kernel_sizes)
@@ -1173,7 +1174,7 @@ class SynthesizerTrnV3(nn.Module):
                 quantized = F.interpolate(quantized, scale_factor=2, mode="nearest")  ##BCT
                 x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge)
         fea = self.bridge(x)
-        fea = F.interpolate(fea, scale_factor=(1.875 if self.version=="v3"else 2), mode="nearest")  ##BCT
+        fea = F.interpolate(fea, scale_factor=(1.875 if self.version == "v3" else 2), mode="nearest")  ##BCT
         fea, y_mask_ = self.wns1(
             fea, mel_lengths, ge
         )  ##If the 1-minute fine-tuning works fine, no need to manually adjust the learning rate.
@@ -1196,9 +1197,9 @@ class SynthesizerTrnV3(nn.Module):
             ge = self.ref_enc(refer[:, :704] * refer_mask, refer_mask)
         y_lengths = torch.LongTensor([int(codes.size(2) * 2)]).to(codes.device)
         if speed == 1:
-            sizee = int(codes.size(2) * (3.875 if self.version=="v3"else 4))
+            sizee = int(codes.size(2) * (3.875 if self.version == "v3" else 4))
         else:
-            sizee = int(codes.size(2) * (3.875 if self.version=="v3"else 4) / speed) + 1
+            sizee = int(codes.size(2) * (3.875 if self.version == "v3" else 4) / speed) + 1
         y_lengths1 = torch.LongTensor([sizee]).to(codes.device)
         text_lengths = torch.LongTensor([text.size(-1)]).to(text.device)
 
@@ -1207,7 +1208,7 @@ class SynthesizerTrnV3(nn.Module):
             quantized = F.interpolate(quantized, scale_factor=2, mode="nearest")  ##BCT
         x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, ge, speed)
         fea = self.bridge(x)
-        fea = F.interpolate(fea, scale_factor=(1.875 if self.version=="v3"else 2), mode="nearest")  ##BCT
+        fea = F.interpolate(fea, scale_factor=(1.875 if self.version == "v3" else 2), mode="nearest")  ##BCT
         ####more wn paramter to learn mel
         fea, y_mask_ = self.wns1(fea, y_lengths1, ge)
         return fea, ge