Fix bugs in install.sh, reduce log noise, and improve error reporting (#2464)

* Update Install.sh

* Format Code

* Delete dev null

* Update README, Support Dark Mode in CSS/JS

GPT_SoVITS/AR/models/t2s_lightning_module.py

@@ -28,7 +28,8 @@ class Text2SemanticLightningModule(LightningModule):
                 self.load_state_dict(
                     torch.load(
                         pretrained_s1,
-                        map_location="cpu", weights_only=False,
+                        map_location="cpu",
+                        weights_only=False,
                     )["weight"],
                 )
             )

GPT_SoVITS/TTS_infer_pack/TTS.py

@@ -32,19 +32,21 @@ from transformers import AutoModelForMaskedLM, AutoTokenizer
 
 from tools.audio_sr import AP_BWE
 from tools.i18n.i18n import I18nAuto, scan_language_list
-from tools.my_utils import load_audio
 from TTS_infer_pack.text_segmentation_method import splits
 from TTS_infer_pack.TextPreprocessor import TextPreprocessor
 from sv import SV
+
 resample_transform_dict = {}
+
+
 def resample(audio_tensor, sr0, sr1, device):
     global resample_transform_dict
     key = "%s-%s-%s" % (sr0, sr1, str(device))
     if key not in resample_transform_dict:
-        resample_transform_dict[key] = torchaudio.transforms.Resample(
-            sr0, sr1
-        ).to(device)
+        resample_transform_dict[key] = torchaudio.transforms.Resample(sr0, sr1).to(device)
     return resample_transform_dict[key](audio_tensor)
+
+
 language = os.environ.get("language", "Auto")
 language = sys.argv[-1] if sys.argv[-1] in scan_language_list() else language
 i18n = I18nAuto(language=language)
@@ -111,6 +113,7 @@ def speed_change(input_audio: np.ndarray, speed: float, sr: int):
 
     return processed_audio
 
+
 class DictToAttrRecursive(dict):
     def __init__(self, input_dict):
         super().__init__(input_dict)
@@ -632,7 +635,9 @@ class TTS:
             )
             self.vocoder.remove_weight_norm()
             state_dict_g = torch.load(
-                "%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,), map_location="cpu", weights_only=False
+                "%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,),
+                map_location="cpu",
+                weights_only=False,
             )
             print("loading vocoder", self.vocoder.load_state_dict(state_dict_g))
 
@@ -752,11 +757,13 @@ class TTS:
 
         if raw_sr != self.configs.sampling_rate:
             audio = raw_audio.to(self.configs.device)
-            if (audio.shape[0] == 2): audio = audio.mean(0).unsqueeze(0)
+            if audio.shape[0] == 2:
+                audio = audio.mean(0).unsqueeze(0)
             audio = resample(audio, raw_sr, self.configs.sampling_rate, self.configs.device)
         else:
             audio = raw_audio.to(self.configs.device)
-            if (audio.shape[0] == 2): audio = audio.mean(0).unsqueeze(0)
+            if audio.shape[0] == 2:
+                audio = audio.mean(0).unsqueeze(0)
 
         maxx = audio.abs().max()
         if maxx > 1:
@@ -775,7 +782,8 @@ class TTS:
             audio = resample(audio, self.configs.sampling_rate, 16000, self.configs.device)
             if self.configs.is_half:
                 audio = audio.half()
-        else:audio=None
+        else:
+            audio = None
         return spec, audio
 
     def _set_prompt_semantic(self, ref_wav_path: str):
@@ -1073,7 +1081,10 @@ class TTS:
 
         ###### setting reference audio and prompt text preprocessing ########
         t0 = time.perf_counter()
-        if (ref_audio_path is not None) and (ref_audio_path != self.prompt_cache["ref_audio_path"] or (self.is_v2pro and self.prompt_cache["refer_spec"][0][1] is None)):
+        if (ref_audio_path is not None) and (
+            ref_audio_path != self.prompt_cache["ref_audio_path"]
+            or (self.is_v2pro and self.prompt_cache["refer_spec"][0][1] is None)
+        ):
             if not os.path.exists(ref_audio_path):
                 raise ValueError(f"{ref_audio_path} not exists")
             self.set_ref_audio(ref_audio_path)
@@ -1212,7 +1223,8 @@ class TTS:
                 t_34 += t4 - t3
 
                 refer_audio_spec = []
-                if self.is_v2pro:sv_emb=[]
+                if self.is_v2pro:
+                    sv_emb = []
                 for spec, audio_tensor in self.prompt_cache["refer_spec"]:
                     spec = spec.to(dtype=self.precision, device=self.configs.device)
                     refer_audio_spec.append(spec)
@@ -1250,9 +1262,13 @@ class TTS:
                         )
                         _batch_phones = torch.cat(batch_phones).unsqueeze(0).to(self.configs.device)
                         if self.is_v2pro != True:
-                            _batch_audio_fragment = self.vits_model.decode(all_pred_semantic, _batch_phones, refer_audio_spec, speed=speed_factor).detach()[0, 0, :]
+                            _batch_audio_fragment = self.vits_model.decode(
+                                all_pred_semantic, _batch_phones, refer_audio_spec, speed=speed_factor
+                            ).detach()[0, 0, :]
                         else:
-                            _batch_audio_fragment = self.vits_model.decode(all_pred_semantic, _batch_phones, refer_audio_spec, speed=speed_factor,sv_emb=sv_emb).detach()[0, 0, :]
+                            _batch_audio_fragment = self.vits_model.decode(
+                                all_pred_semantic, _batch_phones, refer_audio_spec, speed=speed_factor, sv_emb=sv_emb
+                            ).detach()[0, 0, :]
                         audio_frag_end_idx.insert(0, 0)
                         batch_audio_fragment = [
                             _batch_audio_fragment[audio_frag_end_idx[i - 1] : audio_frag_end_idx[i]]
@@ -1266,9 +1282,13 @@ class TTS:
                                 pred_semantic_list[i][-idx:].unsqueeze(0).unsqueeze(0)
                             )  # .unsqueeze(0)#mq要多unsqueeze一次
                             if self.is_v2pro != True:
-                                audio_fragment = self.vits_model.decode(_pred_semantic, phones, refer_audio_spec, speed=speed_factor).detach()[0, 0, :]
+                                audio_fragment = self.vits_model.decode(
+                                    _pred_semantic, phones, refer_audio_spec, speed=speed_factor
+                                ).detach()[0, 0, :]
                             else:
-                                audio_fragment = self.vits_model.decode(_pred_semantic, phones, refer_audio_spec, speed=speed_factor,sv_emb=sv_emb).detach()[0, 0, :]
+                                audio_fragment = self.vits_model.decode(
+                                    _pred_semantic, phones, refer_audio_spec, speed=speed_factor, sv_emb=sv_emb
+                                ).detach()[0, 0, :]
                             batch_audio_fragment.append(audio_fragment)  ###试试重建不带上prompt部分
                 else:
                     if parallel_infer:

GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py

@@ -160,7 +160,9 @@ class TextPreprocessor:
                 else:
                     for tmp in LangSegmenter.getTexts(text):
                         if langlist:
-                            if (tmp["lang"] == "en" and langlist[-1] == "en") or (tmp["lang"] != "en" and langlist[-1] != "en"):
+                            if (tmp["lang"] == "en" and langlist[-1] == "en") or (
+                                tmp["lang"] != "en" and langlist[-1] != "en"
+                            ):
                                 textlist[-1] += tmp["text"]
                                 continue
                         if tmp["lang"] == "en":

GPT_SoVITS/eres2net/ERes2Net.py

@@ -8,7 +8,6 @@
 The global feature fusion (GFF) takes acoustic features of different scales as input to aggregate global signal.
 """
 
-
 import torch
 import math
 import torch.nn as nn
@@ -16,15 +15,14 @@ import torch.nn.functional as F
 import pooling_layers as pooling_layers
 from fusion import AFF
 
-class ReLU(nn.Hardtanh):
 
+class ReLU(nn.Hardtanh):
     def __init__(self, inplace=False):
         super(ReLU, self).__init__(0, 20, inplace)
 
     def __repr__(self):
-        inplace_str = 'inplace' if self.inplace else ''
-        return self.__class__.__name__ + ' (' \
-            + inplace_str + ')'
+        inplace_str = "inplace" if self.inplace else ""
+        return self.__class__.__name__ + " (" + inplace_str + ")"
 
 
 class BasicBlockERes2Net(nn.Module):
@@ -51,9 +49,9 @@ class BasicBlockERes2Net(nn.Module):
         self.shortcut = nn.Sequential()
         if stride != 1 or in_planes != self.expansion * planes:
             self.shortcut = nn.Sequential(
-                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1,
-                          stride=stride, bias=False),
-                nn.BatchNorm2d(self.expansion * planes))
+                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(self.expansion * planes),
+            )
         self.stride = stride
         self.width = width
         self.scale = scale
@@ -86,6 +84,7 @@ class BasicBlockERes2Net(nn.Module):
 
         return out
 
+
 class BasicBlockERes2Net_diff_AFF(nn.Module):
     expansion = 2
 
@@ -115,9 +114,9 @@ class BasicBlockERes2Net_diff_AFF(nn.Module):
         self.shortcut = nn.Sequential()
         if stride != 1 or in_planes != self.expansion * planes:
             self.shortcut = nn.Sequential(
-                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1,
-                          stride=stride, bias=False),
-                nn.BatchNorm2d(self.expansion * planes))
+                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(self.expansion * planes),
+            )
         self.stride = stride
         self.width = width
         self.scale = scale
@@ -151,16 +150,19 @@ class BasicBlockERes2Net_diff_AFF(nn.Module):
 
         return out
 
+
 class ERes2Net(nn.Module):
-    def __init__(self,
+    def __init__(
+        self,
         block=BasicBlockERes2Net,
         block_fuse=BasicBlockERes2Net_diff_AFF,
         num_blocks=[3, 4, 6, 3],
         m_channels=32,
         feat_dim=80,
         embedding_size=192,
-                 pooling_func='TSTP',
-                 two_emb_layer=False):
+        pooling_func="TSTP",
+        two_emb_layer=False,
+    ):
         super(ERes2Net, self).__init__()
         self.in_planes = m_channels
         self.feat_dim = feat_dim
@@ -176,20 +178,24 @@ class ERes2Net(nn.Module):
         self.layer4 = self._make_layer(block_fuse, m_channels * 8, num_blocks[3], stride=2)
 
         # Downsampling module for each layer
-        self.layer1_downsample = nn.Conv2d(m_channels * 2, m_channels * 4, kernel_size=3, stride=2, padding=1, bias=False)
-        self.layer2_downsample = nn.Conv2d(m_channels * 4, m_channels * 8, kernel_size=3, padding=1, stride=2, bias=False)
-        self.layer3_downsample = nn.Conv2d(m_channels * 8, m_channels * 16, kernel_size=3, padding=1, stride=2, bias=False)
+        self.layer1_downsample = nn.Conv2d(
+            m_channels * 2, m_channels * 4, kernel_size=3, stride=2, padding=1, bias=False
+        )
+        self.layer2_downsample = nn.Conv2d(
+            m_channels * 4, m_channels * 8, kernel_size=3, padding=1, stride=2, bias=False
+        )
+        self.layer3_downsample = nn.Conv2d(
+            m_channels * 8, m_channels * 16, kernel_size=3, padding=1, stride=2, bias=False
+        )
 
         # Bottom-up fusion module
         self.fuse_mode12 = AFF(channels=m_channels * 4)
         self.fuse_mode123 = AFF(channels=m_channels * 8)
         self.fuse_mode1234 = AFF(channels=m_channels * 16)
 
-        self.n_stats = 1 if pooling_func == 'TAP' or pooling_func == "TSDP" else 2
-        self.pool = getattr(pooling_layers, pooling_func)(
-            in_dim=self.stats_dim * block.expansion)
-        self.seg_1 = nn.Linear(self.stats_dim * block.expansion * self.n_stats,
-                               embedding_size)
+        self.n_stats = 1 if pooling_func == "TAP" or pooling_func == "TSDP" else 2
+        self.pool = getattr(pooling_layers, pooling_func)(in_dim=self.stats_dim * block.expansion)
+        self.seg_1 = nn.Linear(self.stats_dim * block.expansion * self.n_stats, embedding_size)
         if self.two_emb_layer:
             self.seg_bn_1 = nn.BatchNorm1d(embedding_size, affine=False)
             self.seg_2 = nn.Linear(embedding_size, embedding_size)
@@ -247,14 +253,12 @@ class ERes2Net(nn.Module):
         return fuse_out1234
 
 
-if __name__ == '__main__':
-
+if __name__ == "__main__":
     x = torch.zeros(10, 300, 80)
-    model = ERes2Net(feat_dim=80, embedding_size=192, pooling_func='TSTP')
+    model = ERes2Net(feat_dim=80, embedding_size=192, pooling_func="TSTP")
     model.eval()
     out = model(x)
     print(out.shape)  # torch.Size([10, 192])
 
     num_params = sum(param.numel() for param in model.parameters())
     print("{} M".format(num_params / 1e6))  # 6.61M
-

GPT_SoVITS/eres2net/ERes2NetV2.py

@@ -8,8 +8,6 @@
 both the model parameters and its computational cost.
 """
 
-
-
 import torch
 import math
 import torch.nn as nn
@@ -17,19 +15,17 @@ import torch.nn.functional as F
 import pooling_layers as pooling_layers
 from fusion import AFF
 
-class ReLU(nn.Hardtanh):
 
+class ReLU(nn.Hardtanh):
     def __init__(self, inplace=False):
         super(ReLU, self).__init__(0, 20, inplace)
 
     def __repr__(self):
-        inplace_str = 'inplace' if self.inplace else ''
-        return self.__class__.__name__ + ' (' \
-            + inplace_str + ')'
+        inplace_str = "inplace" if self.inplace else ""
+        return self.__class__.__name__ + " (" + inplace_str + ")"
 
 
 class BasicBlockERes2NetV2(nn.Module):
-
     def __init__(self, in_planes, planes, stride=1, baseWidth=26, scale=2, expansion=2):
         super(BasicBlockERes2NetV2, self).__init__()
         width = int(math.floor(planes * (baseWidth / 64.0)))
@@ -52,12 +48,9 @@ class BasicBlockERes2NetV2(nn.Module):
         self.shortcut = nn.Sequential()
         if stride != 1 or in_planes != self.expansion * planes:
             self.shortcut = nn.Sequential(
-                nn.Conv2d(in_planes,
-                          self.expansion * planes,
-                          kernel_size=1,
-                          stride=stride,
-                          bias=False),
-                nn.BatchNorm2d(self.expansion * planes))
+                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(self.expansion * planes),
+            )
         self.stride = stride
         self.width = width
         self.scale = scale
@@ -90,8 +83,8 @@ class BasicBlockERes2NetV2(nn.Module):
 
         return out
 
-class BasicBlockERes2NetV2AFF(nn.Module):
 
+class BasicBlockERes2NetV2AFF(nn.Module):
     def __init__(self, in_planes, planes, stride=1, baseWidth=26, scale=2, expansion=2):
         super(BasicBlockERes2NetV2AFF, self).__init__()
         width = int(math.floor(planes * (baseWidth / 64.0)))
@@ -119,12 +112,9 @@ class BasicBlockERes2NetV2AFF(nn.Module):
         self.shortcut = nn.Sequential()
         if stride != 1 or in_planes != self.expansion * planes:
             self.shortcut = nn.Sequential(
-                nn.Conv2d(in_planes,
-                          self.expansion * planes,
-                          kernel_size=1,
-                          stride=stride,
-                          bias=False),
-                nn.BatchNorm2d(self.expansion * planes))
+                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(self.expansion * planes),
+            )
         self.stride = stride
         self.width = width
         self.scale = scale
@@ -158,8 +148,10 @@ class BasicBlockERes2NetV2AFF(nn.Module):
 
         return out
 
+
 class ERes2NetV2(nn.Module):
-    def __init__(self,
+    def __init__(
+        self,
         block=BasicBlockERes2NetV2,
         block_fuse=BasicBlockERes2NetV2AFF,
         num_blocks=[3, 4, 6, 3],
@@ -169,8 +161,9 @@ class ERes2NetV2(nn.Module):
         baseWidth=26,
         scale=2,
         expansion=2,
-                 pooling_func='TSTP',
-                 two_emb_layer=False):
+        pooling_func="TSTP",
+        two_emb_layer=False,
+    ):
         super(ERes2NetV2, self).__init__()
         self.in_planes = m_channels
         self.feat_dim = feat_dim
@@ -181,42 +174,29 @@ class ERes2NetV2(nn.Module):
         self.scale = scale
         self.expansion = expansion
 
-        self.conv1 = nn.Conv2d(1,
-                               m_channels,
-                               kernel_size=3,
-                               stride=1,
-                               padding=1,
-                               bias=False)
+        self.conv1 = nn.Conv2d(1, m_channels, kernel_size=3, stride=1, padding=1, bias=False)
         self.bn1 = nn.BatchNorm2d(m_channels)
-        self.layer1 = self._make_layer(block,
-                                       m_channels,
-                                       num_blocks[0],
-                                       stride=1)
-        self.layer2 = self._make_layer(block,
-                                       m_channels * 2,
-                                       num_blocks[1],
-                                       stride=2)
-        self.layer3 = self._make_layer(block_fuse,
-                                       m_channels * 4,
-                                       num_blocks[2],
-                                       stride=2)
-        self.layer4 = self._make_layer(block_fuse,
-                                       m_channels * 8,
-                                       num_blocks[3],
-                                       stride=2)
+        self.layer1 = self._make_layer(block, m_channels, num_blocks[0], stride=1)
+        self.layer2 = self._make_layer(block, m_channels * 2, num_blocks[1], stride=2)
+        self.layer3 = self._make_layer(block_fuse, m_channels * 4, num_blocks[2], stride=2)
+        self.layer4 = self._make_layer(block_fuse, m_channels * 8, num_blocks[3], stride=2)
 
         # Downsampling module
-        self.layer3_ds = nn.Conv2d(m_channels * 4 * self.expansion, m_channels * 8 * self.expansion, kernel_size=3, \
-                                   padding=1, stride=2, bias=False)
+        self.layer3_ds = nn.Conv2d(
+            m_channels * 4 * self.expansion,
+            m_channels * 8 * self.expansion,
+            kernel_size=3,
+            padding=1,
+            stride=2,
+            bias=False,
+        )
 
         # Bottom-up fusion module
         self.fuse34 = AFF(channels=m_channels * 8 * self.expansion, r=4)
 
-        self.n_stats = 1 if pooling_func == 'TAP' or pooling_func == "TSDP" else 2
-        self.pool = getattr(pooling_layers, pooling_func)(
-            in_dim=self.stats_dim * self.expansion)
-        self.seg_1 = nn.Linear(self.stats_dim * self.expansion * self.n_stats,
-                               embedding_size)
+        self.n_stats = 1 if pooling_func == "TAP" or pooling_func == "TSDP" else 2
+        self.pool = getattr(pooling_layers, pooling_func)(in_dim=self.stats_dim * self.expansion)
+        self.seg_1 = nn.Linear(self.stats_dim * self.expansion * self.n_stats, embedding_size)
         if self.two_emb_layer:
             self.seg_bn_1 = nn.BatchNorm1d(embedding_size, affine=False)
             self.seg_2 = nn.Linear(embedding_size, embedding_size)
@@ -228,7 +208,11 @@ class ERes2NetV2(nn.Module):
         strides = [stride] + [1] * (num_blocks - 1)
         layers = []
         for stride in strides:
-            layers.append(block(self.in_planes, planes, stride, baseWidth=self.baseWidth, scale=self.scale, expansion=self.expansion))
+            layers.append(
+                block(
+                    self.in_planes, planes, stride, baseWidth=self.baseWidth, scale=self.scale, expansion=self.expansion
+                )
+            )
             self.in_planes = planes * self.expansion
         return nn.Sequential(*layers)
 
@@ -276,8 +260,8 @@ class ERes2NetV2(nn.Module):
         # else:
         #     return embed_a
 
-if __name__ == '__main__':
 
+if __name__ == "__main__":
     x = torch.randn(1, 300, 80)
     model = ERes2NetV2(feat_dim=80, embedding_size=192, m_channels=64, baseWidth=26, scale=2, expansion=2)
     model.eval()
@@ -286,7 +270,3 @@ if __name__ == '__main__':
     macs, num_params = profile(model, inputs=(x,))
     print("Params: {} M".format(num_params / 1e6))  # 17.86 M
     print("MACs: {} G".format(macs / 1e9))  # 12.69 G
-
-
-
-

GPT_SoVITS/eres2net/ERes2Net_huge.py

@@ -8,7 +8,6 @@
 ERes2Net-huge is an upgraded version of ERes2Net that uses a larger number of parameters to achieve better
 recognition performance. Parameters expansion, baseWidth, and scale can be modified to obtain optimal performance.
 """
-import pdb
 
 import torch
 import math
@@ -17,15 +16,14 @@ import torch.nn.functional as F
 import pooling_layers as pooling_layers
 from fusion import AFF
 
-class ReLU(nn.Hardtanh):
 
+class ReLU(nn.Hardtanh):
     def __init__(self, inplace=False):
         super(ReLU, self).__init__(0, 20, inplace)
 
     def __repr__(self):
-        inplace_str = 'inplace' if self.inplace else ''
-        return self.__class__.__name__ + ' (' \
-            + inplace_str + ')'
+        inplace_str = "inplace" if self.inplace else ""
+        return self.__class__.__name__ + " (" + inplace_str + ")"
 
 
 class BasicBlockERes2Net(nn.Module):
@@ -53,7 +51,8 @@ class BasicBlockERes2Net(nn.Module):
         if stride != 1 or in_planes != self.expansion * planes:
             self.shortcut = nn.Sequential(
                 nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
-                nn.BatchNorm2d(self.expansion * planes))
+                nn.BatchNorm2d(self.expansion * planes),
+            )
         self.stride = stride
         self.width = width
         self.scale = scale
@@ -86,6 +85,7 @@ class BasicBlockERes2Net(nn.Module):
 
         return out
 
+
 class BasicBlockERes2Net_diff_AFF(nn.Module):
     expansion = 4
 
@@ -116,7 +116,8 @@ class BasicBlockERes2Net_diff_AFF(nn.Module):
         if stride != 1 or in_planes != self.expansion * planes:
             self.shortcut = nn.Sequential(
                 nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
-                nn.BatchNorm2d(self.expansion * planes))
+                nn.BatchNorm2d(self.expansion * planes),
+            )
         self.stride = stride
         self.width = width
         self.scale = scale
@@ -141,7 +142,6 @@ class BasicBlockERes2Net_diff_AFF(nn.Module):
             else:
                 out = torch.cat((out, sp), 1)
 
-
         out = self.conv3(out)
         out = self.bn3(out)
 
@@ -151,16 +151,19 @@ class BasicBlockERes2Net_diff_AFF(nn.Module):
 
         return out
 
+
 class ERes2Net(nn.Module):
-    def __init__(self,
+    def __init__(
+        self,
         block=BasicBlockERes2Net,
         block_fuse=BasicBlockERes2Net_diff_AFF,
         num_blocks=[3, 4, 6, 3],
         m_channels=64,
         feat_dim=80,
         embedding_size=192,
-                 pooling_func='TSTP',
-                 two_emb_layer=False):
+        pooling_func="TSTP",
+        two_emb_layer=False,
+    ):
         super(ERes2Net, self).__init__()
         self.in_planes = m_channels
         self.feat_dim = feat_dim
@@ -176,17 +179,22 @@ class ERes2Net(nn.Module):
         self.layer3 = self._make_layer(block_fuse, m_channels * 4, num_blocks[2], stride=2)
         self.layer4 = self._make_layer(block_fuse, m_channels * 8, num_blocks[3], stride=2)
 
-        self.layer1_downsample = nn.Conv2d(m_channels * 4, m_channels * 8, kernel_size=3, padding=1, stride=2, bias=False)
-        self.layer2_downsample = nn.Conv2d(m_channels * 8, m_channels * 16, kernel_size=3, padding=1, stride=2, bias=False)
-        self.layer3_downsample = nn.Conv2d(m_channels * 16, m_channels * 32, kernel_size=3, padding=1, stride=2, bias=False)
+        self.layer1_downsample = nn.Conv2d(
+            m_channels * 4, m_channels * 8, kernel_size=3, padding=1, stride=2, bias=False
+        )
+        self.layer2_downsample = nn.Conv2d(
+            m_channels * 8, m_channels * 16, kernel_size=3, padding=1, stride=2, bias=False
+        )
+        self.layer3_downsample = nn.Conv2d(
+            m_channels * 16, m_channels * 32, kernel_size=3, padding=1, stride=2, bias=False
+        )
 
         self.fuse_mode12 = AFF(channels=m_channels * 8)
         self.fuse_mode123 = AFF(channels=m_channels * 16)
         self.fuse_mode1234 = AFF(channels=m_channels * 32)
 
-        self.n_stats = 1 if pooling_func == 'TAP' or pooling_func == "TSDP" else 2
-        self.pool = getattr(pooling_layers, pooling_func)(
-            in_dim=self.stats_dim * block.expansion)
+        self.n_stats = 1 if pooling_func == "TAP" or pooling_func == "TSDP" else 2
+        self.pool = getattr(pooling_layers, pooling_func)(in_dim=self.stats_dim * block.expansion)
         self.seg_1 = nn.Linear(self.stats_dim * block.expansion * self.n_stats, embedding_size)
         if self.two_emb_layer:
             self.seg_bn_1 = nn.BatchNorm1d(embedding_size, affine=False)
@@ -244,14 +252,13 @@ class ERes2Net(nn.Module):
         out4 = self.layer4(out3)
         fuse_out123_downsample = self.layer3_downsample(fuse_out123)
         fuse_out1234 = self.fuse_mode1234(out4, fuse_out123_downsample).flatten(start_dim=1, end_dim=2)  # bs,20480,T
-        if(if_mean==False):
+        if if_mean == False:
             mean = fuse_out1234[0].transpose(1, 0)  # (T,20480),bs=T
         else:
             mean = fuse_out1234.mean(2)  # bs,20480
         mean_std = torch.cat([mean, torch.zeros_like(mean)], 1)
         return self.seg_1(mean_std)  # (T,192)
 
-
         # stats = self.pool(fuse_out1234)
         # if self.two_emb_layer:
         #     out = F.relu(embed_a)
@@ -280,7 +287,3 @@ class ERes2Net(nn.Module):
         # print(fuse_out1234.shape)
         # print(fuse_out1234.flatten(start_dim=1,end_dim=2).shape)
         # pdb.set_trace()
-
-
-
-

GPT_SoVITS/eres2net/fusion.py

@@ -6,7 +6,6 @@ import torch.nn as nn
 
 
 class AFF(nn.Module):
-
     def __init__(self, channels=64, r=4):
         super(AFF, self).__init__()
         inter_channels = int(channels // r)
@@ -26,4 +25,3 @@ class AFF(nn.Module):
         xo = torch.mul(x, x_att) + torch.mul(ds_y, 2.0 - x_att)
 
         return xo
-

GPT_SoVITS/eres2net/kaldi.py

@@ -144,7 +144,7 @@ def _get_waveform_and_window_properties(
     )
     assert 0 < window_shift, "`window_shift` must be greater than 0"
     assert padded_window_size % 2 == 0, (
-        "the padded `window_size` must be divisible by two." " use `round_to_power_of_two` or change `frame_length`"
+        "the padded `window_size` must be divisible by two. use `round_to_power_of_two` or change `frame_length`"
     )
     assert 0.0 <= preemphasis_coefficient <= 1.0, "`preemphasis_coefficient` must be between [0,1]"
     assert sample_frequency > 0, "`sample_frequency` must be greater than zero"
@@ -441,7 +441,9 @@ def get_mel_banks(
     high_freq: float,
     vtln_low: float,
     vtln_high: float,
-    vtln_warp_factor: float,device=None,dtype=None
+    vtln_warp_factor: float,
+    device=None,
+    dtype=None,
 ) -> Tuple[Tensor, Tensor]:
     """
     Returns:
@@ -457,9 +459,9 @@ def get_mel_banks(
     if high_freq <= 0.0:
         high_freq += nyquist
 
-    assert (
-        (0.0 <= low_freq < nyquist) and (0.0 < high_freq <= nyquist) and (low_freq < high_freq)
-    ), "Bad values in options: low-freq {} and high-freq {} vs. nyquist {}".format(low_freq, high_freq, nyquist)
+    assert (0.0 <= low_freq < nyquist) and (0.0 < high_freq <= nyquist) and (low_freq < high_freq), (
+        "Bad values in options: low-freq {} and high-freq {} vs. nyquist {}".format(low_freq, high_freq, nyquist)
+    )
 
     # fft-bin width [think of it as Nyquist-freq / half-window-length]
     fft_bin_width = sample_freq / window_length_padded
@@ -475,7 +477,7 @@ def get_mel_banks(
 
     assert vtln_warp_factor == 1.0 or (
         (low_freq < vtln_low < high_freq) and (0.0 < vtln_high < high_freq) and (vtln_low < vtln_high)
-    ), "Bad values in options: vtln-low {} and vtln-high {}, versus " "low-freq {} and high-freq {}".format(
+    ), "Bad values in options: vtln-low {} and vtln-high {}, versus low-freq {} and high-freq {}".format(
         vtln_low, vtln_high, low_freq, high_freq
     )
 
@@ -510,7 +512,10 @@ def get_mel_banks(
 
     return bins.to(device=device, dtype=dtype)  # , center_freqs
 
+
 cache = {}
+
+
 def fbank(
     waveform: Tensor,
     blackman_coeff: float = 0.42,
@@ -620,10 +625,30 @@ def fbank(
     # size (num_mel_bins, padded_window_size // 2)
     # print(num_mel_bins, padded_window_size, sample_frequency, low_freq, high_freq, vtln_low, vtln_high, vtln_warp)
 
-    cache_key="%s-%s-%s-%s-%s-%s-%s-%s-%s-%s"%(num_mel_bins, padded_window_size, sample_frequency, low_freq, high_freq, vtln_low, vtln_high, vtln_warp,device,dtype)
+    cache_key = "%s-%s-%s-%s-%s-%s-%s-%s-%s-%s" % (
+        num_mel_bins,
+        padded_window_size,
+        sample_frequency,
+        low_freq,
+        high_freq,
+        vtln_low,
+        vtln_high,
+        vtln_warp,
+        device,
+        dtype,
+    )
     if cache_key not in cache:
         mel_energies = get_mel_banks(
-            num_mel_bins, padded_window_size, sample_frequency, low_freq, high_freq, vtln_low, vtln_high, vtln_warp,device,dtype
+            num_mel_bins,
+            padded_window_size,
+            sample_frequency,
+            low_freq,
+            high_freq,
+            vtln_low,
+            vtln_high,
+            vtln_warp,
+            device,
+            dtype,
         )
         cache[cache_key] = mel_energies
     else:

GPT_SoVITS/eres2net/pooling_layers.py

@@ -11,6 +11,7 @@ class TAP(nn.Module):
     """
     Temporal average pooling, only first-order mean is considered
     """
+
     def __init__(self, **kwargs):
         super(TAP, self).__init__()
 
@@ -25,6 +26,7 @@ class TSDP(nn.Module):
     """
     Temporal standard deviation pooling, only second-order std is considered
     """
+
     def __init__(self, **kwargs):
         super(TSDP, self).__init__()
 
@@ -41,6 +43,7 @@ class TSTP(nn.Module):
     x-vector
     Comment: simple concatenation can not make full use of both statistics
     """
+
     def __init__(self, **kwargs):
         super(TSTP, self).__init__()
 
@@ -59,6 +62,7 @@ class ASTP(nn.Module):
     """Attentive statistics pooling: Channel- and context-dependent
     statistics pooling, first used in ECAPA_TDNN.
     """
+
     def __init__(self, in_dim, bottleneck_dim=128, global_context_att=False):
         super(ASTP, self).__init__()
         self.global_context_att = global_context_att
@@ -66,15 +70,10 @@ class ASTP(nn.Module):
         # Use Conv1d with stride == 1 rather than Linear, then we don't
         # need to transpose inputs.
         if global_context_att:
-            self.linear1 = nn.Conv1d(
-                in_dim * 3, bottleneck_dim,
-                kernel_size=1)  # equals W and b in the paper
+            self.linear1 = nn.Conv1d(in_dim * 3, bottleneck_dim, kernel_size=1)  # equals W and b in the paper
         else:
-            self.linear1 = nn.Conv1d(
-                in_dim, bottleneck_dim,
-                kernel_size=1)  # equals W and b in the paper
-        self.linear2 = nn.Conv1d(bottleneck_dim, in_dim,
-                                 kernel_size=1)  # equals V and k in the paper
+            self.linear1 = nn.Conv1d(in_dim, bottleneck_dim, kernel_size=1)  # equals W and b in the paper
+        self.linear2 = nn.Conv1d(bottleneck_dim, in_dim, kernel_size=1)  # equals V and k in the paper
 
     def forward(self, x):
         """
@@ -88,15 +87,13 @@ class ASTP(nn.Module):
 
         if self.global_context_att:
             context_mean = torch.mean(x, dim=-1, keepdim=True).expand_as(x)
-            context_std = torch.sqrt(
-                torch.var(x, dim=-1, keepdim=True) + 1e-10).expand_as(x)
+            context_std = torch.sqrt(torch.var(x, dim=-1, keepdim=True) + 1e-10).expand_as(x)
             x_in = torch.cat((x, context_mean, context_std), dim=1)
         else:
             x_in = x
 
         # DON'T use ReLU here! ReLU may be hard to converge.
-        alpha = torch.tanh(
-            self.linear1(x_in))  # alpha = F.relu(self.linear1(x_in))
+        alpha = torch.tanh(self.linear1(x_in))  # alpha = F.relu(self.linear1(x_in))
         alpha = torch.softmax(self.linear2(alpha), dim=2)
         mean = torch.sum(alpha * x, dim=2)
         var = torch.sum(alpha * (x**2), dim=2) - mean**2

GPT_SoVITS/export_torch_script.py

@@ -37,10 +37,13 @@ default_config = {
 }
 
 sv_cn_model = None
+
+
 def init_sv_cn(device, is_half):
     global sv_cn_model
     sv_cn_model = SV(device, is_half)
 
+
 def load_sovits_new(sovits_path):
     f = open(sovits_path, "rb")
     meta = f.read(2)
@@ -129,7 +132,9 @@ def sample(
 
 
 @torch.jit.script
-def spectrogram_torch(hann_window:Tensor, y: Tensor, n_fft: int, sampling_rate: int, hop_size: int, win_size: int, center: bool = False):
+def spectrogram_torch(
+    hann_window: Tensor, y: Tensor, n_fft: int, sampling_rate: int, hop_size: int, win_size: int, center: bool = False
+):
     # hann_window = torch.hann_window(win_size, device=y.device, dtype=y.dtype)
     y = torch.nn.functional.pad(
         y.unsqueeze(1),
@@ -380,8 +385,9 @@ class VitsModel(nn.Module):
             self.vq_model = self.vq_model.half()
         self.vq_model = self.vq_model.to(device)
         self.vq_model.eval()
-        self.hann_window = torch.hann_window(self.hps.data.win_length, device=device, dtype= torch.float16 if is_half else torch.float32)
-
+        self.hann_window = torch.hann_window(
+            self.hps.data.win_length, device=device, dtype=torch.float16 if is_half else torch.float32
+        )
 
     def forward(self, text_seq, pred_semantic, ref_audio, speed=1.0, sv_emb=None):
         refer = spectrogram_torch(
@@ -667,7 +673,9 @@ def export(gpt_path, vits_path, ref_audio_path, ref_text, output_path, export_be
     ref_seq = torch.LongTensor([ref_seq_id]).to(device)
     ref_bert = ref_bert_T.T.to(ref_seq.device)
     text_seq_id, text_bert_T, norm_text = get_phones_and_bert(
-        "这是一个简单的示例，真没想到这么简单就完成了。The King and His Stories.Once there was a king. He likes to write stories, but his stories were not good. As people were afraid of him, they all said his stories were good.After reading them, the writer at once turned to the soldiers and said: Take me back to prison, please.", "auto", "v2"
+        "这是一个简单的示例，真没想到这么简单就完成了。The King and His Stories.Once there was a king. He likes to write stories, but his stories were not good. As people were afraid of him, they all said his stories were good.After reading them, the writer at once turned to the soldiers and said: Take me back to prison, please.",
+        "auto",
+        "v2",
     )
     text_seq = torch.LongTensor([text_seq_id]).to(device)
     text_bert = text_bert_T.T.to(text_seq.device)
@@ -747,9 +755,7 @@ def export_prov2(
 
     print(f"device: {device}")
 
-    ref_seq_id, ref_bert_T, ref_norm_text = get_phones_and_bert(
-        ref_text, "all_zh", "v2"
-    )
+    ref_seq_id, ref_bert_T, ref_norm_text = get_phones_and_bert(ref_text, "all_zh", "v2")
     ref_seq = torch.LongTensor([ref_seq_id]).to(device)
     ref_bert = ref_bert_T.T
     if is_half:
@@ -757,7 +763,9 @@ def export_prov2(
     ref_bert = ref_bert.to(ref_seq.device)
 
     text_seq_id, text_bert_T, norm_text = get_phones_and_bert(
-        "这是一个简单的示例，真没想到这么简单就完成了。The King and His Stories.Once there was a king. He likes to write stories, but his stories were not good. As people were afraid of him, they all said his stories were good.After reading them, the writer at once turned to the soldiers and said: Take me back to prison, please.", "auto", "v2"
+        "这是一个简单的示例，真没想到这么简单就完成了。The King and His Stories.Once there was a king. He likes to write stories, but his stories were not good. As people were afraid of him, they all said his stories were good.After reading them, the writer at once turned to the soldiers and said: Take me back to prison, please.",
+        "auto",
+        "v2",
     )
     text_seq = torch.LongTensor([text_seq_id]).to(device)
     text_bert = text_bert_T.T
@@ -930,6 +938,7 @@ class GPT_SoVITS_V2Pro(nn.Module):
         audio = self.vits(text_seq, pred_semantic, ref_audio_sr, speed, sv_emb)
         return audio
 
+
 def test():
     parser = argparse.ArgumentParser(description="GPT-SoVITS Command Line Tool")
     parser.add_argument("--gpt_model", required=True, help="Path to the GPT model file")
@@ -1046,21 +1055,11 @@ def export_symbel(version="v2"):
 def main():
     parser = argparse.ArgumentParser(description="GPT-SoVITS Command Line Tool")
     parser.add_argument("--gpt_model", required=True, help="Path to the GPT model file")
-    parser.add_argument(
-        "--sovits_model", required=True, help="Path to the SoVITS model file"
-    )
-    parser.add_argument(
-        "--ref_audio", required=True, help="Path to the reference audio file"
-    )
-    parser.add_argument(
-        "--ref_text", required=True, help="Path to the reference text file"
-    )
-    parser.add_argument(
-        "--output_path", required=True, help="Path to the output directory"
-    )
-    parser.add_argument(
-        "--export_common_model", action="store_true", help="Export Bert and SSL model"
-    )
+    parser.add_argument("--sovits_model", required=True, help="Path to the SoVITS model file")
+    parser.add_argument("--ref_audio", required=True, help="Path to the reference audio file")
+    parser.add_argument("--ref_text", required=True, help="Path to the reference text file")
+    parser.add_argument("--output_path", required=True, help="Path to the output directory")
+    parser.add_argument("--export_common_model", action="store_true", help="Export Bert and SSL model")
     parser.add_argument("--device", help="Device to use")
     parser.add_argument("--version", help="version of the model", default="v2")
     parser.add_argument("--no-half", action="store_true", help="Do not use half precision for model weights")

GPT_SoVITS/export_torch_script_v3v4.py

@@ -439,6 +439,7 @@ class GPTSoVITSV3(torch.nn.Module):
         wav_gen = torch.cat(wav_gen_list, 2)
         return wav_gen[0][0][:wav_gen_length]
 
+
 class GPTSoVITSV4(torch.nn.Module):
     def __init__(self, gpt_sovits_half, cfm, hifigan):
         super().__init__()
@@ -581,6 +582,7 @@ from process_ckpt import get_sovits_version_from_path_fast, load_sovits_new
 
 v3v4set = {"v3", "v4"}
 
+
 def get_sovits_weights(sovits_path):
     path_sovits_v3 = "GPT_SoVITS/pretrained_models/s2Gv3.pth"
     is_exist_s2gv3 = os.path.exists(path_sovits_v3)
@@ -711,7 +713,6 @@ def export_1(ref_wav_path,ref_wav_text,version="v3"):
         sovits = get_sovits_weights("GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth")
         init_hifigan()
 
-
     dict_s1 = torch.load("GPT_SoVITS/pretrained_models/s1v3.ckpt")
     raw_t2s = get_raw_t2s_model(dict_s1).to(device)
     print("#### get_raw_t2s_model ####")
@@ -755,9 +756,7 @@ def export_1(ref_wav_path,ref_wav_text,version="v3"):
     # phones1, bert1, norm_text1 = get_phones_and_bert(
     #     "你这老坏蛋，我找了你这么久，真没想到在这里找到你。他说。", "all_zh", "v3"
     # )
-    phones1, bert1, norm_text1 = get_phones_and_bert(
-        ref_wav_text, "auto", "v3"
-    )
+    phones1, bert1, norm_text1 = get_phones_and_bert(ref_wav_text, "auto", "v3")
     phones2, bert2, norm_text2 = get_phones_and_bert(
         "这是一个简单的示例，真没想到这么简单就完成了。The King and His Stories.Once there was a king. He likes to write stories, but his stories were not good. As people were afraid of him, they all said his stories were good.After reading them, the writer at once turned to the soldiers and said: Take me back to prison, please.",
         "auto",
@@ -1205,7 +1204,6 @@ def export_2(version="v3"):
     gpt_sovits_v3v4 = gpt_sovits_v3 if version == "v3" else gpt_sovits_v4
     sr = 24000 if version == "v3" else 48000
 
-
     time.sleep(5)
     # print("thread:", torch.get_num_threads())
     # print("thread:", torch.get_num_interop_threads())
@@ -1216,14 +1214,14 @@ def export_2(version="v3"):
         "汗流浃背了呀!老弟~ My uncle has two dogs. One is big and the other is small. He likes them very much. He often plays with them. He takes them for a walk every day. He says they are his good friends. He is very happy with them. 最后还是我得了 MVP....",
         gpt_sovits_v3v4,
         "out.wav",
-        sr
+        sr,
     )
 
     test_export(
         "你小子是什么来路.汗流浃背了呀!老弟~ My uncle has two dogs. He is very happy with them. 最后还是我得了 MVP!",
         gpt_sovits_v3v4,
         "out2.wav",
-        sr
+        sr,
     )
 
     # test_export(

GPT_SoVITS/module/data_utils.py

@@ -252,9 +252,28 @@ class TextAudioSpeakerCollate:
             if self.is_v2Pro:
                 sv_embs[i] = row[4]
         if self.is_v2Pro:
-            return ssl_padded, ssl_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, text_padded, text_lengths,sv_embs
+            return (
+                ssl_padded,
+                ssl_lengths,
+                spec_padded,
+                spec_lengths,
+                wav_padded,
+                wav_lengths,
+                text_padded,
+                text_lengths,
+                sv_embs,
+            )
         else:
-            return ssl_padded, ssl_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, text_padded, text_lengths
+            return (
+                ssl_padded,
+                ssl_lengths,
+                spec_padded,
+                spec_lengths,
+                wav_padded,
+                wav_lengths,
+                text_padded,
+                text_lengths,
+            )
 
 
 class TextAudioSpeakerLoaderV3(torch.utils.data.Dataset):

GPT_SoVITS/module/models.py

@@ -586,12 +586,17 @@ class DiscriminatorS(torch.nn.Module):
 
         return x, fmap
 
+
 v2pro_set = {"v2Pro", "v2ProPlus"}
+
+
 class MultiPeriodDiscriminator(torch.nn.Module):
     def __init__(self, use_spectral_norm=False, version=None):
         super(MultiPeriodDiscriminator, self).__init__()
-        if version in v2pro_set:periods = [2, 3, 5, 7, 11,17,23]
-        else:periods = [2, 3, 5, 7, 11]
+        if version in v2pro_set:
+            periods = [2, 3, 5, 7, 11, 17, 23]
+        else:
+            periods = [2, 3, 5, 7, 11]
 
         discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
         discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
@@ -787,6 +792,7 @@ class CodePredictor(nn.Module):
 
             return pred_codes.transpose(0, 1)
 
+
 class SynthesizerTrn(nn.Module):
     """
     Synthesizer for Training
@@ -983,7 +989,14 @@ class SynthesizerTrn(nn.Module):
         quantized = self.quantizer.decode(codes)
         if self.semantic_frame_rate == "25hz":
             quantized = F.interpolate(quantized, size=int(quantized.shape[-1] * 2), mode="nearest")
-        x, m_p, logs_p, y_mask = self.enc_p(quantized, y_lengths, text, text_lengths, self.ge_to512(ge.transpose(2,1)).transpose(2,1)if self.is_v2pro else ge, speed)
+        x, m_p, logs_p, y_mask = self.enc_p(
+            quantized,
+            y_lengths,
+            text,
+            text_lengths,
+            self.ge_to512(ge.transpose(2, 1)).transpose(2, 1) if self.is_v2pro else ge,
+            speed,
+        )
         z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
 
         z = self.flow(z_p, y_mask, g=ge, reverse=True)
@@ -996,6 +1009,7 @@ class SynthesizerTrn(nn.Module):
         quantized, codes, commit_loss, quantized_list = self.quantizer(ssl)
         return codes.transpose(0, 1)
 
+
 class CFM(torch.nn.Module):
     def __init__(self, in_channels, dit):
         super().__init__()
@@ -1029,7 +1043,18 @@ class CFM(torch.nn.Module):
             t_tensor = torch.ones(x.shape[0], device=x.device, dtype=mu.dtype) * t
             # v_pred = model(x, t_tensor, d_tensor, **extra_args)
             v_pred, text_emb, dt = self.estimator(
-                x, prompt_x, x_lens, t_tensor, d_tensor, mu, use_grad_ckpt=False, drop_audio_cond=False, drop_text=False, infer=True, text_cache=text_cache, dt_cache=dt_cache
+                x,
+                prompt_x,
+                x_lens,
+                t_tensor,
+                d_tensor,
+                mu,
+                use_grad_ckpt=False,
+                drop_audio_cond=False,
+                drop_text=False,
+                infer=True,
+                text_cache=text_cache,
+                dt_cache=dt_cache,
             )
             v_pred = v_pred.transpose(2, 1)
             if self.use_conditioner_cache:
@@ -1048,7 +1073,7 @@ class CFM(torch.nn.Module):
                     drop_text=True,
                     infer=True,
                     text_cache=text_cfg_cache,
-                                    dt_cache=dt_cache
+                    dt_cache=dt_cache,
                 )
                 neg = neg.transpose(2, 1)
                 if self.use_conditioner_cache:

GPT_SoVITS/module/models_onnx.py

@@ -762,8 +762,10 @@ class CodePredictor(nn.Module):
 
             return pred_codes.transpose(0, 1)
 
+
 v2pro_set = {"v2Pro", "v2ProPlus"}
 
+
 class SynthesizerTrn(nn.Module):
     """
     Synthesizer for Training

GPT_SoVITS/module/modules.py

@@ -1,5 +1,4 @@
 import math
-import pdb
 
 import numpy as np
 import torch

GPT_SoVITS/prepare_datasets/2-get-sv.py

@@ -10,7 +10,6 @@ i_part = os.environ.get("i_part")
 all_parts = os.environ.get("all_parts")
 if "_CUDA_VISIBLE_DEVICES" in os.environ:
     os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
-from feature_extractor import cnhubert
 
 opt_dir = os.environ.get("opt_dir")
 sv_path = os.environ.get("sv_path")
@@ -19,19 +18,18 @@ import torch
 is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
 
 import traceback
-import numpy as np
-from scipy.io import wavfile
 import torchaudio
 
 now_dir = os.getcwd()
 sys.path.append(now_dir)
 sys.path.append(f"{now_dir}/GPT_SoVITS/eres2net")
-from tools.my_utils import load_audio, clean_path
+from tools.my_utils import clean_path
 from time import time as ttime
 import shutil
 from ERes2NetV2 import ERes2NetV2
 import kaldi as Kaldi
 
+
 def my_save(fea, path):  #####fix issue: torch.save doesn't support chinese path
     dir = os.path.dirname(path)
     name = os.path.basename(path)
@@ -56,9 +54,10 @@ if torch.cuda.is_available():
 else:
     device = "cpu"
 
+
 class SV:
     def __init__(self, device, is_half):
-        pretrained_state = torch.load(sv_path, map_location='cpu')
+        pretrained_state = torch.load(sv_path, map_location="cpu")
         embedding_model = ERes2NetV2(baseWidth=24, scale=4, expansion=4)
         embedding_model.load_state_dict(pretrained_state)
         embedding_model.eval()
@@ -73,15 +72,22 @@ class SV:
     def compute_embedding3(self, wav):  # (1,x)#-1~1
         with torch.no_grad():
             wav = self.res(wav)
-            if self.is_half==True:wav=wav.half()
-            feat = torch.stack([Kaldi.fbank(wav0.unsqueeze(0), num_mel_bins=80, sample_frequency=16000, dither=0) for wav0 in wav])
+            if self.is_half == True:
+                wav = wav.half()
+            feat = torch.stack(
+                [Kaldi.fbank(wav0.unsqueeze(0), num_mel_bins=80, sample_frequency=16000, dither=0) for wav0 in wav]
+            )
             sv_emb = self.embedding_model.forward3(feat)
         return sv_emb
 
+
 sv = SV(device, is_half)
+
+
 def name2go(wav_name, wav_path):
     sv_cn_path = "%s/%s.pt" % (sv_cn_dir, wav_name)
-    if os.path.exists(sv_cn_path):return
+    if os.path.exists(sv_cn_path):
+        return
     wav_path = "%s/%s" % (wav32dir, wav_name)
     wav32k, sr0 = torchaudio.load(wav_path)
     assert sr0 == 32000

GPT_SoVITS/process_ckpt.py

@@ -17,7 +17,6 @@ def my_save(fea, path):  #####fix issue: torch.save doesn't support chinese path
     shutil.move(tmp_path, "%s/%s" % (dir, name))
 
 
-
 from io import BytesIO
 
 model_version2byte = {
@@ -26,6 +25,8 @@ model_version2byte={
     "v2Pro": b"05",
     "v2ProPlus": b"06",
 }
+
+
 def my_save2(fea, path, model_version):
     bio = BytesIO()
     torch.save(fea, bio)
@@ -50,7 +51,7 @@ def savee(ckpt, name, epoch, steps, hps, model_version=None, lora_rank=None):
         if lora_rank:
             opt["lora_rank"] = lora_rank
             my_save2(opt, "%s/%s.pth" % (hps.save_weight_dir, name), model_version)
-        elif (model_version!=None and "Pro"in model_version):
+        elif model_version != None and "Pro" in model_version:
             my_save2(opt, "%s/%s.pth" % (hps.save_weight_dir, name), model_version)
         else:
             my_save(opt, "%s/%s.pth" % (hps.save_weight_dir, name))
@@ -58,6 +59,7 @@ def savee(ckpt, name, epoch, steps, hps, model_version=None, lora_rank=None):
     except:
         return traceback.format_exc()
 
+
 """
 00:v1
 01:v2

GPT_SoVITS/s2_train.py

@@ -36,7 +36,7 @@ from module.models import (
     MultiPeriodDiscriminator,
     SynthesizerTrn,
 )
-from process_ckpt import savee,my_save2
+from process_ckpt import savee
 
 torch.backends.cudnn.benchmark = False
 torch.backends.cudnn.deterministic = False
@@ -91,7 +91,26 @@ def run(rank, n_gpus, hps):
     train_sampler = DistributedBucketSampler(
         train_dataset,
         hps.train.batch_size,
-        [32,300,400,500,600,700,800,900,1000,1100,1200,1300,1400,1500,1600,1700,1800,1900,],
+        [
+            32,
+            300,
+            400,
+            500,
+            600,
+            700,
+            800,
+            900,
+            1000,
+            1100,
+            1200,
+            1300,
+            1400,
+            1500,
+            1600,
+            1700,
+            1800,
+            1900,
+        ],
         num_replicas=n_gpus,
         rank=rank,
         shuffle=True,
@@ -315,12 +334,39 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
         else:
             ssl, ssl_lengths, spec, spec_lengths, y, y_lengths, text, text_lengths = data
         if torch.cuda.is_available():
-            spec, spec_lengths = (spec.cuda(rank,non_blocking=True,),spec_lengths.cuda(rank,non_blocking=True,),)
-            y, y_lengths = (y.cuda(rank,non_blocking=True,),y_lengths.cuda(rank,non_blocking=True,),)
+            spec, spec_lengths = (
+                spec.cuda(
+                    rank,
+                    non_blocking=True,
+                ),
+                spec_lengths.cuda(
+                    rank,
+                    non_blocking=True,
+                ),
+            )
+            y, y_lengths = (
+                y.cuda(
+                    rank,
+                    non_blocking=True,
+                ),
+                y_lengths.cuda(
+                    rank,
+                    non_blocking=True,
+                ),
+            )
             ssl = ssl.cuda(rank, non_blocking=True)
             ssl.requires_grad = False
             # ssl_lengths = ssl_lengths.cuda(rank, non_blocking=True)
-            text, text_lengths = (text.cuda(rank,non_blocking=True,),text_lengths.cuda(rank,non_blocking=True,),)
+            text, text_lengths = (
+                text.cuda(
+                    rank,
+                    non_blocking=True,
+                ),
+                text_lengths.cuda(
+                    rank,
+                    non_blocking=True,
+                ),
+            )
             if hps.model.version in {"v2Pro", "v2ProPlus"}:
                 sv_emb = sv_emb.cuda(rank, non_blocking=True)
         else:
@@ -334,9 +380,19 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
                 sv_emb = sv_emb.to(device)
         with autocast(enabled=hps.train.fp16_run):
             if hps.model.version in {"v2Pro", "v2ProPlus"}:
-                (y_hat,kl_ssl,ids_slice,x_mask,z_mask,(z, z_p, m_p, logs_p, m_q, logs_q),stats_ssl) = net_g(ssl, spec, spec_lengths, text, text_lengths,sv_emb)
+                (y_hat, kl_ssl, ids_slice, x_mask, z_mask, (z, z_p, m_p, logs_p, m_q, logs_q), stats_ssl) = net_g(
+                    ssl, spec, spec_lengths, text, text_lengths, sv_emb
+                )
             else:
-                (y_hat,kl_ssl,ids_slice,x_mask,z_mask,(z, z_p, m_p, logs_p, m_q, logs_q),stats_ssl,) = net_g(ssl, spec, spec_lengths, text, text_lengths)
+                (
+                    y_hat,
+                    kl_ssl,
+                    ids_slice,
+                    x_mask,
+                    z_mask,
+                    (z, z_p, m_p, logs_p, m_q, logs_q),
+                    stats_ssl,
+                ) = net_g(ssl, spec, spec_lengths, text, text_lengths)
 
             mel = spec_to_mel_torch(
                 spec,
@@ -508,7 +564,14 @@ def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loade
                 % (
                     hps.name,
                     epoch,
-                    savee(ckpt,hps.name + "_e%s_s%s" % (epoch, global_step),epoch,global_step,hps,model_version=None if hps.model.version not in {"v2Pro","v2ProPlus"}else hps.model.version),
+                    savee(
+                        ckpt,
+                        hps.name + "_e%s_s%s" % (epoch, global_step),
+                        epoch,
+                        global_step,
+                        hps,
+                        model_version=None if hps.model.version not in {"v2Pro", "v2ProPlus"} else hps.model.version,
+                    ),
                 )
             )
 

GPT_SoVITS/sv.py

@@ -1,11 +1,16 @@
-import sys,os,torch
+import sys
+import os
+import torch
+
 sys.path.append(f"{os.getcwd()}/GPT_SoVITS/eres2net")
 sv_path = "GPT_SoVITS/pretrained_models/sv/pretrained_eres2netv2w24s4ep4.ckpt"
 from ERes2NetV2 import ERes2NetV2
 import kaldi as Kaldi
+
+
 class SV:
     def __init__(self, device, is_half):
-        pretrained_state = torch.load(sv_path, map_location='cpu', weights_only=False)
+        pretrained_state = torch.load(sv_path, map_location="cpu", weights_only=False)
         embedding_model = ERes2NetV2(baseWidth=24, scale=4, expansion=4)
         embedding_model.load_state_dict(pretrained_state)
         embedding_model.eval()
@@ -18,7 +23,10 @@ class SV:
 
     def compute_embedding3(self, wav):
         with torch.no_grad():
-            if self.is_half==True:wav=wav.half()
-            feat = torch.stack([Kaldi.fbank(wav0.unsqueeze(0), num_mel_bins=80, sample_frequency=16000, dither=0) for wav0 in wav])
+            if self.is_half == True:
+                wav = wav.half()
+            feat = torch.stack(
+                [Kaldi.fbank(wav0.unsqueeze(0), num_mel_bins=80, sample_frequency=16000, dither=0) for wav0 in wav]
+            )
             sv_emb = self.embedding_model.forward3(feat)
         return sv_emb

GPT_SoVITS/text/LangSegmenter/langsegmenter.py

@@ -3,19 +3,25 @@ import re
 
 # jieba静音
 import jieba
+
 jieba.setLogLevel(logging.CRITICAL)
 
 # 更改fast_langdetect大模型位置
 from pathlib import Path
 import fast_langdetect
-fast_langdetect.infer._default_detector = fast_langdetect.infer.LangDetector(fast_langdetect.infer.LangDetectConfig(cache_dir=Path(__file__).parent.parent.parent / "pretrained_models" / "fast_langdetect"))
+
+fast_langdetect.infer._default_detector = fast_langdetect.infer.LangDetector(
+    fast_langdetect.infer.LangDetectConfig(
+        cache_dir=Path(__file__).parent.parent.parent / "pretrained_models" / "fast_langdetect"
+    )
+)
 
 
 from split_lang import LangSplitter
 
 
 def full_en(text):
-    pattern = r'^(?=.*[A-Za-z])[A-Za-z0-9\s\u0020-\u007E\u2000-\u206F\u3000-\u303F\uFF00-\uFFEF]+$'
+    pattern = r"^(?=.*[A-Za-z])[A-Za-z0-9\s\u0020-\u007E\u2000-\u206F\u3000-\u303F\uFF00-\uFFEF]+$"
     return bool(re.match(pattern, text))
 
 
@@ -34,7 +40,7 @@ def full_cjk(text):
         (0x2EBF0, 0x2EE5D),  # CJK Extension H
     ]
 
-    pattern = r'[0-9、-〜。！？.!?… /]+$'
+    pattern = r"[0-9、-〜。！？.!?… /]+$"
 
     cjk_text = ""
     for char in text:
@@ -53,28 +59,28 @@ def split_jako(tag_lang,item):
 
     lang_list: list[dict] = []
     tag = 0
-    for match in re.finditer(pattern, item['text']):
+    for match in re.finditer(pattern, item["text"]):
         if match.start() > tag:
-            lang_list.append({'lang':item['lang'],'text':item['text'][tag:match.start()]})
+            lang_list.append({"lang": item["lang"], "text": item["text"][tag : match.start()]})
 
         tag = match.end()
-        lang_list.append({'lang':tag_lang,'text':item['text'][match.start():match.end()]})
+        lang_list.append({"lang": tag_lang, "text": item["text"][match.start() : match.end()]})
 
-    if tag < len(item['text']):
-        lang_list.append({'lang':item['lang'],'text':item['text'][tag:len(item['text'])]})
+    if tag < len(item["text"]):
+        lang_list.append({"lang": item["lang"], "text": item["text"][tag : len(item["text"])]})
 
     return lang_list
 
 
 def merge_lang(lang_list, item):
-    if lang_list and item['lang'] == lang_list[-1]['lang']:
-        lang_list[-1]['text'] += item['text']
+    if lang_list and item["lang"] == lang_list[-1]["lang"]:
+        lang_list[-1]["text"] += item["text"]
     else:
         lang_list.append(item)
     return lang_list
 
 
-class LangSegmenter():
+class LangSegmenter:
     # 默认过滤器, 基于gsv目前四种语言
     DEFAULT_LANG_MAP = {
         "zh": "zh",
@@ -87,7 +93,6 @@ class LangSegmenter():
         "en": "en",
     }
 
-    
     def getTexts(text):
         lang_splitter = LangSplitter(lang_map=LangSegmenter.DEFAULT_LANG_MAP)
         substr = lang_splitter.split_by_lang(text=text)
@@ -95,18 +100,18 @@ class LangSegmenter():
         lang_list: list[dict] = []
 
         for _, item in enumerate(substr):
-            dict_item = {'lang':item.lang,'text':item.text}
+            dict_item = {"lang": item.lang, "text": item.text}
 
             # 处理短英文被识别为其他语言的问题
-            if full_en(dict_item['text']):  
-                dict_item['lang'] = 'en'
+            if full_en(dict_item["text"]):
+                dict_item["lang"] = "en"
                 lang_list = merge_lang(lang_list, dict_item)
                 continue
 
             # 处理非日语夹日文的问题(不包含CJK)
             ja_list: list[dict] = []
-            if dict_item['lang'] != 'ja':
-                ja_list = split_jako('ja',dict_item)
+            if dict_item["lang"] != "ja":
+                ja_list = split_jako("ja", dict_item)
 
             if not ja_list:
                 ja_list.append(dict_item)
@@ -115,8 +120,8 @@ class LangSegmenter():
             ko_list: list[dict] = []
             temp_list: list[dict] = []
             for _, ko_item in enumerate(ja_list):
-                if ko_item["lang"] != 'ko':
-                    ko_list = split_jako('ko',ko_item)
+                if ko_item["lang"] != "ko":
+                    ko_list = split_jako("ko", ko_item)
 
                 if ko_list:
                     temp_list.extend(ko_list)
@@ -126,10 +131,10 @@ class LangSegmenter():
             # 未存在非日韩文夹日韩文
             if len(temp_list) == 1:
                 # 未知语言检查是否为CJK
-                if dict_item['lang'] == 'x':
-                    cjk_text = full_cjk(dict_item['text'])
+                if dict_item["lang"] == "x":
+                    cjk_text = full_cjk(dict_item["text"])
                     if cjk_text:
-                        dict_item = {'lang':'zh','text':cjk_text}
+                        dict_item = {"lang": "zh", "text": cjk_text}
                         lang_list = merge_lang(lang_list, dict_item)
                     else:
                         lang_list = merge_lang(lang_list, dict_item)
@@ -141,10 +146,10 @@ class LangSegmenter():
             # 存在非日韩文夹日韩文
             for _, temp_item in enumerate(temp_list):
                 # 未知语言检查是否为CJK
-                if temp_item['lang'] == 'x':
-                    cjk_text = full_cjk(dict_item['text'])
+                if temp_item["lang"] == "x":
+                    cjk_text = full_cjk(dict_item["text"])
                     if cjk_text:
-                        dict_item = {'lang':'zh','text':cjk_text}
+                        dict_item = {"lang": "zh", "text": cjk_text}
                         lang_list = merge_lang(lang_list, dict_item)
                     else:
                         lang_list = merge_lang(lang_list, dict_item)
@@ -154,13 +159,13 @@ class LangSegmenter():
         temp_list = lang_list
         lang_list = []
         for _, temp_item in enumerate(temp_list):
-            if temp_item['lang'] == 'x':
+            if temp_item["lang"] == "x":
                 if lang_list:
-                    temp_item['lang'] = lang_list[-1]['lang']
+                    temp_item["lang"] = lang_list[-1]["lang"]
                 elif len(temp_list) > 1:
-                    temp_item['lang'] = temp_list[1]['lang']
+                    temp_item["lang"] = temp_list[1]["lang"]
                 else:
-                    temp_item['lang'] = 'zh'
+                    temp_item["lang"] = "zh"
 
             lang_list = merge_lang(lang_list, temp_item)
 

GPT_SoVITS/text/g2pw/onnx_api.py

@@ -3,7 +3,6 @@
 
 import json
 import os
-import traceback
 import warnings
 import zipfile
 from typing import Any, Dict, List, Tuple
@@ -23,7 +22,8 @@ from .utils import load_config
 onnxruntime.set_default_logger_severity(3)
 try:
     onnxruntime.preload_dlls()
-except:pass
+except:
+    pass
     # traceback.print_exc()
 warnings.filterwarnings("ignore")
 

GPT_SoVITS/text/tone_sandhi.py

@@ -655,11 +655,7 @@ class ToneSandhi:
         while i < len(seg):
             word, pos = seg[i]
             merged = False
-            if (
-                i - 1 >= 0
-                and word == "一"
-                and i + 1 < len(seg)
-            ):
+            if i - 1 >= 0 and word == "一" and i + 1 < len(seg):
                 last = new_seg[-1] if new_seg else seg[i - 1]
                 if last[0] == seg[i + 1][0] and last[1] == "v" and seg[i + 1][1] == "v":
                     combined = last[0] + "一" + seg[i + 1][0]

README.md

@@ -9,10 +9,14 @@ A Powerful Few-shot Voice Conversion and Text-to-Speech WebUI.<br><br>
 
 <!-- img src="https://counter.seku.su/cmoe?name=gptsovits&theme=r34" /><br> -->
 
-[![Open In Colab](https://img.shields.io/badge/Colab-F9AB00?style=for-the-badge&logo=googlecolab&color=525252)](https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/colab_webui.ipynb)
-[![License](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
-[![Huggingface](https://img.shields.io/badge/🤗%20-online%20demo-yellow.svg?style=for-the-badge)](https://huggingface.co/spaces/lj1995/GPT-SoVITS-v2)
-[![Discord](https://img.shields.io/discord/1198701940511617164?color=%23738ADB&label=Discord&style=for-the-badge)](https://discord.gg/dnrgs5GHfG)
+[![Train In Colab](https://img.shields.io/badge/Colab-Training-F9AB00?style=for-the-badge&logo=googlecolab)](https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/Colab-WebUI.ipynb)
+[![License](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge&logo=opensourceinitiative)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
+[![Huggingface](https://img.shields.io/badge/HuggingFace-demo-blue.svg?style=for-the-badge&logo=huggingface)](https://huggingface.co/spaces/lj1995/GPT-SoVITS-v2)
+[![Image Size](https://img.shields.io/docker/image-size/xxxxrt666/gpt-sovits/latest?style=for-the-badge&logo=docker)](https://hub.docker.com/r/xxxxrt666/gpt-sovits)
+
+[![简体中文](https://img.shields.io/badge/简体中文-阅读文档-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e)
+[![English](https://img.shields.io/badge/English-Read%20Docs-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://rentry.co/GPT-SoVITS-guide#/)
+[![Change Log](https://img.shields.io/badge/Change%20Log-View%20Updates-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/docs/en/Changelog_EN.md)
 
 **English** | [**中文简体**](./docs/cn/README.md) | [**日本語**](./docs/ja/README.md) | [**한국어**](./docs/ko/README.md) | [**Türkçe**](./docs/tr/README.md)
 
@@ -128,8 +132,9 @@ Due to rapid development in the codebase and a slower Docker image release cycle
 
 - Check [Docker Hub](https://hub.docker.com/r/xxxxrt666/gpt-sovits) for the latest available image tags
 - Choose an appropriate image tag for your environment
-- `Lite` means the Docker image does not include ASR models and UVR5 models. You can manually download the UVR5 models, while the program will automatically download the ASR models as needed
+- `Lite` means the Docker image **does not include** ASR models and UVR5 models. You can manually download the UVR5 models, while the program will automatically download the ASR models as needed
 - The appropriate architecture image (amd64/arm64) will be automatically pulled during Docker Compose
+- Docker Compose will mount **all files** in the current directory. Please switch to the project root directory and **pull the latest code** before using the Docker image
 - Optionally, build the image locally using the provided Dockerfile for the most up-to-date changes
 
 #### Environment Variables
@@ -333,7 +338,7 @@ Use v4 from v1/v2/v3 environment:
 New Features:
 
 1. Slightly higher VRAM usage than v2, surpassing v4's performance, with v2's hardware cost and speed.
-   [more details](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90features-(%E5%90%84%E7%89%88%E6%9C%AC%E7%89%B9%E6%80%A7))
+   [more details](<https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90features-(%E5%90%84%E7%89%88%E6%9C%AC%E7%89%B9%E6%80%A7)>)
 
 2.v1/v2 and the v2Pro series share the same characteristics, while v3/v4 have similar features. For training sets with average audio quality, v1/v2/v2Pro can deliver decent results, but v3/v4 cannot. Additionally, the synthesized tone and timebre of v3/v4 lean more toward the reference audio rather than the overall training set.
 

api.py

@@ -199,6 +199,8 @@ def is_full(*items):  # 任意一项为空返回False
 
 
 bigvgan_model = hifigan_model = sv_cn_model = None
+
+
 def clean_hifigan_model():
     global hifigan_model
     if hifigan_model:
@@ -208,6 +210,8 @@ def clean_hifigan_model():
             torch.cuda.empty_cache()
         except:
             pass
+
+
 def clean_bigvgan_model():
     global bigvgan_model
     if bigvgan_model:
@@ -217,6 +221,8 @@ def clean_bigvgan_model():
             torch.cuda.empty_cache()
         except:
             pass
+
+
 def clean_sv_cn_model():
     global sv_cn_model
     if sv_cn_model:
@@ -262,7 +268,9 @@ def init_hifigan():
     hifigan_model.eval()
     hifigan_model.remove_weight_norm()
     state_dict_g = torch.load(
-        "%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,), map_location="cpu", weights_only=False
+        "%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,),
+        map_location="cpu",
+        weights_only=False,
     )
     print("loading vocoder", hifigan_model.load_state_dict(state_dict_g))
     if is_half == True:
@@ -272,19 +280,21 @@ def init_hifigan():
 
 
 from sv import SV
+
+
 def init_sv_cn():
     global hifigan_model, bigvgan_model, sv_cn_model
     sv_cn_model = SV(device, is_half)
 
 
 resample_transform_dict = {}
+
+
 def resample(audio_tensor, sr0, sr1, device):
     global resample_transform_dict
     key = "%s-%s-%s" % (sr0, sr1, str(device))
     if key not in resample_transform_dict:
-        resample_transform_dict[key] = torchaudio.transforms.Resample(
-            sr0, sr1
-        ).to(device)
+        resample_transform_dict[key] = torchaudio.transforms.Resample(sr0, sr1).to(device)
     return resample_transform_dict[key](audio_tensor)
 
 
@@ -370,6 +380,7 @@ from process_ckpt import get_sovits_version_from_path_fast, load_sovits_new
 
 def get_sovits_weights(sovits_path):
     from config import pretrained_sovits_name
+
     path_sovits_v3 = pretrained_sovits_name["v3"]
     path_sovits_v4 = pretrained_sovits_name["v4"]
     is_exist_s2gv3 = os.path.exists(path_sovits_v3)
@@ -632,11 +643,13 @@ def get_spepc(hps, filename, dtype, device, is_v2pro=False):
     audio, sr0 = torchaudio.load(filename)
     if sr0 != sr1:
         audio = audio.to(device)
-        if(audio.shape[0]==2):audio=audio.mean(0).unsqueeze(0)
+        if audio.shape[0] == 2:
+            audio = audio.mean(0).unsqueeze(0)
         audio = resample(audio, sr0, sr1, device)
     else:
         audio = audio.to(device)
-        if(audio.shape[0]==2):audio=audio.mean(0).unsqueeze(0)
+        if audio.shape[0] == 2:
+            audio = audio.mean(0).unsqueeze(0)
 
     maxx = audio.abs().max()
     if maxx > 1:
@@ -937,14 +950,22 @@ def get_tts_wav(
         if version not in {"v3", "v4"}:
             if is_v2pro:
                 audio = (
-                    vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers, speed=speed,sv_emb=sv_emb)
+                    vq_model.decode(
+                        pred_semantic,
+                        torch.LongTensor(phones2).to(device).unsqueeze(0),
+                        refers,
+                        speed=speed,
+                        sv_emb=sv_emb,
+                    )
                     .detach()
                     .cpu()
                     .numpy()[0, 0]
                 )
             else:
                 audio = (
-                    vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers, speed=speed)
+                    vq_model.decode(
+                        pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers, speed=speed
+                    )
                     .detach()
                     .cpu()
                     .numpy()[0, 0]
@@ -1108,7 +1129,6 @@ def handle(
         if not default_refer.is_ready():
             return JSONResponse({"code": 400, "message": "未指定参考音频且接口无预设"}, status_code=400)
 
-
     if cut_punc == None:
         text = cut_text(text, default_cut_punc)
     else:

config.py

@@ -144,6 +144,7 @@ webui_port_subfix = 9871
 
 api_port = 9880
 
+
 # Thanks to the contribution of @Karasukaigan and @XXXXRT666
 def get_device_dtype_sm(idx: int) -> tuple[torch.device, torch.dtype, float, float]:
     cpu = torch.device("cpu")
@@ -158,9 +159,12 @@ def get_device_dtype_sm(idx: int) -> tuple[torch.device, torch.dtype, float, flo
     major, minor = capability
     sm_version = major + minor / 10.0
     is_16_series = bool(re.search(r"16\d{2}", name)) and sm_version == 7.5
-    if mem_gb < 4 or sm_version < 5.3:return cpu, torch.float32, 0.0, 0.0
-    if sm_version == 6.1 or is_16_series==True:return cuda, torch.float32, sm_version, mem_gb
-    if sm_version > 6.1:return cuda, torch.float16, sm_version, mem_gb
+    if mem_gb < 4 or sm_version < 5.3:
+        return cpu, torch.float32, 0.0, 0.0
+    if sm_version == 6.1 or is_16_series == True:
+        return cuda, torch.float32, sm_version, mem_gb
+    if sm_version > 6.1:
+        return cuda, torch.float16, sm_version, mem_gb
     return cpu, torch.float32, 0.0, 0.0
 
 

docker-compose.yaml

@@ -12,10 +12,6 @@ services:
       - "9880:9880"
     volumes:
       - .:/workspace/GPT-SoVITS
-      - /dev/null:/workspace/GPT-SoVITS/GPT_SoVITS/pretrained_models
-      - /dev/null:/workspace/GPT-SoVITS/GPT_SoVITS/text/G2PWModel
-      - /dev/null:/workspace/GPT-SoVITS/tools/asr/models
-      - /dev/null:/workspace/GPT-SoVITS/tools/uvr5/uvr5_weights
     environment:
       - is_half=true
     tty: true
@@ -34,10 +30,6 @@ services:
       - "9880:9880"
     volumes:
       - .:/workspace/GPT-SoVITS
-      - /dev/null:/workspace/GPT-SoVITS/GPT_SoVITS/pretrained_models
-      - /dev/null:/workspace/GPT-SoVITS/GPT_SoVITS/text/G2PWModel
-      - /dev/null:/workspace/GPT-SoVITS/tools/asr/models
-      - /dev/null:/workspace/GPT-SoVITS/tools/uvr5/uvr5_weights
       - tools/asr/models:/workspace/models/asr_models
       - tools/uvr5/uvr5_weights:/workspace/models/uvr5_weights
     environment:
@@ -58,10 +50,6 @@ services:
       - "9880:9880"
     volumes:
       - .:/workspace/GPT-SoVITS
-      - /dev/null:/workspace/GPT-SoVITS/GPT_SoVITS/pretrained_models
-      - /dev/null:/workspace/GPT-SoVITS/GPT_SoVITS/text/G2PWModel
-      - /dev/null:/workspace/GPT-SoVITS/tools/asr/models
-      - /dev/null:/workspace/GPT-SoVITS/tools/uvr5/uvr5_weights
     environment:
       - is_half=true
     tty: true
@@ -80,10 +68,6 @@ services:
       - "9880:9880"
     volumes:
       - .:/workspace/GPT-SoVITS
-      - /dev/null:/workspace/GPT-SoVITS/GPT_SoVITS/pretrained_models
-      - /dev/null:/workspace/GPT-SoVITS/GPT_SoVITS/text/G2PWModel
-      - /dev/null:/workspace/GPT-SoVITS/tools/asr/models
-      - /dev/null:/workspace/GPT-SoVITS/tools/uvr5/uvr5_weights
       - tools/asr/models:/workspace/models/asr_models
       - tools/uvr5/uvr5_weights:/workspace/models/uvr5_weights
     environment:

docs/cn/README.md

@@ -7,12 +7,14 @@
 
 <a href="https://trendshift.io/repositories/7033" target="_blank"><img src="https://trendshift.io/api/badge/repositories/7033" alt="RVC-Boss%2FGPT-SoVITS | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
 
-<!-- img src="https://counter.seku.su/cmoe?name=gptsovits&theme=r34" /><br> -->
+[![Train In Colab](https://img.shields.io/badge/Colab-Training-F9AB00?style=for-the-badge&logo=googlecolab)](https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/Colab-WebUI.ipynb)
+[![License](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge&logo=opensourceinitiative)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
+[![Huggingface](https://img.shields.io/badge/HuggingFace-demo-blue.svg?style=for-the-badge&logo=huggingface)](https://huggingface.co/spaces/lj1995/GPT-SoVITS-v2)
+[![Image Size](https://img.shields.io/docker/image-size/xxxxrt666/gpt-sovits/latest?style=for-the-badge&logo=docker)](https://hub.docker.com/r/xxxxrt666/gpt-sovits)
 
-[![Open In Colab](https://img.shields.io/badge/Colab-F9AB00?style=for-the-badge&logo=googlecolab&color=525252)](https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/colab_webui.ipynb)
-[![License](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
-[![Huggingface](https://img.shields.io/badge/🤗%20-online%20demo-yellow.svg?style=for-the-badge)](https://huggingface.co/spaces/lj1995/GPT-SoVITS-v2)
-[![Discord](https://img.shields.io/discord/1198701940511617164?color=%23738ADB&label=Discord&style=for-the-badge)](https://discord.gg/dnrgs5GHfG)
+[![简体中文](https://img.shields.io/badge/简体中文-阅读文档-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e)
+[![English](https://img.shields.io/badge/English-Read%20Docs-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://rentry.co/GPT-SoVITS-guide#/)
+[![Change Log](https://img.shields.io/badge/更新日志-查看更新-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/docs/cn/Changelog_CN.md)
 
 [**English**](../../README.md) | **中文简体** | [**日本語**](../ja/README.md) | [**한국어**](../ko/README.md) | [**Türkçe**](../tr/README.md)
 
@@ -128,8 +130,9 @@ brew install ffmpeg
 
 - 前往 [Docker Hub](https://hub.docker.com/r/xxxxrt666/gpt-sovits) 查看最新可用的镜像标签(tags)
 - 根据你的运行环境选择合适的镜像标签
-- `Lite` Docker 镜像不包含 ASR 模型和 UVR5 模型. 你可以自行下载 UVR5 模型, ASR 模型则会在需要时由程序自动下载
+- `Lite` Docker 镜像**不包含** ASR 模型和 UVR5 模型. 你可以自行下载 UVR5 模型, ASR 模型则会在需要时由程序自动下载
 - 在使用 Docker Compose 时, 会自动拉取适配的架构镜像 (amd64 或 arm64)
+- Docker Compose 将会挂载当前目录的**所有文件**, 请在使用 Docker 镜像前先切换到项目根目录并**拉取代码更新**
 - 可选：为了获得最新的更改, 你可以使用提供的 Dockerfile 在本地构建镜像
 
 #### 环境变量
@@ -329,7 +332,7 @@ python webui.py
 新特性：
 
 1. **相比 V2 占用稍高显存, 性能超过 V4, 在保留 V2 硬件成本和推理速度优势的同时实现更高音质.**
-   [更多详情](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90features-(%E5%90%84%E7%89%88%E6%9C%AC%E7%89%B9%E6%80%A7))
+   [更多详情](<https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90features-(%E5%90%84%E7%89%88%E6%9C%AC%E7%89%B9%E6%80%A7)>)
 
 2. V1/V2 与 V2Pro 系列具有相同特性, V3/V4 则具备相近功能. 对于平均音频质量较低的训练集, V1/V2/V2Pro 可以取得较好的效果, 但 V3/V4 无法做到. 此外, V3/V4 合成的声音更偏向参考音频, 而不是整体训练集的风格.
 

docs/ja/README.md

@@ -5,12 +5,16 @@
 
 [![madewithlove](https://img.shields.io/badge/made_with-%E2%9D%A4-red?style=for-the-badge&labelColor=orange)](https://github.com/RVC-Boss/GPT-SoVITS)
 
-<img src="https://counter.seku.su/cmoe?name=gptsovits&theme=r34" /><br>
+<a href="https://trendshift.io/repositories/7033" target="_blank"><img src="https://trendshift.io/api/badge/repositories/7033" alt="RVC-Boss%2FGPT-SoVITS | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
 
-[![Open In Colab](https://img.shields.io/badge/Colab-F9AB00?style=for-the-badge&logo=googlecolab&color=525252)](https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/colab_webui.ipynb)
-[![License](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
-[![Huggingface](https://img.shields.io/badge/🤗%20-online%20demo-yellow.svg?style=for-the-badge)](https://huggingface.co/spaces/lj1995/GPT-SoVITS-v2)
-[![Discord](https://img.shields.io/discord/1198701940511617164?color=%23738ADB&label=Discord&style=for-the-badge)](https://discord.gg/dnrgs5GHfG)
+[![Train In Colab](https://img.shields.io/badge/Colab-Training-F9AB00?style=for-the-badge&logo=googlecolab)](https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/Colab-WebUI.ipynb)
+[![License](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge&logo=opensourceinitiative)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
+[![Huggingface](https://img.shields.io/badge/HuggingFace-demo-blue.svg?style=for-the-badge&logo=huggingface)](https://huggingface.co/spaces/lj1995/GPT-SoVITS-v2)
+[![Image Size](https://img.shields.io/docker/image-size/xxxxrt666/gpt-sovits/latest?style=for-the-badge&logo=docker)](https://hub.docker.com/r/xxxxrt666/gpt-sovits)
+
+[![简体中文](https://img.shields.io/badge/简体中文-阅读文档-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e)
+[![English](https://img.shields.io/badge/English-Read%20Docs-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://rentry.co/GPT-SoVITS-guide#/)
+[![Change Log](https://img.shields.io/badge/Change%20Log-View%20Updates-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/docs/ja/Changelog_JA.md)
 
 [**English**](../../README.md) | [**中文简体**](../cn/README.md) | **日本語** | [**한국어**](../ko/README.md) | [**Türkçe**](../tr/README.md)
 
@@ -122,8 +126,9 @@ brew install ffmpeg
 
 - [Docker Hub](https://hub.docker.com/r/xxxxrt666/gpt-sovits) で最新のイメージタグを確認してください
 - 環境に合った適切なイメージタグを選択してください
-- `Lite` とは、Docker イメージに ASR モデルおよび UVR5 モデルが含まれていないことを意味します. UVR5 モデルは手動でダウンロードし、ASR モデルは必要に応じてプログラムが自動的にダウンロードします
+- `Lite` とは、Docker イメージに ASR モデルおよび UVR5 モデルが**含まれていない**ことを意味します. UVR5 モデルは手動でダウンロードし、ASR モデルは必要に応じてプログラムが自動的にダウンロードします
 - Docker Compose 実行時に、対応するアーキテクチャ (amd64 または arm64) のイメージが自動的に取得されます
+- Docker Compose は現在のディレクトリ内の**すべてのファイル**をマウントします. Docker イメージを使用する前に、プロジェクトのルートディレクトリに移動し、**コードを最新の状態に更新**してください
 - オプション：最新の変更を反映させるため、提供されている Dockerfile を使ってローカルでイメージをビルドすることも可能です
 
 #### 環境変数
@@ -304,7 +309,7 @@ v2 環境から v3 を使用する方法:
 新機能:
 
 1. **V4 は、V3 で発生していた非整数倍アップサンプリングによる金属音の問題を修正し、音声がこもる問題を防ぐためにネイティブに 48kHz 音声を出力します（V3 はネイティブに 24kHz 音声のみ出力）**. 作者は V4 を V3 の直接的な置き換えとして推奨していますが、さらなるテストが必要です.
-   [詳細はこちら](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v3v4%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7))
+   [詳細はこちら](<https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v3v4%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7)>)
 
 V1/V2/V3 環境から V4 への移行方法:
 
@@ -319,7 +324,7 @@ V1/V2/V3 環境から V4 への移行方法:
 新機能:
 
 1. **V2 と比較してやや高いメモリ使用量ですが、ハードウェアコストと推論速度は維持しつつ、V4 よりも高い性能と音質を実現します. **
-   [詳細はこちら](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90features-(%E5%90%84%E7%89%88%E6%9C%AC%E7%89%B9%E6%80%A7))
+   [詳細はこちら](<https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90features-(%E5%90%84%E7%89%88%E6%9C%AC%E7%89%B9%E6%80%A7)>)
 
 2. V1/V2 と V2Pro シリーズは類似した特徴を持ち、V3/V4 も同様の機能を持っています. 平均音質が低いトレーニングセットの場合、V1/V2/V2Pro は良好な結果を出すことができますが、V3/V4 では対応できません. また、V3/V4 の合成音声はトレーニング全体ではなく、より参考音声に寄った音質になります.
 

docs/ko/README.md

@@ -5,12 +5,16 @@
 
 [![madewithlove](https://img.shields.io/badge/made_with-%E2%9D%A4-red?style=for-the-badge&labelColor=orange)](https://github.com/RVC-Boss/GPT-SoVITS)
 
-<img src="https://counter.seku.su/cmoe?name=gptsovits&theme=r34" /><br>
+<a href="https://trendshift.io/repositories/7033" target="_blank"><img src="https://trendshift.io/api/badge/repositories/7033" alt="RVC-Boss%2FGPT-SoVITS | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
 
-[![Open In Colab](https://img.shields.io/badge/Colab-F9AB00?style=for-the-badge&logo=googlecolab&color=525252)](https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/colab_webui.ipynb)
-[![License](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
-[![Huggingface](https://img.shields.io/badge/🤗%20-online%20demo-yellow.svg?style=for-the-badge)](https://huggingface.co/spaces/lj1995/GPT-SoVITS-v2)
-[![Discord](https://img.shields.io/discord/1198701940511617164?color=%23738ADB&label=Discord&style=for-the-badge)](https://discord.gg/dnrgs5GHfG)
+[![Train In Colab](https://img.shields.io/badge/Colab-Training-F9AB00?style=for-the-badge&logo=googlecolab)](https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/Colab-WebUI.ipynb)
+[![License](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge&logo=opensourceinitiative)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
+[![Huggingface](https://img.shields.io/badge/HuggingFace-demo-blue.svg?style=for-the-badge&logo=huggingface)](https://huggingface.co/spaces/lj1995/GPT-SoVITS-v2)
+[![Image Size](https://img.shields.io/docker/image-size/xxxxrt666/gpt-sovits/latest?style=for-the-badge&logo=docker)](https://hub.docker.com/r/xxxxrt666/gpt-sovits)
+
+[![简体中文](https://img.shields.io/badge/简体中文-阅读文档-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e)
+[![English](https://img.shields.io/badge/English-Read%20Docs-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://rentry.co/GPT-SoVITS-guide#/)
+[![Change Log](https://img.shields.io/badge/Change%20Log-View%20Updates-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/docs/ko/Changelog_KO.md)
 
 [**English**](../../README.md) | [**中文简体**](../cn/README.md) | [**日本語**](../ja/README.md) | **한국어** | [**Türkçe**](../tr/README.md)
 
@@ -122,8 +126,9 @@ brew install ffmpeg
 
 - [Docker Hub](https://hub.docker.com/r/xxxxrt666/gpt-sovits)에서 최신 이미지 태그를 확인하세요
 - 환경에 맞는 적절한 이미지 태그를 선택하세요
-- `Lite` 는 Docker 이미지에 ASR 모델과 UVR5 모델이 포함되어 있지 않음을 의미합니다. UVR5 모델은 사용자가 직접 다운로드해야 하며, ASR 모델은 필요 시 프로그램이 자동으로 다운로드합니다
+- `Lite` 는 Docker 이미지에 ASR 모델과 UVR5 모델이 **포함되어 있지 않음**을 의미합니다. UVR5 모델은 사용자가 직접 다운로드해야 하며, ASR 모델은 필요 시 프로그램이 자동으로 다운로드합니다
 - Docker Compose 실행 시, 해당 아키텍처에 맞는 이미지(amd64 또는 arm64)가 자동으로 다운로드됩니다
+- Docker Compose는 현재 디렉터리의 **모든 파일**을 마운트합니다. Docker 이미지를 사용하기 전에 프로젝트 루트 디렉터리로 이동하여 코드를 **최신 상태로 업데이트**하세요
 - 선택 사항: 최신 변경사항을 반영하려면 제공된 Dockerfile을 사용하여 로컬에서 직접 이미지를 빌드할 수 있습니다
 
 #### 환경 변수
@@ -319,7 +324,7 @@ V1/V2/V3 환경에서 V4로 전환 방법:
 신규 기능:
 
 1. **V2보다 약간 높은 VRAM 사용량이지만 성능은 V4보다 우수하며, V2 수준의 하드웨어 비용과 속도를 유지합니다**.
-   [자세히 보기](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90features-(%E5%90%84%E7%89%88%E6%9C%AC%E7%89%B9%E6%80%A7))
+   [자세히 보기](<https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90features-(%E5%90%84%E7%89%88%E6%9C%AC%E7%89%B9%E6%80%A7)>)
 
 2. V1/V2와 V2Pro 시리즈는 유사한 특징을 가지며, V3/V4도 비슷한 기능을 가지고 있습니다. 평균 음질이 낮은 학습 데이터셋에서는 V1/V2/V2Pro가 좋은 결과를 내지만 V3/V4는 그렇지 못합니다. 또한 V3/V4의 합성 음색은 전체 학습 데이터셋보다는 참고 음성에 더 가깝습니다.
 

docs/tr/README.md

@@ -7,12 +7,14 @@ Güçlü Birkaç Örnekli Ses Dönüştürme ve Metinden Konuşmaya Web Arayüz
 
 <a href="https://trendshift.io/repositories/7033" target="_blank"><img src="https://trendshift.io/api/badge/repositories/7033" alt="RVC-Boss%2FGPT-SoVITS | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
 
-<!-- img src="https://counter.seku.su/cmoe?name=gptsovits&theme=r34" /><br> -->
+[![Train In Colab](https://img.shields.io/badge/Colab-Training-F9AB00?style=for-the-badge&logo=googlecolab)](https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/Colab-WebUI.ipynb)
+[![License](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge&logo=opensourceinitiative)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
+[![Huggingface](https://img.shields.io/badge/HuggingFace-demo-blue.svg?style=for-the-badge&logo=huggingface)](https://huggingface.co/spaces/lj1995/GPT-SoVITS-v2)
+[![Image Size](https://img.shields.io/docker/image-size/xxxxrt666/gpt-sovits/latest?style=for-the-badge&logo=docker)](https://hub.docker.com/r/xxxxrt666/gpt-sovits)
 
-[![Open In Colab](https://img.shields.io/badge/Colab-F9AB00?style=for-the-badge&logo=googlecolab&color=525252)](https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/colab_webui.ipynb)
-[![License](https://img.shields.io/badge/LICENSE-MIT-green.svg?style=for-the-badge)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
-[![Huggingface](https://img.shields.io/badge/🤗%20-online%20demo-yellow.svg?style=for-the-badge)](https://huggingface.co/spaces/lj1995/GPT-SoVITS-v2)
-[![Discord](https://img.shields.io/discord/1198701940511617164?color=%23738ADB&label=Discord&style=for-the-badge)](https://discord.gg/dnrgs5GHfG)
+[![简体中文](https://img.shields.io/badge/简体中文-阅读文档-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e)
+[![English](https://img.shields.io/badge/English-Read%20Docs-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://rentry.co/GPT-SoVITS-guide#/)
+[![Change Log](https://img.shields.io/badge/Change%20Log-View%20Updates-blue?style=for-the-badge&logo=googledocs&logoColor=white)](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/docs/tr/Changelog_TR.md)
 
 [**English**](../../README.md) | [**中文简体**](../cn/README.md) | [**日本語**](../ja/README.md) | [**한국어**](../ko/README.md) | **Türkçe**
 
@@ -124,8 +126,9 @@ Kod tabanı hızla geliştiği halde Docker imajları daha yavaş yayınlandığ
 
 - En güncel kullanılabilir imaj etiketlerini görmek için [Docker Hub](https://hub.docker.com/r/xxxxrt666/gpt-sovits) adresini kontrol edin
 - Ortamınıza uygun bir imaj etiketi seçin
-- `Lite`, Docker imajında ASR modelleri ve UVR5 modellerinin bulunmadığı anlamına gelir. UVR5 modellerini manuel olarak indirebilirsiniz; ASR modelleri ise gerektiğinde program tarafından otomatik olarak indirilir
+- `Lite`, Docker imajında ASR modelleri ve UVR5 modellerinin **bulunmadığı** anlamına gelir. UVR5 modellerini manuel olarak indirebilirsiniz; ASR modelleri ise gerektiğinde program tarafından otomatik olarak indirilir
 - Docker Compose sırasında, uygun mimariye (amd64 veya arm64) ait imaj otomatik olarak indirilir
+- Docker Compose, mevcut dizindeki **tüm dosyaları** bağlayacaktır. Docker imajını kullanmadan önce lütfen proje kök dizinine geçin ve **en son kodu çekin**
 - Opsiyonel: En güncel değişiklikleri almak için, sağlanan Dockerfile ile yerel olarak imajı kendiniz oluşturabilirsiniz
 
 #### Ortam Değişkenleri
@@ -323,7 +326,7 @@ V1/V2/V3 ortamından V4'e geçiş:
 Yeni Özellikler:
 
 1. **V2 ile karşılaştırıldığında biraz daha yüksek VRAM kullanımı sağlar ancak V4'ten daha iyi performans gösterir; aynı donanım maliyeti ve hız avantajını korur**.
-   [Daha fazla bilgi](https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90features-(%E5%90%84%E7%89%88%E6%9C%AC%E7%89%B9%E6%80%A7))
+   [Daha fazla bilgi](<https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90features-(%E5%90%84%E7%89%88%E6%9C%AC%E7%89%B9%E6%80%A7)>)
 
 2. V1/V2 ve V2Pro serisi benzer özelliklere sahipken, V3/V4 de yakın işlevleri paylaşır. Ortalama kalite düşük olan eğitim setleriyle V1/V2/V2Pro iyi sonuçlar verebilir ama V3/V4 veremez. Ayrıca, V3/V4’ün ürettiği ses tonu genel eğitim setine değil, referans ses örneğine daha çok benzemektedir.
 

install.sh

@@ -5,14 +5,62 @@ SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" &>/dev/null && pwd)"
 
 cd "$SCRIPT_DIR" || exit 1
 
-set -e
+RESET="\033[0m"
+BOLD="\033[1m"
+ERROR="\033[1;31m[ERROR]: $RESET"
+WARNING="\033[1;33m[WARNING]: $RESET"
+INFO="\033[1;32m[INFO]: $RESET"
+SUCCESS="\033[1;34m[SUCCESS]: $RESET"
+
+set -eE
+set -o errtrace
+
+trap 'on_error $LINENO "$BASH_COMMAND" $?' ERR
+
+# shellcheck disable=SC2317
+on_error() {
+    local lineno="$1"
+    local cmd="$2"
+    local code="$3"
+
+    echo -e "${ERROR}${BOLD}Command \"${cmd}\" Failed${RESET} at ${BOLD}Line ${lineno}${RESET} with Exit Code ${BOLD}${code}${RESET}"
+    echo -e "${ERROR}${BOLD}Call Stack:${RESET}"
+    for ((i = ${#FUNCNAME[@]} - 1; i >= 1; i--)); do
+        echo -e "  in ${BOLD}${FUNCNAME[i]}()${RESET} at ${BASH_SOURCE[i]}:${BOLD}${BASH_LINENO[i - 1]}${RESET}"
+    done
+    exit "$code"
+}
+
+run_conda_quiet() {
+    local output
+    output=$(conda install --yes --quiet "$@" 2>&1) || {
+        echo -e "${ERROR} Conda install failed:\n$output"
+        exit 1
+    }
+}
+
+run_pip_quiet() {
+    local output
+    output=$(pip install "$@" 2>&1) || {
+        echo -e "${ERROR} Pip install failed:\n$output"
+        exit 1
+    }
+}
+
+run_wget_quiet() {
+    local output
+    output=$(wget --tries=25 --wait=5 --read-timeout=40 --retry-on-http-error=404 "$@" 2>&1) || {
+        echo -e "${ERROR} Wget failed:\n$output"
+        exit 1
+    }
+}
 
 if ! command -v conda &>/dev/null; then
-    echo "Conda Not Found"
+    echo -e "${ERROR}Conda Not Found"
     exit 1
 fi
 
-trap 'echo "Error Occured at \"$BASH_COMMAND\" with exit code $?"; exit 1' ERR
+run_conda_quiet gcc
 
 USE_CUDA=false
 USE_ROCM=false
@@ -34,8 +82,8 @@ print_help() {
     echo "  -h, --help                             Show this help message and exit"
     echo ""
     echo "Examples:"
-    echo "  bash install.sh --source HF --download-uvr5"
-    echo "  bash install.sh --source ModelScope"
+    echo "  bash install.sh --device CU128 --source HF --download-uvr5"
+    echo "  bash install.sh --device MPS --source ModelScope"
 }
 
 # Show help if no arguments provided
@@ -59,8 +107,8 @@ while [[ $# -gt 0 ]]; do
             USE_MODELSCOPE=true
             ;;
         *)
-            echo "Error: Invalid Download Source: $2"
-            echo "Choose From: [HF, HF-Mirror, ModelScope]"
+            echo -e "${ERROR}Error: Invalid Download Source: $2"
+            echo -e "${ERROR}Choose From: [HF, HF-Mirror, ModelScope]"
             exit 1
             ;;
         esac
@@ -86,8 +134,8 @@ while [[ $# -gt 0 ]]; do
             USE_CPU=true
             ;;
         *)
-            echo "Error: Invalid Device: $2"
-            echo "Choose From: [CU126, CU128, ROCM, MPS, CPU]"
+            echo -e "${ERROR}Error: Invalid Device: $2"
+            echo -e "${ERROR}Choose From: [CU126, CU128, ROCM, MPS, CPU]"
             exit 1
             ;;
         esac
@@ -102,22 +150,23 @@ while [[ $# -gt 0 ]]; do
         exit 0
         ;;
     *)
-        echo "Unknown Argument: $1"
-        echo "Use -h or --help to see available options."
+        echo -e "${ERROR}Unknown Argument: $1"
+        echo ""
+        print_help
         exit 1
         ;;
     esac
 done
 
 if ! $USE_CUDA && ! $USE_ROCM && ! $USE_CPU; then
-    echo "Error: Device is REQUIRED"
+    echo -e "${ERROR}Error: Device is REQUIRED"
     echo ""
     print_help
     exit 1
 fi
 
 if ! $USE_HF && ! $USE_HF_MIRROR && ! $USE_MODELSCOPE; then
-    echo "Error: Download Source is REQUIRED"
+    echo -e "${ERROR}Error: Download Source is REQUIRED"
     echo ""
     print_help
     exit 1
@@ -125,55 +174,65 @@ fi
 
 # 安装构建工具
 # Install build tools
+echo -e "${INFO}Detected system: $(uname -s) $(uname -r) $(uname -m)"
 if [ "$(uname)" != "Darwin" ]; then
     gcc_major_version=$(command -v gcc >/dev/null 2>&1 && gcc -dumpversion | cut -d. -f1 || echo 0)
     if [ "$gcc_major_version" -lt 11 ]; then
-        echo "Installing GCC & G++..."
-        conda install -c conda-forge gcc=11 gxx=11 -q -y
+        echo -e "${INFO}Installing GCC & G++..."
+        run_conda_quiet gcc=11 gxx=11
+        echo -e "${SUCCESS}GCC & G++ Installed..."
     else
-        echo "GCC >=11"
+        echo -e "${INFO}Detected GCC Version: $gcc_major_version"
+        echo -e "${INFO}Skip Installing GCC & G++ From Conda-Forge"
     fi
 else
     if ! xcode-select -p &>/dev/null; then
-        echo "Installing Xcode Command Line Tools..."
+        echo -e "${INFO}Installing Xcode Command Line Tools..."
         xcode-select --install
-    fi
-    echo "Waiting For Xcode Command Line Tools Installation Complete..."
+        echo -e "${INFO}Waiting For Xcode Command Line Tools Installation Complete..."
         while true; do
             sleep 20
 
             if xcode-select -p &>/dev/null; then
-            echo "Xcode Command Line Tools Installed"
+                echo -e "${SUCCESS}Xcode Command Line Tools Installed"
                 break
             else
-            echo "Installing，Please Wait..."
+                echo -e "${INFO}Installing，Please Wait..."
             fi
         done
-    conda install -c conda-forge -q -y
+    else
+        XCODE_PATH=$(xcode-select -p)
+        if [[ "$XCODE_PATH" == *"Xcode.app"* ]]; then
+            echo -e "${WARNING} Detected Xcode path: $XCODE_PATH"
+            echo -e "${WARNING} If your Xcode version does not match your macOS version, it may cause unexpected issues during compilation or package builds."
+        fi
+    fi
 fi
 
-echo "Installing ffmpeg and cmake..."
-conda install ffmpeg cmake make -q -y
+echo -e "${INFO}Installing FFmpeg & CMake..."
+run_conda_quiet ffmpeg cmake make
+echo -e "${SUCCESS}FFmpeg & CMake Installed"
 
-echo "Installing unzip..."
-conda install unzip -y --quiet
+echo -e "${INFO}Installing unzip..."
+run_conda_quiet unzip
+echo -e "${SUCCESS}unzip Installed"
 
 if [ "$USE_HF" = "true" ]; then
-    echo "Download Model From HuggingFace"
+    echo -e "${INFO}Download Model From HuggingFace"
     PRETRINED_URL="https://huggingface.co/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/pretrained_models.zip"
     G2PW_URL="https://huggingface.co/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/G2PWModel.zip"
     UVR5_URL="https://huggingface.co/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/uvr5_weights.zip"
     NLTK_URL="https://huggingface.co/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/nltk_data.zip"
     PYOPENJTALK_URL="https://huggingface.co/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/open_jtalk_dic_utf_8-1.11.tar.gz"
 elif [ "$USE_HF_MIRROR" = "true" ]; then
-    echo "Download Model From HuggingFace-Mirror"
+    echo -e "${INFO}Download Model From HuggingFace-Mirror"
     PRETRINED_URL="https://hf-mirror.com/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/pretrained_models.zip"
     G2PW_URL="https://hf-mirror.com/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/G2PWModel.zip"
     UVR5_URL="https://hf-mirror.com/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/uvr5_weights.zip"
     NLTK_URL="https://hf-mirror.com/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/nltk_data.zip"
     PYOPENJTALK_URL="https://hf-mirror.com/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/open_jtalk_dic_utf_8-1.11.tar.gz"
 elif [ "$USE_MODELSCOPE" = "true" ]; then
-    echo "Download Model From ModelScope"
+    echo -e "${INFO}Download Model From ModelScope"
     PRETRINED_URL="https://www.modelscope.cn/models/XXXXRT/GPT-SoVITS-Pretrained/resolve/master/pretrained_models.zip"
     G2PW_URL="https://www.modelscope.cn/models/XXXXRT/GPT-SoVITS-Pretrained/resolve/master/G2PWModel.zip"
     UVR5_URL="https://www.modelscope.cn/models/XXXXRT/GPT-SoVITS-Pretrained/resolve/master/uvr5_weights.zip"
@@ -181,118 +240,129 @@ elif [ "$USE_MODELSCOPE" = "true" ]; then
     PYOPENJTALK_URL="https://www.modelscope.cn/models/XXXXRT/GPT-SoVITS-Pretrained/resolve/master/open_jtalk_dic_utf_8-1.11.tar.gz"
 fi
 
-if [ "$WORKFLOW" = "true" ]; then
-    WGET_CMD=(wget -nv --tries=25 --wait=5 --read-timeout=40 --retry-on-http-error=404)
-else
-    WGET_CMD=(wget --tries=25 --wait=5 --read-timeout=40 --retry-on-http-error=404)
-fi
-
 if find -L "GPT_SoVITS/pretrained_models" -mindepth 1 ! -name '.gitignore' | grep -q .; then
-    echo "Pretrained Model Exists"
+    echo -e "${INFO}Pretrained Model Exists"
+    echo -e "${INFO}Skip Downloading Pretrained Models"
 else
-    echo "Download Pretrained Models"
-    "${WGET_CMD[@]}" "$PRETRINED_URL"
+    echo -e "${INFO}Downloading Pretrained Models..."
+    rm -rf pretrained_models.zip
+    run_wget_quiet "$PRETRINED_URL"
 
     unzip -q -o pretrained_models.zip -d GPT_SoVITS
     rm -rf pretrained_models.zip
+    echo -e "${SUCCESS}Pretrained Models Downloaded"
 fi
 
 if [ ! -d "GPT_SoVITS/text/G2PWModel" ]; then
-    echo "Download G2PWModel"
-    "${WGET_CMD[@]}" "$G2PW_URL"
+    echo -e "${INFO}Downloading G2PWModel.."
+    rm -rf G2PWModel.zip
+    run_wget_quiet "$G2PW_URL"
 
     unzip -q -o G2PWModel.zip -d GPT_SoVITS/text
     rm -rf G2PWModel.zip
+    echo -e "${SUCCESS}G2PWModel Downloaded"
 else
-    echo "G2PWModel Exists"
+    echo -e "${INFO}G2PWModel Exists"
+    echo -e "${INFO}Skip Downloading G2PWModel"
 fi
 
 if [ "$DOWNLOAD_UVR5" = "true" ]; then
     if find -L "tools/uvr5/uvr5_weights" -mindepth 1 ! -name '.gitignore' | grep -q .; then
-        echo "UVR5 Model Exists"
+        echo -e"${INFO}UVR5 Models Exists"
+        echo -e "${INFO}Skip Downloading UVR5 Models"
     else
-        echo "Download UVR5 Model"
-        "${WGET_CMD[@]}" "$UVR5_URL"
+        echo -e "${INFO}Downloading UVR5 Models..."
+        rm -rf uvr5_weights.zip
+        run_wget_quiet "$UVR5_URL"
 
         unzip -q -o uvr5_weights.zip -d tools/uvr5
         rm -rf uvr5_weights.zip
+        echo -e "${SUCCESS}UVR5 Models Downloaded"
     fi
 fi
 
 if [ "$USE_CUDA" = true ] && [ "$WORKFLOW" = false ]; then
-    echo "Checking for CUDA installation..."
+    echo -e "${INFO}Checking For Nvidia Driver Installation..."
     if command -v nvidia-smi &>/dev/null; then
-        echo "CUDA found."
+        echo "${INFO}Nvidia Driver Founded"
     else
+        echo -e "${WARNING}Nvidia Driver Not Found, Fallback to CPU"
         USE_CUDA=false
         USE_CPU=true
-        echo "CUDA not found."
     fi
 fi
 
 if [ "$USE_ROCM" = true ] && [ "$WORKFLOW" = false ]; then
-    echo "Checking for ROCm installation..."
+    echo -e "${INFO}Checking For ROCm Installation..."
     if [ -d "/opt/rocm" ]; then
-        echo "ROCm found."
+        echo -e "${INFO}ROCm Founded"
         if grep -qi "microsoft" /proc/version; then
-            echo "You are running WSL."
+            echo -e "${INFO}WSL2 Founded"
             IS_WSL=true
         else
-            echo "You are NOT running WSL."
             IS_WSL=false
         fi
     else
+        echo -e "${WARNING}ROCm Not Found, Fallback to CPU"
         USE_ROCM=false
         USE_CPU=true
-        echo "ROCm not found."
     fi
 fi
 
 if [ "$USE_CUDA" = true ] && [ "$WORKFLOW" = false ]; then
-    echo "Installing PyTorch with CUDA support..."
     if [ "$CUDA" = 128 ]; then
-        pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu128
+        echo -e "${INFO}Installing PyTorch For CUDA 12.8..."
+        run_pip_quiet torch torchaudio --index-url "https://download.pytorch.org/whl/cu128"
     elif [ "$CUDA" = 126 ]; then
-        pip install torch==2.6 torchaudio --index-url https://download.pytorch.org/whl/cu126
+        echo -e "${INFO}Installing PyTorch For CUDA 12.6..."
+        run_pip_quiet torch torchaudio --index-url "https://download.pytorch.org/whl/cu126"
     fi
 elif [ "$USE_ROCM" = true ] && [ "$WORKFLOW" = false ]; then
-    echo "Installing PyTorch with ROCm support..."
-    pip install torch==2.6 torchaudio --index-url https://download.pytorch.org/whl/rocm6.2
+    echo -e "${INFO}Installing PyTorch For ROCm 6.2..."
+    run_pip_quiet torch torchaudio --index-url "https://download.pytorch.org/whl/rocm6.2"
 elif [ "$USE_CPU" = true ] && [ "$WORKFLOW" = false ]; then
-    echo "Installing PyTorch for CPU..."
-    pip install torch==2.6 torchaudio --index-url https://download.pytorch.org/whl/cpu
+    echo -e "${INFO}Installing PyTorch For CPU..."
+    run_pip_quiet torch torchaudio --index-url "https://download.pytorch.org/whl/cpu"
 elif [ "$WORKFLOW" = false ]; then
-    echo "Unknown Err"
+    echo -e "${ERROR}Unknown Err"
     exit 1
 fi
+echo -e "${SUCCESS}PyTorch Installed"
 
-echo "Installing Python dependencies from requirements.txt..."
+echo -e "${INFO}Installing Python Dependencies From requirements.txt..."
 
-# 刷新环境
-# Refresh environment
 hash -r
 
-pip install -r extra-req.txt --no-deps --quiet
+run_pip_quiet -r extra-req.txt --no-deps
 
-pip install -r requirements.txt --quiet
+run_pip_quiet -r requirements.txt
+
+echo -e "${SUCCESS}Python Dependencies Installed"
 
 PY_PREFIX=$(python -c "import sys; print(sys.prefix)")
 PYOPENJTALK_PREFIX=$(python -c "import os, pyopenjtalk; print(os.path.dirname(pyopenjtalk.__file__))")
 
-"${WGET_CMD[@]}" "$NLTK_URL" -O nltk_data.zip
+echo -e "${INFO}Downloading NLTK Data..."
+rm -rf nltk_data.zip
+run_wget_quiet "$NLTK_URL" -O nltk_data.zip
 unzip -q -o nltk_data -d "$PY_PREFIX"
 rm -rf nltk_data.zip
+echo -e "${SUCCESS}NLTK Data Downloaded"
 
-"${WGET_CMD[@]}" "$PYOPENJTALK_URL" -O open_jtalk_dic_utf_8-1.11.tar.gz
-tar -xvzf open_jtalk_dic_utf_8-1.11.tar.gz -C "$PYOPENJTALK_PREFIX"
+echo -e "${INFO}Downloading Open JTalk Dict..."
 rm -rf open_jtalk_dic_utf_8-1.11.tar.gz
+run_wget_quiet "$PYOPENJTALK_URL" -O open_jtalk_dic_utf_8-1.11.tar.gz
+tar -xzf open_jtalk_dic_utf_8-1.11.tar.gz -C "$PYOPENJTALK_PREFIX"
+rm -rf open_jtalk_dic_utf_8-1.11.tar.gz
+echo -e "${SUCCESS}Open JTalk Dic Downloaded"
 
 if [ "$USE_ROCM" = true ] && [ "$IS_WSL" = true ]; then
-    echo "Update to WSL compatible runtime lib..."
+    echo -e "${INFO}Updating WSL Compatible Runtime Lib For ROCm..."
     location=$(pip show torch | grep Location | awk -F ": " '{print $2}')
     cd "${location}"/torch/lib/ || exit
     rm libhsa-runtime64.so*
     cp /opt/rocm/lib/libhsa-runtime64.so.1.2 libhsa-runtime64.so
+    echo -e "${SUCCESS}ROCm Runtime Lib Updated..."
 fi
 
-echo "Installation completed successfully!"
+echo -e "${SUCCESS}Installation Completed"

tools/assets.py

@@ -1,81 +1,38 @@
 js = """
-function createGradioAnimation() {
+function deleteTheme() {
     
 const params = new URLSearchParams(window.location.search);
-    if (params.get('__theme') !== 'light') { 
-        params.set('__theme', 'light'); // 仅当 __theme 不是 'light' 时设置为 'light'
-        window.location.search = params.toString(); // 更新 URL，触发页面刷新
+if (params.has('__theme')) {
+    params.delete('__theme');
+    const newUrl = `${window.location.pathname}?${params.toString()}`;
+    window.location.replace(newUrl);
 }
 
-    var container = document.createElement('div');
-    container.id = 'gradio-animation';
-    container.style.fontSize = '2em';
-    container.style.fontWeight = '500';
-    container.style.textAlign = 'center';
-    container.style.marginBottom = '20px';
-    container.style.fontFamily = '-apple-system, sans-serif, Arial, Calibri';
-
-    var text = 'Welcome to GPT-SoVITS !';
-    for (var i = 0; i < text.length; i++) {
-        (function(i){
-            setTimeout(function(){
-                var letter = document.createElement('span');
-                letter.style.opacity = '0';
-                letter.style.transition = 'opacity 0.5s';
-                letter.innerText = text[i];
-
-                container.appendChild(letter);
-
-                setTimeout(function() {
-                    letter.style.opacity = '1';
-                }, 50);
-            }, i * 250);
-        })(i);
-    }
-    return 'Animation created';
 }
 """
 
-
 css = """
 /* CSSStyleRule */
-
 .markdown {
-    background-color: lightblue;
     padding: 6px 10px;
 }
 
-.checkbox_info {
-    color: var(--block-title-text-color) !important;
-    font-size: var(--block-title-text-size) !important;
-    font-weight: var(--block-title-text-weight) !important;
-    height: 22px;
-    margin-bottom: 8px !important;
+@media (prefers-color-scheme: light) {
+    .markdown {
+        background-color: lightblue;
+        color: #000;
+    }
+}
+
+@media (prefers-color-scheme: dark) {
+    .markdown {
+        background-color: #4b4b4b;
+        color: rgb(244, 244, 245);
+    }
 }
 
 ::selection {
-    background: #ffc078; !important;
-}
-
-#checkbox_train_dpo input[type="checkbox"]{
-    margin-top: 6px;
-}
-
-#checkbox_train_dpo span {
-    margin-top: 6px;
-}
-
-#checkbox_align_train {
-    padding-top: 18px;
-    padding-bottom: 18px;
-}
-
-#checkbox_align_infer input[type="checkbox"] {
-    margin-top: 10px;
-}
-
-#checkbox_align_infer span {
-    margin-top: 10px;
+    background: #ffc078 !important;
 }
 
 footer {
@@ -91,6 +48,7 @@ footer * {
 }
 
 """
+
 top_html = """
 <div align="center">
     <div style="margin-bottom: 5px; font-size: 15px;">{}</div>

tools/subfix_webui.py

@@ -1,5 +1,6 @@
 import sys
 from tools.i18n.i18n import I18nAuto, scan_language_list
+
 language = sys.argv[-1] if sys.argv[-1] in scan_language_list() else "Auto"
 i18n = I18nAuto(language=language)
 import argparse
@@ -309,7 +310,9 @@ if __name__ == "__main__":
 
     with gr.Blocks(analytics_enabled=False) as demo:
         gr.Markdown(
-            value=i18n("Submit Text: 将当前页所有文本框内容手工保存到内存和文件(翻页前后或者退出标注页面前如果没点这个按钮，你再翻回来就回滚了，白忙活。)")
+            value=i18n(
+                "Submit Text: 将当前页所有文本框内容手工保存到内存和文件(翻页前后或者退出标注页面前如果没点这个按钮，你再翻回来就回滚了，白忙活。)"
+            )
         )
         with gr.Row():
             btn_change_index = gr.Button("Change Index")

tools/uvr5/mdxnet.py

@@ -190,14 +190,14 @@ class Predictor:
             opt_path_vocal = path_vocal[:-4] + ".%s" % format
             opt_path_other = path_other[:-4] + ".%s" % format
             if os.path.exists(path_vocal):
-                os.system("ffmpeg -i \"%s\" -vn \"%s\" -q:a 2 -y" % (path_vocal, opt_path_vocal))
+                os.system('ffmpeg -i "%s" -vn "%s" -q:a 2 -y' % (path_vocal, opt_path_vocal))
                 if os.path.exists(opt_path_vocal):
                     try:
                         os.remove(path_vocal)
                     except:
                         pass
             if os.path.exists(path_other):
-                os.system("ffmpeg -i \"%s\" -vn \"%s\" -q:a 2 -y" % (path_other, opt_path_other))
+                os.system('ffmpeg -i "%s" -vn "%s" -q:a 2 -y' % (path_other, opt_path_other))
                 if os.path.exists(opt_path_other):
                     try:
                         os.remove(path_other)

tools/uvr5/vr.py

@@ -140,7 +140,7 @@ class AudioPre:
                 )
                 if os.path.exists(path):
                     opt_format_path = path[:-4] + ".%s" % format
-                    cmd="ffmpeg -i \"%s\" -vn \"%s\" -q:a 2 -y" % (path, opt_format_path)
+                    cmd = 'ffmpeg -i "%s" -vn "%s" -q:a 2 -y' % (path, opt_format_path)
                     print(cmd)
                     os.system(cmd)
                     if os.path.exists(opt_format_path):
@@ -177,7 +177,7 @@ class AudioPre:
                 )
                 if os.path.exists(path):
                     opt_format_path = path[:-4] + ".%s" % format
-                    cmd="ffmpeg -i \"%s\" -vn \"%s\" -q:a 2 -y" % (path, opt_format_path)
+                    cmd = 'ffmpeg -i "%s" -vn "%s" -q:a 2 -y' % (path, opt_format_path)
                     print(cmd)
                     os.system(cmd)
                     if os.path.exists(opt_format_path):
@@ -307,7 +307,7 @@ class AudioPreDeEcho:
                 )
                 if os.path.exists(path):
                     opt_format_path = path[:-4] + ".%s" % format
-                    cmd="ffmpeg -i \"%s\" -vn \"%s\" -q:a 2 -y" % (path, opt_format_path)
+                    cmd = 'ffmpeg -i "%s" -vn "%s" -q:a 2 -y' % (path, opt_format_path)
                     print(cmd)
                     os.system(cmd)
                     if os.path.exists(opt_format_path):
@@ -340,7 +340,7 @@ class AudioPreDeEcho:
                 )
                 if os.path.exists(path):
                     opt_format_path = path[:-4] + ".%s" % format
-                    cmd="ffmpeg -i \"%s\" -vn \"%s\" -q:a 2 -y" % (path, opt_format_path)
+                    cmd = 'ffmpeg -i "%s" -vn "%s" -q:a 2 -y' % (path, opt_format_path)
                     print(cmd)
                     os.system(cmd)
                     if os.path.exists(opt_format_path):