mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
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Merge branch 'fast_inference' of https://github.com/SapphireLab/GPT-SoVITS-M into fast_inference_
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commit
38dca77477
@ -99,7 +99,6 @@ class TTS_Config:
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configs = yaml.load(f, Loader=yaml.FullLoader)
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return configs
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def save_configs(self, configs_path:str=None)->None:
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configs={
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@ -112,32 +111,31 @@ class TTS_Config:
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"bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",
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"flash_attn_enabled": True
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},
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"custom": {
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"device": str(self.device),
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"is_half": self.is_half,
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"t2s_weights_path": self.t2s_weights_path,
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"vits_weights_path": self.vits_weights_path,
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"bert_base_path": self.bert_base_path,
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"cnhuhbert_base_path": self.cnhuhbert_base_path,
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"flash_attn_enabled": self.flash_attn_enabled
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}
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"custom": self.update_configs()
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}
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if configs_path is None:
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configs_path = self.configs_path
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with open(configs_path, 'w') as f:
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yaml.dump(configs, f)
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def update_configs(self):
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config = {
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"device" : str(self.device),
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"is_half" : self.is_half,
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"t2s_weights_path" : self.t2s_weights_path,
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"vits_weights_path" : self.vits_weights_path,
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"bert_base_path" : self.bert_base_path,
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"cnhuhbert_base_path": self.cnhuhbert_base_path,
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"flash_attn_enabled" : self.flash_attn_enabled
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}
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return config
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def __str__(self):
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string = "----------------TTS Config--------------\n"
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string += "device: {}\n".format(self.device)
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string += "is_half: {}\n".format(self.is_half)
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string += "flash_attn_enabled: {}\n".format(self.flash_attn_enabled)
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string += "bert_base_path: {}\n".format(self.bert_base_path)
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string += "t2s_weights_path: {}\n".format(self.t2s_weights_path)
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string += "vits_weights_path: {}\n".format(self.vits_weights_path)
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string += "cnhuhbert_base_path: {}\n".format(self.cnhuhbert_base_path)
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string += "----------------------------------------\n"
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self.configs = self.update_configs()
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string = "TTS Config".center(100, '-') + '\n'
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for k, v in self.configs.items():
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string += f"{str(k).ljust(20)}: {str(v)}\n"
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string += "-" * 100 + '\n'
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return string
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@ -158,7 +158,6 @@ class TextPreprocessor:
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bert_feature = torch.cat(bert_feature_list, dim=1)
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# phones = sum(phones_list, [])
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norm_text = ''.join(norm_text_list)
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return phones_list, bert_feature, norm_text
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