Update TTS.py: +configs["version"] = self.version

[e1732a364fed]

项目中 TTS_Config 的 init 逻辑是，查看 tts_infer.yaml 的 version 项：

version = configs.get("version", "v2").lower()

但是保存时，这一项却没有保存下来, 这就删除了用户对 version 的配置。
导致下一次加载时，会使用 默认的v2：

    def save_configs(self, configs_path: str = None) -> None:
        configs = deepcopy(self.default_configs)
        if self.configs is not None:
            configs["custom"] = self.update_configs()

        if configs_path is None:
            configs_path = self.configs_path
        with open(configs_path, "w") as f:
            yaml.dump(configs, f)

更新后为

    def save_configs(self, configs_path: str = None) -> None:
        configs = deepcopy(self.default_configs)
        if self.configs is not None:
            configs["custom"] = self.update_configs()
            configs["version"] = self.version

        if configs_path is None:
            configs_path = self.configs_path
        with open(configs_path, "w") as f:
            yaml.dump(configs, f)

另外，tts_infer.yaml 中默认本身是没有给出 version 项的，要用户手动写一行（不是 custom, v1, v2, v3, v4 中的 version, 而是 配置文件中顶级的 version),

以目前的代码，如果默认 tts_infer.yaml 没有 version 这一项，用户又没看代码逻辑的话，就算改了
custom 中的 version 为 v4, 实际version 还是会被代码认成v2！

因此建议 merge 本 fork 之后，改动 tts_infer.yaml ，加上 version 这一行。即如下。

custom:
  bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
  cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
  device: cuda
  is_half: true
  t2s_weights_path: GPT_SoVITS/pretrained_models/s1v3.ckpt
  version: v4
  vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth
v1:
  bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
  cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
  device: cpu
  is_half: false
  t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
  version: v1
  vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth
v2:
  bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
  cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
  device: cpu
  is_half: false
  t2s_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt
  version: v2
  vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth
v3:
  bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
  cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
  device: cpu
  is_half: false
  t2s_weights_path: GPT_SoVITS/pretrained_models/s1v3.ckpt
  version: v3
  vits_weights_path: GPT_SoVITS/pretrained_models/s2Gv3.pth
v4:
  bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
  cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
  device: cpu
  is_half: false
  t2s_weights_path: GPT_SoVITS/pretrained_models/s1v3.ckpt
  version: v4
  vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth
version: v4