GPT-SoVITS/config.py

import sys
import os

import torch,re

from tools.i18n.i18n import I18nAuto, scan_language_list
i18n = I18nAuto(language=os.environ["language"])


pretrained_sovits_name = {
    "v1":"GPT_SoVITS/pretrained_models/s2G488k.pth",
    "v2":"GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth",
    "v3":"GPT_SoVITS/pretrained_models/s2Gv3.pth",###v3v4还要检查vocoder，算了。。。
    "v4":"GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth",
    "v2Pro":"GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro_pre1.pth",
    "v2ProPlus":"GPT_SoVITS/pretrained_models/v2Pro/s2Gv2ProPlus_pre1.pth",
}

pretrained_gpt_name = {
    "v1":"GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
    "v2":"GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt",
    "v3":"GPT_SoVITS/pretrained_models/s1v3.ckpt",
    "v4":"GPT_SoVITS/pretrained_models/s1v3.ckpt",
    "v2Pro":"GPT_SoVITS/pretrained_models/s1v3.ckpt",
    "v2ProPlus":"GPT_SoVITS/pretrained_models/s1v3.ckpt",
}
name2sovits_path={
    # i18n("不训练直接推v1底模！"): "GPT_SoVITS/pretrained_models/s2G488k.pth",
    i18n("不训练直接推v2底模！"): "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth",
    # i18n("不训练直接推v3底模！"): "GPT_SoVITS/pretrained_models/s2Gv3.pth",
    # i18n("不训练直接推v4底模！"): "GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth",
    i18n("不训练直接推v2Pro底模！"): "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro_pre1.pth",
    i18n("不训练直接推v2ProPlus底模！"): "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2ProPlus_pre1.pth",
}
name2gpt_path={
    # i18n("不训练直接推v1底模！"):"GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
    i18n("不训练直接推v2底模！"):"GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt",
    i18n("不训练直接推v3底模！"):"GPT_SoVITS/pretrained_models/s1v3.ckpt",
}
SoVITS_weight_root = ["SoVITS_weights", "SoVITS_weights_v2", "SoVITS_weights_v3", "SoVITS_weights_v4", "SoVITS_weights_v2Pro", "SoVITS_weights_v2ProPlus"]
GPT_weight_root = ["GPT_weights", "GPT_weights_v2", "GPT_weights_v3", "GPT_weights_v4", "GPT_weights_v2Pro", "GPT_weights_v2ProPlus"]
SoVITS_weight_version2root={
    "v1":"SoVITS_weights",
    "v2":"SoVITS_weights_v2",
    "v3":"SoVITS_weights_v3",
    "v4":"SoVITS_weights_v4",
    "v2Pro":"SoVITS_weights_v2Pro",
    "v2ProPlus":"SoVITS_weights_v2ProPlus",
}
GPT_weight_version2root={
    "v1":"GPT_weights",
    "v2":"GPT_weights_v2",
    "v3":"GPT_weights_v3",
    "v4":"GPT_weights_v4",
    "v2Pro":"GPT_weights_v2Pro",
    "v2ProPlus":"GPT_weights_v2ProPlus",
}
def custom_sort_key(s):
    # 使用正则表达式提取字符串中的数字部分和非数字部分
    parts = re.split("(\d+)", s)
    # 将数字部分转换为整数，非数字部分保持不变
    parts = [int(part) if part.isdigit() else part for part in parts]
    return parts

def get_weights_names():
    SoVITS_names = []
    for key in name2sovits_path:
        if os.path.exists(name2sovits_path[key]):SoVITS_names.append(key)
    for path in SoVITS_weight_root:
        for name in os.listdir(path):
            if name.endswith(".pth"):
                SoVITS_names.append("%s/%s" % (path, name))
    GPT_names = []
    for key in name2gpt_path:
        if os.path.exists(name2gpt_path[key]):GPT_names.append(key)
    for path in GPT_weight_root:
        for name in os.listdir(path):
            if name.endswith(".ckpt"):
                GPT_names.append("%s/%s" % (path, name))
    SoVITS_names=sorted(SoVITS_names, key=custom_sort_key)
    GPT_names=sorted(GPT_names, key=custom_sort_key)
    return SoVITS_names, GPT_names

def change_choices():
    SoVITS_names, GPT_names = get_weights_names()
    return {"choices": SoVITS_names, "__type__": "update"}, {
        "choices": GPT_names,
        "__type__": "update",
    }


# 推理用的指定模型
sovits_path = ""
gpt_path = ""
is_half_str = os.environ.get("is_half", "True")
is_half = True if is_half_str.lower() == "true" else False
is_share_str = os.environ.get("is_share", "False")
is_share = True if is_share_str.lower() == "true" else False

cnhubert_path = "GPT_SoVITS/pretrained_models/chinese-hubert-base"
bert_path = "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
pretrained_sovits_path = "GPT_SoVITS/pretrained_models/s2G488k.pth"
pretrained_gpt_path = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"

exp_root = "logs"
python_exec = sys.executable or "python"
if torch.cuda.is_available():
    infer_device = "cuda"
else:
    infer_device = "cpu"

webui_port_main = 9874
webui_port_uvr5 = 9873
webui_port_infer_tts = 9872
webui_port_subfix = 9871

api_port = 9880

if infer_device == "cuda":
    gpu_name = torch.cuda.get_device_name(0)
    if (
        ("16" in gpu_name and "V100" not in gpu_name.upper())
        or "P40" in gpu_name.upper()
        or "P10" in gpu_name.upper()
        or "1060" in gpu_name
        or "1070" in gpu_name
        or "1080" in gpu_name
    ):
        is_half = False

if infer_device == "cpu":
    is_half = False


class Config:
    def __init__(self):
        self.sovits_path = sovits_path
        self.gpt_path = gpt_path
        self.is_half = is_half

        self.cnhubert_path = cnhubert_path
        self.bert_path = bert_path
        self.pretrained_sovits_path = pretrained_sovits_path
        self.pretrained_gpt_path = pretrained_gpt_path

        self.exp_root = exp_root
        self.python_exec = python_exec
        self.infer_device = infer_device

        self.webui_port_main = webui_port_main
        self.webui_port_uvr5 = webui_port_uvr5
        self.webui_port_infer_tts = webui_port_infer_tts
        self.webui_port_subfix = webui_port_subfix

        self.api_port = api_port