| import os
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| import re
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| import sys
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|
|
| import torch
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|
|
| from tools.i18n.i18n import I18nAuto
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|
|
| i18n = I18nAuto(language=os.environ.get("language", "Auto"))
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|
|
| pretrained_sovits_name = {
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| "v1": "GPT_SoVITS/pretrained_models/s2G488k.pth",
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| "v2": "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth",
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| "v3": "GPT_SoVITS/pretrained_models/s2Gv3.pth",
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| "v4": "GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth",
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| "v2Pro": "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro.pth",
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| "v2ProPlus": "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2ProPlus.pth",
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| }
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|
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| pretrained_gpt_name = {
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| "v1": "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
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| "v2": "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt",
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| "v3": "GPT_SoVITS/pretrained_models/s1v3.ckpt",
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| "v4": "GPT_SoVITS/pretrained_models/s1v3.ckpt",
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| "v2Pro": "GPT_SoVITS/pretrained_models/s1v3.ckpt",
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| "v2ProPlus": "GPT_SoVITS/pretrained_models/s1v3.ckpt",
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| }
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| name2sovits_path = {
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|
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| i18n("不训练直接推v2底模！"): "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth",
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|
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| i18n("不训练直接推v2Pro底模！"): "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro.pth",
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| i18n("不训练直接推v2ProPlus底模！"): "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2ProPlus.pth",
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| }
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| name2gpt_path = {
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|
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| i18n(
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| "不训练直接推v2底模！"
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| ): "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt",
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| i18n("不训练直接推v3底模！"): "GPT_SoVITS/pretrained_models/s1v3.ckpt",
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| }
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| SoVITS_weight_root = [
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| "SoVITS_weights",
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| "SoVITS_weights_v2",
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| "SoVITS_weights_v3",
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| "SoVITS_weights_v4",
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| "SoVITS_weights_v2Pro",
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| "SoVITS_weights_v2ProPlus",
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| ]
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| GPT_weight_root = [
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| "GPT_weights",
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| "GPT_weights_v2",
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| "GPT_weights_v3",
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| "GPT_weights_v4",
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| "GPT_weights_v2Pro",
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| "GPT_weights_v2ProPlus",
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| ]
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| SoVITS_weight_version2root = {
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| "v1": "SoVITS_weights",
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| "v2": "SoVITS_weights_v2",
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| "v3": "SoVITS_weights_v3",
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| "v4": "SoVITS_weights_v4",
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| "v2Pro": "SoVITS_weights_v2Pro",
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| "v2ProPlus": "SoVITS_weights_v2ProPlus",
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| }
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| GPT_weight_version2root = {
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| "v1": "GPT_weights",
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| "v2": "GPT_weights_v2",
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| "v3": "GPT_weights_v3",
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| "v4": "GPT_weights_v4",
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| "v2Pro": "GPT_weights_v2Pro",
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| "v2ProPlus": "GPT_weights_v2ProPlus",
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| }
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|
|
|
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| def custom_sort_key(s):
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|
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| parts = re.split("(\d+)", s)
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|
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| parts = [int(part) if part.isdigit() else part for part in parts]
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| return parts
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|
|
|
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| def get_weights_names():
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| SoVITS_names = []
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| for key in name2sovits_path:
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| if os.path.exists(name2sovits_path[key]):
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| SoVITS_names.append(key)
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| for path in SoVITS_weight_root:
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| if not os.path.exists(path):
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| continue
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| for name in os.listdir(path):
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| if name.endswith(".pth"):
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| SoVITS_names.append("%s/%s" % (path, name))
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| if not SoVITS_names:
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| SoVITS_names = [""]
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| GPT_names = []
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| for key in name2gpt_path:
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| if os.path.exists(name2gpt_path[key]):
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| GPT_names.append(key)
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| for path in GPT_weight_root:
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| if not os.path.exists(path):
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| continue
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| for name in os.listdir(path):
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| if name.endswith(".ckpt"):
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| GPT_names.append("%s/%s" % (path, name))
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| SoVITS_names = sorted(SoVITS_names, key=custom_sort_key)
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| GPT_names = sorted(GPT_names, key=custom_sort_key)
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| if not GPT_names:
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| GPT_names = [""]
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| return SoVITS_names, GPT_names
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|
|
|
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| def change_choices():
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| SoVITS_names, GPT_names = get_weights_names()
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| return {"choices": SoVITS_names, "__type__": "update"}, {
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| "choices": GPT_names,
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| "__type__": "update",
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| }
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|
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|
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| sovits_path = ""
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| gpt_path = ""
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| is_half_str = os.environ.get("is_half", "True")
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| is_half = True if is_half_str.lower() == "true" else False
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| is_share_str = os.environ.get("is_share", "False")
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| is_share = True if is_share_str.lower() == "true" else False
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|
|
| cnhubert_path = "GPT_SoVITS/pretrained_models/chinese-hubert-base"
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| bert_path = "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
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| pretrained_sovits_path = "GPT_SoVITS/pretrained_models/s2G488k.pth"
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| pretrained_gpt_path = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
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|
|
| exp_root = "logs"
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| python_exec = sys.executable or "python"
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|
|
| webui_port_main = 9874
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| webui_port_uvr5 = 9873
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| webui_port_infer_tts = 9872
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| webui_port_subfix = 9871
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|
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| api_port = 9880
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|
|
|
|
|
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| def get_device_dtype_sm(idx: int) -> tuple[torch.device, torch.dtype, float, float]:
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| cpu = torch.device("cpu")
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| cuda = torch.device(f"cuda:{idx}")
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| if not torch.cuda.is_available():
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| return cpu, torch.float32, 0.0, 0.0
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| device_idx = idx
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| capability = torch.cuda.get_device_capability(device_idx)
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| name = torch.cuda.get_device_name(device_idx)
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| mem_bytes = torch.cuda.get_device_properties(device_idx).total_memory
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| mem_gb = mem_bytes / (1024**3) + 0.4
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| major, minor = capability
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| sm_version = major + minor / 10.0
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| is_16_series = bool(re.search(r"16\d{2}", name)) and sm_version == 7.5
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| if mem_gb < 4 or sm_version < 5.3:
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| return cpu, torch.float32, 0.0, 0.0
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| if sm_version == 6.1 or is_16_series == True:
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| return cuda, torch.float32, sm_version, mem_gb
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| if sm_version > 6.1:
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| return cuda, torch.float16, sm_version, mem_gb
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| return cpu, torch.float32, 0.0, 0.0
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|
|
|
|
| IS_GPU = True
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| GPU_INFOS: list[str] = []
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| GPU_INDEX: set[int] = set()
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| GPU_COUNT = torch.cuda.device_count()
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| CPU_INFO: str = "0\tCPU " + i18n("CPU训练,较慢")
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| tmp: list[tuple[torch.device, torch.dtype, float, float]] = []
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| memset: set[float] = set()
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|
|
| for i in range(max(GPU_COUNT, 1)):
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| tmp.append(get_device_dtype_sm(i))
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|
|
| for j in tmp:
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| device = j[0]
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| memset.add(j[3])
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| if device.type != "cpu":
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| GPU_INFOS.append(f"{device.index}\t{torch.cuda.get_device_name(device.index)}")
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| GPU_INDEX.add(device.index)
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|
|
| if not GPU_INFOS:
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| IS_GPU = False
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| GPU_INFOS.append(CPU_INFO)
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| GPU_INDEX.add(0)
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|
|
| infer_device = max(tmp, key=lambda x: (x[2], x[3]))[0]
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| is_half = any(dtype == torch.float16 for _, dtype, _, _ in tmp)
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|
|
|
|
| class Config:
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| def __init__(self):
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| self.sovits_path = sovits_path
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| self.gpt_path = gpt_path
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| self.is_half = is_half
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|
|
| self.cnhubert_path = cnhubert_path
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| self.bert_path = bert_path
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| self.pretrained_sovits_path = pretrained_sovits_path
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| self.pretrained_gpt_path = pretrained_gpt_path
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|
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| self.exp_root = exp_root
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| self.python_exec = python_exec
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| self.infer_device = infer_device
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|
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| self.webui_port_main = webui_port_main
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| self.webui_port_uvr5 = webui_port_uvr5
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| self.webui_port_infer_tts = webui_port_infer_tts
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| self.webui_port_subfix = webui_port_subfix
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|
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| self.api_port = api_port
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|
|
