config.py

import os
import re
import subprocess
import sys

import cpuinfo
import gradio as gr
import torch

pretrained_sovits_name = {
    "v2Pro": "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro.pth",
    "v2ProPlus": "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2ProPlus.pth",
}

pretrained_gpt_name = {
    "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 = {
    # "不训练直接推v2Pro底模！": "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro.pth",
    "不训练直接推v2ProPlus底模！": "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2ProPlus.pth",
}
name2gpt_path = {
    "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(r"(\d+)", s[-1])
    # 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(i18n):
    SoVITS_names: list[tuple[str, str]] = []
    for key, value in name2sovits_path.items():
        if os.path.exists(value):
            SoVITS_names.append((i18n(key), value))
    for path in SoVITS_weight_root:
        if not os.path.exists(path):
            continue
        for name in os.listdir(path):
            if name.endswith(".pth"):
                SoVITS_names.append((f"{path}/{name}", f"{path}/{name}"))
    GPT_names: list[tuple[str, str]] = []
    for key, value in name2gpt_path.items():
        if os.path.exists(value):
            GPT_names.append((i18n(key), value))
    for path in GPT_weight_root:
        if not os.path.exists(path):
            continue
        for name in os.listdir(path):
            if name.endswith(".ckpt"):
                GPT_names.append((f"{path}/{name}", f"{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(i18n):
    SoVITS_names, GPT_names = get_weights_names(i18n)
    return gr.update(choices=SoVITS_names), gr.update(choices=GPT_names)


# 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"

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

api_port = 9880


def get_apple_silicon_name():
    result = subprocess.run(["sysctl", "-n", "machdep.cpu.brand_string"], capture_output=True, text=True)
    return result.stdout.strip()


def get_dtype(idx: int):
    if not torch.cuda.is_available():
        return torch.float32
    capability = torch.cuda.get_device_capability(idx)
    major, minor = capability
    sm_version = major + minor / 10.0
    if sm_version > 6.1:
        return torch.float16
    return torch.float32


# 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:0")
    cuda = torch.device(f"cuda:{idx}")
    if torch.mps.is_available():
        return (
            torch.device("mps:0"),
            torch.float16,
            100,
            os.sysconf("SC_PHYS_PAGES") * os.sysconf("SC_PAGE_SIZE") / (1024**3),
        )
    if not torch.cuda.is_available():
        return cpu, torch.float32, 0.0, 0.0
    device_idx = idx
    capability = torch.cuda.get_device_capability(device_idx)
    name = torch.cuda.get_device_name(device_idx)
    mem_bytes = torch.cuda.get_device_properties(device_idx).total_memory
    mem_gb = mem_bytes / (1024**3) + 0.4
    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 is 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


IS_GPU = True
GPU_INFOS: list[str] = []
GPU_INDEX: set[int] = set()
GPU_COUNT = torch.cuda.device_count()
CPU_INFO: str = f"0\t{cpuinfo.get_cpu_info()['brand_raw']}"
tmp: list[tuple[torch.device, torch.dtype, float, float]] = []
memset: set[float] = set()

for i in range(max(GPU_COUNT, 1)):
    tmp.append(get_device_dtype_sm(i))

for j in tmp:
    device = j[0]
    memset.add(j[3])
    if device.type == "cuda":
        GPU_INFOS.append(f"{device.index}\t{torch.cuda.get_device_name(device.index)}")
        GPU_INDEX.add(device.index)
    elif device.type == "mps":
        GPU_INFOS.append(f"0\t{get_apple_silicon_name()}")
        GPU_INDEX.add(0)

if not GPU_INFOS:
    IS_GPU = False
    GPU_INFOS.append(CPU_INFO)
    GPU_INDEX.add(0)
if torch.mps.is_available():
    infer_device = torch.device("mps:0")
else:
    infer_device = max(tmp, key=lambda x: (x[2], x[3]))[0]

is_half = any(dtype == torch.float16 for _, dtype, _, _ in tmp)


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


def get_implement(device: torch.device):
    if torch.cuda.is_available():
        idx = device.index
        capability = torch.cuda.get_device_capability(idx)
        major, minor = capability
        sm_version = major + minor / 10.0
        if sm_version >= 7.5:
            return "flash_attn"
        else:
            if sys.platform == "linux":
                return "sage_attn"
            else:
                return "naive"
    elif torch.mps.is_available():
        return "mlx"
    else:
        return "naive"