utils.py

import os
import torch
import torchvision.transforms as transforms
import huggingface_hub
import modelscope
from PIL import Image


EN_US = os.getenv("LANG") != "zh_CN.UTF-8"

ZH2EN = {
    "上传录音 (>40dB)": "Upload a recording (>40dB)",
    "选择模型": "Select a model",
    "状态栏": "Status",
    "音频文件名": "Audio filename",
    "唱法识别": "Singing method recognition",
    "建议录音时长保持在 5s 左右, 过长会影响识别效率": "It is recommended to keep the recording length around 5s, too long will affect the recognition efficiency.",
    "引用": "Cite",
    "男声 & 美声唱法": "Bel Canto, Male",
    "女声 & 美声唱法": "Bel Canto, Female",
    "男声 & 民族唱法": "Folk Singing, Male",
    "女声 & 民族唱法": "Folk Singing, Female",
}

MODEL_DIR = (
    huggingface_hub.snapshot_download(
        "ccmusic-database/bel_canto",
        cache_dir="./__pycache__",
    )
    if EN_US
    else modelscope.snapshot_download(
        "ccmusic-database/bel_canto",
        cache_dir="./__pycache__",
    )
)


def _L(zh_txt: str):
    return ZH2EN[zh_txt] if EN_US else zh_txt


TRANSLATE = {
    "m_bel": _L("男声 & 美声唱法"),
    "f_bel": _L("女声 & 美声唱法"),
    "m_folk": _L("男声 & 民族唱法"),
    "f_folk": _L("女声 & 民族唱法"),
}
CLASSES = list(TRANSLATE.keys())
TEMP_DIR = "./__pycache__/tmp"
SAMPLE_RATE = 22050


def toCUDA(x):
    if hasattr(x, "cuda"):
        if torch.cuda.is_available():
            return x.cuda()

    return x


def find_wav_files(folder_path=f"{MODEL_DIR}/examples"):
    wav_files = []
    for root, _, files in os.walk(folder_path):
        for file in files:
            if file.endswith(".wav"):
                file_path = os.path.join(root, file)
                wav_files.append(file_path)

    return wav_files


def get_modelist(model_dir=MODEL_DIR, assign_model=""):
    output = []
    for entry in os.listdir(model_dir):
        # 获取完整路径
        full_path = os.path.join(model_dir, entry)
        # 跳过'.git'文件夹
        if entry == ".git" or entry == "examples":
            print(f"跳过 .git 或 examples 文件夹: {full_path}")
            continue

        # 检查条目是文件还是目录
        if os.path.isdir(full_path):
            model = os.path.basename(full_path)
            if assign_model and assign_model.lower() in model:
                output.insert(0, model)
            else:
                output.append(model)

    return output


def embed_img(img_path: str, input_size=224):
    transform = transforms.Compose(
        [
            transforms.Resize([input_size, input_size]),
            transforms.ToTensor(),
            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
        ]
    )
    img = Image.open(img_path).convert("RGB")
    return transform(img).unsqueeze(0)