utils.py

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

MODEL_DIR = snapshot_download(
    f"ccmusic-database/bel_canto",
    cache_dir=f"{os.getcwd()}/__pycache__",
)
TEMP_DIR = f"{os.getcwd()}/flagged"


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):
    try:
        entries = os.listdir(model_dir)
    except OSError as e:
        print(f"无法访问 {model_dir}: {e}")
        return

    # 遍历所有条目
    output = []
    for entry in entries:
        # 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):
            # 打印目录路径
            output.append(os.path.basename(full_path))

    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)