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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/chest_falsetto",
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)
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