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import os
import torch
import numpy as np
from torchvision.transforms import Compose, Resize, Normalize
from huggingface_hub import snapshot_download
MODEL_DIR = snapshot_download(
"ccmusic-database/Guzheng_Tech99",
cache_dir="./__pycache__",
)
def toCUDA(x):
if hasattr(x, "cuda"):
if torch.cuda.is_available():
return x.cuda()
return x
def find_files(folder_path=f"{MODEL_DIR}/examples", ext=".flac"):
audio_files = []
for root, _, files in os.walk(folder_path):
for file in files:
if file.endswith(ext):
file_path = os.path.join(root, file)
audio_files.append(file_path)
return audio_files
def get_modelist(model_dir=MODEL_DIR, assign_model=""):
pt_files = []
for _, _, files in os.walk(model_dir):
for file in files:
if file.endswith(".pt"):
model = os.path.basename(file)[:-3]
if assign_model and assign_model.lower() in model:
pt_files.insert(0, model)
else:
pt_files.append(model)
return pt_files
def embed(input: list, img_size: int):
compose = Compose(
[
Resize([img_size, img_size]),
Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]
)
inputs = []
for x in input:
x = np.array(x).transpose(2, 0, 1)
x = torch.from_numpy(x).repeat(3, 1, 1)
inputs.append(compose(x).float())
return toCUDA(torch.tensor(np.array(inputs)))
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