Spaces:
Running
Running
File size: 3,015 Bytes
7c99075 4240e15 7c99075 4240e15 2b38a7d 4240e15 2b38a7d 4240e15 7c99075 7f4564b 7c99075 4240e15 7c99075 4240e15 7c99075 4240e15 7c99075 4240e15 7c99075 7f4564b 7c99075 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
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": "Upload a recording",
"选择模型": "Select a model",
"状态栏": "Status",
"音频文件名": "Audio filename",
"古筝演奏技法识别": "Guzheng playing tech recognition",
"建议录音时长保持在 3s 左右": "It is recommended to keep the recording length around 3s.",
"引用": "Cite",
"颤音": "chan yin",
"上滑音": "shang hua yin",
"下滑音": "xia hua yin",
"回滑音": "hui hua yin",
"刮奏, 花指": "gua zou, hua zhi",
"摇指": "yao zhi",
"泛音": "fan yin",
"勾, 打, 抹, 托, ...": "gou, da, mo, tuo, ...",
}
MODEL_DIR = (
huggingface_hub.snapshot_download(
"ccmusic-database/GZ_IsoTech",
cache_dir="./__pycache__",
)
if EN_US
else modelscope.snapshot_download(
"ccmusic-database/GZ_IsoTech",
cache_dir="./__pycache__",
)
)
def _L(zh_txt: str):
return ZH2EN[zh_txt] if EN_US else zh_txt
TRANSLATE = {
"vibrato": _L("颤音"),
"upward_portamento": _L("上滑音"),
"downward_portamento": _L("下滑音"),
"returning_portamento": _L("回滑音"),
"glissando": _L("刮奏, 花指"),
"tremolo": _L("摇指"),
"harmonics": _L("泛音"),
"plucks": _L("勾, 打, 抹, 托, ..."),
}
CLASSES = list(TRANSLATE.keys())
TEMP_DIR = "./__pycache__/tmp"
SAMPLE_RATE = 44100
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=".wav"):
wav_files = []
for root, _, files in os.walk(folder_path):
for file in files:
if file.endswith(ext):
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)
|