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import os
import torch
import torch.nn as nn
import huggingface_hub
import modelscope
from torchvision.models import squeezenet1_1
TMP_DIR = "./__pycache__/tmp"
EN_US = os.getenv("LANG") != "zh_CN.UTF-8"
ZH2EN = {
"上传钢琴录音": "Upload a piano recording",
"状态栏": "Status",
"音频文件名": "Audio filename",
"钢琴分类结果": "Piano classification result",
"建议录音时长保持在 3s 左右, 过长会影响识别效率": "It is recommended to keep the duration of recording around 3s, too long will affect the recognition efficiency.",
"引用": "Cite",
"珠江": "Pearl River",
"英昌": "YOUNG CHANG",
"施坦威剧场": "STEINWAY Theater",
"星海": "HSINGHAI",
"卡瓦依": "KAWAI",
"施坦威": "STEINWAY",
"卡瓦依三角": "KAWAI Grand",
"雅马哈": "YAMAHA",
}
MODEL_DIR = (
huggingface_hub.snapshot_download(
"ccmusic-database/pianos",
cache_dir="./__pycache__",
)
if EN_US
else modelscope.snapshot_download(
"ccmusic-database/pianos",
cache_dir="./__pycache__",
)
)
def _L(zh_txt: str):
return ZH2EN[zh_txt] if EN_US else zh_txt
def Classifier(cls_num=8, output_size=512, linear_output=False):
q = (1.0 * output_size / cls_num) ** 0.25
l1 = int(q * cls_num)
l2 = int(q * l1)
l3 = int(q * l2)
if linear_output:
return torch.nn.Sequential(
nn.Dropout(),
nn.Linear(output_size, l3),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(l3, l2),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(l2, l1),
nn.ReLU(inplace=True),
nn.Linear(l1, cls_num),
)
else:
return torch.nn.Sequential(
nn.Dropout(),
nn.Conv2d(output_size, l3, kernel_size=(1, 1), stride=(1, 1)),
nn.ReLU(inplace=True),
nn.AdaptiveAvgPool2d(output_size=(1, 1)),
nn.Flatten(),
nn.Linear(l3, l2),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(l2, l1),
nn.ReLU(inplace=True),
nn.Linear(l1, cls_num),
)
def net(weights=MODEL_DIR + "/save.pt"):
model = squeezenet1_1(pretrained=False)
model.classifier = Classifier()
model.load_state_dict(torch.load(weights, map_location=torch.device("cpu")))
model.eval()
return model
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