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from collections import OrderedDict
import torch
from diffusers import ConfigMixin, ModelMixin
class AudioEmotionClassifierModel(ModelMixin, ConfigMixin):
num_emotion_classes = 9
def __init__(self, num_classifier_layers=5, num_classifier_channels=2048):
super().__init__()
if num_classifier_layers == 1:
self.layers = torch.nn.Linear(1024, self.num_emotion_classes)
else:
layer_list = [
("fc1", torch.nn.Linear(1024, num_classifier_channels)),
("relu1", torch.nn.ReLU()),
]
for n in range(num_classifier_layers - 2):
layer_list.append((f"fc{n+2}", torch.nn.Linear(num_classifier_channels, num_classifier_channels)))
layer_list.append((f"relu{n+2}", torch.nn.ReLU()))
layer_list.append(
(f"fc{num_classifier_layers}", torch.nn.Linear(num_classifier_channels, self.num_emotion_classes))
)
self.layers = torch.nn.Sequential(OrderedDict(layer_list))
def forward(self, x):
x = self.layers(x)
x = torch.softmax(x, dim=-1)
return x
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