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from torch import nn |
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from torch.nn import Sequential |
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from hw_asr.base import BaseModel |
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class BaselineModel(BaseModel): |
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def __init__(self, n_feats, n_class, fc_hidden=512, **batch): |
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super().__init__(n_feats, n_class, **batch) |
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self.net = Sequential( |
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nn.Linear(in_features=n_feats, out_features=fc_hidden), |
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nn.ReLU(), |
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nn.Linear(in_features=fc_hidden, out_features=fc_hidden), |
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nn.ReLU(), |
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nn.Linear(in_features=fc_hidden, out_features=n_class) |
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) |
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def forward(self, spectrogram, **batch): |
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print(self.net(spectrogram.transpose(1, 2)).shape) |
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return {"logits": self.net(spectrogram.transpose(1, 2))} |
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def transform_input_lengths(self, input_lengths): |
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return input_lengths |
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