| | |
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| | | import math |
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| | | import numpy as np |
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| | | |
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| | | import torch |
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| | | import torch.nn.functional as F |
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| | | from torch import nn |
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| | |
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| | | return self.dropout(x) |
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| | | |
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| | | |
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| | | class TransformerEncoder(nn.Module): |
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| | | def __init__(self, idim, n_layers, n_units, |
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| | | e_units=2048, h=8, dropout_rate=0.1, use_pos_emb=False): |
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| | | super(TransformerEncoder, self).__init__() |
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| | | class EENDOLATransformerEncoder(nn.Module): |
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| | | def __init__(self, |
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| | | idim: int, |
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| | | n_layers: int, |
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| | | n_units: int, |
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| | | e_units: int = 2048, |
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| | | h: int = 8, |
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| | | dropout_rate: float = 0.1, |
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| | | use_pos_emb: bool = False): |
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| | | super(EENDOLATransformerEncoder, self).__init__() |
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| | | self.lnorm_in = nn.LayerNorm(n_units) |
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| | | self.n_layers = n_layers |
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| | | self.dropout = nn.Dropout(dropout_rate) |
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