Merge branch 'main' of https://github.com/alibaba-damo-academy/FunASR into ...

雾聪

2023-08-07 f8d1c79fe355efb18ae49e4363307dfec3ab89ce

 funasr/modules/eend_ola/encoder.py

@@ -91,6 +91,7 @@
                 dropout_rate: float = 0.1,
                 use_pos_emb: bool = False):
        super(EENDOLATransformerEncoder, self).__init__()
        self.linear_in = nn.Linear(idim, n_units)
        self.lnorm_in = nn.LayerNorm(n_units)
        self.n_layers = n_layers
        self.dropout = nn.Dropout(dropout_rate)
@@ -104,25 +105,10 @@
            setattr(self, '{}{:d}'.format("ff_", i),
                    PositionwiseFeedForward(n_units, e_units, dropout_rate))
        self.lnorm_out = nn.LayerNorm(n_units)
        if use_pos_emb:
            self.pos_enc = torch.nn.Sequential(
                torch.nn.Linear(idim, n_units),
                torch.nn.LayerNorm(n_units),
                torch.nn.Dropout(dropout_rate),
                torch.nn.ReLU(),
                PositionalEncoding(n_units, dropout_rate),
            )
        else:
            self.linear_in = nn.Linear(idim, n_units)
            self.pos_enc = None

    def __call__(self, x, x_mask=None):
        BT_size = x.shape[0] * x.shape[1]
        if self.pos_enc is not None:
            e = self.pos_enc(x)
            e = e.view(BT_size, -1)
        else:
            e = self.linear_in(x.reshape(BT_size, -1))
        e = self.linear_in(x.reshape(BT_size, -1))
        for i in range(self.n_layers):
            e = getattr(self, '{}{:d}'.format("lnorm1_", i))(e)
            s = getattr(self, '{}{:d}'.format("self_att_", i))(e, x.shape[0], x_mask)
@@ -130,4 +116,4 @@
            e = getattr(self, '{}{:d}'.format("lnorm2_", i))(e)
            s = getattr(self, '{}{:d}'.format("ff_", i))(e)
            e = e + self.dropout(s)
        return self.lnorm_out(e)
        return self.lnorm_out(e)