espnet2.enh.layers.dprnn.DPRNN_TAC

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class espnet2.enh.layers.dprnn.DPRNN_TAC(rnn_type, input_size, hidden_size, output_size, dropout=0, num_layers=1, bidirectional=True)

Bases: Module

Deep duaL-path RNN with TAC applied to each layer/block.

• Parameters:
  • rnn_type – string, select from ‘RNN’, ‘LSTM’ and ‘GRU’.
  • input_size – int, dimension of the input feature. The input should have shape (batch, seq_len, input_size).
  • hidden_size – int, dimension of the hidden state.
  • output_size – int, dimension of the output size.
  • dropout – float, dropout ratio. Default is 0.
  • num_layers – int, number of stacked RNN layers. Default is 1.
  • bidirectional – bool, whether the RNN layers are bidirectional. Default is False.

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(input, num_mic)

Defines the computation performed at every call.

Should be overridden by all subclasses.

NOTE

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.