espnet2.enh.layers.tcn.TemporalConvNet

source

class espnet2.enh.layers.tcn.TemporalConvNet(N, B, H, P, X, R, C, Sc=None, out_channel=None, norm_type='gLN', causal=False, pre_mask_nonlinear='linear', mask_nonlinear='relu')

Bases: Module

Basic Module of tasnet.

• Parameters:
  • N – Number of filters in autoencoder
  • B – Number of channels in bottleneck 1 * 1-conv block
  • H – Number of channels in convolutional blocks
  • P – Kernel size in convolutional blocks
  • X – Number of convolutional blocks in each repeat
  • R – Number of repeats
  • C – Number of speakers
  • Sc – Number of channels in skip-connection paths’ 1x1-conv blocks
  • out_channel – Number of output channels if it is None, N will be used instead.
  • norm_type – BN, gLN, cLN
  • causal – causal or non-causal
  • pre_mask_nonlinear – the non-linear function before masknet
  • mask_nonlinear – use which non-linear function to generate mask

forward(mixture_w)

Keep this API same with TasNet.

• Parameters:mixture_w – [M, N, K], M is batch size
• Returns: [M, C, N, K]
• Return type: est_mask