espnet2.enh.layers.tcn.TemporalConvNet
Less than 1 minute
espnet2.enh.layers.tcn.TemporalConvNet
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