clarity.enhancer.dnn.mc_conv_tasnet module

Adopted from https://github.com/kaituoxu/Conv-TasNet

class clarity.enhancer.dnn.mc_conv_tasnet.ChannelwiseLayerNorm(channel_size)

Bases: Module

Channel-wise Layer Normalization (cLN)

forward(y)

Parameters:

y – [M, N, K], M is batch size, N is channel size, K is length

Returns:

[M, N, K]

Return type:

cLN_y

reset_parameters()

class clarity.enhancer.dnn.mc_conv_tasnet.Chomp1d(chomp_size)

Bases: Module

To ensure the output length is the same as the input.

forward(x)

Parameters:

x – [M, H, Kpad]

Returns:

[M, H, K]

class clarity.enhancer.dnn.mc_conv_tasnet.ConvTasNet(N_spec, N_spat, L, B, H, P, X, R, C, num_channels, norm_type='cLN', causal=False, mask_nonlinear='relu', device=None)

Bases: Module

forward(mixture)

Parameters:

mixture – [M, T], M is batch size, T is #samples

Returns:

[M, C, T]

Return type:

est_source

class clarity.enhancer.dnn.mc_conv_tasnet.Decoder(N, L, device: str | None = None)

Bases: Module

forward(mixture_w, est_mask)

Parameters:

• mixture_w – [M, N, K]

• est_mask – [M, C, N, K]

Returns:

[M, C, T]

Return type:

est_source

class clarity.enhancer.dnn.mc_conv_tasnet.DepthwiseSeparableConv(in_channels, out_channels, kernel_size, stride, padding, dilation, norm_type='gLN', causal=False)

Bases: Module

forward(x)

Parameters:

x – [M, H, K]

Returns:

[M, B, K]

Return type:

result

class clarity.enhancer.dnn.mc_conv_tasnet.GlobalLayerNorm(channel_size)

Bases: Module

Global Layer Normalization (gLN)

forward(y)

Parameters:

y – [M, N, K], M is batch size, N is channel size, K is length

Returns:

[M, N, K]

Return type:

gLN_y

reset_parameters()

class clarity.enhancer.dnn.mc_conv_tasnet.SpatialEncoder(L, N, num_channels)

Bases: Module

Estimation of the nonnegative mixture weight by a 1-D conv layer.

forward(mixture)

Parameters:

mixture – [M, num_channels, T], M is batch size, T is #samples

Returns:

[M, N, K], where K = (T-L)/(L/2)+1 = 2T/L-1

Return type:

mixture_w

class clarity.enhancer.dnn.mc_conv_tasnet.SpectralEncoder(L, N)

Bases: Module

Estimation of the nonnegative mixture weight by a 1-D conv layer.

forward(mixture)

Parameters:

mixture – [M, T], M is batch size, T is #samples

Returns:

[M, N, K], where K = (T-L)/(L/2)+1 = 2T/L-1

Return type:

mixture_w

class clarity.enhancer.dnn.mc_conv_tasnet.TemporalBlock(in_channels, out_channels, kernel_size, stride, padding, dilation, norm_type='gLN', causal=False)

Bases: Module

forward(x)

Parameters:

x – [M, B, K]

Returns:

[M, B, K]

class clarity.enhancer.dnn.mc_conv_tasnet.TemporalConvNet(N_spec, N_spat, B, H, P, X, R, C, norm_type='gLN', causal=False, mask_nonlinear='relu')

Bases: Module

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

clarity.enhancer.dnn.mc_conv_tasnet.chose_norm(norm_type, channel_size)

The input of normlization will be (M, C, K), where M is batch size, C is channel size and K is sequence length.

Parameters:

• () (channel_size)

• ()

Returns:

clarity.enhancer.dnn.mc_conv_tasnet.overlap_and_add(signal, frame_step, device)

Reconstructs a signal from a framed representation.

Adds potentially overlapping frames of a signal with shape […, frames, frame_length], offsetting subsequent frames by frame_step. The resulting tensor has shape […, output_size] where

output_size = (frames - 1) * frame_step + frame_length

Parameters:

• signal – A […, frames, frame_length] Tensor. All dimensions may be unknown, and rank must be at least 2.

• frame_step – An integer denoting overlap offsets. Must be less than or equal to frame_length.

• device – Whether to use ‘cpu’ or ‘cuda’ for processing.

Returns:

A Tensor with shape […, output_size] containing the overlap-added frames of

signal’s inner-most two dimensions.

output_size = (frames - 1) * frame_step + frame_length

Based on https://github.com/tensorflow/tensorflow/blob/r1.12/tensorflow/

contrib/signal/python/ops/reconstruction_ops.py