speechbrain.lobes.models.MetricGAN module

Generator and discriminator used in MetricGAN

Authors: * Szu-Wei Fu 2020

Summary

Classes:

EnhancementGenerator	Simple LSTM for enhancement with custom initialization.
Learnable_sigmoid
MetricDiscriminator	Metric estimator for enhancement training.

Functions:

shifted_sigmoid
xavier_init_layer	Create a layer with spectral norm, xavier uniform init and zero bias

Reference

speechbrain.lobes.models.MetricGAN.xavier_init_layer(in_size, out_size=None, spec_norm=True, layer_type=<class 'torch.nn.modules.linear.Linear'>, **kwargs)

Create a layer with spectral norm, xavier uniform init and zero bias

speechbrain.lobes.models.MetricGAN.shifted_sigmoid(x)

class speechbrain.lobes.models.MetricGAN.Learnable_sigmoid(in_features=257)

Bases: torch.nn.modules.module.Module

forward(x)

training: bool

class speechbrain.lobes.models.MetricGAN.EnhancementGenerator(input_size=257, hidden_size=200, num_layers=2, dropout=0)

Bases: torch.nn.modules.module.Module

Simple LSTM for enhancement with custom initialization.

Parameters

• input_size (int) – Size of the input tensor’s last dimension.

• hidden_size (int) – Number of neurons to use in the LSTM layers.

• num_layers (int) – Number of layers to use in the LSTM.

• dropout (int) – Fraction of neurons to drop during training.

blstm

Use orthogonal init for recurrent layers, xavier uniform for input layers Bias is 0

forward(x, lengths)

training: bool

class speechbrain.lobes.models.MetricGAN.MetricDiscriminator(kernel_size=(5, 5), base_channels=15, activation=<class 'torch.nn.modules.activation.LeakyReLU'>)

Bases: torch.nn.modules.module.Module

Metric estimator for enhancement training.

Consists of:

• four 2d conv layers

• channel averaging

• three linear layers

Parameters

• kernel_size (tuple) – The dimensions of the 2-d kernel used for convolution.

• base_channels (int) – Number of channels used in each conv layer.

forward(x)

training: bool