speechbrain.lobes.models.Cnn14 module

This file implements the CNN14 model from https://arxiv.org/abs/1912.10211

Authors * Cem Subakan 2022 * Francesco Paissan 2022

Summary

Classes:

Cnn14	This class implements the Cnn14 model from https://arxiv.org/abs/1912.10211
ConvBlock	This class implements the convolutional block used in CNN14

Functions:

init_bn	Initialize a Batchnorm layer.
init_layer	Initialize a Linear or Convolutional layer.

Reference

speechbrain.lobes.models.Cnn14.init_layer(layer)

Initialize a Linear or Convolutional layer.

speechbrain.lobes.models.Cnn14.init_bn(bn)

Initialize a Batchnorm layer.

class speechbrain.lobes.models.Cnn14.ConvBlock(in_channels, out_channels, norm_type)

Bases: Module

This class implements the convolutional block used in CNN14

Parameters:

• in_channels (int) – Number of input channels

• out_channels (int) – Number of output channels

• norm_type (str in ['bn', 'in', 'ln']) – The type of normalization

• Example –

• -------- –

• ConvBlock(10 (>>> convblock =) –

• 20 –

• 'ln') –

• torch.rand(5 (>>> x =) –

• 10 –

• 20 –

• 30) –

• convblock(x) (>>> y =) –

• print(y.shape) (>>>) –

• torch.Size([5 –

• 20 –

• 10 –

• 15]) –

init_weight()

Initializes the model convolutional layers and the batchnorm layers

forward(x, pool_size=(2, 2), pool_type='avg')

The forward pass for convblocks in CNN14

Arguments:

xtorch.Tensor

input tensor with shape B x C_in x D1 x D2

where B = Batchsize

C_in = Number of input channel D1 = Dimensionality of the first spatial dim D2 = Dimensionality of the second spatial dim

pool_sizetuple with integer values

Amount of pooling at each layer

pool_typestr in [‘max’, ‘avg’, ‘avg+max’]

The type of pooling

training: bool

class speechbrain.lobes.models.Cnn14.Cnn14(mel_bins, emb_dim, norm_type='bn', return_reps=False)

Bases: Module

This class implements the Cnn14 model from https://arxiv.org/abs/1912.10211

Parameters:

• mel_bins (int) – Number of mel frequency bins in the input

• emb_dim (int) – The dimensionality of the output embeddings

• norm_type (str in ['bn', 'in', 'ln']) – The type of normalization

• return_reps (bool (default=False)) – If True the model returns intermediate representations as well for interpretation

• Example –

• -------- –

• Cnn14(120 (>>> cnn14 =) –

• 256) –

• torch.rand(3 (>>> x =) –

• 400 –

• 120) –

• cnn14.forward(x) (>>> h =) –

• print(h.shape) (>>>) –

• torch.Size([3 –

• 1 –

• 256]) –

training: bool

init_weight()

Initializes the model batch norm layer

forward(x)

The forward pass for the CNN14 encoder

Arguments:

xtorch.Tensor

input tensor with shape B x C_in x D1 x D2

where B = Batchsize

C_in = Number of input channel D1 = Dimensionality of the first spatial dim D2 = Dimensionality of the second spatial dim