speechbrain.nnet.utils module

Assorted reusable neural network modules.

Authors

• Artem Ploujnikov 2023

Summary

Classes:

DoneDetector

A wrapper for the done detector using a model (e.g. a CRDNN) and an output layer.

Reference

class speechbrain.nnet.utils.DoneDetector(model, out)

Bases: Module

A wrapper for the done detector using a model (e.g. a CRDNN) and an output layer.

The goal of using a wrapper is to apply masking before the output layer (e.g. Softmax) so that the model can’t “cheat” by outputting probabilities in the masked area

Parameters:

• model (torch.nn.Module) – the model used to make the prediction

• out (torch.nn.Module) – the output function

Example

>>> import torch
>>> from torch import nn
>>> from speechbrain.nnet.activations import Softmax
>>> from speechbrain.nnet.containers import Sequential
>>> from speechbrain.nnet.linear import Linear
>>> from speechbrain.lobes.models.CRDNN import CRDNN
>>> crdnn = CRDNN(
...     input_size=80,
...     cnn_blocks=1,
...     cnn_kernelsize=3,
...     rnn_layers=1,
...     rnn_neurons=16,
...     dnn_blocks=1,
...     dnn_neurons=16
... )
>>> model_out = Linear(n_neurons=1, input_size=16)
>>> model_act = nn.Sigmoid()
>>> model = Sequential(
...     crdnn,
...     model_out,
...     model_act
... )
>>> out = Softmax(
...     apply_log=False,
... )
>>> done_detector = DoneDetector(
...     model=model,
...     out=out,
... )
>>> preds = torch.randn(4, 10, 80) # Batch x Length x Feats
>>> length = torch.tensor([1., .8, .5, 1.])
>>> preds_len = done_detector(preds, length)
>>> preds_len.shape
torch.Size([4, 10, 1])

forward(feats, length=None)

Computes the forward pass

Parameters:

• feats (torch.Tensor) – the features used for the model (e.g. spectrograms)

• length (torch.Tensor) – a tensor of relative lengths

Returns:

preds – predictions

Return type:

torch.Tensor