speechbrain.lobes.downsampling module

Combinations of processing algorithms to implement downsampling methods.

Authors

• Salah Zaiem

Summary

Classes:

Conv1DDownsampler	1D Convolutional downsampling with a learned convolution
Downsampler	Wrapper for downsampling techniques
PoolingDownsampler	1D Pooling downsampling (non-learned)
SignalDownsampler	Signal downsampling (Decimation)

Reference

class speechbrain.lobes.downsampling.Downsampler(*args, **kwargs)

Bases: Module

Wrapper for downsampling techniques

forward(x)

Downsampling function :param x: Speech samples of shape [B,n_samples] with B the batch size :type x: tensor

training: bool

class speechbrain.lobes.downsampling.SignalDownsampler(downsampling_factor, initial_sampling_rate)

Bases: Downsampler

Signal downsampling (Decimation)

Parameters:

• downsampling_factor (int) – Factor of downsampling (i.e. ratio (length before ds / length after ds))

• initial_sampling_rate (int) – Sampling_rate of the input audios

Example

>>> sd = SignalDownsampler(2,16000)
>>> a = torch.rand([8,28000])
>>> a = sd(a)
>>> print(a.shape)
torch.Size([8, 14000])

training: bool

class speechbrain.lobes.downsampling.Conv1DDownsampler(downsampling_factor, kernel_size)

Bases: Downsampler

1D Convolutional downsampling with a learned convolution

Parameters:

• downsampling_factor (int) – Factor of downsampling (i.e. ratio (length before ds / length after ds))

• kernel_size (int) – Kernel size of the 1D filter (must be an odd integer)

Example

>>> sd = Conv1DDownsampler(3,161)
>>> a = torch.rand([8,33000])
>>> a = sd(a)
>>> print(a.shape)
torch.Size([8, 10947])

training: bool

class speechbrain.lobes.downsampling.PoolingDownsampler(downsampling_factor, kernel_size, padding=0, pool_type='avg')

Bases: Downsampler

1D Pooling downsampling (non-learned)

Parameters:

• downsampling_factor (int) – Factor of downsampling (i.e. ratio (length before ds / length after ds))

• kernel_size (int) – Kernel size of the 1D filter (must be an odd integer)

• padding (int) – The number of padding elements to apply.

• pool_type (string) – Pooling approach, must be within [“avg”,”max”]

Example

>>> sd = PoolingDownsampler(3,41)
>>> a = torch.rand([8,33000])
>>> a = sd(a)
>>> print(a.shape)
torch.Size([8, 10987])

training: bool