speechbrain.nnet.losses module

Losses for training neural networks.

Authors

Mirco Ravanelli 2020
Samuele Cornell 2020
Hwidong Na 2020
Yan Gao 2020
Titouan Parcollet 2020

Summary

Classes:

`AdditiveAngularMargin`	An implementation of Additive Angular Margin (AAM) proposed in the following paper: '''Margin Matters: Towards More Discriminative Deep Neural Network Embeddings for Speaker Recognition''' (https://arxiv.org/abs/1906.07317)
`AngularMargin`	An implementation of Angular Margin (AM) proposed in the following paper: '''Margin Matters: Towards More Discriminative Deep Neural Network Embeddings for Speaker Recognition''' (https://arxiv.org/abs/1906.07317)
`AutoencoderLoss`	An implementation of a standard (non-variational) autoencoder loss
`AutoencoderLossDetails`
`ContrastiveLoss`	Contrastive loss as used in wav2vec2.
`Laplacian`	Computes the Laplacian for image-like data
`LaplacianVarianceLoss`	The Laplacian variance loss - used to penalize blurriness in image-like data, such as spectrograms.
`LogSoftmaxWrapper`	returns: loss (torch.Tensor) -- Learning loss
`PitWrapper`	Permutation Invariant Wrapper to allow Permutation Invariant Training (PIT) with existing losses.
`VariationalAutoencoderLoss`	The Variational Autoencoder loss, with support for length masking
`VariationalAutoencoderLossDetails`

Functions:

`bce_loss`	Computes binary cross-entropy (BCE) loss.
`cal_si_snr`	Calculate SI-SNR.
`cal_snr`	Calculate binaural channel SNR. Arguments: --------- source: [T, E, B, C], Where B is batch size, T is the length of the sources, E is binaural channels, C is the number of sources the ordering is made so that this loss is compatible with the class PitWrapper. estimate_source: [T, E, B, C] The estimated source.
`ce_kd`	Simple version of distillation for cross-entropy loss.
`classification_error`	Computes the classification error at frame or batch level.
`compute_length_mask`	Computes a length mask for the specified data shape :param data: the data shape :type data: torch.tensor :param len_dim: the length dimension (defaults to 1) :type len_dim: int
`compute_masked_loss`	Compute the true average loss of a set of waveforms of unequal length.
`ctc_loss`	CTC loss.
`ctc_loss_kd`	Knowledge distillation for CTC loss.
`distance_diff_loss`	A loss function that can be used in cases where a model outputs an arbitrary probability distribution for a discrete variable on an interval scale, such as the length of a sequence, and the ground truth is the precise values of the variable from a data sample.
`get_mask`	param source:
`get_si_snr_with_pitwrapper`	This function wraps si_snr calculation with the speechbrain pit-wrapper.
`get_snr_with_pitwrapper`	This function wraps snr calculation with the speechbrain pit-wrapper. Arguments: --------- source: [B, T, E, C], Where B is the batch size, T is the length of the sources, E is binaural channels, C is the number of sources the ordering is made so that this loss is compatible with the class PitWrapper. estimate_source: [B, T, E, C] The estimated source.
`kldiv_loss`	Computes the KL-divergence error at the batch level.
`l1_loss`	Compute the true l1 loss, accounting for length differences.
`mse_loss`	Compute the true mean squared error, accounting for length differences.
`nll_loss`	Computes negative log likelihood loss.
`nll_loss_kd`	Knowledge distillation for negative log-likelihood loss.
`reduce_loss`	Performs the specified reduction of the raw loss value
`transducer_loss`	Transducer loss, see `speechbrain/nnet/loss/transducer_loss.py`.
`truncate`	Ensure that predictions and targets are the same length.

Reference

speechbrain.nnet.losses.transducer_loss(logits, targets, input_lens, target_lens, blank_index, reduction='mean', use_torchaudio=True)[source]

Transducer loss, see speechbrain/nnet/loss/transducer_loss.py.

Parameters:

logits (torch.Tensor) – Predicted tensor, of shape [batch, maxT, maxU, num_labels].
targets (torch.Tensor) – Target tensor, without any blanks, of shape [batch, target_len].
input_lens (torch.Tensor) – Length of each utterance.
target_lens (torch.Tensor) – Length of each target sequence.
blank_index (int) – The location of the blank symbol among the label indices.
reduction (str) – Specifies the reduction to apply to the output: ‘mean’ | ‘batchmean’ | ‘sum’.
use_torchaudio (bool) – If True, use Transducer loss implementation from torchaudio, otherwise, use Speechbrain Numba implementation.

class speechbrain.nnet.losses.PitWrapper(base_loss)[source]

Bases: Module

Permutation Invariant Wrapper to allow Permutation Invariant Training (PIT) with existing losses.

Permutation invariance is calculated over the sources/classes axis which is assumed to be the rightmost dimension: predictions and targets tensors are assumed to have shape [batch, …, channels, sources].

Parameters:: base_loss (function) – Base loss function, e.g. torch.nn.MSELoss. It is assumed that it takes two arguments: predictions and targets and no reduction is performed. (if a pytorch loss is used, the user must specify reduction=”none”).
Returns:: pit_loss – Torch module supporting forward method for PIT.
Return type:: torch.nn.Module

Example

>>> pit_mse = PitWrapper(nn.MSELoss(reduction="none"))
>>> targets = torch.rand((2, 32, 4))
>>> p = (3, 0, 2, 1)
>>> predictions = targets[..., p]
>>> loss, opt_p = pit_mse(predictions, targets)
>>> loss
tensor([0., 0.])

reorder_tensor(tensor, p)[source]

Parameters:

tensor (torch.Tensor) – Tensor to reorder given the optimal permutation, of shape [batch, …, sources].
p (list of tuples) – List of optimal permutations, e.g. for batch=2 and n_sources=3 [(0, 1, 2), (0, 2, 1].

Returns:

reordered – Reordered tensor given permutation p.

Return type:

torch.Tensor

forward(preds, targets)[source]

Parameters:

preds (torch.Tensor) – Network predictions tensor, of shape [batch, channels, …, sources].
targets (torch.Tensor) – Target tensor, of shape [batch, channels, …, sources].

Returns:

loss (torch.Tensor) – Permutation invariant loss for current examples, tensor of shape [batch]
perms (list) – List of indexes for optimal permutation of the inputs over sources. e.g., [(0, 1, 2), (2, 1, 0)] for three sources and 2 examples per batch.

training: bool

speechbrain.nnet.losses.ctc_loss(log_probs, targets, input_lens, target_lens, blank_index, reduction='mean')[source]

CTC loss.

Parameters:

predictions (torch.Tensor) – Predicted tensor, of shape [batch, time, chars].
targets (torch.Tensor) – Target tensor, without any blanks, of shape [batch, target_len]
input_lens (torch.Tensor) – Length of each utterance.
target_lens (torch.Tensor) – Length of each target sequence.
blank_index (int) – The location of the blank symbol among the character indexes.
reduction (str) – What reduction to apply to the output. ‘mean’, ‘sum’, ‘batch’, ‘batchmean’, ‘none’. See pytorch for ‘mean’, ‘sum’, ‘none’. The ‘batch’ option returns one loss per item in the batch, ‘batchmean’ returns sum / batch size.

speechbrain.nnet.losses.l1_loss(predictions, targets, length=None, allowed_len_diff=3, reduction='mean')[source]

Compute the true l1 loss, accounting for length differences.

Parameters:

predictions (torch.Tensor) – Predicted tensor, of shape [batch, time, *].
targets (torch.Tensor) – Target tensor with the same size as predicted tensor.
length (torch.Tensor) – Length of each utterance for computing true error with a mask.
allowed_len_diff (int) – Length difference that will be tolerated before raising an exception.
reduction (str) – Options are ‘mean’, ‘batch’, ‘batchmean’, ‘sum’. See pytorch for ‘mean’, ‘sum’. The ‘batch’ option returns one loss per item in the batch, ‘batchmean’ returns sum / batch size.

Example

>>> probs = torch.tensor([[0.9, 0.1, 0.1, 0.9]])
>>> l1_loss(probs, torch.tensor([[1., 0., 0., 1.]]))
tensor(0.1000)

speechbrain.nnet.losses.mse_loss(predictions, targets, length=None, allowed_len_diff=3, reduction='mean')[source]

Compute the true mean squared error, accounting for length differences.

Parameters:

predictions (torch.Tensor) – Predicted tensor, of shape [batch, time, *].
targets (torch.Tensor) – Target tensor with the same size as predicted tensor.
length (torch.Tensor) – Length of each utterance for computing true error with a mask.
allowed_len_diff (int) – Length difference that will be tolerated before raising an exception.
reduction (str) – Options are ‘mean’, ‘batch’, ‘batchmean’, ‘sum’. See pytorch for ‘mean’, ‘sum’. The ‘batch’ option returns one loss per item in the batch, ‘batchmean’ returns sum / batch size.

Example

>>> probs = torch.tensor([[0.9, 0.1, 0.1, 0.9]])
>>> mse_loss(probs, torch.tensor([[1., 0., 0., 1.]]))
tensor(0.0100)

speechbrain.nnet.losses.classification_error(probabilities, targets, length=None, allowed_len_diff=3, reduction='mean')[source]

Computes the classification error at frame or batch level.

Parameters:

probabilities (torch.Tensor) – The posterior probabilities of shape [batch, prob] or [batch, frames, prob]
targets (torch.Tensor) – The targets, of shape [batch] or [batch, frames]
length (torch.Tensor) – Length of each utterance, if frame-level loss is desired.
allowed_len_diff (int) – Length difference that will be tolerated before raising an exception.
reduction (str) – Options are ‘mean’, ‘batch’, ‘batchmean’, ‘sum’. See pytorch for ‘mean’, ‘sum’. The ‘batch’ option returns one loss per item in the batch, ‘batchmean’ returns sum / batch size.

Example

>>> probs = torch.tensor([[[0.9, 0.1], [0.1, 0.9]]])
>>> classification_error(probs, torch.tensor([1, 1]))
tensor(0.5000)

speechbrain.nnet.losses.nll_loss(log_probabilities, targets, length=None, label_smoothing=0.0, allowed_len_diff=3, weight=None, reduction='mean')[source]

Computes negative log likelihood loss.

Parameters:

log_probabilities (torch.Tensor) – The probabilities after log has been applied. Format is [batch, log_p] or [batch, frames, log_p].
targets (torch.Tensor) – The targets, of shape [batch] or [batch, frames].
length (torch.Tensor) – Length of each utterance, if frame-level loss is desired.
allowed_len_diff (int) – Length difference that will be tolerated before raising an exception.
weight (torch.Tensor) – A manual rescaling weight given to each class. If given, has to be a Tensor of size C.
reduction (str) – Options are ‘mean’, ‘batch’, ‘batchmean’, ‘sum’. See pytorch for ‘mean’, ‘sum’. The ‘batch’ option returns one loss per item in the batch, ‘batchmean’ returns sum / batch size.

Example

>>> probs = torch.tensor([[0.9, 0.1], [0.1, 0.9]])
>>> nll_loss(torch.log(probs), torch.tensor([1, 1]))
tensor(1.2040)

speechbrain.nnet.losses.bce_loss(inputs, targets, length=None, weight=None, pos_weight=None, reduction='mean', allowed_len_diff=3, label_smoothing=0.0)[source]

Computes binary cross-entropy (BCE) loss. It also applies the sigmoid function directly (this improves the numerical stability).

Parameters:

inputs (torch.Tensor) – The output before applying the final softmax Format is [batch[, 1]?] or [batch, frames[, 1]?]. (Works with or without a singleton dimension at the end).
targets (torch.Tensor) – The targets, of shape [batch] or [batch, frames].
length (torch.Tensor) – Length of each utterance, if frame-level loss is desired.
weight (torch.Tensor) – A manual rescaling weight if provided it’s repeated to match input tensor shape.
pos_weight (torch.Tensor) – A weight of positive examples. Must be a vector with length equal to the number of classes.
allowed_len_diff (int) – Length difference that will be tolerated before raising an exception.
reduction (str) – Options are ‘mean’, ‘batch’, ‘batchmean’, ‘sum’. See pytorch for ‘mean’, ‘sum’. The ‘batch’ option returns one loss per item in the batch, ‘batchmean’ returns sum / batch size.

Example

>>> inputs = torch.tensor([10.0, -6.0])
>>> targets = torch.tensor([1, 0])
>>> bce_loss(inputs, targets)
tensor(0.0013)

speechbrain.nnet.losses.kldiv_loss(log_probabilities, targets, length=None, label_smoothing=0.0, allowed_len_diff=3, pad_idx=0, reduction='mean')[source]

Computes the KL-divergence error at the batch level. This loss applies label smoothing directly to the targets

Parameters:

probabilities (torch.Tensor) – The posterior probabilities of shape [batch, prob] or [batch, frames, prob].
targets (torch.Tensor) – The targets, of shape [batch] or [batch, frames].
length (torch.Tensor) – Length of each utterance, if frame-level loss is desired.
allowed_len_diff (int) – Length difference that will be tolerated before raising an exception.
reduction (str) – Options are ‘mean’, ‘batch’, ‘batchmean’, ‘sum’. See pytorch for ‘mean’, ‘sum’. The ‘batch’ option returns one loss per item in the batch, ‘batchmean’ returns sum / batch size.

Example

>>> probs = torch.tensor([[0.9, 0.1], [0.1, 0.9]])
>>> kldiv_loss(torch.log(probs), torch.tensor([1, 1]))
tensor(1.2040)

speechbrain.nnet.losses.distance_diff_loss(predictions, targets, length=None, beta=0.25, max_weight=100.0, reduction='mean')[source]

A loss function that can be used in cases where a model outputs an arbitrary probability distribution for a discrete variable on an interval scale, such as the length of a sequence, and the ground truth is the precise values of the variable from a data sample.

The loss is defined as loss_i = p_i * exp(beta * |i - y|) - 1.

The loss can also be used where outputs aren’t probabilities, so long as high values close to the ground truth position and low values away from it are desired

Parameters:

predictions (torch.Tensor) – a (batch x max_len) tensor in which each element is a probability, weight or some other value at that position
targets (torch.Tensor) – a 1-D tensor in which each elemnent is thr ground truth
length (torch.Tensor) – lengths (for masking in padded batches)
beta (torch.Tensor) – a hyperparameter controlling the penalties. With a higher beta, penalties will increase faster
max_weight (torch.Tensor) – the maximum distance weight (for numerical stability in long sequences)
reduction (str) – Options are ‘mean’, ‘batch’, ‘batchmean’, ‘sum’. See pytorch for ‘mean’, ‘sum’. The ‘batch’ option returns one loss per item in the batch, ‘batchmean’ returns sum / batch size

Example

>>> predictions = torch.tensor(
...    [[0.25, 0.5, 0.25, 0.0],
...     [0.05, 0.05, 0.9, 0.0],
...     [8.0, 0.10, 0.05, 0.05]]
... )
>>> targets = torch.tensor([2., 3., 1.])
>>> length = torch.tensor([.75, .75, 1.])
>>> loss = distance_diff_loss(predictions, targets, length)
>>> loss
tensor(0.2967)

speechbrain.nnet.losses.truncate(predictions, targets, allowed_len_diff=3)[source]

Ensure that predictions and targets are the same length.

Parameters:

predictions (torch.Tensor) – First tensor for checking length.
targets (torch.Tensor) – Second tensor for checking length.
allowed_len_diff (int) – Length difference that will be tolerated before raising an exception.

speechbrain.nnet.losses.compute_masked_loss(loss_fn, predictions, targets, length=None, label_smoothing=0.0, mask_shape='targets', reduction='mean')[source]

Compute the true average loss of a set of waveforms of unequal length.

Parameters:

loss_fn (function) – A function for computing the loss taking just predictions and targets. Should return all the losses, not a reduction (e.g. reduction=”none”).
predictions (torch.Tensor) – First argument to loss function.
targets (torch.Tensor) – Second argument to loss function.
length (torch.Tensor) – Length of each utterance to compute mask. If None, global average is computed and returned.
label_smoothing (float) – The proportion of label smoothing. Should only be used for NLL loss. Ref: Regularizing Neural Networks by Penalizing Confident Output Distributions. https://arxiv.org/abs/1701.06548
mask_shape (torch.Tensor) –
the shape of the mask The default is “targets”, which will cause the mask to be the same shape as the targets

Other options include “predictions” and “loss”, which will use the shape of the predictions and the unreduced loss, respectively. These are useful for loss functions that whose output does not match the shape of the targets
reduction (str) – One of ‘mean’, ‘batch’, ‘batchmean’, ‘none’ where ‘mean’ returns a single value and ‘batch’ returns one per item in the batch and ‘batchmean’ is sum / batch_size and ‘none’ returns all.

speechbrain.nnet.losses.compute_length_mask(data, length=None, len_dim=1)[source]

Computes a length mask for the specified data shape :param data: the data shape :type data: torch.tensor :param len_dim: the length dimension (defaults to 1) :type len_dim: int

Returns:: mask – the mask
Return type:: torch.Tensor

Example

>>> data = torch.arange(5)[None, :, None].repeat(3, 1, 2)
>>> data += torch.arange(1, 4)[:, None, None]
>>> data *= torch.arange(1, 3)[None, None, :]
>>> data
tensor([[[ 1,  2],
         [ 2,  4],
         [ 3,  6],
         [ 4,  8],
         [ 5, 10]],

        [[ 2,  4],
         [ 3,  6],
         [ 4,  8],
         [ 5, 10],
         [ 6, 12]],

        [[ 3,  6],
         [ 4,  8],
         [ 5, 10],
         [ 6, 12],
         [ 7, 14]]])
>>> compute_length_mask(data, torch.tensor([1., .4, .8]))
tensor([[[1, 1],
         [1, 1],
         [1, 1],
         [1, 1],
         [1, 1]],

        [[1, 1],
         [1, 1],
         [0, 0],
         [0, 0],
         [0, 0]],

        [[1, 1],
         [1, 1],
         [1, 1],
         [1, 1],
         [0, 0]]])
>>> compute_length_mask(data, torch.tensor([.5, 1., .5]), len_dim=2)
tensor([[[1, 0],
         [1, 0],
         [1, 0],
         [1, 0],
         [1, 0]],

        [[1, 1],
         [1, 1],
         [1, 1],
         [1, 1],
         [1, 1]],

        [[1, 0],
         [1, 0],
         [1, 0],
         [1, 0],
         [1, 0]]])

speechbrain.nnet.losses.reduce_loss(loss, mask, reduction='mean', label_smoothing=0.0, predictions=None, targets=None)[source]

Performs the specified reduction of the raw loss value

Parameters:

loss_fn (function) – A function for computing the loss taking just predictions and targets. Should return all the losses, not a reduction (e.g. reduction=”none”).
predictions (torch.Tensor) – First argument to loss function.
targets (torch.Tensor) – Second argument to loss function.
length (torch.Tensor) – Length of each utterance to compute mask. If None, global average is computed and returned.
label_smoothing (float) – The proportion of label smoothing. Should only be used for NLL loss. Ref: Regularizing Neural Networks by Penalizing Confident Output Distributions. https://arxiv.org/abs/1701.06548
reduction (str) – One of ‘mean’, ‘batch’, ‘batchmean’, ‘none’ where ‘mean’ returns a single value and ‘batch’ returns one per item in the batch and ‘batchmean’ is sum / batch_size and ‘none’ returns all.
predictions – First argument to loss function. Required only if label smoothing is used.
targets – Second argument to loss function. Required only if label smoothing is used.

speechbrain.nnet.losses.get_si_snr_with_pitwrapper(source, estimate_source)[source]

This function wraps si_snr calculation with the speechbrain pit-wrapper.

Arguments:

source: [B, T, C],: Where B is the batch size, T is the length of the sources, C is the number of sources the ordering is made so that this loss is compatible with the class PitWrapper.
estimate_source: [B, T, C]: The estimated source.

Example:

>>> x = torch.arange(600).reshape(3, 100, 2)
>>> xhat = x[:, :, (1, 0)]
>>> si_snr = -get_si_snr_with_pitwrapper(x, xhat)
>>> print(si_snr)
tensor([135.2284, 135.2284, 135.2284])

speechbrain.nnet.losses.get_snr_with_pitwrapper(source, estimate_source)[source]

This function wraps snr calculation with the speechbrain pit-wrapper. Arguments: ——— source: [B, T, E, C],

Where B is the batch size, T is the length of the sources, E is binaural channels, C is the number of sources the ordering is made so that this loss is compatible with the class PitWrapper.

estimate_source: [B, T, E, C]: The estimated source.

speechbrain.nnet.losses.cal_si_snr(source, estimate_source)[source]

Calculate SI-SNR.

Arguments:

source: [T, B, C],: Where B is batch size, T is the length of the sources, C is the number of sources the ordering is made so that this loss is compatible with the class PitWrapper.
estimate_source: [T, B, C]: The estimated source.

Example:

>>> import numpy as np
>>> x = torch.Tensor([[1, 0], [123, 45], [34, 5], [2312, 421]])
>>> xhat = x[:, (1, 0)]
>>> x = x.unsqueeze(-1).repeat(1, 1, 2)
>>> xhat = xhat.unsqueeze(1).repeat(1, 2, 1)
>>> si_snr = -cal_si_snr(x, xhat)
>>> print(si_snr)
tensor([[[ 25.2142, 144.1789],
         [130.9283,  25.2142]]])

speechbrain.nnet.losses.cal_snr(source, estimate_source)[source]

Calculate binaural channel SNR. Arguments: ——— source: [T, E, B, C],

Where B is batch size, T is the length of the sources, E is binaural channels, C is the number of sources the ordering is made so that this loss is compatible with the class PitWrapper.

estimate_source: [T, E, B, C]: The estimated source.

speechbrain.nnet.losses.get_mask(source, source_lengths)[source]

Parameters:

source ([T, B, C]) –
source_lengths ([B]) –

Returns:

mask ([T, B, 1])
Example
———
>>> source = torch.randn(4, 3, 2)
>>> source_lengths = torch.Tensor([2, 1, 4]).int()
>>> mask = get_mask(source, source_lengths)
>>> print(mask)
tensor([[[1.], – [1.], [1.]],
<BLANKLINE> –

[[1.],
[0.], [1.]],
<BLANKLINE> –

[[0.],
[0.], [1.]],
<BLANKLINE> –

[[0.],
[0.], [1.]]])

class speechbrain.nnet.losses.AngularMargin(margin=0.0, scale=1.0)[source]

Bases: Module

An implementation of Angular Margin (AM) proposed in the following paper: ‘’’Margin Matters: Towards More Discriminative Deep Neural Network Embeddings for Speaker Recognition’’’ (https://arxiv.org/abs/1906.07317)

Parameters:

margin (float) – The margin for cosine similiarity
scale (float) – The scale for cosine similiarity

Returns:

predictions

Return type:

torch.Tensor

Example

>>> pred = AngularMargin()
>>> outputs = torch.tensor([ [1., -1.], [-1., 1.], [0.9, 0.1], [0.1, 0.9] ])
>>> targets = torch.tensor([ [1., 0.], [0., 1.], [ 1., 0.], [0.,  1.] ])
>>> predictions = pred(outputs, targets)
>>> predictions[:,0] > predictions[:,1]
tensor([ True, False,  True, False])

forward(outputs, targets)[source]

Compute AM between two tensors

Parameters:

outputs (torch.Tensor) – The outputs of shape [N, C], cosine similarity is required.
targets (torch.Tensor) – The targets of shape [N, C], where the margin is applied for.

Returns:

predictions

Return type:

torch.Tensor

training: bool

class speechbrain.nnet.losses.AdditiveAngularMargin(margin=0.0, scale=1.0, easy_margin=False)[source]

Bases: AngularMargin

An implementation of Additive Angular Margin (AAM) proposed in the following paper: ‘’’Margin Matters: Towards More Discriminative Deep Neural Network Embeddings for Speaker Recognition’’’ (https://arxiv.org/abs/1906.07317)

Parameters:

margin (float) – The margin for cosine similiarity.
scale (float) – The scale for cosine similiarity.

Returns:

predictions – Tensor.

Return type:

torch.Tensor

Example

>>> outputs = torch.tensor([ [1., -1.], [-1., 1.], [0.9, 0.1], [0.1, 0.9] ])
>>> targets = torch.tensor([ [1., 0.], [0., 1.], [ 1., 0.], [0.,  1.] ])
>>> pred = AdditiveAngularMargin()
>>> predictions = pred(outputs, targets)
>>> predictions[:,0] > predictions[:,1]
tensor([ True, False,  True, False])

forward(outputs, targets)[source]

Compute AAM between two tensors

Parameters:

outputs (torch.Tensor) – The outputs of shape [N, C], cosine similarity is required.
targets (torch.Tensor) – The targets of shape [N, C], where the margin is applied for.

Returns:

predictions

Return type:

torch.Tensor

training: bool

class speechbrain.nnet.losses.LogSoftmaxWrapper(loss_fn)[source]

Bases: Module

Returns:

loss (torch.Tensor) – Learning loss
predictions (torch.Tensor) – Log probabilities

Example

>>> outputs = torch.tensor([ [1., -1.], [-1., 1.], [0.9, 0.1], [0.1, 0.9] ])
>>> outputs = outputs.unsqueeze(1)
>>> targets = torch.tensor([ [0], [1], [0], [1] ])
>>> log_prob = LogSoftmaxWrapper(nn.Identity())
>>> loss = log_prob(outputs, targets)
>>> 0 <= loss < 1
tensor(True)
>>> log_prob = LogSoftmaxWrapper(AngularMargin(margin=0.2, scale=32))
>>> loss = log_prob(outputs, targets)
>>> 0 <= loss < 1
tensor(True)
>>> outputs = torch.tensor([ [1., -1.], [-1., 1.], [0.9, 0.1], [0.1, 0.9] ])
>>> log_prob = LogSoftmaxWrapper(AdditiveAngularMargin(margin=0.3, scale=32))
>>> loss = log_prob(outputs, targets)
>>> 0 <= loss < 1
tensor(True)

forward(outputs, targets, length=None)[source]

Parameters:

outputs (torch.Tensor) – Network output tensor, of shape [batch, 1, outdim].
targets (torch.Tensor) – Target tensor, of shape [batch, 1].

Returns:

loss – Loss for current examples.

Return type:

torch.Tensor

training: bool

speechbrain.nnet.losses.ctc_loss_kd(log_probs, targets, input_lens, blank_index, device)[source]

Knowledge distillation for CTC loss.

Reference

Distilling Knowledge from Ensembles of Acoustic Models for Joint CTC-Attention End-to-End Speech Recognition. https://arxiv.org/abs/2005.09310

param log_probs:: Predicted tensor from student model, of shape [batch, time, chars].
type log_probs:: torch.Tensor
param targets:: Predicted tensor from single teacher model, of shape [batch, time, chars].
type targets:: torch.Tensor
param input_lens:: Length of each utterance.
type input_lens:: torch.Tensor
param blank_index:: The location of the blank symbol among the character indexes.
type blank_index:: int
param device:: Device for computing.
type device:: str

speechbrain.nnet.losses.ce_kd(inp, target)[source]

Simple version of distillation for cross-entropy loss.

Parameters:

inp (torch.Tensor) – The probabilities from student model, of shape [batch_size * length, feature]
target (torch.Tensor) – The probabilities from teacher model, of shape [batch_size * length, feature]

speechbrain.nnet.losses.nll_loss_kd(probabilities, targets, rel_lab_lengths)[source]

Knowledge distillation for negative log-likelihood loss.

Reference

Distilling Knowledge from Ensembles of Acoustic Models for Joint CTC-Attention End-to-End Speech Recognition. https://arxiv.org/abs/2005.09310

param probabilities:: The predicted probabilities from the student model. Format is [batch, frames, p]
type probabilities:: torch.Tensor
param targets:: The target probabilities from the teacher model. Format is [batch, frames, p]
type targets:: torch.Tensor
param rel_lab_lengths:: Length of each utterance, if the frame-level loss is desired.
type rel_lab_lengths:: torch.Tensor

Example

>>> probabilities = torch.tensor([[[0.8, 0.2], [0.2, 0.8]]])
>>> targets = torch.tensor([[[0.9, 0.1], [0.1, 0.9]]])
>>> rel_lab_lengths = torch.tensor([1.])
>>> nll_loss_kd(probabilities, targets, rel_lab_lengths)
tensor(-0.7400)

class speechbrain.nnet.losses.ContrastiveLoss(logit_temp)[source]

Bases: Module

Contrastive loss as used in wav2vec2.

Reference

wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations https://arxiv.org/abs/2006.11477

param logit_temp:: A temperature to devide the logits.
type logit_temp:: torch.Float

forward(x, y, negs)[source]

Parameters:

x (torch.Tensor) – Encoded embeddings with shape (B, T, C).
y (torch.Tensor) – Feature extractor target embeddings with shape (B, T, C).
negs (torch.Tensor) – Negative embeddings from feature extractor with shape (N, B, T, C)
sample_negatives (where N is number of negatives. Can be obtained with our) –
lobes/wav2vec2). (function (check in) –

training: bool

class speechbrain.nnet.losses.VariationalAutoencoderLoss(rec_loss=None, len_dim=1, dist_loss_weight=0.001)[source]

Bases: Module

The Variational Autoencoder loss, with support for length masking

From Autoencoding Variational Bayes: https://arxiv.org/pdf/1312.6114.pdf

Parameters:

rec_loss (callable) – a function or module to compute the reconstruction loss
len_dim (int) – the dimension to be used for the length, if encoding sequences of variable length
dist_loss_weight (float) – the relative weight of the distribution loss (K-L divergence)

Example

>>> from speechbrain.nnet.autoencoders import VariationalAutoencoderOutput
>>> vae_loss = VariationalAutoencoderLoss(dist_loss_weight=0.5)
>>> predictions = VariationalAutoencoderOutput(
...     rec=torch.tensor(
...         [[0.8, 1.0],
...          [1.2, 0.6],
...          [0.4, 1.4]]
...         ),
...     mean=torch.tensor(
...         [[0.5, 1.0],
...          [1.5, 1.0],
...          [1.0, 1.4]],
...         ),
...     log_var=torch.tensor(
...         [[0.0, -0.2],
...          [2.0, -2.0],
...          [0.2,  0.4]],
...         ),
...     latent=torch.randn(3, 1),
...     latent_sample=torch.randn(3, 1),
...     latent_length=torch.tensor([1., 1., 1.]),
... )
>>> targets = torch.tensor(
...     [[0.9, 1.1],
...      [1.4, 0.6],
...      [0.2, 1.4]]
... )
>>> loss = vae_loss(predictions, targets)
>>> loss
tensor(1.1264)
>>> details = vae_loss.details(predictions, targets)
>>> details  
VariationalAutoencoderLossDetails(loss=tensor(1.1264),
                                  rec_loss=tensor(0.0333),
                                  dist_loss=tensor(2.1861),
                                  weighted_dist_loss=tensor(1.0930))

forward(predictions, targets, length=None, reduction='batchmean')[source]

Computes the forward pass

Parameters:

predictions (speechbrain.nnet.autoencoders.VariationalAutoencoderOutput) – the variational autoencoder output
targets (torch.Tensor) – the reconstruction targets
length (torch.Tensor) – Length of each sample for computing true error with a mask.

Results

loss: torch.Tensor: the VAE loss (reconstruction + K-L divergence)

details(predictions, targets, length=None, reduction='batchmean')[source]

Gets detailed information about the loss (useful for plotting, logs, etc.)

Parameters:

predictions (speechbrain.nnet.autoencoders.VariationalAutoencoderOutput) – the variational autoencoder output (or a tuple of rec, mean, log_var)
targets (torch.Tensor) – targets for the reconstruction loss
length (torch.Tensor) – Length of each sample for computing true error with a mask.
reduction (str) – The type of reduction to apply

Results

details: VAELossDetails

a namedtuple with the following parameters loss: torch.Tensor

the combined loss

rec_loss: torch.Tensor: the reconstruction loss
dist_loss: torch.Tensor: the distribution loss (K-L divergence), raw value
weighted_dist_loss: torch.Tensor: the weighted value of the distribution loss, as used in the combined loss

training: bool

class speechbrain.nnet.losses.AutoencoderLoss(rec_loss=None, len_dim=1)[source]

Bases: Module

An implementation of a standard (non-variational) autoencoder loss

Parameters:

rec_loss (callable) – the callable to compute the reconstruction loss
len_dim (torch.Tensor) – the dimension index to be used for length

Example

>>> from speechbrain.nnet.autoencoders import AutoencoderOutput
>>> ae_loss = AutoencoderLoss()
>>> rec = torch.tensor(
...   [[0.8, 1.0],
...    [1.2, 0.6],
...    [0.4, 1.4]]
... )
>>> predictions = AutoencoderOutput(
...     rec=rec,
...     latent=torch.randn(3, 1),
...     latent_length=torch.tensor([1., 1.])
... )
>>> targets = torch.tensor(
...     [[0.9, 1.1],
...      [1.4, 0.6],
...      [0.2, 1.4]]
... )
>>> ae_loss(predictions, targets)
tensor(0.0333)
>>> ae_loss.details(predictions, targets)
AutoencoderLossDetails(loss=tensor(0.0333), rec_loss=tensor(0.0333))

forward(predictions, targets, length=None, reduction='batchmean')[source]

Computes the autoencoder loss

Parameters:

predictions (speechbrain.nnet.autoencoders.AutoencoderOutput) – the autoencoder output
targets (torch.Tensor) – targets for the reconstruction loss
length (torch.Tensor) – Length of each sample for computing true error with a mask

details(predictions, targets, length=None, reduction='batchmean')[source]

Gets detailed information about the loss (useful for plotting, logs, etc.)

This is provided mainly to make the loss interchangeable with more complex autoencoder loses, such as the VAE loss.

Parameters:

predictions (speechbrain.nnet.autoencoders.AutoencoderOutput) – the autoencoder output
targets (torch.Tensor) – targets for the reconstruction loss
length (torch.Tensor) – Length of each sample for computing true error with a mask.
reduction (str) – The type of reduction to apply

Results

details: AutoencoderLossDetails

a namedtuple with the following parameters loss: torch.Tensor

the combined loss

rec_loss: torch.Tensor: the reconstruction loss

training: bool

class speechbrain.nnet.losses.VariationalAutoencoderLossDetails(loss, rec_loss, dist_loss, weighted_dist_loss)

Bases: tuple

dist_loss: Alias for field number 2

loss: Alias for field number 0

rec_loss: Alias for field number 1

weighted_dist_loss: Alias for field number 3

class speechbrain.nnet.losses.AutoencoderLossDetails(loss, rec_loss)

Bases: tuple

loss: Alias for field number 0

rec_loss: Alias for field number 1

class speechbrain.nnet.losses.Laplacian(kernel_size, dtype=torch.float32)[source]

Bases: Module

Computes the Laplacian for image-like data

Parameters:

kernel_size (int) – the size of the Laplacian kernel
dtype (torch.dtype) – the data type (optional)

Example

>>> lap = Laplacian(3)
>>> lap.get_kernel()
tensor([[[[-1., -1., -1.],
          [-1.,  8., -1.],
          [-1., -1., -1.]]]])
>>> data = torch.eye(6) + torch.eye(6).flip(0)
>>> data
tensor([[1., 0., 0., 0., 0., 1.],
        [0., 1., 0., 0., 1., 0.],
        [0., 0., 1., 1., 0., 0.],
        [0., 0., 1., 1., 0., 0.],
        [0., 1., 0., 0., 1., 0.],
        [1., 0., 0., 0., 0., 1.]])
>>> lap(data.unsqueeze(0))
tensor([[[ 6., -3., -3.,  6.],
         [-3.,  4.,  4., -3.],
         [-3.,  4.,  4., -3.],
         [ 6., -3., -3.,  6.]]])

get_kernel()[source]: Computes the Laplacian kernel

forward(data)[source]

Computes the Laplacian of image-like data

Parameters:: data (torch.Tensor) – a (B x C x W x H) or (B x C x H x W) tensor with image-like data

training: bool

class speechbrain.nnet.losses.LaplacianVarianceLoss(kernel_size=3, len_dim=1)[source]

Bases: Module

The Laplacian variance loss - used to penalize blurriness in image-like data, such as spectrograms.

The loss value will be the negative variance because the higher the variance, the sharper the image.

Parameters:

kernel_size (int) – the Laplacian kernel size
len_dim (int) – the dimension to be used as the length

Example

>>> lap_loss = LaplacianVarianceLoss(3)
>>> data = torch.ones(6, 6).unsqueeze(0)
>>> data
tensor([[[1., 1., 1., 1., 1., 1.],
         [1., 1., 1., 1., 1., 1.],
         [1., 1., 1., 1., 1., 1.],
         [1., 1., 1., 1., 1., 1.],
         [1., 1., 1., 1., 1., 1.],
         [1., 1., 1., 1., 1., 1.]]])
>>> lap_loss(data)
tensor(-0.)
>>> data = (
...     torch.eye(6) + torch.eye(6).flip(0)
... ).unsqueeze(0)
>>> data
tensor([[[1., 0., 0., 0., 0., 1.],
         [0., 1., 0., 0., 1., 0.],
         [0., 0., 1., 1., 0., 0.],
         [0., 0., 1., 1., 0., 0.],
         [0., 1., 0., 0., 1., 0.],
         [1., 0., 0., 0., 0., 1.]]])
>>> lap_loss(data)
tensor(-17.6000)

forward(predictions, length=None, reduction=None)[source]

Computes the Laplacian loss

Parameters:: predictions (torch.Tensor) – a (B x C x W x H) or (B x C x H x W) tensor
Returns:: loss – the loss value
Return type:: torch.Tensor

training: bool