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Permutation Invariant Training (PIT)

Module Interface

class torchmetrics.PermutationInvariantTraining(metric_func, eval_func='max', **kwargs)[source]

Calculates Permutation invariant training (PIT) that can evaluate models for speaker independent multi- talker speech separation in a permutation invariant way.

As input to forward and update the metric accepts the following input

  • preds (Tensor): float tensor with shape (batch_size,num_speakers,...)

  • target (Tensor): float tensor with shape (batch_size,num_speakers,...)

As output of forward and compute the metric returns the following output

  • pesq (Tensor): float scalar tensor with average PESQ value over samples

Parameters
  • metric_func (Callable) – a metric function accept a batch of target and estimate, i.e. metric_func(preds[:, i, ...], target[:, j, ...]), and returns a batch of metric tensors (batch,)

  • eval_func (Literal[‘max’, ‘min’]) – the function to find the best permutation, can be ‘min’ or ‘max’, i.e. the smaller the better or the larger the better.

  • kwargs (Any) – Additional keyword arguments for either the metric_func or distributed communication, see Advanced metric settings for more info.

Example

>>> import torch
>>> from torchmetrics import PermutationInvariantTraining
>>> from torchmetrics.functional import scale_invariant_signal_noise_ratio
>>> _ = torch.manual_seed(42)
>>> preds = torch.randn(3, 2, 5) # [batch, spk, time]
>>> target = torch.randn(3, 2, 5) # [batch, spk, time]
>>> pit = PermutationInvariantTraining(scale_invariant_signal_noise_ratio, 'max')
>>> pit(preds, target)
tensor(-2.1065)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Functional Interface

torchmetrics.functional.permutation_invariant_training(preds, target, metric_func, eval_func='max', **kwargs)[source]

Calculates Permutation invariant training (PIT) that can evaluate models for speaker independent multi- talker speech separation in a permutation invariant way.

Parameters
  • preds (Tensor) – float tensor with shape (batch_size,num_speakers,...)

  • target (Tensor) – float tensor with shape (batch_size,num_speakers,...)

  • metric_func (Callable) – a metric function accept a batch of target and estimate, i.e. metric_func(preds[:, i, ...], target[:, j, ...]), and returns a batch of metric tensors (batch,)

  • eval_func (Literal[‘max’, ‘min’]) – the function to find the best permutation, can be 'min' or 'max', i.e. the smaller the better or the larger the better.

  • kwargs (Any) – Additional args for metric_func

Return type

Tuple[Tensor, Tensor]

Returns

Tuple of two float tensors. First tensor with shape (batch,) contains the best metric value for each sample and second tensor with shape (batch,) contains the best permutation.

Example

>>> from torchmetrics.functional.audio import scale_invariant_signal_distortion_ratio
>>> # [batch, spk, time]
>>> preds = torch.tensor([[[-0.0579,  0.3560, -0.9604], [-0.1719,  0.3205,  0.2951]]])
>>> target = torch.tensor([[[ 1.0958, -0.1648,  0.5228], [-0.4100,  1.1942, -0.5103]]])
>>> best_metric, best_perm = permutation_invariant_training(
...     preds, target, scale_invariant_signal_distortion_ratio, 'max')
>>> best_metric
tensor([-5.1091])
>>> best_perm
tensor([[0, 1]])
>>> pit_permutate(preds, best_perm)
tensor([[[-0.0579,  0.3560, -0.9604],
         [-0.1719,  0.3205,  0.2951]]])
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