torcheval.metrics.FrechetAudioDistance

class torcheval.metrics.FrechetAudioDistance(preproc: Callable[[Tensor], Tensor], model: Module, embedding_dim: int, device: Optional[device] = None)

Computes the Fréchet distance between predicted and target audio waveforms.

Original paper: https://arxiv.org/abs/1812.08466

Parameters:

• preproc (Callable[[torch.Tensor], torch.Tensor]) – Callable for preprocessing waveforms prior to passing to model.
• model (torch.nn.Module) – Model for generating embeddings from preprocessed waveforms.
• embedding_dim (int) – Size of embedding.
• device (torch.device or None, optional) – Device where computations will be performed. If None, the default device will be used. (Default: None)

__init__(preproc: Callable[[Tensor], Tensor], model: Module, embedding_dim: int, device: Optional[device] = None) → None

Initialize a metric object and its internal states.

Use self._add_state() to initialize state variables of your metric class. The state variables should be either torch.Tensor, a list of torch.Tensor, or a dictionary with torch.Tensor as values

Methods

__init__(preproc, model, embedding_dim[, device])	Initialize a metric object and its internal states.
compute()	Computes the Fréchet distance on the current set of internal states.
load_state_dict(state_dict[, strict])	Loads metric state variables from state_dict.
merge_state(fads)	Merges the states of other FrechetAudioDistance instances into those of the current instance.
reset()	Reset the metric state variables to their default value.
state_dict()	Save metric state variables in state_dict.
to(device, *args, **kwargs)	Move tensors in metric state variables to device.
update(preds, targets)	Update states with a batch of predicted and target waveforms.
with_vggish([device])	Builds an instance of FrechetAudioDistance with TorchAudio's pretrained VGGish model.

Attributes

device

The last input device of Metric.to().