Source code for espnet2.enh.loss.wrappers.dpcl_solver

from espnet2.enh.loss.criterions.abs_loss import AbsEnhLoss
from espnet2.enh.loss.wrappers.abs_wrapper import AbsLossWrapper

[docs]class DPCLSolver(AbsLossWrapper): def __init__(self, criterion: AbsEnhLoss, weight=1.0): super().__init__() self.criterion = criterion self.weight = weight
[docs] def forward(self, ref, inf, others={}): """A naive DPCL solver Args: ref (List[torch.Tensor]): [(batch, ...), ...] x n_spk inf (List[torch.Tensor]): [(batch, ...), ...] others (List): other data included in this solver e.g. "tf_embedding" learned embedding of all T-F bins (B, T * F, D) Returns: loss: (torch.Tensor): minimum loss with the best permutation stats: (dict), for collecting training status others: reserved """ assert "tf_embedding" in others loss = self.criterion(ref, others["tf_embedding"]).mean() stats = dict() stats[] = loss.detach() return loss.mean(), stats, {}