Source code for espnet2.enh.separator.dpcl_separator

from collections import OrderedDict
from typing import Dict, List, Optional, Tuple, Union

import torch
from torch_complex.tensor import ComplexTensor

from espnet2.enh.layers.complex_utils import is_complex
from espnet2.enh.separator.abs_separator import AbsSeparator
from espnet.nets.pytorch_backend.rnn.encoders import RNN

[docs]class DPCLSeparator(AbsSeparator): def __init__( self, input_dim: int, rnn_type: str = "blstm", num_spk: int = 2, nonlinear: str = "tanh", layer: int = 2, unit: int = 512, emb_D: int = 40, dropout: float = 0.0, ): """Deep Clustering Separator. References: [1] Deep clustering: Discriminative embeddings for segmentation and separation; John R. Hershey. et al., 2016; [2] Manifold-Aware Deep Clustering: Maximizing Angles Between Embedding Vectors Based on Regular Simplex; Tanaka, K. et al., 2021; Args: input_dim: input feature dimension rnn_type: string, select from 'blstm', 'lstm' etc. bidirectional: bool, whether the inter-chunk RNN layers are bidirectional. num_spk: number of speakers nonlinear: the nonlinear function for mask estimation, select from 'relu', 'tanh', 'sigmoid' layer: int, number of stacked RNN layers. Default is 3. unit: int, dimension of the hidden state. emb_D: int, dimension of the feature vector for a tf-bin. dropout: float, dropout ratio. Default is 0. """ # noqa: E501 super().__init__() self._num_spk = num_spk self.blstm = RNN( idim=input_dim, elayers=layer, cdim=unit, hdim=unit, dropout=dropout, typ=rnn_type, ) self.linear = torch.nn.Linear(unit, input_dim * emb_D) if nonlinear not in ("sigmoid", "relu", "tanh"): raise ValueError("Not supporting nonlinear={}".format(nonlinear)) self.nonlinear = { "sigmoid": torch.nn.Sigmoid(), "relu": torch.nn.ReLU(), "tanh": torch.nn.Tanh(), }[nonlinear] self.D = emb_D
[docs] def forward( self, input: Union[torch.Tensor, ComplexTensor], ilens: torch.Tensor, additional: Optional[Dict] = None, ) -> Tuple[List[Union[torch.Tensor, ComplexTensor]], torch.Tensor, OrderedDict]: """Forward. Args: input (torch.Tensor or ComplexTensor): Encoded feature [B, T, F] ilens (torch.Tensor): input lengths [Batch] additional (Dict or None): other data included in model NOTE: not used in this model Returns: masked (List[Union(torch.Tensor, ComplexTensor)]): [(B, T, N), ...] ilens (torch.Tensor): (B,) others predicted data, e.g. tf_embedding: OrderedDict[ 'tf_embedding': learned embedding of all T-F bins (B, T * F, D), ] """ # if complex spectrum, if is_complex(input): feature = abs(input) else: feature = input B, T, F = input.shape # x:(B, T, F) x, ilens, _ = self.blstm(feature, ilens) # x:(B, T, F*D) x = self.linear(x) # x:(B, T, F*D) x = self.nonlinear(x) tf_embedding = x.view(B, -1, self.D) if masked = None else: # K-means for batch centers = tf_embedding[:, : self._num_spk, :].detach() dist = torch.empty(B, T * F, self._num_spk, device=tf_embedding.device) last_label = torch.zeros(B, T * F, device=tf_embedding.device) while True: for i in range(self._num_spk): dist[:, :, i] = torch.sum( (tf_embedding - centers[:, i, :].unsqueeze(1)) ** 2, dim=2 ) label = dist.argmin(dim=2) if torch.sum(label != last_label) == 0: break last_label = label for b in range(B): for i in range(self._num_spk): centers[b, i] = tf_embedding[b, label[b] == i].mean(dim=0) label = label.view(B, T, F) masked = [] for i in range(self._num_spk): masked.append(input * (label == i)) others = OrderedDict( {"tf_embedding": tf_embedding}, ) return masked, ilens, others
@property def num_spk(self): return self._num_spk