Source code for espnet2.diar.separator.tcn_separator_nomask

from typing import Tuple, Union

import torch
from packaging.version import parse as V
from torch_complex.tensor import ComplexTensor

from espnet2.diar.layers.tcn_nomask import TemporalConvNet
from espnet2.enh.layers.complex_utils import is_complex
from espnet2.enh.separator.abs_separator import AbsSeparator

is_torch_1_9_plus = V(torch.__version__) >= V("1.9.0")


[docs]class TCNSeparatorNomask(AbsSeparator): def __init__( self, input_dim: int, layer: int = 8, stack: int = 3, bottleneck_dim: int = 128, hidden_dim: int = 512, kernel: int = 3, causal: bool = False, norm_type: str = "gLN", ): """Temporal Convolution Separator Note that this separator is equivalent to TCNSeparator except for not having the mask estimation part. This separator outputs the intermediate bottleneck feats (which is used as the input to diarization branch in enh_diar task). This separator is followed by MultiMask module, which estimates the masks. Args: input_dim: input feature dimension layer: int, number of layers in each stack. stack: int, number of stacks bottleneck_dim: bottleneck dimension hidden_dim: number of convolution channel kernel: int, kernel size. causal: bool, defalut False. norm_type: str, choose from 'BN', 'gLN', 'cLN' """ super().__init__() self.tcn = TemporalConvNet( N=input_dim, B=bottleneck_dim, H=hidden_dim, P=kernel, X=layer, R=stack, norm_type=norm_type, causal=causal, ) self._output_dim = bottleneck_dim
[docs] def forward( self, input: Union[torch.Tensor, ComplexTensor], ilens: torch.Tensor ) -> Tuple[torch.Tensor, torch.Tensor]: """Forward. Args: input (torch.Tensor or ComplexTensor): Encoded feature [B, T, N] ilens (torch.Tensor): input lengths [Batch] Returns: feats (torch.Tensor): [B, T, bottleneck_dim] ilens (torch.Tensor): (B,) """ # if complex spectrum if is_complex(input): feature = abs(input) else: feature = input feature = feature.transpose(1, 2) # B, N, L feats = self.tcn(feature) # [B, bottleneck_dim, L] feats = feats.transpose(1, 2) # B, L, bottleneck_dim return feats, ilens
@property def output_dim(self) -> int: return self._output_dim @property def num_spk(self): return None