Source code for espnet2.enh.separator.dprnn_separator

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

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

from espnet2.enh.layers.complex_utils import is_complex
from espnet2.enh.layers.dprnn import DPRNN, merge_feature, split_feature
from espnet2.enh.separator.abs_separator import AbsSeparator

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

[docs]class DPRNNSeparator(AbsSeparator): def __init__( self, input_dim: int, rnn_type: str = "lstm", bidirectional: bool = True, num_spk: int = 2, predict_noise: bool = False, nonlinear: str = "relu", layer: int = 3, unit: int = 512, segment_size: int = 20, dropout: float = 0.0, ): """Dual-Path RNN (DPRNN) Separator Args: input_dim: input feature dimension rnn_type: string, select from 'RNN', 'LSTM' and 'GRU'. bidirectional: bool, whether the inter-chunk RNN layers are bidirectional. num_spk: number of speakers predict_noise: whether to output the estimated noise signal 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. segment_size: dual-path segment size dropout: float, dropout ratio. Default is 0. """ super().__init__() self._num_spk = num_spk self.predict_noise = predict_noise self.segment_size = segment_size self.num_outputs = self.num_spk + 1 if self.predict_noise else self.num_spk self.dprnn = DPRNN( rnn_type=rnn_type, input_size=input_dim, hidden_size=unit, output_size=input_dim * self.num_outputs, dropout=dropout, num_layers=layer, bidirectional=bidirectional, ) 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]
[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, N] 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. masks: OrderedDict[ 'mask_spk1': torch.Tensor(Batch, Frames, Freq), 'mask_spk2': torch.Tensor(Batch, Frames, Freq), ... 'mask_spkn': torch.Tensor(Batch, Frames, Freq), ] """ # if complex spectrum, if is_complex(input): feature = abs(input) else: feature = input B, T, N = feature.shape feature = feature.transpose(1, 2) # B, N, T segmented, rest = split_feature( feature, segment_size=self.segment_size ) # B, N, L, K processed = self.dprnn(segmented) # B, N*num_spk, L, K processed = merge_feature(processed, rest) # B, N*num_spk, T processed = processed.transpose(1, 2) # B, T, N*num_spk processed = processed.view(B, T, N, self.num_outputs) masks = self.nonlinear(processed).unbind(dim=3) if self.predict_noise: *masks, mask_noise = masks masked = [input * m for m in masks] others = OrderedDict( zip(["mask_spk{}".format(i + 1) for i in range(len(masks))], masks) ) if self.predict_noise: others["noise1"] = input * mask_noise return masked, ilens, others
@property def num_spk(self): return self._num_spk