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