File size: 10,074 Bytes
ad16788 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 |
from collections import OrderedDict
from typing import List
from typing import Tuple
import torch
from torch_complex.tensor import ComplexTensor
from espnet2.enh.layers.dnn_beamformer import DNN_Beamformer
from espnet2.enh.layers.dnn_wpe import DNN_WPE
from espnet2.enh.separator.abs_separator import AbsSeparator
class NeuralBeamformer(AbsSeparator):
def __init__(
self,
input_dim: int,
num_spk: int = 1,
loss_type: str = "mask_mse",
# Dereverberation options
use_wpe: bool = False,
wnet_type: str = "blstmp",
wlayers: int = 3,
wunits: int = 300,
wprojs: int = 320,
wdropout_rate: float = 0.0,
taps: int = 5,
delay: int = 3,
use_dnn_mask_for_wpe: bool = True,
wnonlinear: str = "crelu",
multi_source_wpe: bool = True,
wnormalization: bool = False,
# Beamformer options
use_beamformer: bool = True,
bnet_type: str = "blstmp",
blayers: int = 3,
bunits: int = 300,
bprojs: int = 320,
badim: int = 320,
ref_channel: int = -1,
use_noise_mask: bool = True,
bnonlinear: str = "sigmoid",
beamformer_type: str = "mvdr_souden",
rtf_iterations: int = 2,
bdropout_rate: float = 0.0,
shared_power: bool = True,
# For numerical stability
diagonal_loading: bool = True,
diag_eps_wpe: float = 1e-7,
diag_eps_bf: float = 1e-7,
mask_flooring: bool = False,
flooring_thres_wpe: float = 1e-6,
flooring_thres_bf: float = 1e-6,
use_torch_solver: bool = True,
):
super().__init__()
self._num_spk = num_spk
self.loss_type = loss_type
if loss_type not in ("mask_mse", "spectrum", "spectrum_log", "magnitude"):
raise ValueError("Unsupported loss type: %s" % loss_type)
self.use_beamformer = use_beamformer
self.use_wpe = use_wpe
if self.use_wpe:
if use_dnn_mask_for_wpe:
# Use DNN for power estimation
iterations = 1
else:
# Performing as conventional WPE, without DNN Estimator
iterations = 2
self.wpe = DNN_WPE(
wtype=wnet_type,
widim=input_dim,
wlayers=wlayers,
wunits=wunits,
wprojs=wprojs,
dropout_rate=wdropout_rate,
taps=taps,
delay=delay,
use_dnn_mask=use_dnn_mask_for_wpe,
nmask=1 if multi_source_wpe else num_spk,
nonlinear=wnonlinear,
iterations=iterations,
normalization=wnormalization,
diagonal_loading=diagonal_loading,
diag_eps=diag_eps_wpe,
mask_flooring=mask_flooring,
flooring_thres=flooring_thres_wpe,
use_torch_solver=use_torch_solver,
)
else:
self.wpe = None
self.ref_channel = ref_channel
if self.use_beamformer:
self.beamformer = DNN_Beamformer(
bidim=input_dim,
btype=bnet_type,
blayers=blayers,
bunits=bunits,
bprojs=bprojs,
num_spk=num_spk,
use_noise_mask=use_noise_mask,
nonlinear=bnonlinear,
dropout_rate=bdropout_rate,
badim=badim,
ref_channel=ref_channel,
beamformer_type=beamformer_type,
rtf_iterations=rtf_iterations,
btaps=taps,
bdelay=delay,
diagonal_loading=diagonal_loading,
diag_eps=diag_eps_bf,
mask_flooring=mask_flooring,
flooring_thres=flooring_thres_bf,
use_torch_solver=use_torch_solver,
)
else:
self.beamformer = None
# share speech powers between WPE and beamforming (wMPDR/WPD)
self.shared_power = shared_power and use_wpe
def forward(
self, input: ComplexTensor, ilens: torch.Tensor
) -> Tuple[List[ComplexTensor], torch.Tensor, OrderedDict]:
"""Forward.
Args:
input (ComplexTensor): mixed speech [Batch, Frames, Channel, Freq]
ilens (torch.Tensor): input lengths [Batch]
Returns:
enhanced speech (single-channel): List[ComplexTensor]
output lengths
other predcited data: OrderedDict[
'dereverb1': ComplexTensor(Batch, Frames, Channel, Freq),
'mask_dereverb1': torch.Tensor(Batch, Frames, Channel, Freq),
'mask_noise1': torch.Tensor(Batch, Frames, Channel, Freq),
'mask_spk1': torch.Tensor(Batch, Frames, Channel, Freq),
'mask_spk2': torch.Tensor(Batch, Frames, Channel, Freq),
...
'mask_spkn': torch.Tensor(Batch, Frames, Channel, Freq),
]
"""
# Shape of input spectrum must be (B, T, F) or (B, T, C, F)
assert input.dim() in (3, 4), input.dim()
enhanced = input
others = OrderedDict()
if (
self.training
and self.loss_type is not None
and self.loss_type.startswith("mask")
):
# Only estimating masks during training for saving memory
if self.use_wpe:
if input.dim() == 3:
mask_w, ilens = self.wpe.predict_mask(input.unsqueeze(-2), ilens)
mask_w = mask_w.squeeze(-2)
elif input.dim() == 4:
mask_w, ilens = self.wpe.predict_mask(input, ilens)
if mask_w is not None:
if isinstance(enhanced, list):
# single-source WPE
for spk in range(self.num_spk):
others["mask_dereverb{}".format(spk + 1)] = mask_w[spk]
else:
# multi-source WPE
others["mask_dereverb1"] = mask_w
if self.use_beamformer and input.dim() == 4:
others_b, ilens = self.beamformer.predict_mask(input, ilens)
for spk in range(self.num_spk):
others["mask_spk{}".format(spk + 1)] = others_b[spk]
if len(others_b) > self.num_spk:
others["mask_noise1"] = others_b[self.num_spk]
return None, ilens, others
else:
powers = None
# Performing both mask estimation and enhancement
if input.dim() == 3:
# single-channel input (B, T, F)
if self.use_wpe:
enhanced, ilens, mask_w, powers = self.wpe(
input.unsqueeze(-2), ilens
)
if isinstance(enhanced, list):
# single-source WPE
enhanced = [enh.squeeze(-2) for enh in enhanced]
if mask_w is not None:
for spk in range(self.num_spk):
key = "dereverb{}".format(spk + 1)
others[key] = enhanced[spk]
others["mask_" + key] = mask_w[spk].squeeze(-2)
else:
# multi-source WPE
enhanced = enhanced.squeeze(-2)
if mask_w is not None:
others["dereverb1"] = enhanced
others["mask_dereverb1"] = mask_w.squeeze(-2)
else:
# multi-channel input (B, T, C, F)
# 1. WPE
if self.use_wpe:
enhanced, ilens, mask_w, powers = self.wpe(input, ilens)
if mask_w is not None:
if isinstance(enhanced, list):
# single-source WPE
for spk in range(self.num_spk):
key = "dereverb{}".format(spk + 1)
others[key] = enhanced[spk]
others["mask_" + key] = mask_w[spk]
else:
# multi-source WPE
others["dereverb1"] = enhanced
others["mask_dereverb1"] = mask_w.squeeze(-2)
# 2. Beamformer
if self.use_beamformer:
if (
not self.beamformer.beamformer_type.startswith("wmpdr")
or not self.beamformer.beamformer_type.startswith("wpd")
or not self.shared_power
or (self.wpe.nmask == 1 and self.num_spk > 1)
):
powers = None
# enhanced: (B, T, C, F) -> (B, T, F)
if isinstance(enhanced, list):
# outputs of single-source WPE
raise NotImplementedError(
"Single-source WPE is not supported with beamformer "
"in multi-speaker cases."
)
else:
# output of multi-source WPE
enhanced, ilens, others_b = self.beamformer(
enhanced, ilens, powers=powers
)
for spk in range(self.num_spk):
others["mask_spk{}".format(spk + 1)] = others_b[spk]
if len(others_b) > self.num_spk:
others["mask_noise1"] = others_b[self.num_spk]
if not isinstance(enhanced, list):
enhanced = [enhanced]
return enhanced, ilens, others
@property
def num_spk(self):
return self._num_spk
|