def apply_beamforming(
self,
data,
ilens,
psd_n,
psd_speech,
psd_distortion=None,
rtf_mat=None,
spk=0,
):
"""Beamforming with the provided statistics.
Args:
data (torch.complex64/ComplexTensor): (B, F, C, T)
ilens (torch.Tensor): (B,)
psd_n (torch.complex64/ComplexTensor):
Noise covariance matrix for MVDR (B, F, C, C)
Observation covariance matrix for MPDR/wMPDR (B, F, C, C)
Stacked observation covariance for WPD (B,F,(btaps+1)*C,(btaps+1)*C)
psd_speech (torch.complex64/ComplexTensor):
Speech covariance matrix (B, F, C, C)
psd_distortion (torch.complex64/ComplexTensor):
Noise covariance matrix (B, F, C, C)
rtf_mat (torch.complex64/ComplexTensor):
RTF matrix (B, F, C, num_spk)
spk (int): speaker index
Return:
enhanced (torch.complex64/ComplexTensor): (B, F, T)
ws (torch.complex64/ComplexTensor): (B, F) or (B, F, (btaps+1)*C)
"""
# u: (B, C)
if self.ref_channel < 0:
u, _ = self.ref(psd_speech.to(dtype=data.dtype), ilens)
u = u.double()
else:
if self.beamformer_type.endswith("_souden"):
# (optional) Create onehot vector for fixed reference microphone
u = torch.zeros(
*(data.size()[:-3] + (data.size(-2),)),
device=data.device,
dtype=torch.double
)
u[..., self.ref_channel].fill_(1)
else:
# for simplifying computation in RTF-based beamforming
u = self.ref_channel
if self.beamformer_type in ("mvdr", "mpdr", "wmpdr"):
ws = get_mvdr_vector_with_rtf(
to_double(psd_n),
to_double(psd_speech),
to_double(psd_distortion),
iterations=self.rtf_iterations,
reference_vector=u,
normalize_ref_channel=self.ref_channel,
use_torch_solver=self.use_torch_solver,
diagonal_loading=self.diagonal_loading,
diag_eps=self.diag_eps,
)
enhanced = apply_beamforming_vector(ws, to_double(data))
elif self.beamformer_type == "mvdr_tfs":
assert isinstance(psd_n, (list, tuple))
ws = [
get_mvdr_vector_with_rtf(
to_double(psd_n_i),
to_double(psd_speech),
to_double(psd_distortion),
iterations=self.rtf_iterations,
reference_vector=u,
normalize_ref_channel=self.ref_channel,
use_torch_solver=self.use_torch_solver,
diagonal_loading=self.diagonal_loading,
diag_eps=self.diag_eps,
)
for psd_n_i in psd_n
]
enhanced = stack([apply_beamforming_vector(w, to_double(data)) for w in ws])
with torch.no_grad():
index = enhanced.abs().argmin(dim=0, keepdims=True)
enhanced = enhanced.gather(0, index).squeeze(0)
ws = stack(ws, dim=0)
elif self.beamformer_type in (
"mpdr_souden",
"mvdr_souden",
"wmpdr_souden",
):
ws = get_mvdr_vector(
to_double(psd_speech),
to_double(psd_n),
u,
use_torch_solver=self.use_torch_solver,
diagonal_loading=self.diagonal_loading,
diag_eps=self.diag_eps,
)
enhanced = apply_beamforming_vector(ws, to_double(data))
elif self.beamformer_type == "mvdr_tfs_souden":
assert isinstance(psd_n, (list, tuple))
ws = [
get_mvdr_vector(
to_double(psd_speech),
to_double(psd_n_i),
u,
use_torch_solver=self.use_torch_solver,
diagonal_loading=self.diagonal_loading,
diag_eps=self.diag_eps,
)
for psd_n_i in psd_n
]
enhanced = stack([apply_beamforming_vector(w, to_double(data)) for w in ws])
with torch.no_grad():
index = enhanced.abs().argmin(dim=0, keepdims=True)
enhanced = enhanced.gather(0, index).squeeze(0)
ws = stack(ws, dim=0)
elif self.beamformer_type == "wpd":
ws = get_WPD_filter_with_rtf(
to_double(psd_n),
to_double(psd_speech),
to_double(psd_distortion),
iterations=self.rtf_iterations,
reference_vector=u,
normalize_ref_channel=self.ref_channel,
use_torch_solver=self.use_torch_solver,
diagonal_loading=self.diagonal_loading,
diag_eps=self.diag_eps,
)
enhanced = perform_WPD_filtering(
ws, to_double(data), self.bdelay, self.btaps
)
elif self.beamformer_type == "wpd_souden":
ws = get_WPD_filter_v2(
to_double(psd_speech),
to_double(psd_n),
u,
diagonal_loading=self.diagonal_loading,
diag_eps=self.diag_eps,
)
enhanced = perform_WPD_filtering(
ws, to_double(data), self.bdelay, self.btaps
)
elif self.beamformer_type in ("mwf", "wmwf"):
ws = get_mwf_vector(
to_double(psd_speech),
to_double(psd_n),
u,
use_torch_solver=self.use_torch_solver,
diagonal_loading=self.diagonal_loading,
diag_eps=self.diag_eps,
)
enhanced = apply_beamforming_vector(ws, to_double(data))
elif self.beamformer_type == "sdw_mwf":
ws = get_sdw_mwf_vector(
to_double(psd_speech),
to_double(psd_n),
u,
denoising_weight=self.mwf_mu,
use_torch_solver=self.use_torch_solver,
diagonal_loading=self.diagonal_loading,
diag_eps=self.diag_eps,
)
enhanced = apply_beamforming_vector(ws, to_double(data))
elif self.beamformer_type == "r1mwf":
ws = get_rank1_mwf_vector(
to_double(psd_speech),
to_double(psd_n),
u,
denoising_weight=self.mwf_mu,
use_torch_solver=self.use_torch_solver,
diagonal_loading=self.diagonal_loading,
diag_eps=self.diag_eps,
)
enhanced = apply_beamforming_vector(ws, to_double(data))
elif self.beamformer_type in ("lcmp", "wlcmp", "lcmv"):
ws = get_lcmv_vector_with_rtf(
to_double(psd_n),
to_double(rtf_mat),
reference_vector=spk,
use_torch_solver=self.use_torch_solver,
diagonal_loading=self.diagonal_loading,
diag_eps=self.diag_eps,
)
enhanced = apply_beamforming_vector(ws, to_double(data))
elif self.beamformer_type.startswith("gev"):
ws = get_gev_vector(
to_double(psd_n),
to_double(psd_speech),
mode="power",
diagonal_loading=self.diagonal_loading,
diag_eps=self.diag_eps,
)
enhanced = apply_beamforming_vector(ws, to_double(data))
if self.beamformer_type == "gev_ban":
gain = blind_analytic_normalization(ws, to_double(psd_n))
enhanced = enhanced * gain.unsqueeze(-1)
else:
raise ValueError(
"Not supporting beamformer_type={}".format(self.beamformer_type)
)
return enhanced.to(dtype=data.dtype), ws.to(dtype=data.dtype)
def forward(
self,
data: Union[torch.Tensor, ComplexTensor],
ilens: torch.LongTensor,
powers: Optional[List[torch.Tensor]] = None,
oracle_masks: Optional[List[torch.Tensor]] = None,
) -> Tuple[Union[torch.Tensor, ComplexTensor], torch.LongTensor, torch.Tensor]:
"""DNN_Beamformer forward function.
Notation:
B: Batch
C: Channel
T: Time or Sequence length
F: Freq
Args:
data (torch.complex64/ComplexTensor): (B, T, C, F)
ilens (torch.Tensor): (B,)
powers (List[torch.Tensor] or None): used for wMPDR or WPD (B, F, T)
oracle_masks (List[torch.Tensor] or None): oracle masks (B, F, C, T)
if not None, oracle_masks will be used instead of self.mask
Returns:
enhanced (torch.complex64/ComplexTensor): (B, T, F)
ilens (torch.Tensor): (B,)
masks (torch.Tensor): (B, T, C, F)
"""
# data (B, T, C, F) -> (B, F, C, T)
data = data.permute(0, 3, 2, 1)
data_d = to_double(data)
# mask: [(B, F, C, T)]
if oracle_masks is not None:
masks = oracle_masks
else:
masks, _ = self.mask(data, ilens)
assert self.nmask == len(masks), len(masks)
# floor masks to increase numerical stability
if self.mask_flooring:
masks = [torch.clamp(m, min=self.flooring_thres) for m in masks]
if self.num_spk == 1: # single-speaker case
if self.use_noise_mask:
# (mask_speech, mask_noise)
mask_speech, mask_noise = masks
else:
# (mask_speech,)
mask_speech = masks[0]
mask_noise = 1 - mask_speech
if self.beamformer_type in ("lcmv", "lcmp", "wlcmp"):
raise NotImplementedError("Single source is not supported yet")
beamformer_stats = prepare_beamformer_stats(
data_d,
[mask_speech],
mask_noise,
powers=powers,
beamformer_type=self.beamformer_type,
bdelay=self.bdelay,
btaps=self.btaps,
eps=self.eps,
)
if self.beamformer_type in ("mvdr", "mpdr", "wmpdr", "wpd"):
enhanced, ws = self.apply_beamforming(
data,
ilens,
beamformer_stats["psd_n"],
beamformer_stats["psd_speech"],
psd_distortion=beamformer_stats["psd_distortion"],
)
elif (
self.beamformer_type.endswith("_souden")
or self.beamformer_type == "mwf"
or self.beamformer_type == "wmwf"
or self.beamformer_type == "sdw_mwf"
or self.beamformer_type == "r1mwf"
or self.beamformer_type.startswith("gev")
):
enhanced, ws = self.apply_beamforming(
data,
ilens,
beamformer_stats["psd_n"],
beamformer_stats["psd_speech"],
)
else:
raise ValueError(
"Not supporting beamformer_type={}".format(self.beamformer_type)
)
# (..., F, T) -> (..., T, F)
enhanced = enhanced.transpose(-1, -2)
else: # multi-speaker case
if self.use_noise_mask:
# (mask_speech1, ..., mask_noise)
mask_speech = list(masks[:-1])
mask_noise = masks[-1]
else:
# (mask_speech1, ..., mask_speechX)
mask_speech = list(masks)
mask_noise = None
beamformer_stats = prepare_beamformer_stats(
data_d,
mask_speech,
mask_noise,
powers=powers,
beamformer_type=self.beamformer_type,
bdelay=self.bdelay,
btaps=self.btaps,
eps=self.eps,
)
if self.beamformer_type in ("lcmv", "lcmp", "wlcmp"):
rtf_mat = get_rtf_matrix(
beamformer_stats["psd_speech"],
beamformer_stats["psd_distortion"],
diagonal_loading=self.diagonal_loading,
ref_channel=self.ref_channel,
rtf_iterations=self.rtf_iterations,
use_torch_solver=self.use_torch_solver,
diag_eps=self.diag_eps,
)
enhanced, ws = [], []
for i in range(self.num_spk):
# treat all other speakers' psd_speech as noises
if self.beamformer_type in ("mvdr", "mvdr_tfs", "wmpdr", "wpd"):
enh, w = self.apply_beamforming(
data,
ilens,
beamformer_stats["psd_n"][i],
beamformer_stats["psd_speech"][i],
psd_distortion=beamformer_stats["psd_distortion"][i],
)
elif self.beamformer_type in (
"mvdr_souden",
"mvdr_tfs_souden",
"wmpdr_souden",
"wpd_souden",
"wmwf",
"sdw_mwf",
"r1mwf",
"gev",
"gev_ban",
):
enh, w = self.apply_beamforming(
data,
ilens,
beamformer_stats["psd_n"][i],
beamformer_stats["psd_speech"][i],
)
elif self.beamformer_type == "mpdr":
enh, w = self.apply_beamforming(
data,
ilens,
beamformer_stats["psd_n"],
beamformer_stats["psd_speech"][i],
psd_distortion=beamformer_stats["psd_distortion"][i],
)
elif self.beamformer_type in ("mpdr_souden", "mwf"):
enh, w = self.apply_beamforming(
data,
ilens,
beamformer_stats["psd_n"],
beamformer_stats["psd_speech"][i],
)
elif self.beamformer_type == "lcmp":
enh, w = self.apply_beamforming(
data,
ilens,
beamformer_stats["psd_n"],
beamformer_stats["psd_speech"][i],
rtf_mat=rtf_mat,
spk=i,
)
elif self.beamformer_type in ("lcmv", "wlcmp"):
enh, w = self.apply_beamforming(
data,
ilens,
beamformer_stats["psd_n"][i],
beamformer_stats["psd_speech"][i],
rtf_mat=rtf_mat,
spk=i,
)
else:
raise ValueError(
"Not supporting beamformer_type={}".format(self.beamformer_type)
)
# (..., F, T) -> (..., T, F)
enh = enh.transpose(-1, -2)
enhanced.append(enh)
ws.append(w)
# (..., F, C, T) -> (..., T, C, F)
masks = [m.transpose(-1, -3) for m in masks]
return enhanced, ilens, masks
def forward(
self, data: Union[torch.Tensor, ComplexTensor], ilens: torch.LongTensor
) -> Tuple[Union[torch.Tensor, ComplexTensor], torch.LongTensor, Union[
torch.Tensor, ComplexTensor], ]:
"""DNN_WPE forward function.
Notation:
B: Batch
C: Channel
T: Time or Sequence length
F: Freq or Some dimension of the feature vector
Args:
data: (B, T, C, F)
ilens: (B,)
Returns:
enhanced (torch.Tensor or List[torch.Tensor]): (B, T, C, F)
ilens: (B,)
masks (torch.Tensor or List[torch.Tensor]): (B, T, C, F)
power (List[torch.Tensor]): (B, F, T)
"""
# (B, T, C, F) -> (B, F, C, T)
data = data.permute(0, 3, 2, 1)
enhanced = [data for i in range(self.nmask)]
masks = None
power = None
for i in range(self.iterations):
# Calculate power: (..., C, T)
power = [enh.real**2 + enh.imag**2 for enh in enhanced]
if i == 0 and self.use_dnn_mask:
# mask: (B, F, C, T)
masks, _ = self.mask_est(data, ilens)
# floor masks to increase numerical stability
if self.mask_flooring:
masks = [m.clamp(min=self.flooring_thres) for m in masks]
if self.normalization:
# Normalize along T
masks = [m / m.sum(dim=-1, keepdim=True) for m in masks]
# (..., C, T) * (..., C, T) -> (..., C, T)
power = [p * masks[i] for i, p in enumerate(power)]
# Averaging along the channel axis: (..., C, T) -> (..., T)
power = [p.mean(dim=-2).clamp(min=self.eps) for p in power]
# enhanced: (..., C, T) -> (..., C, T)
# NOTE(kamo): Calculate in double precision
enhanced = [
wpe_one_iteration(
to_double(data.contiguous()),
to_double(p),
taps=self.taps,
delay=self.delay,
inverse_power=self.inverse_power,
) for p in power
]
enhanced = [
enh.to(dtype=data.dtype).masked_fill(
make_pad_mask(ilens, enh.real), 0) for enh in enhanced
]
# (B, F, C, T) -> (B, T, C, F)
enhanced = [enh.permute(0, 3, 2, 1) for enh in enhanced]
if masks is not None:
masks = ([m.transpose(-1, -3) for m in masks]
if self.nmask > 1 else masks[0].transpose(-1, -3))
if self.nmask == 1:
enhanced = enhanced[0]
return enhanced, ilens, masks, power
def apply_beamforming(data, ilens, psd_n, psd_speech, psd_distortion=None):
"""Beamforming with the provided statistics.
Args:
data (torch.complex64/ComplexTensor): (B, F, C, T)
ilens (torch.Tensor): (B,)
psd_n (torch.complex64/ComplexTensor):
Noise covariance matrix for MVDR (B, F, C, C)
Observation covariance matrix for MPDR/wMPDR (B, F, C, C)
Stacked observation covariance for WPD (B,F,(btaps+1)*C,(btaps+1)*C)
psd_speech (torch.complex64/ComplexTensor):
Speech covariance matrix (B, F, C, C)
psd_distortion (torch.complex64/ComplexTensor):
Noise covariance matrix (B, F, C, C)
Return:
enhanced (torch.complex64/ComplexTensor): (B, F, T)
ws (torch.complex64/ComplexTensor): (B, F) or (B, F, (btaps+1)*C)
"""
# u: (B, C)
if self.ref_channel < 0:
u, _ = self.ref(psd_speech.to(dtype=data.dtype), ilens)
u = u.double()
else:
if self.beamformer_type.endswith("_souden"):
# (optional) Create onehot vector for fixed reference microphone
u = torch.zeros(
*(data.size()[:-3] + (data.size(-2),)),
device=data.device,
dtype=torch.double
)
u[..., self.ref_channel].fill_(1)
else:
# for simplifying computation in RTF-based beamforming
u = self.ref_channel
if self.beamformer_type in ("mvdr", "mpdr", "wmpdr"):
ws = get_mvdr_vector_with_rtf(
to_double(psd_n),
to_double(psd_speech),
to_double(psd_distortion),
iterations=self.rtf_iterations,
reference_vector=u,
normalize_ref_channel=self.ref_channel,
use_torch_solver=self.use_torch_solver,
diagonal_loading=self.diagonal_loading,
diag_eps=self.diag_eps,
)
enhanced = apply_beamforming_vector(ws, to_double(data))
elif self.beamformer_type in ("mpdr_souden", "mvdr_souden", "wmpdr_souden"):
ws = get_mvdr_vector(
to_double(psd_speech),
to_double(psd_n),
u,
use_torch_solver=self.use_torch_solver,
diagonal_loading=self.diagonal_loading,
diag_eps=self.diag_eps,
)
enhanced = apply_beamforming_vector(ws, to_double(data))
elif self.beamformer_type == "wpd":
ws = get_WPD_filter_with_rtf(
to_double(psd_n),
to_double(psd_speech),
to_double(psd_distortion),
iterations=self.rtf_iterations,
reference_vector=u,
normalize_ref_channel=self.ref_channel,
use_torch_solver=self.use_torch_solver,
diagonal_loading=self.diagonal_loading,
diag_eps=self.diag_eps,
)
enhanced = perform_WPD_filtering(
ws, to_double(data), self.bdelay, self.btaps
)
elif self.beamformer_type == "wpd_souden":
ws = get_WPD_filter_v2(
to_double(psd_speech),
to_double(psd_n),
u,
diagonal_loading=self.diagonal_loading,
diag_eps=self.diag_eps,
)
enhanced = perform_WPD_filtering(
ws, to_double(data), self.bdelay, self.btaps
)
else:
raise ValueError(
"Not supporting beamformer_type={}".format(self.beamformer_type)
)
return enhanced.to(dtype=data.dtype), ws.to(dtype=data.dtype)
def forward(
self,
data: Union[torch.Tensor, ComplexTensor],
ilens: torch.LongTensor,
powers: Union[List[torch.Tensor], None] = None,
) -> Tuple[Union[torch.Tensor, ComplexTensor], torch.LongTensor, torch.Tensor]:
"""DNN_Beamformer forward function.
Notation:
B: Batch
C: Channel
T: Time or Sequence length
F: Freq
Args:
data (torch.complex64/ComplexTensor): (B, T, C, F)
ilens (torch.Tensor): (B,)
powers (List[torch.Tensor] or None): used for wMPDR or WPD (B, F, T)
Returns:
enhanced (torch.complex64/ComplexTensor): (B, T, F)
ilens (torch.Tensor): (B,)
masks (torch.Tensor): (B, T, C, F)
"""
def apply_beamforming(data, ilens, psd_n, psd_speech, psd_distortion=None):
"""Beamforming with the provided statistics.
Args:
data (torch.complex64/ComplexTensor): (B, F, C, T)
ilens (torch.Tensor): (B,)
psd_n (torch.complex64/ComplexTensor):
Noise covariance matrix for MVDR (B, F, C, C)
Observation covariance matrix for MPDR/wMPDR (B, F, C, C)
Stacked observation covariance for WPD (B,F,(btaps+1)*C,(btaps+1)*C)
psd_speech (torch.complex64/ComplexTensor):
Speech covariance matrix (B, F, C, C)
psd_distortion (torch.complex64/ComplexTensor):
Noise covariance matrix (B, F, C, C)
Return:
enhanced (torch.complex64/ComplexTensor): (B, F, T)
ws (torch.complex64/ComplexTensor): (B, F) or (B, F, (btaps+1)*C)
"""
# u: (B, C)
if self.ref_channel < 0:
u, _ = self.ref(psd_speech.to(dtype=data.dtype), ilens)
u = u.double()
else:
if self.beamformer_type.endswith("_souden"):
# (optional) Create onehot vector for fixed reference microphone
u = torch.zeros(
*(data.size()[:-3] + (data.size(-2),)),
device=data.device,
dtype=torch.double
)
u[..., self.ref_channel].fill_(1)
else:
# for simplifying computation in RTF-based beamforming
u = self.ref_channel
if self.beamformer_type in ("mvdr", "mpdr", "wmpdr"):
ws = get_mvdr_vector_with_rtf(
to_double(psd_n),
to_double(psd_speech),
to_double(psd_distortion),
iterations=self.rtf_iterations,
reference_vector=u,
normalize_ref_channel=self.ref_channel,
use_torch_solver=self.use_torch_solver,
diagonal_loading=self.diagonal_loading,
diag_eps=self.diag_eps,
)
enhanced = apply_beamforming_vector(ws, to_double(data))
elif self.beamformer_type in ("mpdr_souden", "mvdr_souden", "wmpdr_souden"):
ws = get_mvdr_vector(
to_double(psd_speech),
to_double(psd_n),
u,
use_torch_solver=self.use_torch_solver,
diagonal_loading=self.diagonal_loading,
diag_eps=self.diag_eps,
)
enhanced = apply_beamforming_vector(ws, to_double(data))
elif self.beamformer_type == "wpd":
ws = get_WPD_filter_with_rtf(
to_double(psd_n),
to_double(psd_speech),
to_double(psd_distortion),
iterations=self.rtf_iterations,
reference_vector=u,
normalize_ref_channel=self.ref_channel,
use_torch_solver=self.use_torch_solver,
diagonal_loading=self.diagonal_loading,
diag_eps=self.diag_eps,
)
enhanced = perform_WPD_filtering(
ws, to_double(data), self.bdelay, self.btaps
)
elif self.beamformer_type == "wpd_souden":
ws = get_WPD_filter_v2(
to_double(psd_speech),
to_double(psd_n),
u,
diagonal_loading=self.diagonal_loading,
diag_eps=self.diag_eps,
)
enhanced = perform_WPD_filtering(
ws, to_double(data), self.bdelay, self.btaps
)
else:
raise ValueError(
"Not supporting beamformer_type={}".format(self.beamformer_type)
)
return enhanced.to(dtype=data.dtype), ws.to(dtype=data.dtype)
if isinstance(data, ComplexTensor):
complex_wrapper = FC
elif is_torch_1_9_plus and torch.is_complex(data):
complex_wrapper = torch
else:
raise ValueError(
"Please update your PyTorch version to 1.8+ for compelx support."
)
# data (B, T, C, F) -> (B, F, C, T)
data = data.permute(0, 3, 2, 1)
data_d = to_double(data)
# mask: [(B, F, C, T)]
masks, _ = self.mask(data, ilens)
assert self.nmask == len(masks), len(masks)
# floor masks to increase numerical stability
if self.mask_flooring:
masks = [torch.clamp(m, min=self.flooring_thres) for m in masks]
if self.num_spk == 1: # single-speaker case
if self.use_noise_mask:
# (mask_speech, mask_noise)
mask_speech, mask_noise = masks
else:
# (mask_speech,)
mask_speech = masks[0]
mask_noise = 1 - mask_speech
if self.beamformer_type.startswith(
"wmpdr"
) or self.beamformer_type.startswith("wpd"):
if powers is None:
power_input = data_d.real ** 2 + data_d.imag ** 2
# Averaging along the channel axis: (..., C, T) -> (..., T)
powers = (power_input * mask_speech.double()).mean(dim=-2)
else:
assert len(powers) == 1, len(powers)
powers = powers[0]
inverse_power = 1 / torch.clamp(powers, min=self.eps)
psd_speech = get_power_spectral_density_matrix(data_d, mask_speech.double())
if mask_noise is not None and (
self.beamformer_type == "mvdr_souden"
or not self.beamformer_type.endswith("_souden")
):
# MVDR or other RTF-based formulas
psd_noise = get_power_spectral_density_matrix(
data_d, mask_noise.double()
)
if self.beamformer_type == "mvdr":
enhanced, ws = apply_beamforming(
data, ilens, psd_noise, psd_speech, psd_distortion=psd_noise
)
elif self.beamformer_type == "mvdr_souden":
enhanced, ws = apply_beamforming(data, ilens, psd_noise, psd_speech)
elif self.beamformer_type == "mpdr":
psd_observed = complex_wrapper.einsum(
"...ct,...et->...ce", [data_d, data_d.conj()]
)
enhanced, ws = apply_beamforming(
data, ilens, psd_observed, psd_speech, psd_distortion=psd_noise
)
elif self.beamformer_type == "mpdr_souden":
psd_observed = complex_wrapper.einsum(
"...ct,...et->...ce", [data_d, data_d.conj()]
)
enhanced, ws = apply_beamforming(data, ilens, psd_observed, psd_speech)
elif self.beamformer_type == "wmpdr":
psd_observed = complex_wrapper.einsum(
"...ct,...et->...ce",
[data_d * inverse_power[..., None, :], data_d.conj()],
)
enhanced, ws = apply_beamforming(
data, ilens, psd_observed, psd_speech, psd_distortion=psd_noise
)
elif self.beamformer_type == "wmpdr_souden":
psd_observed = complex_wrapper.einsum(
"...ct,...et->...ce",
[data_d * inverse_power[..., None, :], data_d.conj()],
)
enhanced, ws = apply_beamforming(data, ilens, psd_observed, psd_speech)
elif self.beamformer_type == "wpd":
psd_observed_bar = get_covariances(
data_d, inverse_power, self.bdelay, self.btaps, get_vector=False
)
enhanced, ws = apply_beamforming(
data, ilens, psd_observed_bar, psd_speech, psd_distortion=psd_noise
)
elif self.beamformer_type == "wpd_souden":
psd_observed_bar = get_covariances(
data_d, inverse_power, self.bdelay, self.btaps, get_vector=False
)
enhanced, ws = apply_beamforming(
data, ilens, psd_observed_bar, psd_speech
)
else:
raise ValueError(
"Not supporting beamformer_type={}".format(self.beamformer_type)
)
# (..., F, T) -> (..., T, F)
enhanced = enhanced.transpose(-1, -2)
else: # multi-speaker case
if self.use_noise_mask:
# (mask_speech1, ..., mask_noise)
mask_speech = list(masks[:-1])
mask_noise = masks[-1]
else:
# (mask_speech1, ..., mask_speechX)
mask_speech = list(masks)
mask_noise = None
if self.beamformer_type.startswith(
"wmpdr"
) or self.beamformer_type.startswith("wpd"):
if powers is None:
power_input = data_d.real ** 2 + data_d.imag ** 2
# Averaging along the channel axis: (..., C, T) -> (..., T)
powers = [
(power_input * m.double()).mean(dim=-2) for m in mask_speech
]
else:
assert len(powers) == self.num_spk, len(powers)
inverse_power = [1 / torch.clamp(p, min=self.eps) for p in powers]
psd_speeches = [
get_power_spectral_density_matrix(data_d, mask.double())
for mask in mask_speech
]
if mask_noise is not None and (
self.beamformer_type == "mvdr_souden"
or not self.beamformer_type.endswith("_souden")
):
# MVDR or other RTF-based formulas
psd_noise = get_power_spectral_density_matrix(
data_d, mask_noise.double()
)
if self.beamformer_type in ("mpdr", "mpdr_souden"):
psd_observed = complex_wrapper.einsum(
"...ct,...et->...ce", [data_d, data_d.conj()]
)
elif self.beamformer_type in ("wmpdr", "wmpdr_souden"):
psd_observed = [
complex_wrapper.einsum(
"...ct,...et->...ce",
[data_d * inv_p[..., None, :], data_d.conj()],
)
for inv_p in inverse_power
]
elif self.beamformer_type in ("wpd", "wpd_souden"):
psd_observed_bar = [
get_covariances(
data_d, inv_p, self.bdelay, self.btaps, get_vector=False
)
for inv_p in inverse_power
]
enhanced, ws = [], []
for i in range(self.num_spk):
psd_speech = psd_speeches.pop(i)
if (
self.beamformer_type == "mvdr_souden"
or not self.beamformer_type.endswith("_souden")
):
psd_noise_i = (
psd_noise + sum(psd_speeches)
if mask_noise is not None
else sum(psd_speeches)
)
# treat all other speakers' psd_speech as noises
if self.beamformer_type == "mvdr":
enh, w = apply_beamforming(
data, ilens, psd_noise_i, psd_speech, psd_distortion=psd_noise_i
)
elif self.beamformer_type == "mvdr_souden":
enh, w = apply_beamforming(data, ilens, psd_noise_i, psd_speech)
elif self.beamformer_type == "mpdr":
enh, w = apply_beamforming(
data,
ilens,
psd_observed,
psd_speech,
psd_distortion=psd_noise_i,
)
elif self.beamformer_type == "mpdr_souden":
enh, w = apply_beamforming(data, ilens, psd_observed, psd_speech)
elif self.beamformer_type == "wmpdr":
enh, w = apply_beamforming(
data,
ilens,
psd_observed[i],
psd_speech,
psd_distortion=psd_noise_i,
)
elif self.beamformer_type == "wmpdr_souden":
enh, w = apply_beamforming(data, ilens, psd_observed[i], psd_speech)
elif self.beamformer_type == "wpd":
enh, w = apply_beamforming(
data,
ilens,
psd_observed_bar[i],
psd_speech,
psd_distortion=psd_noise_i,
)
elif self.beamformer_type == "wpd_souden":
enh, w = apply_beamforming(
data, ilens, psd_observed_bar[i], psd_speech
)
else:
raise ValueError(
"Not supporting beamformer_type={}".format(self.beamformer_type)
)
psd_speeches.insert(i, psd_speech)
# (..., F, T) -> (..., T, F)
enh = enh.transpose(-1, -2)
enhanced.append(enh)
ws.append(w)
# (..., F, C, T) -> (..., T, C, F)
masks = [m.transpose(-1, -3) for m in masks]
return enhanced, ilens, masks
def prepare_beamformer_stats(
signal,
masks_speech,
mask_noise,
powers=None,
beamformer_type="mvdr",
bdelay=3,
btaps=5,
eps=1e-6,
):
"""Prepare necessary statistics for constructing the specified beamformer.
Args:
signal (torch.complex64/ComplexTensor): (..., F, C, T)
masks_speech (List[torch.Tensor]): (..., F, C, T) masks for all speech sources
mask_noise (torch.Tensor): (..., F, C, T) noise mask
powers (List[torch.Tensor]): powers for all speech sources (..., F, T)
used for wMPDR or WPD beamformers
beamformer_type (str): one of the pre-defined beamformer types
bdelay (int): delay factor, used for WPD beamformser
btaps (int): number of filter taps, used for WPD beamformser
eps (torch.Tensor): tiny constant
Returns:
beamformer_stats (dict): a dictionary containing all necessary statistics
e.g. "psd_n", "psd_speech", "psd_distortion"
Note:
* When `masks_speech` is a tensor or a single-element list, all returned
statistics are tensors;
* When `masks_speech` is a multi-element list, some returned statistics
can be a list, e.g., "psd_n" for MVDR, "psd_speech" and "psd_distortion".
"""
from espnet2.enh.layers.dnn_beamformer import BEAMFORMER_TYPES
assert beamformer_type in BEAMFORMER_TYPES, "%s is not supported yet"
if isinstance(masks_speech, (list, tuple)):
masks_speech = [to_double(m) for m in masks_speech]
else:
masks_speech = [to_double(masks_speech)]
num_spk = len(masks_speech)
if (beamformer_type.startswith("wmpdr")
or beamformer_type.startswith("wpd") or beamformer_type == "wlcmp"
or beamformer_type == "wmwf"):
if powers is None:
power_input = signal.real**2 + signal.imag**2
# Averaging along the channel axis: (..., C, T) -> (..., T)
powers = [(power_input * m).mean(dim=-2) for m in masks_speech]
else:
assert len(powers) == num_spk, (len(powers), num_spk)
inverse_powers = [1 / torch.clamp(p, min=eps) for p in powers]
psd_speeches = [
get_power_spectral_density_matrix(signal, m) for m in masks_speech
]
if (beamformer_type == "mvdr_souden" or beamformer_type == "sdw_mwf"
or beamformer_type == "r1mwf"
or beamformer_type.startswith("mvdr_tfs")
or not beamformer_type.endswith("_souden")):
# MVDR or other RTF-based formulas
if mask_noise is not None:
psd_bg = get_power_spectral_density_matrix(signal,
to_double(mask_noise))
if num_spk == 1:
assert mask_noise is not None
psd_noise = psd_bg
else:
psd_noise = []
for i in range(num_spk):
if beamformer_type.startswith("mvdr_tfs"):
# NOTE: psd_noise is a list only for this beamformer
psd_noise_i = [
psd for j, psd in enumerate(psd_speeches) if j != i
]
else:
psd_sum = sum(psd for j, psd in enumerate(psd_speeches)
if j != i)
psd_noise_i = (psd_bg + psd_sum
if mask_noise is not None else psd_sum)
psd_noise.append(psd_noise_i)
if beamformer_type in (
"mvdr",
"mvdr_souden",
"mvdr_tfs_souden",
"sdw_mwf",
"r1mwf",
"lcmv",
"gev",
"gev_ban",
):
psd_n = psd_noise
elif beamformer_type == "mvdr_tfs":
psd_n = psd_noise
psd_noise = [sum(psd_noise_i) for psd_noise_i in psd_noise]
elif beamformer_type in ("mpdr", "mpdr_souden", "lcmp", "mwf"):
psd_n = einsum("...ct,...et->...ce", signal, signal.conj())
elif beamformer_type in ("wmpdr", "wmpdr_souden", "wlcmp", "wmwf"):
psd_n = [
einsum(
"...ct,...et->...ce",
signal * inv_p[..., None, :],
signal.conj(),
) for inv_p in inverse_powers
]
elif beamformer_type in ("wpd", "wpd_souden"):
psd_n = [
get_covariances(signal, inv_p, bdelay, btaps, get_vector=False)
for inv_p in inverse_powers
]
if num_spk == 1:
psd_speeches = psd_speeches[0]
if isinstance(psd_n, (list, tuple)):
psd_n = psd_n[0]
if beamformer_type in (
"mvdr",
"mpdr",
"wmpdr",
"wpd",
"lcmp",
"wlcmp",
"lcmv",
"mvdr_tfs",
):
return {
"psd_n": psd_n,
"psd_speech": psd_speeches,
"psd_distortion": psd_noise
}
elif (beamformer_type.endswith("_souden")
or beamformer_type.startswith("gev") or beamformer_type == "mwf"
or beamformer_type == "wmwf" or beamformer_type == "sdw_mwf"
or beamformer_type == "r1mwf"):
return {"psd_n": psd_n, "psd_speech": psd_speeches}
