Spaces:
Runtime error
Runtime error
File size: 5,561 Bytes
0102e16 |
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 |
"""DNN beamformer module."""
from typing import Tuple
import torch
from torch.nn import functional as F
from funasr_detach.frontends.utils.beamformer import apply_beamforming_vector
from funasr_detach.frontends.utils.beamformer import get_mvdr_vector
from funasr_detach.frontends.utils.beamformer import (
get_power_spectral_density_matrix, # noqa: H301
)
from funasr_detach.frontends.utils.mask_estimator import MaskEstimator
from torch_complex.tensor import ComplexTensor
class DNN_Beamformer(torch.nn.Module):
"""DNN mask based Beamformer
Citation:
Multichannel End-to-end Speech Recognition; T. Ochiai et al., 2017;
https://arxiv.org/abs/1703.04783
"""
def __init__(
self,
bidim,
btype="blstmp",
blayers=3,
bunits=300,
bprojs=320,
bnmask=2,
dropout_rate=0.0,
badim=320,
ref_channel: int = -1,
beamformer_type="mvdr",
):
super().__init__()
self.mask = MaskEstimator(
btype, bidim, blayers, bunits, bprojs, dropout_rate, nmask=bnmask
)
self.ref = AttentionReference(bidim, badim)
self.ref_channel = ref_channel
self.nmask = bnmask
if beamformer_type != "mvdr":
raise ValueError(
"Not supporting beamformer_type={}".format(beamformer_type)
)
self.beamformer_type = beamformer_type
def forward(
self, data: ComplexTensor, ilens: torch.LongTensor
) -> Tuple[ComplexTensor, torch.LongTensor, ComplexTensor]:
"""The forward function
Notation:
B: Batch
C: Channel
T: Time or Sequence length
F: Freq
Args:
data (ComplexTensor): (B, T, C, F)
ilens (torch.Tensor): (B,)
Returns:
enhanced (ComplexTensor): (B, T, F)
ilens (torch.Tensor): (B,)
"""
def apply_beamforming(data, ilens, psd_speech, psd_noise):
# u: (B, C)
if self.ref_channel < 0:
u, _ = self.ref(psd_speech, ilens)
else:
# (optional) Create onehot vector for fixed reference microphone
u = torch.zeros(
*(data.size()[:-3] + (data.size(-2),)), device=data.device
)
u[..., self.ref_channel].fill_(1)
ws = get_mvdr_vector(psd_speech, psd_noise, u)
enhanced = apply_beamforming_vector(ws, data)
return enhanced, ws
# data (B, T, C, F) -> (B, F, C, T)
data = data.permute(0, 3, 2, 1)
# mask: (B, F, C, T)
masks, _ = self.mask(data, ilens)
assert self.nmask == len(masks)
if self.nmask == 2: # (mask_speech, mask_noise)
mask_speech, mask_noise = masks
psd_speech = get_power_spectral_density_matrix(data, mask_speech)
psd_noise = get_power_spectral_density_matrix(data, mask_noise)
enhanced, ws = apply_beamforming(data, ilens, psd_speech, psd_noise)
# (..., F, T) -> (..., T, F)
enhanced = enhanced.transpose(-1, -2)
mask_speech = mask_speech.transpose(-1, -3)
else: # multi-speaker case: (mask_speech1, ..., mask_noise)
mask_speech = list(masks[:-1])
mask_noise = masks[-1]
psd_speeches = [
get_power_spectral_density_matrix(data, mask) for mask in mask_speech
]
psd_noise = get_power_spectral_density_matrix(data, mask_noise)
enhanced = []
ws = []
for i in range(self.nmask - 1):
psd_speech = psd_speeches.pop(i)
# treat all other speakers' psd_speech as noises
enh, w = apply_beamforming(
data, ilens, psd_speech, sum(psd_speeches) + psd_noise
)
psd_speeches.insert(i, psd_speech)
# (..., F, T) -> (..., T, F)
enh = enh.transpose(-1, -2)
mask_speech[i] = mask_speech[i].transpose(-1, -3)
enhanced.append(enh)
ws.append(w)
return enhanced, ilens, mask_speech
class AttentionReference(torch.nn.Module):
def __init__(self, bidim, att_dim):
super().__init__()
self.mlp_psd = torch.nn.Linear(bidim, att_dim)
self.gvec = torch.nn.Linear(att_dim, 1)
def forward(
self, psd_in: ComplexTensor, ilens: torch.LongTensor, scaling: float = 2.0
) -> Tuple[torch.Tensor, torch.LongTensor]:
"""The forward function
Args:
psd_in (ComplexTensor): (B, F, C, C)
ilens (torch.Tensor): (B,)
scaling (float):
Returns:
u (torch.Tensor): (B, C)
ilens (torch.Tensor): (B,)
"""
B, _, C = psd_in.size()[:3]
assert psd_in.size(2) == psd_in.size(3), psd_in.size()
# psd_in: (B, F, C, C)
psd = psd_in.masked_fill(
torch.eye(C, dtype=torch.bool, device=psd_in.device), 0
)
# psd: (B, F, C, C) -> (B, C, F)
psd = (psd.sum(dim=-1) / (C - 1)).transpose(-1, -2)
# Calculate amplitude
psd_feat = (psd.real**2 + psd.imag**2) ** 0.5
# (B, C, F) -> (B, C, F2)
mlp_psd = self.mlp_psd(psd_feat)
# (B, C, F2) -> (B, C, 1) -> (B, C)
e = self.gvec(torch.tanh(mlp_psd)).squeeze(-1)
u = F.softmax(scaling * e, dim=-1)
return u, ilens
|