File size: 11,541 Bytes
7965430 f5d2dd3 7965430 f5d2dd3 7965430 f5d2dd3 7965430 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 | # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Dict, Optional
import torch
from nemo.collections.audio.losses.audio import calculate_mean
from nemo.collections.audio.parts.utils.audio import wrap_to_pi
from nemo.core.classes import NeuralModule, typecheck
from nemo.core.neural_types import LengthsType, NeuralType, SpectrogramType
from nemo.utils import logging
class SpectrogramToMultichannelFeatures(NeuralModule):
"""Convert a complex-valued multi-channel spectrogram to
multichannel features.
Args:
num_subbands: Expected number of subbands in the input signal
num_input_channels: Optional, provides the number of channels
of the input signal. Used to infer the number
of output channels.
mag_reduction: Reduction across channels. Default `None`, will calculate
magnitude of each channel.
mag_power: Optional, apply power on the magnitude.
use_ipd: Use inter-channel phase difference (IPD).
mag_normalization: Normalization for magnitude features
ipd_normalization: Normalization for IPD features
eps: Small regularization constant.
"""
def __init__(
self,
num_subbands: int,
num_input_channels: Optional[int] = None,
mag_reduction: Optional[str] = None,
mag_power: Optional[float] = None,
use_ipd: bool = False,
mag_normalization: Optional[str] = None,
ipd_normalization: Optional[str] = None,
eps: float = 1e-8,
):
super().__init__()
self.mag_reduction = mag_reduction
self.mag_power = mag_power
self.use_ipd = use_ipd
if mag_normalization not in [None, 'mean', 'mean_var']:
raise NotImplementedError(f'Unknown magnitude normalization {mag_normalization}')
self.mag_normalization = mag_normalization
if ipd_normalization not in [None, 'mean', 'mean_var']:
raise NotImplementedError(f'Unknown ipd normalization {ipd_normalization}')
self.ipd_normalization = ipd_normalization
if self.use_ipd:
self._num_features = 2 * num_subbands
self._num_channels = num_input_channels
else:
self._num_features = num_subbands
self._num_channels = num_input_channels if self.mag_reduction is None else 1
self.eps = eps
logging.debug('Initialized %s with', self.__class__.__name__)
logging.debug('\tnum_subbands: %d', num_subbands)
logging.debug('\tmag_reduction: %s', self.mag_reduction)
logging.debug('\tmag_power: %s', self.mag_power)
logging.debug('\tuse_ipd: %s', self.use_ipd)
logging.debug('\tmag_normalization: %s', self.mag_normalization)
logging.debug('\tipd_normalization: %s', self.ipd_normalization)
logging.debug('\teps: %f', self.eps)
logging.debug('\t_num_features: %s', self._num_features)
logging.debug('\t_num_channels: %s', self._num_channels)
@property
def input_types(self) -> Dict[str, NeuralType]:
"""Returns definitions of module output ports."""
return {
"input": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()),
"input_length": NeuralType(('B',), LengthsType()),
}
@property
def output_types(self) -> Dict[str, NeuralType]:
"""Returns definitions of module output ports."""
return {
"output": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()),
"output_length": NeuralType(('B',), LengthsType()),
}
@property
def num_features(self) -> int:
"""Configured number of features"""
return self._num_features
@property
def num_channels(self) -> int:
"""Configured number of channels"""
if self._num_channels is not None:
return self._num_channels
else:
raise ValueError(
'Num channels is not configured. To configure this, `num_input_channels` '
'must be provided when constructing the object.'
)
@staticmethod
def get_mean_time_channel(input: torch.Tensor, input_length: Optional[torch.Tensor] = None) -> torch.Tensor:
"""Calculate mean across time and channel dimensions.
Args:
input: tensor with shape (B, C, F, T)
input_length: tensor with shape (B,)
Returns:
Mean of `input` calculated across time and channel dimension
with shape (B, 1, F, 1)
"""
assert input.ndim == 4, f'Expected input to have 4 dimensions, got {input.ndim}'
if input_length is None:
mean = torch.mean(input, dim=(-1, -3), keepdim=True)
else:
# temporal mean
mean = calculate_mean(input, input_length, dim=-1, keepdim=True)
# channel mean
mean = torch.mean(mean, dim=-3, keepdim=True)
return mean
@classmethod
def get_mean_std_time_channel(
cls, input: torch.Tensor, input_length: Optional[torch.Tensor] = None, eps: float = 1e-10
) -> torch.Tensor:
"""Calculate mean and standard deviation across time and channel dimensions.
Args:
input: tensor with shape (B, C, F, T)
input_length: tensor with shape (B,)
Returns:
Mean and standard deviation of the `input` calculated across time and
channel dimension, each with shape (B, 1, F, 1).
"""
assert input.ndim == 4, f'Expected input to have 4 dimensions, got {input.ndim}'
if input_length is None:
std, mean = torch.std_mean(input, dim=(-1, -3), unbiased=False, keepdim=True)
else:
mean = cls.get_mean_time_channel(input, input_length)
std = (input - mean).pow(2)
# temporal mean
std = calculate_mean(std, input_length, dim=-1, keepdim=True)
# channel mean
std = torch.mean(std, dim=-3, keepdim=True)
# final value
std = torch.sqrt(std.clamp(eps))
return mean, std
@typecheck(
input_types={
'input': NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()),
'input_length': NeuralType(tuple('B'), LengthsType()),
},
output_types={
'output': NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()),
},
)
def normalize_mean(self, input: torch.Tensor, input_length: torch.Tensor) -> torch.Tensor:
"""Mean normalization for the input tensor.
Args:
input: input tensor
input_length: valid length for each example
Returns:
Mean normalized input.
"""
mean = self.get_mean_time_channel(input=input, input_length=input_length)
output = input - mean
return output
@typecheck(
input_types={
'input': NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()),
'input_length': NeuralType(tuple('B'), LengthsType()),
},
output_types={
'output': NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()),
},
)
def normalize_mean_var(self, input: torch.Tensor, input_length: torch.Tensor) -> torch.Tensor:
"""Mean and variance normalization for the input tensor.
Args:
input: input tensor
input_length: valid length for each example
Returns:
Mean and variance normalized input.
"""
mean, std = self.get_mean_std_time_channel(input=input, input_length=input_length, eps=self.eps)
output = (input - mean) / std
return output
@typecheck()
def forward(self, input: torch.Tensor, input_length: torch.Tensor) -> torch.Tensor:
"""Convert input batch of C-channel spectrograms into
a batch of time-frequency features with dimension num_feat.
The output number of channels may be the same as input, or
reduced to 1, e.g., if averaging over magnitude and not appending individual IPDs.
Args:
input: Spectrogram for C channels with F subbands and N time frames, (B, C, F, N)
input_length: Length of valid entries along the time dimension, shape (B,)
Returns:
num_feat_channels channels with num_feat features, shape (B, num_feat_channels, num_feat, N)
"""
num_input_channels = input.size(1)
# Magnitude spectrum
if self.mag_reduction is None:
mag = torch.abs(input)
elif self.mag_reduction == 'abs_mean':
mag = torch.abs(torch.mean(input, axis=1, keepdim=True))
elif self.mag_reduction == 'mean_abs':
mag = torch.mean(torch.abs(input), axis=1, keepdim=True)
elif self.mag_reduction == 'rms':
mag = torch.sqrt(torch.mean(torch.abs(input) ** 2, axis=1, keepdim=True))
else:
raise ValueError(f'Unexpected magnitude reduction {self.mag_reduction}')
if self.mag_power is not None:
mag = torch.pow(mag, self.mag_power)
if self.mag_normalization == 'mean':
# normalize mean across channels and time steps
mag = self.normalize_mean(input=mag, input_length=input_length)
elif self.mag_normalization == 'mean_var':
# normalize mean and variance across channels and time steps
mag = self.normalize_mean_var(input=mag, input_length=input_length)
features = mag
if self.use_ipd:
if num_input_channels == 1:
# no IPD for single-channel input
ipd = torch.zeros_like(input, dtype=features.dtype, device=features.device)
else:
# Calculate IPD relative to the average spec
spec_mean = torch.mean(input, axis=1, keepdim=True) # channel average
ipd = torch.angle(input) - torch.angle(spec_mean)
# Modulo to [-pi, pi]
ipd = wrap_to_pi(ipd)
if self.ipd_normalization == 'mean':
# normalize mean across channels and time steps
# mean across time
ipd = self.normalize_mean(input=ipd, input_length=input_length)
elif self.ipd_normalization == 'mean_var':
ipd = self.normalize_mean_var(input=ipd, input_length=input_length)
# Concatenate to existing features
features = torch.cat([features.expand(ipd.shape), ipd], axis=2)
if self._num_channels is not None and features.size(1) != self._num_channels:
raise RuntimeError(
f'Number of channels in features {features.size(1)} is different than the configured number of channels {self._num_channels}'
)
return features, input_length
|