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# 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.core.classes import NeuralModule, typecheck
from nemo.core.neural_types import NeuralType, SpectrogramType
class MixtureConsistencyProjection(NeuralModule):
"""Ensure estimated sources are consistent with the input mixture.
Note that the input mixture is assume to be a single-channel signal.
Args:
weighting: Optional weighting mode for the consistency constraint.
If `None`, use uniform weighting. If `power`, use the power of the
estimated source as the weight.
eps: Small positive value for regularization
Reference:
Wisdom et al, Differentiable consistency constraints for improved deep speech enhancement, 2018
"""
def __init__(self, weighting: Optional[str] = None, eps: float = 1e-8):
super().__init__()
self.weighting = weighting
self.eps = eps
if self.weighting not in [None, 'power']:
raise NotImplementedError(f'Weighting mode {self.weighting} not implemented')
@property
def input_types(self) -> Dict[str, NeuralType]:
"""Returns definitions of module output ports."""
return {
"mixture": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()),
"estimate": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()),
}
@property
def output_types(self) -> Dict[str, NeuralType]:
"""Returns definitions of module output ports."""
return {
"output": NeuralType(('B', 'C', 'D', 'T'), SpectrogramType()),
}
@typecheck()
def forward(self, mixture: torch.Tensor, estimate: torch.Tensor) -> torch.Tensor:
"""Enforce mixture consistency on the estimated sources.
Args:
mixture: Single-channel mixture, shape (B, 1, F, N)
estimate: M estimated sources, shape (B, M, F, N)
Returns:
Source estimates consistent with the mixture, shape (B, M, F, N)
"""
if mixture.size(-3) != 1:
raise ValueError(f'Mixture must have a single channel, got shape {mixture.shape}')
# number of sources
M = estimate.size(-3)
# estimated mixture based on the estimated sources
estimated_mixture = torch.sum(estimate, dim=-3, keepdim=True)
# weighting
if self.weighting is None:
weight = 1 / M
elif self.weighting == 'power':
weight = estimate.abs().pow(2)
weight = weight / (weight.sum(dim=-3, keepdim=True) + self.eps)
else:
raise NotImplementedError(f'Weighting mode {self.weighting} not implemented')
# consistent estimate
consistent_estimate = estimate + weight * (mixture - estimated_mixture)
return consistent_estimate
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