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snr.py
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snr.py
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# Copyright The PyTorch Lightning team.
#
# 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.
import torch
from deprecate import deprecated, void
from torch import Tensor
from torchmetrics.functional.audio.sdr import scale_invariant_signal_distortion_ratio
from torchmetrics.utilities import _future_warning
from torchmetrics.utilities.checks import _check_same_shape
def signal_noise_ratio(preds: Tensor, target: Tensor, zero_mean: bool = False) -> Tensor:
r"""Signal-to-noise ratio (SNR_):
.. math::
\text{SNR} = \frac{P_{signal}}{P_{noise}}
where :math:`P` denotes the power of each signal. The SNR metric compares the level
of the desired signal to the level of background noise. Therefore, a high value of
SNR means that the audio is clear.
Args:
preds:
shape ``[...,time]``
target:
shape ``[...,time]``
zero_mean:
if to zero mean target and preds or not
Returns:
snr value of shape [...]
Example:
>>> from torchmetrics.functional.audio import signal_noise_ratio
>>> target = torch.tensor([3.0, -0.5, 2.0, 7.0])
>>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0])
>>> signal_noise_ratio(preds, target)
tensor(16.1805)
References:
[1] Le Roux, Jonathan, et al. "SDR half-baked or well done." IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP) 2019.
"""
_check_same_shape(preds, target)
EPS = torch.finfo(preds.dtype).eps
if zero_mean:
target = target - torch.mean(target, dim=-1, keepdim=True)
preds = preds - torch.mean(preds, dim=-1, keepdim=True)
noise = target - preds
snr_value = (torch.sum(target ** 2, dim=-1) + EPS) / (torch.sum(noise ** 2, dim=-1) + EPS)
snr_value = 10 * torch.log10(snr_value)
return snr_value
@deprecated(target=signal_noise_ratio, deprecated_in="0.7", remove_in="0.8", stream=_future_warning)
def snr(preds: Tensor, target: Tensor, zero_mean: bool = False) -> Tensor:
r"""Signal-to-noise ratio (SNR_)
.. deprecated:: v0.7
Use :func:`torchmetrics.functional.signal_noise_ratio`. Will be removed in v0.8.
Example:
>>> snr(torch.tensor([2.5, 0.0, 2.0, 8.0]), torch.tensor([3.0, -0.5, 2.0, 7.0]))
tensor(16.1805)
"""
return void(preds, target, zero_mean)
def scale_invariant_signal_noise_ratio(preds: Tensor, target: Tensor) -> Tensor:
"""Scale-invariant signal-to-noise ratio (SI-SNR).
Args:
preds:
shape ``[...,time]``
target:
shape ``[...,time]``
Returns:
si-snr value of shape [...]
Example:
>>> import torch
>>> from torchmetrics.functional.audio import scale_invariant_signal_noise_ratio
>>> target = torch.tensor([3.0, -0.5, 2.0, 7.0])
>>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0])
>>> scale_invariant_signal_noise_ratio(preds, target)
tensor(15.0918)
References:
[1] Y. Luo and N. Mesgarani, "TaSNet: Time-Domain Audio Separation Network for Real-Time, Single-Channel Speech
Separation," 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, pp.
696-700, doi: 10.1109/ICASSP.2018.8462116.
"""
return scale_invariant_signal_distortion_ratio(preds=preds, target=target, zero_mean=True)