File size: 7,376 Bytes
9c6594c |
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 |
# Copyright The 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.
from collections.abc import Sequence
from typing import Any, Optional, Union
from torch import Tensor, tensor
from torchmetrics.functional.audio.srmr import (
_srmr_arg_validate,
speech_reverberation_modulation_energy_ratio,
)
from torchmetrics.metric import Metric
from torchmetrics.utilities.imports import (
_GAMMATONE_AVAILABLE,
_MATPLOTLIB_AVAILABLE,
_TORCHAUDIO_AVAILABLE,
)
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not all([_GAMMATONE_AVAILABLE, _TORCHAUDIO_AVAILABLE]):
__doctest_skip__ = ["SpeechReverberationModulationEnergyRatio", "SpeechReverberationModulationEnergyRatio.plot"]
elif not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["SpeechReverberationModulationEnergyRatio.plot"]
class SpeechReverberationModulationEnergyRatio(Metric):
"""Calculate `Speech-to-Reverberation Modulation Energy Ratio`_ (SRMR).
SRMR is a non-intrusive metric for speech quality and intelligibility based on
a modulation spectral representation of the speech signal.
This code is translated from `SRMRToolbox`_ and `SRMRpy`_.
As input to ``forward`` and ``update`` the metric accepts the following input
- ``preds`` (:class:`~torch.Tensor`): float tensor with shape ``(...,time)``
As output of `forward` and `compute` the metric returns the following output
- ``srmr`` (:class:`~torch.Tensor`): float scaler tensor
.. hint::
Using this metrics requires you to have ``gammatone`` and ``torchaudio`` installed.
Either install as ``pip install torchmetrics[audio]`` or ``pip install torchaudio``
and ``pip install git+https://github.com/detly/gammatone``.
.. attention::
This implementation is experimental, and might not be consistent with the matlab
implementation `SRMRToolbox`_, especially the fast implementation.
The slow versions, a) fast=False, norm=False, max_cf=128, b) fast=False, norm=True, max_cf=30, have
a relatively small inconsistency.
Args:
fs: the sampling rate
n_cochlear_filters: Number of filters in the acoustic filterbank
low_freq: determines the frequency cutoff for the corresponding gammatone filterbank.
min_cf: Center frequency in Hz of the first modulation filter.
max_cf: Center frequency in Hz of the last modulation filter. If None is given,
then 30 Hz will be used for `norm==False`, otherwise 128 Hz will be used.
norm: Use modulation spectrum energy normalization
fast: Use the faster version based on the gammatonegram.
Note: this argument is inherited from `SRMRpy`_. As the translated code is based to pytorch,
setting `fast=True` may slow down the speed for calculating this metric on GPU.
Raises:
ModuleNotFoundError:
If ``gammatone`` or ``torchaudio`` package is not installed
Example:
>>> from torch import randn
>>> from torchmetrics.audio import SpeechReverberationModulationEnergyRatio
>>> preds = randn(8000)
>>> srmr = SpeechReverberationModulationEnergyRatio(8000)
>>> srmr(preds)
tensor(0.3191)
"""
msum: Tensor
total: Tensor
full_state_update: bool = False
is_differentiable: bool = True
higher_is_better: bool = True
plot_lower_bound: Optional[float] = None
plot_upper_bound: Optional[float] = None
def __init__(
self,
fs: int,
n_cochlear_filters: int = 23,
low_freq: float = 125,
min_cf: float = 4,
max_cf: Optional[float] = None,
norm: bool = False,
fast: bool = False,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
if not _TORCHAUDIO_AVAILABLE or not _GAMMATONE_AVAILABLE:
raise ModuleNotFoundError(
"speech_reverberation_modulation_energy_ratio requires you to have `gammatone` and"
" `torchaudio>=0.10` installed. Either install as ``pip install torchmetrics[audio]`` or "
"``pip install torchaudio>=0.10`` and ``pip install git+https://github.com/detly/gammatone``"
)
_srmr_arg_validate(
fs=fs,
n_cochlear_filters=n_cochlear_filters,
low_freq=low_freq,
min_cf=min_cf,
max_cf=max_cf,
norm=norm,
fast=fast,
)
self.fs = fs
self.n_cochlear_filters = n_cochlear_filters
self.low_freq = low_freq
self.min_cf = min_cf
self.max_cf = max_cf
self.norm = norm
self.fast = fast
self.add_state("msum", default=tensor(0.0), dist_reduce_fx="sum")
self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
def update(self, preds: Tensor) -> None:
"""Update state with predictions."""
metric_val_batch = speech_reverberation_modulation_energy_ratio(
preds, self.fs, self.n_cochlear_filters, self.low_freq, self.min_cf, self.max_cf, self.norm, self.fast
).to(self.msum.device)
self.msum += metric_val_batch.sum()
self.total += metric_val_batch.numel()
def compute(self) -> Tensor:
"""Compute metric."""
return self.msum / self.total
def plot(self, val: Union[Tensor, Sequence[Tensor], None] = None, ax: Optional[_AX_TYPE] = None) -> _PLOT_OUT_TYPE:
"""Plot a single or multiple values from the metric.
Args:
val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
If no value is provided, will automatically call `metric.compute` and plot that result.
ax: An matplotlib axis object. If provided will add plot to that axis
Returns:
Figure and Axes object
Raises:
ModuleNotFoundError:
If `matplotlib` is not installed
.. plot::
:scale: 75
>>> # Example plotting a single value
>>> import torch
>>> from torchmetrics.audio import SpeechReverberationModulationEnergyRatio
>>> metric = SpeechReverberationModulationEnergyRatio(8000)
>>> metric.update(torch.rand(8000))
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.audio import SpeechReverberationModulationEnergyRatio
>>> metric = SpeechReverberationModulationEnergyRatio(8000)
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(torch.rand(8000)))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
|