-
Notifications
You must be signed in to change notification settings - Fork 388
/
si_snr.py
101 lines (83 loc) · 3.43 KB
/
si_snr.py
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
# 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.
from typing import Any, Callable, Optional
from torch import Tensor, tensor
from torchmetrics.functional.audio.si_snr import si_snr
from torchmetrics.metric import Metric
class SI_SNR(Metric):
"""Scale-invariant signal-to-noise ratio (SI-SNR).
Forward accepts
- ``preds``: ``shape [...,time]``
- ``target``: ``shape [...,time]``
Args:
compute_on_step:
Forward only calls ``update()`` and returns None if this is set to False. default: True
dist_sync_on_step:
Synchronize metric state across processes at each ``forward()``
before returning the value at the step.
process_group:
Specify the process group on which synchronization is called. default: None (which selects the entire world)
dist_sync_fn:
Callback that performs the allgather operation on the metric state. When `None`, DDP
will be used to perform the allgather.
Raises:
TypeError:
if target and preds have a different shape
Returns:
average si-snr value
Example:
>>> import torch
>>> from torchmetrics import SI_SNR
>>> target = torch.tensor([3.0, -0.5, 2.0, 7.0])
>>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0])
>>> si_snr = SI_SNR()
>>> si_snr_val = si_snr(preds, target)
>>> si_snr_val
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.
"""
is_differentiable = True
sum_si_snr: Tensor
total: Tensor
higher_is_better = True
def __init__(
self,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
dist_sync_fn: Optional[Callable[[Tensor], Tensor]] = None,
) -> None:
super().__init__(
compute_on_step=compute_on_step,
dist_sync_on_step=dist_sync_on_step,
process_group=process_group,
dist_sync_fn=dist_sync_fn,
)
self.add_state("sum_si_snr", default=tensor(0.0), dist_reduce_fx="sum")
self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore
"""Update state with predictions and targets.
Args:
preds: Predictions from model
target: Ground truth values
"""
si_snr_batch = si_snr(preds=preds, target=target)
self.sum_si_snr += si_snr_batch.sum()
self.total += si_snr_batch.numel()
def compute(self) -> Tensor:
"""Computes average SI-SNR."""
return self.sum_si_snr / self.total