-
Notifications
You must be signed in to change notification settings - Fork 388
/
wil.py
106 lines (89 loc) · 3.85 KB
/
wil.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
102
103
104
105
106
# 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, List, Optional, Union
from torch import Tensor, tensor
from torchmetrics.functional.text.wil import _wil_compute, _wil_update
from torchmetrics.metric import Metric
class WordInfoLost(Metric):
r"""
Word Information Lost (WordInfoLost_) is a metric of the performance of an automatic speech
recognition system. This value indicates the percentage of words that were incorrectly predicted
between a set of ground-truth sentences and a set of hypothesis sentences.
The lower the value, the better the performance of the ASR system with a WordInfoLost of
0 being a perfect score.
Word Information Lost rate can then be computed as:
.. math::
wil = 1 - \frac{C}{N} + \frac{C}{P}
where:
- C is the number of correct words,
- N is the number of words in the reference
- P is the number of words in the prediction
Args:
compute_on_step:
Forward only calls ``update()`` and return None if this is set to False.
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.
dist_sync_fn:
Callback that performs the allgather operation on the metric state. When ``None``, DDP
will be used to perform the allgather
Examples:
>>> from torchmetrics import WordInfoLost
>>> preds = ["this is the prediction", "there is an other sample"]
>>> target = ["this is the reference", "there is another one"]
>>> metric = WordInfoLost()
>>> metric(preds, target)
tensor(0.6528)
"""
is_differentiable = False
higher_is_better = False
errors: Tensor
target_total: Tensor
preds_total: Tensor
def __init__(
self,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
dist_sync_fn: Callable = 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("errors", tensor(0.0), dist_reduce_fx="sum")
self.add_state("target_total", tensor(0.0), dist_reduce_fx="sum")
self.add_state("preds_total", tensor(0.0), dist_reduce_fx="sum")
def update(self, preds: Union[str, List[str]], target: Union[str, List[str]]) -> None: # type: ignore
"""Store predictions/references for computing Word Information Lost scores.
Args:
preds:
Transcription(s) to score as a string or list of strings
target:
Reference(s) for each speech input as a string or list of strings
"""
errors, target_total, preds_total = _wil_update(preds, target)
self.errors += errors
self.target_total += target_total
self.preds_total += preds_total
def compute(self) -> Tensor:
"""Calculate the Word Information Lost.
Returns:
Word Information Lost score
"""
return _wil_compute(self.errors, self.target_total, self.preds_total)