refactor: wer

CHANGELOG.md

@@ -55,6 +55,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - Renamed IoU -> Jaccard Index ([#662](https://github.com/PyTorchLightning/metrics/pull/662))
 
+- Renamed `WER` -> `WordErrorRate` and `wer` -> `word_error_rate` ([#714](https://github.com/PyTorchLightning/metrics/pull/714))
+
 
 - Renamed correlation coefficient classes: ([#710](https://github.com/PyTorchLightning/metrics/pull/710))
   * `MatthewsCorrcoef` -> `MatthewsCorrCoef`

tests/text/test_wer.py

@@ -11,8 +11,8 @@
 else:
     compute_measures = Callable
 
-from torchmetrics.functional.text.wer import wer
-from torchmetrics.text.wer import WER
+from torchmetrics.functional.text.wer import word_error_rate
+from torchmetrics.text.wer import WordErrorRate
 
 
 def _compute_wer_metric_jiwer(prediction: Union[str, List[str]], reference: Union[str, List[str]]):
@@ -36,7 +36,7 @@ def test_wer_class(self, ddp, dist_sync_on_step, preds, targets):
             ddp=ddp,
             preds=preds,
             targets=targets,
-            metric_class=WER,
+            metric_class=WordErrorRate,
             sk_metric=_compute_wer_metric_jiwer,
             dist_sync_on_step=dist_sync_on_step,
         )
@@ -46,7 +46,7 @@ def test_wer_functional(self, preds, targets):
         self.run_functional_metric_test(
             preds,
             targets,
-            metric_functional=wer,
+            metric_functional=word_error_rate,
             sk_metric=_compute_wer_metric_jiwer,
         )
 
@@ -55,6 +55,6 @@ def test_wer_differentiability(self, preds, targets):
         self.run_differentiability_test(
             preds=preds,
             targets=targets,
-            metric_module=WER,
-            metric_functional=wer,
+            metric_module=WordErrorRate,
+            metric_functional=word_error_rate,
         )

torchmetrics/__init__.py

@@ -78,6 +78,7 @@
     SacreBLEUScore,
     SQuAD,
     TranslationEditRate,
+    WordErrorRate,
     WordInfoLost,
     WordInfoPreserved,
 )
@@ -154,6 +155,7 @@
     "SymmetricMeanAbsolutePercentageError",
     "TranslationEditRate",
     "WER",
+    "WordErrorRate",
     "CharErrorRate",
     "MatchErrorRate",
     "WordInfoLost",

torchmetrics/functional/__init__.py

@@ -73,7 +73,7 @@
 from torchmetrics.functional.text.sacre_bleu import sacre_bleu_score
 from torchmetrics.functional.text.squad import squad
 from torchmetrics.functional.text.ter import translation_edit_rate
-from torchmetrics.functional.text.wer import wer
+from torchmetrics.functional.text.wer import wer, word_error_rate
 from torchmetrics.functional.text.wil import word_information_lost
 from torchmetrics.functional.text.wip import word_information_preserved
 
@@ -140,6 +140,7 @@
     "symmetric_mean_absolute_percentage_error",
     "translation_edit_rate",
     "wer",
+    "word_error_rate",
     "char_error_rate",
     "match_error_rate",
     "word_information_lost",

torchmetrics/functional/text/__init__.py

@@ -19,6 +19,6 @@
 from torchmetrics.functional.text.sacre_bleu import sacre_bleu_score  # noqa: F401
 from torchmetrics.functional.text.squad import squad  # noqa: F401
 from torchmetrics.functional.text.ter import translation_edit_rate  # noqa: F401
-from torchmetrics.functional.text.wer import wer  # noqa: F401
+from torchmetrics.functional.text.wer import wer, word_error_rate  # noqa: F401
 from torchmetrics.functional.text.wil import word_information_lost  # noqa: F401
 from torchmetrics.functional.text.wip import word_information_preserved  # noqa: F401

torchmetrics/functional/text/wer.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 from typing import List, Tuple, Union
+from warnings import warn
 
 import torch
 from torch import Tensor, tensor
@@ -61,7 +62,7 @@ def _wer_compute(errors: Tensor, total: Tensor) -> Tensor:
     return errors / total
 
 
-def wer(
+def word_error_rate(
     predictions: Union[str, List[str]],
     references: Union[str, List[str]],
 ) -> Tensor:
@@ -79,8 +80,27 @@ def wer(
     Examples:
         >>> predictions = ["this is the prediction", "there is an other sample"]
         >>> references = ["this is the reference", "there is another one"]
-        >>> wer(predictions=predictions, references=references)
+        >>> word_error_rate(predictions=predictions, references=references)
         tensor(0.5000)
     """
     errors, total = _wer_update(predictions, references)
     return _wer_compute(errors, total)
+
+
+def wer(
+    predictions: Union[str, List[str]],
+    references: Union[str, List[str]],
+) -> Tensor:
+    """Word error rate (WER_) is a common metric of the performance of an automatic speech recognition system.
+
+    .. deprecated:: v0.7
+        Use :func:`torchmetrics.fuctional.word_error_rate`. Will be removed in v0.8.
+
+    Examples:
+        >>> predictions = ["this is the prediction", "there is an other sample"]
+        >>> references = ["this is the reference", "there is another one"]
+        >>> wer(predictions=predictions, references=references)
+        tensor(0.5000)
+    """
+    warn("`wer` was renamed to `word_error_rate` in v0.7 and it will be removed in v0.8", DeprecationWarning)
+    return word_error_rate(predictions, references)

torchmetrics/text/__init__.py

@@ -18,6 +18,6 @@
 from torchmetrics.text.sacre_bleu import SacreBLEUScore  # noqa: F401
 from torchmetrics.text.squad import SQuAD  # noqa: F401
 from torchmetrics.text.ter import TranslationEditRate  # noqa: F401
-from torchmetrics.text.wer import WER  # noqa: F401
+from torchmetrics.text.wer import WER, WordErrorRate  # noqa: F401
 from torchmetrics.text.wil import WordInfoLost  # noqa: F401
 from torchmetrics.text.wip import WordInfoPreserved  # noqa: F401

torchmetrics/text/wer.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from typing import Any, Callable, List, Optional, Union
+from warnings import warn
 
 import torch
 from torch import Tensor, tensor
@@ -20,7 +21,7 @@
 from torchmetrics.metric import Metric
 
 
-class WER(Metric):
+class WordErrorRate(Metric):
     r"""
     Word error rate (WER_) is a common metric of the performance of an automatic speech recognition system.
     This value indicates the percentage of words that were incorrectly predicted.
@@ -57,7 +58,7 @@ class WER(Metric):
     Examples:
         >>> predictions = ["this is the prediction", "there is an other sample"]
         >>> references = ["this is the reference", "there is another one"]
-        >>> metric = WER()
+        >>> metric = WordErrorRate()
         >>> metric(predictions, references)
         tensor(0.5000)
     """
@@ -100,3 +101,29 @@ def compute(self) -> Tensor:
             Word error rate score
         """
         return _wer_compute(self.errors, self.total)
+
+
+class WER(WordErrorRate):
+    r"""
+    Word error rate (WER_) is a common metric of the performance of an automatic speech recognition system.
+
+    .. deprecated:: v0.7
+        Use :class:`torchmetrics.WordErrorRate`. Will be removed in v0.8.
+
+    Examples:
+        >>> predictions = ["this is the prediction", "there is an other sample"]
+        >>> references = ["this is the reference", "there is another one"]
+        >>> metric = WER()
+        >>> metric(predictions, references)
+        tensor(0.5000)
+    """
+
+    def __init__(
+        self,
+        compute_on_step: bool = True,
+        dist_sync_on_step: bool = False,
+        process_group: Optional[Any] = None,
+        dist_sync_fn: Callable = None,
+    ):
+        warn("`WER` was renamed to `WordErrorRate` in v0.7 and it will be removed in v0.8", DeprecationWarning)
+        super().__init__(compute_on_step, dist_sync_on_step, process_group, dist_sync_fn)