Renaming repo to `phone_errors`

README.md

@@ -1,10 +1,10 @@
 ---
-title: phone_distance
+title: phone_errors
 tags:
 - evaluate
 - metric
 description: >-
-    Measures of distance in terms of articulatory phonological features can help understand differences 
+    Error rates in terms of distance between articulatory phonological features can help understand differences 
     between strings in the International Phonetic Alphabet (IPA) in a linguistically motivated way. 
     This is useful when evaluating speech recognition or orthographic to IPA conversion tasks.
 sdk: gradio
@@ -16,14 +16,14 @@ pinned: false
 # Metric Card for Phone Distance
 
 ## Metric Description
-Measures of distance in terms of articulatory phonological features can help understand differences between strings in the International Phonetic Alphabet (IPA) in a linguistically motivated way. 
+Error rates in terms of distance between articulatory phonological features can help understand differences between strings in the International Phonetic Alphabet (IPA) in a linguistically motivated way. 
 This is useful when evaluating speech recognition or orthographic to IPA conversion tasks. These are Levenshtein distances for comparing strings where the smallest unit of measurement is based on phones or articulatory phonological features, rather than Unicode characters. 
 
 ## How to Use
 
 ```python
 import evaluate
-phone_distance = evaluate.load("ginic/phone_distance")
+phone_distance = evaluate.load("ginic/phone_errors")
 phone_distance.compute(predictions=["bob", "ði"], references=["pop", "ðə"])
 ```
 

app.py

@@ -2,5 +2,5 @@ import evaluate
 from evaluate.utils import launch_gradio_widget
 
 
-module = evaluate.load("ginic/phone_distance")
+module = evaluate.load("ginic/phone_errors")
 launch_gradio_widget(module)

phone_distance.py

@@ -38,9 +38,9 @@ _CITATION = """\
 """
 
 _DESCRIPTION = """
-Measures of distance in terms of articulatory phonological features can help understand differences 
+Error rates in terms of distance between articulatory phonological features can help understand differences 
 between strings in the International Phonetic Alphabet (IPA) in a linguistically motivated way. 
-This is useful when evaluating speech recognition or orthographic to IPA conversion tasks. 
+This is useful when evaluating speech recognition or orthographic to IPA conversion tasks.
 """
 
 
@@ -71,12 +71,12 @@ Returns:
 
 Examples:
     Compare articulatory differences in voicing in "bob" vs. "pop" and different pronunciations of "the":
->>> phone_distance = evaluate.load("ginic/phone_distance")
+>>> phone_distance = evaluate.load("ginic/phone_errors")
 >>> phone_distance.compute(predictions=["bob", "ði"], references=["pop", "ðə"])
 {'phone_error_rates': [0.6666666666666666, 0.5], 'mean_phone_error_rate': 0.5833333333333333, 'phone_feature_error_rates': [0.08333333333333333, 0.125], 'mean_phone_feature_error_rates': 0.10416666666666666, 'feature_error_rates': [0.027777777777777776, 0.0625], 'mean_feature_error_rates': 0.04513888888888889}
 
     Normalize PFER by the length of string with largest number of phones: 
->>> phone_distance = evaluate.load("ginic/phone_distance")
+>>> phone_distance = evaluate.load("ginic/phone_errors")
 >>> phone_distance.compute(predictions=["bob", "ði"], references=["pop", "ðə"], is_normalize_pfer=True)
 
 """