Downgrade gradio version to fix table display for examples

README.md

@@ -9,7 +9,7 @@ description: >-
   Alphabet (IPA) in a linguistically motivated way.  This is useful when
   evaluating speech recognition or orthographic to IPA conversion tasks.
 sdk: gradio
-sdk_version: 3.50.2
+sdk_version: 3.19.1
 app_file: app.py
 pinned: false
 ---
@@ -17,8 +17,8 @@ pinned: false
 # Metric Card for Phone Errors
 
 ## Metric Description
-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. 
+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
 
@@ -31,7 +31,7 @@ phone_errors.compute(predictions=["bob", "ði"], references=["pop", "ðə"])
 ### Inputs
 - **predictions** (`list` of `str`): Transcriptions to score.
 - **references** (`list` of `str`) : Reference strings serving as ground truth.
-- **feature_model** (`str`): Set which panphon.distance.Distance feature parsing model is used, choose from `"strict"`, `"permissive"`, `"segment"`. Defaults to `"segment"`. 
+- **feature_model** (`str`): Set which panphon.distance.Distance feature parsing model is used, choose from `"strict"`, `"permissive"`, `"segment"`. Defaults to `"segment"`.
 - **is_normalize_pfer** (`bool`): Set to `True `to normalize PFER by the largest number of phones in the prediction, reference pair. Defaults to `False`. When this is used PFER will no longer obey the triangle inequality.
 
 
@@ -39,9 +39,9 @@ phone_errors.compute(predictions=["bob", "ði"], references=["pop", "ðə"])
 The computation returns a dictionary with the following key and values:
  - **phone_error_rates** (`list` of `float`): Phone error rate (PER) gives edit distance in terms of phones for each prediction-reference pair, rather than Unicode characters, since phones can consist of multiple characters. It is normalized by the number of phones of the reference string. The result with have the same length as the input prediction and reference lists.
  - **mean_phone_error_rate** (`float`): Overall mean of PER.
- - **phone_feature_error_rates** (`list` of `float`): Phone feature error rate (PFER) is Levenshtein distance between strings where distance between individual phones is computed using Hamming distance between phonetic features for each prediction-reference pair. By default it is a metric that obeys the triangle equality, but can also be normalized by number of phones. 
+ - **phone_feature_error_rates** (`list` of `float`): Phone feature error rate (PFER) is Levenshtein distance between strings where distance between individual phones is computed using Hamming distance between phonetic features for each prediction-reference pair. By default it is a metric that obeys the triangle equality, but can also be normalized by number of phones.
  - **mean_phone_feature_error_rate** (`float`):  Overall mean of PFER.
- - **feature_error_rates** (`list` of `float`): Feature error rate (FER) is the edit distance in terms of articulatory features normalized by the number of phones in the reference, computed for each prediction-reference pair. 
+ - **feature_error_rates** (`list` of `float`): Feature error rate (FER) is the edit distance in terms of articulatory features normalized by the number of phones in the reference, computed for each prediction-reference pair.
  - **mean_feature_error_rate** (`float`): Overall mean of FER.
 
 
@@ -50,27 +50,27 @@ The computation returns a dictionary with the following key and values:
 
 ### Examples
 
-Simplest use case to compute phone error rates between two IPA strings: 
+Simplest use case to compute phone error rates between two IPA strings:
 ```python
 >>> phone_errors.compute(predictions=["bob", "ði", "spin"], references=["pop", "ðə", "spʰin"])
-{'phone_error_rates': [0.6666666666666666, 0.5, 0.25], 'mean_phone_error_rate': 0.47222222222222215, 
- 'phone_feature_error_rates': [0.08333333333333333, 0.125, 0.041666666666666664], 'mean_phone_feature_error_rate': 0.08333333333333333, 
+{'phone_error_rates': [0.6666666666666666, 0.5, 0.25], 'mean_phone_error_rate': 0.47222222222222215,
+ 'phone_feature_error_rates': [0.08333333333333333, 0.125, 0.041666666666666664], 'mean_phone_feature_error_rate': 0.08333333333333333,
   'feature_error_rates': [0.027777777777777776, 0.0625, 0.30208333333333337], 'mean_feature_error_rate': 0.13078703703703706}
 ```
 
-Normalize phone feature error rate by the length of the reference string: 
+Normalize phone feature error rate by the length of the reference string:
 ```python
 >>> phone_errors.compute(predictions=["bob", "ði"], references=["pop", "ðə"], is_normalize_pfer=True)
-{'phone_error_rates': [0.6666666666666666, 0.5], 'mean_phone_error_rate': 0.5833333333333333, 
- 'phone_feature_error_rates': [0.027777777777777776, 0.0625], 'mean_phone_feature_error_rate': 0.04513888888888889, 
+{'phone_error_rates': [0.6666666666666666, 0.5], 'mean_phone_error_rate': 0.5833333333333333,
+ 'phone_feature_error_rates': [0.027777777777777776, 0.0625], 'mean_phone_feature_error_rate': 0.04513888888888889,
  'feature_error_rates': [0.027777777777777776, 0.0625], 'mean_feature_error_rate': 0.04513888888888889}
 ```
 
 Error rates may be greater than 1.0 if the reference string is shorter than the prediction string:
 ```python
 >>> phone_errors.compute(predictions=["bob"], references=["po"])
-{'phone_error_rates': [1.0], 'mean_phone_error_rate': 1.0, 
- 'phone_feature_error_rates': [1.0416666666666667], 'mean_phone_feature_error_rate': 1.0416666666666667, 
+{'phone_error_rates': [1.0], 'mean_phone_error_rate': 1.0,
+ 'phone_feature_error_rates': [1.0416666666666667], 'mean_phone_feature_error_rate': 1.0416666666666667,
  'feature_error_rates': [0.020833333333333332], 'mean_feature_error_rate': 0.020833333333333332}
 ```
 
@@ -85,7 +85,7 @@ ValueError: one or more references are empty strings
 
 ## Limitations and Bias
 - Phone error rate and feature error rate can be greater than 1.0 if the reference string is shorter than the prediction string.
-- Since these are error rates, not edit distances, the reference strings cannot be empty. 
+- Since these are error rates, not edit distances, the reference strings cannot be empty.
 
 ## Citation
 ```bibtex
@@ -105,6 +105,6 @@ ValueError: one or more references are empty strings
 ```
 
 ## Further References
-- PER and PFER are used as evaluation metrics in [Universal Automatic Phonetic Transcription into the International Phonetic Alphabet (Taguchi et al.)](https://www.isca-archive.org/interspeech_2023/taguchi23_interspeech.html) 
+- PER and PFER are used as evaluation metrics in [Universal Automatic Phonetic Transcription into the International Phonetic Alphabet (Taguchi et al.)](https://www.isca-archive.org/interspeech_2023/taguchi23_interspeech.html)
 - Pierce Darragh's blog post [Introduction to Phonology, Part 3: Phonetic Features](https://pdarragh.github.io/blog/2018/04/26/intro-to-phonology-pt-3/) gives an overview of phonetic features for speech sounds.
 - [panphon Github repository](https://github.com/dmort27/panphon)

requirements.txt

@@ -1,2 +1,2 @@
-evaluate==0.4.3
+evaluate
 git+https://github.com/dmort27/panphon.git