Update Space (evaluate main: e179b5b8)

README.md

@@ -1,6 +1,6 @@
 ---
 title: WER
-emoji: 🤗
+emoji: 🤗 
 colorFrom: blue
 colorTo: red
 sdk: gradio
@@ -8,55 +8,10 @@ sdk_version: 3.0.2
 app_file: app.py
 pinned: false
 tags:
-  - evaluate
-  - metric
-description: >-
-  Word error rate (WER) is a common metric of the performance of an automatic
-  speech recognition system.
-
-
-  The general difficulty of measuring performance lies in the fact that the
-  recognized word sequence can have a different length from the reference word
-  sequence (supposedly the correct one). The WER is derived from the Levenshtein
-  distance, working at the word level instead of the phoneme level. The WER is a
-  valuable tool for comparing different systems as well as for evaluating
-  improvements within one system. This kind of measurement, however, provides no
-  details on the nature of translation errors and further work is therefore
-  required to identify the main source(s) of error and to focus any research
-  effort.
-
-
-  This problem is solved by first aligning the recognized word sequence with the
-  reference (spoken) word sequence using dynamic string alignment. Examination
-  of this issue is seen through a theory called the power law that states the
-  correlation between perplexity and word error rate.
-
-
-  Word error rate can then be computed as:
-
-
-  WER = (S + D + I) / N = (S + D + I) / (S + D + C)
-
-
-  where
-
-
-  S is the number of substitutions,
-
-  D is the number of deletions,
-
-  I is the number of insertions,
-
-  C is the number of correct words,
-
-  N is the number of words in the reference (N=S+D+C).
-
-
-  This value indicates the average number of errors per reference word. The
-  lower the value, the better the
-
-  performance of the ASR system with a WER of 0 being a perfect score.
+- evaluate
+- metric
 ---
+
 # Metric Card for WER
 
 ## Metric description