Add description to card metadata

README.md

@@ -1,6 +1,6 @@
 ---
 title: WER
-emoji: 🤗 
+emoji: 🤗
 colorFrom: blue
 colorTo: red
 sdk: gradio
@@ -8,10 +8,55 @@ sdk_version: 3.0.2
 app_file: app.py
 pinned: false
 tags:
-- evaluate
-- metric
----
+  - 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.
+---
 # Metric Card for WER
 
 ## Metric description