julien-c HF staff commited on
Commit
9170004
1 Parent(s): 86e1c71

Add description to card metadata

Browse files

Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.

CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.

Character error rate can be computed as:

CER = (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 characters,
N is the number of characters in the reference (N=S+D+C).

CER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.

Files changed (1) hide show
  1. README.md +38 -4
README.md CHANGED
@@ -1,6 +1,6 @@
1
  ---
2
  title: CER
3
- emoji: 🤗
4
  colorFrom: blue
5
  colorTo: red
6
  sdk: gradio
@@ -8,10 +8,44 @@ sdk_version: 3.0.2
8
  app_file: app.py
9
  pinned: false
10
  tags:
11
- - evaluate
12
- - metric
13
- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
14
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
15
  # Metric Card for CER
16
 
17
  ## Metric description
 
1
  ---
2
  title: CER
3
+ emoji: 🤗
4
  colorFrom: blue
5
  colorTo: red
6
  sdk: gradio
 
8
  app_file: app.py
9
  pinned: false
10
  tags:
11
+ - evaluate
12
+ - metric
13
+ description: >-
14
+ Character error rate (CER) is a common metric of the performance of an
15
+ automatic speech recognition system.
16
+
17
+
18
+ CER is similar to Word Error Rate (WER), but operates on character instead of
19
+ word. Please refer to docs of WER for further information.
20
+
21
+
22
+ Character error rate can be computed as:
23
+
24
+
25
+ CER = (S + D + I) / N = (S + D + I) / (S + D + C)
26
+
27
+
28
+ where
29
 
30
+
31
+ S is the number of substitutions,
32
+
33
+ D is the number of deletions,
34
+
35
+ I is the number of insertions,
36
+
37
+ C is the number of correct characters,
38
+
39
+ N is the number of characters in the reference (N=S+D+C).
40
+
41
+
42
+ CER's output is not always a number between 0 and 1, in particular when there
43
+ is a high number of insertions. This value is often associated to the
44
+ percentage of characters that were incorrectly predicted. The lower the value,
45
+ the better the
46
+
47
+ performance of the ASR system with a CER of 0 being a perfect score.
48
+ ---
49
  # Metric Card for CER
50
 
51
  ## Metric description