Update README.md

README.md

@@ -26,7 +26,7 @@ model-index:
     metrics:
        - name: Test WER
          type: wer
-         value: 32.18
+         value: 32.20
         
 ---
 
@@ -81,7 +81,7 @@ chars_to_mapping = {
 "ﻟ": "ل", "ﻡ": "م", "ﻢ": "م", "ﻪ": "ه", "ﻮ": "و", "ئ": "ی", 'ﺍ': "ا", 'ة': "ه",
 'ﯾ': "ی", 'ﯿ': "ی", 'ﺒ': "ب", 'ﺖ': "ت", 'ﺪ': "د", 'ﺮ': "ر", 'ﺴ': "س", 'ﺷ': "ش",
 'ﺸ': "ش", 'ﻋ': "ع", 'ﻤ': "م", 'ﻥ': "ن", 'ﻧ': "ن", 'ﻭ': "و", 'ﺭ': "ر", "ﮔ": "گ",
-"\u200c": " ", "\u200d": " ", "\u200e": " ", "\u200f": " ", "\ufeff": " ",
+"\\u200c": " ", "\\u200d": " ", "\\u200e": " ", "\\u200f": " ", "\\ufeff": " ",
 }
 
 def multiple_replace(text, chars_to_mapping):
@@ -217,11 +217,6 @@ predicted: من سفر کردم را دوست دارم
 
 The model can be evaluated as follows on the Persian (Farsi) test data of Common Voice.
 
-```bash
-!mkdir cer
-!wget -O cer/cer.py https://huggingface.co/ctl/wav2vec2-large-xlsr-cantonese/raw/main/cer.py
-```
-
 ```python
 import librosa
 import torch
@@ -250,7 +245,7 @@ chars_to_mapping = {
 "ﻟ": "ل", "ﻡ": "م", "ﻢ": "م", "ﻪ": "ه", "ﻮ": "و", "ئ": "ی", 'ﺍ': "ا", 'ة': "ه",
 'ﯾ': "ی", 'ﯿ': "ی", 'ﺒ': "ب", 'ﺖ': "ت", 'ﺪ': "د", 'ﺮ': "ر", 'ﺴ': "س", 'ﺷ': "ش",
 'ﺸ': "ش", 'ﻋ': "ع", 'ﻤ': "م", 'ﻥ': "ن", 'ﻧ': "ن", 'ﻭ': "و", 'ﺭ': "ر", "ﮔ": "گ",
-"\u200c": " ", "\u200d": " ", "\u200e": " ", "\u200f": " ", "\ufeff": " ",
+"\\u200c": " ", "\\u200d": " ", "\\u200e": " ", "\\u200f": " ", "\\ufeff": " ",
 }
 
 def multiple_replace(text, chars_to_mapping):
@@ -311,15 +306,11 @@ dataset = dataset.map(speech_file_to_array_fn)
 result = dataset.map(predict)
 
 wer = load_metric("wer")
-cer = load_metric("./cer")
-
 print("WER: {:.2f}".format(100 * wer.compute(predictions=result["predicted"], references=result["sentence"])))
-print("CER: {:.2f}".format(100 * cer.compute(predictions=result["predicted"], references=result["sentence"])))
 ```
 
 **Test Result:** 
-- WER: 32.18%
-- CER: 8.27%
+- WER: 32.20%
 
 
 ## Training