Update README.md

README.md

@@ -78,6 +78,17 @@ model-index:
         - name: Test WER
           type: wer
           value: 8.0
+    - task:
+        type: Automatic Speech Recognition
+        name: automatic-speech-recognition
+      dataset:
+        name: Mozilla Common Voice 8.0
+        type: mozilla-foundation/common_voice_8_0
+        args: en
+      metrics:
+        - name: Test WER
+          type: wer
+          value: 9.48
     - task:
         type: Automatic Speech Recognition
         name: automatic-speech-recognition
@@ -201,8 +212,6 @@ The list of the available models in this collection is shown in the following ta
 | Version | Tokenizer | Vocabulary Size | LS test-other | LS test-clean | WSJ Eval92 | WSJ Dev93 | NSC Part 1 | MLS Test | MLS Dev | MCV Test 6.1 |Train Dataset |
 |---------|-----------------------|-----------------|---------------|---------------|------------|-----------|-------|------|-----|-------|---------|
 | 1.6.0 | SentencePiece Unigram | 128 | 4.3 | 2.2 | 2.0 | 2.9 | 7.0 | 7.2 | 6.5 | 8.0 | NeMo ASRSET 2.0 |
-| 1.0.0 | SentencePiece Unigram | 128 | 5.4 | 2.5 | 2.1 | 3.0 | 7.9 | - | - | - | NeMo ASRSET 1.4.1 |
-| rc1.0.0 | WordPiece | 128 | 6.3 | 2.7 | - | - | - | - | - | - | LibriSpeech |
 
 
 You may use language models to improve the accuracy of the models. The WER(%) of the latest model with different language modeling techniques are reported in the follwoing table.