Update README.md

README.md

@@ -13,7 +13,7 @@ widget:
 - example_title: Librispeech sample 2
   src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
 model-index:
-- name: patrickvonplaten/wav2vec2-large-960h-lv60-self-4-gram
+- name: patrickvonplaten/hubert-xlarge-ls960-ft-4-gram
   results:
   - task:
       name: Automatic Speech Recognition
@@ -25,17 +25,17 @@ model-index:
     metrics:
     - name: Test WER
       type: wer
-      value: 1.84
+      value: 1.71
 ---
 
 # Wav2Vec2-Base-960h + 4-gram
 
-This model is identical to [Facebook's Wav2Vec2-Large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self), but is 
+This model is identical to [Facebook's hubert-xlarge-ls960-ft](https://huggingface.co/facebook/hubert-xlarge-ls960-ft), but is 
 augmented with an English 4-gram. The `4-gram.arpa.gz` of [Librispeech's official ngrams](https://www.openslr.org/11) is used.
  
  ## Evaluation
  
- This code snippet shows how to evaluate **patrickvonplaten/wav2vec2-large-960h-lv60-self-4-gram** on LibriSpeech's "clean" and "other" test data.
+ This code snippet shows how to evaluate **patrickvonplaten/hubert-xlarge-ls960-ft-4-gram** on LibriSpeech's "clean" and "other" test data.
  
 ```python
 from datasets import load_dataset
@@ -43,7 +43,7 @@ from transformers import AutoModelForCTC, AutoProcessor
 import torch
 from jiwer import wer
 
-model_id = "patrickvonplaten/wav2vec2-large-960h-lv60-self-4-gram"
+model_id = "patrickvonplaten/hubert-xlarge-ls960-ft-4-gram"
 
 librispeech_eval = load_dataset("librispeech_asr", "other", split="test")
 
@@ -71,4 +71,4 @@ print(wer(result["text"], result["transcription"]))
 
 | "clean" | "other" |
 |---|---|
-| 1.84 | 3.71 |
+| 1.71 | 3.06 |