Update README.md

README.md

@@ -219,13 +219,13 @@ ar_sample = next(iter(stream_data))["audio"]["array"]
 Next, we load the model and processor
 
 ```py
-from transformers import Wav2Vec2ForCTC, AutoFeatureExtractor
+from transformers import Wav2Vec2ForSequenceClassification, AutoFeatureExtractor
 import torch
 
 model_id = "facebook/mms-lid-126"
 
 processor = AutoFeatureExtractor.from_pretrained(model_id)
-model = Wav2Vec2ForCTC.from_pretrained(model_id)
+model = Wav2Vec2ForSequenceClassification.from_pretrained(model_id)
 ```
 
 Now we process the audio data, pass the processed audio data to the model to classify it into a language, just like we usually do for Wav2Vec2 audio classification models such as [ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition](https://huggingface.co/harshit345/xlsr-wav2vec-speech-emotion-recognition)