Update README.md

README.md

@@ -42,8 +42,8 @@ from datasets import load_dataset
 from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 
 test_dataset = load_dataset("common_voice", "fr")
-processor = Wav2Vec2Processor.from_pretrained("Nhut/wav2vec2-large-xlsr-53-french")
-model = Wav2Vec2ForCTC.from_pretrained("Nhut/wav2vec2-large-xlsr-53-french")
+processor = Wav2Vec2Processor.from_pretrained("Nhut/wav2vec2-large-xlsr-french")
+model = Wav2Vec2ForCTC.from_pretrained("Nhut/wav2vec2-large-xlsr-french")
 resampler = torchaudio.transforms.Resample(48_000, 16_000)
 
 # Preprocessing the datasets.
@@ -79,8 +79,8 @@ import re
 
 test_dataset = load_dataset("common_voice", "fr")
 wer = load_metric("wer")
-processor = Wav2Vec2Processor.from_pretrained("Nhut/wav2vec2-large-xlsr-53-French")
-model = Wav2Vec2ForCTC.from_pretrained("Nhut/wav2vec2-large-xlsr-53-French")
+processor = Wav2Vec2Processor.from_pretrained("Nhut/wav2vec2-large-xlsr-french")
+model = Wav2Vec2ForCTC.from_pretrained("Nhut/wav2vec2-large-xlsr-french")
 model.to("cuda")
 chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�]'
 resampler = torchaudio.transforms.Resample(48_000, 16_000)