Update README.md

README.md

@@ -41,7 +41,7 @@ import torchaudio
 from datasets import load_dataset
 from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 
-test_dataset = load_dataset("common_voice", "fr")
+test_dataset = load_dataset("common_voice", "fr", split="test[:20%]")
 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)
@@ -108,11 +108,11 @@ result = test_dataset.map(evaluate, batched=True, batch_size=8)
 print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
 ```
 
-**Test Result**: 49.59 % 
+**Test Result**: 29.31 % 
 ## Training
 
 V1 of the Common Voice `train`, `validation` datasets were used for training.
 
 ## Testing
 
-V6.1 of the Common Voice `Test` dataset were used for training.
+20% of V6.1 of the Common Voice `Test` dataset were used for training.