Update README.md

README.md

@@ -42,7 +42,7 @@ import torchaudio
 from datasets import load_dataset
 from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 
-test_dataset = load_dataset("common_voice", "vi", split="test") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
+test_dataset = load_dataset("common_voice", "vi", split="test")
 
 processor = Wav2Vec2Processor.from_pretrained("not-tanh/wav2vec2-large-xlsr-53-vietnamese")
 model = Wav2Vec2ForCTC.from_pretrained("not-tanh/wav2vec2-large-xlsr-53-vietnamese")
@@ -71,7 +71,7 @@ print("Reference:", test_dataset["sentence"][:2])
 
 ## Evaluation
 
-The model can be evaluated as follows on the {language} test data of Common Voice.  # TODO: replace #TODO: replace language with your {language}, *e.g.* French
+The model can be evaluated as follows on the Vietnamese test data of Common Voice.
 
 
 ```python
@@ -124,6 +124,6 @@ print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"],
 ## Training
 ## TODO
 
-The Common Voice `train`, `validation`, and `vivos` datasets were used for training
+The Common Voice `train`, `validation`, the VIVOS and FOSD datasets were used for training
 
-The script used for training can be found ... # TODO: fill in a link to your training script here. If you trained your model in a colab, simply fill in the link here. If you trained the model locally, it would be great if you could upload the training script on github and paste the link here.
+The script used for training can be found ... # TODO