upd readme

README.md

@@ -25,9 +25,9 @@ model-index:
          value: 37.72
 ---
 
-# Wav2Vec2-Large-XLSR-53-Chech
+# Wav2Vec2-Large-XLSR-53-Ukrainian
 
-Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Czech using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
+Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Ukrainian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
 
 When using this model, make sure that your speech input is sampled at 16kHz.
 
@@ -42,7 +42,7 @@ import torchaudio
 from datasets import load_dataset
 from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 
-test_dataset = load_dataset("common_voice", "cs", split="test[:2%]")
+test_dataset = load_dataset("common_voice", "uk", split="test[:2%]")
 
 processor = Wav2Vec2Processor.from_pretrained("arampacha/wav2vec2-large-xlsr-ukrainian")
 model = Wav2Vec2ForCTC.from_pretrained("arampacha/wav2vec2-large-xlsr-ukrainian")
@@ -82,11 +82,11 @@ from datasets import load_dataset, load_metric
 from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 import re
 
-test_dataset = load_dataset("common_voice", "cs", split="test")
+test_dataset = load_dataset("common_voice", "uk", split="test")
 
 wer = load_metric("wer")
-processor = Wav2Vec2Processor.from_pretrained("arampacha/wav2vec2-large-xlsr-czech")
-model = Wav2Vec2ForCTC.from_pretrained("arampacha/wav2vec2-large-xlsr-czech")
+processor = Wav2Vec2Processor.from_pretrained("arampacha/wav2vec2-large-xlsr-ukrainian")
+model = Wav2Vec2ForCTC.from_pretrained("arampacha/wav2vec2-large-xlsr-ukrainian")
 model.to("cuda")
 
 chars_to_ignore = [",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�", '«', '»', '—', '…', '(', ')', '*', '”', '“']