Update README.md

README.md

@@ -39,7 +39,20 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+```
+from transformers import AutoModelForCTC, Wav2Vec2BertProcessor
+
+model = AutoModelForCTC.from_pretrained("tbkazakova/wav2vec-bert-2.0-even-pakendorf")
+processor = Wav2Vec2BertProcessor.from_pretrained("tbkazakova/wav2vec-bert-2.0-even-pakendorf")
+
+data, sampling_rate = librosa.load('audio.wav')
+librosa.resample(data, orig_sr=sampling_rate, target_sr=16000)
+logits = model(torch.tensor(processor(data,
+                                      sampling_rate=16000).input_features[0]).unsqueeze(0)).logits
+
+pred_ids = torch.argmax(logits, dim=-1)[0]
+print(processor.decode(pred_ids))
+```
 
 ## Intended uses & limitations