Add usage example

README.md

@@ -45,5 +45,30 @@ Model size is 15%  less then original whisper-small:
 
 You can fine-tune this model on your data to achive better performance.
 
+## Usage
+Model can be used as an original whisper:
+
+```python
+>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
+>>> import torchaudio
+
+>>> # load audio
+>>> wav, sr = torchaudio.load("audio.wav")
+
+>>> # load model and processor
+>>> processor = WhisperProcessor.from_pretrained("waveletdeboshir/whisper-small-ru-pruned")
+>>> model = WhisperForConditionalGeneration.from_pretrained("waveletdeboshir/whisper-small-ru-pruned")
+
+>>> input_features = processor(wav[0], sampling_rate=sr, return_tensors="pt").input_features 
+
+>>> # generate token ids
+>>> predicted_ids = model.generate(input_features)
+>>> # decode token ids to text
+>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
+['<|startoftranscript|><|ru|><|transcribe|><|notimestamps|> Начинаем работу.<|endoftext|>']
+
+```
+The context tokens can be removed from the start of the transcription by setting `skip_special_tokens=True`.
+
 ## Colab for pruning
 TODO