Add sample usage from GitHub README

README.md

@@ -14,6 +14,44 @@ tags:
 
 Lite-Whisper is a compressed version of OpenAI Whisper with LiteASR. See our [GitHub repository](https://github.com/efeslab/LiteASR) and [paper](https://arxiv.org/abs/2502.20583) for details.
 
+## Quick Start
+The easiest way to run our model is to use our integration with HuggingFace Transformers library.
+We provide model weights for the compressed version of OpenAI Whisper series [here](https://huggingface.co/efficient-speech).
+
+```python
+import librosa 
+import torch
+from transformers import AutoProcessor, AutoModel
+
+device = "cuda:0"
+dtype = torch.float16
+
+# load the compressed Whisper model
+model = AutoModel.from_pretrained(
+    "efficient-speech/lite-whisper-large-v3-turbo", 
+    trust_remote_code=True, 
+)
+model.to(dtype).to(device)
+
+# we use the same processor as the original model
+processor = AutoProcessor.from_pretrained("openai/whisper-large-v3")
+
+# set the path to your audio file
+path = "path/to/audio.wav"
+audio, _ = librosa.load(path, sr=16000)
+
+input_features = processor(audio, sampling_rate=16000, return_tensors="pt").input_features
+input_features = input_features.to(dtype).to(device)
+
+predicted_ids = model.generate(input_features)
+transcription = processor.batch_decode(
+    predicted_ids, 
+    skip_special_tokens=True
+)[0]
+
+print(transcription)
+```
+
 ## Benchmark Results
 
 Following is the average word error rate (WER) evaluated on the [ESB datasets](https://huggingface.co/datasets/hf-audio/esb-datasets-test-only-sorted):