Add quick start code and citation to model card (#1)

- Add quick start code and citation to model card (9aa404281aa9d5e3dc472eda671afb58127097d3)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,13 +1,13 @@
 ---
-license: apache-2.0
-library_name: transformers
 base_model: openai/whisper-large-v3-turbo
-tags:
-  - audio
-  - automatic-speech-recognition
-  - whisper
-  - hf-asr-leaderboard
+library_name: transformers
+license: apache-2.0
 pipeline_tag: automatic-speech-recognition
+tags:
+- audio
+- automatic-speech-recognition
+- whisper
+- hf-asr-leaderboard
 ---
 
 # Model Card for Lite-Whisper large-v3-turbo-acc
@@ -32,4 +32,59 @@ Following is the average word error rate (WER) evaluated on the [ESB datasets](h
 | [lite-whisper-large-v3-turbo](https://huggingface.co/efficient-speech/lite-whisper-large-v3-turbo) | 12.6 | 374M | 172M |
 | [lite-whisper-large-v3-turbo-fast](https://huggingface.co/efficient-speech/lite-whisper-large-v3-turbo-fast) | 20.1 | 313M | 172M |
 |   |   |   |   |
-| [whisper-medium](https://huggingface.co/openai/whisper-medium) | 14.8 | 306M | 457M |
+| [whisper-medium](https://huggingface.co/openai/whisper-medium) | 14.8 | 306M | 457M |
+
+## 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)
+```
+
+## Citation
+
+If you use LiteASR in your research, please cite the following paper:
+
+```
+@misc{kamahori2025liteasrefficientautomaticspeech,
+      title={LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation}, 
+      author={Keisuke Kamahori and Jungo Kasai and Noriyuki Kojima and Baris Kasikci},
+      year={2025},
+      eprint={2502.20583},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2502.20583}, 
+}
+```