Improve model card with abstract and sample usage

README.md

@@ -14,6 +14,49 @@ 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.
 
+## Abstract
+
+Modern automatic speech recognition (ASR) models, such as OpenAI's Whisper, rely on deep encoder-decoder architectures, and their encoders are a critical bottleneck for efficient deployment due to high computational intensity. We introduce LiteASR, a low-rank compression scheme for ASR encoders that significantly reduces inference costs while maintaining transcription accuracy. Our approach leverages the strong low-rank properties observed in intermediate activations: by applying principal component analysis (PCA) with a small calibration dataset, we approximate linear transformations with a chain of low-rank matrix multiplications, and further optimize self-attention to work in reduced dimensionality. Evaluation results show that our method can compress Whisper large-v3's encoder size by over 50%, matching Whisper medium's size with better transcription accuracy, thereby establishing a new Pareto frontier of accuracy and efficiency.
+
+## Sample Usage
+
+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-small",
+    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):
@@ -40,19 +83,18 @@ Following is the average word error rate (WER) evaluated on the [ESB datasets](h
 | [lite-whisper-medium](https://huggingface.co/efficient-speech/lite-whisper-medium) | 14.50 | 239.99M | 456.64M |
 | [lite-whisper-medium-fast](https://huggingface.co/efficient-speech/lite-whisper-medium-fast) | 14.52 | 215.31M | 456.64M |
 
-
 ## 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}, 
+      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}, 
+      url={https://arxiv.org/abs/2502.20583},
 }
 ```