Improve model card: Add abstract and Transformers sample usage

This PR improves the model card by:
- Updating the main heading to the official paper title for better discoverability and context.
- Adding the full paper abstract to provide a comprehensive overview of the model's methodology and contributions.
- Including a "Sample Usage" section with a `transformers`-compatible code snippet, directly sourced from the GitHub README. This enables users to easily get started with the model.

Existing metadata, benchmark results, and citation information remain unchanged as they are already accurate and well-formatted.

README.md

@@ -10,9 +10,51 @@ tags:
 - hf-asr-leaderboard
 ---
 
-<!-- Provide a quick summary of what the model is/does. -->
+# LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation
 
-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.
+LiteASR is a compression scheme for automatic speech recognition (ASR) models that leverages the low-rank properties of activation values. Our method can compress OpenAI's Whisper encoder by up to ~50%. For more details, see our [paper](https://arxiv.org/abs/2502.20583) and [GitHub repository](https://github.com/efeslab/LiteASR).
+
+## 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-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
 
@@ -47,12 +89,12 @@ 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},
 }
 ```