Update README.md

README.md

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 **Whisper Speaker Identification (WSI)** is a state-of-the-art speaker identification model designed for multilingual scenarios.The WSI model adapts OpenAI's Whisper encoder and fine-tunes it with a projection head using triplet loss-based metric learning. This approach enhances its ability to generate discriminative, language-agnostic speaker embeddings.WSI demonstrates state-of-the-art performance on multilingual datasets, achieving lower Equal Error Rates (EER) and higher F1 Scores compared to models such as **pyannote/wespeaker-voxceleb-resnet34-LM** and **speechbrain/spkrec-ecapa-voxceleb**.
 
-## Installation
 
-Install the `whisper-speaker-id` library via pip:
-
-```
-pip install whisper-speaker-id 
-```
-
-## Usage
-
-The `wsi` library provides a simple interface to use the WSI model for embedding generation and speaker similarity tasks. 
-
-## Download the model from Huggingface
-
-[WSI Model on Hugging Face](https://huggingface.co/emon-j/WSI)
-
-### Generate Speaker Embeddings  
-
-```python
-from whisper-speaker-id import load_model, process_single_audio
-model, feature_extractor = load_model(
-    model_path_or_repo_id="emon-j/WSI",
-    filename="wsi.pth"
-)
-# Process an audio file
-embedding = process_single_audio(model, feature_extractor, "path/to/audio.wav")
-print("Speaker Embedding:", embedding)
-```
-
-### Calculate Similarity Between Two Audio Files
-
-```python
-from whisper-speaker-id import load_model, process_audio_pair
-
-model, feature_extractor = load_model(
-    model_path_or_repo_id="emon-j/WSI",
-    filename="wsi.pth"
-)
-
-# Compute similarity between two audio files
-similarity = process_audio_pair(
-    model, feature_extractor, "path/to/audio1.wav", "path/to/audio2.wav"
-)
-print("Similarity Score:", similarity)
-```
 
 ### Cite This Work