Update README.md

README.md

@@ -1,3 +1,73 @@
----
-license: cc-by-nc-sa-4.0
----
+---
+license: cc-by-nc-sa-4.0
+datasets:
+- voice-is-cool/voxtube
+base_model:
+- openai/whisper-tiny
+library_name: transformers
+tags:
+- speaker-verification
+- voice
+- audio
+- speaker-recognition
+- speaker-embedding
+- speaker-identification
+- speaker
+- whisper
+- voxtube
+---
+# Whisper Speaker Identification (WSI)
+
+**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 wsi import load_model, process_single_audio
+
+# Load the model and feature extractor
+model, feature_extractor = load_model("path/to/wsi_model.pth")
+
+# Generate embeddings for 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 wsi import load_model, process_audio_pair
+
+# Load the model and feature extractor
+model, feature_extractor = load_model("path/to/wsi_model.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
+
+Comming Soon!
+
+### License
+
+This project is licensed under the CC BY-NC-SA 4.0 License.