Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+
+# Conformer based multilingual speaker encoder
+
+## Summary
+
+This is a massively multilingual conformer-based speaker recognition model. 
+
+The model was trained with public data only.
+
+The paper: https://arxiv.org/abs/2104.02125
+
+```
+@inproceedings{chojnacka2021speakerstew,
+  title={{SpeakerStew: Scaling to many languages with a triaged multilingual text-dependent and text-independent speaker verification system}},
+  author={Chojnacka, Roza and Pelecanos, Jason and Wang, Quan and Moreno, Ignacio Lopez},
+  booktitle={Prod. Interspeech},
+  year={2021}
+}
+```
+
+## Usage
+
+Run use this model, you will need to use the `siglingvo` library: https://github.com/google/speaker-id/tree/master/lingvo
+
+Since lingvo does not support Python 3.11 yet, make sure your Python is up to 3.10.
+
+Install the library:
+
+```
+pip install sidlingvo
+```
+
+Example usage:
+
+```Python
+import os
+from sidlingvo import wav_to_dvector
+from huggingface_hub import hf_hub_download
+
+repo_id = "tflite-hub/conformer-speaker-encoder"
+model_path = "models"
+hf_hub_download(repo_id=repo_id, filename="vad_long_model.tflite", local_dir=model_path)
+hf_hub_download(repo_id=repo_id, filename="vad_long_mean_stddev.csv", local_dir=model_path)
+hf_hub_download(repo_id=repo_id, filename="conformer_tisid_medium..tflite", local_dir=model_path)
+
+enroll_wav_files = ["your_first_wav_file.wav"]
+test_wav_file = "your_second_wav_file.wav"
+runner = wav_to_dvector.WavToDvectorRunner(
+    vad_model_file=os.path.join(model_path, "vad_long_model.tflite"),
+    vad_mean_stddev_file=os.path.join(model_path, "vad_long_mean_stddev.csv"),
+    tisid_model_file=os.path.join(model_path, "conformer_tisid_medium.tflite"))
+score = runner.compute_score(enroll_wav_files, test_wav_file)
+print("Speaker similarity score:", score)
+```