metadata
license: apache-2.0
tags:
- audio
- speech
- speaker
- speaker-recognition
- speaker-embedding
- speaker-verification
- speaker-identification
- speaker-encoder
- tflite
- voice
library_name: sidlingvo
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:
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)