metadata
language: en
thumbnail: null
tags:
- embeddings
- Speaker
- Verification
- Identification
- pytorch
- xvectors
- TDNN
license: apache-2.0
datasets:
- voxceleb
metrics:
- EER
- min_dct
Speaker Verification with xvector embeddings on Voxceleb
This repository provides all the necessary tools to extract speaker embeddings with a pretrained TDNN model using SpeechBrain. The system is trained on Voxceleb 1+ Voxceleb2 training data.
For a better experience, we encourage you to learn more about SpeechBrain. The given ASR model performance on Voxceleb1-test set are:
Release | EER(%) | minDCF |
---|---|---|
05-03-21 | -.- | -.- |
Pipeline description
This system is composed of a TDNN model coupled with statistical pooling. The system is trained with Categorical Cross-Entropy Loss.
Install SpeechBrain
First of all, please install SpeechBrain with the following command:
pip install speechbrain
Please notice that we encourage you to read our tutorials and learn more about SpeechBrain.
Compute your speaker embeddings
import torchaudio
from speechbrain.pretrained import SpeakerRecognition
verification = SpeakerRecognition.from_hparams(source="speechbrain/spkrec-xvect-voxceleb")
signal, fs =torchaudio.load('samples/audio_samples/example1.wav')
embeddings = verification.encode(signal)
Referencing xvectors
author = {David Snyder and
Daniel Garcia{-}Romero and
Alan McCree and
Gregory Sell and
Daniel Povey and
Sanjeev Khudanpur},
title = {Spoken Language Recognition using X-vectors},
booktitle = {Odyssey 2018},
pages = {105--111},
year = {2018},
}
Referencing SpeechBrain
@misc{SB2021,
author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
title = {SpeechBrain},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/speechbrain/speechbrain}},
}