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 model performance on Voxceleb1-test set (Cleaned) is:

Release	EER(%)
05-03-21	3.2

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 EncoderClassifier
classifier = EncoderClassifier.from_hparams(source="speechbrain/spkrec-xvect-voxceleb", savedir="pretrained_models/spkrec-xvect-voxceleb")
signal, fs =torchaudio.load('samples/audio_samples/example1.wav')
embeddings = classifier.encode_batch(signal)

Inference on GPU

To perform inference on the GPU, add run_opts={"device":"cuda"} when calling the from_hparams method.

Training

The model was trained with SpeechBrain (aa018540). To train it from scratch follows these steps:

Clone SpeechBrain:

git clone https://github.com/speechbrain/speechbrain/

Install it:

cd speechbrain
pip install -r requirements.txt
pip install -e .

Run Training:

cd  recipes/VoxCeleb/SpeakerRec/
python train_speaker_embeddings.py hparams/train_x_vectors.yaml --data_folder=your_data_folder

You can find our training results (models, logs, etc) here.

Limitations

The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.

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}},
  }

About SpeechBrain

SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains. Website: https://speechbrain.github.io/ GitHub: https://github.com/speechbrain/speechbrain