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VoxCeleb Disentangler

This repository contains the environment-disentangled representation learning framework for training speaker recognition models described in the paper 'Disentangled Representation Learning for Environment-agnostic Speaker Recognition'.

This repository is based on the original repository voxceleb_trainer(Link), so you can follow original process of the repository. For adeversarial-learning phase, we refer to voxceleb_unsupervised(Link), an unsupervised learning framework based on voxceleb_trainer.

Dependencies

pip install -r requirements.txt

Data preparation

The following script can be used to download and prepare the VoxCeleb dataset for training.

python ./dataprep.py --save_path data --download --user USERNAME --password PASSWORD 
python ./dataprep.py --save_path data --extract
python ./dataprep.py --save_path data --convert

In order to use data augmentation, also run:

python ./dataprep.py --save_path data --augment

In addition to the Python dependencies, wget and ffmpeg must be installed on the system.

Dataset setup

For this repository, you can follow the example setup below (Optional).

datasets/
└──voxceleb1/
└──voxceleb2/
└──manifests/
  -- train_list.txt
  -- veri_test2.txt

you can download voxceleb1-based verification set files in here

Training examples

Our environment-disentangler is implemented based on two popular models - ResNetSE34V2 and ECAPA_TDNN.

  • ResNetSE34V2 with Disentangler:
python ./trainSpeakerNet.py --config ./configs/ResNetSE34V2_SFAP_AE.yaml
  • ECAPA_TDNN with Disentangler:
python ./trainSpeakerNet.py --config ./configs/ECAPA_SFAP_AE.yaml

(Our ECAPA_TDNN model's code is based on here)

You can pass individual arguments that are defined in trainSpeakerNet.py by --{ARG_NAME} {VALUE}. Note that the configuration file overrides the arguments passed via command line.

Pretrained models

A pretrained model of ResNetSE34V2, can be downloaded from here.

The following script should return: (EER, %) Vox1-O : 0.8457, VoxSRC22 : 3.0123, VoxSRC23 : 5.4046.

You will be given an option to save the scores.

python ./trainSpeakerNet.py --eval --config ./configs/ResNetSE34V2_SFAP_AE.yaml --initial_model pretrained/baseline_resnetse34V2_de.model --save_path exps/test --eval_frames 400

A pretrained model of ECAPA_TDNN, can be downloaded from here.

The following script should return: (EER, %) Vox1-O : 0.8188, VoxSRC22 : 3.0814, VoxSRC23 : 5.7594.

python ./trainSpeakerNet.py --eval --config ./configs/ECAPA_SFAP_AE.yaml --initial_model pretrained/baseline_ecapatdnn_de.model --save_path exps/test --eval_frames 400

You can access the pretrained models in pretrained/ directory.

Implemented loss functions

-- Original(`voxceleb_trainer`) --
Softmax (softmax)
AM-Softmax (amsoftmax)
AAM-Softmax (aamsoftmax)
GE2E (ge2e)
Prototypical (proto)
Triplet (triplet)
Angular Prototypical (angleproto)
-- Ours --
Mean Average Pearson's correlation (MAPC)

Implemented models and encoders(pooling layers)

ResNetSE34L   (SAP, ASP)
ResNetSE34V2  (SAP, ASP) + Disentangler (Implemented)
VGGVox40      (SAP, TAP, MAX)
ECAPA_TDNN    (ECA) + Disentangler      (Implemented)

Data augmentation

--augment True enables online data augmentation, described in voxceleb_trainer.

Adding new models and loss functions

You can add new models and loss functions to models and loss directories respectively. See the existing definitions for examples.

Accelerating training

  • Use --mixedprec flag to enable mixed precision training. This is recommended for Tesla V100, GeForce RTX 20 series or later models.

  • Use --distributed flag to enable distributed training.

    • GPU indices should be set before training using the command export CUDA_VISIBLE_DEVICES=0,1,2,3.

    • If you are running more than one distributed training session, you need to change the --port argument.