Low-resource speaker recognition systems

This repository is used for researching the effectiveness of wav2vec2 for speaker recognition in low-resource data conditions.

See the (pre-print) paper at: https://arxiv.org/abs/2203.14688

You can download the data here: https://surfdrive.surf.nl/files/index.php/s/497wbcet5SvMTUe

It includes:

tiny-few-speakers: as shards_tiny_few.tar.gz
tiny-few-sessions: as shards_tiny_many_low.tar.gz
tiny-many-sessions: as shards_tiny_many_high.tar.gz
vox2: needs to be downloaded manually, see instructions below
meta folder: as meta.tar.gz

See the "Data preparation" section below for instructions for reproducing the data.

Setting up

Dependencies

If poetry is not installed, see https://python-poetry.org/docs/. We also expect at least python 3.8 on the system. If this is not the case, look into https://github.com/pyenv/pyenv for an easy tool to install a specific python version on your system.

The python dependencies can be installed (in a project-specific virtual environment) by:

$ ./scripts/setup_dependencies.sh

To access the virtual environment, activate it with poetry shell or prepend poetry run to the command you want to run in the virtual environment.

Environment

Copy the example environment variables:

$ cp .env.example .env

Then fill in .env accordingly.

Data preparation

We we will work towards the following folder structure under $DATA_FOLDER specified in .env. Note: all the data (zip files, extracted files, data shards) will take approximate 1 TB of storage space.

${DATA_FOLDER}
├── archives
├── meta
│   ├── iden_split.txt
│   ├── list_test_all2.txt
│   ├── list_test_all.txt
│   ├── list_test_hard2.txt
│   ├── list_test_hard.txt
│   ├── veri_test2.txt
│   ├── veri_test.txt
│   ├── vox1_meta.csv
│   └── vox2_meta.csv
├── shards_eval
│   ├── extended
│   ├── hard
│   └── original
├── shards_vox2_full
├── shards_vox2_tiny_deep
├── shards_vox2_tiny_shallow
├── voxceleb1
│   ├── test
│   └── train
└── voxceleb2
    └── train

Downloading pre-processed data

If you don't want to manually go through the preprocessing pipeline, you can download our version of the data here:

We provide the shards_* and meta folder. Make sure to place the data at $DATA_FOLDER location, as specified in your .env file.

Downloading voxceleb1 and voxceleb2

I've experienced that the download links for voxceleb1/2 can be unstable. I recommend manually downloading the dataset from the google drive link displayed on https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html.

You should end up 4 zip files, which are to be placed in $DATA_FOLDER/archives.

vox1_dev_wav.zip
vox1_test_wav.zip
vox2_dev_aac.zip
vox2_test_aac.zip

Converting voxceleb2 to wav format

We need to convert the voxceleb2 to wav data before we can continue. We will convert the vox2_dev_aac.zip and vox2_test_aac.zip zip files to respectively vox2_dev_wav.zip and vox2_test_wav.zip with the script below:

source .env

PDIR=$PWD # folder where this README is located
D=$DATA_FOLDER # location of data - should be set in .env file
T=$TEMP_FOLDER # location of temporary disk location where data will be unzipped
WORKERS=$(nproc --all) # number of CPUs available 

# extract voxceleb 2 data
cd $D
mkdir -p $T/train $T/test

unzip archives/vox2_dev_aac.zip -d $T/train
unzip archives/vox2_test_aac.zip -d $T/test

# run the conversion script
cd $PDIR
poetry run python scripts/data/voxceleb2_convert_to_wav.py $T --num_workers $WORKERS

# rezip the converted data
cd $T/train
zip $D/archives/vox2_dev_wav.zip wav -r

cd $T/test
zip $D/archives/vox2_test_wav.zip wav -r

# delete the unzipped .m4a files
rm -r $T/train $T/test

creating datasets

You can now run scripts/data/pipeline.sh to:

Create the validation set
Create the three tiny dataset splits (and the original vox2 split)
Create the evaluation sets and trial lists

Experiments

The following section provides the commands to reproduce the experiments. You can remove the hydra/launcher=... argument if you intend to run the experiment on a machine without SLURM. If you do intend to use SLURM, you should edit config/hydra/launcher/slurm.yaml accordingly (namely, the partition parameter probably doesn't make sense).

If you do not have access to a GPU with 24GB of VRAM, you can reduce the batch size by e.g. a factor 2, (See config/speaker/dataloader/speaker.yaml) and add, e.g. trainer.accumulate_grad_batches=2 to the command.

X-vector

Initial naive hyperparameter search grid:

python run.py -m \
+experiment=xvector_full,xvector_tiny_few,xvector_tiny_many_low,xvector_tiny_many_high \
optim.algo.lr=1e-7,1e-6,1e-5,1e-4,1e-3,1e-2 \
tag=naive_grid \
enroll_training_data=false post_process_scores=false \
hydra/launcher=slurm hydra.launcher.array_parallelism=3

Follow-up detailed hyperparameter search grid:

tiny-few

python run.py -m \
+experiment=xvector_tiny_few \
optim.algo.lr=1.78e-3,3.16e-3,5.62e-3,1.78e-2,3.16e-2,5.62e-2 \
tag=detailed_grid \
enroll_training_data=false post_process_scores=false \
hydra/launcher=slurm hydra.launcher.array_parallelism=3

tiny-many-high

python run.py -m \
+experiment=xvector_tiny_many_high \
optim.algo.lr=1.78e-3,3.16e-3,5.62e-3,1.78e-2,3.16e-2,5.62e-2 \
tag=detailed_grid \
enroll_training_data=false post_process_scores=false \
hydra/launcher=slurm hydra.launcher.array_parallelism=3

tiny-many-low

python run.py -m \
+experiment=xvector_tiny_many_low \
optim.algo.lr=1.78e-5,3.16e-5,5.62e-5,1.78e-4,3.16e-4,5.62e-4 \
tag=detailed_grid \
enroll_training_data=false post_process_scores=false \
hydra/launcher=slurm hydra.launcher.array_parallelism=3

full voxceleb2

python run.py -m \
+experiment=xvector_full \
optim.algo.lr=1.78e-4,3.16e-4,5.62e-4,1.78e-3,3.16e-3,5.62e-3 \
tag=detailed_grid \
enroll_training_data=false post_process_scores=false \
hydra/launcher=slurm hydra.launcher.array_parallelism=3

Number of steps with best LR

tiny-few

python run.py -m \
+experiment=xvector_tiny_few \
optim.algo.lr=1.78E-02 \
tag=step_exp \
enroll_training_data=false post_process_scores=false \
trainer.max_steps=25_000,50_000,100_000,400_000 \
seed=7348,12935,6622 \
data.use_additional_test_sets=true \
hydra/launcher=slurm_11vram hydra.launcher.array_parallelism=1

tiny-many-high

python run.py -m \
+experiment=xvector_tiny_many_high \
optim.algo.lr=1.78E-03 \
tag=step_exp \
enroll_training_data=false post_process_scores=false \
trainer.max_steps=25_000,50_000,100_000,400_000 \
seed=32188,60801,73677 \
data.use_additional_test_sets=true \
hydra/launcher=slurm_11vram hydra.launcher.array_parallelism=1

tiny-many-low

python run.py -m \
+experiment=xvector_tiny_many_low \
optim.algo.lr=1.00E-04 \
tag=step_exp \
enroll_training_data=false post_process_scores=false \
trainer.max_steps=25_000,50_000,100_000,400_000 \
seed=30921,90124,70956 \
data.use_additional_test_sets=true \
hydra/launcher=slurm_11vram hydra.launcher.array_parallelism=1

full voxceleb2

python run.py -m \
+experiment=xvector_full \
optim.algo.lr=3.16E-03 \
tag=step_exp \
enroll_training_data=false post_process_scores=false \
trainer.max_steps=25_000,50_000,100_000,400_000 \
seed=51412,38396,11558 \
data.use_additional_test_sets=true \
hydra/launcher=slurm_11vram hydra.launcher.array_parallelism=1

ECAPA-TDNN

Initial naive hyperparameter search grid:

python run.py -m \
+experiment=ecapa_full,ecapa_tiny_few,ecapa_tiny_many_low,ecapa_tiny_many_high \
optim.algo.lr=1e-7,1e-6,1e-5,1e-4,1e-3,1e-2 \
tag=naive_grid \
enroll_training_data=false post_process_scores=false \
data.use_additional_test_sets=true \
hydra/launcher=slurm hydra.launcher.array_parallelism=3

Follow-up detailed hyperparameter search grid:

tiny-few

python run.py -m \
+experiment=ecapa_tiny_few \
optim.algo.lr=1.78e-3,3.16e-3,5.62e-3,1.78e-2,3.16e-2,5.62e-2 \
tag=detailed_grid \
enroll_training_data=false post_process_scores=false \
hydra/launcher=slurm hydra.launcher.array_parallelism=3

tiny-many-high

python run.py -m \
+experiment=ecapa_tiny_many_high \
optim.algo.lr=1.78e-3,3.16e-3,5.62e-3,1.78e-2,3.16e-2,5.62e-2 \
tag=detailed_grid \
enroll_training_data=false post_process_scores=false \
hydra/launcher=slurm hydra.launcher.array_parallelism=3

tiny-many-low

python run.py -m \
+experiment=ecapa_tiny_many_low \
optim.algo.lr=1.78e-3,3.16e-3,5.62e-3,1.78e-2,3.16e-2,5.62e-2 \
tag=detailed_grid \
enroll_training_data=false post_process_scores=false \
hydra/launcher=slurm hydra.launcher.array_parallelism=3

full voxceleb2

python run.py -m \
+experiment=ecapa_full \
optim.algo.lr=1.78e-3,3.16e-3,5.62e-3,1.78e-2,3.16e-2,5.62e-2 \
tag=detailed_grid \
enroll_training_data=false post_process_scores=false \
hydra/launcher=slurm hydra.launcher.array_parallelism=3

Number of steps with best LR

tiny-few

python run.py -m \
+experiment=ecapa_tiny_few \
optim.algo.lr=1.78E-02 \
tag=step_exp \
enroll_training_data=true post_process_scores=true \
trainer.max_steps=25_000,50_000,100_000,400_000 \
seed=87510,83832,49529 \
data.use_additional_test_sets=true \
hydra/launcher=slurm_11vram hydra.launcher.array_parallelism=1

tiny-many-high

python run.py -m \
+experiment=ecapa_tiny_many_high \
optim.algo.lr=5.62E-03 \
tag=step_exp \
enroll_training_data=true post_process_scores=true \
trainer.max_steps=25_000,50_000,100_000,400_000 \
seed=89540,80774,47997 \
data.use_additional_test_sets=true \
hydra/launcher=slurm_11vram hydra.launcher.array_parallelism=1

tiny-many-low

python run.py -m \
+experiment=ecapa_tiny_many_low \
optim.algo.lr=5.62E-03 \
tag=step_exp \
enroll_training_data=true post_process_scores=true \
trainer.max_steps=25_000,50_000,100_000,400_000 \
seed=54190,54160,86823 \
data.use_additional_test_sets=true \
hydra/launcher=slurm_11vram hydra.launcher.array_parallelism=1

full voxceleb2

python run.py -m \
+experiment=ecapa_full \
optim.algo.lr=5.62E-03 \
tag=step_exp \
enroll_training_data=true post_process_scores=true \
trainer.max_steps=25_000,50_000,100_000,400_000 \
seed=69317,43142,8754 \
data.use_additional_test_sets=true \
hydra/launcher=slurm_11vram hydra.launcher.array_parallelism=1

wav2vec2

Initial hyperparameter search:

python run.py -m \
+experiment=wav2vec2_full,wav2vec2_tiny_few,wav2vec2_tiny_many_high,wav2vec2_tiny_many_low \
optim.algo.lr=1e-7,1e-6,1e-5,1e-4,1e-3,1e-2 \
tag=naive_grid \
enroll_training_data=false post_process_scores=false \
hydra/launcher=slurm_24vram hydra.launcher.array_parallelism=6

Follow-up detailed hyperparameter search grid:

tiny-few

python run.py -m \
+experiment=wav2vec2_tiny_few \
optim.algo.lr=1.78e-6,3.16e-6,5.62e-6,1.78e-5,3.16e-5,5.62e-5 \
tag=detailed_grid \
enroll_training_data=false post_process_scores=false \
hydra/launcher=slurm_24vram hydra.launcher.array_parallelism=3

tiny-many-high

python run.py -m \
+experiment=wav2vec2_tiny_many_high \
optim.algo.lr=1.78e-5,3.16e-5,5.62e-5,1.78e-4,3.16e-4,5.62e-4 \
tag=detailed_grid \
enroll_training_data=false post_process_scores=false \
hydra/launcher=slurm_24vram hydra.launcher.array_parallelism=3

tiny-many-low

python run.py -m \
+experiment=wav2vec2_tiny_many_low \
optim.algo.lr=1.78e-5,3.16e-5,5.62e-5,1.78e-4,3.16e-4,5.62e-4 \
tag=detailed_grid \
enroll_training_data=false post_process_scores=false \
hydra/launcher=slurm_24vram hydra.launcher.array_parallelism=3

full voxceleb2

python run.py -m \
+experiment=wav2vec2_full \
optim.algo.lr=1.78e-5,3.16e-5,5.62e-5,1.78e-4,3.16e-4,5.62e-4 \
tag=detailed_grid \
enroll_training_data=false post_process_scores=false \
hydra/launcher=slurm_24vram hydra.launcher.array_parallelism=3

Number of steps with best LR

tiny-few

python run.py -m \
+experiment=wav2vec2_tiny_few \
optim.algo.lr=5.62E-06 \
tag=step_exp \
enroll_training_data=true post_process_scores=true \
trainer.max_steps=25_000,50_000,100_000,400_000 \
seed=62204,20427,456 \
data.use_additional_test_sets=true \
hydra/launcher=slurm_24vram

tiny-many-high

python run.py -m \
+experiment=wav2vec2_tiny_many_high \
optim.algo.lr=1.782E-04 \
tag=step_exp \
enroll_training_data=true post_process_scores=true \
trainer.max_steps=25_000,50_000,100_000,400_000 \
seed=73232,95015,69018 \
data.use_additional_test_sets=true \
hydra/launcher=slurm_24vram

tiny-many-low

python run.py -m \
+experiment=wav2vec2_tiny_many_low \
optim.algo.lr=1.782E-04 \
tag=step_exp \
enroll_training_data=true post_process_scores=true \
trainer.max_steps=25_000,50_000,100_000,400_000 \
seed=45979,43354,68179 \
data.use_additional_test_sets=true \
hydra/launcher=slurm_24vram

full voxceleb2

python run.py -m \
+experiment=wav2vec2_full \
optim.algo.lr=1.782E-04 \
tag=step_exp \
enroll_training_data=true post_process_scores=true \
trainer.max_steps=25_000,50_000,100_000,400_000 \
seed=69317,43142,8754 \
data.use_additional_test_sets=true \
hydra/launcher=slurm_24vram

Ablation

baseline

python3 run.py -m \
+experiment=ablation_wav2vec2_tiny_few,ablation_wav2vec2_tiny_many_high \
seed=787,23916 \
tag=baseline \
hydra/launcher=slurm_24vram

LR schedule

constant LR

python3 run.py -m \
+experiment=ablation_wav2vec2_tiny_few,ablation_wav2vec2_tiny_many_high \
optim/schedule=constant \
seed=11469,787,23916 \
tag=lr_constant \
hydra/launcher=slurm_24vram

exponentially decaying LR

python3 run.py -m \
+experiment=ablation_wav2vec2_tiny_few,ablation_wav2vec2_tiny_many_high \
optim/schedule=exp_decay \
seed=11469,787,23916 \
tag=lr_exp_decay \
hydra/launcher=slurm_24vram

Cyclic learning rate (but only 1 cycle), baseline has 4 cycles

python3 run.py -m \
+experiment=ablation_wav2vec2_tiny_few,ablation_wav2vec2_tiny_many_high \
++optim.schedule.scheduler.step_size_up=25_000 \
++optim.schedule.scheduler.step_size_down=25_000 \
seed=11469,787,23916 \
tag=lr_1_cycle \
hydra/launcher=slurm_24vram

Weights

Random initialization instead of pre-trained weights, nothing frozen

python3 run.py -m \
+experiment=ablation_wav2vec2_tiny_few,ablation_wav2vec2_tiny_many_high \
++network.reset_weights=true \
++network.completely_freeze_feature_extractor=false \
++network.wav2vec_initially_frozen=false \
seed=11469,787,23916 \
tag=weights_random_init \
hydra/launcher=slurm_24vram

Pretrained initialization, nothing frozen

python3 run.py -m \
+experiment=ablation_wav2vec2_tiny_few,ablation_wav2vec2_tiny_many_high \
++network.completely_freeze_feature_extractor=false \
++network.wav2vec_initially_frozen=false \
seed=11469,787,23916 \
tag=weights_no_freeze \
hydra/launcher=slurm_24vram

freeze 1st cycle

python3 run.py -m \
+experiment=ablation_wav2vec2_tiny_few,ablation_wav2vec2_tiny_many_high \
++network.completely_freeze_feature_extractor=false \
++network.wav2vec_initially_frozen=true \
++network.num_frozen_steps=12500 \
seed=11469,787,23916 \
tag=weights_freeze_cycle \
hydra/launcher=slurm_24vram

freeze CNN

python3 run.py -m \
+experiment=ablation_wav2vec2_tiny_few,ablation_wav2vec2_tiny_many_high \
++network.completely_freeze_feature_extractor=true \
++network.wav2vec_initially_frozen=false \
seed=11469,787,23916 \
tag=weights_freeze_cycle_cnn \
hydra/launcher=slurm_24vram

regularisation

No regularisation

python3 run.py -m \
+experiment=ablation_wav2vec2_tiny_few,ablation_wav2vec2_tiny_many_high \
network/regularisation=wav2vec2_none \
seed=11469,787,23916 \
tag=reg_none \
hydra/launcher=slurm_24vram

Only dropout

python3 run.py -m \
+experiment=ablation_wav2vec2_tiny_few,ablation_wav2vec2_tiny_many_high \
network/regularisation=wav2vec2_only_dropout \
seed=11469,787,23916 \
tag=reg_dropout \
hydra/launcher=slurm_24vram

Only masking

python3 run.py -m \
+experiment=ablation_wav2vec2_tiny_few,ablation_wav2vec2_tiny_many_high \
network/regularisation=wav2vec2_only_masking \
seed=11469,787,23916 \
tag=reg_mask \
hydra/launcher=slurm_24vram

Only layerdrop

python3 run.py -m \
+experiment=ablation_wav2vec2_tiny_few,ablation_wav2vec2_tiny_many_high \
network/regularisation=wav2vec2_only_layerdrop \
seed=11469,787,23916 \
tag=reg_layerdrop \
hydra/launcher=slurm_24vram

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Low-resource speaker recognition systems

Setting up

Dependencies

Environment

Data preparation

Downloading pre-processed data

Downloading voxceleb1 and voxceleb2

Converting voxceleb2 to wav format

creating datasets

Experiments

X-vector

Initial naive hyperparameter search grid:

Follow-up detailed hyperparameter search grid:

tiny-few

tiny-many-high

tiny-many-low

full voxceleb2

Number of steps with best LR

tiny-few

tiny-many-high

tiny-many-low

full voxceleb2

ECAPA-TDNN

Initial naive hyperparameter search grid:

Follow-up detailed hyperparameter search grid:

tiny-few

tiny-many-high

tiny-many-low

full voxceleb2

Number of steps with best LR

tiny-few

tiny-many-high

tiny-many-low

full voxceleb2

wav2vec2

Follow-up detailed hyperparameter search grid:

tiny-few

tiny-many-high

tiny-many-low

full voxceleb2

Number of steps with best LR

tiny-few

tiny-many-high

tiny-many-low

full voxceleb2

Ablation

baseline

LR schedule

Weights

regularisation