Fine-tuning wav2vec2 for speaker recognition
This is the code used to run the experiments in https://arxiv.org/abs/2109.15053. Detailed logs of each training run can be found here:
- x-vector: https://www.comet.ml/nik-vaessen-ru-nl/xvector-sv-ce?shareable=eVKNVMJUEV0WZd5FxXKeOFl2B
- ecapa-tdnn: https://www.comet.ml/nik-vaessen-ru-nl/ecapa-tdnn?shareable=Cis9Pp3RMwgirkJQp8wvHWlfs
- w2v2-ce: https://www.comet.ml/nik-vaessen-ru-nl/wav2vec2-sv-ce?shareable=8B3IJfHK4T1UF1jG0wasPHJb5
- w2v2-aam: https://www.comet.ml/nik-vaessen-ru-nl/wav2vec2-sv-aam?shareable=wGswdkZvqET0Iy7lmC2ORpMhO
- w2v2-bce: https://www.comet.ml/nik-vaessen-ru-nl/wav2vec2-sv-bce?shareable=SPGxZGBNAmFaxVsmsDBisFfrU
Installing 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:
$ poetry shell # enter project-specific virtual environment
From now on, every command which should be run under the virtual environment (which looks like (wav2vec-speaker-identification-
) which is shortened to (xxx) $ .
Then install all required python packages:
(xxx) $ pip install -U pip
(xxx) $ poetry update # install dependencies
Because PyTorch is currently serving the packages on PiPY incorrectly, we need to use pip to install the specific PyTorch versions we need.
(xxx) $ pip install -r requirements/requirements_cuda101.txt # if CUDA 10.1
(xxx) $ pip install -r requirements/requirements_cuda110.txt # if CUDA 11.0
Make sure to modify/create a requirements file for your operating system and CUDA version.
Finally, install the local package in the virtual environment by running
(xxx) $ poetry install
Setting up the environment
Copy the example environment variables:
$ cp .env.example .env
You can then fill in .env accordingly.
Downloading and using voxceleb1 and 2
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 should be placed in $DATA_FOLDER/voxceleb_archives.
vox1_dev_wav.zipvox1_test_wav.zipvox2_dev_aac.zipvox2_test_aac.zip
You should also download the meta files of voxceleb. You can use preparation_scripts/download_pretrained_models.sh to download them to the expected location $DATA_FOLDER/voxceleb_meta.
Converting voxceleb2 data from .m4a to .wav
This requires ffmpeg to be installed on the machine. Check with ffmpeg -version. Assuming the voxceleb2 data is placed at $DATA_FOLDER/voxceleb_archives/vox2_dev_aac.zip and $DATA_FOLDER/voxceleb_archives/vox2_test_aac.zip, run the following commands, starting from the root project directory.
source .env
PDIR=$PWD # folder where this README is located
D=$DATA_FOLDER # location of data - should be set in .env file
WORKERS=$(nproc --all) # number of CPUs available
# extract voxceleb 2 data
cd $D
mkdir -p convert_tmp/train convert_tmp/test
unzip voxceleb_archives/vox2_dev_aac.zip -d convert_tmp/train
unzip voxceleb_archives/vox2_test_aac.zip -d convert_tmp/test
# run the conversion script
cd $PDIR
poetry run python preparation_scripts/voxceleb2_convert_to_wav.py $D/convert_tmp --num_workers $WORKERS
# rezip the converted data
cd $D/convert_tmp/train
zip $D/voxceleb_archives/vox2_dev_wav.zip wav -r
cd $D/convert_tmp/test
zip $D/voxceleb_archives/vox2_test_wav.zip wav -r
# delete the unzipped .m4a files
cd $D
rm -r convert_tmp
Note that this process can take a few hours on a fast machine and day(s) on a single (slow) cpu. Make sure to save the vox2_dev_wav.zip and vox2_test_wav.zip files somewhere secure, so you don't have redo this process :).
Downloading pre-trained models.
You can run ./preparation_scripts/download_pretrained_models.sh to download the pre-trained models of wav2vec2 to the required $DATA_DIRECTORY/pretrained_models directory.
Running the experiments
Below we show all the commands for training the specified network. They should reproduce the results in the paper. Note that we used a SLURM GPU cluster and each command therefore includes hydra/launcher=slurm. If you want to reproduce these locally these lines need to be removed.
wav2vec2-sv-ce
auto_lr_find
python run.py +experiment=speaker_wav2vec2_ce \
tune_model=True data/module=voxceleb1 \
trainer.auto_lr_find=auto_lr_find tune_iterations=5000
5k iters, visually around 1e-4
grid search
grid = 1e-5, 5e-5, 9e-5, 1e-4, 2e-4, 5e-4, 1e-3
python run.py -m +experiment=speaker_wav2vec2_ce \
data.dataloader.train_batch_size=66 \
optim.algo.lr=1e-5,5e-5,9e-5,1e-4,2e-4,5e-4,1e-3 \
hydra/launcher=slurm hydra.launcher.exclude=cn104 hydra.launcher.array_parallelism=7
best performance n=3
python run.py -m +experiment=speaker_wav2vec2_ce \
data.dataloader.train_batch_size=66 optim.algo.lr=9e-5 \
seed=26160,79927,90537 \
hydra/launcher=slurm hydra.launcher.exclude=cn104 hydra.launcher.array_parallelism=3
best pooling n=3
python run.py -m +experiment=speaker_wav2vec2_ce \
data.dataloader.train_batch_size=66 optim.algo.lr=9e-5 \
seed=168621,597558,440108 \
network.stat_pooling_type=mean,mean+std,attentive,quantile,first,first+cls,last,middle,random,max \
hydra/launcher=slurm hydra.launcher.exclude=cn104 hydra.launcher.array_parallelism=4
wav2vec2-sv-aam
aam with m=0.2 and s=30
auto_lr_find
python run.py +experiment=speaker_wav2vec2_ce \
tune_model=True data/module=voxceleb1 \
trainer.auto_lr_find=auto_lr_find tune_iterations=5000 \
optim/loss=aam_softmax
grid search
python run.py -m +experiment=speaker_wav2vec2_aam \
data.dataloader.train_batch_size=66 \
optim.algo.lr=1e-5,5e-5,9e-5,1e-4,2e-4,5e-4,1e-3 \
hydra/launcher=slurm hydra.launcher.exclude=cn104 hydra.launcher.array_parallelism=7
same grid
best performance n=3
python run.py -m +experiment=speaker_wav2vec2_aam \
data.dataloader.train_batch_size=66 optim.algo.lr=0.00005 \
seed=29587,14352,70814 \
hydra/launcher=slurm hydra.launcher.exclude=cn104 hydra.launcher.array_parallelism=3
best pooling n=3
python run.py -m +experiment=speaker_wav2vec2_aam \
data.dataloader.train_batch_size=66 optim.algo.lr=0.00005 \
seed=392401,39265,62634 \
network.stat_pooling_type=mean,mean+std,attentive,quantile,first,first+cls,last,middle,random,max \
hydra/launcher=slurm hydra.launcher.exclude=cn104 hydra.launcher.array_parallelism=4
wav2vec2-sv-bce
auto_lr_find
python run.py +experiment=speaker_wav2vec2_pairs \
tune_model=True data/module=voxceleb1_pairs \
trainer.auto_lr_find=auto_lr_find tune_iterations=5000
grid search
5e-6,7e6,9e-6,1e-5,2e-5,3e-5,4e-5,1e-4
python run.py -m +experiment=speaker_wav2vec2_pairs \
optim.algo.lr=5e-6,7e-6,9e-6,1e-5,2e-5,3e-5,4e-5,1e-4 \
data.dataloader.train_batch_size=32 \
hydra/launcher=slurm hydra.launcher.exclude=cn104 hydra.launcher.array_parallelism=8
best performance n=4
python run.py -m +experiment=speaker_wav2vec2_pairs \
optim.algo.lr=0.00003 data.dataloader.train_batch_size=32 \
seed=154233,979426,971817,931201 \
hydra/launcher=slurm hydra.launcher.exclude=cn104 hydra.launcher.array_parallelism=4
xvector
auto_lr_find
python run.py +experiment=speaker_xvector \
tune_model=True data/module=voxceleb1 \
trainer.auto_lr_find=auto_lr_find tune_iterations=5000
grid search
1e-5,6e-5,1e-4,2e-4,3e-4,4e-4,8e-4,1e-3
python run.py -m +experiment=speaker_xvector \
optim.algo.lr=1e-5,6e-5,1e-4,2e-4,3e-4,4e-4,8e-4,1e-3 \
data.dataloader.train_batch_size=66 \
hydra/launcher=slurm hydra.launcher.exclude=cn105 hydra.launcher.array_parallelism=8
best performance n=3
python run.py -m +experiment=speaker_xvector \
optim.algo.lr=0.0004 trainer.max_steps=100_000 \
data.dataloader.train_batch_size=66 \
seed=82713,479728,979292 \
hydra/launcher=slurm hydra.launcher.exclude=cn105 hydra.launcher.array_parallelism=6 \
ecapa-tdnn
auto_lr_find
python run.py +experiment=speaker_ecapa_tdnn \
tune_model=True data/module=voxceleb1 \
trainer.auto_lr_find=auto_lr_find tune_iterations=5000
grid search
5e-6,1e-5,5e-4,1e-4,5e-3,7e-4,9e-4,1e-3
python run.py -m +experiment=speaker_ecapa_tdnn \
optim.algo.lr=5e-6,1e-5,5e-4,1e-4,5e-3,7e-4,9e-4,1e-3 \
data.dataloader.train_batch_size=66 \
hydra/launcher=slurm hydra.launcher.exclude=cn105 hydra.launcher.array_parallelism=8
best performance n=3
python run.py -m +experiment=speaker_ecapa_tdnn \
optim.algo.lr=0.001 trainer.max_steps=100_000 \
data.dataloader.train_batch_size=66 \
seed=494671,196126,492116 \
hydra/launcher=slurm hydra.launcher.exclude=cn105 hydra.launcher.array_parallelism=6
Ablation
baseline
python run.py -m +experiment=speaker_wav2vec2_aam \
data.dataloader.train_batch_size=66 optim.algo.lr=0.00005 \
seed=392401,39265,62634 network.stat_pooling_type=first+cls \
hydra/launcher=slurm hydra.launcher.array_parallelism=3
unfrozen feature extractor
python run.py -m +experiment=speaker_wav2vec2_aam \
data.dataloader.train_batch_size=66 optim.algo.lr=0.00005 \
seed=914305,386390,865459 network.stat_pooling_type=first+cls \
network.completely_freeze_feature_extractor=False tag=no_freeze \
hydra/launcher=slurm hydra.launcher.array_parallelism=3 hydra.launcher.exclude=cn104
no pre-trained weights
python run.py -m +experiment=speaker_wav2vec2_aam \
data.dataloader.train_batch_size=66 optim.algo.lr=0.00005 \
seed=517646,414321,137524 network.stat_pooling_type=first+cls \
network.completely_freeze_feature_extractor=False network.reset_weights=True tag=no_pretrain \
hydra/launcher=slurm hydra.launcher.array_parallelism=3 hydra.launcher.exclude=cn104
no layerdrop
python run.py -m +experiment=speaker_wav2vec2_aam \
data.dataloader.train_batch_size=66 optim.algo.lr=0.00005 \
seed=15249,728106,821754 network.stat_pooling_type=first+cls \
network.layerdrop=0.0 tag=no_layer \
hydra/launcher=slurm hydra.launcher.array_parallelism=3
no dropout
python run.py -m +experiment=speaker_wav2vec2_aam \
data.dataloader.train_batch_size=66 optim.algo.lr=0.00005 \
seed=627687,883727,154405 network.stat_pooling_type=first+cls \
network.layerdrop=0.0 network.attention_dropout=0 \
network.feat_proj_dropout=0 network.hidden_dropout=0 tag=no_drop \
hydra/launcher=slurm hydra.launcher.array_parallelism=3
no time masking
python run.py -m +experiment=speaker_wav2vec2_aam \
data.dataloader.train_batch_size=66 optim.algo.lr=0.00005 \
seed=602400,553540,419322 network.stat_pooling_type=first+cls \
network.layerdrop=0.0 network.attention_dropout=0 network.feat_proj_dropout=0 \
network.hidden_dropout=0 network.mask_time_prob=0 tag=no_mask \
hydra/launcher=slurm hydra.launcher.array_parallelism=3
batch size 32
python run.py -m +experiment=speaker_wav2vec2_aam \
data.dataloader.train_batch_size=32 trainer.max_steps=200_000 \
optim.algo.lr=0.00005 network.stat_pooling_type=first+cls \
tag=bs_32 seed=308966,753370,519822 \
hydra/launcher=slurm hydra.launcher.array_parallelism=3
batch size 128
python run.py -m +experiment=speaker_wav2vec2_aam \
data.dataloader.train_batch_size=128 trainer.max_steps=50_000 \
optim.algo.lr=0.00005 seed=54375,585956,637400 \
network.stat_pooling_type=first+cls tag=bs_128 \
hydra/launcher=slurm hydra.launcher.array_parallelism=3 hydra.launcher.exclude=cn104
constant lr=3e-6
python run.py -m +experiment=speaker_wav2vec2_aam \
data.dataloader.train_batch_size=66 optim.algo.lr=3e-6 \
seed=549686,190215,637679 network.stat_pooling_type=first+cls \
optim/schedule=constant tag=lr_low \
hydra/launcher=slurm hydra.launcher.array_parallelism=3
constant lr=5e-5
python run.py -m +experiment=speaker_wav2vec2_aam \
data.dataloader.train_batch_size=66 optim.algo.lr=0.00005 \
seed=419703,980724,124995 network.stat_pooling_type=first+cls \
optim/schedule=constant tag=lr_same \
hydra/launcher=slurm hydra.launcher.array_parallelism=3
tri_stage
python run.py -m +experiment=speaker_wav2vec2_aam \
data.dataloader.train_batch_size=66 optim.algo.lr=0.00005 \
seed=856797,952324,89841 network.stat_pooling_type=first+cls \
optim/schedule=tri_stage tag=lr_3stage \
optim.schedule.scheduler.lr_lambda.initial_lr=1e-7 optim.schedule.scheduler.lr_lambda.final_lr=1e-7 \
hydra/launcher=slurm hydra.launcher.array_parallelism=3
exp decay
python run.py -m +experiment=speaker_wav2vec2_aam \
data.dataloader.train_batch_size=66 optim.algo.lr=0.00005 seed=962764,682423,707761 \
network.stat_pooling_type=first+cls optim/schedule=exp_decay tag=lr_exp_decay \
optim.schedule.scheduler.lr_lambda.final_lr=1e-7 \
hydra/launcher=slurm hydra.launcher.array_parallelism=3
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