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
216 Nov 27, 2022
1 Feb 08, 2022
37 Jan 04, 2023
27 Jan 05, 2023
80 Jan 02, 2023
46 Dec 28, 2022
877 Jan 05, 2023
75 Dec 05, 2022
151 Dec 14, 2022
102 Dec 19, 2022
3 Jan 04, 2022
15.3k Jan 03, 2023
3.1k Dec 29, 2022
69 Jan 03, 2023
28 Oct 30, 2022
223 Jan 02, 2023
128 Aug 24, 2022
70 Dec 29, 2022
47 Dec 20, 2022
73 Dec 11, 2022