Efficient Speech Processing Tookit for Automatic Speaker Recognition
| HuggingFace |
The authors' PyTorch implementation and pretrained models of EfficientTDNN.
- 17 June 2022: EfficientTDNN are published in IEEE/ACM Transactions on Audio, Speech, and Language Processing.
- EfficientTDNN: Efficient Architecture Search for Speaker Recognition [arXiv] [IEEE/ACM TASLP]
Model | Training Dataset | Link |
---|---|---|
EfficientTDNN | VoxCeleb2 | HuggingFace: mechanicalsea/efficient-tdnn |
- PyTorch version >= 1.7.1
- Python version >= 3.7.9
- To install sugar:
git clone https://github.com/mechanicalsea/sugar.git
cd sugar
pip install --editable .
# [Option] Requrements Packages: numpy, scipy, pandas, matplotlib, pyyaml, thop, geatpy, torch, torchvision, torchaudio, scikit-learn
We provide pre-trained models for extracting speaker embeddings via huggingface.
- EfficientTDNN
An example of training the TDNN supernet is given as follows.
task='' # training stage, e.g., supernet, supernet_kernel, supernet_kernel_depth, ...
cycle_step=34120 # a half cycle of learning rate scheduler, e.g., GPU 1: 68248, GPU 2: 34120, GPU 4: 17056
second=48000 # largest stage: 32000, other stages: 48000
initial_weights='' # required to these stages except for the largest stage
logdir=''
python scripts/train_vox2_veri.py --distributed --augment \
--task supernet_width1 --epochs 64 --cycle-step ${cycle_step} --second ${second} \
--initial-weights ${initial_weights} \
--logdir ${logdir}
import torch
from sugar.models import WrappedModel
wav_input_16khz = torch.randn(1,10000).cuda()
repo_id = "mechanicalsea/efficient-tdnn"
supernet_filename = "depth/depth.torchparams"
subnet_filename = "depth/depth.ecapa-tdnn.3.512.512.512.512.5.3.3.3.1536.bn.tar"
subnet, info = WrappedModel.from_pretrained(repo_id=repo_id, supernet_filename=supernet_filename, subnet_filename=subnet_filename)
subnet = subnet.cuda()
subnet = subnet.eval()
embedding = subnet(wav_input_16khz)
Please, cite EfficientTDNN if you use it for your research or business.
@article{wr-efficienttdnn-2022,
author={Wang, Rui and Wei, Zhihua and Duan, Haoran and Ji, Shouling and Long, Yang and Hong, Zhen},
journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
title={EfficientTDNN: Efficient Architecture Search for Speaker Recognition},
year={2022},
volume={30},
number={},
pages={2267-2279},
doi={10.1109/TASLP.2022.3182856}}
For help or issues using EfficientTDNN models, please submit a GitHub issue.
For other communications related to EfficientTDNN, please contact Rui Wang ([email protected]
).