hyperion-ml 0.3.1

Toolkit for speaker recognition

HYPERION

Hyperion is a Speaker Recognition Toolkit based on PyTorch and numpy. It provides:

• x-Vector architectures: ResNet, Res2Net, Spine2Net, ECAPA-TDNN, EfficientNet, Transformers and others.
• Embedding preprocessing tools: PCA, LDA, NAP, Centering/Whitening, Length Normalization, CORAL
• Several flavours of PLDA back-ends: Full-rank PLDA, Simplified PLDA, PLDA
• Calibration and Fusion tools
• Recipes for popular datasets: VoxCeleb, NIST-SRE, VOiCES

The full API is described in the documentation page https://hyperion-ml.readthedocs.io

Installation Instructions

Prerequisites

We use anaconda or miniconda, though you should be able to make it work in other python distributions
To start, you should create a new enviroment and install PyTorch>=1.9, (older versions are not supported any longer) e.g.:

conda create --name ${your_env} python=3.8
conda activate ${your_env}
conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=10.2 -c pytorch

In next Hyperion versions, we will upgrade to Pytorch>=1.9 and drop compatibility with older PyTorch versions.

Installing Hyperion

• First, clone the repo:

git clone https://github.com/hyperion-ml/hyperion.git

• You can choose to install hyperion in the environment

cd hyperion
pip install -e .

• Or add the hyperion toolkit to the PYTHONPATH envirnoment variable This option will allow you to share the same environment if you are working with several hyperion branches at the same time, while installing it requires to have an enviroment per branch. For this, you need to install the requirements

cd hyperion
pip install -r requirements.txt

Then add these lines to your ~/.bashrc or to each script that uses hyperion

HYP_ROOT= #substitute this by your hyperion location
export PYTHONPATH=${HYP_ROOT}:$PYTHONPATH
export PATH=${HYP_ROOT}/bin:$PATH

Recipes

There are recipes for several tasks in the ./egs directory.

Prerequistes to run the recipes

These recipes require some extra tools (e.g. sph2pipe), which need to be installed first:

./install_egs_requirements.sh 

Most recipes do not require Kaldi, only the older ones using Kaldi x-vectors, so we do not install it by default. If you are going to need it install it yourself. Then make a link in ./tools to your kaldi installation

cd tools
ln -s ${your_kaldi_path} kaldi
cd -

Finally configure the python and environment name that you intend to use to run the recipes. For that run

./prepare_egs_paths.sh

This script will ask for the path to your anaconda installation and enviromentment name. It will also detect if hyperion is already installed in the environment, otherwise it will add hyperion to your python path. This will create the file

tools/path.sh

which sets all the enviroment variables required to run the recipes. This has been tested only on JHU computer grids, so you may need to modify this file manually to adapt it to your grid.

Recipes structure

The structure of the recipes is very similar to Kaldi, so if should be familiar for most people. Data preparation is also similar to Kaldi. Each dataset has a directory with files like

wav.scp
utt2spk
spk2utt
...

Running the recipes

Contrary to other toolkits, the recipes do not contain a single run.sh script to run all the steps of the recipe. Since some recipes have many steps and most times you don't want to run all of then from the beginning, we have split the recipe in several run scripts. The scripts have a number indicating the order in the sequence. For example,

run_001_prepare_data.sh
run_002_compute_vad.sh
run_010_prepare_audios_to_train_xvector.sh
run_011_train_xvector.sh
run_030_extract_xvectors.sh
run_040_evaluate_plda_backend.sh

will evaluate the recipe with the default configuration. The default configuration is in the file default_config.sh

We also include extra configurations, which may change the hyperparamters of the recipe. For example:

• Acoustic features
• Type of the x-vector neural netwok
• Hyper-parameters of the models
• etc.

Extra configs are in the global_conf directory of the recipe. Then you can run the recipe with the alternate config as:

run_001_prepare_data.sh --config-file global_conf/alternative_conf.sh
run_002_compute_vad.sh --config-file global_conf/alternative_conf.sh
run_010_prepare_audios_to_train_xvector.sh --config-file global_conf/alternative_conf.sh
run_011_train_xvector.sh --config-file global_conf/alternative_conf.sh
run_030_extract_xvectors.sh --config-file global_conf/alternative_conf.sh
run_040_evaluate_plda_backend.sh --config-file global_conf/alternative_conf.sh

Note that many alternative configus share hyperparameters with the default configs. That means that you may not need to rerun all the steps to evaluate a new configuration. It mast cases you just need to re-run the steps from the neural network training to the end.

Citing

Each recipe README.md file contains the bibtex to the works that should be cited if you use that recipe in your research

Directory structure:

• The directory structure of the repo looks like this:

hyperion
hyperion/egs
hyperion/hyperion
hyperion/resources
hyperion/tests
hyperion/tools

• Directories:
  • hyperion: python classes with utilities for speaker and language recognition
  • egs: recipes for sevaral tasks: VoxCeleb, SRE18/19/20, voices, ...
  • tools: contains external repos and tools like kaldi, python, cudnn, etc.
  • tests: unit tests for the classes in hyperion
  • resources: data files required by unittest or recipes