hyperion-ml 0.3.2
Toolkit for speaker recognition
Requires Python: >=3.7
Keywords: speaker recognition,adversarial attacks,NIST SRE,x-vectors

# HYPERION

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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](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
```

### Installing Hyperion

- First, clone the repo:
```bash
git clone https://github.com/hyperion-ml/hyperion.git
```

- You can choose to install hyperion in the environment
```bash
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
```bash
cd hyperion
pip install -r requirements.txt
```
Then add these lines to your `~/.bashrc` or to each script that uses hyperion
```bash
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:
```bash
./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
```bash
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
```bash
./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,
```bash
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:
```bash
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:
```bash
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