sv0

ECAPA-TDNN with VoxCeleb

This example contains code used to train a ECAPA-TDNN model with VoxCeleb dataset

Overview

All the scripts you need are in the run.sh. There are several stages in the run.sh, and each stage has its function.

Stage	Function
0	Process data. It includes: (1) Download the VoxCeleb1 dataset (2) Download the VoxCeleb2 dataset (3) Convert the VoxCeleb2 m4a to wav format (4) Get the manifest files of the train, development and test dataset (5) Download the RIR Noise dataset and Get the noise manifest files for augmentation
1	Train the model
2	Test the speaker verification with VoxCeleb trial

You can choose to run a range of stages by setting the stage and stop_stage .

For example, if you want to execute the code in stage 1 and stage 2, you can run this script:

bash run.sh --stage 1 --stop_stage 2

Or you can set stage equal to stop-stage to only run one stage. For example, if you only want to run stage 0, you can use the script below:

bash run.sh --stage 1 --stop_stage 1

The document below will describe the scripts in the run.sh in detail.

The environment variables

The path.sh contains the environment variable.

source path.sh

This script needs to be run first.

And another script is also needed:

source ${MAIN_ROOT}/utils/parse_options.sh

It will support the way of using --variable value in the shell scripts.

The local variables

Some local variables are set in the run.sh. gpus denotes the GPU number you want to use. If you set gpus=, it means you only use CPU. stage denotes the number of the stage you want to start from in the experiments. stop stage denotes the number of the stage you want to end at in the experiments. conf_path denotes the config path of the model. exp_dir denotes the experiment directory, e.g. "exp/ecapa-tdnn-vox12-big/"

You can set the local variables when you use the run.sh

For example, you can set the gpus when you use the command line.:

bash run.sh --gpus 0,1 

Stage 0: Data processing

To use this example, you need to process data firstly and you can use stage 0 in the run.sh to do this. The code is shown below:

 if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
     # prepare data
     bash ./local/data.sh || exit -1
 fi

Stage 0 is for processing the data. If you only want to process the data. You can run

bash run.sh --stage 0 --stop_stage 0

You can also just run these scripts in your command line.

source path.sh
bash ./local/data.sh

After processing the data, the data directory will look like this:

data/
├── rir_noise
│   ├── csv
│   │   ├── noise.csv
│   │   └── rir.csv
│   ├── manifest.pointsource_noises
│   ├── manifest.real_rirs_isotropic_noises
│   └── manifest.simulated_rirs
├── vox
│   ├── csv
│   │   ├── dev.csv
│   │   ├── enroll.csv
│   │   ├── test.csv
│   │   └── train.csv
│   └── meta
│       └── label2id.txt
└── vox1
    ├── list_test_all2.txt
    ├── list_test_all.txt
    ├── list_test_hard2.txt
    ├── list_test_hard.txt
    ├── manifest.dev
    ├── manifest.test
    ├── veri_test2.txt
    ├── veri_test.txt
    ├── voxceleb1.dev.meta
    └── voxceleb1.test.meta

Stage 1: Model training

If you want to train the model. you can use stage 1 in the run.sh. The code is shown below.

if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
     # train model, all `ckpt` under `exp` dir
     CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path}  ${ckpt}
 fi

If you want to train the model, you can use the script below to execute stage 0 and stage 1:

bash run.sh --stage 0 --stop_stage 1

or you can run these scripts in the command line (only use CPU).

source path.sh
bash ./local/data.sh ./data/ conf/ecapa_tdnn.yaml
CUDA_VISIBLE_DEVICES= ./local/train.sh ./data/ exp/ecapa-tdnn-vox12-big/ conf/ecapa_tdnn.yaml

Stage 2: Model Testing

The test stage is to evaluate the model performance. The code of the test stage is shown below:

 if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
     # test ckpt avg_n
     CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${dir} ${exp_dir} ${conf_path} || exit -1
 fi

If you want to train a model and test it, you can use the script below to execute stage 0, stage 1 and stage 2:

bash run.sh --stage 0 --stop_stage 2

or you can run these scripts in the command line (only use CPU).

source path.sh
bash ./local/data.sh ./data/ conf/ecapa_tdnn.yaml
CUDA_VISIBLE_DEVICES= ./local/train.sh ./data/ exp/ecapa-tdnn-vox12-big/ conf/ecapa_tdnn.yaml
CUDA_VISIBLE_DEVICES= ./local/test.sh ./data/ exp/ecapa-tdnn-vox12-big/ conf/ecapa_tdnn.yaml

3: Pretrained Model

You can get the pretrained models from this.

using the tar scripts to unpack the model and then you can use the script to test the model.

For example:

wget https://paddlespeech.bj.bcebos.com/vector/voxceleb/sv0_ecapa_tdnn_voxceleb12_ckpt_0_2_1.tar.gz
tar -xvf sv0_ecapa_tdnn_voxceleb12_ckpt_0_2_1.tar.gz
source path.sh
# If you have processed the data and get the manifest file， you can skip the following 2 steps

CUDA_VISIBLE_DEVICES= bash ./local/test.sh ./data sv0_ecapa_tdnn_voxceleb12_ckpt_0_2_1/model/ conf/ecapa_tdnn.yaml

The performance of the released models are shown in this