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README.md
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_0.tar.gz
tar -xvf sv0_ecapa_tdnn_voxceleb12_ckpt_0_2_0.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_1_2/model/ conf/ecapa_tdnn.yaml
The performance of the released models are shown in this