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# ECAPA-TDNN with VoxCeleb
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This example contains code used to train a ECAPA-TDNN model with [VoxCeleb dataset](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/index.html#about)
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## Overview
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All the scripts you need are in the `run.sh`. There are several stages in the `run.sh`, and each stage has its function.
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| Stage | Function |
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|:---- |:----------------------------------------------------------- |
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| 0 | Process data. It includes: <br> (1) Download the VoxCeleb1 dataset <br> (2) Download the VoxCeleb2 dataset <br> (3) Convert the VoxCeleb2 m4a to wav format <br> (4) Get the manifest files of the train, development and test dataset <br> (5) Download the RIR Noise dataset and Get the noise manifest files for augmentation |
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| 1 | Train the model |
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| 2 | Test the speaker verification with VoxCeleb trial|
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You can choose to run a range of stages by setting the `stage` and `stop_stage `.
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For example, if you want to execute the code in stage 1 and stage 2, you can run this script:
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```bash
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bash run.sh --stage 1 --stop_stage 2
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```
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Or you can set `stage` equal to `stop-stage` to only run one stage.
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For example, if you only want to run `stage 0`, you can use the script below:
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```bash
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bash run.sh --stage 1 --stop_stage 1
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```
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The document below will describe the scripts in the `run.sh` in detail.
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## The environment variables
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The path.sh contains the environment variable.
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```bash
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source path.sh
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```
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This script needs to be run first.
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And another script is also needed:
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```bash
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source ${MAIN_ROOT}/utils/parse_options.sh
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```
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It will support the way of using `--variable value` in the shell scripts.
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## The local variables
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Some local variables are set in the `run.sh`.
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`gpus` denotes the GPU number you want to use. If you set `gpus=`, it means you only use CPU.
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`stage` denotes the number of the stage you want to start from in the experiments.
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`stop stage` denotes the number of the stage you want to end at in the experiments.
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`conf_path` denotes the config path of the model.
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`exp_dir` denotes the experiment directory, e.g. "exp/ecapa-tdnn-vox12-big/"
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You can set the local variables when you use the `run.sh`
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For example, you can set the `gpus` when you use the command line.:
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```bash
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bash run.sh --gpus 0,1
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```
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## Stage 0: Data processing
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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:
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```bash
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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# prepare data
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bash ./local/data.sh || exit -1
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fi
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```
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Stage 0 is for processing the data. If you only want to process the data. You can run
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```bash
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bash run.sh --stage 0 --stop_stage 0
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```
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You can also just run these scripts in your command line.
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```bash
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source path.sh
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bash ./local/data.sh
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```
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After processing the data, the `data` directory will look like this:
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```bash
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data/
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├── rir_noise
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│ ├── csv
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│ │ ├── noise.csv
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│ │ └── rir.csv
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│ ├── manifest.pointsource_noises
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│ ├── manifest.real_rirs_isotropic_noises
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│ └── manifest.simulated_rirs
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├── vox
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│ ├── csv
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│ │ ├── dev.csv
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│ │ ├── enroll.csv
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│ │ ├── test.csv
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│ │ └── train.csv
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│ └── meta
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│ └── label2id.txt
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└── vox1
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├── list_test_all2.txt
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├── list_test_all.txt
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├── list_test_hard2.txt
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├── list_test_hard.txt
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├── manifest.dev
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├── manifest.test
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├── veri_test2.txt
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├── veri_test.txt
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├── voxceleb1.dev.meta
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└── voxceleb1.test.meta
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```
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## Stage 1: Model training
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If you want to train the model. you can use stage 1 in the `run.sh`. The code is shown below.
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```bash
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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# train model, all `ckpt` under `exp` dir
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CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${ckpt}
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fi
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```
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If you want to train the model, you can use the script below to execute stage 0 and stage 1:
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```bash
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bash run.sh --stage 0 --stop_stage 1
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```
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or you can run these scripts in the command line (only use CPU).
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```bash
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source path.sh
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bash ./local/data.sh ./data/ conf/ecapa_tdnn.yaml
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CUDA_VISIBLE_DEVICES= ./local/train.sh ./data/ exp/ecapa-tdnn-vox12-big/ conf/ecapa_tdnn.yaml
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```
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## Stage 2: Model Testing
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The test stage is to evaluate the model performance. The code of the test stage is shown below:
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```bash
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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# test ckpt avg_n
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CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${dir} ${exp_dir} ${conf_path} || exit -1
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fi
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```
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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:
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```bash
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bash run.sh --stage 0 --stop_stage 2
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```
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or you can run these scripts in the command line (only use CPU).
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```bash
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source path.sh
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bash ./local/data.sh ./data/ conf/ecapa_tdnn.yaml
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CUDA_VISIBLE_DEVICES= ./local/train.sh ./data/ exp/ecapa-tdnn-vox12-big/ conf/ecapa_tdnn.yaml
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CUDA_VISIBLE_DEVICES= ./local/test.sh ./data/ exp/ecapa-tdnn-vox12-big/ conf/ecapa_tdnn.yaml
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```
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## 3: Pretrained Model
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You can get the pretrained models from [this](../../../docs/source/released_model.md).
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using the `tar` scripts to unpack the model and then you can use the script to test the model.
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For example:
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```
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wget https://paddlespeech.bj.bcebos.com/vector/voxceleb/sv0_ecapa_tdnn_voxceleb12_ckpt_0_2_1.tar.gz
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tar -xvf sv0_ecapa_tdnn_voxceleb12_ckpt_0_2_1.tar.gz
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source path.sh
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# If you have processed the data and get the manifest file， you can skip the following 2 steps
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CUDA_VISIBLE_DEVICES= bash ./local/test.sh ./data sv0_ecapa_tdnn_voxceleb12_ckpt_0_2_1/model/ conf/ecapa_tdnn.yaml
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```
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The performance of the released models are shown in [this](./RESULTS.md)
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