声纹识别

requirements

python==3.8
tensorboardX==2.6
tensorboard==2.11.2
numpy==1.23.5
librosa==0.9.2
scikit-learn==1.2.2
matplotlib==3.6.3
torch==1.13.1
torchaudio==0.13.1            

File Structure

.
├── audio.py
├── data
│   ├── dev
│   ├── test
│   └── train
├── eval.py
├── fine_tuning.py
├── img
├── loader.py
├── logs
│   └── acc
│       ├── test_acc
│       │   
│       └── train_acc         
├── loss.py
├── models
│   ├── tdnn.py
│   ├── tdnn_module.py
│   └── tdnn_pretrain.py
├── param.model
├── test.py
├── tools.py
└── train.py

Usage

若要对模型进行微调，先下载本人训练好的模型param.model，并将该模型放在 fine_tuning.py 的同一目录下，然后运行 fine_tuning.py。

若想从零开始训练出一个模型，则运行 train.py 进行训练。

若要对模型进行评估，则运行 test.py。

Dataset

这是我所用的数据集：https://pan.baidu.com/s/1_KrjPB27AHPrBa_1AeMQSQ?pwd=0mag 提取码：0mag

当然，也可以用自己的数据集。只需在 train.py 的相同目录下创建 data 文件夹，并在 data 下创建子文件夹 train，然后将自己的训练数据放到 train 中。目前，这代码仅支持 .wav 格式的训练音频。

Reference

Original ECAPA-TDNN paper

@inproceedings{desplanques2020ecapa,
  title={{ECAPA-TDNN: Emphasized Channel Attention, propagation and aggregation in TDNN based speaker verification}},
  author={Desplanques, Brecht and Thienpondt, Jenthe and Demuynck, Kris},
  booktitle={Interspeech 2020},
  pages={3830--3834},
  year={2020}
}

Acknowledge

We study many useful projects in our codeing process, which includes:

Ecapa-tdnn: Emphasized channel attention, propagation and aggregation in tdnn based speaker verification.

clovaai/voxceleb_trainer.

lawlict/ECAPA-TDNN.

TaoRuijie/ECAPA-TDNN

Thanks for these authors to open source their code!