Unofficial reimplementation of ECAPA-TDNN for speaker recognition (EER=0.86 for Vox1_O when train only in Vox2)

During evaluation, the current implementation calculates the similarity scores one by one using a for loop, that could be slow when the size of "lines" gets larger. Is there an elegant way of vectorizing it?

我看到推理代码中： with torch.no_grad(): embedding_1 = self.speaker_encoder.forward(data_1, aug = False) embedding_1 = F.normalize(embedding_1, p=2, dim=1) embedding_2 = self.speaker_encoder.forward(data_2, aug = False) embedding_2 = F.normalize(embedding_2, p=2, dim=1) embeddings[file] = [embedding_1, embedding_2] 其中，data1是语音全部的数据，data2是分割后又stack的数据。对于不同长度的语音，data1和data2是没有规定长度的？都可以输入到self.speaker_encoder.forward计算embedding？？？

When I followed the Data preparation part in the link and ran the this code python3 dataprep.py --save_path data --download --user USERNAME --password PASSWORD , I met with the following error.

--2021-11-26 14:04:56-- http://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_dev_wav_partaa Resolving www.robots.ox.ac.uk (www.robots.ox.ac.uk)... 129.67.94.2 Connecting to www.robots.ox.ac.uk (www.robots.ox.ac.uk)|129.67.94.2|:80... connected. HTTP request sent, awaiting response... 301 Moved Permanently Location: https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_dev_wav_partaa [following] --2021-11-26 14:04:58-- https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_dev_wav_partaa Connecting to www.robots.ox.ac.uk (www.robots.ox.ac.uk)|129.67.94.2|:443... connected. HTTP request sent, awaiting response... 404 Not Found 2021-11-26 14:04:59 ERROR 404: Not Found.

Traceback (most recent call last): File "Downloads/voxceleb_trainer-master/dataprep.py", line 176, in download(args,fileparts) File "Downloads/voxceleb_trainer-master/dataprep.py", line 58, in download raise ValueError('Download failed %s. If download fails repeatedly, use alternate URL on the VoxCeleb website.'%url) ValueError: Download failed http://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_dev_wav_partaa. If download fails repeatedly, use alternate URL on the VoxCeleb website.

How can I solve this problem? Thanks!

I want to add some chinese audios to the training data.

Can I use your pretrain.model and continue to train using my data,

Or Do I have to download all the VoxCeleb1data plusing my data, and train it from the beginning?

Thank you for your reply.

Hi

I found out there are some differences between your code configrations and original configurations in ECAPA.

The most important one is in your code, you just random choose 1 of the 6 noise to add . And in ECAPA, they use all 6 noise methods which means they have a largger dataset.

I trained the 512 channels model, which only can achieve 1.16 EER (1.01 in ECAPA) , but your result in 1024 channel is even better than ECAPA. So is there any secret you holding about training skill? or you changed the configrations in your upload code ( I just copy your project and change the channel num, and everything else stays the same). OR because the tiny differences in your code leads it is better on a large model.

And thank you for your excellent work! Any help will be appriciated!

Best

Hello, thank you so much for contributing this project. I am training this model recently. I also use one 3090 and the same setting as you. But i need spend about 20 hours for each epoch. Do you know what's the reason? Thank you so much for your answering in advance.

Hi! I'm new at neural networks and i'm having trouble discovering how to evaluate your implementation. By now I'm using an audio dataset which is different from your --eval_path and --eval_list, so I'm running this command:

python trainECAPAModel.py --eval --initial_model exps/pretrain.model --eval_list /eval_list_directory --eval_path /eval_path_directory

Is this the correct way to evaluate your implementation? Should I use any different argument? The point is I don't think I understand what exps/pretrain.model is, so I don't know how to use it.

Looking forward to your response! Thanks

Hi！Ruijie，在B站关注你好久了！最近在做SASVC的比赛，发现用了你这个仓库做ASV 的 Baseline code. 你在Readme中写了这个ECAPA-TDNN结果是as-norm后的结果，可我没有在你的代码里找到任何关于backend norm的部分。请问是typo吗？还是您没有向本仓库中添加那一段代码?

Hi, author. I am training the ECAPA-TDNN model end-to-end by using: python trainECAPAModel.py However, I found that while training, the training time per epoch is very long. After checking, I found that the GPU memory is occupied, but its utilization is 0. I manually set model.cuda(), but it does not work. I'm wondering what part of the program should I change to make the model load successfully.

I have read your great work in SELF-SUPERVISED SPEAKER RECOGNITION WITH LOSS-GATED LEARNING（ICASSP 2022).

I attempt to follow Stage II in your paper, which shows great gains in your experiments.

If these parts of codes are available, it will benefit a lot.

Thanks a lot.

I notice that in dataloader, the size of training set is the same size as original audio size after augmentation.

So, adding augmentation is not to increase the amount of training data, only to increase the diversity of it ?

Hi!

I'm having trouble understanding ECAPA-TDNN architecture.

To be specific, I don't understand what does the elements in ECAPA-TDNN do (PreEmphasis,MelSpectrogram,FBankAug,conv1d,relu, batchNorm1d, bottleneck, Attention...) in the context of speaker verification?

What about classifier AAAsoftmax, optimizer Adam and scheduler stepLR?

Thanks for your attention and time!