I followed the stages in "$root_path/examples/hi_xiaowen/s0/run.sh" , and try to reproduce network perfermance using config/mdtc.yaml, num_average=10, max_epoch=80, but after training and check results(score.txt, stats.0.txt, stats.1.txt), the score, fa and recall seems very strange:

1. the highest score comes out at first 1/2 frame, the keyword speech has not be spoken at that time.
2. most scores are lower than 0.5.
3. because we config two keywords, but most postive wav got valuable and high score at both two output points. this is strange, when i change to ds_tcn config, the score seems correct.

Hi,

I'm facing the following issues using wekws:

1. It is overfitting after 2-4 epochs only. (Even train on hundreds, or thousands of hours of data).
2. High False positive. When there are many keywords like 20, it's confusing between those, and more bias towards keywords than Freetext (-1) class.
3. Confuse between similar sounding words, and predict freetext as a keyword.

Can you please suggest any solution for it?

Thanks

Hi, I train a model on my custom dataset, with a single keyword. Using compute_accuracy.py, I'm getting, 97% accuracy on the test dataset.

But when I tried to plot the DET curve using, plot_det_curve.py, the output DET curve image is empty. (Before running this, I already compute score.txt, and stats.txt)

Thanks

Hi, Would like know how to prepare dataset for RIR & Musan Augmentation I go through the script, and understand that it needs data in .mdb format that should be inside lmdb folder. I have raw audio files, how to prepare data for it? Also, would like to know, is there any flag in the configuration file, which I can use as a flag to apply augmentation or not.

Thanks

Describe the bug

I have a server with 4GPU gtx1080 ubuntu 16.4

When I run train process using run.sh, if already another train task was already running, it will occur error:

Start training ... [W socket.cpp:401] [c10d] The server socket has failed to bind to [::]:29400 (errno: 98 - Address already in use). [W socket.cpp:401] [c10d] The server socket has failed to bind to 0.0.0.0:29400 (errno: 98 - Address already in use). [E socket.cpp:435] [c10d] The server socket has failed to listen on any local network address.

How to solve this case ?

Traceback (most recent call last): File "kws/bin/train.py", line 230, in main() File "kws/bin/train.py", line 141, in main model = init_model(configs['model']) File "/home/pengteng.spt/wekws-master/kws/model/kws_model.py", line 125, in init_model causal = configs['backbone']['causal'] KeyError: 'causal' Traceback (most recent call last): File "kws/bin/train.py", line 230, in main() File "kws/bin/train.py", line 141, in main model = init_model(configs['model']) File "/home/pengteng.spt/wekws-master/kws/model/kws_model.py", line 125, in init_model causal = configs['backbone']['causal'] KeyError: 'causal' ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 101738) of binary: /home/pengteng.spt/miniconda2/envs/wenet/bin/python

Hi, Is there any way to train a KWS model, in case we want to train a new wake word model, without having much data?

Or can I finetune an existing model on the new wake word, which is trained on a good amount of data? If yes, how?

Thanks

Hello I compared the result of WeKws fft and kaldi srfft, I found that the result is different, could you tell me which method of fft do we used ? the project is clean and light, I liked it!

Thanks

AttributeError: 'MDTC' object has no attribute 'padding' Now, model backbone not has attribute 'padding in wekws and all model input cache is default torch.zeros(0, 0, 0, dtype=torch.float) in train.py

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https://commonvoice.mozilla.org/en/datasets

Finding single word datasets for English is hard and the Single Word Dataset from Common Voice is a rarity being multi national. Also it has a very useful sample 'Hey' that can be concatenated with other keywords. I used Sox to arrange by pitch/trim and then used Hey from Common Voice and Marvin from the Google Dataset to concatenate 'Hey Marvin' to create a good phonetically unique keyword.

WeKws is a community-driven project and we love your feedback and proposals on where we should be heading. Feel free to volunteer yourself if you are interested in trying out some items(they do not have to be on the list).

The following items are in 2.0:

• [ ] Rubustness, improve the robustness by learning acoustic feature rather than other features of the keywords.
• [ ] Various on-device chips support.
• [ ] Unsupervised model or pretrained model exploration.

Hi, I train a new model after changing Fbins to 80. The model trained successfully, but when I tried to test the model on some test cases, getting the following error:

RuntimeError: The size of tensor a (80) must match the size of tensor b (40) at non-singleton dimension 2

I just change the hyperparameter in conf file, apart from this, do we need to make change anywhere else in the code? Is it something hard coded?

I wonder why there isn't any GRU-based model implemented here.

Reading your paper, especially Fig. 1-4, I have the impression that GRU-based model performed better than TCN-based counterpart. I understand that for production system there are more considerations/constraints (model size, FLOPS/energy, etc.) than just FAR/FRR. Just want to make sure I didn't miss anything.

Describe the bug pointer returned by LookupCustomMetadataMap must be released using allocator.Free(); (https://github.com/wenet-e2e/wekws/blob/main/runtime/core/kws/keyword_spotting.cc#L38-L41)

您好，你们的工程非常棒，集合了小型的优秀的唤醒词模型以及提出创新性的了max_pooling loss．从我们用自己的数据跑你们的模型来看，比较容易过拟合，具体表现： １，训练集loss过快收敛，训练集acc过快的到达95%以上，大概两个step的时间

2，验证集的数据稍微和训练集有些不一致，loss就比较大，验证集acc=0．如果从同类的数据集中划出一部分数据作为验证集，剩余的作为训练集，loss就比较正常，acc也能达到95%以上.

3，和验证集比较类似的测试集（包括纯干净的数据)，测试结果也不佳，激活很差，有的激活率为0

4，从我们的实验结果来看，我们最终的测试集得和训练集尽可能的像，哪怕有比较小的差距，测试结果都是一边倒，个位数的识别率．

5，不知道你们有没有这样的情况，或者说我们还有哪里的技术点没有get到？有没有一些解决方案？ 谢谢，期待你们的回复．