hello，the question about Model convergence

Open quyuquan2019 opened this issue 4 years ago • 10 comments

I encountered a problem during training and I was puzzled for a day. According to your network structure, I use 230 speakers, input 's shape = [20000,400,24], I use RTX2080 training on the server, the parameter setting is the learning rate 0.0001, batch_size = 50, I trained for about 8 hours to find the model Loss did not fall, and kept floating up and down. I am wondering why the model does not converge?

May 14 '20 10:05 quyuquan2019

hey, thank you for paying interest in this repo, I made it mostly for educational purpose, Im working on better version, that will use faster TDNN, meanwhile you could switch to one else by yourself your use pyannote package

Jul 06 '20 19:07 Dannynis

many thanks for the repo, i found it is very useful to learn the x-vector concept.

but I am curious, that even i used the default dataset and xvector - gpu.ipynb, i cannot train the model to get

from start: loss: 0.2348794427783556 to loss: 0.008

my loss keep at this value. so weird. loss: 1.3787403551848396

Thanks in advance if you could provide any insight!

Jul 22 '20 14:07 ustraymond

one possibly reasons is, when i ran the notebook, i uncommented the preparing dataset code. and i changed 'train_cleaned' to 'train'.

`X = [] y = [] max_total_context = 400 _min,_max = float('inf'),-float('inf')

for wav_name in os.listdir('train'): if wav_name.endswith('wav'): print (wav_name) rate, wav = wavfile.read(r'train/'+wav_name) for chunked_wav in tqdm(chunks(wav,int(len(wav)/4))): X_sample = mfcc(chunked_wav,samplerate= rate,numcep=24 ,nfilt=26,nfft=1024) _min = min(np.amin(X_sample),_min) _max = max(np.amax(X_sample),_max)

        for chunked_X_sample in list(chunks(X_sample,max_total_context)):
            if len(chunked_X_sample) == max_total_context:
                X.append(chunked_X_sample)
                y.append(to_categorical(clases.index(wav_name.split('.wav')[0]),len(clases)))
            else:
                print ('discarded box')

X = (X - _min) / (_max-_min) #with open('X_data.pkl','wb') as f:

pickle.dump((X,y),f)`

Jul 22 '20 14:07 ustraymond

Try changing the optimizer to Adam. I trained for five days and the loss dropped from 5.3 to 0.2, but when I was going to extract x-vector for LDA operation, I found something wrong

Jul 23 '20 01:07 quyuquan2019

many thanks for reply!

it did work, just need more patience. may i know a bit more detail on what you meant "extract x-vector for LDA operation, I found something wrong"? what was wrong? your "xvector_ext_and_lda_scatter.ipynb" seem gave a reasonable plot? many thanks!

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Jul 23 '20 13:07 ustraymond

How many people are in your training set? When my loss value is 0.2, I thought my model has been trained, so I plan to randomly extract 5 x-vectors and use LDA to reduce them to a two-dimensional plane to observe it. Distribution, but I found that I can't do it. Also, I observed that there are only 5 people in the data set on this code, and each person has 30 minutes. Are you sure that such training will not overfit?

Jul 24 '20 01:07 quyuquan2019

It is suggested that you can try to replace the original data set, use your own data set to extract x-vector, and verify whether speaker recognition can be performed. I'm currently in the exploratory stage

Jul 24 '20 01:07 quyuquan2019

How many people are in your training set? When my loss value is 0.2, I thought my model has been trained, so I plan to randomly extract 5 x-vectors and use LDA to reduce them to a two-dimensional plane to observe it. Distribution, but I found that I can't do it. Also, I observed that there are only 5 people in the data set on this code, and each person has 30 minutes. Are you sure that such training will not overfit?

thx for insight, I only trained with the default 4 people datasets. yes, i guess there is overfitting. I may try more speakers, but I also discovered this, d-vector, it seems good but complex code to me. https://github.com/resemble-ai/Resemblyzer

Aug 01 '20 12:08 ustraymond

but this repo is good, and I learned what is x-vector, and how to implement one from this repo, cheer!

Aug 01 '20 12:08 ustraymond

Indeed, I think this library is great. I will not use Kaldi. I think all pytorch versions are easier to read and understand for me, but this library is a bit rough. I think the final x-vector performance may not be as good as Kaldi.

Aug 02 '20 01:08 quyuquan2019

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hello，the question about Model convergence

pickle.dump((X,y),f)`

xvector_pytorch
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