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PyTorch implementation of "Generalized End-to-End Loss for Speaker Verification" by Wan, Li et al.

License: BSD 3-Clause "New" or "Revised" License

Python 100.00%

pytorch speaker-identification speaker-verification

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pytorch_speaker_verification's Issues

align_embeddings()

What's the role of the function " align embeddings() "?
Thank you
@HarryVolek

how to print predict result?

when test the trianed model .how to print the predict result. I am not mean EER/.

Workflow of the project

Can you describe the workflow of the project.
for example.
Step 1. run the preprocess script
Step 2. run the dvector_create script
Step 3. and maybe you can take it from here

I can't load the trained model

@HarryVolek I have get the model of TIMIT database ,and get thi information
"Done, trained model saved at ./speech_id_checkpoint/final_epoch_950_batch_id_141.model"
But when I do test , I cant load the trained model.The log is
Traceback (most recent call last):
File "train_speech_embedder.py", line 168, in
test('speech_id_checkpoint/ckpt_epoch_840_batch_id_141.pth')
File "train_speech_embedder.py", line 109, in test
embedder_net.load_state_dict(torch.load(model_path))
File "/media/diskc/wq/.local/lib/python3.5/site-packages/torch/nn/modules/module.py", line 721, in load_state_dict
self.class.name, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for SpeechEmbedder:
Missing key(s) in state_dict: "LSTM_stack.weight_ih_l1", "LSTM_stack.bias_hh_l1", "LSTM_stack.bias_hh_l0", "LSTM_stack.bias_ih_l0", "LSTM_stack.weight_hh_l2", "LSTM_stack.weight_hh_l1", "LSTM_stack.bias_ih_l1", "LSTM_stack.weight_ih_l0", "LSTM_stack.weight_ih_l2", "LSTM_stack.weight_hh_l0", "LSTM_stack.bias_hh_l2", "LSTM_stack.bias_ih_l2", "projection.weight", "projection.bias".
Unexpected key(s) in state_dict: "module.LSTM_stack.weight_ih_l0", "module.LSTM_stack.weight_hh_l0", "module.LSTM_stack.bias_ih_l0", "module.LSTM_stack.bias_hh_l0", "module.LSTM_stack.weight_ih_l1", "module.LSTM_stack.weight_hh_l1", "module.LSTM_stack.bias_ih_l1", "module.LSTM_stack.bias_hh_l1", "module.LSTM_stack.weight_ih_l2", "module.LSTM_stack.weight_hh_l2", "module.LSTM_stack.bias_ih_l2", "module.LSTM_stack.bias_hh_l2", "module.projection.weight", "module.projection.bias".

poor performance

I am training the network on VCTK corpus (framerate =48kHZ
109 speakers with an average of 300 utterances per speaker)
I got a very high EER ( 0.45) and I couldn't understand why the performance is poor (because of frame rate or there is not enough data or there is a problem in the model )
Any thoughts (for data augmentation I don't think adding noise because the model removes the noise in the data preprocessing )
Your help is much appreciated.
Thank you.

Sliding window implementation for training

I haven't found sliding window implementation for training as described in the Google paper (fig.3). Only first and last 180 frames were taken from each segment. Why don't you implement sliding window? Could you explain the reason?

change float to int sir

EER

Sir can you tell me how much you got EER for TIMIT database
you didnt mention in readme

torch.norm will give a scalar, Is this a bug?

PyTorch_Speaker_Verification/speech_embedder_net.py

Line 32 in 29232c8

x = x/torch.norm(x)

torch.norm will give a scalar, and In paper eq(4), I think x should be normalized by each embedding.
F.normalize(x, dim=-1, eps=1e-6), Am I right?

about the sampling rate

how much does the sampling rate affect the inference? Can I train the network at 16khz sampling rate while inference at other sampling rate such as 32khz or 48khz?

train_speech_embedder.py:batch_id

UnboundLocalError: local variable 'batch_id' referenced before assignment

Different input size between training d-vector modle and creating dvector for uis-rnn

when training d-vector model ,the input size is batchsizex180x40,but in creating dvecor feature for uis-rnn ,the input size of the d-vector model is batchsizex24x40.Does it matter?

How to use dvector_create.py

Hi!

Could you please explain how to run dvector_create.py on the TIMIT dataset?

This program tries to load some .wav files (line 91). However, the original data in TIMIT are .WAV files. After preprocessing, they are converted to .npy files. But where to find .wav files?

Thanks!

confusion about enrollment and inference handling? shape issue...?

#18 (comment)

In train_speech_embedder.py, separated into enrollment and verification.

enrollment_batch, verification_batch = torch.split(mel_db_batch, int(mel_db_batch.size(1)/2), dim=1)

in the same i followed, separated into two functions, like enrollment, verification.
in enrollment,

enrollment_batch, _ = torch.split(mel_db_batch, int(mel_db_batch.size(1)/2), dim=1)
torch.save("./dummy/speaker_embedding.pt", enrollment_centroids)

in verification,

_ , verification_batch = torch.split(mel_db_batch, int(mel_db_batch.size(1)/2), dim=1)
enrollment_centroids = torch.load("./dummy/speaker_embedding.pt")
sim_matrix = get_cossim(verification_embeddings, enrollment_centroids)

is this corrcect procedure or not?

then which way to handle enrollment and inference for speaker identification?

i am beginner in pytorch, any suggestions sir.

Thank you.

is this d-vector embedding input "GE2E Speaker Verification" is moreover same as UIS-RNN or not?

sir i have one doubt.

is this d-vector embedding PyTorch implementation of "Generalized End-to-End Loss for Speaker Verification" is moreover common as UIS-RNN or not?

accuracy for d-vector embeddings?

is it satisfying continuous d-vector embeddings(as sequences) or not?

sir one you saw this url. i did TIMIT dataset to generated the d-vector embeddings. but how to feed or initialize this embeddings i don't know.

google/uis-rnn#6 (comment)

Some of the libraries are only able to produce per-utterance d-vector embeddings, while for UIS-RNN, we require continuous d-vector embeddings (as sequences). We have no guarantee which third-party library supports this

can you help me sir? thank you

No backend error (in librosa.core.load in preprocessing data)

Hi I am facing this error when I am loading TIMIT database utterances
Your help is much appreciated.
(I installed ffmpeg but I had the same error)

The format of label is different between your code(dvector_create.py) and the example data in uis-rnn.

@HarryVolek The label in example data in uis-rnn starts with the name of audio, but in your code the label has no the name of audio. Does it matter?

How to Use Your Audio Data Set

Hello, this code is for TIMIT data set. If you change it into your own audio data, it will not work. If you use your own audio data as a data set, how can I do it? Thank you very much for your guidance. #

Speaker Verification Predict

Hi, first of all, thank you for your awesome work. I intend to use this for speaker verification, I know a speaker and I need to know if another audio belongs to him or not. I have some questions regarding your repo:

I had thought of saving the speaker embeddings, that is equivalent to the part of the code where you calculate the centroid right? I could calculate the centroid of all my known speakers and just load it later.
As I said my intention is to give the network and audio and test it against a known speaker, but I am a bit lost on how could I do that with your code, could you point me in the right direction? I get lost after you calculate the sim_matrix and the FAR FRR and thresh parameters.
I think that my main question is how do I fix that threshold parameter and how do I get a binary output meaning if two inputs correspond to the same person or not.

Once more thank you for your work!

lstm input wrong shape

I am not 100% sure but as I understand the input for the lstm layer should be in shape (seq_len, batch, input_size) according to the documentation. The input for the SpeechEmbedder is in the shape of (batch, seq_len, input_size). Is this a mistake or do I understand something wrong?

no of epochs

my training no of speakers is 5000. how many epochs will i set for training. i think 950 is too much na

Pre-trained model

Hi! Do you have any plans on sharing a pre-trained model on TIMIT?

perm action

Hi, sir. I don't think that the perm and unperm acitions in your code make any difference. Because the perm action is along the batch dim, in the forward process, the different data along the batch dim are unrelated. Or I misunderstood your purpose?

how about wav which haven't been trained ?

@HarryVolek
thanks for your project ,I have run it succesfully .
but there is another problem,if there is a person's speech wav that haven't been trained ,can this verify it out?
I mean we haven't trained any speech wav of the new person.

how to finetune TIMIT model with my own speakers audios?

@HarryVolek Sir, i have model for our PyTorch_Speaker_Verification ( epochs: 950 Max training speaker epoch for TIMIT)
sir i have a doubt. In that pytorch model, we will finetune(transfer learning/ Retrain) with my own speakers audios again.
Is it possible or not sir? which way to implement this?

AND another doubt, if we have only 10-20 speakers audio utterence, will it gives good accuracy or not?
(or)
in this 10-20 speakers audio utterence to build own model, will it gives good accuracy or not?

In that Voice Activity Detection how can we achieve "Wake word detection" sir? then how can i do this in dynamic way?

i am more concentrate on this. thank you for your initiative.
thank you lots @HarryVolek sir

GE2E loss can work with cnn

I try to replace your LSTM with CNN but the loss doesn't decrease. The input, loss function are the same. Can you explain why is happening?

what's the result of timit database for uis-rnn?

@HarryVolek what's the result of timit database for uis-rnn? My result is very bad.

OOM

Hi! I try to run this code in Linux on cpu with 16g memory. The cost of memory was growth fast util OS kill the process. It only run about 30 iterations.
Do you have some idea to fix the problem? What should I do to debug?
Thank you!

Question about L2 norm of embedding

Thanks for this package. I was wondering about the L2 norm being applied in speech_embedder_net.py line 32. It looks like the paper describes an L2 norm being calculated per embedding and then dividing the embedding by that value. However, I believe the code as written calculates the norm over the entire matrix. Is this intentional? That is, I would have thought line 32 should be: x = x / torch.norm(x, dim=1).unsqueeze(1). This calculates the norm per embedding.

Question on time cost during each iteration by uis-rnn

Sir,
I've trained the d-vectors by your code, but it takes much more time than UIS-RNN's own test sequence which have the same sequence length and dims during each iteration. The phenomenon is just like what I described in the following question.
Thank you in advance.
https://github.com/google/uis-rnn/issues/27

Issue in Using test_features in UIS_RNN

HI HarryVolek,

I have created embeddings for Timit model and generated the d-vectors as you explained in README but when i try to test the features in USI_RNN it throw the following error

"ValueError: test_sequence must be 2-dim array."
guess we need to reshape the test_sequence

Correctness of d-vectors

How to evaluate the correctness of d-vectors. my d-vectors change when I change th dataset on which I train my LSTM

Why dont change the learning rate?

In you code ,the learning rate allways keeps 0.01 and does not change during the train process.

Overfitting

Hi,

I see the model is overfitting on the mix of data I am using. Can you please suggest some regularisation technique which might help here? Shall I add more data?

Sun Jul 7 10:31:55 2019 Epoch:1700[30/79],Iteration:134251 Loss:0.0008 TLoss:0.9285

Sun Jul 7 10:31:58 2019 Epoch:1700[60/79],Iteration:134281 Loss:0.0011 TLoss:0.6694

Sun Jul 7 10:32:05 2019 Epoch:1701[30/79],Iteration:134330 Loss:0.0631 TLoss:0.4391

Why do you use the different vad method for preparing data for d-vector and for uis-rnn?

In your code, librosa.effects.split(utter, top_db=30) for d-vector and webrtcvad for uis-rnn.

Embedder-net()

Hi,I have some questions (this is my graduation project and this is extremly important for me )

-the output of embedder-net() function is a [N,256] I need to understand what is N exactly is it the number of sliding windows (240ms)?
-Can we use this output (embedder-net() function output) for speaker diarization (can we apply clustering algorithms to this sequences for speaker diarization)?
-Can I understand how did you build train-sequence and train-cluster-id (the input of uis rnn ) because my dataset is different from TIMIT-corpus (Timit-corpus is a speaker recognition dataset not a speaker diarization dataset )?
this is a link to the corpus I am using : https://github.com/EMRAI/emrai-synthetic-diarization-corpus
Thank you in advance for your help

error in data_preprocess.py (save_spectrogram_tisv() function)

I got this error in save_spectrogram_tisv() fucction.
Your help is much appreciated.

Bitrate/Sample Rate Differences

How much of an impact does the sample rate and bitrate have? My use case is that I need to perform speaker verification across multiple sample rates/codecs such as AMR and EVRC. I will of course be converting them to wav for training but since I will be performing the verification across multiple sample rates, I'm wondering if I should adapt my training set to have varying sample rates as well. Thoughts?

Frame rate problem

I am working on VCTK corpus (48kHZ)
I can't train my network and preprocess the data using this frame rate and always I got this error (mainly in preprocess and get_STFT_frames function )
What should I modify in the code ?
thank you for help

)

Calculating EER

I can't understand well the process of calculating EER .
I would be greatful if someone could explain this to me .

Excessive memory use due to train.num_workers > 0

Hello, thanks for sharing your code.

I've succesfully tried it with TIMIT data. However, I have come across a memory issue when training on a much larger dataset (over 7000 speakers) - after the first epoch, used memory starts rapidly increasing and eventually surpasses available RAM.

From what I've been able to find, this is actually caused by a known problem with PyTorch DataLoader:

1: CPU memory gradually leaks when num_workers > 0 in the DataLoader
2: https://forums.fast.ai/t/runtimeerror-dataloader-worker-is-killed-by-signal/31277

The above links mention some possible workarounds you might be able to use in your code. Barring that, setting train.num_workers to zero (default is 8) solves this problem - possibly at the cost of speed, although in my case the training speed actually improved.

Even if you're not able to fix this completely, I would suggest at least changing the default train.num_workers setting from 8 to 0.

(Note: I suspect this may also be the cause of issue #20)

Question about inference

Hi @HarryVolek,

I trained the model correctly. But now I have some .wav as inputs so how can I use the trained model in order to do inference ?
Also, could the inference be used for speaker identification (verify if the utterance belongs to one of a set of N speakers) or is it just valid for speaker verification (verify if the utterance is for the claimed speaker) ?
Thanks in advance,
Tony,

About Data_loader.py, "utterance = utterance[:, :, 160]", why we use the num 160?

Question about the pipeline.

Thanks for sharing your great work. As a newer to to speaker verification, I have two question here.
First, what's the purpose of perm and unperm in training script.
Second, since the original data matrix is disordered by the perm index, so how the loss is calculated between different speakers?

Thanks a lot!

feature extraction

sir can you explain how you extracted features for every utterance. and finally for one speaker the features will be (12,40,180) can you explain dimentions.

License

I'm unable to find the license that's associated with this file. Would you mind adding a license? If possible, can you add a permissive license like the MIT or BSD? I'm unable to use any software at my company if it doesn't have an obvious license associated with it.

Thanks!

DataLoader

hello
I can't execute this part of code on Jupyter Notebook (there are no errors but I can't execute it an i works on google colab )
train_dataset = SpeakerDatasetEMRAIreprocessed()
train_loader = DataLoader(train_dataset, batch_size=N, shuffle=True, num_workers=num_workers, drop_last=True)
Thank you for help

Two question on Embeddings

About the align_embeddings

After calculating all the Embeddings window-by-window, you then calculate the average Embedding with align_embeddings function. But why here you hardcode the threshold, and what do they mean ?

PyTorch_Speaker_Verification/dvector_create.py

Lines 60 to 67 in 07f996a

 for i, embedding in enumerate(embeddings): 

 if (i*.12)+.24 < j*.401: 

 end = end + 1 

 else: 

 partitions.append((start,end)) 

 start = end 

 end = end + 1 

 j += 1

In the original paper, it says "the final utterance-wise d-vector is generated by L2 normalizing the window-wise d-vectors, then taking the element-wise average".

About the embedding frame.

In your implementation of data_preprocess.py, you only save the first N frame and the last N frame as below to .npy file, which may drop a lot of data if current utterance consists long frames:

PyTorch_Speaker_Verification/data_preprocess.py

Lines 30 to 45 in 07f996a

 utterances_spec = [] 

 for utter_name in os.listdir(folder): 

 if utter_name[-4:] == '.WAV': 

 utter_path = os.path.join(folder, utter_name) # path of each utterance 

 utter, sr = librosa.core.load(utter_path, hp.data.sr) # load utterance audio 

 intervals = librosa.effects.split(utter, top_db=30) # voice activity detection 

 for interval in intervals: 

 if (interval[1]-interval[0]) > utter_min_len: # If partial utterance is sufficient long, 

 utter_part = utter[interval[0]:interval[1]] # save first and last 180 frames of spectrogram. 

 S = librosa.core.stft(y=utter_part, n_fft=hp.data.nfft, 

 win_length=int(hp.data.window * sr), hop_length=int(hp.data.hop * sr)) 

 S = np.abs(S) ** 2 

 mel_basis = librosa.filters.mel(sr=hp.data.sr, n_fft=hp.data.nfft, n_mels=hp.data.nmels) 

 S = np.log10(np.dot(mel_basis, S) + 1e-6) # log mel spectrogram of utterances 

 utterances_spec.append(S[:, :hp.data.tisv_frame]) # first 180 frames of partial utterance 

 utterances_spec.append(S[:, -hp.data.tisv_frame:]) # last 180 frames of partial utterance

Why don't use all the frames to get more data into training ? Like:

utterances_spec = []
j = hp.data.tisv_frame
while j < S.shape[1]:
    utterances_spec.append(S[:, j - hp.data.tisv_frame:j])
    j += hp.data.tisv_frame

=====Update====
As for the second question, I tried and know why, is it because most sentences are no longer than 2 * tisv_frame, so you just crop the head and the tail ?

trained model used in uis-rnn

when I trained a modle with this project ,but when using it in uis rnn, it come out errors:
here the train log.

/home/rice/anaconda3/envs/pytorch/bin/python3.6 /home/rice/PycharmProjects/uis-rnn-master/demo.py
Iter: 0 Training Loss: -292.7123
Negative Log Likelihood: 1.1230 Sigma2 Prior: -293.8359 Regularization: 0.0006
Iter: 10 Training Loss: -292.9026
Negative Log Likelihood: 1.0294 Sigma2 Prior: -293.9326 Regularization: 0.0006
Iter: 20 Training Loss: -293.1618
Negative Log Likelihood: 0.9518 Sigma2 Prior: -294.1143 Regularization: 0.0006
Iter: 30 Training Loss: -293.3418
Negative Log Likelihood: 0.8811 Sigma2 Prior: -294.2235 Regularization: 0.0006
Iter: 40 Training Loss: -293.6046
Negative Log Likelihood: 0.7965 Sigma2 Prior: -294.4017 Regularization: 0.0006
Iter: 50 Training Loss: -293.7784
Negative Log Likelihood: 0.7400 Sigma2 Prior: -294.5190 Regularization: 0.0006
Iter: 60 Training Loss: -294.0261
Negative Log Likelihood: 0.6534 Sigma2 Prior: -294.6801 Regularization: 0.0006
Iter: 70 Training Loss: -294.1154
Negative Log Likelihood: 0.6151 Sigma2 Prior: -294.7312 Regularization: 0.0006
Iter: 80 Training Loss: -294.3496
Negative Log Likelihood: 0.5512 Sigma2 Prior: -294.9014 Regularization: 0.0006
Iter: 90 Training Loss: -294.6035
Negative Log Likelihood: 0.4841 Sigma2 Prior: -295.0882 Regularization: 0.0006
Iter: 100 Training Loss: -294.6783
Negative Log Likelihood: 0.4460 Sigma2 Prior: -295.1250 Regularization: 0.0006
Iter: 110 Training Loss: -294.9049
Negative Log Likelihood: 0.3866 Sigma2 Prior: -295.2922 Regularization: 0.0006
Iter: 120 Training Loss: -295.1215
Negative Log Likelihood: 0.3335 Sigma2 Prior: -295.4556 Regularization: 0.0006
Iter: 130 Training Loss: -295.2138
Negative Log Likelihood: 0.3084 Sigma2 Prior: -295.5229 Regularization: 0.0006
Iter: 140 Training Loss: -295.3630
Negative Log Likelihood: 0.2796 Sigma2 Prior: -295.6432 Regularization: 0.0006
Iter: 150 Training Loss: -295.5717
Negative Log Likelihood: 0.2386 Sigma2 Prior: -295.8109 Regularization: 0.0006
Iter: 160 Training Loss: -295.6212
Negative Log Likelihood: 0.2382 Sigma2 Prior: -295.8600 Regularization: 0.0006
Iter: 170 Training Loss: -295.8949
Negative Log Likelihood: 0.1961 Sigma2 Prior: -296.0916 Regularization: 0.0006
Iter: 180 Training Loss: -296.0236
Negative Log Likelihood: 0.1764 Sigma2 Prior: -296.2007 Regularization: 0.0006
Iter: 190 Training Loss: -296.1873
Negative Log Likelihood: 0.1511 Sigma2 Prior: -296.3391 Regularization: 0.0006
Iter: 200 Training Loss: -296.3225
Negative Log Likelihood: 0.1493 Sigma2 Prior: -296.4724 Regularization: 0.0006
Iter: 210 Training Loss: -296.3659
Negative Log Likelihood: 0.1548 Sigma2 Prior: -296.5213 Regularization: 0.0006
Iter: 220 Training Loss: -296.5490
Negative Log Likelihood: 0.1174 Sigma2 Prior: -296.6670 Regularization: 0.0006
Iter: 230 Training Loss: -296.6978
Negative Log Likelihood: 0.1129 Sigma2 Prior: -296.8114 Regularization: 0.0006
Iter: 240 Training Loss: -296.7265
Negative Log Likelihood: 0.1292 Sigma2 Prior: -296.8564 Regularization: 0.0006
Iter: 250 Training Loss: -296.9639
Negative Log Likelihood: 0.1191 Sigma2 Prior: -297.0836 Regularization: 0.0006
Iter: 260 Training Loss: -297.1142
Negative Log Likelihood: 0.0966 Sigma2 Prior: -297.2114 Regularization: 0.0006
Iter: 270 Training Loss: -297.3285
Negative Log Likelihood: 0.0972 Sigma2 Prior: -297.4264 Regularization: 0.0006
Iter: 280 Training Loss: -297.3492
Negative Log Likelihood: 0.1149 Sigma2 Prior: -297.4648 Regularization: 0.0006
Iter: 290 Training Loss: -297.5171
Negative Log Likelihood: 0.0907 Sigma2 Prior: -297.6085 Regularization: 0.0006
Iter: 300 Training Loss: -297.6031
Negative Log Likelihood: 0.1217 Sigma2 Prior: -297.7255 Regularization: 0.0006
Iter: 310 Training Loss: -297.7408
Negative Log Likelihood: 0.1000 Sigma2 Prior: -297.8414 Regularization: 0.0006
Iter: 320 Training Loss: -297.9149
Negative Log Likelihood: 0.0958 Sigma2 Prior: -298.0113 Regularization: 0.0006
Iter: 330 Training Loss: -298.0205
Negative Log Likelihood: 0.1114 Sigma2 Prior: -298.1325 Regularization: 0.0006
Iter: 340 Training Loss: -298.1181
Negative Log Likelihood: 0.0878 Sigma2 Prior: -298.2065 Regularization: 0.0006
Iter: 350 Training Loss: -298.3074
Negative Log Likelihood: 0.0924 Sigma2 Prior: -298.4005 Regularization: 0.0006
Iter: 360 Training Loss: -298.4767
Negative Log Likelihood: 0.0768 Sigma2 Prior: -298.5542 Regularization: 0.0006
Iter: 370 Training Loss: -298.5929
Negative Log Likelihood: 0.0933 Sigma2 Prior: -298.6869 Regularization: 0.0006
Iter: 380 Training Loss: -298.7109
Negative Log Likelihood: 0.0876 Sigma2 Prior: -298.7992 Regularization: 0.0006
Iter: 390 Training Loss: -298.7951
Negative Log Likelihood: 0.0971 Sigma2 Prior: -298.8928 Regularization: 0.0006
Iter: 400 Training Loss: -298.9722
Negative Log Likelihood: 0.0889 Sigma2 Prior: -299.0617 Regularization: 0.0006
Iter: 410 Training Loss: -299.0759
Negative Log Likelihood: 0.1211 Sigma2 Prior: -299.1976 Regularization: 0.0006
Iter: 420 Training Loss: -299.3126
Negative Log Likelihood: 0.0831 Sigma2 Prior: -299.3963 Regularization: 0.0006
Iter: 430 Training Loss: -299.3480
Negative Log Likelihood: 0.1019 Sigma2 Prior: -299.4505 Regularization: 0.0006
Iter: 440 Training Loss: -299.5879
Negative Log Likelihood: 0.0894 Sigma2 Prior: -299.6780 Regularization: 0.0006
Iter: 450 Training Loss: -299.6454
Negative Log Likelihood: 0.1086 Sigma2 Prior: -299.7546 Regularization: 0.0006
Iter: 460 Training Loss: -299.8495
Negative Log Likelihood: 0.0820 Sigma2 Prior: -299.9322 Regularization: 0.0006
Iter: 470 Training Loss: -299.9776
Negative Log Likelihood: 0.0821 Sigma2 Prior: -300.0603 Regularization: 0.0006
Iter: 480 Training Loss: -300.1432
Negative Log Likelihood: 0.0770 Sigma2 Prior: -300.2209 Regularization: 0.0006
Iter: 490 Training Loss: -300.1850
Negative Log Likelihood: 0.0830 Sigma2 Prior: -300.2686 Regularization: 0.0006
Iter: 500 Training Loss: -300.3543
Negative Log Likelihood: 0.0824 Sigma2 Prior: -300.4373 Regularization: 0.0006
Iter: 510 Training Loss: -300.4816
Negative Log Likelihood: 0.0881 Sigma2 Prior: -300.5703 Regularization: 0.0006
Iter: 520 Training Loss: -300.6026
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Negative Log Likelihood: 3.0123 Sigma2 Prior: -731.5533 Regularization: 0.0009
Iter: 19999 Training Loss: -731.6766
Negative Log Likelihood: 2.5480 Sigma2 Prior: -734.2255 Regularization: 0.0009
Done training with 20000 iterations
Traceback (most recent call last):
File "/home/rice/PycharmProjects/uis-rnn-master/demo.py", line 85, in
main()
File "/home/rice/PycharmProjects/uis-rnn-master/demo.py", line 81, in main
diarization_experiment(model_args, training_args, inference_args)
File "/home/rice/PycharmProjects/uis-rnn-master/demo.py", line 61, in diarization_experiment
predicted_label = model.predict(test_sequence, inference_args)
File "/home/rice/PycharmProjects/uis-rnn-master/uisrnn/uisrnn.py", line 573, in predict
return self.predict_single(test_sequences, args)
File "/home/rice/PycharmProjects/uis-rnn-master/uisrnn/uisrnn.py", line 503, in predict_single
raise ValueError('test_sequence must be 2-dim array.')
ValueError: test_sequence must be 2-dim array.

Process finished with exit code 1

can you help me slove this problem?

N = 1 --> EER : 0.00 (thres:0.00, FAR:0.00, FRR:0.00) for enrollment and verification.???

Number of speakers in batch N = 1 then,
EER : 0.00 (thres:0.00, FAR:0.00, FRR:0.00)

torch.Size([1, 4, 160, 40]) mel_db_batch.size()
4 mel_db_batch.size(1)
0 batch_id
torch.Size([1, 2, 160, 40]) enrollment_batch size
torch.Size([1, 2, 160, 40]) verification_batch size
torch.Size([2, 160, 40]) reshape tensor enrollment_batch size
torch.Size([2, 160, 40]) reshape tensor verification_batch size
2 reshape tensor verification_batch size(0)
[0, 1] perm
[0, 1] unperm
torch.Size([2, 160, 40]) verification_batch
torch.Size([2, 256]) enrollment_embeddings
torch.Size([2, 256]) verification_embeddings
torch.Size([2, 256]) verification_embeddings
torch.Size([1, 2, 256]) enrollment_embeddings.size()
torch.Size([1, 2, 256]) verification_embeddings.size()
torch.Size([1, 256]) enrollment_centroids.size()
tensor([[[0.7973],
         [0.7973]]], grad_fn=<CopySlices>) sim_matrix
torch.Size([1, 2, 1]) sim_matrix.size()

EER : 0.00 (thres:0.00, FAR:0.00, FRR:0.00)

EER across 10 epochs: 0.0000

if N > 1 means,
EER : 0.01 (thres:0.60, FAR:0.03, FRR:0.00)

EER : 0.00 (thres:0.71, FAR:0.00, FRR:0.00)
torch.Size([4, 6, 160, 40]) mel_db_batch.size()
6 mel_db_batch.size(1)
11 batch_id
torch.Size([4, 3, 160, 40]) enrollment_batch size
torch.Size([4, 3, 160, 40]) verification_batch size
torch.Size([12, 160, 40]) reshape tensor enrollment_batch size
torch.Size([12, 160, 40]) reshape tensor verification_batch size
12 reshape tensor verification_batch size(0)
[2, 6, 5, 4, 7, 8, 9, 1, 0, 10, 3, 11] perm
[8, 7, 0, 10, 3, 2, 1, 4, 5, 6, 9, 11] unperm
torch.Size([12, 160, 40]) verification_batch
torch.Size([12, 256]) enrollment_embeddings
torch.Size([12, 256]) verification_embeddings
torch.Size([12, 256]) verification_embeddings
torch.Size([4, 3, 256]) enrollment_embeddings.size()
torch.Size([4, 3, 256]) verification_embeddings.size()
torch.Size([4, 256]) enrollment_centroids.size()

torch.Size([4, 3, 4]) sim_matrix.size()

EER : 0.01 (thres:0.60, FAR:0.03, FRR:0.00)

How can i fit best threshold for single speaker enrollment vs inference in testing.

@HarryVolek Thanks sir.

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	for i, embedding in enumerate(embeddings):
	if (i.12)+.24 < j.401:
	end = end + 1
	else:
	partitions.append((start,end))
	start = end
	end = end + 1
	j += 1

	utterances_spec = []
	for utter_name in os.listdir(folder):
	if utter_name[-4:] == '.WAV':
	utter_path = os.path.join(folder, utter_name) # path of each utterance
	utter, sr = librosa.core.load(utter_path, hp.data.sr) # load utterance audio
	intervals = librosa.effects.split(utter, top_db=30) # voice activity detection
	for interval in intervals:
	if (interval[1]-interval[0]) > utter_min_len: # If partial utterance is sufficient long,
	utter_part = utter[interval[0]:interval[1]] # save first and last 180 frames of spectrogram.
	S = librosa.core.stft(y=utter_part, n_fft=hp.data.nfft,
	win_length=int(hp.data.window * sr), hop_length=int(hp.data.hop * sr))
	S = np.abs(S) ** 2
	mel_basis = librosa.filters.mel(sr=hp.data.sr, n_fft=hp.data.nfft, n_mels=hp.data.nmels)
	S = np.log10(np.dot(mel_basis, S) + 1e-6) # log mel spectrogram of utterances
	utterances_spec.append(S[:, :hp.data.tisv_frame]) # first 180 frames of partial utterance
	utterances_spec.append(S[:, -hp.data.tisv_frame:]) # last 180 frames of partial utterance