Comments (5)
How to fix the total number of speakers? In most call center scenario, there are only 2 or 3 speakers.
from uis-rnn.
@fanlu The whole idea of UIS-RNN is to be able to handle unbounded number of speakers by learning from examples, instead of enforcing the number of speakers.
If you train UIS-RNN with call center audios where there are always 2 or 3 speakers, it should be able to predict at most 2 or 3 speakers, without requiring additional constraints.
However, since you asked, let me create a feature request issue for it. But likely we won't work on it for any time soon.
from uis-rnn.
Hi, do you have any update about this issue?
Or do you have any suggestion related to the input parameter adjustment when the system tends to add too many speakers?
from uis-rnn.
@suzinia Unfortunately no, since some core members have left the team.
You can try to locally apply #56 to constrain the number of speakers. It's not really very correct, but may solve your immediate problem.
from uis-rnn.
Thanks, I'll try that out!
from uis-rnn.
Related Issues (20)
- Embedding Extraction Procedure HOT 1
- about model HOT 1
- [Bug] Predict method does not finish HOT 3
- what is train data format? HOT 1
- Question about custom data generator
- uis-rnn gives different result on broken audios and continuous audios HOT 5
- how to control the number of different speaker when predicting? HOT 1
- Unable to convert pytorch model to tensorflow in Diarization on mobile device. HOT 2
- [Question] Are input d-vectors for training assumed L2-normalized? HOT 8
- Change input size HOT 1
- No module named coverage HOT 1
- Is is possible to pre-load the model for multiple request? HOT 1
- [Question] About num_non_zero HOT 1
- [Question] The dimension of toy test data [test_sequence] is (25, 95, 256) what does the first 2 dimension represent? Toy train data [train_sequence] has dimension (4627, 256) which is understandable. HOT 1
- Is there a way to fine tune an already existing pre-trained model? HOT 1
- rnn initial state trainable HOT 1
- Any documentations on training from scratch using custom data in other languages ? HOT 1
- [Bug] Making a prediction on CPU after training on GPU
- Predicted labels doesn't match with Ground truth labels but the accuracy of test results is 0.8% HOT 1
- assign gpu with arguments
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