Comments (3)
Hello,
I haven't found a speaker change embodiment in the code. No training process was found for audio containing multiple speakers. In the pre-processing of training data, audio segments of the same individual is collected together, but how is the training process? Could you explain the generative process of uis-rnn?
Thank you in advance for your help.
from uis-rnn.
1: The function sample_permuted_segments
seems not work in utils.py
, it generates always the same permutation.
2: And also, why need to collect the same cluster in resize_sequence
, the overlapping window to calculate speaker embedding will not make sense from this way.
from uis-rnn.
- You mean the outputs in a single call are all identical sequences? Or do you mean when you call the function multiple times for the same input you always get same output? If latter, check if it is because you fixed the random seed of numpy.
- There's additional segment level aggregation logic after sliding windows. Segments are not overlapping. See section 2 of this paper.
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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from uis-rnn.