Comments (1)
wq2012
commented on July 28, 2024
It depends on what you want to work on.
You can use any dataset that satisfies the definition of supervised clustering, meaning you can extract sequences of features, and associate those features with ground truth labels. Features can be speaker embeddings, face embeddings, etc.
Example datasets include NIST SRE 2000 CALLHOME for speaker diarization. But for any dataset, you need to process them yourself to extract features and align the features with labels. This library only provides the API for the clustering part.
More details are in the README.md file and the paper on arXiv.
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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
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- 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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