Comments (3)
For training the d-vector model, I would suggest using all possible speaker-labelled datasets you could find, including:
- VCTK
- LibriSpeech
- VoxCeleb
- VoxCeleb2
- Datasets with similar acoustic environments to your own
The first 4 sum up to almost 10K different speakers. Although it's still much smaller than what we use internally (100K+ speakers), it would produce reasonably good results. (according to our experience, training d-vector model with less than 3~5K speakers is usually bad)
Then for training UIS-RNN, you could consider using:
- NIST SRE disk-6 + disk-8
- ICSI
- Fisher
- Datasets with similar acoustic environment and dialogue style to your own
The last one should not cover the same speakers to your testing test, but should have the same acoustic environment and dialogue style to your testing set. UIS-RNN is for supervised diarization, meaning you cannot expect it to work when trained in one domain and tested in a totally different domain:
https://www.youtube.com/watch?v=pGkqwRPzx9U&t=7m56s
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Pytorch TIMIT d-vector embeddings for my own datasets not giving good results for UIS-RNN archietecture. then how can we improvise accuracy? any suggestions for me also.?
@Aurora11111 sir, i also facing this issue. 👍
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@wq2012 thanks for your suggestions,I will have a try.
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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
- [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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