Closed Aurora11111 closed 5 years ago
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. :+1:
For training the d-vector model, I would suggest using all possible speaker-labelled datasets you could find, including:
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:
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
@wq2012 thanks for your suggestions,I will have a try.
Describe the question
when I tested my own datasets which haven't been trained ,the result is really bad,can you give me some suggestion?
My background
Have I read the
README.md
file?Have I searched for similar questions from closed issues?
Have I tried to find the answers in the paper Fully Supervised Speaker Diarization?
Have I tried to find the answers in the reference Speaker Diarization with LSTM?
Have I tried to find the answers in the reference Generalized End-to-End Loss for Speaker Verification?