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Compute embeddings from stream & unsupervised diarization #10
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I've investigated these areas but haven't yet implemented anything for them, even though I am considering it.
You might also be able to work with similarity. E.g. if you add these lines in demo 2 after having computed the continuous embedding:
Clearly you can detect some speakers there, by looking for pattern of high similarity:
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This is a demo I meant to make too, but it's certainly more work than the other 5. Hope we'll get there. |
Thanks for your detailed explanations.
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I mean that at this point in the function: https://github.com/resemble-ai/Resemblyzer/blob/master/resemblyzer/voice_encoder.py#L151, the variable |
Got it! Thank you very much for clearing these doubts. I will close this and will update here when I will make significant progress with unsupervised and streaming diarization. |
Sure, it's fine if you leave it open until we figure it out. |
Hi, @shashankpr |
Hi @nikitalpopov , |
@shashankpr |
@CorentinJ @shashankpr |
Hi, great work and great repo really. Your code and examples helped me understand the flow very easily.
I am currently working on a speaker identification task wherein I want to detect "who spoke when" with low latency. There are two tasks that I need to overcome and I was wondering if you had already worked on them or have plans to in future. If not, then I would be glad to contribute to your repo as a PR. The tasks are as follows:
I know that they can be done with few tweaks but I would like to know your insight on them if you had already worked or have idea about them.
Thanks!
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