pyDenStream
Implementation of the DenStream algorithm in Python. (by MrParosk)
uis-rnn
This is the library for the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm, corresponding to the paper Fully Supervised Speaker Diarization. (by google)
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| pyDenStream | uis-rnn | |
|---|---|---|
| 1 | 2 | |
| 6 | 1,419 | |
| - | 0.3% | |
| 0.0 | 0.6 | |
| 3 months ago | 11 months ago | |
| Python | Python | |
| - | Apache License 2.0 |
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pyDenStream
Posts with mentions or reviews of pyDenStream.
We have used some of these posts to build our list of alternatives
and similar projects.
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[P] Implementation of DenStream
The implementation can be found here: https://github.com/MrParosk/pyDenStream
uis-rnn
Posts with mentions or reviews of uis-rnn.
We have used some of these posts to build our list of alternatives
and similar projects.
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Putting my degree to use. (Exclude Specials and Guests)
Discussion: - When I started this, I thought I would use something like the VoxSort Diarization and it would be easy. But these apps are terrible, especially in recognizing Joey apart from Garnt. Connor has a distinct voice so it was recognizable but still bad. But I didn't think Joey's and Garnt's voices were so similar. - Tested the thing and it's accuracy is almost 99%. - You can still improve this by cutting the episode into smaller chunk but 1 second is the maximum for my computer, any smaller than that i will run out of RAM. I can work to get around this but hey I'm lazy. - The library to implement yourself from google.
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Finally, my degree can be useful
I used this algorithm from Google to determine "who spoke when".
What are some alternatives?
When comparing pyDenStream and uis-rnn you can also consider the following projects:
pyannote-audio - Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
lightning-bolts - Toolbox of models, callbacks, and datasets for AI/ML researchers.
impfuzzy - Fuzzy Hash calculated from import API of PE files
stringlifier - Stringlifier is on Opensource ML Library for detecting random strings in raw text. It can be used in sanitising logs, detecting accidentally exposed credentials and as a pre-processing step in unsupervised ML-based analysis of application text data.
hover - :speedboat: Label data at scale. Fun and precision included.