Point-Processes
uis-rnn
| Point-Processes | uis-rnn | |
|---|---|---|
| 2 | 3 | |
| 37 | 1,530 | |
| - | 0.3% | |
| 0.0 | 3.5 | |
| over 1 year ago | 8 months ago | |
| Python | Python | |
| - | Apache License 2.0 |
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Point-Processes
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[R] DeepDPM: Deep Clustering With an Unknown Number of Clusters
https://github.com/VincentGranville/Point-Processes/commit/2c2ed7cc989711d0a40d96fb6f194c690fcded8f (left is original data)
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What is the hardest thing to learn in statistics?
As for confidence regions, you show them the a plot like this one. Each contour line defines a confidence region of a certain level (that can be determined accurately). This stuff is familiar to hikers using a map to navigate the terrain. No math involved in the whole teaching experience, other than stuff from elementary school.
uis-rnn
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[D] Is there a way to distinguish different human voices from 1 audio file ?
Looks like you can get an put of the box here: https://github.com/google/uis-rnn
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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?
dpmmpythonStreaming - Python wrapper for the DPMMSubClusterStreaming.jl Julia package.
pyannote-audio - Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
GPflow - Gaussian processes in TensorFlow
pyDenStream - Implementation of the DenStream algorithm in Python.
DeepDPM - "DeepDPM: Deep Clustering With An Unknown Number of Clusters" [Ronen, Finder, and Freifeld, CVPR 2022]
lightning-bolts - Toolbox of models, callbacks, and datasets for AI/ML researchers.
Stochastic-Processes - My book: Gentle Introduction to Chaotic Dynamical Systems. Includes stochastic dynamical systems and statistical properties of numeration systems in any dimension.
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
hover - :speedboat: Label data at scale. Fun and precision included.
ECAPA-TDNN - Unofficial reimplementation of ECAPA-TDNN for speaker recognition (EER=0.86 for Vox1_O when train only in Vox2)
Clover - An Efficient DNA Clustering algorithm based on Tree Structure.