n2d
A deep clustering algorithm. Code to reproduce results for our paper N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding. (by rymc)
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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| n2d | uis-rnn | |
|---|---|---|
| 1 | 3 | |
| 122 | 1,529 | |
| - | 0.3% | |
| 0.0 | 3.5 | |
| 6 months ago | 8 months ago | |
| Python | Python | |
| GNU General Public License v3.0 only | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
n2d
Posts with mentions or reviews of n2d.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-09-12.
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Time-Series image clustering. Advice needed!
So far I found some approaches that looks promising, for example n2d or k-means with DTW distance, and there are some more (e.g. T-DPSOM), but I want to start from these.
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. The last one was on 2022-10-03.
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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?
When comparing n2d and uis-rnn you can also consider the following projects:
minisom - :red_circle: MiniSom is a minimalistic implementation of the Self Organizing Maps
pyannote-audio - Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
pyDenStream - Implementation of the DenStream algorithm in Python.
lightning-bolts - Toolbox of models, callbacks, and datasets for AI/ML researchers.
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.