uis-rnn
Clover
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uis-rnn | Clover | |
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3 | 1 | |
1,529 | 11 | |
0.3% | - | |
3.5 | 1.7 | |
8 months ago | about 1 year ago | |
Python | Python | |
Apache License 2.0 | GNU General Public License v3.0 only |
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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".
Clover
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R&D: Clover, Tree Structure-based Efficient DNA Clustering for DNA-Based Storage
Deoxyribonucleic acid (DNA)-based data storage is a promising new storage technology which has the advantage of high storage capacity and long storage time compared with traditional storage media. However, the synthesis and sequencing process of DNA can randomly generate many types of errors, which makes it more difficult to cluster DNA sequences to recover DNA information. Currently, the available DNA clustering algorithms are targeted at DNA sequences in the biological domain, which not only cannot adapt to the characteristics of sequences in DNA storage, but also tend to be unacceptably time-consuming for billions of DNA sequences in DNA storage. In this paper, we propose an efficient DNA clustering method termed Clover for DNA storage with linear computational complexity and low memory. Clover avoids the computation of the Levenshtein distance by using a tree structure for interval-specific retrieval. We argue through theoretical proofs that Clover has standard linear computational complexity, low space complexity, etc. Experiments show that our method can cluster 10 million DNA sequences into 50 000 classes in 10 s and meet an accuracy rate of over 99%. Furthermore, we have successfully completed an unprecedented clustering of 10 billion DNA data on a single home computer and the time consumption still satisfies the linear relationship. Clover is freely available at https://github.com/Guanjinqu/Clover.
What are some alternatives?
pyannote-audio - Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
similarity - TensorFlow Similarity is a python package focused on making similarity learning quick and easy.
pyDenStream - Implementation of the DenStream algorithm in Python.
dedupe - :id: A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution.
lightning-bolts - Toolbox of models, callbacks, and datasets for AI/ML researchers.
awesome-community-detection - A curated list of community detection research papers with implementations.
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
Unsupervised-Classification - SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]
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)