soft-vc
simclr
| soft-vc | simclr | |
|---|---|---|
| 2 | 13 | |
| 376 | 3,949 | |
| - | 1.1% | |
| 2.2 | 2.9 | |
| 2 months ago | 12 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | Apache License 2.0 |
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soft-vc
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Where can I find more info on the various model files
Also been looking into CycleGANs to do "voice conversions." I found that the term "voice conversion" is a research-friendly way to say you are doing voice clones. I found this one really good and I've been quite impressed : https://github.com/bshall/soft-vc
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Divine conference of the 'sisters' :P
but Lydiasarunrat (my project-partner) had different ideas, and knew of the existence of https://github.com/bshall/soft-vc which he also slightly adjusted to get better results. The title of the work that were basing the female voice-pack conversion on: "{A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion}". It's a beautiful title.. very.. Sekiro lore-friendly 🤣 - use this for Pro Shinobi whisper voice-line conversion! 😄
simclr
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Positive and Negative Sampling Strategies for Representation Learning in Semantic Search
For visual representations, you could look into SimCLR and MoCo. https://github.com/google-research/simclr https://github.com/facebookresearch/moco
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[D] Why is random cropping necessary in SimCLR?
Yeah I think so, it's not hard to check https://github.com/google-research/simclr/blob/2fc637bdd6a723130db91b377ac15151e01e4fc2/data_util.py
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[R] Deep Learning with a Small Training Batch (or Lack Thereof). Part 1
Code for https://arxiv.org/abs/2006.10029 found: https://github.com/google-research/simclr
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[D] Current trends in computer vision related to unsupervised learning
SimCLR v2.0 - https://arxiv.org/abs/2006.10029
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Self-Supervised Contrastive Learning model for video dataset?
My data consists of binary labels (normal & anomalous) where the videos are already broken up into frames in the directory, I'm looking for a model where I can feed a normal-labeled video alongside an anomalous-labeled video like visualized in this example from the SimCLR Repo, a dog will represent the normal video and the chair the anomalous video.
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[P] Choosing a self-supervised learning framework that's easy to use
No, go to the "tf2" folder in the repo root. https://github.com/google-research/simclr/tree/master/tf2
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[D] What is meant by width in the SimCLRv2 paper?
Code for https://arxiv.org/abs/2006.10029 found: https://github.com/google-research/simclr
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[D] Funding PhD in Europe
[1] https://github.com/google-research/simclr [2] https://www.tensorflow.org/tfrc?hl=en&authuser=2
What are some alternatives?
silero-models - Silero Models: pre-trained speech-to-text, text-to-speech and text-enhancement models made embarrassingly simple
swav - PyTorch implementation of SwAV https//arxiv.org/abs/2006.09882
SfMLearner - An unsupervised learning framework for depth and ego-motion estimation from monocular videos
Unsupervised-Classification - SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]
unsupervised-depth-completion-visual-inertial-odometry - Tensorflow and PyTorch implementation of Unsupervised Depth Completion from Visual Inertial Odometry (in RA-L January 2020 & ICRA 2020)
SimCLR - PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations
torchlars - A LARS implementation in PyTorch
Supervised-Constrastive-Learning-in-TensorFlow-2 - Implements the ideas presented in https://arxiv.org/pdf/2004.11362v1.pdf by Khosla et al. [Moved to: https://github.com/sayakpaul/Supervised-Contrastive-Learning-in-TensorFlow-2]
CodeSearchNet - Datasets, tools, and benchmarks for representation learning of code.
moco - PyTorch implementation of MoCo: https://arxiv.org/abs/1911.05722