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Top 10 Jupyter Notebook pretrained-model Projects
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pyannote-audio
Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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silero-models
Silero Models: pre-trained speech-to-text, text-to-speech and text-enhancement models made embarrassingly simple
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super-gradients
Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.
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SaaSHub
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continual-pretraining-nlp-vision
Code to reproduce experiments from the paper "Continual Pre-Training Mitigates Forgetting in Language and Vision" https://arxiv.org/abs/2205.09357
The part of CLIP[1] that you need to know to understand this is that it embeds text and images into the same space. ie: the word "dog" is close to images of dogs. Normally this space is a high dimensional real space. Think 512-dimensional or 512 floating point numbers. When you want to measure "closeness" between vectors in this space cosine similarity[2] is a natural choice.
Why would you want to quantize values? Well, instead of using a 32-bit float for each dimension, what if you could get away with 1-bit? You would save you 31x the space. Often you'll want to embed millions or billions of pieces of text or images, so the savings represent a huge speed & cost savings and if accuracy isn't impacted too much then it could be worth it.
If you naively clip the floats of an existing model, it severely impacts accuracy. However, if you train a model from scratch that produces binary outputs, then it appears to perform better.
There is one twist. Deep learning models rely on gradient descent to train and binary output doesn't produce useful gradients. We use cosine similarity on floating point vectors and hamming distance on bit vectors. Is there a function that behaves like hamming distance but is nicely differentiable? We can then use this function during training and then vanilla hamming distance during inference. It seems like they've done that.
I'd suggest playing around with OpenCLIP[3]. My background is in data science but all my CLIP knowledge comes from doing a side project over the course of a couple weekends.
1. https://huggingface.co/docs/transformers/model_doc/clip
2. https://en.wikipedia.org/wiki/Cosine_similarity
3. https://github.com/mlfoundations/open_clip
pyannote/pyannote-audio
Project mention: Weird A.I. Yankovic, a cursed deep dive into the world of voice cloning | news.ycombinator.com | 2023-10-02I doubt it's currently actually "the best open source text to speech", but the answer I came up with when throwing a couple of hours at the problem some months ago was "Silero" [0, 1].
Following the "standalone" guide [2], it was pretty trivial to make the model render my sample text in about 100 English "voices" (many of which were similar to each other, and in varying quality). Sampling those, I got about 10 that were pretty "good". And maybe 6 that were the "best ones" (pretty natural, not annoying to listen to).
IIRC the license was free for noncommercial use only. I'm not sure exactly "how open source" they are, but it was simple to install the dependencies and write the basic Python to try it out; I had to write a for loop to try all the voices like I wanted. I ended using something else for the project for other reasons, but this could still be fairly good backup option for some use cases IMO.
[0] https://github.com/snakers4/silero-models#text-to-speech
Most computer vision models are trained to predict on a preset list of label classes. In object detection, for instance, many of the most popular models like YOLOv8 and YOLO-NAS are pretrained with the classes from the MS COCO dataset. If you download the weights checkpoints for these models and run prediction on your dataset, you will generate object detection bounding boxes for the 80 COCO classes.
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Jupyter Notebook pretrained-models related posts
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GitHub - Deci-AI/super-gradients: Easily train or fine-tune SOTA co...GitHub - Deci-AI/super-gradients: Easily train or fine-tune SOTA co...
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🚀 Deep Learning for Deep Objects: ZoeDepth is an AI Model for Multi-Domain Depth Estimation
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[R] HugsVision: A easy-to-use HuggingFace wrapper for computer vision
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A note from our sponsor - SaaSHub
www.saashub.com | 31 May 2024
Index
What are some of the best open-source pretrained-model projects in Jupyter Notebook? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | open_clip | 8,705 |
| 2 | pyannote-audio | 5,266 |
| 3 | silero-models | 4,631 |
| 4 | super-gradients | 4,370 |
| 5 | ZoeDepth | 2,027 |
| 6 | Entity | 672 |
| 7 | glasses | 426 |
| 8 | HugsVision | 188 |
| 9 | gan-vae-pretrained-pytorch | 170 |
| 10 | continual-pretraining-nlp-vision | 14 |
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