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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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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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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
While OpenAI’s CLIP model has garnered a lot of attention, it is far from the only game in town—and far from the best! On the OpenCLIP leaderboard, for instance, the largest and most capable CLIP model from OpenAI ranks just 41st(!) in its average zero-shot accuracy across 38 datasets.
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.
Jupyter Notebook pretrained-models related posts
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A note from our sponsor - WorkOS
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Index
What are some of the best open-source pretrained-model projects in Jupyter Notebook? This list will help you:
Project | Stars | |
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1 | open_clip | 8,391 |
2 | pyannote-audio | 5,027 |
3 | silero-models | 4,546 |
4 | super-gradients | 4,322 |
5 | ZoeDepth | 1,939 |
6 | Entity | 660 |
7 | glasses | 413 |
8 | HugsVision | 188 |
9 | gan-vae-pretrained-pytorch | 162 |
10 | continual-pretraining-nlp-vision | 14 |
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