magenta
pluginbase
Our great sponsors
| magenta | pluginbase | |
|---|---|---|
| 7 | - | |
| 18,925 | 1,075 | |
| 0.3% | - | |
| 1.7 | 0.0 | |
| 21 days ago | almost 3 years ago | |
| Python | Python | |
| Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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.
magenta
- Run this on your PC for me and I'll pay you
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💊Your daily dose of machine learning : Neural style transfer...but fast!
Paper : https://arxiv.org/abs/1705.06830v2 Code : https://github.com/magenta/magenta/blob/main/magenta/models/arbitrary_image_stylization/README.md
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Training Magenta Fast Style Transfer from scratch?
I'm interested in retraining https://github.com/magenta/magenta/tree/main/magenta/models/arbitrary_image_stylization from scratch in order to test it on higher resolutions. Unfortunately it seems that some of the training sets are no longer available (Kaggle painters for instance).
- Quickdraw dataset
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Show HN: Are you playing your violin (viola, guitar, etc.) in tune?
The problem with FFTs is that for the lower frequencies you have very few bins, but at the higher end you get ridiculous accuracy and there is no easy way to make this more linear. Binning on the high end saves some space but doesn't make the low any more accurate.
So you need to run multiple methods in parallel and decide based on the very rough distribution of the energy in the spectrum which method has the biggest chance of success, or, alternatively, to use the output of both methods to drive some logic that will assign a weight to the output of each.
It's a tricky problem, to put it mildly. Also, this is the simplest form of the problem, doing this accurately for multiple pitches at once is much harder.
Another source of inspiration is the 'onsets and frames' software that powers some automated transcription software:
https://github.com/magenta/magenta/tree/master/magenta/model...
I think if this code is over your head that maybe a good introduction course on signal processing would be a nice thing to have under your belt.
Best of luck!
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What is a direction to head into once learning the basics in Python?
As mentioned in other comments, doing a project would help to take the next step, especially something that'd help you personally. You might be able to do something related to music as well, see https://github.com/vinta/awesome-python#audio for some modules. There's https://github.com/magenta/magenta for generating art/music. Specializing in a niche area could help if you decide do freelancing, write books, etc.
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Does Anyone Know Python Packages That Can Generate Sound Signals And Play Them?
check out https://github.com/magenta/magenta (found it via https://github.com/vinta/awesome-python#miscellaneous)
pluginbase
We haven't tracked posts mentioning pluginbase yet.
Tracking mentions began in Dec 2020.
What are some alternatives?
blinker - A fast Python in-process signal/event dispatching system.
Tryton - Mirror of tryton
itsdangerous - Safely pass trusted data to untrusted environments and back.
Pychievements - The Python Achievements Framework!
attrs - Python Classes Without Boilerplate
cppimport - Import C++ files directly from Python!
import_string
riprova - Versatile async-friendly retry package with multiple backoff strategies