SpectroGenV2
Hides images into sound (by usr-ein)
nnAudio
Audio processing by using pytorch 1D convolution network (by KinWaiCheuk)
| SpectroGenV2 | nnAudio | |
|---|---|---|
| 1 | 1 | |
| 14 | 962 | |
| - | - | |
| 1.8 | 5.3 | |
| about 3 years ago | 3 months ago | |
| Python | Python | |
| GNU General Public License v3.0 only | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
SpectroGenV2
Posts with mentions or reviews of SpectroGenV2.
We have used some of these posts to build our list of alternatives
and similar projects.
nnAudio
Posts with mentions or reviews of nnAudio.
We have used some of these posts to build our list of alternatives
and similar projects.
-
LEAF: A Learnable Frontend for Audio Classification
FYI nnAudio has learnable STFT and Mel kernels https://github.com/KinWaiCheuk/nnAudio
What are some alternatives?
When comparing SpectroGenV2 and nnAudio you can also consider the following projects:
igel - a delightful machine learning tool that allows you to train, test, and use models without writing code
ultimatevocalremovergui - GUI for a Vocal Remover that uses Deep Neural Networks.
kapre - kapre: Keras Audio Preprocessors
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.