semvec/pypi-packages huggingface dataset:
planet-generator-sdk 0.1.2
Python SDk for Planet API, the 2D planet sprite generator.
License: MIT


# Planet Generator - Python SDK

Planet API is a **2D planet sprite generator**. Our internal process procedurally generate surfaces, map them to a true sphere, to finally render a planet at 1080x1080 with transparent background.
This SDK facilitate the interaction with the API and implement masks features to add lighting and solar effect to the planets.

This API currently under development. During this phase, it can be accessed for free. [See here how to get your key](https://planet.joffreybvn.be/).

## Installation

```shell
  pip install planet-generator-sdk
```

## Quickstart

Generate a 2D planet sprite with masks:

```python
from planet_generator import Client

# Create a Planet API client
planet_api = Client.from_api_key(
    api_key="YOUR API KEY HERE"
)

# Generate a new planet
planet = planet_api.get_planet()
sprite = planet.sprite.get_image(apply_masks=True)

sprite  # Contains the 2D planet sprite
```
pyspeechanalysis 0.0.1
Module for Speech Processing
License: MIT
Keywords: python,speech,signal,processing,speech processing,signal processing

# PySpeechAnalysis

* Now Support
    - autocorrelation, average_magnitude_difference, bandpass_filter, covariance, discrete_cosine_transfrom, 
    - framming, hamming_window, mel_filter, moving_average, normalize_signal, 
    - pre_emphasis, power_spectrum, standard_deviation 

## Example

How to use

```python
from scipy.io import wavfile
from pyspeechanalysis import SpeechProcessing as SP

inputfile = r'./audio.wav'
[fs, X] = wavfile.read(inputfile)

# PRE-PROCESSING
x = SP.normalize_signal(X, is_figure = True)

y = SP.pre_emphasis(x, is_figure = False)

# FRAMING AND OVERLAPPING
PERCENT = 80
FRAME_MILLISEC = 0.032
frame = SP.set_frame(fs, FRAME_MILLISEC)
signal_vector = SP.framming(y, frame, PERCENT)
```

Check out: https://edocstudio.info
model-creator-bird-sing-v2 0.2.0
Autoencoder singing
License: MIT

# IA singing bird

![alt text](readme_image/intro_image.jpg)

This is my final project of my engineering school. It has been made in one month with an other student
who works in cloud computing. The idea was to generate birds songs with an Artificial Intelligence. We
started with a tutorial from Valerio Velardo on sound VAE. We changed a bit the model to make it more 
more complex. We trained the model on Deep Learning machins because we needed GPU. Moreover, we tried 
different achitectures to find the best model.

## The model



## How to use the model

This work is made to be test and used again by professionals of birds. You can install this part of the work on Pypi :

pip install model-creator-bird-sing-v2