semvec/pypi-packages huggingface dataset:
curlconverter 1.0.1
The library is responsible for parsing a cURL request string and converting it into a Python dictionary containing the method, URL, headers, and data
Requires Python: >=3.6,<4.0

Here's an example of how to use the curlconverter library:

from curlconverter import CurlConverter

# Example cURL command string
curl_string = 'curl \'https://api.example.com\' -H \'Content-Type: application/json\' -d \'{"foo": "bar"}\''

# Create an instance of CurlConverter with the cURL string
curl_converter = CurlConverter(curl_string)

# Convert the cURL string to a dictionary
parsed_data = curl_converter.convert()

# Print the parsed data
print(parsed_data)


Output:
{'method': 'GET', 'url': 'https://api.example.com', 'headers': {'Content-Type': 'application/json'}, 'data': '{"foo": "bar"}'}


In this example, we create an instance of CurlConverter with a cURL command string that makes a GET request to https://api.example.com with a Content-Type header and a JSON payload. We then call the convert() method on the instance to parse the cURL string and return a dictionary with the parsed data. Finally, we print the parsed data to the console.
BanglaTTS 0.0.3
An open-source offline text-to-speech package for Bangla language. Convert text to speech in male or female voice.
Requires Python: >=3.6
Keywords: python,text to speech,bangla text to speech,bangla tts,offline bangla tts,offline bangla text to speech

# BanglaTTS
BanglaTTS is a text-to-speech (TTS) system for Bangla language that works in offline mode. You can convert text to speech in male or female voice.

## Features
* Convert any bangla text to speech.
* Save the converted speech as an audio file.

## Requirements
* Python 3.6 or higher

## Installation
```bash
pip install BanglaTTS
```

## Usage
```python
from banglatts import BanglaTTS


tts = BanglaTTS(save_location="save_model_location")
path = tts("আমি বাংলায় কথা বলতে পারি।", voice='female', filename='1.wav') # voice can be male or female
```
dss-projectfutura 0.2.4
Content Generation based on Caches
Requires Python: >=3.7


# DSSv3g
This is the third version of the DSS text generator, but built in a more generic way to allow for any kind of dataset.
Lots of new features have been implemented, helping DSS*v3g* become actually useful.


DSS*v3g* is still built using the caches architecture, but additionally includes a tokenization step that allows any kind of data to be learnt.

## Variants
DSS has had a progression in versions and capability. Here you can explore their branches.
DSS is an architecture for building content generation ML models using caches.

### DSSv1
> Character-by-character text generation using caches. 4 char keys, 1 char values. JSON-encoded.

### DSSv2
> Word-by-word text generation using caches. 1 word keys, 1 word values. JSON-encoded.

### DSSv3
> Word-by word text generation using caches. Variable word keys, 1 word values. JSON-encoded with brotli compression.

#### DSSv3g
The most capable DSS variant to date, capable of text generation, music generation, code completion and (limited) chatting.
> General (integer tokens) version of DSS content generation using caches. Variable token keys, 1 token values. Binary encoded with brotli compression.

#### DSSv3i
Currently highly experimental and only implemented in JavaScript for easy graphics debugging.
> Image generation using the DSS caches architecture. Variable-*k* scalar keys, pixel scalar values. Binary encoded with brotli compression.

## Klarmuz
DSS*v3g* powers Klarmuz, the open platform for AI content generation. This project is possible thanks to jDev Studios and their fantastic Klarmuz compute units.
For examples on running DSS*v3g* models, please check out the [Klarmuz Platform](https://jdevstudios.es/klarmuz).