semvec/pypi-packages huggingface dataset:
pathenger 0.3.0
A utility package for determining the paths of executable files and temporary directories, especially useful for applications packaged with PyInstaller.
Requires Python: >=3.6

# pathenger

pathenger is a utility package for Python, offering a straightforward way to determine the paths of executable files and temporary directories. It is especially useful for applications packaged with PyInstaller, facilitating access to crucial path information in different execution environments.

## Features

- **Executable Path Detection**: Easily retrieve the path of the executable file when your application is packaged with PyInstaller.
- **Temporary Directory Path**: Obtain the path to the temporary directory used by PyInstaller in one-file mode, allowing for efficient management of temporary files.

## Installation

Install pathenger directly from PyPI to add it to your project:

```bash
pip install pathenger
```

## Usage

pathenger simplifies the process of getting the executable or script path, and the temporary directory path, especially for PyInstaller-packaged applications. Here's how to use it:

```python
from pathenger import *

# Get the path of the executable or script
executable_path = get_executable_path()
print("Executable/Script Path:", executable_path)

# Get the path of the temporary directory (for PyInstaller one-file mode)
temp_path = get_temp_path()
print("Temporary Directory Path:", temp_path)
```
pnm 0.0.1
Convert audio to phonetic text and practice improving your speech accent.
Requires Python: <4,>=3.5
Keywords: phonetics, machine learning, neural network, english, speech, practice

# pnm

pnm is an audio-to-phoneme conversion tool designed to transform spoken English into phonetic transcriptions. This project is a mini-project derived from a larger, unfinished personal project aimed at creating a tool for English phonetic practice. Although the main project wasn't completed, PNM is being transformed into a Python library for open-source use.

Currently, the tool is a work-in-progress but is functional and offers a simple way to convert audio into phonemes.

It is possible to classify the speech quality of the person training using the pnm tool. By analyzing the phonetic transcriptions generated from the spoken audio (by token confidence). This analysis can help in evaluating the quality of the speaker’s pronunciation and progress over time, allowing for personalized feedback during training. 

## Installation

To install the required dependencies, use the following command:

For cpu

```bash
pip install "pnm[cpu]"
```

For cuda 11.X
```bash
pip install "pnm[gpu]"
```

For cuda 12.X
```bash
pip install "pnm[gpu]" --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/
```

## Usage

### Command Line Interface

For get the phonemes of an audio file:

```bash
pnm file --file_path path/to/audio.wav
```

For get the phonemes of an audio recorder (default input device):

```bash
pnm recorder
```

For practice (default input device):

```bash
pnm practice
```

# Images

<p align="center">
  <img src="https://raw.githubusercontent.com/pstwh/pnm/main/examples/image.png" width="768" />
</p>
cura 0.0.2
Cura is a tool to manage and grow Communities.
Requires Python: >=3.12
Keywords: web3, social

`Cura <https://cura.openrank.com>`_ is a tool to manage and grow Communities.

It gives Farcaster channels their own AI agent.
This agent helps community owners and moderators curate people and content,
manage the community wallet, distribute rewards for positive participation.
If the community wants, they can launch their own currency or token.
It's simple, intuitive and gives valuable insights to a channel moderator,
using OpenRank under the hood.
milvus-model 0.2.12
Model components for PyMilvus, the Python SDK for Milvus
Requires Python: >=3.8

# Milvus Model Lib

The milvus-model library provides model components for PyMilvus, the official Python SDK for Milvus, an open-source vector database built for AI applications.

## Installation

`milvus-model` supports Python 3.8 and above. Install it via pip:
```bash
pip install milvus-model
```
This command installs milvus-model directly. If you're working within the PyMilvus ecosystem and want to include milvus-model as an optional component, you can install it using:
```bash
pip install pymilvus[model] 
# or pip install "pymilvus[model]" for zsh.
```
To upgrade milvus-model to the latest version, use:
```
pip install milvus-model --upgrade
```
If milvus-model was initially installed as part of the PyMilvus optional components, you should also upgrade PyMilvus to ensure compatibility. This can be done with:
```
pip install pymilvus[model] --upgrade
```
If you need to install a specific version of milvus-model, specify the version number:
```bash
pip install milvus-model==0.2.0
```
This command installs version 0.2.0 of milvus-model.