semvec/pypi-packages huggingface dataset:
pvcheetah 3.0.2
Cheetah Speech-to-Text Engine.
Requires Python: >=3.9
Keywords: Speech-to-Text, Speech Recognition, Voice Recognition, ASR, Automatic Speech Recognition

# Cheetah Binding for Python

## Cheetah Speech-to-Text Engine

Made in Vancouver, Canada by [Picovoice](https://picovoice.ai)

Cheetah is an on-device streaming speech-to-text engine. Cheetah is:

- Private; All voice processing runs locally.
- [Accurate](https://picovoice.ai/docs/benchmark/stt/)
- [Compact and Computationally-Efficient](https://github.com/Picovoice/speech-to-text-benchmark#rtf)
- Cross-Platform:
    - Linux (x86_64), macOS (x86_64, arm64), and Windows (x86_64, arm64)
    - Android and iOS
    - Chrome, Safari, Firefox, and Edge
    - Raspberry Pi (3, 4, 5)

## Compatibility

- Python 3.9+
- Runs on Linux (x86_64), macOS (x86_64, arm64), Windows (x86_64, arm64), and Raspberry Pi (3, 4, 5).

## Installation

```console
pip3 install pvcheetah
```

## AccessKey

Cheetah requires a valid Picovoice `AccessKey` at initialization. `AccessKey` acts as your credentials when using Cheetah SDKs.
You can get your `AccessKey` for free. Make sure to keep your `AccessKey` secret.
Signup or Login to [Picovoice Console](https://console.picovoice.ai/) to get your `AccessKey`.

## Usage

Create an instance of the engine and transcribe audio:

```python
import pvcheetah

handle = pvcheetah.create(access_key='${ACCESS_KEY}')

def get_next_audio_frame():
    pass

while True:
    partial_transcript, is_endpoint = handle.process(get_next_audio_frame())
    if is_endpoint:
        final_transcript = handle.flush()
```

Replace `${ACCESS_KEY}` with yours obtained from [Picovoice Console](https://console.picovoice.ai/). When done be sure
to explicitly release the resources using `handle.delete()`.

### Language Model

The Cheetah Python SDK comes preloaded with a default English language model (`.pv` file).
Default models for other supported languages can be found in [lib/common](../../lib/common).

Create custom language models using the [Picovoice Console](https://console.picovoice.ai/). Here you can train
language models with custom vocabulary and boost words in the existing vocabulary.

Pass in the `.pv` file via the `model_path` argument:
```python
cheetah = pvcheetah.create(
    access_key='${ACCESS_KEY}',
    model_path='${MODEL_FILE_PATH}')
```

## Demos

[pvcheetahdemo](https://pypi.org/project/pvcheetahdemo/) provides command-line utilities for processing audio using
Cheetah.
pvcobra 3.0.2
Cobra voice activity detection (VAD) engine
Requires Python: >=3.9
Keywords: voice activity detection engine, VAD

# Cobra Voice Activity Detection Engine

Made in Vancouver, Canada by [Picovoice](https://picovoice.ai)

Cobra is a highly accurate and lightweight voice activity detection (VAD) engine.

## Compatibility

- Python 3.9+
- Runs on Linux (x86_64), macOS (x86_64), Windows (x86_64, arm64), and Raspberry Pi (3, 4, 5).

## Installation

```console
pip3 install pvcobra
```

## AccessKey

Cobra requires a valid Picovoice `AccessKey` at initialization. `AccessKey` acts as your credentials when using Cobra SDKs.
You can get your `AccessKey` for free. Make sure to keep your `AccessKey` secret.
Signup or Login to [Picovoice Console](https://console.picovoice.ai/) to get your `AccessKey`.

## Usage

Create an instance of the engine

```python
import pvcobra

handle = pvcobra.create(access_key=${AccessKey})
```
Replace `${AccessKey}` with your AccessKey obtained from [Picovoice Console](https://console.picovoice.ai/). `handle` is
an instance of Cobra.

When initialized, the valid sample rate is given by `handle.sample_rate`. Expected frame length (number of audio samples
in an input array) is `handle.frame_length`. The engine accepts 16-bit linearly-encoded PCM and operates on
single-channel audio.

```python
def get_next_audio_frame():
    pass

while True:
    voice_probability = handle.process(get_next_audio_frame())
```

When done, resources have to be released explicitly:

```python
handle.delete()
```

## Demos

[pvcobrademo](https://pypi.org/project/pvcobrademo/) provides command-line utilities for processing real-time
audio (i.e. microphone) and files using Cobra.
ml-automater 0.1.0
An all-in-one automated ML pipeline for feature engineering, optimization, and evaluation.
Requires Python: >=3.8

# ML-Automator ðŸš€

**ML-Automator** is a powerful, low-code machine learning utility library that automates Feature Engineering, Hyperparameter Optimization, and Model Evaluation.


## ðŸŒŸ Features

- **Automated Feature Engineering**: Handle Target Encoding, Scaling, and Imputation in one line.
- **Optuna-Powered Optimization**: Pre-configured search spaces for RandomForest, XGBoost, CatBoost, LightGBM, and more.
- **Deep Evaluation**:
    - Multi-model score comparison.
    - Interactive ROC and Calibration curves using Plotly.
    - Automated Learning Curve analysis.
    - Automatic report generation (CSV/Excel/PNG).

## ðŸ“‚ Project Structure
Your library is organized into three core modules:
1. `feature_engineering.py`: Data preprocessing and importance extraction.
2. `models_optimizer.py`: Optuna-based hyperparameter tuning.
3. `trainer.py`: Model training, cross-validation, and visualization.

## ðŸš€ Quick Start

### 1. Automation at its Best
```python
from automater.feature_engineering import FeatureEvaluation
from sklearn.ensemble import RandomForestClassifier

evaluator = FeatureEvaluation(X, y)
processed_x, importance_df = evaluator.fit_all_at_once(RandomForestClassifier())
omnicode 0.0.1
OmniCode - Universal code intelligence toolkit (coming soon)
Requires Python: >=3.10
Keywords: ai, code, intelligence, toolkit

# OmniCode

Universal code intelligence toolkit.

**Status:** Under development. This package is a placeholder for the upcoming release.

## Coming Soon

- Code analysis and understanding
- Multi-language support
- AI-powered code intelligence

## License

Apache 2.0
shipthisapi-python 3.0.5
ShipthisAPI async utility package

# ShipthisAPI Python 

[![PyPI version](https://badge.fury.io/py/shipthisapi-python.svg)](https://badge.fury.io/py/shipthisapi-python)

A Python wrapper package for ShipthisAPI for making api calls using python.


Features
--------

* Demo setup with ``demo.py``
* Simple Step API calls.

Quickstart
----------


Install the latest `shipthisapi-python`
if you haven't installed it yet.


    pip install shipthisapi-python


[Generate a API Key For your Organisation](https://shipthis.crisp.help/en/article/how-to-generate-an-api-key-1qnsq1d/?bust=1633352029281)


```python
from ShipthisAPI.shipthisapi import ShipthisAPI

x_api_key = '<your_api_key>'
organisation = 'demo'
region_id='<your_region_id>'
location_id='<your_location_id>'


shipthisapi = ShipthisAPI(organisation=organisation, x_api_key=x_api_key, region_id=region_id, location_id=location_id)


# Get Organisation/User Info 

print(shipthisapi.info())


# Get Invoice Collection List

print(shipthisapi.get_list(collection_name='invoice'))

# Get all Sea list

print(shipthisapi.get_list(collection_name="sea_shipment", params={"count": 2}))


```


Documentation
-----------

For more details related to available fields and operations check our [developer portal](https://developer.shipthis.co)


 Available Operations 

-  Get Organisation / User Information
-  Create New Collection Entry
-  Update Collection
-  Get Collection Items
-  Delete an existing collection item



Support
------------------

If you have any queries related to usage of the api please raise a issue [here](https://github.com/shipthisco/shipthisapi-python)
vim-eof-comment 0.8.0
Adds Vim EOF modeline comments for given filetypes in given directories
Requires Python: >=3.10
Keywords: eof comments, eof, files, preprocessing, text, vim modeline, vim, vim-eof

# vim-eof-comment

![GitHub Repo stars](https://img.shields.io/github/stars/DrKJeff16/vim-eof-comment?style=flat-square)![GitHub Release](https://img.shields.io/github/v/release/DrKJeff16/vim-eof-comment?include_prereleases&sort=semver&display_name=release&style=flat-square)

[PyPI Package][pypi] | [Source Code][source]

Ensure Vim EOF comments in given files.

---

## About

This tool adds a [Vim modeline comment](https://neovim.io/doc/user/options.html#_2.-automatically-setting-options) at the end of the target files.

---

## Install

```bash
pip install vim-eof-comment
```

---

## Usage

General usage is as follows:

```bash
vim-eof-comment [-h] [-v] [-V] -e EXT1[,EXT2[,EXT3[,...]]] [-i EXT1:INDENT[:Y/N][,...]] [-n] dir1 [dir2 [...]]
```

You can also call it as a module:

```bash
python -m vim_eof_comment [-h] [-v] [-V] -e EXT1[,EXT2[,EXT3[,...]]] [-i EXT1:INDENT[:Y/N][,...]] [-n] dir1 [dir2 [...]]
```

### Example

```bash
vim-eof-comment -e py,md,lua .
```

---

## License

[GNU GPL-v2.0][license]

[license]: https://github.com/DrKJeff16/vim_eof_comment/blob/main/LICENSE
[pypi]: https://pypi.org/project/vim-eof-comment/
[source]: https://github.com/DrKJeff16/vim_eof_comment

<!-- vim: set ts=2 sts=2 sw=2 et ai si sta: -->
pvkoala 3.0.1
Koala Noise Suppression Engine.
Requires Python: >=3.9
Keywords: Noise Cancellation, Noise Suppression, Noise Removal, Speech Enhancement, Speech Denoising

# Koala Binding for Python

## Koala Noise Suppression Engine

Made in Vancouver, Canada by [Picovoice](https://picovoice.ai)

Koala is an on-device noise suppression engine. Koala is:

- Private; All voice processing runs locally.
- Cross-Platform:
  - Linux (x86_64), macOS (x86_64, arm64), Windows (x86_64, arm64)
  - Android and iOS
  - Chrome, Safari, Firefox, and Edge
  - Raspberry Pi (3, 4, 5)

## Compatibility

- Python 3.9 or higher
- Runs on Linux (x86_64), macOS (x86_64, arm64), Windows (x86_64, arm64), and Raspberry Pi (3, 4, 5).

## Installation

```console
pip3 install pvkoala
```

## AccessKey

Koala requires a valid Picovoice `AccessKey` at initialization. `AccessKey` acts as your credentials when using Koala
SDKs. You can get your `AccessKey` for free. Make sure to keep your `AccessKey` secret.
Signup or Login to [Picovoice Console](https://console.picovoice.ai/) to get your `AccessKey`.

### Usage

Create an instance of the engine and enhance audio:

```python
import pvkoala

koala = pvkoala.create(access_key='${ACCESS_KEY}')

def get_next_audio_frame():
    pass

while True:
    enhanced_audio = koala.process(get_next_audio_frame())
```

Replace `${ACCESS_KEY}` with yours obtained from [Picovoice Console](https://console.picovoice.ai/).
The input audio must come from a single-channel stream with integer 16-bit encoding. The sample rate must be identical
to `koala.sample_rate`. The stream must be split into *frames* with a fixed length in samples that can be obtained
from `koala.frame_length`.

The output of `koala.process()` is a frame of enhanced audio with the same 16-bit integer encoding. The delay in
samples between the start time of the input frame and the start time of the output frame can be attained from
`koala.delay_sample`.

In case the next audio frame does not directly follow the previous one, call `koala.reset()`.
When done be sure to explicitly release the resources using `koala.delete()`.

## Demos

[pvkoalademo](https://pypi.org/project/pvkoalademo/) provides command-line utilities for processing audio using Koala.
iflow-mcp_waii-ai-waii-mcp-hands-on-lab 0.1.0
Talk to your database using WAII and MCP through Claude Desktop
Requires Python: >=3.10

# waii-mcp-hands-on-lab
Talk to your database using WAII and MCP through Claude Desktop

## Prerequisites

Before you begin, we will use a basic MCP server to confirm the environment. 
Please follow the setup instructions in [PREREQUISITES.md](PREREQUISITES.md) to install and configure all required components.

## Next Steps

Ready to start building your first MCP server? Follow these steps:

1. [Configuring the Waii MCP Server](lab-docs/1_configuring_waii_mcp_server.md) - Set up your configuration so Claude can connect to your server
2. [Building the Waii MCP Server](lab-docs/ref_building_mcp_server.md) - Step-by-step guide to building your own MCP server from scratch

## References

- [Model Context Protocol Server Documentation](https://modelcontextprotocol.io/quickstart/server) - Official guide for building MCP servers
- [Claude Desktop Download](https://claude.ai/download) - Download Claude Desktop application