semvec/pypi-packages huggingface dataset:
verstack 4.3.11
Machine learning tools to make a Data Scientist's work more efficient
License: MIT
Keywords: impute, missing, values, stratify, nan, continuous, multiprocessing, concurrent, timer

.. image:: https://img.shields.io/pepy/dt/verstack
   :target: https://pypi.org/project/verstack/

.. image:: https://img.shields.io/badge/version-4.3.11-success.svg?color=blue
   :target: https://pypi.org/project/verstack/

.. image:: logo.png

**verstack** is a set of Machine learning tools to make a Data Scientist's work efficient.

The package contains multiple tools for: training & tuning machine learning models, creating ensembles, working with datetime objects, data transformation & wrangling, imputing missing values, concurrency work and many more.

Please refer to the `official documentation <https://verstack.readthedocs.io>`_ for more information.

The project was created by Danil Zherebtsov in 2020.

It is currently maintained by a single contributor with occasional contributions by the active members of the community.

Installation
~~~~~~~~~~~~

.. code-block:: console

  $ pip install verstack
  $ pip install --upgrade verstack


Dependencies
------------

- Python (>= 3.6)
- numpy
- pandas<=2.0.3
- scikit-learn>=0.23.2,<=1.1.3
- lightgbm>=3.3.0,<=4.0.0
- optuna>=2.10.0,<=3.2.0
- plotly>=5.3.1,<=5.11.0
- matplotlib
- python-dateutil>=2.8.1,<=2.8.2
- holidays==0.11.3.1
- mlxtend
- category_encoders>=2.4.0,<=2.5.1
- fastparquet

=======

Development
-----------

I welcome new contributors of all experience levels. ``verstack`` community goals are to be helpful, welcoming, and effective.
`Development Guide <https://scikit-learn.org/stable/developers/index.html>`_
based on scikit-learn best practices has detailed information about contributing code, documentation, tests, and more. 

Important links
---------------

- Official source code repo: https://github.com/DanilZherebtsov/verstack
- Issue tracker: https://github.com/DanilZherebtsov/verstack/issues

Source code
-----------

You can check the latest sources with the command::

    git clone https://github.com/DanilZherebtsov/verstack.git

Submitting a Pull Request
-------------------------

Before opening a Pull Request, have a look at the full Contributing page to make sure your code complies
with the following guidelines: https://scikit-learn.org/stable/developers/index.html

Communication
-------------

- Author email: danil.com@me.com
- `Author profile <https://www.linkedin.com/in/danil-zherebtsov/>`_
 
Citation
--------

If you use verstack in a media/research publication, we would appreciate citations to this repository.
naijaligo-asr 0.1.1
ASR SDK for Nigerian languages Yoruba, Igbo, Hausa and Nigerian-accented English using CTranslate2-converted Whisper models
License: MIT
Requires Python: >=3.9

# NaijaLingo ASR SDK

ASR SDK for Nigerian languages using CTranslate2-converted Whisper models.

## Install

```bash
pip install naijaligo-asr
```

## Quickstart

```python
from naijaligo_asr import transcribe

text = transcribe("/path/to/audio.wav", language="yo")
print(text)
```

## CLI

```bash
naijaligo-asr --audio /path/to/audio.wav --language yo
```

## Docker usage

Build the image (CPU):

```bash
docker build -t naijaligo-asr:cpu .
```

Run transcription via CLI:

```bash
docker run --rm -v $(pwd):/data naijaligo-asr:cpu \
  naijaligo-asr --audio /data/audio.mp3 --language yo
```

For GPU (CUDA), use a CUDA-enabled base and `--gpus all` (to be added later).

## Supported languages

- yo: Yoruba
- ig: Igbo
- ha: Hausa
- en: Nigerian-accented English

## Notes
- Uses faster-whisper (CTranslate2 backend)
- Accepts file paths (mp3/wav/m4a/etc.) via librosa, or a numpy array (mono 16k)
- Task is transcription only; set `task="transcribe"` and the language code.

## Logging

Set via CLI `--log-level INFO` or env `NAIJALINGO_ASR_LOG=INFO`.