semvec/pypi-packages huggingface dataset:
inaSpeechSegmenter 0.8.0
CNN-based audio segmentation toolkit. Does voice activity detection, speech detection, music detection, noise detection, speaker gender recognition.
License: MIT
Requires Python: >=3.8
Keywords: speech-segmentation, audio-analysis, music-detection, noise-detection, speech-detection, speech-music, gender-classification, gender-representation, speaker-gender, speech, music, noise, gender, voice-activity-detection, speech-activity-detection, VAD, praat

Split audio signal into homogeneous zones of speech, music and noise. Then detects speaker gender.  

inaSpeechSegmenter has been presented at the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2018 conference in Calgary, Canada. If you use this toolbox in your research, you can cite the following work in your publications :


```bibtex
@inproceedings{ddoukhanicassp2018,
  author = {Doukhan, David and Carrive, Jean and Vallet, Félicien and Larcher, Anthony and Meignier, Sylvain},
  title = {An Open-Source Speaker Gender Detection Framework for Monitoring Gender Equality},
  year = {2018},
  organization={IEEE},
  booktitle={Acoustics Speech and Signal Processing (ICASSP), 2018 IEEE International Conference on}
}
```

inaSpeechSegmenter was ranked #1 against 6 open-source VAD system on a French TV and
radio benchmark : LIUM_SpkDiarization, Pyannote, Rvad, Silero, Speechbrain
```bibtex
@inproceedings{doukhan-etal-2024-inagvad,
    title = "{I}na{GVAD} : A Challenging {F}rench {TV} and Radio Corpus Annotated for Speech Activity Detection and Speaker Gender Segmentation",
    author = "Doukhan, David  and Maertens, Christine  and Le Personnic, William  and Speroni, Ludovic  and Dehak, Reda",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.785/",
    pages = "8963--8974",
}
```

inaSpeechSegmenter won MIREX 2018 speech detection challenge.  
http://www.music-ir.org/mirex/wiki/2018:Music_and_or_Speech_Detection_Results  
Details on the speech detection submodule can be found bellow:  


```bibtex
@inproceedings{ddoukhanmirex2018,
  author = {Doukhan, David and Lechapt, Eliott and Evrard, Marc and Carrive, Jean},
  title = {INA’S MIREX 2018 MUSIC AND SPEECH DETECTION SYSTEM},
  year = {2018},
  booktitle={Music Information Retrieval Evaluation eXchange (MIREX 2018)}
}
```
pandaflow 1.0.0
Command-line utilities for processing CSV files with regex and transformation strategies
License: MIT
Requires Python: >=3.9

# 📦 PandaFlow

![PyPI](https://img.shields.io/pypi/v/pandaflow) ![License](https://img.shields.io/pypi/l/license/pandaflow) ![Coverage](https://codecov.io/gh/user/repo/branch/main/graph/badge.svg) [![CI](https://github.com/giacomos85/pandaflow/actions/workflows/ci.yml/badge.svg)](https://github.com/giacomos85/pandaflow/actions/workflows/ci.yml)

<img src="doc/source/_static/logo.png" alt="Project Logo" width="400">

**PandaFlow** is a modular, strategy-driven CLI tool for transforming CSV files using declarative configuration. Built for maintainability, extensibility, and safety, it empowers users to define transformation pipelines with schema-aware validation and plugin-based logic.

---

## 🚀 Features

- ✅ **Strategy Pattern** — Each transformation is encapsulated in a reusable strategy  
- 🧩 **Config-Driven Execution** — Define pipelines in YAML, TOML, or JSON  
- 🔍 **Schema Validation** — Pydantic-powered rule parsing with clear error reporting  
- 🛠 **CLI Interface** — Run transformations with intuitive commands and feedback  
- 🔌 **Plugin Architecture** — Easily extend with custom strategies  
- 🧪 **Test Coverage** — Comprehensive suite with CI-ready structure  

---

## ⚙️ Usage

### CLI

```bash
pandaflow run --config config.json --input data.csv --output transformed.csv
```

## Config Example

```json
{
  "meta": {
    "csv_separator": ","
  },
  "match": {
    "filename": null,
    "glob": "**/bgsaxo*.csv",
    "regex": null
  },
  "rules": [
    {
      "field": "__md5__",
      "strategy": "hash",
      "source": [
        "ID Cliente",
        "Data della negoziazione",
        "Data valuta",
        "Tipo",
        "Nome prodotto",
        "Instrument ISIN",
        "Valuta strumento",
        "Mercato",
        "Simbolo strumento",
        "_Tipo",
        "Importo contabilizzato",
        "ID ordine",
        "Tasso di conversione"
      ],
      "function": "calculate_md5"
    },
    {
      "field": "__source__",
      "strategy": "constant",
      "value": "bgsaxo"
    }
  ]
}
```

## 🧰 Development Setup

Install Poetry
```bash
curl -sSL https://install.python-poetry.org | python3 -`
```
Install Dependencies

```bash
poetry install
```
consensusclustering 0.2.4
Python implementation of Consensus Clustering by Monti et al. (2003)
License: MIT
Requires Python: >=3.11

# Consensus Clustering

![PyPI](https://img.shields.io/pypi/v/consensusclustering)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/consensusclustering)
![PyPI - Wheel](https://img.shields.io/pypi/wheel/consensusclustering)
![PyPI - License](https://img.shields.io/pypi/l/consensusclustering)
![Codecov](https://img.shields.io/codecov/c/github/burtonrj/consensusclustering)
![GitHub Workflow Status (with event)](https://img.shields.io/github/actions/workflow/status/burtonrj/consensusclustering/build.yaml)

## Description

`consensusclustering` is a python implementation of the Consensus Clustering
algorithm for finding the optimal number of clusters by means of clustering
stability under resampling, as originally proposed by [Monti et al. Consensus
Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene
Expression Microarray Data (2003)](https://link.springer.com/article/10.1023/A:1023949509487).

## Installation

`consensusclustering` can be installed from PyPi using pip:

```bash
pip install consensusclustering
```

## Usage

See the 'examples' directory for a detailed overview of the package's functionality.

## License

`consensusclustering` is licensed under the MIT license, is free to use, and comes with no warranty whatsoever.