semvec/pypi-packages huggingface dataset:
argus-ticket-rt 1.2.0
Allow argus-server to create tickets in Request Tracker
Requires Python: >=3.10

# argus_ticket_rt

This is a plugin to create tickets in Request Tracker from [Argus](https://github.com/Uninett/argus-server)

The API supported is RT V2. RT 4.4 needs [RT::Extension::REST2](https://github.com/bestpractical/rt-extension-rest2)
to support V2, while RT 5.0.0 and later has the support included.

## Settings

* `TICKET_PLUGIN` must be set to `"argus_ticket_rt.RequestTrackerPlugin"`
* `TICKET_ENDPOINT`: Link to instance, absolute URL
* `TICKET_AUTHENTICATION_SECRET`: Standard username/password or token:

    ```
    {
        "username": username,
        "password": password
    }
    ```

    or

    ```
    {
        "token": token
    }
    ```

    It is recommended to create an RT API user and to ensure that that user has
    the necessary authorization for creating tickets in the relevant queue.

    Username and password login can be disabled if the login happens through an
    external provider. In that case an authentication token should be used.

    If authentication tokens are enabled in RT they can be created under
    "Logged in as ..." -> "Settings" -> "Auth tokens".

    This could however be turned off side-wide or just for that specific user.
    In that case please contact the responsible RT administrator.

* `TICKET_INFORMATION`:

    Queue (obligatory)

    ```
    {
        "queue": queue_name
    }
    ```

    ### Custom fields (optional)

    There are two ways of automatically filling custom fields:

    1. Custom fields that are always the same, independent of the incident.
    These will be set in `custom_fields_set` with the name of the custom field as key and the fixed value as value.


        ```
        {
            "custom_fields_set" : {
                "name_of_custom_field": set_value,
            }
        }
        ```

    2. Custom fields that are filled by attributes of the Argus incident. These are set in `custom_fields_mapping` with the name of the custom field as key and the name of the attribute as it is returned by the API  as value (e.g. `start_time`). If the information can be found in the tags the value consists of a dictionary with `tag` as the key and the name of the tag as the value (e.g. {"tag": "host"}).

        ```
        {
            "custom_fields_mapping" : {
                "name_of_custom_field": attribute_of_incident,
                "name_of_custom_field": {"tag": name_of_tag},
            }
        }
        ```

## Library

The library used is [rt](https://pypi.org/project/rt/)
instead of [rt-client](https://pypi.org/project/rt-client/)
because "rt-client" does not support token authentication.

## Code style

argus_ticket_rt uses black as a source code formatter. Black can be installed
by running

```console
$ pip install black
```

A pre-commit hook will format new code automatically before committing.
To enable this pre-commit hook, run

```console
$ pre-commit install
```
diarize-whisper 0.1.4
Librairie pour la transcription ASR et la diarisation
Requires Python: >=3.10

# diarize_whisper

**diarize_whisper** est une librairie Python permettant la transcription automatique (ASR) et la diarisation d'un fichier audio en utilisant des modèles HuggingFace. Elle offre des outils pour :

- Convertir un fichier audio en format WAV mono 16kHz.
- Découper l'audio en segments de durée configurable.
- Transcrire chaque segment via un modèle ASR (par exemple, `openai/whisper-large-v3-turbo`).
- Appliquer une diarisation avec le modèle `pyannote/speaker-diarization-3.1` pour identifier les différents locuteurs.
- Fusionner les résultats de la transcription et de la diarisation afin de reconstruire les dialogues avec attribution des locuteurs.
- Fournir une interface en ligne de commande (CLI) pour faciliter l'exécution du pipeline complet.

## Table des matières

- [Fonctionnalités](#fonctionnalités)
- [Installation](#installation)
- [Utilisation](#utilisation)
  - [Utilisation en tant que librairie Python](#utilisation-en-tant-que-librairie-python)
  - [Utilisation via la CLI](#utilisation-via-la-cli)
- [Structure du projet](#structure-du-projet)
- [Configuration](#configuration)
- [Licence](#licence)

## Fonctionnalités

- **Conversion Audio**  
  Convertit n'importe quel fichier audio en un fichier WAV mono 16kHz afin d'assurer la compatibilité avec les modèles de transcription.

- **Segmentation Audio**  
  Découpe le fichier WAV en segments de durée fixe (par défaut 25 secondes, mais paramétrable) pour faciliter le traitement par le modèle ASR.

- **Transcription (ASR)**  
  Utilise un modèle de transcription (ASR) de HuggingFace pour extraire le texte du fichier audio. Le modèle par défaut est `openai/whisper-large-v3-turbo`.

- **Diarisation**  
  Applique la diarisation afin d'identifier et d'attribuer les segments de texte aux différents locuteurs à l'aide du modèle `pyannote/speaker-diarization-3.1`.

- **Fusion et Reconstruction des Dialogues**  
  Combine les résultats de l'ASR et de la diarisation, ajuste les timestamps et attribue les dialogues aux locuteurs, en affinant la segmentation pour de meilleurs résultats.

- **Interface en Ligne de Commande**  
  Un outil CLI (`asr_diarization`) permet d'exécuter l'ensemble du pipeline (conversion, segmentation, ASR, diarisation et reconstruction) en une seule commande.

## Installation

### Prérequis

- Python 3.10 ou supérieur.
- FFMPEG installé sur le poste.
- Un environnement compatible avec `torch`, `pydub`, `pyannote.audio` et `transformers`.

## Utilisation
explain-log 0.1.2
Explain your DevOps logs instantly using OpenAI with a colorful CLI.
Requires Python: >=3.8

# Explain-Log 🔥
> Understand your DevOps and Data Engineering logs instantly — powered by OpenAI.

---

## 🚀 What is Explain-Log?

**Explain-Log** is a colorful, intelligent command-line tool that reads any log file,  
explains the root cause of errors, and suggests fixes — powered by OpenAI's GPT models.

✅ Supports piped input from Kubernetes, Airflow, Spark, Jenkins, Terraform logs and more.  
✅ Provides clear warnings before sending logs externally.  
✅ Bright, beautiful CLI interface built with `rich`.

---

## 📦 Installation

**Option 1: From PyPI**

```bash
pip install explain-log
```

**Option 2: Using Docker**

```bash
docker pull yourdockerhubusername/explain-log
```

---

## 🛠 Usage

**Explain a log file:**

```bash
explain-log my-error.log
```

**Explain logs piped from another command:**

```bash
k logs my-pod-name -n my-namespace | explain-log --force
```

⚠️ When using piped input, you must add `--force` to skip manual confirmation.

---

## ⚙️ Options

| Option | Description |
|:---|:---|
| `--no-mask` | Disable auto-masking of sensitive data (emails, IPs, URLs) |
| `--force` | Skip security confirmation (required when piping logs) |

---

## 🎨 CLI Features

- Beautiful bright colored output
- Safety-first design (asks for confirmation before sending logs to OpenAI)
- Professional banners, warnings, and success messages
- Markdown-formatted explanations
- Works with stdin piping (`|`) or from log files

---

## 🛡 Security Notice

Explain-Log by default warns you and asks for your consent before sending any logs to OpenAI servers.  
You control when and what is sent.

---

## 🐳 Docker Usage

Run without installing Python:

```bash
docker run --rm yourdockerhubusername/explain-log --help
```

You can also pipe logs:

```bash
k logs mypod | docker run --rm -i yourdockerhubusername/explain-log --force
```

(Use `-i` to pass piped input into Docker.)

---

## 📄 License

This project is licensed under the [MIT License](LICENSE).

---

## ✨ Author

Made with ❤️ by [Your Name]  
GitHub: [github.com/yourgithub](https://github.com/yourgithub)