semvec/pypi-packages huggingface dataset:
ovos-dialog-normalizer-plugin 0.0.2a2
A question solver plugin for ovos
License: MIT
Keywords: ovos plugin utterance dialog TTS normalization

# OVOS Dialog Normalizer

a dialog transformer plugins for OVOS

## Description

This plugin will prepare text for TTS, it will expand contractions and pronounce digits, ensuring the TTS engine pronounces words correctly

- "I'm" -> "I am"
- "Dr." -> "Doctor"
- "1" -> "one"

## Install

`pip install ovos-dialog-normalizer-plugin`


## Configuration

All you need to do is add a entry in your `mycroft.conf` under `"dialog_transformers"` to enable the plugin

```json
"dialog_transformers": {
    "ovos-dialog-normalizer-plugin": {}
}
```

## Contributing

Pull Requests welcome! 

Adding new expansions should be straightforward, to improve number handling please refer to [ovos-number-parser](https://github.com/OpenVoiceOS/ovos-number-parser)

## Credits

<img src="img.png" width="128"/>

> This plugin was funded by the Ministerio para la Transformación Digital y de la Función Pública and Plan de Recuperación, Transformación y Resiliencia - Funded by EU – NextGenerationEU within the framework of the project ILENIA with reference 2022/TL22/00215337
EcoAI-Guide 2025.12.19192943
The system aims to process concise, structured prompts to help users understand the limitations of fine-tuning AI models, and to suggest alternative, resource-efficient strategies for model customizat
License: MIT
Requires Python: >=3.9

# EcoAI-Guide

EcoAI-Guide is a structured, resource-efficient decision-making tool designed to help users understand and optimize AI model customization strategies. By processing concise prompts, it guides users through evaluating the limitations of fine-tuning models and exploring alternative methods that reduce resource consumption. The system extracts key insights from user input and provides clear, actionable guidance to support effective AI deployment without unnecessary model modification.

## Features
- Analyzes user prompts to identify core insights and constraints
- Suggests sustainable and resource-efficient strategies for AI model customization
- Integrates with various language models for flexible deployment
- Provides structured, easy-to-understand guidance to enhance decision-making

## Installation
To install EcoAI-Guide, clone the repository and install required dependencies:
```bash
git clone <repository_url>
cd <repository_directory>
pip install -r requirements.txt
```

## Usage
Call the `EcoAI_Guide` function with your user input and optional API key or language model instance:
```python
from your_module_name import EcoAI_Guide

result = EcoAI_Guide("Your prompt here", api_key="your_api_key")
print(result)
```

## How It Works
- Initializes a language model, defaulting to the OpenAI-based `ChatLLM7` if no model or API key is provided
- Processes user prompts through a structured pattern-matching system (`llmatch`)
- Extracts key insights and guidance for resource-efficient AI strategy development
- Returns a list of tailored recommendations or insights based on the input

## Contributing
Contributions are welcome! Please fork the repository and submit pull requests with improvements or new features.

## License
This project is licensed under the MIT License. See the LICENSE file for details.

## Troubleshooting
- Ensure your api_key is valid and has appropriate permissions
- Check dependencies in `requirements.txt`
- Reach out via GitHub issues for support

## Acknowledgements
This project is inspired by best practices in sustainable AI deployment and resource-efficient model tuning.