yarngpt 0.2.0
A Python wrapper for YarnGPT text-to-speech model with multi-language support
Requires Python: >=3.6

# YarnGPT Python Wrapper Library

## Version 0.2.0
- Added support for local speakers
- Added support for multiple languages
- Added support for multiple languages

## Description
YarnGPT is a Python wrapper for the YarnGPT text-to-speech model, designed to synthesize natural Nigerian speech in multiple languages using a pure language modeling approach. This library provides a simple API to convert text into audio output, allowing users to select from various preset voices, languages, and adjust generation parameters.

## Features
- Supports multiple Nigerian languages: English, Yoruba, Igbo, and Hausa
- Rich set of voices for each language:
  - English: idera, chinenye, jude, emma, umar, joke, zainab, osagie, remi, tayo
  - Yoruba: abayomi, aisha, folake
  - Igbo: chioma, obinna, adanna
  - Hausa: amina, fatima, ibrahim, yusuf
- Utilizes Hugging Face's model caching for efficient model loading
- Exposes a straightforward API function: generate_speech(text, speaker, language, temperature, repetition_penalty, max_length)
- Allows customization of generation parameters
- Includes unit tests to ensure core functionality

## Installation
1. Create and activate a virtual environment:
   - On Linux/MacOS:
   ```bash
   python3 -m venv env
   source env/bin/activate
   ```
   - On Windows:
   ```bash
   python -m venv env
   env\Scripts\activate
   ```

2. Install the package:
   ```bash
   pip install yarngpt
   ```

## Usage
Basic usage to generate and save audio:
```python
from yarngpt import generate_speech
import torchaudio

# Generate English speech with default speaker
audio = generate_speech("Hello, this is a test.", language="english")

# Generate Yoruba speech with a Yoruba voice
audio = generate_speech("Bawo ni?", speaker="abayomi", language="yoruba")

# Save the generated audio
torchaudio.save("output.wav", audio, sample_rate=24000)
```

For Jupyter Notebook users, you can also play the audio directly:
```python
from yarngpt import generate_speech
import torchaudio
from IPython.display import Audio

# Generate speech in different languages
english_audio = generate_speech("Hello!", speaker="idera", language="english")
yoruba_audio = generate_speech("Bawo ni?", speaker="abayomi", language="yoruba")
igbo_audio = generate_speech("Kedu?", speaker="chioma", language="igbo")
hausa_audio = generate_speech("Sannu!", speaker="amina", language="hausa")

# Save and play the audio
torchaudio.save("output.wav", english_audio, sample_rate=24000)
Audio("output.wav")
```

## Parameter Options
- `text`: The input string to convert to speech
- `speaker`: Choose from available voices by language (see Features section for full list)
- `language`: The language for speech generation ("english", "yoruba", "igbo", "hausa")
- `temperature`: Controls the randomness of generation (default is 0.1)
- `repetition_penalty`: A factor to reduce repetitive output (default is 1.1)
- `max_length`: The maximum length of the generated output tokens (default is 4000)

## Testing
Run the unit tests to verify functionality:
```bash
python -m unittest discover -s tests
```

## License
This project is licensed under the MIT License.

## Acknowledgments
- Built as a contribution to yarngpt projects
- Utilizes Hugging Face's model caching and the transformers library
- Special thanks to the open-source community for their ongoing support

For more details and documentation, visit the GitHub repository: https://github.com/jerryola1