Metadata-Version: 2.1
Name: EasyTTS
Version: 0.1.8
Summary: Ready-to-use Multilingual Text-To-Speech (TTS) package.
Home-page: https://EasyTTS.github.io/
Author: Yanis Labrak & Others
Author-email: yanis.labrak@univ-avignon.fr
License: UNKNOWN
Description: <p align="center">
          <img src="https://raw.githubusercontent.com/qanastek/EasyTTS/main/ressources/images/logo_name_transparent.png" alt="drawing" width="250"/>
        </p>
        
        [![PyPI version](https://badge.fury.io/py/EasyTTS.svg)](https://badge.fury.io/py/EasyTTS)
        [![GitHub Issues](https://img.shields.io/github/issues/qanastek/EasyTTS.svg)](https://github.com/qanastek/EasyTTS/issues)
        [![Contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg)](CONTRIBUTING.md)
        [![License: MIT](https://img.shields.io/badge/License-MIT-brightgreen.svg)](https://opensource.org/licenses/MIT)
        
        EasyTTS is an open-source and ready-to-use Multilingual Text-To-Speech (TTS) package.
        
        The goal is to simplify usages of **state-of-the-art** text-to-speech models for a variety of languages (french, english, ...).
        
        ⚠️ EasyTTS is currently in beta. ⚠️
        
        # Quick installation
        
        EasyTTS is constantly evolving. New features, tutorials, and documentation will appear over time. EasyTTS can be installed via PyPI to rapidly use the standard library. Moreover, a local installation can be used by those users than want to run experiments and modify/customize the toolkit. EasyTTS supports both CPU and GPU computations. Please note that CUDA must be properly installed to use GPUs.
        
        ## Anaconda setup
        
        ```bash
        conda create --name EasyTTS python=3.7 -y
        conda activate EasyTTS
        pip install git+https://github.com/repodiac/german_transliterate
        ```
        
        More information on managing environments with Anaconda can be found in [the conda cheat sheet](https://docs.conda.io/projects/conda/en/4.6.0/_downloads/52a95608c49671267e40c689e0bc00ca/conda-cheatsheet.pdf).
        
        ## Install via PyPI
        
        Once you have created your Python environment (Python 3.7+) you can simply type:
        
        ```bash
        pip install EasyTTS
        pip install git+https://github.com/repodiac/german_transliterate
        ```
        
        ## Install with GitHub
        
        Once you have created your Python environment (Python 3.7+) you can simply type:
        
        ```bash
        git clone https://github.com/qanastek/EasyTTS.git
        cd EasyTTS
        pip install -r requirements.txt
        pip install --editable .
        ```
        
        Any modification made to the `EasyTTS` package will be automatically interpreted as we installed it with the `--editable` flag.
        
        # Example Usage
        
        ```python
        import soundfile as sf
        from EasyTTS.inference.TTS import TTS
        
        tts = TTS(lang="fr") # Instantiate the model for your language
        audio = tts.predict(text="Bonjour à tous") # Make a prediction
        
        sf.write('./audio_pip.wav', audio, 22050, "PCM_16") # Save output in .WAV file
        ```
        
        # Model architectures
        
        1. **[Tacotron 2](https://github.com/NVIDIA/tacotron2)** (from Google Research &  University of California, Berkeley) released with the paper [NATURAL TTS SYNTHESIS BY CONDITIONING WAVENET ON MEL SPECTROGRAM PREDICTIONS](https://arxiv.org/pdf/1712.05884.pdf), by Jonathan Shen, Ruoming Pang, Ron J. Weiss, Mike Schuster, Navdeep Jaitly, Zongheng Yang, Zhifeng Chen, Yu Zhang, Yuxuan Wang, RJ Skerry-Ryan, Rif A. Saurous, Yannis Agiomyrgiannakis and Yonghui Wu.
        
        # Datasets used
        
        1. **[SynPaFlex](http://synpaflex.irisa.fr/fr/)** (from IRISA, LLF (Laboratoire de Linguistique Formelle de Nantes), LIUM (Le Mans Université) and ATILF (Analyse et Traitement Informatique de la Langue Française)) released with the paper [SynPaFlex-Corpus: An Expressive French Audiobooks Corpus Dedicated to Expressive Speech Synthesis](https://hal.archives-ouvertes.fr/hal-01826690), by Aghilas Sini, Damien Lolive, Gaëlle Vidal, Marie Tahon and Élisabeth Delais-Roussarie.
        
        # Build PyPi package
        
        Build: `python setup.py sdist bdist_wheel`
        
        Upload: `twine upload dist/*`
        
Keywords: python,transformers,huggingface,wrapper,toolkit,speech,text-to-speech,text2speech,text-2-speech,T2S,easy,voice,vocal synthesis,synthesis,Speech synthesis
Platform: UNKNOWN
Requires-Python: >=3.7
Description-Content-Type: text/markdown
