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README update
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<p align="center"><img src="https://user-images.githubusercontent.com/1402048/52643646-c2102980-2edd-11e9-8c37-b72f3c89a640.png" data-canonical-src="
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" width="320" height="95" /></p>
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<br/>
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<p align='center'>
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<img src="https://travis-ci.org/mozilla/TTS.svg?branch=dev"/>
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<a href='https://discourse.mozilla.org/c/tts'><img src="https://img.shields.io/badge/discourse-online-green.svg"/></a>
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<a href='https://opensource.org/licenses/MPL-2.0'> <img src="https://img.shields.io/badge/License-MPL%202.0-brightgreen.svg"/></a>
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</p>
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This project is a part of [Mozilla Common Voice](https://voice.mozilla.org/en).
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<br/>
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This project is a part of [Mozilla Common Voice](https://voice.mozilla.org/en).
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Mozilla TTS aims a deep learning based Text2Speech engine, low in cost and high in quality.
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## [Mozilla TTS Tutorials and Notebooks](https://github.com/mozilla/TTS/wiki/TTS-Notebooks-and-Tutorials)
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## Datasets and Data-Loading
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TTS provides a generic dataloader easy to use for your custom dataset.
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You just need to write a simple function to format the dataset. Check ```datasets/preprocess.py``` to see some examples.
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TTS provides a generic dataloader easy to use for your custom dataset.
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You just need to write a simple function to format the dataset. Check ```datasets/preprocess.py``` to see some examples.
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After that, you need to set ```dataset``` fields in ```config.json```.
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Some of the public datasets that we successfully applied TTS:
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