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# Parler-TTS
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> [!IMPORTANT]
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> We're proud to release Parler-TTS v0.1, our first 300M parameter model, trained on 10.5K hours of audio data.
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> In the coming weeks, we'll be working on scaling up to 50k hours of data, in preparation for the v1 model.
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Parler-TTS is a lightweight text-to-speech (TTS) model that can generate high-quality, natural sounding speech in the style of a given speaker (gender, pitch, speaking style, etc). It is a reproduction of work from the paper [Natural language guidance of high-fidelity text-to-speech with synthetic annotations](https://www.text-description-to-speech.com) by Dan Lyth and Simon King, from Stability AI and Edinburgh University respectively.
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Contrarily to other TTS models, Parler-TTS is a **fully open-source** release. All of the datasets, pre-processing, training code and weights are released publicly under permissive license, enabling the community to build on our work and develop their own powerful TTS models.
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This repository contains the inference and training code for Parler-TTS. It is designed to accompany the [Data-Speech](https://github.com/huggingface/dataspeech) repository for dataset annotation.
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> [!IMPORTANT]
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> We're proud to release Parler-TTS v0.1, our first 300M parameter model, trained on 10.5K hours of audio data.
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> In the coming weeks, we'll be working on scaling up to 50k hours of data, in preparation for the v1 model.
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## 📖 Quick Index
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* [Installation](#installation)

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