

Parler TTS
Overview :
Parler-TTS is a lightweight text-to-speech (TTS) model developed by Hugging Face that can generate high-quality, natural-sounding speech in a given speaker style (gender, tone, speaking style, etc.). It is an open-source implementation of the paper "Natural language guidance of high-fidelity text-to-speech with synthetic annotations" by Dan Lyth and Simon King from Stability AI and the University of Edinburgh, respectively. Unlike other TTS models, Parler-TTS is fully open-source, including the dataset, preprocessing, training code, and weights. Features include:
* Generation of high-quality, natural-sounding speech output
* Flexible usage and deployment
* Provision of a rich annotated speech dataset.
Pricing: Free.
Target Users :
Generate natural-sounding voices, customize specific speaker styles, and provide a rich set of annotated speech datasets.
Use Cases
Customizing the speaking style of generated voices
Quickly deploy and use natural-sounding speech output
Providing rich resources for training and improving TTS models
Features
Generate high-quality, natural-sounding speech output
Customize speech based on given speaker styles
Easy-to-use installation and deployment
Provide an open-source annotated speech dataset
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