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Yoach Lacombe

ylacombe

yoachlacombe

ylacombe

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Yesterday, we released Parler-TTS and Data-Speech, fully open-source reproduction of work from the paper: Natural language guidance of high-fidelity text-to-speech with synthetic annotations (2402.01912)

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).

parler-tts/parler-tts-fully-open-source-high-quality-tts-models-66164ad285ba03e8ffde214c

Parler-TTS Mini v0.1, is the first iteration Parler-TTS model trained using 10k hours of narrated audiobooks. It generates high-quality speech with features that can be controlled using a simple text prompt (e.g. gender, background noise, speaking rate, pitch and reverberation).

To improve the prosody and naturalness of the speech further, we're scaling up the amount of training data to 50k hours of speech. The v1 release of the model will be trained on this data, as well as inference optimisations, such as flash attention and torch compile.

parler-tts/parler_tts_mini_v0.1

Data-Speech can be used for annotating speech characteristics in a large-scale setting.

parler-tts/open-source-speech-datasets-annotated-using-data-speech-661648ffa0d3d76bfa23d534

This work is both scalable and easily modifiable and will hopefully help the TTS research community explore new ways of conditionning speech synthesis.

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.