#Parler-TTS

Data-Speech provides efficient scripts for speech dataset annotation, emphasizing audio transformation and automated tagging essential for next-gen text-to-speech models. This toolset aids in crafting natural language labeled datasets, inspired by significant research on speech synthesis. It works seamlessly with renowned datasets like LibriTTS-R and MLS and integrates with the Parler-TTS library for both inference and training. Discover streamlined processes for annotating and generating detailed language prompts from speech data.

Parler-TTS is an open-source model for generating high-quality text-to-speech in different speaker styles. It provides complete access to datasets, training codes, and model weights under permissive licenses. The model supports rapid synthesis and is trained on extensive audiobook data, making it a suitable framework for researchers and developers. Parler-TTS allows for the customization of speech features through simple text prompts.