Parler-TTS is a lightweight text-to-speech model and library for generating high-quality natural-sounding speech from text with controllable voice characteristics.
It supports prompting for attributes such as gender, pitch, speaking style, recording quality, and background conditions, and the repository includes both inference code and training tooling so users can run the model or train and fine-tune their own variants.
This is free and open source software.
Key Features
- Generates high-quality speech from text.
- Allows voice control through natural language descriptions.
- Supports named speakers for more consistent voice generation.
- Includes inference code for running pretrained models.
- Provides training and fine-tuning tools for custom models.
- Offers inference optimisations including SDPA, Flash Attention 2, torch.compile, and streaming support.
- Publishes model weights, datasets, and code under a permissive license.
Website: github.com/huggingface/parler-tts
Support:
Developer: Hugging Face
License: Apache License 2.0
Parler-TTS is written in Python. Learn Python with our recommended free books and free tutorials.
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