# Chinese and English Support

Llasa-1B
Llasa 1B
Llasa-1B is a text-to-speech model developed by the Audio Lab at the Hong Kong University of Science and Technology. Based on the LLaMA architecture and integrated with speech tokens from the XCodec2 codec, it converts text into natural and fluent speech. The model has been trained on 250,000 hours of Chinese and English speech data and supports generating speech from plain text, as well as utilizing given voice prompts for synthesis. Its main advantage is the ability to produce high-quality multilingual speech, making it suitable for various applications such as audiobooks and voice assistants. The model is licensed under CC BY-NC-ND 4.0, prohibiting commercial use.
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Llasa-3B
Llasa 3B
Llasa-3B is a powerful text-to-speech (TTS) model developed based on the LLaMA architecture, focused on Chinese and English speech synthesis. By integrating XCodec2's speech encoding technology, it efficiently converts text into natural and fluent speech. Its main advantages include high-quality speech output, support for multilingual synthesis, and flexible speech prompting capabilities. This model is suitable for various applications requiring speech synthesis, such as audiobook production and voice assistant development. Its open-source nature also allows developers to explore and expand its functionalities freely.
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104.9K
Chinese Picks
Aijia Writing
Aijia Writing
Aijia Writing is a desktop software developed based on the latest AI large language model technology, supporting both Chinese and English writing without the need for a GPU graphics card or a complex Python environment. It can quickly generate lengthy texts on everyday computers, featuring fast generation speed, simple installation and use, and low hardware requirements. The software's background leverages AI technology to enhance writing efficiency and lower technical barriers, enabling more people to easily use AI in their creations. Aijia Writing is free software aimed at everyday writing and content creators, helping them swiftly produce high-quality text content.
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58.5K
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