Llama-OuteTTS-1.0-1B

> [!IMPORTANT] > **Important Sampling Considerations** > > When using OuteTTS version 1.0, it is crucial to use the settings specified in the Sampling Configuration section. > > The **repetition penalty implementation** is particularly important - this model requires penalization applied to a **64-token recent window**, > rather than across the entire context window. Penalizing the entire context will cause the model to produce **broken or low-quality output**. > > Currently, **llama.cpp** delivers the most reliable and consistent output quality by default. > Both **llama.cpp** and **EXL2** support this windowed sampling approach, while **Transformers** doesn't. > > To address this limitation, I've implemented a **windowed repetition penalty** for the **Hugging Face Transformers** backend in the **OuteTTS** library, > which significantly improves output quality and resolves sampling issues, providing comparable results to llama.cpp. This update brings significant improvements in speech synthesis and voice cloning—delivering a more powerful, accurate, and user-friendly experience in a compact size. The model is designed to be used with a speaker reference. Without one, it generates random vocal characteristics, often leading to lower-quality outputs. The model inherits the referenced speaker's emotion, style, and accent. When transcribing to other languages with the same speaker, you may observe the model retaining the original accent.