Principle:Ggml org Llama cpp Text To Speech
| Knowledge Sources | |
|---|---|
| Domains | Text_To_Speech, Audio |
| Last Updated | 2026-02-15 00:00 GMT |
Overview
Text To Speech is the principle of generating audio waveforms from text input using neural speech synthesis models.
Description
This principle covers the text-to-speech pipeline implemented in llama.cpp, which uses language models adapted for speech synthesis to convert text prompts into audio output. It includes support for different TTS model architectures including the OuteTTS approach, which uses a language model to generate discrete audio token sequences that are then decoded into waveforms.
Usage
Apply this principle when building applications that need to convert text into spoken audio, such as voice assistants, audiobook generation, or accessibility tools that read text aloud.
Theoretical Basis
Neural text-to-speech systems in the LLM paradigm treat speech generation as a sequence-to-sequence problem. The input text is tokenized and processed by a language model that has been trained to predict discrete audio tokens (codebook indices from a neural audio codec). These audio tokens are then decoded by a vocoder or audio codec decoder into continuous audio waveforms. The OuteTTS approach specifically uses a standard transformer language model fine-tuned on interleaved text and audio tokens, allowing the same model architecture used for text generation to be repurposed for speech synthesis with minimal architectural changes.