> For the complete documentation index, see [llms.txt](https://docs.aimlapi.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.aimlapi.com/api-references/speech-models/text-to-speech.md). # Text-to-Speech Overview of the capabilities of AIML API Text-to-Speech (TTS) models. ## Overview Text-to-speech (TTS) models convert written text into natural-sounding speech, enabling a wide range of applications, from voice assistants and audiobooks to accessibility tools for visually impaired users. These models use deep learning techniques, such as neural vocoders and transformer-based architectures, to generate human-like speech with variations in tone, pitch, and emphasis. Many modern TTS systems support multiple languages, voices, and even emotional expressions for more engaging and realistic audio output. Advanced TTS models offer features like speaker adaptation, voice cloning, and fine-tuned prosody control, allowing for highly customizable speech synthesis. Some solutions run on-device for real-time applications, while cloud-based TTS services provide scalable, high-quality synthesis for larger workloads. Developers can integrate TTS into their applications through APIs, enabling dynamic voice generation for customer support, content creation, and assistive technologies. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.aimlapi.com/api-references/speech-models/text-to-speech.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.