Speechify's Text-to-Speech API is designed to seamlessly integrate cutting-edge TTS capabilities into your digital offerings, providing an unparalleled auditory experience for your users. Ideal for automating customer calls, educational platforms, media and entertainment, accessibility, gaming, and more.
Speechify API is built upon a cutting-edge, proprietary AI model developed in-house by our team of researchers. This model has been behind Speechify's Reader Apps—the world's largest text-to-speech consumer app, with a user base of over 23 million people. For more than two years, it has been powering not only our reader apps but also the text-to-speech experiences of Medium.com, Artifact, Walmart, Quadrant, Carnegie Learning, and hundreds of other products. We are thrilled to now open our technology to the world, enabling any business or developer to harness the power of our state-of-the-art AI model and elevate their audio experiences.
Getting Started
If you're new to Speechify AI API, or to HTTP APIs in general, we have prepared the Quickstart guide to get you covered. Please make sure that you have followed every step there to streamline your API usage experience.
Full API Documentation: https://docs.sws.speechify.com/reference/getspeech
Models
Speechify's advanced text-to-speech models are designed to meet specific user needs, from simple text reading to complex multilingual and emotional tone integration. This page describes the models that are available through the API.
Simba English
Speechify's Simba English text-to-speech model offers standard capabilities designed to deliver clear and natural voice output for reading texts. The model focuses on delivering a consistent user experience, supporting fine-tuning, and zero-shot voice cloning. The audio output of this model is distinctively different from other models.
Key Features
Voice Clarity: Produces clear and natural speech.
Consistency: Maintains uniform quality across all outputs.
Zero-shot voice cloning: Creates a voice clone from a short audio sample.
Fine-tuning: Creates a voice clone from hours of the speaker's audio, providing significantly better results than zero-shot voice cloning.