swivid/f5-tts

F5-TTS is a Python library that converts written text into spoken audio, a task commonly called text-to-speech. What makes it notable is the technique it uses: a method called flow matching, which guides a model to generate speech that sounds natural and closely matches the style of a short audio clip you provide as a reference. The name comes from an academic paper published by researchers at Shanghai Jiao Tong University and partner labs. To use it, you give the system a short recording of a voice (a few seconds of speech), and it can then read new text aloud in that same voice. This makes it useful for voice cloning, audiobook narration, or any situation where you want consistent synthetic speech from a specific speaker. The web interface, built with a tool called Gradio, lets you experiment without writing any code. A command-line version is also available for more automated workflows. The library supports multiple modes. Basic mode generates speech from a single voice. Multi-style and multi-speaker modes let you mix different voices or speaking styles in a single output, which is useful for narrating dialogue or stories with different characters. There is also a voice chat mode that pairs the speech engine with a language model so you can have a spoken conversation with an AI. Installation requires a machine with a compatible graphics card (NVIDIA, AMD, or Intel) or an Apple Silicon Mac, since the underlying models are computationally demanding. A Docker container is also provided for easier deployment. Developers who want to train the model on their own data or fine-tune it for a specific voice can do so using either a web interface or a configuration file. The project is the official code release accompanying the F5-TTS research paper and includes benchmark results showing the model can generate speech with very low latency on server-grade hardware.