F5-TTS
F5-TTS is an open-source text-to-speech project for generating speech from text, with command-line inference and a Gradio web interface.
What F5-TTS is
F5-TTS is an open-source text-to-speech project for generating speech from text, with command-line inference and a Gradio web interface. Its approach uses flow matching and a Diffusion Transformer with ConvNeXt V2; the project also includes E2 TTS, which uses a Flat-UNet Transformer. The Gradio app supports chunked inference, multiple styles and speakers, and voice chat powered by Qwen2.5-3B-Instruct. Custom inference instructions cover adding language support. Users can install it as a Python package, run it from an editable local clone, or build and run it with Docker. Training and fine-tuning are supported through Hugging Face Accelerate or a Gradio app. Installation guidance covers NVIDIA, AMD and Intel GPUs, as well as Apple Silicon; AMD ROCm instructions specify Linux. The project also describes deployment with Triton and TensorRT-LLM. Requirements include Python 3.10 or newer and FFmpeg. The code is under the MIT License, but pre-trained models use CC-BY-NC, and commercial use is listed as unavailable.
Good
- Offers command-line and Gradio interfaces.
- Supports multiple styles and speakers.
- Training and fine-tuning are supported.
- Installation guidance covers several GPU types.
- Custom language support instructions are linked.
Watch out
- Requires Python 3.10 or newer and FFmpeg.
- Pre-trained models use CC-BY-NC licensing.
- Commercial use is listed as unavailable.
- AMD ROCm instructions specify Linux.
Who it suits
F5-TTS may suit developers and researchers seeking an open-source speech-generation project with configurable inference and training options. It is not suitable for commercial use under the listed terms for the pre-trained models.
Verdict
F5-TTS offers several ways to run and adapt speech generation, including a web interface, CLI and training paths. Review the separate code and model licenses before use, especially for commercial projects.
The facts
Each one read from F5-TTS's own pages, with the date it was checked.
What it does
- API access
- Yeschecked 20 Sep 2026
- Commercial use
- Nochecked 20 Sep 2026
- Custom languages
- The README links to custom inference instructions for adding language support.checked 8 Oct 2026
- Deployment
- The README describes a deployment solution using Triton and TensorRT-LLM.checked 8 Oct 2026
- Hardware and operating systems
- The installation instructions cover NVIDIA, AMD, and Intel GPUs and Apple Silicon; the AMD ROCm instructions specify Linux.checked 8 Oct 2026
- Hardware requirements
- The installation instructions cover NVIDIA, AMD, Intel, and Apple Silicon setups; the AMD ROCm instructions specify Linux.checked 8 Oct 2026
- Inference features
- The Gradio app supports basic text-to-speech with chunk inference, multi-style and multi-speaker generation, and voice chat powered by Qwen2.5-3B-Instruct.checked 8 Oct 2026
- Interfaces
- The project provides a Gradio web interface and command-line inference.checked 8 Oct 2026
- License
- The code is released under the MIT License, while the pre-trained models are licensed under CC-BY-NC.checked 8 Oct 2026
- Model availability
- The README links F5-TTS and E2 TTS base models on Hugging Face, Model Scope, and Wisemodel.checked 8 Oct 2026
- Model design
- The project describes F5-TTS as a Diffusion Transformer with ConvNeXt V2 and includes E2 TTS, a Flat-UNet Transformer.checked 8 Oct 2026
- Purpose
- F5-TTS is an open-source text-to-speech project that uses flow matching to generate fluent and faithful speech.checked 8 Oct 2026
- Related tools
- The project acknowledges Vocos and BigVGAN as vocoders and FunASR and faster-whisper among its evaluation tools.checked 8 Oct 2026
- Requirements
- The installation instructions call for Python 3.10 or newer and FFmpeg.checked 8 Oct 2026
- Runtime benchmark
- The README reports a 253 ms average latency for F5-TTS Base with Vocos at concurrency 2 on a single L20 GPU in its stated benchmark setup.checked 8 Oct 2026
- Support guidance
- The README recommends searching GitHub issues for help with problems encountered.checked 8 Oct 2026
- Training
- Training and fine-tuning are supported through Hugging Face Accelerate or a Gradio app.checked 8 Oct 2026
- Voice cloning
- Yeschecked 20 Sep 2026
- Ways to run
- The project can be installed as a Python package, run from a local editable clone, or built and run with Docker.checked 8 Oct 2026
- Web interface
- The project provides Gradio commands for launching a web interface and a share link.checked 8 Oct 2026
Where it runs
- Platforms
- web,linux,macos,api,self_hostedchecked 20 Sep 2026
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