# F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching
[](https://github.com/SWivid/F5-TTS)
[](https://arxiv.org/abs/2410.06885)
[](https://swivid.github.io/F5-TTS/)
[](https://huggingface.co/spaces/mrfakename/E2-F5-TTS)
[](https://modelscope.cn/studios/modelscope/E2-F5-TTS)
[](https://x-lance.sjtu.edu.cn/)
[](https://www.pcl.ac.cn)
**F5-TTS**: Diffusion Transformer with ConvNeXt V2, faster trained and inference.
**E2 TTS**: Flat-UNet Transformer, closest reproduction from [paper](https://arxiv.org/abs/2406.18009).
**Sway Sampling**: Inference-time flow step sampling strategy, greatly improves performance
### Thanks to all the contributors !
## News
- **2025/03/12**: 🔥 F5-TTS v1 base model with better training and inference performance. [Few demo](https://swivid.github.io/F5-TTS_updates).
- **2024/10/08**: F5-TTS & E2 TTS base models on [🤗 Hugging Face](https://huggingface.co/SWivid/F5-TTS), [🤖 Model Scope](https://www.modelscope.cn/models/SWivid/F5-TTS_Emilia-ZH-EN), [🟣 Wisemodel](https://wisemodel.cn/models/SJTU_X-LANCE/F5-TTS_Emilia-ZH-EN).
## Installation
### Create a separate environment if needed
```bash
# Create a python 3.10 conda env (you could also use virtualenv)
conda create -n f5-tts python=3.10
conda activate f5-tts
```
### Install PyTorch with matched device
NVIDIA GPU
> ```bash
> # Install pytorch with your CUDA version, e.g.
> pip install torch==2.4.0+cu124 torchaudio==2.4.0+cu124 --extra-index-url https://download.pytorch.org/whl/cu124
> ```
AMD GPU
> ```bash
> # Install pytorch with your ROCm version (Linux only), e.g.
> pip install torch==2.5.1+rocm6.2 torchaudio==2.5.1+rocm6.2 --extra-index-url https://download.pytorch.org/whl/rocm6.2
> ```
Intel GPU
> ```bash
> # Install pytorch with your XPU version, e.g.
> # Intel® Deep Learning Essentials or Intel® oneAPI Base Toolkit must be installed
> pip install torch torchaudio --index-url https://download.pytorch.org/whl/test/xpu
>
> # Intel GPU support is also available through IPEX (Intel® Extension for PyTorch)
> # IPEX does not require the Intel® Deep Learning Essentials or Intel® oneAPI Base Toolkit
> # See: https://pytorch-extension.intel.com/installation?request=platform
> ```
Apple Silicon
> ```bash
> # Install the stable pytorch, e.g.
> pip install torch torchaudio
> ```
### Then you can choose one from below:
> ### 1. As a pip package (if just for inference)
>
> ```bash
> pip install f5-tts
> ```
>
> ### 2. Local editable (if also do training, finetuning)
>
> ```bash
> git clone https://github.com/SWivid/F5-TTS.git
> cd F5-TTS
> # git submodule update --init --recursive # (optional, if need > bigvgan)
> pip install -e .
> ```
### Docker usage also available
```bash
# Build from Dockerfile
docker build -t f5tts:v1 .
# Run from GitHub Container Registry
docker container run --rm -it --gpus=all --mount 'type=volume,source=f5-tts,target=/root/.cache/huggingface/hub/' -p 7860:7860 ghcr.io/swivid/f5-tts:main
# Quickstart if you want to just run the web interface (not CLI)
docker container run --rm -it --gpus=all --mount 'type=volume,source=f5-tts,target=/root/.cache/huggingface/hub/' -p 7860:7860 ghcr.io/swivid/f5-tts:main f5-tts_infer-gradio --host 0.0.0.0
```
## Inference
### 1. Gradio App
Currently supported features:
- Basic TTS with Chunk Inference
- Multi-Style / Multi-Speaker Generation
- Voice Chat powered by Qwen2.5-3B-Instruct
- [Custom inference with more language support](src/f5_tts/infer/SHARED.md)
```bash
# Launch a Gradio app (web interface)
f5-tts_infer-gradio
# Specify the port/host
f5-tts_infer-gradio --port 7860 --host 0.0.0.0
# Launch a share link
f5-tts_infer-gradio --share
```
NVIDIA device docker compose file example
```yaml
services:
f5-tts:
image: ghcr.io/swivid/f5-tts:main
ports:
- "7860:7860"
environment:
GRADIO_SERVER_PORT: 7860
entrypoint: ["f5-tts_infer-gradio", "--port", "7860", "--host", "0.0.0.0"]
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
volumes:
f5-tts:
driver: local
```
### 2. CLI Inference
```bash
# Run with flags
# Leave --ref_text "" will have ASR model transcribe (extra GPU memory usage)
f5-tts_infer-cli --model F5TTS_v1_Base \
--ref_audio "provide_prompt_wav_path_here.wav" \
--ref_text "The content, subtitle or transcription of reference audio." \
--gen_text "Some text you want TTS model generate for you."
# Run with default setting. src/f5_tts/infer/examples/basic/basic.toml
f5-tts_infer-cli
# Or with your own .toml file
f5-tts_infer-cli -c custom.toml
# Multi voice. See src/f5_tts/infer/README.md
f5-tts_infer-cli -c src/f5_tts/infer/examples/multi/story.toml
```
### 3. More instructions
- In order to have better generation results, take a moment to read [detailed guidance](src/f5_tts/infer).
- The [Issues](https://github.com/SWivid/F5-TTS/issues?q=is%3Aissue) are very useful, please try to find the solution by properly searching the keywords of problem encountered. If no answer found, then feel free to open an issue.
## Training
### 1. With Hugging Face Accelerate
Refer to [training & finetuning guidance](src/f5_tts/train) for best practice.
### 2. With Gradio App
```bash
# Quick start with Gradio web interface
f5-tts_finetune-gradio
```
Read [training & finetuning guidance](src/f5_tts/train) for more instructions.
## [Evaluation](src/f5_tts/eval)
## Development
Use pre-commit to ensure code quality (will run linters and formatters automatically):
```bash
pip install pre-commit
pre-commit install
```
When making a pull request, before each commit, run:
```bash
pre-commit run --all-files
```
Note: Some model components have linting exceptions for E722 to accommodate tensor notation.
## Acknowledgements
- [E2-TTS](https://arxiv.org/abs/2406.18009) brilliant work, simple and effective
- [Emilia](https://arxiv.org/abs/2407.05361), [WenetSpeech4TTS](https://arxiv.org/abs/2406.05763), [LibriTTS](https://arxiv.org/abs/1904.02882), [LJSpeech](https://keithito.com/LJ-Speech-Dataset/) valuable datasets
- [lucidrains](https://github.com/lucidrains) initial CFM structure with also [bfs18](https://github.com/bfs18) for discussion
- [SD3](https://arxiv.org/abs/2403.03206) & [Hugging Face diffusers](https://github.com/huggingface/diffusers) DiT and MMDiT code structure
- [torchdiffeq](https://github.com/rtqichen/torchdiffeq) as ODE solver, [Vocos](https://huggingface.co/charactr/vocos-mel-24khz) and [BigVGAN](https://github.com/NVIDIA/BigVGAN) as vocoder
- [FunASR](https://github.com/modelscope/FunASR), [faster-whisper](https://github.com/SYSTRAN/faster-whisper), [UniSpeech](https://github.com/microsoft/UniSpeech), [SpeechMOS](https://github.com/tarepan/SpeechMOS) for evaluation tools
- [ctc-forced-aligner](https://github.com/MahmoudAshraf97/ctc-forced-aligner) for speech edit test
- [mrfakename](https://x.com/realmrfakename) huggingface space demo ~
- [f5-tts-mlx](https://github.com/lucasnewman/f5-tts-mlx/tree/main) Implementation with MLX framework by [Lucas Newman](https://github.com/lucasnewman)
- [F5-TTS-ONNX](https://github.com/DakeQQ/F5-TTS-ONNX) ONNX Runtime version by [DakeQQ](https://github.com/DakeQQ)
## Citation
If our work and codebase is useful for you, please cite as:
```
@article{chen-etal-2024-f5tts,
title={F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching},
author={Yushen Chen and Zhikang Niu and Ziyang Ma and Keqi Deng and Chunhui Wang and Jian Zhao and Kai Yu and Xie Chen},
journal={arXiv preprint arXiv:2410.06885},
year={2024},
}
```
## License
Our code is released under MIT License. The pre-trained models are licensed under the CC-BY-NC license due to the training data Emilia, which is an in-the-wild dataset. Sorry for any inconvenience this may cause.