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F5-TTS: Diffusion Transformer with ConvNeXt V2, faster trained and inference.
E2 TTS: Flat-UNet Transformer, closest reproduction from paper.
Sway Sampling: Inference-time flow step sampling strategy, greatly improves performance
# 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 your CUDA version, e.g. pip install torch==2.3.0+cu118 torchaudio==2.3.0+cu118 --extra-index-url https://download.pytorch.org/whl/cu118
# 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
# 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
# Install the stable pytorch, e.g. pip install torch torchaudio
1. As a pip package (if just for inference)
pip install git+https://github.com/SWivid/F5-TTS.git2. Local editable (if also do training, finetuning)
git clone https://github.com/SWivid/F5-TTS.git cd F5-TTS # git submodule update --init --recursive # (optional, if need > bigvgan) pip install -e .
# Build from Dockerfile
docker build -t f5tts:v1 .
# Or pull from GitHub Container Registry
docker pull ghcr.io/swivid/f5-tts:main
Currently supported features:
# 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
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
# Run with flags
# Leave --ref_text "" will have ASR model transcribe (extra GPU memory usage)
f5-tts_infer-cli \
--model "F5-TTS" \
--ref_audio "ref_audio.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
Read training & finetuning guidance for more instructions.
# Quick start with Gradio web interface
f5-tts_finetune-gradio
Use pre-commit to ensure code quality (will run linters and formatters automatically)
pip install pre-commit
pre-commit install
When making a pull request, before each commit, run:
pre-commit run --all-files
Note: Some model components have linting exceptions for E722 to accommodate tensor notation
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},
}
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.
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