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
- 2025/03/12: 🔥 F5-TTS v1 base model with better training and inference performance. Few demo.
- 2024/10/08: F5-TTS & E2 TTS base models on 🤗 Hugging Face, 🤖 Model Scope, 🟣 Wisemodel.
# Create a python 3.10 conda env (you could also use virtualenv)
conda create -n f5-tts python=3.10
conda activate f5-ttsNVIDIA GPU
# 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
# 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
# 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
# Install the stable pytorch, e.g. pip install torch torchaudio
pip install f5-ttsgit 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 .
# 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 0.0.0.0Deployment solution with Triton and TensorRT-LLM.
Decoding on a single L20 GPU, using 26 different prompt_audio & target_text pairs.
| Model | Concurrency | Avg Latency | RTF | Mode |
|---|---|---|---|---|
| F5-TTS Base (Vocos) | 2 | 253 ms | 0.0394 | Client-Server |
| F5-TTS Base (Vocos) | 1 (Batch_size) | - | 0.0402 | Offline TRT-LLM |
| F5-TTS Base (Vocos) | 1 (Batch_size) | - | 0.1467 | Offline Pytorch |
See detailed instructions for more information.
- In order to achieve desired performance, take a moment to read detailed guidance.
- By properly searching the keywords of problem encountered, issues are very helpful.
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
# 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 --shareNVIDIA device docker compose file example
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 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.tomlRefer to training & finetuning guidance for best practice.
# Quick start with Gradio web interface
f5-tts_finetune-gradioRead training & finetuning guidance for more instructions.
Use pre-commit to ensure code quality (will run linters and formatters automatically):
pip install pre-commit
pre-commit installWhen making a pull request, before each commit, run:
pre-commit run --all-filesNote: Some model components have linting exceptions for E722 to accommodate tensor notation.
- E2-TTS brilliant work, simple and effective
- Emilia, WenetSpeech4TTS, LibriTTS, LJSpeech valuable datasets
- lucidrains initial CFM structure with also bfs18 for discussion
- SD3 & Hugging Face diffusers DiT and MMDiT code structure
- torchdiffeq as ODE solver, Vocos and BigVGAN as vocoder
- FunASR, faster-whisper, UniSpeech, SpeechMOS for evaluation tools
- ctc-forced-aligner for speech edit test
- mrfakename huggingface space demo ~
- f5-tts-mlx Implementation with MLX framework by Lucas Newman
- F5-TTS-ONNX ONNX Runtime version by DakeQQ
- Yuekai Zhang Triton and TensorRT-LLM support ~
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.
